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  • 1
    facet.materialart.
    Unknown
    Reimer, Berlin
    In:  Herausgeberexemplar
    Publication Date: 2024-04-24
    Description: Fennoskandien ist seit jeher ein Gebiet, auf das sich die geowissenschaftlichen Interessen konzentrierten. Ein Grund dafür ist die seit langem bekannte Landhebung von ca. 10 mm pro Jahr im Zentrum, das im nördlichen Teil des Bottnischen Meerbusens liegt. Neben den seismischen Untersuchungen der Struktur der Lithosphäre wird seit 1966 mit Hilfe der Präzisionsgravimetrie entlang der sog. Landhebungslinien versucht, durch wiederholte Schweremessungen den Effekt der Landhebung zu erfassen und dadurch, neben den Präzisionsnivellements, auch Hinweise auf den Mechanismus zu erhalten. Parallel dazu wurden an vielen Orten gravimetrische Gezeitenbeobachtungen durchgeführt, um realistische Gezeitenparameter für die Korrektur der Präzisionsgravimetrie zu ermitteln. Dabei stellte sich heraus, daß im Bereich der norwegischen Küste eine starke Anomalie der Parameter von bis zu 35% in der Amplitude und mehr als 20° in der Phase festzustellen ist, die man auf die Wirkung der ozeanischen Auf lastgezeiten zurückführen kann. Die vorliegende Arbeit befaßt sich mit den gravimetrischen Gezeitenregistrierungen entlang der Geotraverse "Blaue Straße". Dieses Profil beginnt an der norwegischen Küste in der Nähe des Polarkreises und läuft mit etwa 125° in südöstlicher Richtung bis an den Finnischen Meerbusen nahe der sowjetischen Grenze. Die Besonderheit der Stationsauswahl liegt in der Verdichtung der Meßorte im Bereich der Küste: Hierdurch ist es möglich, das Abklingen des Effektes der ozeanischen Auflast zu erfassen. Dies erlaubt gezielte Mode 11 Untersuchungen zur Überprüfung von lokalen Meeresgezeitenmodellen mit einer unabhängigen Methode: Neben den elastischen Eigenschaften der Lithosphäre, insbesondere der Tiefe der Krusten-/Mantelgrenze gehen lediglich die Verteilung der Amplituden und Phasen der Meeresgezeit in die Rechnung ein, nicht aber die Parameter und die Randbedingungen, die zu ihrer Modellierung benötigt werden. Durch die Verdichtung der Stationen im Küstenbereich können auch Aufschlüsse über die Struktur der Lithosphäre, vor allem die Moho - Tiefe, gewonnen werden. Es standen für die Messungen in den Jahren 1980 bis 1983 fünf Gravimeter zur Verfügung, mit denen insgesamt sieben Stationen vermessen wurden. Die Registrierzeiträume lagen zwischen 10 und 26 Monaten. Im Folgenden werden die bisherigen geowissenschaftlichen Befunde für Fennoskandien und für das Pof il "Blaue Staße" im Besonderen zusammengefaßt, die Methoden der Auflastberechnung diskutiert und die Problematik der Modellierung der Meeresgezeiten dargestellt. Die Messungen schließen intensive experimentelle Arbeiten wie auch Überlegungen zur Datenbearbeitung und Analyse ein. Besonderer Wert war dabei auf die Kalibrierung der Gravimeter zu legen, die im Berliner Gezeitenobservatorium (Insulaner) erfolgte. Die Interpretation der Residuen der Welle M2 hinsichtlich der Auf lastgezeiten ergab schließlich, daß die Schelfgezeiten bei der Modellierung in der Phase um 15 bis 20° verzögert werden müßten, um der Beobachtung zu entsprechen. Hierdurch wird natürlich das Meeresgezeitenmodell nicht korrigiert, die gefundenen Abweichungen sind aber ein Hinweis für die Berechnung der Modelle. Überdies ergab sich eine besonders gute Übereinstimmung für eine flache Kruste. Dies steht im Einklang mit seismischen Ergebnissen aus dem Küstenbereich und bestätigt das Fehlen einer Gebirgswurzel unter den Kaledoniden. Auch für das Residuum der Welle 01 wird eine Inkonsistenz mit dem Meeresgezeitenmodell festgestellt, die sich hier allerdings nicht aus der Modellierung der Schelfgezeiten erklären läßt: Die Amplituden von 01 liegen bei 3 bis 5 cm. Die lokale Wirkung ist demnach gegenüber M2 (80 bis 100 cm) zu klein. Für die Korrekturen der Präzisionsschweremessungen komplettiert dieses Profil das bereits vorhandene Stationsnetz und ermöglicht die flächenhafe Interpolation. Daraus lassen sich dann später unter Hinzuziehung der anderen Hauptwellen realistische Gezeitenkorrekturen für alle Stationen ermitteln.
    Description: Fennoscandia has always been an area of special interest for geoscientists. One reason for that ist certainly the well known land uplift of about 10mm/yr in its center, which is located in the northern part of the Gulf of Bothnia. Besides the seismic research of the structure of the Lithosphere since 1966 precise gravity surveys are carried out along the so called land uplift lines. By repetition of these measurements it is hoped to monitor the effect of land uplift, and to get indications of its mechanism. In addition tidal gravity observations were carried out at many places to achieve realistic tidal parameters for the correction of the precise gravity surveys. It was found that esp. adjacent to the sea there is a strong anomaly of the parameters of up to 35% in amplitude and more than 20° in phase. This can be allocated to the effect of the ocean tides. The present work deals with the tidal gravity measurements along the geotraverse 'Blue Road'. This profile starts at the Norwegian coast close to the polar circle running about 125° south-east till the Gulf of Finland, close to the Sowjet border. The speciality of the choice of the station locations is in their concentration near the coast. By these means it is possible to monitor the decrease of the effect of the ocean loading. This enables the check of local ocean tide models by an independent method: In addition to the elastic properties of the Lithosphere, esp. the depth of the crust/mantle boundary, only the distribution of the calculated ocean tidal amplitudes and phases are included into the calculations; the parameters and boundary conditions necessary for the calculation of these models are not used. By concentration of the stations near the coast also hints on the structure of the Lithosphere, esp. the depth of the Moho can be achieved. The measurements were carried out during a period from 1980 to 1983, and five gravimeters were available to measure at seven stations. The recording intervals were between 10 and 23 months each. In the following the geoscientif ic findings obtained up to now for Fennoscandia are put together, in detail for the 'Blue Road' profile. The methods of loading calculations and the problem of modelling the ocean tides are reviewed. The preparations of the measurements include intense experimental work as well as efforts in data treatment and analysis. Special care was necessary for the calibration of the gravimeters which was performed at the Tidal Observatory Berlin (Insulaner). The interpretation of the tidal residuals of the constituent M2 resulted in the fact, that the shelf tides should be retarded by 15 to 20° to fit the observations. Of course, this is no way to correct the tidal chart, but it provides boundary conditions for the calculation of these models. In addition, the best fit was achieved assuming a fairly thin crust in the coastal area. This corresponds to seismic results and confirms the lack of a mountain root below the Caledonides. For the residual of 01 the interpretation showed a significant restvector, too. But here it is not possible to use the shelf area for an explanation: In contrary to M2 (amplitudes of up to 100 cm) the 01 amplitudes of the shelf tides are only in the order of 3 to 5 cm, which is too small. These profile measurements complete the Fennoscandian net of tidal results. This enables the spatial interpolation of the values and, by addition of the other main tidal constituents, it will be possible to derive realistic tidal corections for the repeated precise gravity surveys.
    Description: thesis
    Description: DFG, SUB Göttingen
    Keywords: ddc:550 ; Geophysik
    Language: German
    Type: doc-type:book
    Format: 195
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  • 2
    Publication Date: 2024-04-24
    Description: In a regional study based upon a large set of sea-level data and meteorological data, the long-period variations in sea-level and the fluctuations of meteorological parameters have been investigated for the Norwegian coast. In the period range between 1 and 20 years, the sea-level is found to be dominated by a few distinct signals. The interaction between meteorological parameters and sea-level is discussed. The sea-level is found to respond to long-period air pressure variations as an inverse barometer. Most of the sea-level signals with periods between 1 and 5 years are at least partly due to similar signals in air pressure and/or wind stress. In the Chandler band, the meteorological parameters contain at least two distinct signals. The oceanic pole tide signal is masked by the atmospheric forcing of the sea-level. This leads to temporal variations in the apparent pole tide, which are uncorrelated with the temporal variations of the Chandler wobble. The air pressure corrected pole tide is still not an equilibrium tide, but the large deviations in phase from equilibrium may well be due to unaccounted effects of wind stress and temperature. The Chandler wobble in polar motion is important as the driving force of the pole tide. In an extensive numerical investigation, the simple and physically reasonable model of a single frequency, damped, harmonic oscillation, which is frequently excited at random times and with random amplitude is found to explain all features of the observed Chandler and annual wobbles. Comparing the results from the numerical investigations to the results from analyses of the polar motion gives strong evidence for the Chandler wobble period to be either 428.5± 1.0 days or 437.2±0.8 days and not the usually assumed period of ≈434 days. No final decision can be made which of the two periods is the true Chandler period, though the shorter one is favoured by several facts. The signals in meteorological parameters can all be related to the variations in sunspot numbers. A non-linear response of the atmosphere to the annual solar forcing, which is modulated by the sunspot cycles explains the observed frequency patterns. The excess of the observed sunspot effects in surface air temperature over those predicted from simple climate models by a factor of 10 indicates the existence of strong positive feedback mechanisms, which are responsible for the signals in air pressure and wind stress, too. The 18.6-year nodal tide lags the equilibrium tide by 0.8 radian, while the amplitudes exceed the equilibrium amplitudes by a factor of 3 to 5. At most parts of the coast, the nodal modulation of the amplitude of the fortnightly lunar tide Mf also lags the equilibrium modulation by 0.5 to 0.7 radian. The amplitude of the nodal modulation is close to equilibrium, except for Oslo and Bergen. At the latter stations, resonance effects may modify the modulation. Mf itself is found to have an amplitude of two to three times the equilibrium amplitude and a phase lag of slightly more than π. Determining the land uplift at the Norwegian coast from the trend in sea-level leads to a varying pattern of isolines, with the land uplift gradient being perpendicular to the general direction of the coast line, and being rather large at parts of the coast. At most parts of the coast, the zero line is further out in the sea than given on other published charts.
    Description: Die vorliegende Arbeit über langperiodische Variationen des Meeresspiegels entlang der norwegischen Küste basiert auf umfangreichen Datensätzen von Meeresgezeiten und Meteorologie. Alle verwendeten Meeresgezeitendaten lagen als Stundenstützstellen vor, die nach sorgfältiger Fehlersuche auf monatliche Mittelwerte reduziert wurden. Die längsten Meeresgezeiten-Registrierungen an der norwegischen Küste sind für Oslo und Bergen verfügbar, wo die Zeitreihen das Intervall von 1914 bis 1985 umfassen. Für sieben Stationen lagen Registrierungen von 1952 bis 1985 vor, und für jeweils weitere 2 Stationen waren Registrierung für die Zeiträume von 1961 bis 1985 bzw. von 1970 bis 1985 verfügbar. Die meteorologischen Daten standen an drei Station für die längeren Zeiträume von 1952 bis 1983 (Oslo und Bergen) bzw. von 1957 bis 1975 (Narvik ) zur Verfügung, und für 15 Stationen waren die Daten für den kürzeren Zeitraum von 1970 bis 1984 vorhanden. Dabei ist der Luftdruck mit einem Stützstellenabstand von drei Stunden registriert worden, während die übrigen Parameter (Windgeschwindigkeit und -richtung, Lufttemperatur) mit einem Stützstellenabstand von 6 Stunden vorliegen. Auch hier wurden aus den Daten monatliche Mittelwerte berechnet. Da die Wirkung des Windes auf die Meeresoberfläche als dem tangentialen Windstress proportional angenommen wird, wurden aus den Winddaten monatliche Mittelwerte für die beiden tangentialen Windstress-Komponenten berechnet. In dem Periodenbereich von einem bis etwa zwanzig Jahren sind die Schwankungen des Meeresspiegels von einigen wenigen, fast-periodischen Variationen bestimmt. Die Perioden, die in den Meeresspiegelschwankungen gefunden wurden, sind überwiegend auch in den untersuchten meteorologischen Parametern zu finden. Dabei sind diese Signale in Periode, Amplitude und Phase räumlich sehr homogen. Bei der Untersuchung der Signale in den meteorologischen Parametern Luftdruck, Ost-West- und Nord-Süd- Komponente des Windstress und Lufttemperatur zeigte sich, daß der überwiegende Teil der Perioden in ein Muster paßt, daß sich aus der Modulation der jährlichen solaren Anregung der Atmosphäre durch den Sonnenfleckenzyklus ergibt. Aus der Literatur ist bekannt, daß die Variationen in den Sonnenflecken mit Schwankungen in der Solarkonstanten und damit mit Variationen im Wärme-Input in das System Atmosphäre-Ozean verbunden sind. Die in der Lufttemperatur gefundenen Schwankungen mit Perioden zwischen einem und zwanzig Jahren haben Amplituden von ≈0.5°C und liegen damit um eine Größenordnung höhere als die aus einfachen Klima-Modellen abgeschätzten Effekte des Sonnenfleckenzyklus. Nur wenn man die Existenz positiver Rückkopplungs- Mechanismen annimmt, können die in den Beobachtungen gefundenen Variationen erklärt werden. Diese Rückkopplungen sind in beobachteten Veränderungen der vorherrschenden Wetterbedingungen mit dem Sonnenfleckenzyklus zu suchen. Änderun- gen der vorherrschenden Wetterbedingungen erklären dann auch das Vorhandensein dieser Perioden in Luftdruck und Windstress. Benutzt man die monatlichen Mittelwerte um die Antwort des Meeresspiegels auf Luftdruckschwankungen zu ermitteln, so ergibt sich für den größten Teil der Küste eine isostatische Antwort, vergleichbar einem inversen Barometer. Abweichungen finden sich insbesondere an Stationen am Ende von Fjorden (Oslofjord, Ofotfjord). Dort ist dann aber die Korrelation zwischen Windstress und Meeresspiegel höher als an Stationen an der offenen Küste, so daß diese Abweichungen von der isostatischen Antwort in wesentlichen durch Windeffekte bewirkt werden. Im Bereich der Chandler Periode sind im Luftdruck mindestens zwei Signale vorhanden, die mit Perioden von ≈1.14 Jahren und ≈1.22 Jahren die vom Chandler Wobble bewirkte Poltide verdecken. Die Signale im Luftdruck haben Amplituden (bis zu 200 Pa), die bei einer isostatischen Antwort des Meeresspiegels (etwa -1 cm/HPa) zu Signalen im Meeresspiegel führen, deren Amplituden deutlich über der Amplitude einer Gleichgewichts-Poltide (etwa 0.5 cm an der norwegischen Küste) liegen. Zu diesen Signalen im Luftdruck kommen noch Signale im Windstress und in der Lufttemperatur hinzu. Die Veränderungen in der Lufttemperatur können mit einiger Phasenverschiebung zu Signalen in der Wassertemperatur und über den Dichteeffekt zu entsprechenden Signalen im Meeresspiegel führen. Signale in der Temperatur des Meerwassers im Chandler-Band sind aus der Literatur bekannt. Diese komplizierte Wechselwirkung zwischen Atmosphäre und Ozean führt zu einer Verdeckung der vom Chandler Wobble bewirkten Poltide. Die Frequenz-Unterschiede zwischen den Signalen im Chandler-Band in der Atmosphäre und der Chandler Periode selbst (die auch der Periode der Poltide entspricht), führen zu langperiodischen Modulationen einer scheinbaren Poltide. Dadurch sind die beobachteten zeitlichen Variationen der Poltide nicht korreliert mit den zeitlichen Variation des Chandler Wobbles. Wird der isostatische Effekt des Luftdrucks auf den Meeresspiegel eliminiert, so ergibt sich eine Poltide, deren Amplitude nahe der Gleichgewichtsamplitude liegt. Der wesentliche Teil der Amplituden-Überhöhung der beobachteten Poltide gegenüber der Gleichgewichtsamplitude ist damit auf Luftdruckeffekte zurückzuführen. Die Phasenbeziehung zwischen beobachteter Poltide und der Gleichgewichtstide zeigt allerdings starke zeitliche Variationen. So ist im Interval von 1970 bis 1979 die Phase der beobachtete Poltide nahe der Gleichgewichtstide, während sich für das Interval von 1957 bis 1979 deutliche Abweichungen ergeben. Diese zeitliche Variabilität ist durch das Zusammenwirken verschiedener Signale im Chandler Band zu erklären. Insbesondere die in der Temperatur und im Wind gefundenen Signale mit Perioden nahe bei 14 Monaten können diese Variationen bewirken. Insgesamt wurden keine Hinweise gefunden auf eine von der Gleichgewichtstide abweichende Poltide. Der Chandler Wobble in der Polbewegung ist als Ursache für die Poltide wichtig für die vorliegende Arbeit. Da die in der Literatur publizierten Resultate bezüglich des Chandler Wobbles sehr widersprüchlich sind, wurden die Polbewegungsdaten des ILS/IPMS und vom BIH im Rahmen dieser Arbeit erneut analysiert. Mit Hilfe umfangreicher numerischer Untersuchungen konnte gezeigt werden, daß das einfache und physikalisch sinnvolle Modell eines mono-frequenten Chandler Wobbles, der häufig an zufälligen Zeitpunkten mit zufälliger Amplitude angeregt wird, ausreicht, um alle Eigenschaften des beobachteten Chandler Wobbles zu erklären. Durch Vergleich der Modellrechungen mit den Ergebnissen aus der beobachteten Polbewegung ergab sich, daß die tatsächliche Chandler Periode entweder bei 428.5± 1.0 Tagen oder bei 437.2±0.8 Tagen liegt, und nicht bei den üblicherweise angenommenen ≈434 Tagen. Dieses überraschende Ergebnis ist von großer Bedeutung für alle Arbeiten zur rheologischen Struktur der Erde. Dabei ist anzumerken, daß noch keine endgültige Aussage möglich ist, welche der beiden Perioden der tatsächlichen Chandler Periode entspricht. Allerdings wird die Periode von 428.5 Tagen durch einige Ergebnisse bevorzugt. Die in der Literatur üblicherweise genannte Periode von 434 Tagen ergibt sich gewöhnlich aus stark geglätteten Spektren. Diese Glättung führt bei einer Anregung nach dem oben beschriebenen Modell zu fehlerhaften Perioden. Die Untersuchung der aus den Polbewegungen ermittelten Gleichgewichtsbewegungen des Meeresspiegels erbrachte eine weitere interessante Korrelation: Eliminiert man aus diesen Gleichgewichtsbewegungen Poltide und jährliche Variation, so findet sich in den Residuen eine quasi-periodische Schwankung mit einer Periode von grob 30 Jahren. Diese Variation in den Gleichgewichtsbewegungen ist auf die als Markowitz Wobble bezeichnete Polbewegung zurückzuführen. Eine entsprechende, phasengleiche Variation findet sich auch in den Residuen der Meeresspiegelschwankungen, wenn man alle wichtigen Signale mit Perioden von einschließlich einem Jahr bis hin zu etwa 10 Jahren eliminiert. Falls diese Korrelation nicht zufällig ist, so müßte sie auch global zu finden sein. In der globalen Oberflächentemperatur und in der Änderung der Tageslänge finden sich ebenfalls Variationen, die mit den im residualen Meeresspiegel und in den Gleichgewichtsbewegungen gefundenen Signalen korrelieren, wobei die Variationen in der globalen Temperatur gegenüber den andern Parametern phasenverschoben sind. Die nodale Tide mit einer Periode von 18.6 Jahren zeigt entlang der norwegischen Küste gegenüber der Gleichgewichtstide eine Phasenverschiebung von etwa 0.8 Radian, und die Amplituden liegen um einen Faktor von 3 bis 5 über der entsprechenden Gleichgewichtsamplitude. Die Untersuchung der Variationen der Amplitude der vierzehntägigen Tide Mf zeigt eine ähnlich große Phasenverschiebung zwischen der beobachteten und der erwarteten nodalen Modulation, wobei hier die relativen Amplituden für alle Stationen mit Ausnahme von Oslo und Bergen nahe den Erwartungen liegen. Allerdings ist hier zu bemerken, daß die Amplituden der Mf um den Faktor 2 bis 3 über der Gleichgewichtstide liegen, und somit auch die Modulationen entsprechend erhöht sind. Die Unterschiede in Oslo und Bergen deuten auf Resonanz-Effekte hin. Der Trend im Meeresspiegel relativ zum Land ist an der norwegischen Küste im wesentlichen auf die Landhebung infolge der post-glazialen Entlastungsbewegungen zurückzuführen. Bestimmt man die Isolinien der Landhebungen aus den in dieser Arbeit ermittelten Trends, so ergeben sich Linien, die zu dem generellen Verlauf der Küste parallel sind. Bei Ålesund ergibt sich eine deutliche Verzerrung dieser Linien. In Ålesund finden sich starke zeitliche Variationen im Trend, die mit kleinräumigen Prozessen in Verbindung stehen müssen. Generell ist der Gradient der Landhebung senkrecht zum großräumigen Verlauf der Küste. Der hier ermittelte Gradient ist aber höher als in bisher publizierten Arbeiten, und die Lage der Nullinie ist weiter zur See hin verschoben.
    Description: thesis
    Description: DFG, SUB Göttingen
    Keywords: ddc:551.4 ; Geophysik ; Sea level ; Meeresspiegel
    Language: English
    Type: doc-type:book
    Format: 184
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  • 3
    Publication Date: 2024-04-24
    Description: Se caracterizó y evaluó el estado de conservación de los pastos marinos en áreas de interés conservacionista del Archipiélago Sabana-Camagüey en el periodo 2001-2003, considerando variables descriptivas de las angiospermas marinas, de la estructura del macrofitobentos y abióticas para conocer las causas de su afectación. Las áreas estudiadas fueron las bahías de Cárdenas, Santa Clara, Buena Vista, de Perros, Jigüey, La Gloria y Nuevitas, así como algunas lagunas arrecifales. Los pastos marinos mejor conservados se encontraron en las zonas con mayor intercambio con el océano, destacándose las lagunas arrecifales. El deterioro de los pastos marinos se debió principalmente al aumento de la turbidez por contaminación orgánica cerca de la isla principal en sectores costeros de las bahías de Cárdenas, Santa Clara y Buena Vista, y a este factor, junto con el incremento de la salinidad, en las bahías de Perros y Jigüey. Las condiciones más propicias para el desarrollo de los pastos parecen ser: la visibilidad submarina 〉 1 m, la salinidad 〈 43 ups, la variabilidad de salinidad 〈 10 ups, la DQO 〈 5,6 mgO2 L-1 y el nitrógeno total 〈 173 μM en el agua. La especie de angiosperma dominante fue Thalassia testudinum, seguida por Syringodium filiforme y Halodule wrightii, que dominaron donde disminuyó la luz y aumentaron los nutrientes. El inventario del macrofitobentos en el ASC acumuló 227 especies (100 Rhodophyta, 26 Ochrophyta, 96 Chlorophyta y cinco Magnoliophyta), con 66 nuevos registros para la zona y 16, para Cuba. Las macroalgas más frecuentes fueron de los órdenes Bryopsidales (Chlorophyta) y Ceramiales (Rhodophyta). La estructura del macrofitobentos estuvo modulada por los mismos factores que afectan el desarrollo de las angiospermas, con una riqueza específica menor donde está deteriorado el hábitat.
    Description: PhD
    Repository Name: AquaDocs
    Type: Thesis/Dissertation
    Format: 103pp.
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  • 4
    Publication Date: 2024-04-24
    Description: Groundwater is a vital resource for humans, non-human species, and ecosystems. It has allowed the development of human evolution and civilizations throughout history (e.g., Wittfogel 1956, Tempelhoff et al. 2009, Cuthbert and Ashley 2014, Roberts 2014). However, it faces multiple potential threats that make it vulnerable and fragile. Climate change and human activities are the primary causes that have led to water cycle disruptions, particularly a decline in groundwater quality and quantity (e.g., Gleeson et al. 2020, Chaminé et al. 2022, Richardson et al. 2023). Climate variability has induced droughts, floods, and other extreme weather conditions, significantly impacting groundwater in many regions. Meanwhile, human activities such as over-abstraction, ground contamination, deforestation, land-use change, and other anthropogenic pressures have further compromised groundwater status. Nonetheless, groundwater continues to fulfill water demands in many regions or during specific periods. Therefore, concerted efforts are imperative to ensure its sustainability. So, conservation practices and nature-based solutions must be adopted to efficiently manage groundwater and shield it from additional potential hazards or risks (e.g., contamination, pollution, or over-abstraction). Failure to act quickly can result in the loss of this critical resource, with severe consequences for the economy, society, and ecosystems. From this perspective, it is imperative to prioritize actions underscored by technical-scientific integrity, environmental responsibility, societal sensitivity, and ethical practices.
    Description: Published
    Description: 97
    Description: OS: Terza missione
    Description: OSA5: Energia e georisorse
    Description: JCR Journal
    Keywords: groundwater ; resource management ; sustainability ; hydrogeoethics ; geoethics ; societal well-being ; 05.03. Educational, History of Science, Public Issues ; 03.02. Hydrology ; 04.04. Geology ; 05.09. Miscellaneous
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: article
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  • 5
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    In:  EPIC3BIOspektrum, 28(3), pp. 269-271, ISSN: 0947-0867
    Publication Date: 2024-04-24
    Repository Name: EPIC Alfred Wegener Institut
    Type: Article , isiRev
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  • 6
    Publication Date: 2024-04-24
    Repository Name: EPIC Alfred Wegener Institut
    Type: Article , isiRev
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  • 7
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    Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung
    In:  EPIC3Expeditionsprogramm Polarstern, Bremerhaven, Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung, 20 p., pp. 1-20
    Publication Date: 2024-04-24
    Repository Name: EPIC Alfred Wegener Institut
    Type: Expedition program , notRev
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  • 8
    Publication Date: 2024-04-24
    Description: In the marine realm, microorganisms are responsible for the bulk of primary production, thereby sustaining marine life across all trophic levels. Longhurst provinces have distinct microbial fingerprints; however, little is known about how microbial diversity and primary productivity change at finer spatial scales. Here, we sampled the Atlantic Ocean from south to north (~50°S–50°N), every ~0.5° latitude. We conducted measurements of primary productivity, chlorophyll-a and relative abundance of 16S and 18S rRNA genes, alongside analyses of the physicochemical and hydrographic environment. We analysed the diversity of autotrophs, mixotrophs and heterotrophs, and noted distinct patterns among these guilds across provinces with high and low chlorophyll-a conditions. Eukaryotic autotrophs and prokaryotic heterotrophs showed a shared inter-province diversity pattern, distinct from the diversity pattern shared by mixotrophs, cyanobacteria and eukaryotic heterotrophs. Additionally, we calculated samplewise productivity-specific length scales, the potential horizontal displacement of microbial communities by surface currents to an intrinsic biological rate (here, specific primary productivity). This scale provides key context for our trophically disaggregated diversity analysis that we could relate to underlying oceanographic features. We integrate this element to provide more nuanced insights into the mosaic-like nature of microbial provincialism, linking diversity patterns to oceanographic transport through primary production.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Article , peerRev
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  • 9
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    Alfred-Wegener-Institute for Polar- and Marine Research
    In:  EPIC3Expeditionsprogramm Polarstern, Bremerhaven, Germany, Alfred-Wegener-Institute for Polar- and Marine Research, 45 p.
    Publication Date: 2024-04-24
    Repository Name: EPIC Alfred Wegener Institut
    Type: Expedition program , notRev
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  • 10
    Publication Date: 2024-04-24
    Description: Contrasting models predict two different climate change scenarios for the Southern Ocean (SO), forecasting either less or stronger vertical mixing of the water column. To investigate the responses of SO phytoplankton to these future conditions, we sampled a natural diatom dominated (63%) community from today’s relatively moderately mixed Drake Passage waters with both low availabilities of iron (Fe) and light. The phytoplankton community was then incubated at these ambient open ocean conditions (low Fe and low light, moderate mixing treatment), representing a control treatment. In addition, the phytoplankton was grown under two future mixing scenarios based on current climate model predictions. Mixing was simulated by changes in light and Fe availabilities. The two future scenarios consisted of a low mixing scenario (low Fe and higher light) and a strong mixing scenario (high Fe and low light). In addition, communities of each mixing scenario were exposed to ambient and low pH, the latter simulating ocean acidification (OA). The effects of the scenarios on particulate organic carbon (POC) production, trace metal to carbon ratios, photophysiology and the relative numerical contribution of diatoms and nanoflagellates were assessed. During the first growth phase, at ambient pH both future mixing scenarios promoted the numerical abundance of diatoms (∼75%) relative to nanoflagellates. This positive effect, however, vanished in response to OA in the communities of both future mixing scenarios (∼65%), with different effects for their productivity. At the end of the experiment, diatoms remained numerically the most abundant phytoplankton group across all treatments (∼80%). In addition, POC production was increased in the two future mixing scenarios under OA. Overall, this study suggests a continued numerical dominance of diatoms as well as higher carbon fixation in response to both future mixing scenarios under OA, irrespective of different changes in light and Fe availability.
    Repository Name: EPIC Alfred Wegener Institut
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  • 11
    Publication Date: 2024-04-24
    Description: Mesopelagic fish (meso-fish) are central species within the Southern Ocean (SO). However, their ecosystem role and adaptive capacity to climate change are rarely integrated into marine protected area (MPAs) assessments. This is a pity given their importance as crucial prey and predators in food webs, coupled with the impacts of climate change. Here, we estimate the habitat distribution of nine meso-fish using an ensemble model approach (MAXENT, random forest, and boosted regression tree). Four climate model simulations were used to project their distribution under two representative concentration pathways (RCP4.5 and RCP8.5) for short-term (2006–2055) and long-term (2050–2099) periods. In addition, we assess the ecological representativeness of established and proposed MPAs under climate change scenarios using meso-fish as indicator species. Our models show that all species shift poleward in the future. Lanternfishes (family Myctophidae) are predicted to migrate poleward more than other families (Paralepididae, Nototheniidae, Bathylagidae, and Gonostomatidae). In comparison, lanternfishes were projected to increase habitat area in the eastern SO but lose area in the western SO; the opposite was projected for species in other families. Important areas (IAs) of meso-fish are mainly distributed near the Antarctic Peninsula and East Antarctica. Proposed MPAs cover 23% of IAs at present and 38% of IAs in the future (RCP8.5, long-term future). Many IAs of meso-fish still need to be included in MPA proposals, such as the Prydz Bay and the seas around the Antarctic Peninsula. Our results provide a framework for designing new MPAs incorporating climate change adaptation strategies for MPA management.
    Repository Name: EPIC Alfred Wegener Institut
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  • 12
    Publication Date: 2024-04-24
    Description: Sediment reworking by benthic infauna, namely bioturbation, is of pivotal importance in expansive soft-sediment environments such as the Wadden Sea. Bioturbating fauna facilitate ecosystem functions such as bentho-pelagic coupling and sediment nutrient remineralization capacities. Yet, these benthic fauna are expected to be profoundly affected by current observed rising sea temperatures. In order to predict future changes in ecosystem functioning in soft-sediment environments like the Wadden Sea, knowledge on the underlying processes such as sediment reworking, is crucial. Here, we tested how temperature affects bioturbation and its associated ecosystem processes, such as benthic nutrient fluxes and sediment oxygen consumption, using luminophore tracers and sediment incubation cores. We used a controlled mesocosm experiment set-up with key Wadden Sea benthos species: the burrowing polychaetes Arenicola marina and Hediste diversicolor, the bivalve Cerastoderma edule, and the tube-building polychaete Lanice conchilega. The highest bioturbation rates were observed from A. marina, reaching up to 375 cm2yr−1; followed by H. diversicolor, with 124 cm2yr−1 being the peak bioturbation rate for the ragworm. Additionally, the sediment reworking activity of A. marina facilitated nearly double the amount of silicate efflux compared to any other species. Arenicola marina and H. diversicolor accordingly facilitated stronger nutrient effluxes under a warmer temperature than L. conchilega and C. edule. The oxygen uptake of A. marina and H. diversicolor within the sediment incubation cores was correspondingly enhanced with a higher temperature. Thus, increases in sea temperatures may initially be beneficial to ecosystem functioning in the Wadden Sea as faunal bioturbation is definitely expedited, leading to a tighter coupling between the sediment and overlying water column. The enhanced bioturbation activity, oxygen consumption, and facilitated nutrient effluxes from these invertebrates themselves, will aid in the ongoing high levels of primary productivity and organic matter production.
    Repository Name: EPIC Alfred Wegener Institut
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  • 13
    Publication Date: 2024-04-24
    Description: 〈jats:title〉Abstract〈/jats:title〉〈jats:sec〉〈jats:title〉Aim〈/jats:title〉〈jats:p〉Within the intensively‐studied, well‐documented latitudinal diversity gradient, the deep‐sea biodiversity of the present‐day Norwegian Sea stands out with its notably low diversity, constituting a steep latitudinal diversity gradient in the North Atlantic. The reason behind this has long been a topic of debate and speculation. Most prominently, it is explained by the deep‐sea glacial disturbance hypothesis, which states that harsh environmental glacial conditions negatively impacted Norwegian Sea diversities, which have not yet fully recovered. Our aim is to empirically test this hypothesis. Specific research questions are: (1) Has deep‐sea biodiversity been lower during glacials than during interglacials? 〈jats:italic〉(〈/jats:italic〉2) Was there any faunal shift at the Mid‐Brunhes Event (MBE) when the mode of glacial–interglacial climatic change was altered?〈/jats:p〉〈/jats:sec〉〈jats:sec〉〈jats:title〉Location〈/jats:title〉〈jats:p〉Norwegian Sea, deep sea (1819–2800 m), coring sites MD992277, PS1243, and M23352.〈/jats:p〉〈/jats:sec〉〈jats:sec〉〈jats:title〉Time period〈/jats:title〉〈jats:p〉620.7–1.4 ka (Middle Pleistocene–Late Holocene).〈/jats:p〉〈/jats:sec〉〈jats:sec〉〈jats:title〉Taxa studied〈/jats:title〉〈jats:p〉Ostracoda (Crustacea).〈/jats:p〉〈/jats:sec〉〈jats:sec〉〈jats:title〉Methods〈/jats:title〉〈jats:p〉We empirically test the deep‐sea glacial disturbance hypothesis by investigating whether diversity in glacial periods is consistently lower than diversity in interglacial periods. Additionally, we apply comparative analyses to determine a potential faunal shift at the MBE, a Pleistocene event describing a fundamental shift in global climate.〈/jats:p〉〈/jats:sec〉〈jats:sec〉〈jats:title〉Results〈/jats:title〉〈jats:p〉The deep Norwegian Sea diversity was not lower during glacial periods compared to interglacial periods. Holocene diversity was exceedingly lower than that of the last glacial period. Faunal composition changed substantially between pre‐ and post‐MBE.〈/jats:p〉〈/jats:sec〉〈jats:sec〉〈jats:title〉Main conclusions〈/jats:title〉〈jats:p〉These results reject the glacial disturbance hypothesis, since the low glacial diversity is the important precondition here. The present‐day‐style deep Norwegian Sea ecosystem was established by the MBE, more specifically by MBE‐induced changes in global climate, which has led to more dynamic post‐MBE conditions. In a broader context, this implies that the MBE has played an important role in the establishment of the modern polar deep‐sea ecosystem and biodiversity in general.〈/jats:p〉〈/jats:sec〉
    Repository Name: EPIC Alfred Wegener Institut
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  • 14
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    American Geophysical Union (AGU)
    In:  EPIC3Journal of Geophysical Research: Solid Earth, American Geophysical Union (AGU), 129(3), ISSN: 2169-9313
    Publication Date: 2024-04-24
    Description: 〈jats:title〉Abstract〈/jats:title〉〈jats:p〉Extensive investigation of continental rift systems has been fundamental for advancing the understanding of extensional tectonics and modes of formation of new ocean basins. However, current rift classification schemes do not account for conjugate end members formed by Large Igneous Province crust, referring to thick mafic crust, sometimes including continental fragments. Here, we investigate the rifting of William's Ridge (Kerguelen Plateau) and Broken Ridge, components of the Kerguelen Large Igneous Province now situated in the Southeast Indian Ocean, and incorporate these end members into the deformation migration concept for rifted margins. We use multichannel seismic reflection profiles and data from scientific drill cores acquired on both conjugate margins to propose, for the first time, a combined tectono‐stratigraphic framework. We interpret seismic patterns, tectonic features, and magnetic anomaly picks to determine an across‐strike structural domain classification. This interpretation considers the rift system overall to be “magma‐poor” despite being located proximal to the Kerguelen plume but suggests that syn‐rift interaction between the Kerguelen mantle plume and the lithospheric structure of William's Ridge and Broken Ridge has controlled the along‐strike segmentation of both conjugates. We integrate seismic reflection and bathymetric data to test the hypothesis of predominantly transform motion, between the Australian and Antarctic plates, in Late Cretaceous and Paleogene time.〈/jats:p〉
    Repository Name: EPIC Alfred Wegener Institut
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  • 15
    Publication Date: 2024-04-24
    Description: Ongoing climate warming in the western Canadian Arctic is leading to thawing of permafrost soils and subsequent mobilization of its organic matter pool. Part of this mobilized terrestrial organic matter enters the aquatic system as dissolved organic matter (DOM) and is laterally transported from land to sea. Mobilized organic matter is an important source of nutrients for ecosystems, as it is available for microbial breakdown, and thus a source of greenhouse gases. We are beginning to understand spatial controls on the release of DOM as well as the quantities and fate of this material in large Arctic rivers. Yet, these processes remain systematically understudied in small, high-Arctic watersheds, despite the fact that these watersheds experience the strongest warming rates in comparison. Here, we sampled soil (active layer and permafrost) and water (porewater and stream water) from a small ice wedge polygon (IWP) catchment along the Yukon coast, Canada, during the summer of 2018. We assessed the organic carbon (OC) quantity (using dissolved (DOC) and particulate OC (POC) concentrations and soil OC content), quality (δ13C DOC, optical properties and source apportionment) and bioavailability (incubations; optical indices such as slope ratio, Sr; and humification index, HIX) along with stream water properties (temperature, T; pH; electrical conductivity, EC; and water isotopes). We classify and compare different landscape units and their soil horizons that differ in microtopography and hydrological connectivity, giving rise to differences in drainage capacity. Our results show that porewater DOC concentrations and yield reflect drainage patterns and waterlogged conditions in the watershed. DOC yield (in mg DOC g−1 soil OC) generally increases with depth but shows a large variability near the transition zone (around the permafrost table). Active-layer porewater DOC generally is more labile than permafrost DOC, due to various reasons (heterogeneity, presence of a paleo-active-layer and sampling strategies). Despite these differences, the very long transport times of porewater DOC indicate that substantial processing occurs in soils prior to release into streams. Within the stream, DOC strongly dominates over POC, illustrated by ratios around 50, yet storm events decrease that ratio to around 5. Source apportionment of stream DOC suggests a contribution of around 50 % from permafrost/deep-active-layer OC, which contrasts with patterns observed in large Arctic rivers (12 ± 8 %; Wild et al., 2019). Our 10 d monitoring period demonstrated temporal DOC patterns on multiple scales (i.e., diurnal patterns, storm events and longer-term trends), underlining the need for high-resolution long-term monitoring. First estimates of Black Creek annual DOC (8.2 ± 6.4 t DOC yr−1) and POC (0.21 ± 0.20 t yr−1) export allowed us to make a rough upscaling towards the entire Yukon Coastal Plain (34.51 ± 2.7 kt DOC yr−1 and 8.93 ± 8.5 kt POC yr−1). Rising Arctic temperatures, increases in runoff, soil organic matter (OM) leaching, permafrost thawing and primary production are likely to increase the net lateral OC flux. Consequently, altered lateral fluxes may have strong impacts on Arctic aquatic ecosystems and Arctic carbon cycling.
    Repository Name: EPIC Alfred Wegener Institut
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  • 16
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    Academy of Sciences of the Republic of Sakha (Yakutia)
    In:  EPIC3ARCTIC AND SUBARCTIC NATURAL RESOURCES, Academy of Sciences of the Republic of Sakha (Yakutia), 28(4), pp. 584-594, ISSN: 2618-9712
    Publication Date: 2024-04-24
    Description: 〈jats:p〉This study presents the results of research on the climatic signal of radial growth of Siberian larch (〈jats:italic〉Larix cajanderi Mayr〈/jats:italic〉.) in the Omoloy River Basin, (north-eastYakutia). Tree-ring width chronologies were obtained from three sites  located in the valley complexes of subarctic tundra and forest-tundra ecotone, with chronologies spanning up to 498 years. Comparative analysis of radial growth dynamics and its statistical parameters indicated similar variability patterns within the study region. Dendroclimatic analysis revealed that the primary limiting factor determining the magnitude of radial growth in Siberianlarch is the air temperature during the first half of the growing season. Increasing temperatures have led to an increased role of precipitation and changes in the strength of growth-temperature correlations, especially in northern sites.This study highlights the potential for dendroclimatic and dendroecological researchin northern Yakutia.〈/jats:p〉
    Repository Name: EPIC Alfred Wegener Institut
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  • 17
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    Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung
    In:  EPIC3Expeditionsprogramm Polarstern, Bremerhaven, Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung, 40 p., pp. 1-40
    Publication Date: 2024-04-24
    Repository Name: EPIC Alfred Wegener Institut
    Type: Expedition program , notRev
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  • 18
    Publication Date: 2024-04-24
    Description: Palaeoenvironmental records from permafrost sequences complemented by infrared stimulated luminescence (IRSL) and 230Th/U dates from Bol'shoy Lyakhovsky Island (73°20′N, 141°30′E) document the environmental history in the region for at least the past 200 ka. Pollen spectra and insect fauna indicate that relatively wet grasssedge tundra habitats dominated during an interstadial c. 200-170 ka BP. Summers were rather warm and wet, while stable isotopes reflect severe winter conditions. The pollen spectra reflect sparser grass-sedge vegetation during a Taz (Late Saalian) stage, c. 170-130 ka BP, with environmental conditions much more severe compared with the previous interstadial. Open Poaceae and Artemisia plant associations dominated vegetation at the beginning of the Kazantsevo (Eemian) c. 130 ka BP. Some shrubs (Alnus fruticosa, Salix, Betula nana) grew in more protected and wetter places as well. The climate was relatively warm during this time, resulting in the melting of Saalian ice wedges. Later, during the interglacial optimum, shrub tundra with Alnus fruticosa and Betula nana s.l. dominated vegetation. Climate was relatively wet and warm. Quantitative pollen-based climate reconstruction suggests that mean July temperatures were 4-5°C higher than the present during the optimum of the Eemian, while late Eemian records indicate significant climate deterioration. © 2004 Taylor & Francis.
    Repository Name: EPIC Alfred Wegener Institut
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  • 19
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    American Association for the Advancement of Science (AAAS)
    In:  EPIC3Science Advances, American Association for the Advancement of Science (AAAS), 9(44), pp. eadg2639-eadg2639, ISSN: 2375-2548
    Publication Date: 2024-04-24
    Description: Paleoceanographic reconstructions show that the strength of North Atlantic currents decreased during the Little Ice Age. In contrast, the role of ocean circulation in climate regulation during earlier historical epochs of the Common Era (C.E.) remains unclear. Here, we reconstruct sea surface temperature (SST) and salinity in the Caribbean Basin for the past 1700 years using the isotopic and elemental composition of planktic foraminifera tests. Centennial-scale SST and salinity variations in the Caribbean co-occur with (hydro)climate changes in the Northern Hemisphere and are linked to a North Atlantic SST forcing. Cold phases around 600, 800, and 1400 to 1600 C.E. are characterized by Caribbean salinification and Gulf of Mexico freshening that implies reductions in the strength of North Atlantic surface circulation. We suggest that the associated changes in the meridional salt advection contributed to the historical climate variability of the C.E.
    Repository Name: EPIC Alfred Wegener Institut
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  • 20
    Publication Date: 2024-04-24
    Description: 〈jats:p〉Reconstructing interglacial marine environments helps us understand the climate change mechanisms of the past. To contribute to this body of knowledge, we studied a high-resolution 455 cm-thick sediment sequence of the Boreal (Eemian) marine beds directly overlying Moscovian (Saalian) moraine in the Bychye-2 section on the Pyoza River. We analyzed lithological and microfossil (foraminifers, ostracods, pollen, aquatic palynomorphs) variations at the studied site. Stratigraphical zonation is based on the local and well-established regional pollen zones, correlated with the western European pollen zones. The studied marine beds accumulated from the end of the Moscovian glacial (>131 ka) until ca. 119.5 ka. We distinguished three successive phases: a seasonally sea-ice-covered, relatively deep, freshened basin in the initial rapid flooding stage (>131–130.5 ka); a deep basin in the maximum flooding phase with less extensive sea ice cover (130.5–130.25 ka); and a shallow basin with reduced sea ice cover (130.25–119.5 ka). According to a pollen zone comparison with other sites, the regional glacioisostatic rebound started ca. 130 ka. The diverse warm-water assemblages of benthic foraminifers and ostracods containing typical Baltic Sea species occurred during the regression, mainly 128–124 ka, thus giving evidence for a relatively long-lasting connection between the White and Baltic Seas.〈/jats:p〉
    Repository Name: EPIC Alfred Wegener Institut
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  • 21
    Publication Date: 2024-04-24
    Description: Around 9.7 million years ago there was an abrupt collapse in diversity; the so-called 'Vallesian Crisis'. This was seen as the transition point to a climate with more seasonality and open landscapes.Using a fossil dataset from Miocene Eurasia, the influence of dataset quality on the severity of this crisis, which animals were most affected and their distribution patterns was studied.The crisis’ victims have three things in common: they are mainly forest dwellers, they date back to the Middle Miocene (16-11.1 Ma), and they are rare during the early Vallesian. The high Vallesian biodiversity was caused by the arrival of new immigrants in addition to older transient groups, possibly due to the unique coastal conditions.Major differences existed between the coastal region and the interior of the Iberian Peninsula. Some species appeared in the Vallès-Penedès, but never reached the inland. The 'Vallesian Crisis', while seen understandably as a time of abrupt and severe extinction, was thus largely a local turnover event.
    Repository Name: National Museum of Natural History, Netherlands
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  • 22
    Publication Date: 2024-04-24
    Description: New records of notoaturine water mites (Aturidae: Notoaturinae) from New Zealand are presented. Five new species are described: Evidaturus longiscutatus n. sp., Kritaturus (Kritaturus) longipalpis n. sp., Planaturus simpsonensis n. sp., Tryssaturus longwood n. sp. and Zelandalbia thibaulti n. sp. The males are described for the first time for Planaturus pileatus Smit, 2017 and Zelandalbia cf. hopkinsi Imamura, 1978 and the females are described for the first time for Kritaturus (Kritaturus) sornus Cook, 1983, Paratryssaturus zodelus Cook, 1983 and Taintaturus accidens Cook, 1983.
    Keywords: new species; taxonomy; new records; notoaturine mites; New Zealand
    Repository Name: National Museum of Natural History, Netherlands
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  • 23
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    In:  Characterization, Prediction and Modelling of Crustal Present-Day In-Situ Stresses | Geological Society special publication
    Publication Date: 2024-04-24
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  • 24
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    Geological Society of London
    In:  Geological Society Special Publication
    Publication Date: 2024-04-24
    Language: English
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  • 25
    Publication Date: 2024-04-24
    Description: Multi-archive studies of climate events and archive-specific response times require synchronous time scales. Aligning common variations in the cosmogenic radionuclide production rate via curve fitting methods provides a tool for the continuous synchronization of natural environmental archives down to decadal precision. Based on this approach, we synchronize 10Be records from Western Gotland Basin (WGB, Baltic Sea) and Lake Kälksjön (KKJ, central Sweden) sediments to the 14C production time series from the IntCal20 calibration curve during the Mid-Holocene period ~6400 to 5200 a BP. Before the synchronization, we assess and reduce non-production variability in the 10Be records by using 10Be/9Be ratios and removing common variability with the TOC record from KKJ sediments based on regression analysis. The synchronizations to the IntCal20 14C production time scale suggest decadal to multi-decadal refinements of the WGB and KKJ chronologies. These refinements reduce the previously centennial chronological uncertainties of both archives to about ± 20 (WGB) and ±40 (KKJ) years. Combining proxy time series from the synchronized archives enables us to interpret a period of ventilation in the deep central Baltic Sea basins from ~6250 to 6000 a BP as possibly caused by inter-annual cooling reducing vertical water temperature gradients allowing deep water formation during exceptionally cold winters.
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  • 26
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    In:  Characterization, Prediction and Modelling of Crustal Present-Day In-Situ Stresses | Geological Society special publication
    Publication Date: 2024-04-24
    Description: Geomechanics has a marked impact on the safe and sustainable use of the subsurface. This Special Publication contains contributions detailing the latest efforts in present-day in-situ stress characterization, prediction and modelling from the borehole to plate-tectonic scale. A particular emphasis is on the uncertainties that are often associated with geomechanics.
    Language: English
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  • 27
    Publication Date: 2024-04-24
    Description: Supraglacial discharge of limiting micronutrients such as iron (Fe) into high-nutrient low-chlorophyll (HNLC) regions like the Southern Ocean has recently drawn global attention. In this study, we aim to understand the contribution of cryoconite holes (comprising a meltwater column with an underlying layer of sediment) to the discharge of Fe through the glacier runoff. Cryoconite hole meltwater collected from the Larsemann Hills, East Antarctica showed a higher concentration of dissolved Fe (dFe: 71.2 μgL−1) and total Fe extractable from suspended sediments (exFe: 362.1 μgL−1) than in the adjacent streams (dFe: 30.5 μgL−1; exFe: 21.2 μgL−1) and melt pools (dFe: 42.3 μgL−1; exFe: 5.8 μgL−1). Predictive pathways (using PICRUSt2) show that cryoconite hole bacterial communities could acquire Fe and other trace elements using different mechanisms, such as the biosynthesis of siderophores, and transport proteins, therefore influencing the trace metal chemistry in these and other environments that drain cryoconite hole contents. Estimated discharge of dFe (11.4 kg km−2 a−1) and exFe (57.9 kg km−2 a−1) within cryoconite holes are 2 and 17 times higher, respectively than the discharge from the adjacent supraglacial streams, indicating that cryoconite holes are an important source of potentially bioavailable Fe to the HNLC region.
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  • 28
    Publication Date: 2024-04-24
    Description: Die DIN 19700:2004-07 "Stauanlagen fordert für Hochwasserrückhaltebecken und Talsperren die Prüfung der Einwirkung von Erdbeben. Ausgehend von der Erdbebengefahr am Standort müssen Nachweise hinsichtlich der Zuverlässigkeit der Stauanlage im Erdbebenfall geführt werden. Für die Betreiber und die Wasserbehörden gibt das vorliegende Kompendium einen zusammenfassenden Überblick über die Grundlagen der Erdbebennachweisführung entsprechend den DIN-Vorgaben. Die Anforderungen an die Nachweisführung sowie eine Leistungsbeschreibung für die zu beauftragenden Fachbüros werden gegeben. Ferner werden Hinweise zum Betrieb der Stauanlage erläutert. Es wird dargestellt, wie die Stauanlagen in Hinsicht auf eine Erdbebeneinwirkung zu überwachen sind.
    Language: German
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  • 29
    Publication Date: 2024-04-24
    Description: Desert environments constitute one of the largest and yet most fragile ecosystems on Earth. Under the absence of regular precipitation, microorganisms are the main ecological component mediating nutrient fluxes by using soil components, like minerals and salts, and atmospheric gases as a source for energy and water. While most of the previous studies on microbial ecology of desert environments have focused on surface environments, little is known about microbial life in deeper sediment layers. Our study is extending the limited knowledge about microbial communities within the deeper subsurface of the hyperarid core of the Atacama Desert. By employing intracellular DNA extraction and subsequent 16S rRNA sequencing of samples collected from a soil pit in the Yungay region of the Atacama Desert, we unveiled a potentially viable microbial subsurface community residing at depths down to 4.20 m. In the upper 80 cm of the playa sediments, microbial communities were dominated by Firmicutes taxa showing a depth-related decrease in biomass correlating with increasing amounts of soluble salts. High salt concentrations are possibly causing microbial colonization to cease in the lower part of the playa sediments between 80 and 200 cm depth. In the underlying alluvial fan deposits, microbial communities reemerge, possibly due to gypsum providing an alternative water source. The discovery of this deeper subsurface community is reshaping our understanding of desert soils, emphasizing the need to consider subsurface environments in future explorations of arid ecosystems.
    Language: English
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  • 30
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    In:  Geophysical Journal International
    Publication Date: 2024-04-24
    Description: The current crustal stress field is of key importance to understand geodynamic processes and to assess stability aspects during subsurface usage. To provide a 3-D continuous description of the stress state, linear elastic forward geomechanical-numerical models are used. These models solve the equilibrium of forces between gravitational volume forces and surfaces forces im- posed mainly by plate tectonics. The latter are responsible for the horizontal stress anisotropy and impose the inverse problem to estimate horizontal displacement boundary conditions that provide a fit best to horizontal stress magnitude data within the model volume. Ho wever , horizontal stress magnitude data have high uncertainties and they are sparse, clustered and not necessaril y representati ve for a larger rock v olume. Even w hen Bay esian statistics are incor - porated and additional stress information such as borehole failure observations or formation integrity test are used to further constrain the solution space, this approach may result in a low accuracy of the model results, that is the result is not correct. Here, we present an alternative approach that removes the dependence of the solution space based on stress magnitude data to avoid potential low accuracy . Initially , a solution space that contains all stress states that are physically reasonable is defined. Stress magnitude data and the additional stress information are then used in a Bayesian framework to e v aluate which solutions are more likely than others. We first show and validate our approach with a generic truth model and then apply it to a case study of the Molasse foreland basin of the Alps in Southern Germany. The results show that the model’s ability to predict a reliable stress state is increasing while the number of likely solutions may also increase, and that outlier of stress magnitude data can be identified. This alternative approach results in a substantial increase in computational speed as we perform most of the calculations anal yticall y.
    Language: English
    Type: info:eu-repo/semantics/article
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  • 31
    Publication Date: 2024-04-24
    Description: Abstract
    Description: Since the early 1990s, the GFZ has operated a global GNSS station network with currently about 70 stations for precise satellite clock & orbit determination, realization of the terrestrial reference frame, radio occultation measurements or studies on crust dynamics. A subset of these stations contributes also to the tracking networks of the International GNSS Service (IGS) and the EUREF Permanent GNSS Network (EPN). Other stations contribute to GFZ observatories (IPOC, DESERVE, TERENO), to the GPS Atmosphere Sounding Project (GASP), to WMO Global Climate Observing System Reference Upper-Air Network (GRUAN) or to other external cooperations. We offer data of 51 GFZ GNSS stations under this DOI. Nearly all stations are equipped with Javad or Septentrio hardware. Depending on the location and hardware they provide data of GPS (L1 / L2 / L5), GLONASS (L1 / L2 / L3), Galileo (E1 / E5a / E5b / E6), BeiDou (B1 / B2 / B3), QZSS (L1 / L2 / L5 / L6), NAVIC (L5), and SBAS (L1 / L5). The GNSS Station Nework Site (https://isdc.gfz-potsdam.de/gnss-station-network/) provides direct access to the 1s and 30s sampled RINEX data (near real-time, file based) and to real-time streams. Real-time streams are available for stations contributing to the IGS. Raw data GNSS binary raw observations are available upon request. All GFZ Stations follow the site guidelines of the International GNSS Service (https://kb.igs.org/hc/en-us/articles/202011433-Current-IGS-Site-Guidelines) Station specific metadata can be found at our metadata portal SEMISYS. An overview of the list of stations with direct links to the station specific metadata in semisys is available via ftp://datapub.gfz-potsdam.de/download/10.5880.GFZ.1.1.2020.001/2020-001_Ramatschi-et-al_List-of-GFZ-GNSS-Stations-with-links-to-SEMISYS.pdf.
    Description: Methods
    Description: Our GNSS stations are equipped with antenna, receiver, data logger (PC), and communication. For nearly all stations binary GNSS receiver messages are streamed in real time via local internet / GSM / VSAT to the GFZ data center and collected there in 15 minute data files, as it is done on site. After comparing the data files collected at GFZ with the raw messages stored locally, non identical or missing files are send from the site using scp. Data files are then converted to the RINEX (http://acc.igs.org/misc/rinex304.pdf) format using vendor-provided software. Real-time streams are provided by converting the binary message stream into RTCM format using the Alberding EuroNET software. Stations without real-time capabilities transfer binary files only. If no internet connection is available data will be stored locally and transmitted as soon as the connection is re-established.
    Type: Dataset , Dataset
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  • 32
    Publication Date: 2024-04-24
    Description: Abstract
    Description: This dataset contains simulated vegetation and fire variables using the LPJmLv5.6-SPITFIRE and LPJmLv5.6-SPITFIRE-BASE coupled vegetation-fire model. LPJmL is a Dynamic Global Vegetation Model (DGVM), which simulates impacts of climate change and vegetation including carbon, water and energy fluxes on land. SPITFIRE is a process-based fire model that is developed at the Potsdam Institute for Climate Impact Research (PIK) simulating ignitions, fire spread, fuel combustion and plant mortality. BASE is an empirical burned area model, developed at Senckenberg – Leibniz Institution for Biodiversity and Earth System Research (SGN), that is based on remotely sensed information using generalised linear model (GLM) techniques provided by data sources from within the HORIZON2020 project FirEUrisk and elsewhere. The dataset contains a set of future changes in vegetation and fire variables under future climate and land-use change at the European (ET) scale at 9 km covering 2000-2100 for both couple vegetation-fire models. The models were forced with 5 climate models from the SSP126 and SSP370 climate scenarios (its downscaling to ~9 km grid cell resolution) as well as the land-use projections corresponding to those climate scenarios (provided at ~9 km grid cell resolution). The variables provided in this dataset are at monthly and annual temporal resolution. The simulated changes in fire and vegetation spatio-temporal patterns are the result of changes in climate and land-use and subsequent fire-vegetation feedbacks. This data has been developed in the course of the HORIZON2020 project FirEUrisk (Deliverable 3.4; Grant Agreement no. 101003890).
    Keywords: vegetation-fire model ; burnt area ; vegetation cover ; fire regime ; EARTH SCIENCE 〉 BIOSPHERE 〉 TERRESTRIAL ECOSYSTEMS 〉 FORESTS ; EARTH SCIENCE 〉 BIOSPHERE 〉 VEGETATION 〉 BIOMASS ; EARTH SCIENCE 〉 BIOSPHERE 〉 VEGETATION 〉 CARBON ; EARTH SCIENCE 〉 BIOSPHERE 〉 VEGETATION 〉 VEGETATION COVER ; EARTH SCIENCE SERVICES 〉 ENVIRONMENTAL ADVISORIES 〉 FIRE ADVISORIES 〉 WILDFIRES ; EARTH SCIENCE SERVICES 〉 MODELS 〉 DYNAMIC VEGETATION/ECOSYSTEM MODELS
    Type: Dataset , Dataset
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  • 33
    Publication Date: 2024-04-24
    Description: Abstract
    Description: Understanding the factors leading to large earthquakes in the coupling zone of convergent margins and their interrelation with surface deformation were the main aims of the international and interdisciplinary research initiative TIPTEQ (“From The Incoming Plate To megaThrust EarthQuake Processes”). Between Nov. 2004 and Oct. 2005 we deployed 2 temporary, amphibious seismic arrays in South-Central Chile. In this region the 1960 Mw = 9.5 earthquake nucleated. The northern network between 37° and 39°S was formed by up to 120 digitally recording land stations (equipped with short-period sensors) and 10 Ocean Bottom Seismometers/Hydrophones (OBS/OBH). Waveform data are available from the GEOFON data centre, under network code ZW, and are available under CC-BY 4.0 license according to GIPP-rules.
    Keywords: Broadband seismic waveforms ; Seismic monitoring ; temporary local seismic network ; Monitoring system ; EARTH SCIENCE 〉 SOLID EARTH ; In Situ/Laboratory Instruments 〉 Magnetic/Motion Sensors 〉 Seismometers ; In Situ Land-based Platforms 〉 GEOPHYSICAL STATIONS/NETWORKS ; In Situ Land-based Platforms 〉 GEOPHYSICAL STATIONS/NETWORKS 〉 SEISMOLOGICAL STATIONS
    Type: Dataset , Seismic Network
    Format: ~500G
    Format: .mseed
    Format: XML
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  • 34
    Publication Date: 2024-04-24
    Description: Abstract
    Description: This data set includes the results of high-resolution digital elevation models (DEM) and digital image correlation (DIC) analysis applied to analogue modelling experiments. Twenty generic analogue models are extended on top of a rubber sheet. Two benchmark experiments are also reported. Detailed descriptions of the experiments can be found in Liu et al. (submitted) to which this data set is supplement. The data presented here are visualized as topography and the horizontal cumulative surface strain (principal strain and slip rake).
    Keywords: EPOS ; multi-scale laboratories ; analogue models of geologic processes ; analogue modelling results ; depression ; Digital Image Correlation (DIC) / Particle Image Velocimetry (PIV) ; Digital Image Correlation (DIC) / Particle Image Velocimetry (PIV) 〉 StrainMaster (La Vision GmbH) ; Extension box ; fault ; graben ; graben ; High frame rate camera ; horst ; normal fault ; Poisson ratio ; rift valley ; rifting ; Sand 〉 Quartz Sand ; Sandbox ; Silicon/Silly putty/PDMS ; SLR camera ; Structure from Motion (SfM) ; Structure from Motion (SfM) 〉 Photoscan (Agisoft) ; tectonic and structural features ; tectonic process ; tectonic process 〉 continental_breakup ; tectonic process 〉 continental_breakup 〉 rifting ; tectonic setting 〉 extended terrane setting ; tectonic setting 〉 extended terrane setting 〉 continental rift setting ; tectonic setting 〉 intraplate tectonic setting ; wrench fault
    Type: Dataset , Dataset
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  • 35
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    Unknown
    GEOMAR Helmholtz Centre for Ocean Research
    Publication Date: 2024-04-24
    Description: Vessel-mounted Acoustic Doppler Current Profilers (ADCPs) provide velocity profiles of the upper ocean along the ship track. They are a key tool in oceanographic research to study the oceanic circulation and the associated distribution of mass, heat, contaminants and other tracers. In order to obtain high-quality ocean current data from vessel-mounted ADCP measurements, a number of requirements must be met, from system installation and data acquisition measures to certain essential processing steps. Here, we present an open-source Python toolbox called OSADCP for scientists to convert, clean, calibrate and organize binary raw vessel-mounted ADCP data for scientific use. The toolbox is designed to process ADCP measurements in deep water by Teledyne RDI Ocean Surveyor ADCPs and the data acquisition software VMDAS.
    Type: Software , NonPeerReviewed
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  • 36
    Publication Date: 2024-04-24
    Description: Der Biodiversitätsverlust schreitet in bedrohlichem Ausmaß voran. Mit dem Global Biodiversity Framework und voraussichtlich dem Nature Restoration Law bestehen nun auf internationaler und europäischer Ebene vielversprechende Ansätze, ihm Herr zu werden. Jetzt ist der Bundesgesetzgeber – nicht zuletzt aus verfassungsrechtlichen Erwägungen – aufgerufen, daran anzuknüpfen. Dazu bietet sich die Regelungsform eines Rahmen- und Politikplanungsgesetzes an, wie sie schon aus dem Klimaschutzgesetz und dem Klimaanpassungsgesetz bekannt ist. Der Aufsatz beleuchtet den internationalen, europa- und verfassungsrechtlichen Hintergrund eines solchen ‘Biodiversitätsschutzgesetzes’ und diskutiert – unter Zusammenarbeit sowohl rechts- als auch naturwissenschaftlicher Autor:innen – formale und materielle Ausgestaltungsmöglichkeiten.
    Type: Article , PeerReviewed
    Format: text
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  • 37
    Publication Date: 2024-04-24
    Description: The expedition AL570 with the RV Alkor was carried out within the framework of the interdisciplinary DAM MGF-OSTSEE Project “Potential effects of closure for bottom fishing in the marine protected areas (MPAs) of the western Baltic Sea – baseline observations” funded by the Ministry of Education and Research (BMBF). Within MGF-OSTSEE a consortium of scientists from various institutions investigates how benthic ecosystems in Natura 2000 areas within the German exclusive economic zone develop after the exclusion of bottom trawling. Major goals of the project are i. the initial assessment of the environmental state and its variability in- and outside the three Natura 2000 areas Fehmarnbelt, Oder- and Rönnebank under the ongoing pressure of bottom trawling and ii. the general assessment of the effect of bottom trawling on benthic communities and benthic biogeochemical functioning as well as their development after fishery exclusion. The cruise AL570 concludes a series of three previous expeditions EMB238 (2020) and EMB267/268 (2021) and aimed to survey all components of the benthic food web including prokaryotes, protozoans, meiofauna and macrofauna, as well as sediment properties and biogeochemical processes in selected working areas in- and outside of the MPA. The working program comprised 156 station activities of various gears for biological and biogeochemical sampling of sediments. Solute exchange between the sediment and the water column was investigated using Landers and a novel underwater vehicle the Deep-Sea Rover (DSR) Panta Rhei. Investigations in the water column, seafloor observation and deployments of a dredge supplemented the station work. Due to stormy weather in situ solute fluxe measurements were not performed at the Rönnebank.
    Type: Report , NonPeerReviewed
    Format: text
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  • 38
    facet.materialart.
    Unknown
    PANGAEA
    In:  Supplement to: Gerdes, Klaas; Martínez Arbizu, Pedro; Schwarz-Schampera, Ulrich; Schwentner, Martin; Kihara, Terue Cristina (2019): Detailed Mapping of Hydrothermal Vent Fauna: A 3D Reconstruction Approach Based on Video Imagery. Frontiers in Marine Science, 6, https://doi.org/10.3389/fmars.2019.00096
    Publication Date: 2024-04-24
    Description: We used ROV video imagery of a hydrothermal vent field on the southwestern Indian Ridge in the Indian Ocean. Structure from Motion photogrammetry was applied to build a high resolution 3D reconstruction model of an active hydrothermal chimney complex and to project quantified abundances. This technique works for any kind of video imagery, regardless of its initial purpose and can be implemented in marine monitoring and management to identify important ecological areas. Likewise, the reconstruction was used to infer terrain variables at scales of megabenthic specimens, which were related to the abundances of the faunal assemblages. Based on the terrain variables the applied random forest model predicted the faunal assemblage distribution with an accuracy of 84.97 %. The most important structuring variables were the distances to diffuse- and black fluid exits, as well as the height of the chimney complex. This novel approach enabled us to classify quantified abundances of megabenthic taxa to distinct faunal assemblages and relate terrain variables to their distribution. The successful prediction of faunal assemblage occurrences further supports the importance of abiotic terrain variables as key structuring factors in hydrothermal systems.
    Keywords: INDEX2016; INDEX2016_16ROV; Pourquoi Pas ? (2005); South East Indian Ridge; VICTOR; Victor6000 ROV
    Type: Dataset
    Format: application/zip, 3 datasets
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  • 39
    Publication Date: 2024-04-24
    Description: Current warming, shifting hydrological regimes and accelerated permafrost thaw in the catchment of the Arctic rivers will affect their water biogeochemistry. The Lena River is the second largest Arctic river and 71 % of its catchment is characterized by continuous permafrost. Monitoring of Arctic rivers will enable to observe expected changes in matter transport such as an increase of dissolved organic matter (DOM) re-mobilization from permafrost. A number of biogeochemical variables are presented here in a unique high frequency throughout the whole year. The sampling of Lena River water is done near the Research Station Samoylov Island in the central Lena River Delta. The Samoylov research station allows a unique chance for continuous sampling since it operates throughout the year.
    Keywords: biogeochemistry; CDOM; DOC; DOM; major ions; Olenekskaya_Ch; RIVER; Sampling river; stable water isotopes
    Type: Dataset
    Format: application/zip, 10 datasets
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  • 40
    facet.materialart.
    Unknown
    PANGAEA
    In:  Supplement to: Lischka, Silke; Hagen, Wilhelm (2006): Seasonal lipid dynamics of the copepods Pseudocalanus minutus (Calanoida) and Oithona similis (Cyclopoida) in the Arctic Kongsfjorden (Svalbard). Marine Biology, 150(3), 443-454, https://doi.org/10.1007/s00227-006-0359-4
    Publication Date: 2024-04-24
    Description: Seasonal lipid dynamics of various developmental stages were investigated in Pseudocalanus minutus and Oithona similis. For P. minutus, the dominance of 16:1(n−7), 16:4(n−3) and 20:5(n−3) fatty acids indicated a diatom-based nutrition in spring, whereas 22:6(n−3), 16:0, 18:2(n−6) and 18:1(n−9) pointed to a flagellate-based diet during the rest of the year as well as omnivorous/carnivorous low-level feeding during winter. The shorter-chain fatty alcohols 14:0 and 16:0 prevailed, also reflecting biosynthetic processes typical of omnivores or carnivores. Altogether, the lipid signatures characterized P. minutus as an opportunistic feeder. In contrast, O. similis had consistently high amounts of the 18:1(n−9) fatty acid in all stages and during all seasons pointing to a generally omnivorous/carnivorous/detritivorous diet. Furthermore, the fatty alcohol 20:1(n−9) reached high percentages especially in adult females and males, and feeding on Calanus faecal pellets is suggested. Fatty alcohols, as wax ester moieties, revealed significant seasonal variations in O. similis and a seasonal trend towards wax ester accumulation in autumn in P. minutus. P. minutus utilized its lipid deposits for development in the copepodite stages III and IV and for gonad maturation in CV and females during the dark season. However, CVs and females depended on the spring phytoplankton bloom for final maturation processes and reproduction. O. similis fueled gonad maturation and egg production for reproduction in June by wax esters, whereas reproduction in August/September co-occurred with the accumulation of new depot lipids. Both species revealed significantly higher wax ester levels in deeper (〉50 m) as compared to surface (0–50 m) dwelling individuals related to a descent prior to overwintering.
    Keywords: APN; Apstein plankton net; Kongsfjorden_98-99; Kongsfjorden, Spitsbergen, Arctic; Priority Programme 1158 Antarctic Research with Comparable Investigations in Arctic Sea Ice Areas; SPP1158
    Type: Dataset
    Format: application/zip, 2 datasets
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  • 41
    facet.materialart.
    Unknown
    PANGAEA
    In:  Woods Hole Oceanographic Institution, Department of Geology & Geophysics | Supplement to: Thornalley, David J R; Oppo, Delia W; Ortega, Pablo; Robson, Jon I; Brierley, Chris M; Davis, Renee; Hall, Ian R; Moffa-Sanchez, Paola; Rose, Neil L; Spooner, Peter T; Yashayaev, Igor M; Keigwin, Lloyd D (2018): Anomalously weak Labrador Sea convection and Atlantic overturning during the past 150 years. Nature, 556(7700), 227-230, https://doi.org/10.1038/s41586-018-0007-4
    Publication Date: 2024-04-24
    Description: The Atlantic meridional overturning circulation (AMOC) is a system of ocean currents that has an essential role in Earth's climate, redistributing heat and influencing the carbon cycle. The AMOC has been shown to be weakening in recent years1; this decline may reflect decadal-scale variability in convection in the Labrador Sea, but short observational datasets preclude a longer-term perspective on the modern state and variability of Labrador Sea convection and the AMOC. Here we provide several lines of palaeo-oceanographic evidence that Labrador Sea deep convection and the AMOC have been anomalously weak over the past 150 years or so (since the end of the Little Ice Age, LIA, approximately AD 1850) compared with the preceding 1,500 years. Our palaeoclimate reconstructions indicate that the transition occurred either as a predominantly abrupt shift towards the end of the LIA, or as a more gradual, continued decline over the past 150 years; this ambiguity probably arises from non-AMOC influences on the various proxies or from the different sensitivities of these proxies to individual components of the AMOC. We suggest that enhanced freshwater fluxes from the Arctic and Nordic seas towards the end of the LIA—sourced from melting glaciers and thickened sea ice that developed earlier in the LIA—weakened Labrador Sea convection and the AMOC. The lack of a subsequent recovery may have resulted from hysteresis or from twentieth-century melting of the Greenland Ice Sheet. Our results suggest that recent decadal variability in Labrador Sea convection and the AMOC has occurred during an atypical, weak background state. Future work should aim to constrain the roles of internal climate variability and early anthropogenic forcing in the AMOC weakening described here. The data presented here is the supporting data for Thornalley et al. 2018 (see details below) and is derived from cores KNR-178-56JPC and KNR-178-48JPC. It includes the mean sortable silt size, details of radiocarbon dating, the % nps and binned sub-surface temperature reconstructions.
    Keywords: Atlantic meridional overturning circulation; ATLAS; A Trans-Atlantic assessment and deep-water ecosystem-based spatial management plan for Europe; deep water formation; sortable silt; subsurface ocean temperatures
    Type: Dataset
    Format: application/zip, 22 datasets
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  • 42
    Publication Date: 2024-04-24
    Description: The distance between a remotely operated vehicle (ROV) and the sea-ice underside was measured by a single-beam upward-looking acoustic sonar altimeter (Tritech PA500) attached to the ROV during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition between November 2019 and September 2020. Sea-ice draft was derived by subtracting the distance to the sea-ice underside from the ROV depth, uncorrected for ROV attitude (pitch, roll). An offset between the depth reference (ROV bumper bars) and the altimeter of 0.105 m is accounted for in the presented data.
    Keywords: Arctic Ocean; AWI_SeaIce; BEAST; FRAM; FRontiers in Arctic marine Monitoring; MOSAiC; MOSAiC20192020; MOSAiC expedition; Multidisciplinary drifting Observatory for the Study of Arctic Climate; Polarstern; PS122/1; PS122/1_10-113; PS122/1_5-62; PS122/1_6-118; PS122/1_6-16; PS122/1_6-31; PS122/1_7-18; PS122/1_7-55; PS122/1_8-125; PS122/1_9-22; PS122/2; PS122/2_18-10; PS122/2_18-19; PS122/2_18-89; PS122/2_19-115; PS122/2_19-27; PS122/2_20-101; PS122/2_20-23; PS122/2_21-125; PS122/2_21-36; PS122/2_22-107; PS122/2_22-45; PS122/2_23-116; PS122/2_23-29; PS122/2_24-70; PS122/2_24-97; PS122/2_25-104; PS122/2_25-44; PS122/3; PS122/3_29-14; PS122/3_29-65; PS122/3_30-69; PS122/3_31-17; PS122/3_31-75; PS122/3_32-11; PS122/3_32-34; PS122/3_32-78; PS122/3_33-27; PS122/3_33-83; PS122/3_34-20; PS122/3_35-32; PS122/3_35-95; PS122/3_36-112; PS122/3_36-125; PS122/3_36-24; PS122/3_37-108; PS122/3_37-19; PS122/3_37-20; PS122/3_38-50; PS122/3_38-85; PS122/3_38-91; PS122/3_39-152; PS122/3_39-20; PS122/3_39-77; PS122/4; PS122/4_44-162; PS122/4_44-191; PS122/4_44-206; PS122/4_45-129; PS122/4_45-149; PS122/4_45-61; PS122/4_46-172; PS122/4_46-174; PS122/4_46-175; PS122/4_46-176; PS122/4_46-177; PS122/4_46-37; PS122/4_47-135; PS122/4_47-31; PS122/4_48-213; PS122/4_48-4; PS122/4_49-105; PS122/5; PS122/5_59-269; PS122/5_59-369; PS122/5_60-165; PS122/5_60-166; PS122/5_60-167; PS122/5_60-28; PS122/5_60-5; PS122/5_61-156; PS122/5_61-200; PS122/5_61-35; PS122/5_62-103; PS122/5_62-165; PS122/5_62-65; Remotely operated sensor platform BEAST; Remotely operated vehicle (ROV); Sea ice; Sea-ice draft; Sea Ice Physics @ AWI
    Type: Dataset
    Format: application/zip, 90 datasets
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  • 43
    Publication Date: 2024-04-24
    Description: This data set is a higher-processing-level version of Gridded segments of sea-ice or snow surface elevation and freeboard from helicopter-borne laser scanner during the MOSAiC expedition, version 1 (Hutter et al., 2022; doi:10.1594/PANGAEA.950339), where the individual 30-second segments of the small scale grid flights have been combined into merged grids. The data were collected using a near-infrared, line-scanning Riegl VQ-580 airborne laser scanner (https://hdl.handle.net/10013/sensor.7ebb63c3-dc3b-4f0f-9ca5-f1c6e5462a31 & https://hdl.handle.net/10013/sensor.7a931b33-72ca-46d0-b623-156836ac9550) mounted in a helicopter along the MOSAiC drift from the north of the Laptev Sea, across the central Arctic Ocean, and towards the Fram Strait from September 2019 to October 2020. The merged data are stored in netCDF and geotiff format. The data are drift corrected using the position and heading data of RV Polarstern and elevation offset corrected using overlapping segments to overcome degraded GPS altitude data 〉85°N. For the flights with degraded GPS altitude quality, we provide only a freeboard estimate. The merged grids include all data variables of the gridded 30-s segments: surface elevation, freeboard (estimate), freeboard uncertainty, estimated sea surface height, surface reflectance, echo width, and number of points used in the interpolation. Also the calculated elevation offset correction term is provided for each flight as a csv file.
    Keywords: 20191002_01; 20191020_01; 20191112_02; 20191119_01; 20191130_01; 20191224_01; 20191225_01; 20191228_01; 20200107_01; 20200108_01; 20200108_03; 20200108_04; 20200116_01; 20200121_01; 20200123_02; 20200128_01; 20200204_01; 20200212_01; 20200217_02; 20200227_01; 20200321_01; 20200423_01; Airborne laser scanning; Arctic Ocean; Freeboard; HELI; Helicopter; IceSense; MOSAiC; MOSAiC20192020; MOSAIC-HELI; Multidisciplinary drifting Observatory for the Study of Arctic Climate; Polarstern; PS122_4_44_78_2020061601; PS122_4_45_112_2020070401; PS122_4_45_36_2020063001; PS122_4_46_36_2020070701; PS122_4_47_96_2020071701; PS122_4_48_69_2020072201; PS122/1; PS122/1_2-167; PS122/1_2-57; PS122/1_7-25; PS122/1_8-23; PS122/1_9-98; PS122/2; PS122/2_17-101; PS122/2_17-98; PS122/2_17-99; PS122/2_19-44; PS122/2_19-46; PS122/2_19-52; PS122/2_19-53; PS122/2_20-52; PS122/2_21-41; PS122/2_21-78; PS122/2_22-16; PS122/2_23-14; PS122/2_24-31; PS122/2_25-8; PS122/3; PS122/3_29-49; PS122/3_32-42; PS122/3_32-70; PS122/3_35-49; PS122/3_37-63; PS122/3_39-109; PS122/4; PS122/4_44-78; PS122/4_45-112; PS122/4_45-36; PS122/4_46-36; PS122/4_47-96; PS122/4_48-69; PS122/5; PS122/5_61-190; PS122/5_61-62; PS122/5_62-166; PS122/5_62-67; Remote Sensing of the Seasonal Evolution of Climate-relevant Sea Ice Properties; Sea ice; Surface Elevation
    Type: Dataset
    Format: application/zip, 35 datasets
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  • 44
    Publication Date: 2024-04-24
    Description: This data set is a higher-processing-level version of Geolocated sea-ice or snow surface elevation point clouds from helicopter-borne laser scanner during the MOSAiC expedition, version 1 (Jutila et al., 2022; doi:10.1594/PANGAEA.950509), where the surface elevation point cloud has been converted to freeboard using automatic open water detection scheme and projected onto a regular 0.5-meter grid. The data were collected using a near-infrared, line-scanning Riegl VQ-580 airborne laser scanner (hdl:10013/sensor.7ebb63c3-dc3b-4f0f-9ca5-f1c6e5462a31 & hdl:10013/sensor.7a931b33-72ca-46d0-b623-156836ac9550) mounted in a helicopter along the MOSAiC drift from the north of the Laptev Sea, across the central Arctic Ocean, and towards the Fram Strait from September 2019 to October 2020. The flights are both small scale, ~5x5 km grid patterns mainly over the central observatory, and large scale, few tens of km away from RV Polarstern, triangle patterns, or transects. The gridded data are stored in 30-second along-track segments in netCDF format. For the small scale grid flights, the data are drift corrected using the position and heading data of RV Polarstern and elevation offset corrected using overlapping segments to overcome degraded GPS altitude data 〉85°N. Open water points are identified to derive a freeboard estimate from the surface elevations. For the flights with degraded GPS altitude quality, we provide only a freeboard estimate (grid pattern flights) or no freeboard (transects). The gridded 30-s segments include as data variables: surface elevation, freeboard (estimate), freeboard uncertainty, estimated sea surface height, surface reflectance, echo width, and number of points used in the interpolation. In addition, list of detected open water points and an overview figure of each flight is provided.
    Keywords: Airborne laser scanning; Arctic; Freeboard; Helicopter; IceSense; MOSAiC; MOSAiC20192020; MOSAIC-HELI; Multidisciplinary drifting Observatory for the Study of Arctic Climate; Remote Sensing of the Seasonal Evolution of Climate-relevant Sea Ice Properties; Sea ice; Surface Elevation
    Type: Dataset
    Format: application/zip, 64 datasets
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  • 45
    Publication Date: 2024-04-24
    Description: This data set provides high-resolution geolocated point clouds of sea-ice or snow surface elevation for mapping temporal and spatial evolution of sea-ice conditions such as freeboard, roughness, or the size and spatial distributions of surface features. The surface elevation data are referenced to the DTU21 mean sea surface height and are not corrected for sea-ice drift during acquisition. The data were collected using a near-infrared, line-scanning Riegl VQ-580 airborne laser scanner (hdl:10013/sensor.7ebb63c3-dc3b-4f0f-9ca5-f1c6e5462a31 & hdl:10013/sensor.7a931b33-72ca-46d0-b623-156836ac9550) mounted in a helicopter along the MOSAiC drift from the north of the Laptev Sea, across the central Arctic Ocean, and towards the Fram Strait from September 2019 to October 2020. The flights are both small scale, ~5x5 km grid patterns mainly over the central observatory, and large scale, few tens of km away from RV Polarstern, triangle patterns, or transects. The point cloud data are stored in 5-min along-track segments in a custom binary format, for which we provide a python-based parsing tool in awi-als-toolbox (https://github.com/awi-als-toolbox/awi-als-toolbox), together with corresponding metadata json and line-shot quicklook png files. The point cloud data includes as variables: surface elevation (referenced to DTU mean sea surface height), surface reflectance, and echo width. The degraded GPS altitude data 〉85°N may cause undulations in the along-track surface elevations, which are not corrected for in this data product.
    Keywords: 20191002_01; 20191020_01; 20191029_01; 20191105_01; 20191112_01; 20191112_02; 20191119_01; 20191130_01; 20191206_01; 20191224_01; 20191225_01; 20191228_01; 20191230_01; 20200107_01; 20200107_02; 20200108_01; 20200108_03; 20200108_04; 20200116_01; 20200116_02; 20200121_01; 20200123_01; 20200123_02; 20200125_01; 20200128_01; 20200202_01; 20200204_01; 20200209_01; 20200212_01; 20200217_01; 20200217_02; 20200227_01; 20200321_01; 20200321_02; 20200423_01; Airborne laser scanning; Arctic; Arctic Ocean; HELI; Helicopter; IceSense; MOSAiC; MOSAiC20192020; MOSAIC-HELI; Multidisciplinary drifting Observatory for the Study of Arctic Climate; Polarstern; PS122_1_2_45_2019092801; PS122_4_44_27_2020061101; PS122_4_44_65_2020061502; PS122_4_44_78_2020061601; PS122_4_45_112_2020070401; PS122_4_45_36_2020063001; PS122_4_45_37_2020063002; PS122_4_46_36_2020070701; PS122_4_46_39_2020070703; PS122_4_46_97_2020071101; PS122_4_47_96_2020071701; PS122_4_48_69_2020072201; PS122_4_50_32_2020080601; PS122_4_50_45_2020080701; PS122/1; PS122/1_10-78; PS122/1_2-167; PS122/1_2-45; PS122/1_2-57; PS122/1_5-9; PS122/1_6-11; PS122/1_7-24; PS122/1_7-25; PS122/1_8-23; PS122/1_9-98; PS122/2; PS122/2_17-101; PS122/2_17-98; PS122/2_17-99; PS122/2_18-7; PS122/2_19-44; PS122/2_19-45; PS122/2_19-46; PS122/2_19-51; PS122/2_19-52; PS122/2_19-53; PS122/2_20-52; PS122/2_20-53; PS122/2_21-122; PS122/2_21-41; PS122/2_21-77; PS122/2_21-78; PS122/2_22-16; PS122/2_22-97; PS122/2_23-109; PS122/2_23-14; PS122/2_24-31; PS122/2_25-7; PS122/2_25-8; PS122/3; PS122/3_29-49; PS122/3_32-42; PS122/3_32-70; PS122/3_32-71; PS122/3_33-17; PS122/3_35-48; PS122/3_35-49; PS122/3_37-63; PS122/3_37-66; PS122/3_39-109; PS122/4; PS122/4_44-27; PS122/4_44-65; PS122/4_44-78; PS122/4_45-112; PS122/4_45-36; PS122/4_45-37; PS122/4_46-36; PS122/4_46-39; PS122/4_46-97; PS122/4_47-96; PS122/4_48-69; PS122/4_50-32; PS122/4_50-45; PS122/5; PS122/5_59-139; PS122/5_61-190; PS122/5_61-62; PS122/5_61-63; PS122/5_62-166; PS122/5_62-67; PS122/5_63-118; PS122/5_63-3; Remote Sensing of the Seasonal Evolution of Climate-relevant Sea Ice Properties; Sea ice; Surface Elevation
    Type: Dataset
    Format: application/zip, 64 datasets
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  • 46
    Publication Date: 2024-04-24
    Description: pH values were obtained using a SBE18 pH sensor (Seabird) mounted on the remotely operated vehicle (ROV) during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition between November 2019 and September 2020. The values were derived from the sensor voltages using the same calibration during the entire expedition.
    Keywords: Arctic Ocean; AWI_SeaIce; BEAST; FRAM; FRontiers in Arctic marine Monitoring; MOSAiC; MOSAiC20192020; MOSAiC expedition; Multidisciplinary drifting Observatory for the Study of Arctic Climate; pH; Polarstern; PS122/1; PS122/1_10-113; PS122/1_5-62; PS122/1_6-118; PS122/1_6-16; PS122/1_6-31; PS122/1_7-18; PS122/1_7-55; PS122/1_8-125; PS122/1_9-22; PS122/2; PS122/2_18-10; PS122/2_18-19; PS122/2_18-89; PS122/2_19-115; PS122/2_19-27; PS122/2_20-101; PS122/2_20-23; PS122/2_21-125; PS122/2_21-36; PS122/2_22-107; PS122/2_22-45; PS122/2_23-116; PS122/2_23-29; PS122/2_24-70; PS122/2_24-97; PS122/2_25-104; PS122/2_25-44; PS122/3; PS122/3_29-14; PS122/3_29-65; PS122/3_30-69; PS122/3_31-17; PS122/3_31-75; PS122/3_32-11; PS122/3_32-33; PS122/3_32-34; PS122/3_32-78; PS122/3_33-27; PS122/3_33-83; PS122/3_34-20; PS122/3_35-32; PS122/3_35-95; PS122/3_36-112; PS122/3_36-125; PS122/3_36-24; PS122/3_37-108; PS122/3_37-19; PS122/3_37-20; PS122/3_38-50; PS122/3_38-85; PS122/3_38-91; PS122/3_39-111; PS122/3_39-152; PS122/3_39-20; PS122/3_39-77; PS122/4; PS122/4_44-162; PS122/4_44-191; PS122/4_44-206; PS122/4_45-129; PS122/4_45-149; PS122/4_45-61; PS122/4_46-172; PS122/4_46-174; PS122/4_46-175; PS122/4_46-176; PS122/4_46-177; PS122/4_46-37; PS122/4_47-135; PS122/4_47-31; PS122/4_48-213; PS122/4_48-4; PS122/4_49-105; PS122/5; PS122/5_59-269; PS122/5_59-369; PS122/5_60-165; PS122/5_60-166; PS122/5_60-167; PS122/5_60-28; PS122/5_60-5; PS122/5_61-156; PS122/5_61-200; PS122/5_61-35; PS122/5_62-103; PS122/5_62-165; PS122/5_62-65; Remotely operated sensor platform BEAST; Remotely operated vehicle (ROV); Sea ice; Sea Ice Physics @ AWI
    Type: Dataset
    Format: application/zip, 93 datasets
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  • 47
    Publication Date: 2024-04-24
    Description: Fluorometric data on chlorophyll a concentration, Fluorescent Dissolved Organic Matter (FDOM) concentration, and optical backscatter were measured by a triplet fluorometer (ECO-Puck BBFL2SSC, Wetlabs) attached to a remotely operated vehicle (ROV) during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition between November 2019 and September 2020. Data use manufacturer calibration.
    Keywords: Arctic Ocean; AWI_SeaIce; BEAST; FRAM; FRontiers in Arctic marine Monitoring; MOSAiC; MOSAiC20192020; MOSAiC expedition; Multidisciplinary drifting Observatory for the Study of Arctic Climate; Polarstern; PS122/1; PS122/1_10-113; PS122/1_5-62; PS122/1_6-118; PS122/1_6-16; PS122/1_6-31; PS122/1_7-18; PS122/1_7-55; PS122/1_8-125; PS122/1_9-22; PS122/2; PS122/2_18-10; PS122/2_18-19; PS122/2_18-89; PS122/2_19-115; PS122/2_19-27; PS122/2_20-101; PS122/2_20-23; PS122/2_21-125; PS122/2_21-36; PS122/2_22-107; PS122/2_22-45; PS122/2_23-116; PS122/2_23-29; PS122/2_24-70; PS122/2_24-97; PS122/2_25-104; PS122/2_25-44; PS122/3; PS122/3_29-14; PS122/3_29-65; PS122/3_30-69; PS122/3_31-17; PS122/3_31-75; PS122/3_32-11; PS122/3_32-33; PS122/3_32-34; PS122/3_32-78; PS122/3_33-27; PS122/3_33-83; PS122/3_34-20; PS122/3_35-32; PS122/3_35-95; PS122/3_36-112; PS122/3_36-125; PS122/3_36-24; PS122/3_37-108; PS122/3_37-19; PS122/3_37-20; PS122/3_38-50; PS122/3_38-85; PS122/3_38-91; PS122/3_39-111; PS122/3_39-152; PS122/3_39-20; PS122/3_39-77; PS122/4; PS122/4_44-162; PS122/4_44-191; PS122/4_44-206; PS122/4_45-129; PS122/4_45-149; PS122/4_45-61; PS122/4_46-172; PS122/4_46-174; PS122/4_46-175; PS122/4_46-176; PS122/4_46-177; PS122/4_46-37; PS122/4_47-135; PS122/4_47-31; PS122/4_48-213; PS122/4_48-4; PS122/4_49-105; PS122/5; PS122/5_59-269; PS122/5_59-369; PS122/5_60-165; PS122/5_60-166; PS122/5_60-167; PS122/5_60-28; PS122/5_60-5; PS122/5_61-156; PS122/5_61-200; PS122/5_61-35; PS122/5_62-103; PS122/5_62-165; PS122/5_62-65; Remotely operated sensor platform BEAST; Remotely operated vehicle (ROV); Sea ice; Sea Ice Physics @ AWI
    Type: Dataset
    Format: application/zip, 93 datasets
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  • 48
    Publication Date: 2024-04-24
    Description: Absorbance and spectral absorption coefficient (SAC) parameters as measured by a VIPER G2 spectral transmissometer (TriOS) mounted in the sensor skid of a remotely operated vehicle (ROV) during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition between November 2019 and September 2020. Data use manufacturer calibration. The path length was 250 mm and the wavelength range 360-750 nm. More technical details can be found here: https://www.trios.de/en/viper.html.
    Keywords: Arctic Ocean; attenuation coefficient; AWI_SeaIce; BEAST; FRAM; FRontiers in Arctic marine Monitoring; MOSAiC; MOSAiC20192020; MOSAiC expedition; Multidisciplinary drifting Observatory for the Study of Arctic Climate; Polarstern; PS122/1; PS122/1_10-113; PS122/1_5-62; PS122/1_6-118; PS122/1_6-16; PS122/1_6-31; PS122/1_7-18; PS122/1_7-55; PS122/1_8-125; PS122/1_9-22; PS122/2; PS122/2_18-10; PS122/2_18-19; PS122/2_18-89; PS122/2_19-115; PS122/2_19-27; PS122/2_20-101; PS122/2_20-23; PS122/2_21-125; PS122/2_21-36; PS122/2_22-107; PS122/2_22-45; PS122/2_23-116; PS122/2_23-29; PS122/2_24-70; PS122/2_24-97; PS122/2_25-104; PS122/2_25-44; PS122/3; PS122/3_29-14; PS122/3_29-65; PS122/3_30-69; PS122/3_31-17; PS122/3_31-75; PS122/3_32-11; PS122/3_32-33; PS122/3_32-34; PS122/3_32-78; PS122/3_33-27; PS122/3_33-83; PS122/3_34-20; PS122/3_35-32; PS122/3_35-95; PS122/3_36-112; PS122/3_36-125; PS122/3_36-24; PS122/3_37-108; PS122/3_37-19; PS122/3_37-20; PS122/3_38-50; PS122/3_38-85; PS122/3_38-91; PS122/3_39-111; PS122/3_39-152; PS122/3_39-20; PS122/3_39-77; PS122/4; PS122/4_44-162; PS122/4_44-191; PS122/4_44-206; PS122/4_45-129; PS122/4_45-149; PS122/4_45-61; PS122/4_46-172; PS122/4_46-174; PS122/4_46-175; PS122/4_46-176; PS122/4_46-177; PS122/4_46-37; PS122/4_47-135; PS122/4_47-31; PS122/4_48-213; PS122/4_48-4; PS122/4_49-105; PS122/5; PS122/5_59-269; PS122/5_59-369; PS122/5_60-165; PS122/5_60-166; PS122/5_60-167; PS122/5_60-28; PS122/5_60-5; PS122/5_61-156; PS122/5_61-200; PS122/5_61-35; PS122/5_62-103; PS122/5_62-165; PS122/5_62-65; Remotely operated sensor platform BEAST; Remotely operated vehicle (ROV); Sea ice; Sea Ice Physics @ AWI
    Type: Dataset
    Format: application/zip, 92 datasets
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  • 49
    Publication Date: 2024-04-24
    Description: Videos as recorded by a HD-zoom camera (Bowtech Surveyor WAHD) with a 10:1 optical zoom attached to a remotely operated vehicle (ROV) during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition between November 2019 and September 2020.
    Keywords: Arctic Ocean; AWI_SeaIce; BEAST; FRAM; FRontiers in Arctic marine Monitoring; MOSAiC; MOSAiC20192020; MOSAiC expedition; Multidisciplinary drifting Observatory for the Study of Arctic Climate; Polarstern; PS122/1; PS122/1_10-113; PS122/1_5-62; PS122/1_6-118; PS122/1_6-16; PS122/1_6-31; PS122/1_7-18; PS122/1_7-55; PS122/1_8-125; PS122/1_9-22; PS122/2; PS122/2_18-10; PS122/2_18-19; PS122/2_18-89; PS122/2_19-115; PS122/2_19-27; PS122/2_20-101; PS122/2_20-23; PS122/2_21-125; PS122/2_21-36; PS122/2_22-107; PS122/2_22-45; PS122/2_23-116; PS122/2_23-29; PS122/2_24-70; PS122/2_24-97; PS122/2_25-104; PS122/2_25-44; PS122/3; PS122/3_29-14; PS122/3_29-65; PS122/3_30-69; PS122/3_31-17; PS122/3_31-75; PS122/3_32-11; PS122/3_32-33; PS122/3_32-34; PS122/3_32-78; PS122/3_33-27; PS122/3_33-83; PS122/3_34-20; PS122/3_35-32; PS122/3_35-95; PS122/3_36-112; PS122/3_36-125; PS122/3_36-24; PS122/3_37-108; PS122/3_37-19; PS122/3_37-20; PS122/3_38-50; PS122/3_38-85; PS122/3_38-91; PS122/3_39-111; PS122/3_39-152; PS122/3_39-20; PS122/3_39-77; PS122/4; PS122/4_44-162; PS122/4_44-191; PS122/4_44-206; PS122/4_45-129; PS122/4_45-149; PS122/4_45-61; PS122/4_46-172; PS122/4_46-174; PS122/4_46-175; PS122/4_46-176; PS122/4_46-177; PS122/4_46-37; PS122/4_47-135; PS122/4_47-31; PS122/4_48-213; PS122/4_48-4; PS122/4_49-105; PS122/5; PS122/5_59-269; PS122/5_59-369; PS122/5_60-165; PS122/5_60-167; PS122/5_60-28; PS122/5_60-5; PS122/5_61-156; PS122/5_61-200; PS122/5_61-35; PS122/5_62-103; PS122/5_62-165; PS122/5_62-65; Remotely operated sensor platform BEAST; Remotely operated vehicle (ROV); Sea ice; Sea Ice Physics @ AWI
    Type: Dataset
    Format: application/zip, 142 datasets
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  • 50
    Publication Date: 2024-04-24
    Description: Nitrate and UV-absorbance spectra were measured by a SUNA V2 UV-spectrometer (Satlantic) mounted in the sensor skid of a remotely operated vehicle (ROV) during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition between November 2019 and September 2020. Data use manufacturer calibration.
    Keywords: Arctic Ocean; AWI_SeaIce; BEAST; FRAM; FRontiers in Arctic marine Monitoring; MOSAiC; MOSAiC20192020; MOSAiC expedition; Multidisciplinary drifting Observatory for the Study of Arctic Climate; Polarstern; PS122/1; PS122/1_10-113; PS122/1_5-62; PS122/1_6-118; PS122/1_6-16; PS122/1_6-31; PS122/1_7-18; PS122/1_7-55; PS122/1_9-22; PS122/2; PS122/2_18-10; PS122/2_18-19; PS122/2_18-89; PS122/2_19-115; PS122/2_19-27; PS122/2_20-101; PS122/2_20-23; PS122/2_21-125; PS122/2_21-36; PS122/2_22-107; PS122/2_22-45; PS122/2_23-116; PS122/2_23-29; PS122/2_24-70; PS122/2_24-97; PS122/2_25-104; PS122/2_25-44; PS122/3; PS122/3_29-14; PS122/3_29-65; PS122/3_30-69; PS122/3_31-17; PS122/3_31-75; PS122/3_32-11; PS122/3_32-33; PS122/3_32-34; PS122/3_32-78; PS122/3_33-27; PS122/3_33-83; PS122/3_34-20; PS122/3_35-32; PS122/3_35-95; PS122/3_36-112; PS122/3_36-125; PS122/3_36-24; PS122/3_37-108; PS122/3_37-19; PS122/3_37-20; PS122/3_38-50; PS122/3_38-85; PS122/3_38-91; PS122/3_39-111; PS122/3_39-152; PS122/3_39-20; PS122/3_39-77; PS122/4; PS122/4_44-162; PS122/4_44-191; PS122/4_44-206; PS122/4_45-129; PS122/4_45-149; PS122/4_46-177; PS122/4_46-37; PS122/4_47-135; PS122/4_48-213; PS122/4_49-105; PS122/5; PS122/5_59-269; PS122/5_59-369; PS122/5_60-5; PS122/5_61-200; PS122/5_62-65; Remotely operated sensor platform BEAST; Remotely operated vehicle (ROV); Sea ice; Sea Ice Physics @ AWI
    Type: Dataset
    Format: application/zip, 71 datasets
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  • 51
    Publication Date: 2024-04-24
    Description: Water/ice velocity data and instrument status from a Nortek Aquadopp Profiler 2MHz acoustic doppler current profiler (ADCP) attached to a remotely operated vehicle (ROV) during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition between November 2019 and September 2020. The Aquadopp System Integrator Manual by Nortek AS can be found here: https://sensor.awi.de/rest/sensors/onlineResources/getOnlineResourcesFile/1764/system-integrator-manual_Mar2016.pdf
    Keywords: ADCP; Arctic Ocean; AWI_SeaIce; BEAST; FRAM; FRontiers in Arctic marine Monitoring; MOSAiC; MOSAiC20192020; MOSAiC expedition; Multidisciplinary drifting Observatory for the Study of Arctic Climate; Polarstern; PS122/1; PS122/1_10-113; PS122/1_5-62; PS122/1_6-118; PS122/1_6-16; PS122/1_6-31; PS122/1_7-18; PS122/1_7-55; PS122/1_9-22; PS122/2; PS122/2_18-10; PS122/2_18-19; PS122/2_18-89; PS122/2_19-115; PS122/2_19-27; PS122/2_20-101; PS122/2_20-23; PS122/2_21-125; PS122/2_21-36; PS122/2_22-107; PS122/2_22-45; PS122/2_23-116; PS122/2_23-29; PS122/2_24-70; PS122/2_24-97; PS122/2_25-104; PS122/2_25-44; PS122/3; PS122/3_29-14; PS122/3_29-65; PS122/3_30-69; PS122/3_31-17; PS122/3_31-75; PS122/3_32-11; PS122/3_32-33; PS122/3_32-34; PS122/3_32-78; PS122/3_33-27; PS122/3_33-83; PS122/3_34-20; PS122/3_35-32; PS122/3_35-95; PS122/3_36-112; PS122/3_36-125; PS122/3_36-24; PS122/3_37-108; PS122/3_37-19; PS122/3_37-20; PS122/3_38-50; PS122/3_38-85; PS122/3_38-91; PS122/3_39-111; PS122/3_39-152; PS122/3_39-20; PS122/3_39-77; PS122/4; PS122/4_44-162; PS122/4_44-191; PS122/4_44-206; PS122/4_45-129; PS122/4_45-149; PS122/4_45-61; PS122/4_46-172; PS122/4_46-174; PS122/4_46-175; PS122/4_46-176; PS122/4_46-177; PS122/4_46-37; PS122/4_47-135; PS122/4_47-31; PS122/4_48-213; PS122/4_48-4; PS122/4_49-105; PS122/5; PS122/5_59-269; PS122/5_59-369; PS122/5_60-165; PS122/5_60-166; PS122/5_60-167; PS122/5_60-28; PS122/5_60-5; PS122/5_61-156; PS122/5_61-200; PS122/5_61-35; PS122/5_62-103; PS122/5_62-165; PS122/5_62-65; Remotely operated sensor platform BEAST; Remotely operated vehicle (ROV); Sea ice; Sea Ice Physics @ AWI
    Type: Dataset
    Format: application/zip, 184 datasets
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  • 52
    Publication Date: 2024-04-24
    Description: Conductivity, temperature, and pressure were measured by a Glider Payload CTD (SBE GPCTD, Seabird). Oxygen frequency was measured by an oxygen optode (SBE 43F DO, Seabird). Both instruments were mounted in the sensor skid of a remotely operated vehicle (ROV) during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition between November 2019 and September 2020. Data use manufacturer calibration. The Gibbs SeaWater (GSW) Oceanographic Toolbox of TEOS-10 was used to derive other hydrographic data. The conversion from oxygen frequency to dissolved oxygen concentration was performed using the OOI L2 data product DOCONCF (Vardaro, 2014).
    Keywords: Arctic Ocean; AWI_SeaIce; BEAST; FRAM; FRontiers in Arctic marine Monitoring; GPCTD; MOSAiC; MOSAiC20192020; MOSAiC expedition; Multidisciplinary drifting Observatory for the Study of Arctic Climate; Polarstern; PS122/2; PS122/2_18-10; PS122/2_18-19; PS122/2_18-89; PS122/2_19-115; PS122/2_19-27; PS122/2_20-101; PS122/2_20-23; PS122/2_21-125; PS122/2_21-36; PS122/2_22-107; PS122/2_22-45; PS122/2_23-116; PS122/2_23-29; PS122/2_24-70; PS122/2_24-97; PS122/2_25-104; PS122/2_25-44; PS122/3; PS122/3_29-14; PS122/3_29-65; PS122/3_30-69; PS122/3_31-17; PS122/3_31-75; PS122/3_32-11; PS122/3_32-33; PS122/3_32-34; PS122/3_32-78; PS122/3_33-27; PS122/3_33-83; PS122/3_34-20; PS122/3_35-32; PS122/3_35-95; PS122/3_36-112; PS122/3_36-125; PS122/3_36-24; PS122/3_37-108; PS122/3_37-19; PS122/3_37-20; PS122/3_38-50; PS122/3_38-85; PS122/3_38-91; PS122/3_39-111; PS122/3_39-152; PS122/3_39-20; PS122/3_39-77; PS122/4; PS122/4_44-162; PS122/4_44-191; PS122/4_44-206; PS122/4_45-129; PS122/4_45-149; PS122/4_45-61; PS122/4_46-172; PS122/4_46-174; PS122/4_46-175; PS122/4_46-176; PS122/4_46-177; PS122/4_46-37; PS122/4_47-135; PS122/4_47-31; PS122/4_48-213; PS122/4_48-4; PS122/4_49-105; PS122/5; PS122/5_59-269; PS122/5_59-369; PS122/5_60-165; PS122/5_60-166; PS122/5_60-167; PS122/5_60-28; PS122/5_60-5; PS122/5_61-156; PS122/5_61-200; PS122/5_61-35; PS122/5_62-103; PS122/5_62-165; PS122/5_62-65; Remotely operated sensor platform BEAST; Remotely operated vehicle (ROV); Sea Ice Physics @ AWI
    Type: Dataset
    Format: application/zip, 84 datasets
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  • 53
    Publication Date: 2024-04-24
    Description: The distance between a remotely operated vehicle (ROV) and the sea-ice underside was measured by a single-beam upward-looking acoustic sonar altimeter (Tritech PA500) attached to the ROV during the ARTofMELT2023 expedition in May and June 2023. Sea-ice draft was derived by subtracting the distance to the sea-ice underside from the ROV depth, uncorrected for ROV attitude (pitch, roll). An offset between the depth reference (ROV bumper bars) and the altimeter of 0.105 m is accounted for in the presented data.
    Keywords: ARTofMELT; ARTofMELT2023; Atmospheric rivers and the onset of sea ice melt 2023; AWI_SeaIce; BEAST; FRAM; FRontiers in Arctic marine Monitoring; Remotely operated vehicle (ROV); Sea ice; Sea Ice Physics @ AWI
    Type: Dataset
    Format: application/zip, 18 datasets
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  • 54
    Publication Date: 2024-04-24
    Description: Firn cores OH-7 and OH-11 were retrieved from Plateau Laclavere, a small ice cap on the northernmost end of the Antarctic Peninsula, at about 1130 m above sea level (a.s.l.). OH-7 was drilled in January 2014 to a depth of 15.31 m using a mechanical 9 cm diameter drilling device (Rufli auger). OH-11 was drilled in January 2015 to a depth of 20.44 m. Firn core LP-01 was recovered from Plateau Louis Phillipe, which is located approximately 40 km south of Plateau Laclavere, at about 1390 m a.s.l. The core was drilled in January 2016 to a depth of 21.38 m. Cores OH-11 and LP-01 were obtained using a portable solar-powered and electrically operated ice-core drill (Backpack Drill; icedrill.ch AG). Subsamples for stable water isotope analysis were obtained from the three cores at 5 cm resolution. Stable water isotope measurements of OH-7 and LP-01 were performed at the ISOLAB Stable Isotope Facility of the Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research (AWI) in Potsdam, Germany, in summer 2017 and autumn 2018, respectively, using cavity ring-down spectrometers L2130-i and L2140-i (Picarro Inc.) coupled to an auto-sampler (L2130-i: PAL HTC-xt, CTC Analytics AG; L2140-i: Picarro Autosampler, Picarro Inc.). Stable water isotope measurements of OH-11 were conducted at the Stable Isotope Laboratory of the Universidad Nacional Andrés Bello (UNAB) in Viña del Mar, Chile, in autumn 2015 with an off-axis integrated cavity output spectrometer (TLWIA 45EP; Los Gatos Research). The three cores have not been dated yet. The data has been used in combination with data on the stable water isotope composition of three other firn cores from the same study area (doi:10.1594/PANGAEA.871083; doi:10.1594/PANGAEA.939718) to identify common isotopic patterns and to investigate their spatial and temporal variability.
    Keywords: Antarctic Peninsula; Firn chemistry; firn core; proxies; stable water isotopes
    Type: Dataset
    Format: application/zip, 3 datasets
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  • 55
    Publication Date: 2024-04-24
    Description: Conductivity, temperature, and pressure were measured by a Glider Payload CTD (SBE GPCTD, Seabird). Oxygen frequency was measured by an oxygen optode (SBE 43F DO, Seabird). Both instruments were mounted in the sensor skid of a remotely operated vehicle (ROV) during the ARTofMELT2023 expedition in May and June 2023. Data use manufacturer calibration. The Gibbs SeaWater (GSW) Oceanographic Toolbox of TEOS-10 was used to derive other hydrographic data. The conversion from oxygen frequency to dissolved oxygen concentration was performed using the OOI L2 data product DOCONCF (Vardaro, 2014).
    Keywords: ARTofMELT; ARTofMELT2023; Atmospheric rivers and the onset of sea ice melt 2023; AWI_SeaIce; BEAST; FRAM; FRontiers in Arctic marine Monitoring; Remotely operated vehicle (ROV); Sea ice; Sea Ice Physics @ AWI
    Type: Dataset
    Format: application/zip, 19 datasets
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  • 56
    Publication Date: 2024-04-24
    Description: Upward-looking still images as acquired by a photo camera (Tiger Shark, Imenco) with internal flash and 4 x zoom attached to a remotely operated vehicle (ROV) during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition between November 2019 and September 2020.
    Keywords: Arctic Ocean; AWI_SeaIce; BEAST; FRAM; FRontiers in Arctic marine Monitoring; MOSAiC; MOSAiC20192020; MOSAiC expedition; Multidisciplinary drifting Observatory for the Study of Arctic Climate; Polarstern; PS122/1; PS122/1_10-113; PS122/1_5-62; PS122/1_6-118; PS122/1_6-16; PS122/1_6-31; PS122/1_7-18; PS122/1_7-55; PS122/1_8-125; PS122/1_9-22; PS122/2; PS122/2_18-10; PS122/2_18-19; PS122/2_18-89; PS122/2_19-115; PS122/2_19-27; PS122/2_20-101; PS122/2_20-23; PS122/2_21-125; PS122/2_21-36; PS122/2_22-107; PS122/2_22-45; PS122/2_23-116; PS122/2_23-29; PS122/2_24-70; PS122/2_24-97; PS122/2_25-104; PS122/2_25-44; PS122/3; PS122/3_29-14; PS122/3_29-65; PS122/3_30-69; PS122/3_31-75; PS122/3_32-11; PS122/3_32-33; PS122/3_32-34; PS122/3_32-78; PS122/3_33-83; PS122/3_34-20; PS122/3_35-32; PS122/3_35-95; PS122/3_36-112; PS122/3_36-125; PS122/3_36-24; PS122/3_37-108; PS122/3_37-19; PS122/3_37-20; PS122/3_38-50; PS122/3_38-85; PS122/3_38-91; PS122/3_39-111; PS122/3_39-152; PS122/3_39-20; PS122/3_39-77; PS122/4; PS122/4_44-162; PS122/4_44-191; PS122/4_44-206; PS122/4_45-129; PS122/4_45-149; PS122/4_45-61; PS122/4_46-172; PS122/4_46-174; PS122/4_46-175; PS122/4_46-37; PS122/4_47-135; PS122/4_47-31; PS122/4_48-213; PS122/4_48-4; PS122/4_49-105; PS122/5; PS122/5_59-269; PS122/5_59-369; PS122/5_60-165; PS122/5_60-166; PS122/5_60-167; PS122/5_60-28; PS122/5_60-5; PS122/5_61-156; PS122/5_61-200; PS122/5_61-35; PS122/5_62-103; PS122/5_62-165; PS122/5_62-65; Remotely operated sensor platform BEAST; Remotely operated vehicle (ROV); Sea ice; Sea Ice Physics @ AWI
    Type: Dataset
    Format: application/zip, 88 datasets
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  • 57
    Publication Date: 2024-04-24
    Description: pH values were obtained using a SBE18 pH sensor (Seabird) mounted on the remotely operated vehicle (ROV) during the ARTofMELT2023 expedition in May and June 2023. The values were derived from the sensor voltages using the same calibration during the entire expedition.
    Keywords: ARTofMELT; ARTofMELT2023; Atmospheric rivers and the onset of sea ice melt 2023; AWI_SeaIce; BEAST; FRAM; FRontiers in Arctic marine Monitoring; Remotely operated vehicle (ROV); Sea ice; Sea Ice Physics @ AWI
    Type: Dataset
    Format: application/zip, 19 datasets
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  • 58
    Publication Date: 2024-04-24
    Description: The dataset comprises the main geochemical characteristics of purified lake sediment samples from Lake Bolshoye Shchuchye, in the Polar Ural based on EDS and stable isotope data. Moreover, core segment (column A), composite depth (in cm; column B); calibrated age (in cal ka BP; column C) are given. Details on coring and age model are given in Lenz et al. (2021) Energy-Dispersive X-ray Spectroscopy (EDS) was carried out with a scanning electron microscope (SEM) at the German Research Centre for Geosciences (GFZ Potsdam, Germany) to assess contamination of all diatom samples (following Chapligin et al., 2012). Three replicate analyses were carried out with an excited-area size with a radius of ~200 μm at an acceleration voltage of 20.0 kV. All detectable elements were normalized to 100% weight. The results were expressed as weight percentages (in %) and displayed as oxides: SiO2 content (%); Al2O3 content (%); Na2O content (%); MgO content (%); K2O content (%); CaO content (%); MnO content (%); FeO content (%): Total sum (%) of the purified sediment sample (columns D to L). Details are given in Meyer et al. (2022) The diatom oxygen isotope composition (δ18Odiatom) from lacustrine sediments helps tracing the hydrological and climate dynamics in individual lake catchments. The oxygen isotope data has been generated in the ISOLAB Facility Potsdam including all d18Odiatom values (all in ‰ vs. VSMOW). The measured δ18O values (δ18Omeas), the standard deviation (SD) and number of replicates (N) are given (columns M to O), as well as the calculated contamination (ccont; in %) and δ18O values corrected for contamination (δ18Ocorr) (columns P to Q). The details of the contamination correction and isotope analytics are given in Meyer et al. (2022)
    Keywords: AGE; Aluminium oxide; biogenic silica; Calcium oxide; chironomid-inferred temperature reconstructions; Climate change; Co1321; Contamination; Core; Corrected; DEPTH, sediment/rock; Diatom; Diatoms, δ18O; Diatoms, δ18O, standard deviation; hydrological fluctuations; Iron oxide, FeO; Isotope ratio mass spectrometry; Lake Bolshoye Shchuchye, Polar Urals, Russia; Lake sediment; Magnesium oxide; Manganese oxide; oxygen isotopes; Paleolimnological Transect; PCUWI; Piston corer, UWITEC; PLOT; Potassium oxide; Replicates; Scanning electron microscope (SEM) equipped with electron-dispersive x-ray spectroscopy (EDX); Silicon dioxide; Sodium oxide; Total; δ18O, adjusted/corrected
    Type: Dataset
    Format: text/tab-separated-values, 720 data points
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  • 59
    Publication Date: 2024-04-24
    Description: Seawater samples have been taken from the station Ocean City on the main MOSAiC ice floe on legs 1, 2, and 3. Water samples for measurement of stable water isotopes (δ18O, δD) were collected in 50-mL glass screw-cap narrow-neck vials (VWR international LLC, Germany), sealed with Parafilm M and stored at +4 °C from the end of the expedition until the measurement. Oxygen and hydrogen isotope analyses were carried out at the ISOLAB Facility at AWI Potsdam (https://hdl.handle.net/10013/sensor.ddc92f54-4c63-492d-81c7-696260694001) with mass spectrometers (DELTA-S Finnigan MAT, USA): https://hdl.handle.net/10013/sensor.af148dea-fe65-4c87-9744-50dc4c81f7c9 https://hdl.handle.net/10013/sensor.62e86761-9fae-4f12-9c10-9b245028ea4c employing the equilibration method (details in Meyer et al., 2000). δ18O and δD values were given in per mil (‰) vs. Vienna standard mean ocean water (V-SMOW) as the standard. The second order parameter d excess was computed according to: d excess = δD-8 δ18O (Dansgaard, 1964).
    Keywords: Arctic Ocean; Calculated after Dansgaard (1964); CTD/Rosette; CTD-RO; DATE/TIME; DEPTH, water; Deuterium excess; Event label; isotopes; Mass spectrometer Finnigan MAT Delta-S (ISOLAB); Mosaic; MOSAiC; MOSAiC20192020; Multidisciplinary drifting Observatory for the Study of Arctic Climate; Polarstern; PS122/1; PS122/1_10-45; PS122/1_11-40; PS122/1_2-68; PS122/1_4-37; PS122/1_5-46; PS122/1_6-38; PS122/1_7-40; PS122/1_8-16; PS122/1_9-28; PS122/2; PS122/2_18-16; PS122/2_19-4; PS122/2_20-17; PS122/2_21-101; PS122/2_21-114; PS122/2_21-128; PS122/2_21-26; PS122/2_22-18; PS122/2_22-3; PS122/2_22-71; PS122/2_23-17; PS122/2_23-4; PS122/2_23-70; PS122/2_24-47; PS122/2_25-4; PS122/3; PS122/3_29-74; PS122/3_29-8; PS122/3_30-38; PS122/3_30-9; PS122/3_31-18; PS122/3_31-81; PS122/3_32-12; PS122/3_32-77; PS122/3_33-82; PS122/3_34-17; PS122/3_34-76; PS122/3_35-25; PS122/3_35-92; PS122/3_36-115; PS122/3_36-19; PS122/3_37-116; PS122/3_37-15; PS122/3_38-100; PS122/3_38-31; PS122/3_39-16; Sample code/label; Sample ID; Sample type; seawater; Station label; δ18O, water; δ Deuterium, water
    Type: Dataset
    Format: text/tab-separated-values, 762 data points
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  • 60
    Publication Date: 2024-04-24
    Description: Sea ice cores were collected from the Biogeochemistry (BGC) team at different stations located on the main ice floe of MOSAiC expedition. Flat sea ice on the floe was categorized into three types based on the age: sea ice which grew during the same winter referred to as First-Year-Ice (FYI); sea ice which had survived one or more summer melting periods referred to as Second-Year-Ice (SYI). Sea ice cores were collected using a Kovacs Mark II 9 cm diameter corer. The core was extracted and placed in an aluminum holder equipped with a metric ruler. Using a standard Kovacs ice thickness gauge, the freeboard was taken and the length of the core was measured. The snow on top of the sea ice was brushed off the top of the cores to minimize the snow affecting the ice surface. Onboard RV Polarstern, the cores were cut in 10 cm sections using a handsaw at 4° C (leg 1) or an electric saw at -20° C (legs 2 and 3). Each section was transferred into a gas-tight TedlarTM bag. The closed bags were carefully degassed with a vacuum pump (NKF Neuberger, type N035). Melting occurred within 12 to 15 hours in a water bath in the dark. After shaking the melted ice within the TedlarTM bags, discrete sampling started by first rising the melt water carefully through a Tygon tube connected with the opened valves of the gas tide bags and then into prepared sample vials. Here we present the data from samples collected at Main Core Site (MCS) at the Dark Sector (DS). Oxygen and hydrogen isotope analyses were carried out at the ISOLAB Facility at AWI Potsdam (https://hdl.handle.net/10013/sensor.ddc92f54-4c63-492d-81c7-696260694001) with mass spectrometers (DELTA-S Finnigan MAT, USA): https://hdl.handle.net/10013/sensor.af148dea-fe65-4c87-9744-50dc4c81f7c9 https://hdl.handle.net/10013/sensor.62e86761-9fae-4f12-9c10-9b245028ea4c employing the equilibration method (details in Meyer et al., 2000). δ18O and δD values were given in per mil (‰) vs. Vienna standard mean ocean water (V-SMOW) as the standard. The second order parameter d excess was computed according to: d excess = δD-8 δ18O (Dansgaard, 1964).
    Keywords: Arctic Ocean; Calculated after Dansgaard (1964); DATE/TIME; DEPTH, ice/snow; Deuterium excess; Event label; IC; Ice corer; Mass spectrometer Finnigan MAT Delta-S (ISOLAB); Mosaic; MOSAiC; MOSAiC20192020; Multidisciplinary drifting Observatory for the Study of Arctic Climate; Polarstern; PS122/1; PS122/1_10-16; PS122/1_10-19; PS122/1_5-3; PS122/1_5-78; PS122/1_5-81; PS122/1_6-10; PS122/1_7-6; PS122/1_7-9; PS122/1_8-2; PS122/1_9-30; PS122/1_9-6; PS122/2; PS122/2_17-3; PS122/2_19-7; PS122/2_20-5; PS122/2_21-13; PS122/2_22-7; PS122/2_23-3; PS122/3; PS122/3_32-63; PS122/3_33-18; PS122/3_35-11; PS122/3_36-21; PS122/3_36-4; PS122/3_38-16; PS122/3_38-24; PS122/3_39-18; PS122/3_39-7; Salinity; Sample code/label; Sample ID; Sample type; Sea ice; Station label; δ18O, water; δ Deuterium, water
    Type: Dataset
    Format: text/tab-separated-values, 2179 data points
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  • 61
    Publication Date: 2024-04-24
    Description: Second-year sea-ice thickness, draft, salinity, temperature, and density were measured during near-weekly surveys at the main second-year ice coring site (MCS-SYI) during the MOSAiC expedition (legs 1 to 3) and new second-year ice coring site leg 4, since the earlier site was not accessible any longer. The ice cores were extracted either with a 9-cm (Mark II) or 7.25-cm (Mark III) internal diameter ice corers (Kovacs Enterprise, US). This data set includes data from 18 coring site visits and were performed from 28 October 2019 to 20 July 2020 at coring locations within 50 m to each other in the MOSAiC Central Observatory. During each coring event, ice temperature was measured in situ from a separate temperature core, using Testo 720 thermometers in drill holes with a length of half-core-diameter at 5-cm vertical resolution. Ice bulk practical salinity was measured from melted core sections at 5-cm resolution using a YSI 30 conductivity meter. Ice density was measured using the hydrostatic weighing method (Pustogvar and Kulyakhtin, 2016) from a density core in the freezer laboratory onboard Polarstern at the temperature of –15°C. Relative volumes of brine and gas were estimated from ice salinity, temperature and density using Cox and Weeks (1983) for cold ice and Leppäranta and Manninen (1988) for ice warmer than –2°C. The data contains the event label (1), time (2), and global coordinates (3,4) of each coring measurement and sample IDs (13, 15). Each salinity core has its manually measured ice thickness (5), ice draft (6), core length (7), and mean snow height (22). Each core section has the total length of its top (8) and bottom (9) measured in situ, as well estimated depth of section top (10), bottom (11), and middle (12). The depth estimates assume that the total length of all core sections is equal to the measured ice thickness. Each core section has the value of its practical salinity (14), isotopic values (16, 17, 18) (Meyer et al., 2000), as well as sea ice temperature (19) and ice density (20) interpolated to the depth of salinity measurements. The global coordinates of coring sites were measured directly. When it was not possible, coordinates of the nearby temperature buoy 2019T62 (legs 1-3) or 2019T61 (leg 4) were used. Ice mass balance buoy 2019T62 installation is described in doi:10.1594/PANGAEA.940231, ice mass balance buoy 2020T61 installation is described in doi: 10.1594/PANGAEA.926580. Brine volume (21) fraction estimates are presented only for fraction values from 0 to 30%. Each core section also has comments (23) describing if the sample is from a new coring site or has any other special characteristics. Macronutrients from the salinity core will be published in a subsequent version of this data set.
    Keywords: Arctic; Arctic Ocean; Arctic Research Icebreaker Consortium: A strategy for meeting the needs for marine-based research in the Arctic; ARICE; Calculated; Comment; Core length; cores; DATE/TIME; density; Density, ice; Depth, adjusted; Depth, adjusted bottom; Depth, adjusted top; Depth, ice/snow, bottom/maximum; Depth, ice/snow, top/minimum; Deuterium excess; Ecological monitoring; Event label; HAVOC; Hydrostatic weighing; IC; Ice corer; ICEGAUGE; Ice thickness gauge; Isotopic liquid water analyzer; LATITUDE; LONGITUDE; MOSAiC; MOSAiC_ECO; MOSAiC_ICE; MOSAiC20192020; Multidisciplinary drifting Observatory for the Study of Arctic Climate; Physical properties; Polarstern; PS122/1; PS122/1_10-16; PS122/1_5-78; PS122/1_6-36; PS122/1_7-53; PS122/1_7-9; PS122/1_9-11; PS122/2; PS122/2_20-5; PS122/2_22-7; PS122/2_25-15; PS122/3; PS122/3_33-18; PS122/3_35-4; PS122/3_36-4; PS122/3_38-16; PS122/3_39-18; PS122/4; PS122/4_45-29; PS122/4_46-20; PS122/4_47-18; PS122/4_48-25; Ridges - Safe HAVens for ice-associated Flora and Fauna in a Seasonally ice-covered Arctic OCean; Salinity; Temperature; Salinometer, inductive; Sample ID; Sea ice; Sea ice draft; Sea ice salinity; Sea ice thickness; Snow height; Tape measure; Temperature, ice/snow; Thermometer; time-series; Volume, brine; δ18O, water; δ Deuterium, water
    Type: Dataset
    Format: text/tab-separated-values, 9395 data points
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  • 62
    Publication Date: 2024-04-24
    Description: Compiled marine radiocarbon data from the global ocean, derived from planktonic/benthic foraminifera and corals, spanning the last deglaciation, with associated calendar ages that are independently derived and/or are consistent with the Marine20 radiocarbon calibration curve. The data have been screened and include data flags pertaining to anomalous values (e.g. negative offsets relative to the contemporary atmosphere), low sedimentation rates 〈2 cm/kyr, and/or deviations from dominant regional-temporal trends. The data are further grouped by ocean basin, and according to their associated calendar ages as belonging to a succession of time slices across the last deglaciation: the Last Glacial Maximum (LGM), Heinrich Stadial 1 (HS1), the Bolling-Allerod (BA), the Younger Dryas, the Early Holocene (7-11ka BP), and the late Holocene (〈6 ka BP).
    Keywords: 0050PG; 0066PG; 145-887; 146-893A; 167-1019; 17/1GCA; 1K-SUERC; 1P-OS-75; 1P-SUERC; 202-1240; 22SL; 310-M0015A; 310-M0016A; 310-M0018A; 310-M0020A; 310-M0021A; 310-M0023A; 310-M0023B; 310-M0024A; 310-M0025A; 310-M0025B; 310-M0026A; 313-M0027A; 313-M0029A; 341-U1419; 35MF20120125, OISO_21, INDIEN SUD 2; 47396B; 4P-OS-75; 4P-SUERC; 50-37KL; 5P-OS-79; 5P-SUERC; 64-480; 90b; 94-609; AII125-8-55; AII125-8-56; AK-AA-1; Akademik M.A. Lavrentiev; AK-BC-2; AK-F-1; AK-L-1; ALIENOR; also published as VM28-122; ALV; ALV-3884; ALV-3885; ALV-3887; ALV-3890; ALV-3891; ALV-3892; ALV-4162; ALVIN; ANT-XI/4; ANT-XXIII/9; ANT-XXVI/2; ARA04-43E; Argentine Basin; ARK-II/5; ARK-X/2; AT07-35; AT12-01; Atlantis (1997); B34-91; BC; Bering Sea; BO04-PC11; Box corer; Brazil Basin; Burdwood_Bank; CALYPSO; CALYPSO2; Calypso Corer; Calypso Corer II; Calypso square corer; Calypso Square Core System; CASQ; CASQS; CD159; CD159-10; CD38-17P; CDRILL; Cenderawasih Bay; CH84-14; Charles Darwin; CHAT 10K; CHAT-10K; CHAT-16K; CHAT-3K; CHAT-5K; CHR-4; CHR-5; CHR-6; CHR-7; COMPCORE; Composite Core; Conrad Rise; Coral; Core; CORE; Core drilling; Corner Rise; DAPC2; deglacial; Denmark Strait; DH117; DH43; DH74; DP Hunter; DR23; DR27; DR34; DR35; DR38; DR40; Drake Passage; Dredge; DRG; DRILL; Drill9A_Tasmaloum; Drilling/drill rig; Eastern Equatorial Pacific; Eastern slope of Kurile Basin; East Pacific; EBA1; EBA10; EBA11; EBA2; EBA3; EBA4; EBA5; EBA6; EBA7; EBA8; EBA9; EBB1; EBB2; EBB3; EBB4; EBB5; EBB6; EBB7; ENG-111; Equatorial East Pacific; Equatorial Indian Ocean; ESTASE1; ET97-7T; EW0408; EW0408-26JC; EW0408-87JC; Exp310; Exp313; Exp341; f0001carcs; F2-92-P3; F8-90-G21; FLAMINGO; Foraminifera; GC; GC_POI; GeoB1503-1; GeoB2104-3; GeoB7149-2; GeoB7162-6; GeoB7163-7; GeoB7167-6; GEOSCIENCES, MARMARCORE; GGC; GGC5; gh02-1030; Giant box corer; Giant gravity corer; Giant piston corer; GIK23243-2 PS05/431; GIK23415-9; GKG; Glomar Challenger; GPC; Gramberg Seamount; Gravity corer; Gravity corer (Kiel type); Gravity corer (POI); GS07-150-20/2A; Gulf of California; H209; H213; HU72-021-3; HU72-021-7; HU89038-8PC; IMAGES I; IMAGES III - IPHIS; IMAGES IV-IPHIS III; IMAGES V; IMAGES VII - WEPAMA; IMAGES XII - MARCO POLO; IMAGES XV - Pachiderme; Indian Ocean; INOPEX; Interim_Seamount; James Cook; Japan Trench; JC094; JC094_GRM; Jean Charcot; JFA17; JFA2; JFA20; JFA24; JF-FI-19PC; Joides Resolution; JPC; JT96-09; JT96-09PC; Jumbo Piston Core; KAL; KALMAR II; Kasten corer; Kayd; KL; KN_USA; KN11002; KN159-5; Knorr; KNR110-50; KNR110-66; KNR110-82a; KNR110-82GGC; KNR140; KNR140-01JPC; KNR140-02JPC; KNR140-12JPC; KNR140-2-12JPC; KNR140-2-22JPC; KNR140-22JPC; KNR140-2-30GGC; KNR140-2-51GGC; KNR140-26GGC; KNR140-30GGC; KNR140-37JPC; KNR140-39GGC; KNR140-43GGC; KNR140-50GGC; KNR140-51GGC; KNR140-56GGC; KNR140-66GGC; KNR159-5; KNR159-5-78GGC; KNR176-17GC; KNR176-2; KNR176-2-JPC30; KNR178; KNR178-2GGC; KNR178-32JPC; KNR195-5-CDH23; KNR195-5-CDH26; KNR195-5-CDH41; KNR195-5-GGC43; KNR197-10; KNR197-10CDH42; KNR197-10-CDH42; KNR197-10-CDH46; KNR197-10-GGC17; KNR197-10-GGC5; KNR198-CDH36; KNR198-GGC15; KNR198-GGC35; KNR31GPC5; KNR73-3PC; KNR73-4PC; KNR73-6PG; KOL; KOMEX; KOMEX II; KR02-15-PC06; Kronotsky Peninsula; KT89-18-P4; Lakshadweep Sea; Laurentian fan; Leg145; Leg146; Leg167; Leg202; Leg64; Leg94; Le Suroît; LMG06-05-9; LPAZ21P; LV27/GREGORY; LV27-2-4; LV29-114-3; LV29-2; M16/2; M17/2; M23/2; marine; Marion Dufresne (1972); Marion Dufresne (1995); MAT-1A; MAT-3A; Maurice Ewing; MD012386; MD01-2386; MD012420; MD01-2420; MD01-2461; MD02-2461; MD022489; MD02-2489; MD03-2697; MD03-2707; MD04-2829CQ; MD04-2845; MD052896; MD05-2896; MD052904; MD05-2904; MD07-3076; MD07-3076Q; MD07-3088; MD08-3169; MD09-3256; MD09-3256Q; MD09-3257; MD101; MD106; MD111; MD114; MD122; MD123; MD12-3396Cq; MD13; MD134; MD141; MD147; MD159; MD173; MD189; MD77-176; MD952002; MD95-2002; MD952007; MD95-2007; MD972106; MD97-2106; MD972121; MD97-2121; MD972138; MD97-2138; MD982165; MD98-2165; MD982181; MD98-2181; MD99-2331; MD99-2334; ME0005-24JC; Melville; Meriadzec; Meteor (1986); ML1208-01PC; MR01-K03; MR06-04_PC04A; MUC; MULT; MultiCorer; Multiple investigations; Mururoa; MV1007; MV1007-DO3; MV99-GC31; MV99-GC38; MV99-MC17/GC32/PC10; MV99-MC19; MV99-PC08; NA064-117-1; NA064-118-1; NA87-22; Nathaniel B. Palmer; NBP0805; NBP0805-DR23; NBP0805-DR27; NBP0805-DR34; NBP0805-DR35; NBP0805-DR36; NBP0805-DR38; NBP0805-DR40; NBP0805-TB04; NBP1103; NBP1103-DH07; NBP1103-DH112; NBP1103-DH113; NBP1103-DH115; NBP1103-DH117; NBP1103-DH120; NBP1103-DH134; NBP1103-DH14; NBP1103-DH140; NBP1103-DH143; NBP1103-DH15; NBP1103-DH22; NBP1103-DH43; NBP1103-DH74; NBP1103-DH95; Nesmeyanov25-1-GGC15; Nesmeyanov25-1-GGC18; Nesmeyanov25-1-GGC20; Nesmeyanov25-1-GGC27; New England Mountains; New Jersey Shallow Shelf; North Atlantic; North Atlantic/FLANK; Northeast Atlantic; North East Atlantic; North Pacific/Gulf of California/BASIN; North Pacific Ocean; Northwest Atlantic; Norwegian Sea; OCE326-GGC14; OCE326-GGC26; OCE326-GGC5; off Chile; off Nova Scotia; OK-3; OK-8; OKB-36A; OKB-53B; OKB-B3; OSIRIS III; Pacific Ocean; PALEOCINAT; PALEOCINAT II; papua; PC; PC75-1; PC75-2; Philippine Sea; PICABIA; Piston corer; Piston corer (BGR type); Piston corer (Kiel type); PLDS-007G; PLDS-1; Pleiades; Polarstern; PS05; PS1243-2; PS2606-6; PS2644-5; PS30; PS30/144; PS31; PS31/160-5; PS69; PS69/907-2; PS69/912-3; PS69/912-4; PS75/059-2; PS75/100-4; PS75/104-1; PS75 BIPOMAC; PUCK; radiocarbon dates; RAPiD-10-1P; RBDASS05; RBDASS05_H11; RBDASS05_H15; RC11; RC1112; RC11-238; RC24; RC24-8GC; RC27; RC27-14; RC27-23; Remote operated vehicle; Remote operated vehicle Jason II; RETRO-2; RGF_Barbados; RGF-12; RGF-15; RGF-16; RGF-9; RNDB-GGC15; RNDB-GGC5; RNDB-PC11; RNDB-PC13; Robert Conrad; ROV; ROVJ; RR0503-06JPC; RR0503-36JPC; RR0503-64JPC; RR0503-79JPC; RR0503-831C; RR0503-83GC; S67-FFC15; S794; S931; S938; Sakhalin shelf and slope; Sars_Seamount; SC4_ST2_SW2_SX1; Scotia Sea; Sea of Okhotsk; SEDCO; Sediment corer; Shackleton_Fracture_Zone; SHAK03-6K; SHAK05-3K; SHAK06-4K; SHAK10-10K; SHAK14-4G; Shirshov Ridge; Sindhu Sadhana; Site_1471; Site_2088; Site_21210009; Site_2307; Site_2631; Site_2657; Site_2706; Site_2774; Site_47396; Site_654; Site_660; Site_936; SK129-CR2; SL; Smithsonian_48735.1; SO156/2; SO156/3; SO161/5; SO161/5_22SL; SO178; SO178-13-6; SO201/2; SO201-2-101; SO201-2-12KL; SO201-2-77; SO201-2-85; SO202/1; SO202/1_18-6; SO213/2; SO213/2_76-2; SO213/2_79-2; SO213/2_82-1; SO213/2_84-1; Sonne; SOPATRA; South Atlantic; South Atlantic Ocean; South China Sea; Southern Alaska Margin: Tectonics, Climate and Sedimentation; South of Iceland; South Pacific Ocean; South Tasman Rise; Southwest Pacific; Southwest Pacific Ocean; SPOC; SS152; SS172; SSD-044; SSD044_GC-01; Station 6, MD189-3396; St Kildas Basin; SU90-08; SU92; SU92-03; Submersible Alvin; TAH-01A-3A; TAH-02A-4F; TAH-02A-4G; TAH-02A-4H; TAH-02A-5D; TAH-02A-5F; TAH-02A-5G; TAH-02A-5H; TAH-03A-1; TAH-03A-1A; TAH-03A-1E; Tahiti, offshore Faaa; Tahiti, offshore Maraa; Tahiti, offshore Tiarei; Tahiti Sea Level; Ta-P6; Ta-P7; Ta-P8; Tasman Sea; Thomas G. Thompson; Thomas G. Thompson (1964); Thomas Washington; TN228; TN228_J2_382; TN228_J2_383; TN228_J2_387; TN228_J2_389; TN228_J2_393; TN228_J2_395; TNO57-21; TR163-22; TR163-23; TR163-31; TROPICS; TT154-10; TTN013-18; TTXXX; U938; U939; urlepa; V19; V19-27; V21; V21-40; V28; V28-122; V28-238; V34; V34-98; V35; V35-5; V35-6; VAY1; VEM1_8; Vema; ventilation; Vigo; VINO19-4-GGC17; VINO19-4GGC-37; VINO19-4-GGC37; VM21-30; VNTR01; VNTR01-10GC; Vuolep Allakasjaure; W8709A; W8709A-13; Wecoma
    Type: Dataset
    Format: application/x-hdf, 493.1 kBytes
    Location Call Number Expected Availability
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  • 63
    Publication Date: 2024-04-24
    Description: Compiled marine radiocarbon data from the global ocean, derived from planktonic/benthic foraminifera and corals, spanning the last deglaciation, with associated calendar ages that are independently derived and/or are consistent with the Marine20 radiocarbon calibration curve. The data have been screened and include data flags pertaining to anomalous values (e.g. negative offsets relative to the contemporary atmosphere), low sedimentation rates 〈2 cm/kyr, and/or deviations from dominant regional-temporal trends. The data are further grouped by ocean basin, and according to their associated calendar ages as belonging to a succession of time slices across the last deglaciation: the Last Glacial Maximum (LGM), Heinrich Stadial 1 (HS1), the Bolling-Allerod (BA), the Younger Dryas, the Early Holocene (7-11ka BP), and the late Holocene (〈6 ka BP).
    Keywords: 0050PG; 0066PG; 145-887; 146-893A; 167-1019; 17/1GCA; 1K-SUERC; 1P-OS-75; 1P-SUERC; 202-1240; 22SL; 310-M0015A; 310-M0016A; 310-M0018A; 310-M0020A; 310-M0021A; 310-M0023A; 310-M0023B; 310-M0024A; 310-M0025A; 310-M0025B; 310-M0026A; 313-M0027A; 313-M0029A; 341-U1419; 35MF20120125, OISO_21, INDIEN SUD 2; 47396B; 4P-OS-75; 4P-SUERC; 50-37KL; 5P-OS-79; 5P-SUERC; 64-480; 90b; 94-609; AII125-8-55; AII125-8-56; AK-AA-1; Akademik M.A. Lavrentiev; AK-BC-2; AK-F-1; AK-L-1; ALIENOR; also published as VM28-122; ALV; ALV-3884; ALV-3885; ALV-3887; ALV-3890; ALV-3891; ALV-3892; ALV-4162; ALVIN; ANT-XI/4; ANT-XXIII/9; ANT-XXVI/2; ARA04-43E; Argentine Basin; ARK-II/5; ARK-X/2; AT07-35; AT12-01; Atlantis (1997); B34-91; BC; Bering Sea; BO04-PC11; Box corer; Brazil Basin; Burdwood_Bank; CALYPSO; CALYPSO2; Calypso Corer; Calypso Corer II; Calypso square corer; Calypso Square Core System; CASQ; CASQS; CD159; CD159-10; CD38-17P; CDRILL; Cenderawasih Bay; CH84-14; Charles Darwin; CHAT 10K; CHAT-10K; CHAT-16K; CHAT-3K; CHAT-5K; CHR-4; CHR-5; CHR-6; CHR-7; COMPCORE; Composite Core; Conrad Rise; Coral; Core; CORE; Core drilling; Corner Rise; DAPC2; deglacial; Denmark Strait; DH117; DH43; DH74; DP Hunter; DR23; DR27; DR34; DR35; DR38; DR40; Drake Passage; Dredge; DRG; DRILL; Drill9A_Tasmaloum; Drilling/drill rig; Eastern Equatorial Pacific; Eastern slope of Kurile Basin; East Pacific; EBA1; EBA10; EBA11; EBA2; EBA3; EBA4; EBA5; EBA6; EBA7; EBA8; EBA9; EBB1; EBB2; EBB3; EBB4; EBB5; EBB6; EBB7; ENG-111; Equatorial East Pacific; Equatorial Indian Ocean; ESTASE1; ET97-7T; EW0408; EW0408-26JC; EW0408-87JC; Exp310; Exp313; Exp341; f0001carcs; F2-92-P3; F8-90-G21; FLAMINGO; Foraminifera; GC; GC_POI; GeoB1503-1; GeoB2104-3; GeoB7149-2; GeoB7162-6; GeoB7163-7; GeoB7167-6; GEOSCIENCES, MARMARCORE; GGC; GGC5; gh02-1030; Giant box corer; Giant gravity corer; Giant piston corer; GIK23243-2 PS05/431; GIK23415-9; GKG; Glomar Challenger; GPC; Gramberg Seamount; Gravity corer; Gravity corer (Kiel type); Gravity corer (POI); GS07-150-20/2A; Gulf of California; H209; H213; HU72-021-3; HU72-021-7; HU89038-8PC; IMAGES I; IMAGES III - IPHIS; IMAGES IV-IPHIS III; IMAGES V; IMAGES VII - WEPAMA; IMAGES XII - MARCO POLO; IMAGES XV - Pachiderme; Indian Ocean; INOPEX; Interim_Seamount; James Cook; Japan Trench; JC094; JC094_GRM; Jean Charcot; JFA17; JFA2; JFA20; JFA24; JF-FI-19PC; Joides Resolution; JPC; JT96-09; JT96-09PC; Jumbo Piston Core; KAL; KALMAR II; Kasten corer; Kayd; KL; KN_USA; KN11002; KN159-5; Knorr; KNR110-50; KNR110-66; KNR110-82a; KNR110-82GGC; KNR140; KNR140-01JPC; KNR140-02JPC; KNR140-12JPC; KNR140-2-12JPC; KNR140-2-22JPC; KNR140-22JPC; KNR140-2-30GGC; KNR140-2-51GGC; KNR140-26GGC; KNR140-30GGC; KNR140-37JPC; KNR140-39GGC; KNR140-43GGC; KNR140-50GGC; KNR140-51GGC; KNR140-56GGC; KNR140-66GGC; KNR159-5; KNR159-5-78GGC; KNR176-17GC; KNR176-2; KNR176-2-JPC30; KNR178; KNR178-2GGC; KNR178-32JPC; KNR195-5-CDH23; KNR195-5-CDH26; KNR195-5-CDH41; KNR195-5-GGC43; KNR197-10; KNR197-10CDH42; KNR197-10-CDH42; KNR197-10-CDH46; KNR197-10-GGC17; KNR197-10-GGC5; KNR198-CDH36; KNR198-GGC15; KNR198-GGC35; KNR31GPC5; KNR73-3PC; KNR73-4PC; KNR73-6PG; KOL; KOMEX; KOMEX II; KR02-15-PC06; Kronotsky Peninsula; KT89-18-P4; Lakshadweep Sea; Laurentian fan; Leg145; Leg146; Leg167; Leg202; Leg64; Leg94; Le Suroît; LMG06-05-9; LPAZ21P; LV27/GREGORY; LV27-2-4; LV29-114-3; LV29-2; M16/2; M17/2; M23/2; marine; Marion Dufresne (1972); Marion Dufresne (1995); MAT-1A; MAT-3A; Maurice Ewing; MD012386; MD01-2386; MD012420; MD01-2420; MD01-2461; MD02-2461; MD022489; MD02-2489; MD03-2697; MD03-2707; MD04-2829CQ; MD04-2845; MD052896; MD05-2896; MD052904; MD05-2904; MD07-3076; MD07-3076Q; MD07-3088; MD08-3169; MD09-3256; MD09-3256Q; MD09-3257; MD101; MD106; MD111; MD114; MD122; MD123; MD12-3396Cq; MD13; MD134; MD141; MD147; MD159; MD173; MD189; MD77-176; MD952002; MD95-2002; MD952007; MD95-2007; MD972106; MD97-2106; MD972121; MD97-2121; MD972138; MD97-2138; MD982165; MD98-2165; MD982181; MD98-2181; MD99-2331; MD99-2334; ME0005-24JC; Melville; Meriadzec; Meteor (1986); ML1208-01PC; MR01-K03; MR06-04_PC04A; MUC; MULT; MultiCorer; Multiple investigations; Mururoa; MV1007; MV1007-DO3; MV99-GC31; MV99-GC38; MV99-MC17/GC32/PC10; MV99-MC19; MV99-PC08; NA064-117-1; NA064-118-1; NA87-22; Nathaniel B. Palmer; NBP0805; NBP0805-DR23; NBP0805-DR27; NBP0805-DR34; NBP0805-DR35; NBP0805-DR36; NBP0805-DR38; NBP0805-DR40; NBP0805-TB04; NBP1103; NBP1103-DH07; NBP1103-DH112; NBP1103-DH113; NBP1103-DH115; NBP1103-DH117; NBP1103-DH120; NBP1103-DH134; NBP1103-DH14; NBP1103-DH140; NBP1103-DH143; NBP1103-DH15; NBP1103-DH22; NBP1103-DH43; NBP1103-DH74; NBP1103-DH95; Nesmeyanov25-1-GGC15; Nesmeyanov25-1-GGC18; Nesmeyanov25-1-GGC20; Nesmeyanov25-1-GGC27; New England Mountains; New Jersey Shallow Shelf; North Atlantic; North Atlantic/FLANK; Northeast Atlantic; North East Atlantic; North Pacific/Gulf of California/BASIN; North Pacific Ocean; Northwest Atlantic; Norwegian Sea; OCE326-GGC14; OCE326-GGC26; OCE326-GGC5; off Chile; off Nova Scotia; OK-3; OK-8; OKB-36A; OKB-53B; OKB-B3; OSIRIS III; Pacific Ocean; PALEOCINAT; PALEOCINAT II; papua; PC; PC75-1; PC75-2; Philippine Sea; PICABIA; Piston corer; Piston corer (BGR type); Piston corer (Kiel type); PLDS-007G; PLDS-1; Pleiades; Polarstern; PS05; PS1243-2; PS2606-6; PS2644-5; PS30; PS30/144; PS31; PS31/160-5; PS69; PS69/907-2; PS69/912-3; PS69/912-4; PS75/059-2; PS75/100-4; PS75/104-1; PS75 BIPOMAC; PUCK; radiocarbon dates; RAPiD-10-1P; RBDASS05; RBDASS05_H11; RBDASS05_H15; RC11; RC1112; RC11-238; RC24; RC24-8GC; RC27; RC27-14; RC27-23; Remote operated vehicle; Remote operated vehicle Jason II; RETRO-2; RGF_Barbados; RGF-12; RGF-15; RGF-16; RGF-9; RNDB-GGC15; RNDB-GGC5; RNDB-PC11; RNDB-PC13; Robert Conrad; ROV; ROVJ; RR0503-06JPC; RR0503-36JPC; RR0503-64JPC; RR0503-79JPC; RR0503-831C; RR0503-83GC; S67-FFC15; S794; S931; S938; Sakhalin shelf and slope; Sars_Seamount; SC4_ST2_SW2_SX1; Scotia Sea; Sea of Okhotsk; SEDCO; Sediment corer; Shackleton_Fracture_Zone; SHAK03-6K; SHAK05-3K; SHAK06-4K; SHAK10-10K; SHAK14-4G; Shirshov Ridge; Sindhu Sadhana; Site_1471; Site_2088; Site_21210009; Site_2307; Site_2631; Site_2657; Site_2706; Site_2774; Site_47396; Site_654; Site_660; Site_936; SK129-CR2; SL; Smithsonian_48735.1; SO156/2; SO156/3; SO161/5; SO161/5_22SL; SO178; SO178-13-6; SO201/2; SO201-2-101; SO201-2-12KL; SO201-2-77; SO201-2-85; SO202/1; SO202/1_18-6; SO213/2; SO213/2_76-2; SO213/2_79-2; SO213/2_82-1; SO213/2_84-1; Sonne; SOPATRA; South Atlantic; South Atlantic Ocean; South China Sea; Southern Alaska Margin: Tectonics, Climate and Sedimentation; South of Iceland; South Pacific Ocean; South Tasman Rise; Southwest Pacific; Southwest Pacific Ocean; SPOC; SS152; SS172; SSD-044; SSD044_GC-01; Station 6, MD189-3396; St Kildas Basin; SU90-08; SU92; SU92-03; Submersible Alvin; TAH-01A-3A; TAH-02A-4F; TAH-02A-4G; TAH-02A-4H; TAH-02A-5D; TAH-02A-5F; TAH-02A-5G; TAH-02A-5H; TAH-03A-1; TAH-03A-1A; TAH-03A-1E; Tahiti, offshore Faaa; Tahiti, offshore Maraa; Tahiti, offshore Tiarei; Tahiti Sea Level; Ta-P6; Ta-P7; Ta-P8; Tasman Sea; Thomas G. Thompson; Thomas G. Thompson (1964); Thomas Washington; TN228; TN228_J2_382; TN228_J2_383; TN228_J2_387; TN228_J2_389; TN228_J2_393; TN228_J2_395; TNO57-21; TR163-22; TR163-23; TR163-31; TROPICS; TT154-10; TTN013-18; TTXXX; U938; U939; urlepa; V19; V19-27; V21; V21-40; V28; V28-122; V28-238; V34; V34-98; V35; V35-5; V35-6; VAY1; VEM1_8; Vema; ventilation; Vigo; VINO19-4-GGC17; VINO19-4GGC-37; VINO19-4-GGC37; VM21-30; VNTR01; VNTR01-10GC; Vuolep Allakasjaure; W8709A; W8709A-13; Wecoma
    Type: Dataset
    Format: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet, 11.1 kBytes
    Location Call Number Expected Availability
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  • 64
    Publication Date: 2024-04-24
    Description: This global database (CoastDOM v.1) contains both previously published and unpublished measurements of Dissolved organic carbon (DOC), nitrogen (DON) and phosphorus (DOP) in coastal waters. The dataset also contains hydrographic data such as temperature and salinity and, to the extent possible, other biogeochemical variables (e.g., Chlorophyll-a, inorganic nutrients) and the inorganic carbon system (e.g., dissolved inorganic carbon and total alkalinity). The data included were collected from 1978 to 2022 and consist of 62339 data points for DOC, 20360 for DON and 13440 for DOP.
    Keywords: Alkalinity, total; Ammonium; Analytical method; Carbon, inorganic, dissolved; Carbon, organic, dissolved; Carbon, organic, particulate; Chlorophyll a; Coastal waters; Comment; DATE/TIME; DEPTH, water; Dissolved Organic Carbon; Dissolved Organic Matter; dissolved organic nitrogen; dissolved organic phosphorus; ELEVATION; global database;; Hydrogen phosphate; Institution; LATITUDE; Location; LONGITUDE; Nitrate and Nitrite; Nitrogen, organic, dissolved; Nitrogen, particulate; Nitrogen, total dissolved; Phosphorus, organic, dissolved; Phosphorus, particulate; Phosphorus, total dissolved; Principal investigator; Quality flag, alkalinity, total; Quality flag, ammonium; Quality flag, carbon, inorganic, dissolved; Quality flag, carbon, organic, dissolved; Quality flag, carbon, organic, particulate; Quality flag, chlorophyll a; Quality flag, hydrogen phosphate; Quality flag, nitrate and nitrite; Quality flag, nitrogen, particulate; Quality flag, nitrogen, total dissolved; Quality flag, phosphorus, particulate; Quality flag, phosphorus, total dissolved; Reference/source; Salinity; Sample ID; Suspended solids, total; Temperature, water; World Oceans Circulation Experiment (WOCE) quality flags according to Jiang et al. (2022)
    Type: Dataset
    Format: text/tab-separated-values, 1286555 data points
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  • 65
    Publication Date: 2024-04-24
    Description: Copper (Cu) is an important micronutrient involved in different phytoplankton metabolic processes. In seawater, Cu speciation is controlled by the formation of organic complexes with natural binding ligands, which controls its bioavailability and toxicity. The information about Cu organic speciation in the ocean is scarce, particularly in the Arctic Ocean where the ongoing climate change is altering the seawater chemistry. This dataset contains measurements of Cu-binding ligands from station depth profiles sampled between from the Norwegian Coast to the Central Arctic Ocean. Samples were collected during GEOTRACES expedition GN04 (Polarstern cruise PS94) between 17 August and 14 October 2015, following GEOTRACES guidelines (https://www.geotraces.org). The Competitive-Ligand Exchange Adsorptive Cathodic Stripping Voltammetry (CLE-ACSV), using Salicylaldoxime (SA) as competitive ligand (Campos and van den Berg, 1994), was the technique applied to determine the Cu-binding ligand concentrations (LCu) and conditional stability constants (logKcu2+L). The concentration of dissolved copper used for the analysis has been reported by Gerringa et al. (2021) (doi:10.1594/PANGAEA.932797).
    Keywords: Arctic Ocean; ARK-XXIX/3; Barents Sea; Bottle number; CLE-ACSV; Competitive-ligand exchange adsorptive cathodic stripping voltammetry; copper; Copper-binding ligand; Copper-binding ligand, conditional stability constant; Copper-binding ligand, conditional stability constant, standard deviation; Copper-binding ligand, standard deviation; Cruise/expedition; CTD, Sea-Bird SBE 911plus; CTD/Rosette; CTD/Rosette, ultra clean; CTD-RO; CTD-UC; DATE/TIME; DEPTH, water; ELEVATION; Eurasian Basin; Event label; GEOTRACES; Global marine biogeochemical cycles of trace elements and their isotopes; GN04; LATITUDE; LONGITUDE; Norwegian Sea; Organic-binding ligands; Polarstern; Pressure, water; PS94; PS94/004-2; PS94/032-4; PS94/050-3; PS94/054-3; PS94/058-7; PS94/064-2; PS94/069-2; PS94/070-4; PS94/081-10; PS94/087-1; PS94/091-2; PS94/096-4; PS94/099-3; PS94/117-3; PS94/119-1; PS94/121-2; PS94/125-3; PS94/130-2; PS94/134-2; PS94/147-2; PS94/169-2; PS94/173-2; Station label
    Type: Dataset
    Format: text/tab-separated-values, 1806 data points
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  • 66
    Publication Date: 2024-04-24
    Keywords: Aluminium oxide; Burubaital; Calculated; Chiang-Dao; Epoch; Event label; Formation; Germanium; Germanium/Silicon ratio; Inductively coupled plasma atomic emission spectroscope (ICP-AES); Inductively coupled plasma - mass spectrometry (ICP-MS); Kazakhstan; ORDINAL NUMBER; OUTCROP; Outcrop sample; Sample code/label; Silicon; Silicon dioxide
    Type: Dataset
    Format: text/tab-separated-values, 252 data points
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  • 67
    Publication Date: 2024-04-24
    Keywords: Aluminium oxide; Boulonnais-Cliff; Calculated; Enrichment factor; Epoch; Event label; Formation; France; Germanium; Germanium/Silicon ratio; Inductively coupled plasma atomic emission spectroscope (ICP-AES); Inductively coupled plasma - mass spectrometry (ICP-MS); La-Serre-section; Molybdenum; ORDINAL NUMBER; OUTCROP; Outcrop sample; Sample code/label; Silicon dioxide; Uranium
    Type: Dataset
    Format: text/tab-separated-values, 753 data points
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  • 68
    Publication Date: 2024-04-24
    Keywords: 113-692B; 113-693B; Aluminium oxide; Calculated; DRILL; Drilling/drill rig; DSDP/ODP/IODP sample designation; Enrichment factor; Event label; Germanium; Germanium/Silicon ratio; Inductively coupled plasma atomic emission spectroscope (ICP-AES); Inductively coupled plasma - mass spectrometry (ICP-MS); Joides Resolution; Leg113; Molybdenum; Ocean Drilling Program; ODP; Sample code/label; Silicon dioxide; Uranium; Weddell Sea
    Type: Dataset
    Format: text/tab-separated-values, 710 data points
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  • 69
    Publication Date: 2024-04-24
    Keywords: 6,9,12,15-Hexadecatetraenoic acid of total fatty acids; 6,9,12-Hexadecatrienoic acid of total fatty acids; 9,12-Hexadecadienoic acid of total fatty acids; Age, comment; all-cis-4,7,10,13,16,19-Docosahexaenoic acid of total fatty acids; all-cis-5,8,11,14,17-Eicosapentaenoic acid of total fatty acids; all-cis-5,8,11,14-Eicosatetraenoic acid of total fatty acids; all-cis-6,9,12,15-Octadecatetraenoic acid of total fatty acids; all-cis-7,10,13,16,19-Docosapentaenoic acid of total fatty acids; all-cis-8,11,14,17-Eicosatetraenoic acid of total fatty acids; all-cis-9,12,15-Octadecatrienoic acid of total fatty acids; all-cis-9,12-Octadecadienoic acid of total fatty acids; cis-11-Docosenoic acid of total fatty acids; cis-11-Hexadecenoic acid of total fatty acids (IUPAC: (11Z)-hexadec-11-enoic acid); cis-11-Icosenoic acid of total fatty acids; cis-11-Octadecenoic acid of total fatty acids (IUPAC: Octadec-11-enoic acid); cis-13-Docosenoic acid of total fatty acids; cis-13-Icosenoic acid of total fatty acids; cis-9-Hexadecenoic acid of total fatty acids (IUPAC: (9Z)-hexadec-9-enoic acid); cis-9-Octadecenoic acid of total fatty acids (IUPAC: Octadec-9-enoic acid); DATE/TIME; Date/time end; DIVER; Fatty acids, standard deviation; Fatty alcohols; Fatty alcohols, standard deviation; Hexadecanoic acid of total fatty acids; Hexadecanol of total fatty alcohols; High Performance Thin Layer Chromatography (HPTLC); Icosanoic acid of total fatty acids; Kongsfjorden_2001; Kongsfjorden, Spitsbergen, Arctic; Month; Octadecanoic acid of total fatty acids; Pentadecanoic acid of total fatty acids; Replicates; Sampling by diver; Tetradecanoic acid of total fatty acids; Tetradecanol of total alcohols
    Type: Dataset
    Format: text/tab-separated-values, 462 data points
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  • 70
    Publication Date: 2024-04-24
    Keywords: 6,9,12,15-Hexadecatetraenoic acid of total fatty acids; 6,9,12-Hexadecatrienoic acid of total fatty acids; 9,12-Hexadecadienoic acid of total fatty acids; all-cis-4,7,10,13,16,19-Docosahexaenoic acid of total fatty acids; all-cis-5,8,11,14,17-Eicosapentaenoic acid of total fatty acids; all-cis-5,8,11,14-Eicosatetraenoic acid of total fatty acids; all-cis-6,9,12,15-Octadecatetraenoic acid of total fatty acids; all-cis-7,10,13,16,19-Docosapentaenoic acid of total fatty acids; all-cis-8,11,14,17-Eicosatetraenoic acid of total fatty acids; all-cis-9,12,15-Octadecatrienoic acid of total fatty acids; all-cis-9,12-Octadecadienoic acid of total fatty acids; cis-11-Docosenoic acid of total fatty acids; cis-11-Hexadecenoic acid of total fatty acids (IUPAC: (11Z)-hexadec-11-enoic acid); cis-11-Icosenoic acid of total fatty acids; cis-11-Octadecenoic acid of total fatty acids (IUPAC: Octadec-11-enoic acid); cis-13-Docosenoic acid of total fatty acids; cis-13-Icosenoic acid of total fatty acids; cis-15-Docosenoic acid of total fatty acids; cis-9-Hexadecenoic acid of total fatty acids (IUPAC: (9Z)-hexadec-9-enoic acid); cis-9-Octadecenoic acid of total fatty acids (IUPAC: Octadec-9-enoic acid); Compounds; DIVER; Fatty acids, standard deviation; Fatty alcohols; Fatty alcohols, standard deviation; Hexadecanoic acid of total fatty acids; Hexadecanol of total fatty alcohols; High Performance Thin Layer Chromatography (HPTLC); Icosanoic acid of total fatty acids; Kongsfjorden_2001; Kongsfjorden, Spitsbergen, Arctic; Octadecanoic acid of total fatty acids; Pentadecanoic acid of total fatty acids; Replicates; Sampling by diver; Tetradecanoic acid of total fatty acids; Tetradecanol of total alcohols
    Type: Dataset
    Format: text/tab-separated-values, 118 data points
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  • 71
    Publication Date: 2024-04-24
    Keywords: DATE/TIME; Date/time end; DIVER; Fatty alcohols; Fatty alcohols, standard deviation; Hexadecanol of total fatty alcohols; High Performance Thin Layer Chromatography (HPTLC); Kongsfjorden_2001; Kongsfjorden, Spitsbergen, Arctic; Month; Replicates; Sampling by diver; Tetradecanol of total alcohols
    Type: Dataset
    Format: text/tab-separated-values, 58 data points
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  • 72
    Publication Date: 2024-04-24
    Keywords: all-cis-4,7,10,13,16,19-Docosahexaenoic acid of total fatty acids; all-cis-5,8,11,14,17-Eicosapentaenoic acid of total fatty acids; Amundsen Gulf, Canada; Area/locality; Beaufort Sea; CCGSA_4-10_CFL08; CCGS Amundsen; CFL08_02; CFL08_416; CFL08_D19-2; CFL08_D26; CFL08_D29-2; CFL08_D33-2; CFL08_D34-2; CFL08_D36-1; CFL08_D41-1; CFL08_F6; CFL08_F7-2; Circumpolar Flaw Lead Leg 4-10a; cis-11-Docosenoic acid of total fatty acids; cis-11-Icosenoic acid of total fatty acids; cis-11-Octadecenoic acid of total fatty acids (IUPAC: Octadec-11-enoic acid); cis-13-Docosenoic acid of total fatty acids; cis-15-Tetracosenoic acid of total fatty acids; cis-9-Hexadecenoic acid of total fatty acids (IUPAC: (9Z)-hexadec-9-enoic acid); cis-9-Octadecenoic acid of total fatty acids (IUPAC: Octadec-9-enoic acid); DATE/TIME; Depth, bottom/max; Depth, top/min; Depth comment; Elevation of event; Event label; Fatty acids; Fatty acids, standard deviation; Fatty acid trophic marker; Fatty alcohols; Fatty alcohols, standard deviation; Hexadecanoic acid of total fatty acids; Hexadecanol of total fatty alcohols; High Performance Liquid Chromatography (HPLC); Latitude of event; Longitude of event; Monounsaturated fatty acids of total fatty acids; MULT; Multiple investigations; Number of individuals; Octadecanoic acid of total fatty acids; Ontogenetic stage; Polyunsaturated fatty acids of total fatty acids; Sample amount; Sample type; Saturated fatty acids of total fatty acids; Species; Standard deviation; Station label; Tetradecanoic acid of total fatty acids; Tetradecanol of total alcohols; Visual observation
    Type: Dataset
    Format: text/tab-separated-values, 682 data points
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  • 73
    Publication Date: 2024-04-24
    Description: We present a high-resolution airborne radar data set (CHIRP 2019) for the Princess Ragnhild Coast, Dronning Maud Land, East Antarctica. The radar data has been acquired in December 2018 and January 2019 with the Alfred Wegener Institute (AWI) multichannel ultra-wideband radar mounted on the Polar6 aircraft. Radar profiles cover the Princess Ragnhild Coast in Dronning Maud Land, East Antarctica (20° E - 28° E). They cover the western Roi Baudouin Ice Shelf, the Jotneisen Ice Shelf, and the eastern Munisen Ice Shelf as well as the three ice rises Hamarryggen, Lokeryggen and Derwael. Even though the Princess Ragnhild coast has been close to balance in recent decades and is likely dynamically stable , the individual catchments are sensitive to increased ocean melting because some tributary glaciers rest on a retrograde, landward sloping bed . Together, the catchments drain a land ice mass with a eustatic sea-level equivalent of 2 m. We provide radar data in two processing levels: (1) unfocussed SAR (qlook), and (2) foscussed SAR (standard).
    Keywords: AC; Aircraft; AWI UWB; Date/Time of event; Derwael Ice Rise; East Antarctica; Event label; GIS file; ice rise; Ice shelf; Image; Internal Reflection Horizon; IRH; JuRaS, CHIRP; netCDF file; P6_215_UWB_2018; P6_215_UWB_2018_1812311001; P6_215_UWB_2018_1901061201; P6_215_UWB_2018_1901061302; P6_215_UWB_2018_1901071401; POLAR 6; Profile; radio-echo sounding; Roi Baudouin Ice Shelf; Ultra-wideband radar (UWB), MCoRDS 5
    Type: Dataset
    Format: text/tab-separated-values, 315 data points
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  • 74
    Publication Date: 2024-04-24
    Description: This data set is a higher-processing-level version of Geolocated sea-ice or snow surface elevation point clouds from helicopter-borne laser scanner during the MOSAiC expedition, version 1 (Jutila et al., 2022; doi:10.1594/PANGAEA.950509), where the surface elevation point cloud has been converted to freeboard using automatic open water detection scheme and projected onto a regular 0.5-meter grid. The data were collected using a near-infrared, line-scanning Riegl VQ-580 airborne laser scanner (hdl:10013/sensor.7ebb63c3-dc3b-4f0f-9ca5-f1c6e5462a31 & hdl:10013/sensor.7a931b33-72ca-46d0-b623-156836ac9550) mounted in a helicopter along the MOSAiC drift from the north of the Laptev Sea, across the central Arctic Ocean, and towards the Fram Strait from September 2019 to October 2020. The flights are both small scale, ~5x5 km grid patterns mainly over the central observatory, and large scale, few tens of km away from RV Polarstern, triangle patterns, or transects. The gridded data are stored in 30-second along-track segments in netCDF format. For the small scale grid flights, the data are drift corrected using the position and heading data of RV Polarstern and elevation offset corrected using overlapping segments to overcome degraded GPS altitude data 〉85°N. Open water points are identified to derive a freeboard estimate from the surface elevations. For the flights with degraded GPS altitude quality, we provide only a freeboard estimate (grid pattern flights) or no freeboard (transects). The gridded 30-s segments include as data variables: surface elevation, freeboard (estimate), freeboard uncertainty, estimated sea surface height, surface reflectance, echo width, and number of points used in the interpolation. In addition, list of detected open water points and an overview figure of each flight is provided.
    Keywords: 20200107_02; Airborne laser scanning; Arctic; Arctic Ocean; Binary Object; DATE/TIME; Flight number; Freeboard; HELI; Helicopter; IceSense; LATITUDE; LONGITUDE; MOSAiC; MOSAiC20192020; MOSAIC-HELI; Multidisciplinary drifting Observatory for the Study of Arctic Climate; Polarstern; PS122/2; PS122/2_19-45; Remote Sensing of the Seasonal Evolution of Climate-relevant Sea Ice Properties; Sea ice; Surface Elevation
    Type: Dataset
    Format: text/tab-separated-values, 366 data points
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  • 75
    Publication Date: 2024-04-24
    Description: This data set is a higher-processing-level version of Geolocated sea-ice or snow surface elevation point clouds from helicopter-borne laser scanner during the MOSAiC expedition, version 1 (Jutila et al., 2022; doi:10.1594/PANGAEA.950509), where the surface elevation point cloud has been converted to freeboard using automatic open water detection scheme and projected onto a regular 0.5-meter grid. The data were collected using a near-infrared, line-scanning Riegl VQ-580 airborne laser scanner (hdl:10013/sensor.7ebb63c3-dc3b-4f0f-9ca5-f1c6e5462a31 & hdl:10013/sensor.7a931b33-72ca-46d0-b623-156836ac9550) mounted in a helicopter along the MOSAiC drift from the north of the Laptev Sea, across the central Arctic Ocean, and towards the Fram Strait from September 2019 to October 2020. The flights are both small scale, ~5x5 km grid patterns mainly over the central observatory, and large scale, few tens of km away from RV Polarstern, triangle patterns, or transects. The gridded data are stored in 30-second along-track segments in netCDF format. For the small scale grid flights, the data are drift corrected using the position and heading data of RV Polarstern and elevation offset corrected using overlapping segments to overcome degraded GPS altitude data 〉85°N. Open water points are identified to derive a freeboard estimate from the surface elevations. For the flights with degraded GPS altitude quality, we provide only a freeboard estimate (grid pattern flights) or no freeboard (transects). The gridded 30-s segments include as data variables: surface elevation, freeboard (estimate), freeboard uncertainty, estimated sea surface height, surface reflectance, echo width, and number of points used in the interpolation. In addition, list of detected open water points and an overview figure of each flight is provided.
    Keywords: Airborne laser scanning; Arctic; Arctic Ocean; Binary Object; DATE/TIME; Flight number; Freeboard; HELI; Helicopter; IceSense; LATITUDE; LONGITUDE; MOSAiC; MOSAiC20192020; MOSAIC-HELI; Multidisciplinary drifting Observatory for the Study of Arctic Climate; Polarstern; PS122/2; PS122/2_19-51; Remote Sensing of the Seasonal Evolution of Climate-relevant Sea Ice Properties; Sea ice; Surface Elevation
    Type: Dataset
    Format: text/tab-separated-values, 10 data points
    Location Call Number Expected Availability
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  • 76
    Publication Date: 2024-04-24
    Description: This data set is a higher-processing-level version of Geolocated sea-ice or snow surface elevation point clouds from helicopter-borne laser scanner during the MOSAiC expedition, version 1 (Jutila et al., 2022; doi:10.1594/PANGAEA.950509), where the surface elevation point cloud has been converted to freeboard using automatic open water detection scheme and projected onto a regular 0.5-meter grid. The data were collected using a near-infrared, line-scanning Riegl VQ-580 airborne laser scanner (hdl:10013/sensor.7ebb63c3-dc3b-4f0f-9ca5-f1c6e5462a31 & hdl:10013/sensor.7a931b33-72ca-46d0-b623-156836ac9550) mounted in a helicopter along the MOSAiC drift from the north of the Laptev Sea, across the central Arctic Ocean, and towards the Fram Strait from September 2019 to October 2020. The flights are both small scale, ~5x5 km grid patterns mainly over the central observatory, and large scale, few tens of km away from RV Polarstern, triangle patterns, or transects. The gridded data are stored in 30-second along-track segments in netCDF format. For the small scale grid flights, the data are drift corrected using the position and heading data of RV Polarstern and elevation offset corrected using overlapping segments to overcome degraded GPS altitude data 〉85°N. Open water points are identified to derive a freeboard estimate from the surface elevations. For the flights with degraded GPS altitude quality, we provide only a freeboard estimate (grid pattern flights) or no freeboard (transects). The gridded 30-s segments include as data variables: surface elevation, freeboard (estimate), freeboard uncertainty, estimated sea surface height, surface reflectance, echo width, and number of points used in the interpolation. In addition, list of detected open water points and an overview figure of each flight is provided.
    Keywords: 20191230_01; Airborne laser scanning; Arctic; Arctic Ocean; Binary Object; DATE/TIME; Flight number; Freeboard; HELI; Helicopter; IceSense; LATITUDE; LONGITUDE; MOSAiC; MOSAiC20192020; MOSAIC-HELI; Multidisciplinary drifting Observatory for the Study of Arctic Climate; Polarstern; PS122/2; PS122/2_18-7; Remote Sensing of the Seasonal Evolution of Climate-relevant Sea Ice Properties; Sea ice; Surface Elevation
    Type: Dataset
    Format: text/tab-separated-values, 370 data points
    Location Call Number Expected Availability
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  • 77
    Publication Date: 2024-04-24
    Description: This data set is a higher-processing-level version of Geolocated sea-ice or snow surface elevation point clouds from helicopter-borne laser scanner during the MOSAiC expedition, version 1 (Jutila et al., 2022; doi:10.1594/PANGAEA.950509), where the surface elevation point cloud has been converted to freeboard using automatic open water detection scheme and projected onto a regular 0.5-meter grid. The data were collected using a near-infrared, line-scanning Riegl VQ-580 airborne laser scanner (hdl:10013/sensor.7ebb63c3-dc3b-4f0f-9ca5-f1c6e5462a31 & hdl:10013/sensor.7a931b33-72ca-46d0-b623-156836ac9550) mounted in a helicopter along the MOSAiC drift from the north of the Laptev Sea, across the central Arctic Ocean, and towards the Fram Strait from September 2019 to October 2020. The flights are both small scale, ~5x5 km grid patterns mainly over the central observatory, and large scale, few tens of km away from RV Polarstern, triangle patterns, or transects. The gridded data are stored in 30-second along-track segments in netCDF format. For the small scale grid flights, the data are drift corrected using the position and heading data of RV Polarstern and elevation offset corrected using overlapping segments to overcome degraded GPS altitude data 〉85°N. Open water points are identified to derive a freeboard estimate from the surface elevations. For the flights with degraded GPS altitude quality, we provide only a freeboard estimate (grid pattern flights) or no freeboard (transects). The gridded 30-s segments include as data variables: surface elevation, freeboard (estimate), freeboard uncertainty, estimated sea surface height, surface reflectance, echo width, and number of points used in the interpolation. In addition, list of detected open water points and an overview figure of each flight is provided.
    Keywords: 20191225_01; Airborne laser scanning; Arctic; Arctic Ocean; Binary Object; DATE/TIME; Flight number; Freeboard; HELI; Helicopter; IceSense; LATITUDE; LONGITUDE; MOSAiC; MOSAiC20192020; MOSAIC-HELI; Multidisciplinary drifting Observatory for the Study of Arctic Climate; Polarstern; PS122/2; PS122/2_17-99; Remote Sensing of the Seasonal Evolution of Climate-relevant Sea Ice Properties; Sea ice; Surface Elevation
    Type: Dataset
    Format: text/tab-separated-values, 374 data points
    Location Call Number Expected Availability
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  • 78
    Publication Date: 2024-04-24
    Description: This data set is a higher-processing-level version of Geolocated sea-ice or snow surface elevation point clouds from helicopter-borne laser scanner during the MOSAiC expedition, version 1 (Jutila et al., 2022; doi:10.1594/PANGAEA.950509), where the surface elevation point cloud has been converted to freeboard using automatic open water detection scheme and projected onto a regular 0.5-meter grid. The data were collected using a near-infrared, line-scanning Riegl VQ-580 airborne laser scanner (hdl:10013/sensor.7ebb63c3-dc3b-4f0f-9ca5-f1c6e5462a31 & hdl:10013/sensor.7a931b33-72ca-46d0-b623-156836ac9550) mounted in a helicopter along the MOSAiC drift from the north of the Laptev Sea, across the central Arctic Ocean, and towards the Fram Strait from September 2019 to October 2020. The flights are both small scale, ~5x5 km grid patterns mainly over the central observatory, and large scale, few tens of km away from RV Polarstern, triangle patterns, or transects. The gridded data are stored in 30-second along-track segments in netCDF format. For the small scale grid flights, the data are drift corrected using the position and heading data of RV Polarstern and elevation offset corrected using overlapping segments to overcome degraded GPS altitude data 〉85°N. Open water points are identified to derive a freeboard estimate from the surface elevations. For the flights with degraded GPS altitude quality, we provide only a freeboard estimate (grid pattern flights) or no freeboard (transects). The gridded 30-s segments include as data variables: surface elevation, freeboard (estimate), freeboard uncertainty, estimated sea surface height, surface reflectance, echo width, and number of points used in the interpolation. In addition, list of detected open water points and an overview figure of each flight is provided.
    Keywords: 20200121_01; Airborne laser scanning; Arctic; Arctic Ocean; Binary Object; DATE/TIME; Flight number; Freeboard; HELI; Helicopter; IceSense; LATITUDE; LONGITUDE; MOSAiC; MOSAiC20192020; MOSAIC-HELI; Multidisciplinary drifting Observatory for the Study of Arctic Climate; Polarstern; PS122/2; PS122/2_21-41; Remote Sensing of the Seasonal Evolution of Climate-relevant Sea Ice Properties; Sea ice; Surface Elevation
    Type: Dataset
    Format: text/tab-separated-values, 456 data points
    Location Call Number Expected Availability
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  • 79
    Publication Date: 2024-04-24
    Description: This data set is a higher-processing-level version of Geolocated sea-ice or snow surface elevation point clouds from helicopter-borne laser scanner during the MOSAiC expedition, version 1 (Jutila et al., 2022; doi:10.1594/PANGAEA.950509), where the surface elevation point cloud has been converted to freeboard using automatic open water detection scheme and projected onto a regular 0.5-meter grid. The data were collected using a near-infrared, line-scanning Riegl VQ-580 airborne laser scanner (hdl:10013/sensor.7ebb63c3-dc3b-4f0f-9ca5-f1c6e5462a31 & hdl:10013/sensor.7a931b33-72ca-46d0-b623-156836ac9550) mounted in a helicopter along the MOSAiC drift from the north of the Laptev Sea, across the central Arctic Ocean, and towards the Fram Strait from September 2019 to October 2020. The flights are both small scale, ~5x5 km grid patterns mainly over the central observatory, and large scale, few tens of km away from RV Polarstern, triangle patterns, or transects. The gridded data are stored in 30-second along-track segments in netCDF format. For the small scale grid flights, the data are drift corrected using the position and heading data of RV Polarstern and elevation offset corrected using overlapping segments to overcome degraded GPS altitude data 〉85°N. Open water points are identified to derive a freeboard estimate from the surface elevations. For the flights with degraded GPS altitude quality, we provide only a freeboard estimate (grid pattern flights) or no freeboard (transects). The gridded 30-s segments include as data variables: surface elevation, freeboard (estimate), freeboard uncertainty, estimated sea surface height, surface reflectance, echo width, and number of points used in the interpolation. In addition, list of detected open water points and an overview figure of each flight is provided.
    Keywords: 20200123_02; Airborne laser scanning; Arctic; Arctic Ocean; Binary Object; DATE/TIME; Flight number; Freeboard; HELI; Helicopter; IceSense; LATITUDE; LONGITUDE; MOSAiC; MOSAiC20192020; MOSAIC-HELI; Multidisciplinary drifting Observatory for the Study of Arctic Climate; Polarstern; PS122/2; PS122/2_21-78; Remote Sensing of the Seasonal Evolution of Climate-relevant Sea Ice Properties; Sea ice; Surface Elevation
    Type: Dataset
    Format: text/tab-separated-values, 214 data points
    Location Call Number Expected Availability
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  • 80
    Publication Date: 2024-04-24
    Description: This data set is a higher-processing-level version of Geolocated sea-ice or snow surface elevation point clouds from helicopter-borne laser scanner during the MOSAiC expedition, version 1 (Jutila et al., 2022; doi:10.1594/PANGAEA.950509), where the surface elevation point cloud has been converted to freeboard using automatic open water detection scheme and projected onto a regular 0.5-meter grid. The data were collected using a near-infrared, line-scanning Riegl VQ-580 airborne laser scanner (hdl:10013/sensor.7ebb63c3-dc3b-4f0f-9ca5-f1c6e5462a31 & hdl:10013/sensor.7a931b33-72ca-46d0-b623-156836ac9550) mounted in a helicopter along the MOSAiC drift from the north of the Laptev Sea, across the central Arctic Ocean, and towards the Fram Strait from September 2019 to October 2020. The flights are both small scale, ~5x5 km grid patterns mainly over the central observatory, and large scale, few tens of km away from RV Polarstern, triangle patterns, or transects. The gridded data are stored in 30-second along-track segments in netCDF format. For the small scale grid flights, the data are drift corrected using the position and heading data of RV Polarstern and elevation offset corrected using overlapping segments to overcome degraded GPS altitude data 〉85°N. Open water points are identified to derive a freeboard estimate from the surface elevations. For the flights with degraded GPS altitude quality, we provide only a freeboard estimate (grid pattern flights) or no freeboard (transects). The gridded 30-s segments include as data variables: surface elevation, freeboard (estimate), freeboard uncertainty, estimated sea surface height, surface reflectance, echo width, and number of points used in the interpolation. In addition, list of detected open water points and an overview figure of each flight is provided.
    Keywords: 20200209_01; Airborne laser scanning; Arctic; Arctic Ocean; Binary Object; DATE/TIME; Flight number; Freeboard; HELI; Helicopter; IceSense; LATITUDE; LONGITUDE; MOSAiC; MOSAiC20192020; MOSAIC-HELI; Multidisciplinary drifting Observatory for the Study of Arctic Climate; Polarstern; PS122/2; PS122/2_23-109; Remote Sensing of the Seasonal Evolution of Climate-relevant Sea Ice Properties; Sea ice; Surface Elevation
    Type: Dataset
    Format: text/tab-separated-values, 352 data points
    Location Call Number Expected Availability
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  • 81
    Publication Date: 2024-04-24
    Description: This data set is a higher-processing-level version of Geolocated sea-ice or snow surface elevation point clouds from helicopter-borne laser scanner during the MOSAiC expedition, version 1 (Jutila et al., 2022; doi:10.1594/PANGAEA.950509), where the surface elevation point cloud has been converted to freeboard using automatic open water detection scheme and projected onto a regular 0.5-meter grid. The data were collected using a near-infrared, line-scanning Riegl VQ-580 airborne laser scanner (hdl:10013/sensor.7ebb63c3-dc3b-4f0f-9ca5-f1c6e5462a31 & hdl:10013/sensor.7a931b33-72ca-46d0-b623-156836ac9550) mounted in a helicopter along the MOSAiC drift from the north of the Laptev Sea, across the central Arctic Ocean, and towards the Fram Strait from September 2019 to October 2020. The flights are both small scale, ~5x5 km grid patterns mainly over the central observatory, and large scale, few tens of km away from RV Polarstern, triangle patterns, or transects. The gridded data are stored in 30-second along-track segments in netCDF format. For the small scale grid flights, the data are drift corrected using the position and heading data of RV Polarstern and elevation offset corrected using overlapping segments to overcome degraded GPS altitude data 〉85°N. Open water points are identified to derive a freeboard estimate from the surface elevations. For the flights with degraded GPS altitude quality, we provide only a freeboard estimate (grid pattern flights) or no freeboard (transects). The gridded 30-s segments include as data variables: surface elevation, freeboard (estimate), freeboard uncertainty, estimated sea surface height, surface reflectance, echo width, and number of points used in the interpolation. In addition, list of detected open water points and an overview figure of each flight is provided.
    Keywords: 20200204_01; Airborne laser scanning; Arctic; Arctic Ocean; Binary Object; DATE/TIME; Flight number; Freeboard; HELI; Helicopter; IceSense; LATITUDE; LONGITUDE; MOSAiC; MOSAiC20192020; MOSAIC-HELI; Multidisciplinary drifting Observatory for the Study of Arctic Climate; Polarstern; PS122/2; PS122/2_23-14; Remote Sensing of the Seasonal Evolution of Climate-relevant Sea Ice Properties; Sea ice; Surface Elevation
    Type: Dataset
    Format: text/tab-separated-values, 466 data points
    Location Call Number Expected Availability
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  • 82
    Publication Date: 2024-04-24
    Description: This data set is a higher-processing-level version of Geolocated sea-ice or snow surface elevation point clouds from helicopter-borne laser scanner during the MOSAiC expedition, version 1 (Jutila et al., 2022; doi:10.1594/PANGAEA.950509), where the surface elevation point cloud has been converted to freeboard using automatic open water detection scheme and projected onto a regular 0.5-meter grid. The data were collected using a near-infrared, line-scanning Riegl VQ-580 airborne laser scanner (hdl:10013/sensor.7ebb63c3-dc3b-4f0f-9ca5-f1c6e5462a31 & hdl:10013/sensor.7a931b33-72ca-46d0-b623-156836ac9550) mounted in a helicopter along the MOSAiC drift from the north of the Laptev Sea, across the central Arctic Ocean, and towards the Fram Strait from September 2019 to October 2020. The flights are both small scale, ~5x5 km grid patterns mainly over the central observatory, and large scale, few tens of km away from RV Polarstern, triangle patterns, or transects. The gridded data are stored in 30-second along-track segments in netCDF format. For the small scale grid flights, the data are drift corrected using the position and heading data of RV Polarstern and elevation offset corrected using overlapping segments to overcome degraded GPS altitude data 〉85°N. Open water points are identified to derive a freeboard estimate from the surface elevations. For the flights with degraded GPS altitude quality, we provide only a freeboard estimate (grid pattern flights) or no freeboard (transects). The gridded 30-s segments include as data variables: surface elevation, freeboard (estimate), freeboard uncertainty, estimated sea surface height, surface reflectance, echo width, and number of points used in the interpolation. In addition, list of detected open water points and an overview figure of each flight is provided.
    Keywords: 20200217_01; Airborne laser scanning; Arctic; Arctic Ocean; Binary Object; DATE/TIME; Flight number; Freeboard; HELI; Helicopter; IceSense; LATITUDE; LONGITUDE; MOSAiC; MOSAiC20192020; MOSAIC-HELI; Multidisciplinary drifting Observatory for the Study of Arctic Climate; Polarstern; PS122/2; PS122/2_25-7; Remote Sensing of the Seasonal Evolution of Climate-relevant Sea Ice Properties; Sea ice; Surface Elevation
    Type: Dataset
    Format: text/tab-separated-values, 352 data points
    Location Call Number Expected Availability
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  • 83
    Publication Date: 2024-04-24
    Description: This data set is a higher-processing-level version of Geolocated sea-ice or snow surface elevation point clouds from helicopter-borne laser scanner during the MOSAiC expedition, version 1 (Jutila et al., 2022; doi:10.1594/PANGAEA.950509), where the surface elevation point cloud has been converted to freeboard using automatic open water detection scheme and projected onto a regular 0.5-meter grid. The data were collected using a near-infrared, line-scanning Riegl VQ-580 airborne laser scanner (hdl:10013/sensor.7ebb63c3-dc3b-4f0f-9ca5-f1c6e5462a31 & hdl:10013/sensor.7a931b33-72ca-46d0-b623-156836ac9550) mounted in a helicopter along the MOSAiC drift from the north of the Laptev Sea, across the central Arctic Ocean, and towards the Fram Strait from September 2019 to October 2020. The flights are both small scale, ~5x5 km grid patterns mainly over the central observatory, and large scale, few tens of km away from RV Polarstern, triangle patterns, or transects. The gridded data are stored in 30-second along-track segments in netCDF format. For the small scale grid flights, the data are drift corrected using the position and heading data of RV Polarstern and elevation offset corrected using overlapping segments to overcome degraded GPS altitude data 〉85°N. Open water points are identified to derive a freeboard estimate from the surface elevations. For the flights with degraded GPS altitude quality, we provide only a freeboard estimate (grid pattern flights) or no freeboard (transects). The gridded 30-s segments include as data variables: surface elevation, freeboard (estimate), freeboard uncertainty, estimated sea surface height, surface reflectance, echo width, and number of points used in the interpolation. In addition, list of detected open water points and an overview figure of each flight is provided.
    Keywords: 20200217_02; Airborne laser scanning; Arctic; Arctic Ocean; Binary Object; DATE/TIME; Flight number; Freeboard; HELI; Helicopter; IceSense; LATITUDE; LONGITUDE; MOSAiC; MOSAiC20192020; MOSAIC-HELI; Multidisciplinary drifting Observatory for the Study of Arctic Climate; Polarstern; PS122/2; PS122/2_25-8; Remote Sensing of the Seasonal Evolution of Climate-relevant Sea Ice Properties; Sea ice; Surface Elevation
    Type: Dataset
    Format: text/tab-separated-values, 442 data points
    Location Call Number Expected Availability
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  • 84
    Publication Date: 2024-04-24
    Description: This data set is a higher-processing-level version of Geolocated sea-ice or snow surface elevation point clouds from helicopter-borne laser scanner during the MOSAiC expedition, version 1 (Jutila et al., 2022; doi:10.1594/PANGAEA.950509), where the surface elevation point cloud has been converted to freeboard using automatic open water detection scheme and projected onto a regular 0.5-meter grid. The data were collected using a near-infrared, line-scanning Riegl VQ-580 airborne laser scanner (hdl:10013/sensor.7ebb63c3-dc3b-4f0f-9ca5-f1c6e5462a31 & hdl:10013/sensor.7a931b33-72ca-46d0-b623-156836ac9550) mounted in a helicopter along the MOSAiC drift from the north of the Laptev Sea, across the central Arctic Ocean, and towards the Fram Strait from September 2019 to October 2020. The flights are both small scale, ~5x5 km grid patterns mainly over the central observatory, and large scale, few tens of km away from RV Polarstern, triangle patterns, or transects. The gridded data are stored in 30-second along-track segments in netCDF format. For the small scale grid flights, the data are drift corrected using the position and heading data of RV Polarstern and elevation offset corrected using overlapping segments to overcome degraded GPS altitude data 〉85°N. Open water points are identified to derive a freeboard estimate from the surface elevations. For the flights with degraded GPS altitude quality, we provide only a freeboard estimate (grid pattern flights) or no freeboard (transects). The gridded 30-s segments include as data variables: surface elevation, freeboard (estimate), freeboard uncertainty, estimated sea surface height, surface reflectance, echo width, and number of points used in the interpolation. In addition, list of detected open water points and an overview figure of each flight is provided.
    Keywords: 20200321_01; Airborne laser scanning; Arctic; Arctic Ocean; Binary Object; DATE/TIME; Flight number; Freeboard; HELI; Helicopter; IceSense; LATITUDE; LONGITUDE; MOSAiC; MOSAiC20192020; MOSAIC-HELI; Multidisciplinary drifting Observatory for the Study of Arctic Climate; Polarstern; PS122/3; PS122/3_32-70; Remote Sensing of the Seasonal Evolution of Climate-relevant Sea Ice Properties; Sea ice; Surface Elevation
    Type: Dataset
    Format: text/tab-separated-values, 466 data points
    Location Call Number Expected Availability
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  • 85
    Publication Date: 2024-04-24
    Description: This data set is a higher-processing-level version of Geolocated sea-ice or snow surface elevation point clouds from helicopter-borne laser scanner during the MOSAiC expedition, version 1 (Jutila et al., 2022; doi:10.1594/PANGAEA.950509), where the surface elevation point cloud has been converted to freeboard using automatic open water detection scheme and projected onto a regular 0.5-meter grid. The data were collected using a near-infrared, line-scanning Riegl VQ-580 airborne laser scanner (hdl:10013/sensor.7ebb63c3-dc3b-4f0f-9ca5-f1c6e5462a31 & hdl:10013/sensor.7a931b33-72ca-46d0-b623-156836ac9550) mounted in a helicopter along the MOSAiC drift from the north of the Laptev Sea, across the central Arctic Ocean, and towards the Fram Strait from September 2019 to October 2020. The flights are both small scale, ~5x5 km grid patterns mainly over the central observatory, and large scale, few tens of km away from RV Polarstern, triangle patterns, or transects. The gridded data are stored in 30-second along-track segments in netCDF format. For the small scale grid flights, the data are drift corrected using the position and heading data of RV Polarstern and elevation offset corrected using overlapping segments to overcome degraded GPS altitude data 〉85°N. Open water points are identified to derive a freeboard estimate from the surface elevations. For the flights with degraded GPS altitude quality, we provide only a freeboard estimate (grid pattern flights) or no freeboard (transects). The gridded 30-s segments include as data variables: surface elevation, freeboard (estimate), freeboard uncertainty, estimated sea surface height, surface reflectance, echo width, and number of points used in the interpolation. In addition, list of detected open water points and an overview figure of each flight is provided.
    Keywords: 20200321_02; Airborne laser scanning; Arctic; Arctic Ocean; Binary Object; DATE/TIME; Flight number; Freeboard; HELI; Helicopter; IceSense; LATITUDE; LONGITUDE; MOSAiC; MOSAiC20192020; MOSAIC-HELI; Multidisciplinary drifting Observatory for the Study of Arctic Climate; Polarstern; PS122/3; PS122/3_32-71; Remote Sensing of the Seasonal Evolution of Climate-relevant Sea Ice Properties; Sea ice; Surface Elevation
    Type: Dataset
    Format: text/tab-separated-values, 340 data points
    Location Call Number Expected Availability
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  • 86
    Publication Date: 2024-04-24
    Description: This data set is a higher-processing-level version of Geolocated sea-ice or snow surface elevation point clouds from helicopter-borne laser scanner during the MOSAiC expedition, version 1 (Jutila et al., 2022; doi:10.1594/PANGAEA.950509), where the surface elevation point cloud has been converted to freeboard using automatic open water detection scheme and projected onto a regular 0.5-meter grid. The data were collected using a near-infrared, line-scanning Riegl VQ-580 airborne laser scanner (hdl:10013/sensor.7ebb63c3-dc3b-4f0f-9ca5-f1c6e5462a31 & hdl:10013/sensor.7a931b33-72ca-46d0-b623-156836ac9550) mounted in a helicopter along the MOSAiC drift from the north of the Laptev Sea, across the central Arctic Ocean, and towards the Fram Strait from September 2019 to October 2020. The flights are both small scale, ~5x5 km grid patterns mainly over the central observatory, and large scale, few tens of km away from RV Polarstern, triangle patterns, or transects. The gridded data are stored in 30-second along-track segments in netCDF format. For the small scale grid flights, the data are drift corrected using the position and heading data of RV Polarstern and elevation offset corrected using overlapping segments to overcome degraded GPS altitude data 〉85°N. Open water points are identified to derive a freeboard estimate from the surface elevations. For the flights with degraded GPS altitude quality, we provide only a freeboard estimate (grid pattern flights) or no freeboard (transects). The gridded 30-s segments include as data variables: surface elevation, freeboard (estimate), freeboard uncertainty, estimated sea surface height, surface reflectance, echo width, and number of points used in the interpolation. In addition, list of detected open water points and an overview figure of each flight is provided.
    Keywords: 20200423_01; Airborne laser scanning; Arctic; Arctic Ocean; Binary Object; DATE/TIME; Flight number; Freeboard; HELI; Helicopter; IceSense; LATITUDE; LONGITUDE; MOSAiC; MOSAiC20192020; MOSAIC-HELI; Multidisciplinary drifting Observatory for the Study of Arctic Climate; Polarstern; PS122/3; PS122/3_37-63; Remote Sensing of the Seasonal Evolution of Climate-relevant Sea Ice Properties; Sea ice; Surface Elevation
    Type: Dataset
    Format: text/tab-separated-values, 434 data points
    Location Call Number Expected Availability
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  • 87
    Publication Date: 2024-04-24
    Description: This data set is a higher-processing-level version of Geolocated sea-ice or snow surface elevation point clouds from helicopter-borne laser scanner during the MOSAiC expedition, version 1 (Jutila et al., 2022; doi:10.1594/PANGAEA.950509), where the surface elevation point cloud has been converted to freeboard using automatic open water detection scheme and projected onto a regular 0.5-meter grid. The data were collected using a near-infrared, line-scanning Riegl VQ-580 airborne laser scanner (hdl:10013/sensor.7ebb63c3-dc3b-4f0f-9ca5-f1c6e5462a31 & hdl:10013/sensor.7a931b33-72ca-46d0-b623-156836ac9550) mounted in a helicopter along the MOSAiC drift from the north of the Laptev Sea, across the central Arctic Ocean, and towards the Fram Strait from September 2019 to October 2020. The flights are both small scale, ~5x5 km grid patterns mainly over the central observatory, and large scale, few tens of km away from RV Polarstern, triangle patterns, or transects. The gridded data are stored in 30-second along-track segments in netCDF format. For the small scale grid flights, the data are drift corrected using the position and heading data of RV Polarstern and elevation offset corrected using overlapping segments to overcome degraded GPS altitude data 〉85°N. Open water points are identified to derive a freeboard estimate from the surface elevations. For the flights with degraded GPS altitude quality, we provide only a freeboard estimate (grid pattern flights) or no freeboard (transects). The gridded 30-s segments include as data variables: surface elevation, freeboard (estimate), freeboard uncertainty, estimated sea surface height, surface reflectance, echo width, and number of points used in the interpolation. In addition, list of detected open water points and an overview figure of each flight is provided.
    Keywords: Airborne laser scanning; Arctic; Arctic Ocean; Binary Object; DATE/TIME; Flight number; Freeboard; HELI; Helicopter; IceSense; LATITUDE; LONGITUDE; MOSAiC; MOSAiC20192020; MOSAIC-HELI; Multidisciplinary drifting Observatory for the Study of Arctic Climate; Polarstern; PS122_4_44_65_2020061502; PS122/4; PS122/4_44-65; Remote Sensing of the Seasonal Evolution of Climate-relevant Sea Ice Properties; Sea ice; Surface Elevation
    Type: Dataset
    Format: text/tab-separated-values, 290 data points
    Location Call Number Expected Availability
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  • 88
    Publication Date: 2024-04-24
    Description: This data set is a higher-processing-level version of Geolocated sea-ice or snow surface elevation point clouds from helicopter-borne laser scanner during the MOSAiC expedition, version 1 (Jutila et al., 2022; doi:10.1594/PANGAEA.950509), where the surface elevation point cloud has been converted to freeboard using automatic open water detection scheme and projected onto a regular 0.5-meter grid. The data were collected using a near-infrared, line-scanning Riegl VQ-580 airborne laser scanner (hdl:10013/sensor.7ebb63c3-dc3b-4f0f-9ca5-f1c6e5462a31 & hdl:10013/sensor.7a931b33-72ca-46d0-b623-156836ac9550) mounted in a helicopter along the MOSAiC drift from the north of the Laptev Sea, across the central Arctic Ocean, and towards the Fram Strait from September 2019 to October 2020. The flights are both small scale, ~5x5 km grid patterns mainly over the central observatory, and large scale, few tens of km away from RV Polarstern, triangle patterns, or transects. The gridded data are stored in 30-second along-track segments in netCDF format. For the small scale grid flights, the data are drift corrected using the position and heading data of RV Polarstern and elevation offset corrected using overlapping segments to overcome degraded GPS altitude data 〉85°N. Open water points are identified to derive a freeboard estimate from the surface elevations. For the flights with degraded GPS altitude quality, we provide only a freeboard estimate (grid pattern flights) or no freeboard (transects). The gridded 30-s segments include as data variables: surface elevation, freeboard (estimate), freeboard uncertainty, estimated sea surface height, surface reflectance, echo width, and number of points used in the interpolation. In addition, list of detected open water points and an overview figure of each flight is provided.
    Keywords: Airborne laser scanning; Arctic; Arctic Ocean; Binary Object; DATE/TIME; Flight number; Freeboard; HELI; Helicopter; IceSense; LATITUDE; LONGITUDE; MOSAiC; MOSAiC20192020; MOSAIC-HELI; Multidisciplinary drifting Observatory for the Study of Arctic Climate; Polarstern; PS122/5; PS122/5_63-118; Remote Sensing of the Seasonal Evolution of Climate-relevant Sea Ice Properties; Sea ice; Surface Elevation
    Type: Dataset
    Format: text/tab-separated-values, 258 data points
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  • 89
    Publication Date: 2024-04-24
    Description: This data set is a higher-processing-level version of Geolocated sea-ice or snow surface elevation point clouds from helicopter-borne laser scanner during the MOSAiC expedition, version 1 (Jutila et al., 2022; doi:10.1594/PANGAEA.950509), where the surface elevation point cloud has been converted to freeboard using automatic open water detection scheme and projected onto a regular 0.5-meter grid. The data were collected using a near-infrared, line-scanning Riegl VQ-580 airborne laser scanner (hdl:10013/sensor.7ebb63c3-dc3b-4f0f-9ca5-f1c6e5462a31 & hdl:10013/sensor.7a931b33-72ca-46d0-b623-156836ac9550) mounted in a helicopter along the MOSAiC drift from the north of the Laptev Sea, across the central Arctic Ocean, and towards the Fram Strait from September 2019 to October 2020. The flights are both small scale, ~5x5 km grid patterns mainly over the central observatory, and large scale, few tens of km away from RV Polarstern, triangle patterns, or transects. The gridded data are stored in 30-second along-track segments in netCDF format. For the small scale grid flights, the data are drift corrected using the position and heading data of RV Polarstern and elevation offset corrected using overlapping segments to overcome degraded GPS altitude data 〉85°N. Open water points are identified to derive a freeboard estimate from the surface elevations. For the flights with degraded GPS altitude quality, we provide only a freeboard estimate (grid pattern flights) or no freeboard (transects). The gridded 30-s segments include as data variables: surface elevation, freeboard (estimate), freeboard uncertainty, estimated sea surface height, surface reflectance, echo width, and number of points used in the interpolation. In addition, list of detected open water points and an overview figure of each flight is provided.
    Keywords: Airborne laser scanning; Arctic; Arctic Ocean; Binary Object; DATE/TIME; Flight number; Freeboard; HELI; Helicopter; IceSense; LATITUDE; LONGITUDE; MOSAiC; MOSAiC20192020; MOSAIC-HELI; Multidisciplinary drifting Observatory for the Study of Arctic Climate; Polarstern; PS122/5; PS122/5_62-67; Remote Sensing of the Seasonal Evolution of Climate-relevant Sea Ice Properties; Sea ice; Surface Elevation
    Type: Dataset
    Format: text/tab-separated-values, 380 data points
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  • 90
    Publication Date: 2024-04-24
    Description: This data set provides high-resolution geolocated point clouds of sea-ice or snow surface elevation for mapping temporal and spatial evolution of sea-ice conditions such as freeboard, roughness, or the size and spatial distributions of surface features. The surface elevation data are referenced to the DTU21 mean sea surface height and are not corrected for sea-ice drift during acquisition. The data were collected using a near-infrared, line-scanning Riegl VQ-580 airborne laser scanner (hdl:10013/sensor.7ebb63c3-dc3b-4f0f-9ca5-f1c6e5462a31 & hdl:10013/sensor.7a931b33-72ca-46d0-b623-156836ac9550) mounted in a helicopter along the MOSAiC drift from the north of the Laptev Sea, across the central Arctic Ocean, and towards the Fram Strait from September 2019 to October 2020. The flights are both small scale, ~5x5 km grid patterns mainly over the central observatory, and large scale, few tens of km away from RV Polarstern, triangle patterns, or transects. The point cloud data are stored in 5-min along-track segments in a custom binary format, for which we provide a python-based parsing tool in awi-als-toolbox (https://github.com/awi-als-toolbox/awi-als-toolbox), together with corresponding metadata json and line-shot quicklook png files. The point cloud data includes as variables: surface elevation (referenced to DTU mean sea surface height), surface reflectance, and echo width. The degraded GPS altitude data 〉85°N may cause undulations in the along-track surface elevations, which are not corrected for in this data product.
    Keywords: 20191105_01; Airborne laser scanning; Arctic; Binary Object; DATE/TIME; Flight number; HELI; Helicopter; IceSense; LATITUDE; LONGITUDE; MOSAiC; MOSAiC20192020; MOSAIC-HELI; Multidisciplinary drifting Observatory for the Study of Arctic Climate; Polarstern; PS122/1; PS122/1_6-11; Remote Sensing of the Seasonal Evolution of Climate-relevant Sea Ice Properties; Sea ice; Surface Elevation
    Type: Dataset
    Format: text/tab-separated-values, 18 data points
    Location Call Number Expected Availability
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  • 91
    Publication Date: 2024-04-24
    Description: This data set provides high-resolution geolocated point clouds of sea-ice or snow surface elevation for mapping temporal and spatial evolution of sea-ice conditions such as freeboard, roughness, or the size and spatial distributions of surface features. The surface elevation data are referenced to the DTU21 mean sea surface height and are not corrected for sea-ice drift during acquisition. The data were collected using a near-infrared, line-scanning Riegl VQ-580 airborne laser scanner (hdl:10013/sensor.7ebb63c3-dc3b-4f0f-9ca5-f1c6e5462a31 & hdl:10013/sensor.7a931b33-72ca-46d0-b623-156836ac9550) mounted in a helicopter along the MOSAiC drift from the north of the Laptev Sea, across the central Arctic Ocean, and towards the Fram Strait from September 2019 to October 2020. The flights are both small scale, ~5x5 km grid patterns mainly over the central observatory, and large scale, few tens of km away from RV Polarstern, triangle patterns, or transects. The point cloud data are stored in 5-min along-track segments in a custom binary format, for which we provide a python-based parsing tool in awi-als-toolbox (https://github.com/awi-als-toolbox/awi-als-toolbox), together with corresponding metadata json and line-shot quicklook png files. The point cloud data includes as variables: surface elevation (referenced to DTU mean sea surface height), surface reflectance, and echo width. The degraded GPS altitude data 〉85°N may cause undulations in the along-track surface elevations, which are not corrected for in this data product.
    Keywords: 20191112_02; Airborne laser scanning; Arctic; Binary Object; DATE/TIME; Flight number; HELI; Helicopter; IceSense; LATITUDE; LONGITUDE; MOSAiC; MOSAiC20192020; MOSAIC-HELI; Multidisciplinary drifting Observatory for the Study of Arctic Climate; Polarstern; PS122/1; PS122/1_7-25; Remote Sensing of the Seasonal Evolution of Climate-relevant Sea Ice Properties; Sea ice; Surface Elevation
    Type: Dataset
    Format: text/tab-separated-values, 20 data points
    Location Call Number Expected Availability
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  • 92
    Publication Date: 2024-04-24
    Description: This data set provides high-resolution geolocated point clouds of sea-ice or snow surface elevation for mapping temporal and spatial evolution of sea-ice conditions such as freeboard, roughness, or the size and spatial distributions of surface features. The surface elevation data are referenced to the DTU21 mean sea surface height and are not corrected for sea-ice drift during acquisition. The data were collected using a near-infrared, line-scanning Riegl VQ-580 airborne laser scanner (hdl:10013/sensor.7ebb63c3-dc3b-4f0f-9ca5-f1c6e5462a31 & hdl:10013/sensor.7a931b33-72ca-46d0-b623-156836ac9550) mounted in a helicopter along the MOSAiC drift from the north of the Laptev Sea, across the central Arctic Ocean, and towards the Fram Strait from September 2019 to October 2020. The flights are both small scale, ~5x5 km grid patterns mainly over the central observatory, and large scale, few tens of km away from RV Polarstern, triangle patterns, or transects. The point cloud data are stored in 5-min along-track segments in a custom binary format, for which we provide a python-based parsing tool in awi-als-toolbox (https://github.com/awi-als-toolbox/awi-als-toolbox), together with corresponding metadata json and line-shot quicklook png files. The point cloud data includes as variables: surface elevation (referenced to DTU mean sea surface height), surface reflectance, and echo width. The degraded GPS altitude data 〉85°N may cause undulations in the along-track surface elevations, which are not corrected for in this data product.
    Keywords: 20191230_01; Airborne laser scanning; Arctic; Arctic Ocean; Binary Object; DATE/TIME; Flight number; HELI; Helicopter; IceSense; LATITUDE; LONGITUDE; MOSAiC; MOSAiC20192020; MOSAIC-HELI; Multidisciplinary drifting Observatory for the Study of Arctic Climate; Polarstern; PS122/2; PS122/2_18-7; Remote Sensing of the Seasonal Evolution of Climate-relevant Sea Ice Properties; Sea ice; Surface Elevation
    Type: Dataset
    Format: text/tab-separated-values, 38 data points
    Location Call Number Expected Availability
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  • 93
    Publication Date: 2024-04-24
    Description: This data set provides high-resolution geolocated point clouds of sea-ice or snow surface elevation for mapping temporal and spatial evolution of sea-ice conditions such as freeboard, roughness, or the size and spatial distributions of surface features. The surface elevation data are referenced to the DTU21 mean sea surface height and are not corrected for sea-ice drift during acquisition. The data were collected using a near-infrared, line-scanning Riegl VQ-580 airborne laser scanner (hdl:10013/sensor.7ebb63c3-dc3b-4f0f-9ca5-f1c6e5462a31 & hdl:10013/sensor.7a931b33-72ca-46d0-b623-156836ac9550) mounted in a helicopter along the MOSAiC drift from the north of the Laptev Sea, across the central Arctic Ocean, and towards the Fram Strait from September 2019 to October 2020. The flights are both small scale, ~5x5 km grid patterns mainly over the central observatory, and large scale, few tens of km away from RV Polarstern, triangle patterns, or transects. The point cloud data are stored in 5-min along-track segments in a custom binary format, for which we provide a python-based parsing tool in awi-als-toolbox (https://github.com/awi-als-toolbox/awi-als-toolbox), together with corresponding metadata json and line-shot quicklook png files. The point cloud data includes as variables: surface elevation (referenced to DTU mean sea surface height), surface reflectance, and echo width. The degraded GPS altitude data 〉85°N may cause undulations in the along-track surface elevations, which are not corrected for in this data product.
    Keywords: 20200108_01; Airborne laser scanning; Arctic; Arctic Ocean; Binary Object; DATE/TIME; Flight number; HELI; Helicopter; IceSense; LATITUDE; LONGITUDE; MOSAiC; MOSAiC20192020; MOSAIC-HELI; Multidisciplinary drifting Observatory for the Study of Arctic Climate; Polarstern; PS122/2; PS122/2_19-46; Remote Sensing of the Seasonal Evolution of Climate-relevant Sea Ice Properties; Sea ice; Surface Elevation
    Type: Dataset
    Format: text/tab-separated-values, 18 data points
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  • 94
    Publication Date: 2024-04-24
    Description: This data set provides high-resolution geolocated point clouds of sea-ice or snow surface elevation for mapping temporal and spatial evolution of sea-ice conditions such as freeboard, roughness, or the size and spatial distributions of surface features. The surface elevation data are referenced to the DTU21 mean sea surface height and are not corrected for sea-ice drift during acquisition. The data were collected using a near-infrared, line-scanning Riegl VQ-580 airborne laser scanner (hdl:10013/sensor.7ebb63c3-dc3b-4f0f-9ca5-f1c6e5462a31 & hdl:10013/sensor.7a931b33-72ca-46d0-b623-156836ac9550) mounted in a helicopter along the MOSAiC drift from the north of the Laptev Sea, across the central Arctic Ocean, and towards the Fram Strait from September 2019 to October 2020. The flights are both small scale, ~5x5 km grid patterns mainly over the central observatory, and large scale, few tens of km away from RV Polarstern, triangle patterns, or transects. The point cloud data are stored in 5-min along-track segments in a custom binary format, for which we provide a python-based parsing tool in awi-als-toolbox (https://github.com/awi-als-toolbox/awi-als-toolbox), together with corresponding metadata json and line-shot quicklook png files. The point cloud data includes as variables: surface elevation (referenced to DTU mean sea surface height), surface reflectance, and echo width. The degraded GPS altitude data 〉85°N may cause undulations in the along-track surface elevations, which are not corrected for in this data product.
    Keywords: 20200116_02; Airborne laser scanning; Arctic; Arctic Ocean; Binary Object; DATE/TIME; Flight number; HELI; Helicopter; IceSense; LATITUDE; LONGITUDE; MOSAiC; MOSAiC20192020; MOSAIC-HELI; Multidisciplinary drifting Observatory for the Study of Arctic Climate; Polarstern; PS122/2; PS122/2_20-53; Remote Sensing of the Seasonal Evolution of Climate-relevant Sea Ice Properties; Sea ice; Surface Elevation
    Type: Dataset
    Format: text/tab-separated-values, 36 data points
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  • 95
    Publication Date: 2024-04-24
    Description: This data set provides high-resolution geolocated point clouds of sea-ice or snow surface elevation for mapping temporal and spatial evolution of sea-ice conditions such as freeboard, roughness, or the size and spatial distributions of surface features. The surface elevation data are referenced to the DTU21 mean sea surface height and are not corrected for sea-ice drift during acquisition. The data were collected using a near-infrared, line-scanning Riegl VQ-580 airborne laser scanner (hdl:10013/sensor.7ebb63c3-dc3b-4f0f-9ca5-f1c6e5462a31 & hdl:10013/sensor.7a931b33-72ca-46d0-b623-156836ac9550) mounted in a helicopter along the MOSAiC drift from the north of the Laptev Sea, across the central Arctic Ocean, and towards the Fram Strait from September 2019 to October 2020. The flights are both small scale, ~5x5 km grid patterns mainly over the central observatory, and large scale, few tens of km away from RV Polarstern, triangle patterns, or transects. The point cloud data are stored in 5-min along-track segments in a custom binary format, for which we provide a python-based parsing tool in awi-als-toolbox (https://github.com/awi-als-toolbox/awi-als-toolbox), together with corresponding metadata json and line-shot quicklook png files. The point cloud data includes as variables: surface elevation (referenced to DTU mean sea surface height), surface reflectance, and echo width. The degraded GPS altitude data 〉85°N may cause undulations in the along-track surface elevations, which are not corrected for in this data product.
    Keywords: 20200123_01; Airborne laser scanning; Arctic; Arctic Ocean; Binary Object; DATE/TIME; Flight number; HELI; Helicopter; IceSense; LATITUDE; LONGITUDE; MOSAiC; MOSAiC20192020; MOSAIC-HELI; Multidisciplinary drifting Observatory for the Study of Arctic Climate; Polarstern; PS122/2; PS122/2_21-77; Remote Sensing of the Seasonal Evolution of Climate-relevant Sea Ice Properties; Sea ice; Surface Elevation
    Type: Dataset
    Format: text/tab-separated-values, 50 data points
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  • 96
    Publication Date: 2024-04-24
    Description: This data set provides high-resolution geolocated point clouds of sea-ice or snow surface elevation for mapping temporal and spatial evolution of sea-ice conditions such as freeboard, roughness, or the size and spatial distributions of surface features. The surface elevation data are referenced to the DTU21 mean sea surface height and are not corrected for sea-ice drift during acquisition. The data were collected using a near-infrared, line-scanning Riegl VQ-580 airborne laser scanner (hdl:10013/sensor.7ebb63c3-dc3b-4f0f-9ca5-f1c6e5462a31 & hdl:10013/sensor.7a931b33-72ca-46d0-b623-156836ac9550) mounted in a helicopter along the MOSAiC drift from the north of the Laptev Sea, across the central Arctic Ocean, and towards the Fram Strait from September 2019 to October 2020. The flights are both small scale, ~5x5 km grid patterns mainly over the central observatory, and large scale, few tens of km away from RV Polarstern, triangle patterns, or transects. The point cloud data are stored in 5-min along-track segments in a custom binary format, for which we provide a python-based parsing tool in awi-als-toolbox (https://github.com/awi-als-toolbox/awi-als-toolbox), together with corresponding metadata json and line-shot quicklook png files. The point cloud data includes as variables: surface elevation (referenced to DTU mean sea surface height), surface reflectance, and echo width. The degraded GPS altitude data 〉85°N may cause undulations in the along-track surface elevations, which are not corrected for in this data product.
    Keywords: 20200212_01; Airborne laser scanning; Arctic; Arctic Ocean; Binary Object; DATE/TIME; Flight number; HELI; Helicopter; IceSense; LATITUDE; LONGITUDE; MOSAiC; MOSAiC20192020; MOSAIC-HELI; Multidisciplinary drifting Observatory for the Study of Arctic Climate; Polarstern; PS122/2; PS122/2_24-31; Remote Sensing of the Seasonal Evolution of Climate-relevant Sea Ice Properties; Sea ice; Surface Elevation
    Type: Dataset
    Format: text/tab-separated-values, 42 data points
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  • 97
    Publication Date: 2024-04-24
    Description: This data set provides high-resolution geolocated point clouds of sea-ice or snow surface elevation for mapping temporal and spatial evolution of sea-ice conditions such as freeboard, roughness, or the size and spatial distributions of surface features. The surface elevation data are referenced to the DTU21 mean sea surface height and are not corrected for sea-ice drift during acquisition. The data were collected using a near-infrared, line-scanning Riegl VQ-580 airborne laser scanner (hdl:10013/sensor.7ebb63c3-dc3b-4f0f-9ca5-f1c6e5462a31 & hdl:10013/sensor.7a931b33-72ca-46d0-b623-156836ac9550) mounted in a helicopter along the MOSAiC drift from the north of the Laptev Sea, across the central Arctic Ocean, and towards the Fram Strait from September 2019 to October 2020. The flights are both small scale, ~5x5 km grid patterns mainly over the central observatory, and large scale, few tens of km away from RV Polarstern, triangle patterns, or transects. The point cloud data are stored in 5-min along-track segments in a custom binary format, for which we provide a python-based parsing tool in awi-als-toolbox (https://github.com/awi-als-toolbox/awi-als-toolbox), together with corresponding metadata json and line-shot quicklook png files. The point cloud data includes as variables: surface elevation (referenced to DTU mean sea surface height), surface reflectance, and echo width. The degraded GPS altitude data 〉85°N may cause undulations in the along-track surface elevations, which are not corrected for in this data product.
    Keywords: 20200321_01; Airborne laser scanning; Arctic; Arctic Ocean; Binary Object; DATE/TIME; Flight number; HELI; Helicopter; IceSense; LATITUDE; LONGITUDE; MOSAiC; MOSAiC20192020; MOSAIC-HELI; Multidisciplinary drifting Observatory for the Study of Arctic Climate; Polarstern; PS122/3; PS122/3_32-70; Remote Sensing of the Seasonal Evolution of Climate-relevant Sea Ice Properties; Sea ice; Surface Elevation
    Type: Dataset
    Format: text/tab-separated-values, 48 data points
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  • 98
    Publication Date: 2024-04-24
    Description: This data set provides high-resolution geolocated point clouds of sea-ice or snow surface elevation for mapping temporal and spatial evolution of sea-ice conditions such as freeboard, roughness, or the size and spatial distributions of surface features. The surface elevation data are referenced to the DTU21 mean sea surface height and are not corrected for sea-ice drift during acquisition. The data were collected using a near-infrared, line-scanning Riegl VQ-580 airborne laser scanner (hdl:10013/sensor.7ebb63c3-dc3b-4f0f-9ca5-f1c6e5462a31 & hdl:10013/sensor.7a931b33-72ca-46d0-b623-156836ac9550) mounted in a helicopter along the MOSAiC drift from the north of the Laptev Sea, across the central Arctic Ocean, and towards the Fram Strait from September 2019 to October 2020. The flights are both small scale, ~5x5 km grid patterns mainly over the central observatory, and large scale, few tens of km away from RV Polarstern, triangle patterns, or transects. The point cloud data are stored in 5-min along-track segments in a custom binary format, for which we provide a python-based parsing tool in awi-als-toolbox (https://github.com/awi-als-toolbox/awi-als-toolbox), together with corresponding metadata json and line-shot quicklook png files. The point cloud data includes as variables: surface elevation (referenced to DTU mean sea surface height), surface reflectance, and echo width. The degraded GPS altitude data 〉85°N may cause undulations in the along-track surface elevations, which are not corrected for in this data product.
    Keywords: Airborne laser scanning; Arctic; Arctic Ocean; Binary Object; DATE/TIME; Flight number; HELI; Helicopter; IceSense; LATITUDE; LONGITUDE; MOSAiC; MOSAiC20192020; MOSAIC-HELI; Multidisciplinary drifting Observatory for the Study of Arctic Climate; Polarstern; PS122_4_45_37_2020063002; PS122/4; PS122/4_45-37; Remote Sensing of the Seasonal Evolution of Climate-relevant Sea Ice Properties; Sea ice; Surface Elevation
    Type: Dataset
    Format: text/tab-separated-values, 36 data points
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  • 99
    Publication Date: 2024-04-24
    Description: This data set provides high-resolution geolocated point clouds of sea-ice or snow surface elevation for mapping temporal and spatial evolution of sea-ice conditions such as freeboard, roughness, or the size and spatial distributions of surface features. The surface elevation data are referenced to the DTU21 mean sea surface height and are not corrected for sea-ice drift during acquisition. The data were collected using a near-infrared, line-scanning Riegl VQ-580 airborne laser scanner (hdl:10013/sensor.7ebb63c3-dc3b-4f0f-9ca5-f1c6e5462a31 & hdl:10013/sensor.7a931b33-72ca-46d0-b623-156836ac9550) mounted in a helicopter along the MOSAiC drift from the north of the Laptev Sea, across the central Arctic Ocean, and towards the Fram Strait from September 2019 to October 2020. The flights are both small scale, ~5x5 km grid patterns mainly over the central observatory, and large scale, few tens of km away from RV Polarstern, triangle patterns, or transects. The point cloud data are stored in 5-min along-track segments in a custom binary format, for which we provide a python-based parsing tool in awi-als-toolbox (https://github.com/awi-als-toolbox/awi-als-toolbox), together with corresponding metadata json and line-shot quicklook png files. The point cloud data includes as variables: surface elevation (referenced to DTU mean sea surface height), surface reflectance, and echo width. The degraded GPS altitude data 〉85°N may cause undulations in the along-track surface elevations, which are not corrected for in this data product.
    Keywords: Airborne laser scanning; Arctic; Arctic Ocean; Binary Object; DATE/TIME; Flight number; HELI; Helicopter; IceSense; LATITUDE; LONGITUDE; MOSAiC; MOSAiC20192020; MOSAIC-HELI; Multidisciplinary drifting Observatory for the Study of Arctic Climate; Polarstern; PS122_4_46_36_2020070701; PS122/4; PS122/4_46-36; Remote Sensing of the Seasonal Evolution of Climate-relevant Sea Ice Properties; Sea ice; Surface Elevation
    Type: Dataset
    Format: text/tab-separated-values, 28 data points
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  • 100
    Publication Date: 2024-04-24
    Description: This data set is a higher-processing-level version of Gridded segments of sea-ice or snow surface elevation and freeboard from helicopter-borne laser scanner during the MOSAiC expedition, version 1 (Hutter et al., 2022; doi:10.1594/PANGAEA.950339), where the individual 30-second segments of the small scale grid flights have been combined into merged grids. The data were collected using a near-infrared, line-scanning Riegl VQ-580 airborne laser scanner (hdl:10013/sensor.7ebb63c3-dc3b-4f0f-9ca5-f1c6e5462a31 & hdl:10013/sensor.7a931b33-72ca-46d0-b623-156836ac9550) mounted in a helicopter along the MOSAiC drift from the north of the Laptev Sea, across the central Arctic Ocean, and towards the Fram Strait from September 2019 to October 2020. The merged data are stored in netCDF and geotiff format. The data are drift corrected using the position and heading data of RV Polarstern and elevation offset corrected using overlapping segments to overcome degraded GPS altitude data 〉85°N. For the flights with degraded GPS altitude quality, we provide only a freeboard estimate. The merged grids include all data variables of the gridded 30-s segments: surface elevation, freeboard (estimate), freeboard uncertainty, estimated sea surface height, surface reflectance, echo width, and number of points used in the interpolation. Also the calculated elevation offset correction term is provided for each flight as a csv file.
    Keywords: 20191225_01; Airborne laser scanning; Arctic Ocean; Binary Object; DATE/TIME; Flight number; Freeboard; HELI; Helicopter; IceSense; LATITUDE; LONGITUDE; MOSAiC; MOSAiC20192020; MOSAIC-HELI; Multidisciplinary drifting Observatory for the Study of Arctic Climate; Polarstern; PS122/2; PS122/2_17-99; Remote Sensing of the Seasonal Evolution of Climate-relevant Sea Ice Properties; Sea ice; Surface Elevation
    Type: Dataset
    Format: text/tab-separated-values, 20 data points
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