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  • 1
    Publication Date: 2024-06-04
    Language: English
    Type: info:eu-repo/semantics/article
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  • 2
    Publication Date: 2024-06-04
    Description: China has made substantial investment in agricultural research and development (R&D) to promote technological change (TC). Although the role of TC in enhancing agricultural production and mitigating environmental impacts is widely recognized in separate contexts, knowledge about its’ effects on food security and the environment, especially with a focus on China, is still lacking. This study uses an agro-economic optimization model to assess the impact of TC on food security and climate change mitigation. Our results indicate that TC plays an important role in improving agricultural productivity, which, in turn, contributes to a comparative advantage in agricultural trade. It also strengthens food security through lowering food prices. By contrast, a higher TC level increases greenhouse gas (GHG) emissions, albeit marginally, due to higher agricultural production for exports. This indicates a rebound effect of agricultural productivity on GHG emissions. Therefore, additional efforts are required in China to improve food security without compromising GHG mitigation.
    Language: English
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  • 3
    Publication Date: 2024-06-04
    Description: Global hydrological models (GHMs) are widely used to assess the impact of climate change on streamflow, floods, and hydrological droughts. For the 'model evaluation and impact attribution' part of the current round of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3a), modelling teams generated historical simulations based on observed climate and direct human forcings with updated model versions. Here we provide a comprehensive evaluation of daily and maximum annual discharge based on ISIMIP3a simulations from nine GHMs by comparing the simulations to observational data from 644 river gauge stations. We also assess low flows and the effects of different river routing schemes. We find that models can reproduce variability in daily and maximum annual discharge, but tend to overestimate both quantities, as well as low flows. Models perform better at stations in wetter areas and at lower elevations. Discharge routed with the river routing model CaMa-Flood can improve the performance of some models, but for others, variability is overestimated, leading to reduced model performance. This study indicates that areas for future model development include improving the simulation of processes in arid regions and cold dynamics at high elevations. We further suggest that studies attributing observed changes in discharge to historical climate change using the current model ensemble will be most meaningful in humid areas, at low elevations, and in places with a regular seasonal discharge as these are the regions where the underlying dynamics seem to be best represented.
    Language: English
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  • 4
    Publication Date: 2024-06-04
    Description: Quantitative climate mobility research has, so far, focused primarily on climate change impacts on migration outcomes. This focus has led to a separation between quantitative climate migration research and the broader field of migration studies. In this paper ways are proposed for quantitative research to better address the complexity in the relationship between climate change and mobility. First technical suggestions are presented to improve upon migration model setups and designs and highlight promising developments. Then it is argued that quantitative methodologies can broaden the scope of research inquiries by examining how climate mitigation and adaptation efforts influence mobility, as well as assessing how mobility itself impacts vulnerability.
    Language: English
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  • 5
    Publication Date: 2024-06-04
    Description: This paper aims to improve the Soil and Water Assessment Tool (SWAT) model performance across the Major River Basins in Madagascar (MRBM), specifically for SWAT simulation in the Manambolo, Onilahy, Mananara, and Mandrare basins. A multi-gauge calibration was carried out to compare the performance of SWAT+ Toolbox, and R-SWAT, SWAT+ Editor Hard calibration on a monthly time step for the periods 1982–1999. We found that the SWAT+ model generated greater surface runoff, while the SWAT model resulted in higher groundwater flow in both CSFR and CHIRPS datasets. It has been demonstrated that the SWAT+ Toolbox had more potential in calibrating runoff across the MRBM compared to R-SWAT. Calibration in both methods led to a reduction in surface runoff, percolation, water yield, and curve number but increased the lateral flow, evapotranspiration (ET), and groundwater flow. The results showed that the multi-gauge calibrations did not significantly enhance simulation performance in the MRBM compared to single-site calibration. The performance of the SWAT+ model for runoff simulation within the SWAT+ Toolbox and R-SWAT was unsatisfactory for most basins (NSE 〈 0) except for Betsiboka, Mahavavy, Tsiribihina, Mangoro, and Mangoky basins (NSE = 0.40–0.70; R2 = 0.45–0.80, PBIAS≤ ±25), whether considering the CHIRPS or CSFR datasets. Further study is still required to address this issue.
    Language: English
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  • 6
    Publication Date: 2024-06-04
    Description: Central Asia (CA) is among the world's most vulnerable regions to climate change. Increasing anthropogenic greenhouse gas concentrations (GHGs) are the primary forcing of the current and future climate system for the time scale of a century. By analysing observation datasets, we show that a warming of 1.2°C led to a decrease of 20% in snow-depth CA during the last 70 years, especially over the mountains. In recent decades, longer summer times and fewer icing days (more than 20 days·year−1) have exposed unprecedented shock to CA's climate system's components. Furthermore, we analyse 442 model simulations from Coupled Model Inter-comparison Project Phase 5 and 6 (CMIP5, CMIP6) and show that CMIP6 simulations are generally warmer and wetter than the CMIP5 ones in CA. For instance, under the highest emission scenarios (RCP8.5 and SSP5-8.5), CMIP6 projects a 6.1°C increase, while CMIP5 projects a 5.3°C increase, suggesting CMIP6 anticipates greater warming with high emissions. In contrast to CMIP6, the CMIP5 precipitation trends suggest a potential nonlinear relationship between increased greenhouse gas emissions and changes in precipitation, though the impact is much less pronounced than the temperature changes. Our analysis shows that CMIP6 models are more sensitive to temperature rise than CMIP5 ones. Both simulation sets' ensemble means capture well the observed warming trend. The imposed snow-melting leads to an increase in the run-off in the vicinity of glaciers. Such climatic shifts lead to more flooding events in CA. Given the projected warming range of 2–6°C in CA at the end of the century in various scenarios and models, such warming trends might be catastrophic in this region. The seasonal cycle of the temperature change indicates an extension of the glacier's melting period under future scenarios with fossil-fueled development. The models' uncertainty increases for the far-future time-slice, and warming larger than 4°C in CA is very likely among all the models and during all the seasons if no sustainable action is taken. This study also incorporates a detailed Köppen climate classification analysis, revealing significant shifts towards warmer climate categories in Central Asia, which may have profound implications for regional hydrological cycles and water resource management, particularly in the Amu Darya and Syr Darya river basins under warmer scenario by the end of the century. The Tundra and ice cap climate categories will lose more than 60% of their coverage at the end of the century compared to the historical period in the Amu Darya and Syr Darya river basins.
    Language: English
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  • 7
    Publication Date: 2024-06-04
    Description: In response to the climate and biodiversity crisis, the number of transdisciplinary research projects in which researchers partner with sustainability initiatives to foster transformative change is increasing globally. To enable and catalyze substantial transformative change, transformative transdisciplinary research (TTDR) is urgently needed to provide knowledge and guidance for actions. We review prominent discussions on TTDR and draw on our experiences from research projects in the Global South and North. Drawing on this, we identify key gaps and stimulate debate on how sustainability researchers can enable and catalyze transformative change by advancing five priority areas: clarify what TTDR is, conduct meaningful people-centric research, unpack how to act at deep leverage points, improve engagement with diverse knowledge systems, and explore potentials and risks of global digitalization for transformative change.
    Language: English
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  • 8
    Publication Date: 2024-06-04
    Description: This article explores the role of energy in regionalization processes, assessing the case of natural gas finds in the Eastern Mediterranean (East Med). It makes three observations. First, we show that energy resources are a defining factor in shaping a region by rearranging the interactions and networks of actors involved in regionalization processes. Second, we demonstrate that such “energization” processes are not only—and not even primarily—attributable to security practices pursued by state actors. Regionalization underpinned by energy as the key governance object is characterized by a variety of actors, including governments, but also international energy companies, investors, consumers, and regulators. Third, we posit that regionalization processes cannot be fully understood without appreciating the importance of existing global and regional governance frameworks and the values ascribed to the physical resource by international market forces. The findings call on International Relations to go beyond analyzing the East Med energy region through the prism of security studies, which arguably is a function of both theoretical path dependence and a lack of attention to the insights from energy studies. Instead, a multidisciplinary research agenda promises to strengthen academic inquiry into regionalization dynamics in the East Med and the role of regions in world politics more broadly.
    Language: English
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  • 9
    Publication Date: 2024-06-04
    Description: As climate targets tighten, all countries must transition toward a renewable electricity system, but conflicts about generation and infrastructure deployment impede transition progress. Although the triggers of opposition are well studied, what people want remains understudied. We survey citizen preferences for a renewable electricity future through a conjoint analysis among 4,103 individuals in Denmark, Portugal, Poland, and Germany. With our study we go beyond the Likert scale survey approach specifically seeking trade-offs and contextualized preferences for regional electricity system designs. We show the importance of identifying both the ‘‘least preferred’’ and ‘‘most preferred’’ solutions and highlighting the possibility of identifying very different systems with identical utility. Lastly, our research actively bridges the divide between social aspects and techno-economic modeling, promoting their integration. We show that the most preferred system design in all four countries is a predominantly regional one, based on rooftop solar, communally owned, and not relying on transmission expansion.
    Language: English
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  • 10
    Publication Date: 2024-06-04
    Description: Various analyses show that right-wing populist parties (RWP) tend to be sceptical of climate science and policy. This points to a blank space in the dominant analyses of populism: their blindness towards society-nature relations. This paper aims to develop an approach grounded in Cultural Political Economy (CPE) that can be used to decipher the mediation of RWP within the context of economic, political, and cultural developments as well as society–nature relations. Against this background, the argument is developed that RWP is concerned not only with countering migration and processes of societal liberalisation, but also with defending an existing way of life that is firmly rooted in the destructive appropriation of nature. As a current of right-wing politics, RWP defends the imperial mode of living by expressing scepticism towards the existence of anthropogenic climate change. The paper contributes to a better understanding of the political economy of RWP by linking the dimensions of social domination with the appropriation of nature.
    Language: English
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  • 11
    Publication Date: 2024-06-04
    Description: Governments and international organizations are increasingly using public funds to mobilize and leverage private finance for climate projects in the Global South. An important international organization in the effort to mobilize the private sector for financing climate mitigation and adaptation in the Global South is the Green Climate Fund (GCF). The GCF was established under the UNFCCC in 2010 and is the world’s largest dedicated multilateral climate fund. The GCF differs from other intergovernmental institutions through its fund-wide inclusion of the private sector, ranging from project design and financing to project implementation. In this paper, we investigate private sector involvement in the GCF through a qualitative exploratory research approach. We ask two main questions: Do private sector projects deliver on their ambitious goals? What are the tensions, if any, between private sector engagement and other principles of the GCF (most importantly the principles of country ownership, mitigation/adaptation balance, transparency, and civil society participation)? This paper argues that private sector involvement does not provide an easy way out of the financial constraints of public climate financing. We show that the GCF fails to deliver on its ambitious goals in private sector engagement for a number of reasons. First, private sector interest in GCF projects is thus far underwhelming. Second, there are strong tradeoffs between private sector projects and the Global Partnership for Effective Development Co-operation (GPEDC) principles of country ownership, transparency, and civil society participation. Third, private sector involvement is creating a mitigation bias within the GCF portfolio. Fourth, while the private sector portfolio is good at channeling funds to particularly vulnerable countries, it does so mostly through large multi-country projects with weak country ownership. Fifth, there is a danger that private climate financing based on loans and equity might add to the debt burden of developing countries, destabilize financial markets, and further increase dependency on the Global North.
    Language: English
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  • 12
    Publication Date: 2024-06-04
    Description: Real-world labs are witnessing continued growth and institutionalization in the field of transformation-oriented sustainability research, as well as in adjacent disciplines. With their experimental research agendas, these labs aim at sustainability transformations, however, there is still a need to improve the understanding of their impacts. Drawing from this Special Issue’s contributions, we offer a broad overview of the impacts achieved by various real-world labs, highlight the diverse areas and forms of impact, and elucidate strategies as well as mechanisms for achieving impact. We present methodological advances, and address common challenges along with potential solutions for understanding and realizing impact.
    Language: English
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  • 13
    Publication Date: 2024-06-04
    Description: Policymakers and governments increasingly frame climate protection in terms of green growth, arguing that continued economic growth and climate protection are complementary and mutually beneficial. With such framing, governments hope to overcome the global common goods problem associated with climate change and to enable higher ambition on climate action within and across states. Yet, no empirical evidence to date has been provided on how widespread the support for green growth is in international climate politics. This paper, therefore, investigates which countries employ green growth framings at UNFCCC negotiations, and whether this relates to domestic factors, in particular economic structure, level of development and climate impacts. We conduct panel-data analysis on green growth positions derived from hand-coding a unique dataset of High-level Segment statements at the Convention of the Parties (COPs) from 2010 to 2019 for 151 countries. The results reveal that, to date, green growth proponents are those countries with the most advanced national clean energy technology (CET) capacities–as measured by the Green Complexity Index. The findings highlight that green growth is not promoted by all countries at international climate negotiations. Key policy insights In international climate negotiations, climate protection is increasingly framed as a green growth opportunity to motivate global ambition. Clean Energy Technology (CET) leading countries are more likely to use green growth framings than other less-technologically advanced peers and those with high exposure to climate risks. Mechanisms to support green growth pathways for all countries should be scaled up, including technology transfer and finance to foster local capacities and human capital. Given that green growth framings are not universally endorsed, further emphasis should be placed on additional co-benefits of climate action beyond economic growth, such as food and energy security, adaptation and resilience-building.
    Language: English
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  • 14
    Publication Date: 2024-06-04
    Description: Real-world laboratories (RwLs) are gaining further traction as a means to achieve systemic impacts towards sustainability transformation. To guide the analysis of intended impacts, we introduce the concept of leverage points, discerning where, how, and to what end RwLs intervene in systems. Building on conceptual reasoning, we further develop our argument by exploring two RwL cases. Examining RwLs through the lens of the leverage points opens the way for a balanced and comprehensive approach to systemic experimentation. We invite RwL researchers and practitioners to further advance RwLs’ transformative capacity by targeting the design and emerging direction of a system, contributing to a culture of sustainability.
    Language: English
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  • 15
    Publication Date: 2024-06-04
    Description: Observed climate changes in Pacific island countries (PICs) are causing detrimental effects on the health of communities. Increased frequency and intensity of cyclones, more extremely hot days, and changes in rainfall patterns can change the geographic distribution of vector-borne diseases, decrease food and water security and safety, and strain health service capacity. These impacts are projected to worsen with additional climate change in the absence of strong and effective mitigation and adaptation measures. Health vulnerability and adaptation assessments conducted in twelve PICs in 2014 highlighted significant knowledge gaps on the national health risks of climate change and on adaptation implementation and policy translation. We synthesize recent research to identify approaches to support evidence-based policymaking to increase resilience of health systems in the Pacific. Broad areas where further and substantial investment and support are needed include: (i) health workforce capacity development; (ii) enhanced surveillance and monitoring systems, and (iii) research to enhance understanding of risks and effective interventions and their subsequent translation into practice and policy. Finally, health facilities need urgent upgrades; many are old and located in coastal areas, and are heavy users of coal-fired electricity.
    Language: English
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  • 16
    Publication Date: 2024-06-04
    Description: Abstract
    Description: This data publication provides the results of the investigations and measurements of thermal rock properties conducted on site in the Tournemire field laboratory and at the Thermal Petrophysics Lab at GFZ. The thermal characterization of the clayey Jurassic (Upper Toarcian, ca. 180 My old) is contributing to the site characterization of the Tournemire Underground Research Lab (URL), located in Southern France. This URL is installed in a former railway tun-nel to better understand the physical processes resulting from thermal and hydrau-lic loading in a small fault zone in a highly consolidated shale formation (Bonnelye et al., 2023). At the Tournemire site, faults and fractures of different sizes extend from the surface (sedimentary cover) to the crystalline basement. At one specific gallery (Gallery East 03) installed in the former tunnel, thermally controlled in-situ fluid injection experiments are scheduled on a strike-slip fault zone outcropping at the URL (Bonnelye et al., 2023). In 2022, we visited the URL for baseline characteri-zation of thermal properties and to study the heterogeneity of the clay-dominated formation. Therefore, we took the chance to collect data and samples for a laborato-ry measurement campaign and to measure thermal conductivity in-situ in the tun-nel wall of Gallery East 03. The thermal data shall provide the baseline for the pa-rameterization of future numerical 3D models to better understand the thermal-hydraulic processes related to the experiment. This data publication provides the results of the investigations and measurements conducted on-site in the field la-boratory and at the Thermal Petrophysics Lab at GFZ.
    Keywords: thermal conductivity ; claystone ; host rocks ; URL ; compound material 〉 sedimentary material 〉 sedimentary rock 〉 generic mudstone 〉 mudstone 〉 claystone ; EARTH SCIENCE 〉 SOLID EARTH 〉 ROCKS/MINERALS/CRYSTALS 〉 SEDIMENTARY ROCKS 〉 SEDIMENTARY ROCK FORMATION
    Type: Dataset , Dataset
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  • 17
    Publication Date: 2024-06-04
    Description: Yedoma is a permafrost deposit widely distributed across the Arctic and found exclusively within the unglaciated regions in northern Siberia, Alaska, and the Yukon, which are the core regions of Beringia. Yedoma deposits accumulated during the late Pleistocene Stage and are characterized by their predominantly fine-grained texture and association with syngenetic perma-frost formation. The very high ground ice content is most commonly present as pore ice and wedge ice that formed contemporaneously with sediment deposition. In the last decade, research has transitioned from debates about the origin of the Yedoma deposits towards increasing attention on the large carbon and nitrogen pools in Yedoma, their vulnerability to thaw, and increasing mobilization as the climate has warmed across the Arctic. In addition to classical cryolithological and sedimentological research, new methods such as stable isotope paleoclimate reconstruction and ancient sedimentary DNA studies have been more widely applied to better understand the characteristics of Yedoma deposits and helped emphasize their value as archives of Quaternary climate and paleoecological conditions during Ice Age Beringia.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Inbook , peerRev
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  • 18
    Publication Date: 2024-06-04
    Description: 〈jats:p〉The Arctic is sensitive to cloud radiative forcing. Due to the limited number of aerosols present throughout much of the year, cloud formation is susceptible to the presence of cloud condensation nuclei and ice nucleating particles (INPs). Primary biological aerosol particles (PBAP) contribute to INPs and can impact cloud phase, lifetime, and radiative properties. We present yearlong observations of hyperfluorescent aerosols (HFA), tracers for PBAP, conducted with a Wideband Integrated Bioaerosol Sensor, New Electronics Option during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition (October 2019–September 2020) in the central Arctic. We investigate the influence of potential anthropogenic and natural sources on the characteristics of the HFA and relate our measurements to INP observations during MOSAiC. Anthropogenic sources influenced HFA during the Arctic haze period. But surprisingly, we also found sporadic “bursts” of HFA with the characteristics of PBAP during this time, albeit with unclear origin. The characteristics of HFA between May and August 2020 and in October 2019 indicate a strong contribution of PBAP to HFA. Notably from May to August, PBAP coincided with the presence of INPs nucleating at elevated temperatures, that is, >−9°C, suggesting that HFA contributed to the “warm INP” concentration. The air mass residence time and area between May and August and in October were dominated by the open ocean and sea ice, pointing toward PBAP sources from within the Arctic Ocean. As the central Arctic changes drastically due to climate warming with expected implications on aerosol–cloud interactions, we recommend targeted observations of PBAP that reveal their nature (e.g., bacteria, diatoms, fungal spores) in the atmosphere and in relevant surface sources, such as the sea ice, snow on sea ice, melt ponds, leads, and open water, to gain further insights into the relevant source processes and how they might change in the future.〈/jats:p〉
    Repository Name: EPIC Alfred Wegener Institut
    Type: Article , isiRev
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  • 19
    Publication Date: 2024-06-04
    Description: Published
    Description: Refereed
    Keywords: ASFA_2015::M::Marine sciences ; ASFA_2015::O::Oceans ; ASFA_2015::O::Oceanography
    Repository Name: AquaDocs
    Type: Journal Contribution
    Format: 1101
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  • 20
    Publication Date: 2024-06-04
    Description: The study was conducted between August and September of 2021 in order to determine the density, ecological index, distribution pattern, geographic distribution, environmental factors, and the relationship between hermit crabs and environmental factors. Hermit crabs were gathered using a quadratic transect and sample plots, while environmental variables were measured in situ. PCA and CCA multivariate statistics were used to determine the characteristics and correlation between hermit crabs and environmental factors. Hermit crabs were discovered to be comprised of two species (Clibanariuslongitarsus and C. infraspinatus) and one family (Diogenidae), with the highest density found in the C. longitarsus species (1.22 ± 0.57 – 4.68 3.53 ind/m2), diversity index is categorized as moderate (2.01), the geographical distribution is abundant (〉80%), and environmental factors are categorized as good. In addition, Stations I and II have high DO, TOM, mangrove density, C. longitarsus, and C. infraspinatus parameters, whereas Station III has high salinity, pH, and temperature, with salinity, mangrove density, and TOM being the most influential parameters on hermit crab density.
    Description: Published
    Description: Refereed
    Keywords: Clibanarius, ecological index, distribution, environmental parameter ; ASFA_2015::E::Ecological distribution ; ASFA_2015::M::Marine crustaceans ; ASFA_2015::D::Density (population) ; ASFA_2015::M::Marine ecology ; ASFA_2015::M::Mangroves ; ASFA_2015::G::Geographical distribution ; ASFA_2015::E::Environmental factors
    Repository Name: AquaDocs
    Type: Journal Contribution
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  • 21
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    Universidade Estadual de Maringá. Departamento de Biologia. Programa de Pós-Graduação em Ecologia de Ambientes Aquáticos Continentais.
    Publication Date: 2024-06-04
    Description: Plastics are pervasive contaminants worldwide, accumulating from the poles to the equator, spanning pristine environments to deep ocean basins.This workexplores how seasonal variations in a floodplain influence the ingestion of plastics by freshwater fish and systematically examines trends and gaps in Brazilian research related to plastic pollution.Additionally, the potential relationship between anthropogenic activities and the amounts of plastic found in Brazilian aquatic environmentswas investigated.Among the 23 fish species analyzed in the Upper Paraná River floodplain, nine were ingested plastics, and the particles were associated with fishingactivity and domestic waste. Seasonality played a crucial role in the amounts of plastic ingested by these species, with the highest number of particles recorded during the wet season. As for trends and gaps in Brazilian research on plastic pollution, a substantial number of publications related to marine environments, microplastics, and fishwas identified. Conversely, freshwater environments and invertebrates are underexplored.Considering the distribution of studies within Brazilian biomes, the Pantanaland Cerrado had the lowest number of publications—an alarming trend considering the significant rivers and basins they house.Finally, themodels were unable to find strong and significant correlations between the number of plastics and anthropogenic activities in Brazilian municipalities.The lack of significant relationships may be attributed to the limitations of our dataset, specificallydue to the low number of studies.Nevertheless, other factors influenced the quantities of plastic detected.In biotic samples, the number of ingested plastics was influenced by the animal group, with reptiles, birds, and fish being the most affected groups. For abiotic samples, the type of environment emerged as a significant factor.In sediment samples, the quantity of plastics was higher in estuarine and freshwater environments. In water samples, the quantity of plastics was higher exclusively in freshwater environments.The finds of this workcontribute to new research on plastic pollution in Brazilian aquatic environments, expanding theunderstanding of the dynamics of plastics in freshwater environments and their interaction with various organisms.
    Description: Plásticos são contaminantes onipresentes no planeta, acumulando-se dos polos ao equador, desde ambientes pristinos em grandes altitudes até bacias oceânicas profundas. Investigou-se como a ingestão de plásticos por peixes de água doce é influenciada pelas variações sazonais de uma planície de inundação, bem como as tendências e lacunas da pesquisa brasileira em relação a poluição plástica. Investigou-se também a possível relação entre atividades antropogênicas e as quantidades de plástico encontradas nos ambientes aquáticos brasileiros. Das 23 espécies de peixes analisadas na planície de inundação do alto rio Paraná, nove ingeriram plásticos, e as partículas encontradas são associadas com a atividade pesqueira e o lixo doméstico. A sazonalidade promovida pelo ciclo hidrológico desempenhou um papel importante nas quantidades de plásticos ingeridas por essas espécies, onde o maior número de partículas foi registrado durante o período de cheias. Em relação as tendências e lacunas da pesquisa brasileira sobre a poluição plástica, encontrou-se um grande número de publicações para ambientes marinhos, microplásticos e peixes. Ambientes de água doce e invertebrados permanecem pouco estudados no país. Considerando a distribuição dos estudos dentro dos biomas brasileiros, Pantanal e Cerrado foram os biomas com o menor número de publicações, um fato preocupante visto os importantes rios e bacias que estes abrigam. Por fim, os modelos não encontraram correlações entre a quantidade de plásticos e as atividades antropogênicas dos municípios brasileiros. A ausência dessas relações pode estar relacionada às limitações do conjunto de dados, especificamente devido ao baixo número de estudos com dados disponíveis. Entretanto, outros fatores influenciaram as quantidades de plásticos encontradas. Em amostras bióticas, o número de plásticos ingeridos esteve associado ao grupo animal, sendo os répteis, aves e peixes os mais afetados. Para amostras abióticas, o tipo de ambiente foi um fator influente. Em amostras de sedimento o número de plásticos foi maior para ambientes estuarinos e de água doce. Para as amostras de água, o número de plásticos foi maior apenas para ambientes de água doce. Devido aos efeitos negativos da poluição plástica e à importância econômica e ecológica das espécies afetadas, os resultados desse estudo representam um passo importante na avaliação dos impactos gerados nas populações de peixes de água doce pela ingestão de plásticos. Espera-se que estes resultados contribuam para o direcionamento de novas pesquisas em relação a poluição plástica nos ambientes aquáticos brasileiros, e que estes estudos expandam nosso conhecimento sobre a dinâmica dos plásticos em ambientes de água doce, assim como sua interação com diferentes organismos.
    Description: PhD
    Keywords: Organismos de água doce ; Peixes de água doce ; Ecossistemas aquáticos de água doce ; Ingestão de plásticos ; Poluição aquática ; Plásticos ; Impactos ambientais ; Impactos antropogênicos ; Revisão sistemática ; ASFA_2015::F::Freshwater fish ; ASFA_2015::F::Freshwater ecology ; ASFA_2015::O::Organisms (aquatic) ; ASFA_2015::A::Aquatic ecology ; ASFA_2015::E::Ecosystems ; ASFA_2015::P::Pollution ; ASFA_2015::E::Environmental impact ; ASFA_2015::A::Anthropogenic effects ; ASFA_2015::S::Systematics ; ASFA_2015::P::Plastics ; ASFA_2015::L::Literature reviews
    Repository Name: AquaDocs
    Type: Thesis/Dissertation
    Format: 106pp.
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  • 22
    Publication Date: 2024-06-04
    Description: Space plasma turbulence plays a relevant role in several plasma environments, such as solar wind and the Earth’s magnetosphere–ionosphere system, and is essential for describing their complex coupling. This interaction gives rise to various phenomena, including ionospheric irregularities and the amplification of magnetospheric and ionospheric currents. The structure and dynamics of these currents have relevant implications, for example, in studying ionospheric heating and the nature of electric and magnetic field fluctuations in the auroral and polar environments. In this study, we investigate the nature of small-scale fluctuations characterizing the ionospheric magnetic field in response to different geomagnetic conditions. We use high-resolution (50 Hz) magnetic data from the ESA’s Swarm mission, collected during a series of high-latitude crossings, to probe the scaling features of magnetic field fluctuations in auroral and polar cap regions at spatial scales still poorly explored. Our findings reveal that magnetic field fluctuations in field-aligned currents (FACs) and polar cap regions across both hemispheres are characterized by different scaling properties, suggesting a distinct driver of turbulence. Furthermore, we find that geomagnetic activity significantly influences the nature of energy dissipation in FAC regions, leading to more localized filamentary structures toward smaller scales.
    Description: Published
    Description: 1928
    Description: OSA3: Climatologia e meteorologia spaziale
    Description: JCR Journal
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: article
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  • 23
    Publication Date: 2024-06-04
    Description: This Special Issue aims to highlight the pivotal role of the minerals found in alkaline igneous rocks in tracing magmatic processes. In different geodynamic contexts, minerals such as alkali-feldspar and clinopyroxene exhibit a largely variable chemical and isotopic composition. Such a variability can be profitably employed to investigate the evolution processes undergone by magmas during their movement from the mantle source up to the surface, especially when magma evolution occurs in open-system conditions.....
    Description: Published
    Description: 7
    Description: JCR Journal
    Keywords: Alkaline igneous rocks ; P–T–fO2 conditions ; Geothermobarometry ; Radiogenic and stable isotopes ; Diffusion chronometry ; Timescales of magmatic processes
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: article
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  • 24
    Publication Date: 2024-06-04
    Description: Entoloma is one of the largest genera of Agaricales in terms of species diversity and is widespread throughout the world. In the present study, four new species, namely Entoloma brunneofbrillosum, E. humidiphilum, E. ochraceodiscum, and E. colchicum, are introduced as new to science. These species are described based on specimens collected in Cyprus, Georgia, Hungary, Italy, Russia, Spain, and Türkiye, including morphological characteristics and phylogenetic analyses of the nuclear ribosomal DNA internal transcribed spacer (ITS) sequences. Entoloma brunneofbrillosum is recognized by its brown to dark brown pileus with conspicuous dark, radial fbrils, a pale brown stipe with glistening fbrils, and usually fusiform to broadly clavate cheilocystidia. It belongs to the /Undulatosporum clade. Entoloma humidiphilum (subg. Alboleptonia) is close to E. niveum from New Zealand but difers by a completely pruinose or minutely squamulose pileus surface, narrowly cylindrical to cylindrical pileipellis elements with a deep median constriction, and by occurring in riparian habitats. Entoloma ochraceodiscum is characterized by funnel-shaped basidiomata with a deeply depressed yellowish-brown pileus and belongs to the section Griseorubida. Entoloma colchicum (subg. Nolanea) is similar to E. ortonii but difers by its distinctive radially fbrillose or velutinous pileus and the absence of odour. The new species are presented with photographs, line drawings, and comparisons with similar taxa.
    Keywords: Basidiomycota · Molecular systematics · Molecular marker · New species · Taxonomy
    Repository Name: National Museum of Natural History, Netherlands
    Type: info:eu-repo/semantics/article
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  • 25
    Publication Date: 2024-06-04
    Description: The integrated approach of molecular phylogenetic and morphological analyses has revolutionized the systematics and our understanding of the evolutionary relationships of marine taxa. One such group is the hexacorallian order Zoantharia Rafinesque, 1815. The monotypic genus Thoracactis Gravier, 1918 has been little investigated since its placement within the order Zoantharia more than 100 years ago. Here, we examined museum specimens collected from the Cape Verde Islands (eastern Atlantic) and newly collected specimens from Brazil (southwestern Atlantic), using a combined molecular and morphological approach. Our results conclusively show Thoracactis to be referable to the family Parazoanthidae. Morphological data show that Thoracactis topsenti Gravier, 1918, the type species of this monotypic genus, has a cyclically transitional arrangement of its sphincter muscle, and this arrangement has previously been reported from the Parazoanthidae. Thoracactis can be distinguished from other hexasterophoran glass-sponge-associated genera (Churabana Kise, Montenegro & Reimer, 2022, Parachurabana Kise, 2023, and Vitrumanthus Kise, Montenegro & Reimer, 2022) by a combination of morphological, ecological and molecular phylogenetic data. In addition, molecular phylogenetic analyses clearly indicate that Thoracactis topsenti is placed within Parazoanthidae. These results are yet another demonstration of the utility of comprehensive combined approaches. From now, research attention should focus on the revision of remaining taxonomic questions within the family Epizoanthidae, with the goal of a comprehensively revised suborder Macrocnemina within reach.
    Keywords: glass sponge ‒ molecular phylogenetics ‒ sphincter muscle ‒ topotypes ‒ zoantharian
    Repository Name: National Museum of Natural History, Netherlands
    Type: info:eu-repo/semantics/article
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  • 26
    Publication Date: 2024-06-04
    Keywords: macroevolution ; microbes ; prokaryotes ; habitat transitions ; specialization ; diversification ; myxobacteria ; comparative phylogenetics
    Repository Name: National Museum of Natural History, Netherlands
    Type: info:eu-repo/semantics/article
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  • 27
    Publication Date: 2024-06-04
    Description: Aim: The efficiency of animal-mediated seed dispersal is threatened by the decline of animal populations, especially in tropical forests. We hypothesise that large-seeded plants with animal-mediated dispersal tend to have limited geographic ranges and face an increased risk of extinction due to the potential decline in seed dispersal by large-bodied fruit-eating and seed-dispersing animals (frugivores). Location: Atlantic Forest, Brazil, South America. Taxon: Angiosperms. Methods: First, we collected dispersal-related traits (dispersal syndrome, fruit size, and seed size), growth form (tree, climber, and other) and preferred vegetation type (open and closed) data for 1052 Atlantic Forest plant species. Next, we integrated these with occurrence records, extinction risk assessments, and phylogenetic trees. Finally, we performed phylogenetic generalised least squares regressions to test the direct and interactive effects of dispersal-related traits and vegetation type on geographical range size. Results: Large-seeded species had smaller range sizes than small-seeded species, but only for species with animal-mediated dispersal, not for those dispersed by abiotic mechanisms. However, plants with abiotic dispersal had overall smaller range sizes than plants with animal-mediated dispersal. Furthermore, we found that species restricted to forests had smaller ranges than those occurring in open or mixed vegetation. Finally, at least 29% of the Atlantic Forest flora is threatened by extinction, but this was not related to plant dispersal syndromes. Main Conclusions: Large-seeded plants with animal-mediated dispersal may be suffering from dispersal limitation, potentially due to past and ongoing defaunation of large-bodied frugivores, leading to small range sizes. Other factors, such as deforestation and fragmentation, will probably modulate the effects of dispersal on range size, and ultimately extinction. Our study sheds light on the relationship between plant traits, mutualistic interactions, and distribution that are key to the functioning of tropical forests.
    Keywords: defaunation ; extinction risk ; frugivory ; phylogeny ; range size ; seed dispersal ; tropical forest
    Repository Name: National Museum of Natural History, Netherlands
    Type: info:eu-repo/semantics/article
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  • 28
    Publication Date: 2024-06-04
    Description: Data presented here were collected between January 2021 to December 2021 within the research unit DynaCom (Spatial community ecology in highly dynamic landscapes: From island biogeography to metaecosystems, https://uol.de/dynacom/ ) of the Universities of Oldenburg, Göttingen, and Münster, the iDiv Leipzig and the Nationalpark Niedersächsisches Wattenmeer. Experimental islands and saltmarsh enclosed plots were created in the back barrier tidal flat and in the saltmarsh zone of the island of Spiekeroog. Meteorological data were collected near the experimental setup, with a locally installed weather station located approximately 500m north of the southern shoreline. The weather station system used here was a ClimaSensor US 4.920x.00.00x that was pre-calibrated by the manufacturer (Adolf Thies GmbH & Co. KG, D-Göttingen). Data were recorded and saved within the Meteo-Online (V4.5.0.20253) software in a sampling interval of 1 min, with an averaging time of 10 s. Date and time were given in UTC and the position was derived from the internal GPS system. Data handling was performed according to Zielinski et al. (2018): Post-processing of collected data was done using MATLAB (R2018a). Quality control was performed by (a) erasing data covering maintenance activities, (b) removing outliers, defined as data exhibiting changes of more than two standard deviations within one time step, and (c) visually checks.
    Keywords: BEFmate; biodiversity - ecosystem functioning; DynaCom; experimental islands; FOR 2716: Spatial community ecology in highly dynamic landscapes: from island biogeography to metaecosystems; Metacommunity; meteorology; salt marsh; Spiekeroog
    Type: Dataset
    Format: application/zip, 12 datasets
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  • 29
    Publication Date: 2024-06-04
    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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  • 30
    Publication Date: 2024-06-04
    Description: This data data set is the taxonomically harmonized pollen data from records 2831 sites. 1032 sites are located in North America, 1075 sites in Europe, 488 sites in Asia, 150 sites in South America, 54 in Africa and 32 in the Indopacific. Most of the data where retrieved from the Neotoma Paleoecology Database (https://www.neotomadb.org/), with additional data from Cao et al. (2020; https://doi.org/10.5194/essd-12-119-2020), Cao et al. (2013, https://doi.org/10.1016/j.revpalbo.2013.02.003) and our own collection for the Asian sector. The ages of the samples refer to the newly established LegacyAge 1.0 framework (https://doi.pangaea.de/10.1594/PANGAEA.933132). The 10,110 original pollen taxa names and notations were harmonized to 1002 taxa names. We present the table with the harmonization approach crossreferencing the original taxa with the harmonized taxa name. The harmonised pollen data are presented as counts (when available) and as percentage values. We complement the data publication by providing the source information on the references (most data are related to Neotoma) as a table linked to each Dataset ID. The data set and site IDs are from Neotoma if the data sets are derived from the Neotoma repository. In case of our own data collection efforts (Cao et al. (2020), Cao et al. (2013) and our own data) we used the already published PANGAEA event names in case they are related to the data or created own site names with referencing to geographical regions similar to the Neotoma data naming principle.
    Keywords: AWI_Envi; fossil pollen; Neotoma; paleoecology; Polar Terrestrial Environmental Systems @ AWI; taxonomically harmonized
    Type: Dataset
    Format: application/zip, 14 datasets
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  • 31
    Publication Date: 2024-06-04
    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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  • 32
    Publication Date: 2024-06-04
    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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  • 33
    Publication Date: 2024-06-04
    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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  • 34
    Publication Date: 2024-06-04
    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
    Location Call Number Expected Availability
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  • 35
    Publication Date: 2024-06-04
    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
    Location Call Number Expected Availability
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  • 36
    Publication Date: 2024-06-04
    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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  • 37
    Publication Date: 2024-06-04
    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
    Location Call Number Expected Availability
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  • 38
    Publication Date: 2024-06-04
    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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  • 39
    Publication Date: 2024-06-04
    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
    Location Call Number Expected Availability
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  • 40
    Publication Date: 2024-06-04
    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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  • 41
    Publication Date: 2024-06-04
    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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  • 42
    Publication Date: 2024-06-04
    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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  • 43
    Publication Date: 2024-06-04
    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
    Location Call Number Expected Availability
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  • 44
    Publication Date: 2024-06-04
    Description: These bundled biogeochemical data of sediment core EN20001, from Lake Khamra (59.99095° N, 112.98345° E), in SW Yakutia consist of four datasets: (1) Radiocarbon age dating of bulk sediments from sediment core EN20001 from Lake Khamra, measured at AWI MICADAS; (2) Element composition of the sediment core EN20001 from Lake Khamra, measured at the Bundesanstalt für Geowissenschaften und Rohstoffe (BGR); (3) TOC and TN of the sediment core EN20001 from Lake Khamra, measured in the sediment laboratory at AWI, Potsdam; (4) Pollen and non-pollen palynomorphs of the sediment core EN20001 from Lake Khamra, measured at AWI, Potsdam. This study was additionally supported by a short-term grant (not numbered) from AWI Graduate School (POLMAR), and PhD Completion Scholarship (not numbered) provided by University of Potsdam.
    Keywords: AWI_Envi; Boreal; Lake sediment; Lake sediment core; lake sediment proxies; Land cover; non-pollen palynomorphs; Polar Terrestrial Environmental Systems @ AWI; Pollen; pollen analysis; pollen and spores; radiocarbon dating; Russia; sakha; Sakha Republic; Siberia; subarctic; TN; TOC; Vegetation; XRF; XRF core scanner data; Yakutia
    Type: Dataset
    Format: application/zip, 4 datasets
    Location Call Number Expected Availability
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  • 45
    Publication Date: 2024-06-04
    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
    Location Call Number Expected Availability
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  • 46
    Publication Date: 2024-06-04
    Description: The open source Video In Situ Snowfall Sensor (VISSS) is a novel instrument for the characterization of particle shape and size in snowfall. The VISSS consists of two cameras with LED backlights and telecentric lenses that allow accurate sizing and combine a large observation volume with relatively high resolution and a design that limits wind disturbance. Here, movies and images of falling precipitation particles are provided for station Ny-Ålesund from July 2022 to December 2023. For further details on the VISSS Sensor see Maahn et al. (2024).
    Keywords: AC3; Arctic Amplification; In-situ; Ny-Ålesund; snowfall; snowflake
    Type: Dataset
    Format: application/zip, 523 datasets
    Location Call Number Expected Availability
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  • 47
    facet.materialart.
    Unknown
    PANGAEA
    In:  Supplement to: Checkley, David M; Dickson, Andrew G; Takahashi, Motomitsu; Radich, J Adam; Eisenkolb, Nadine; Asch, Rebecca (2009): Elevated CO2 enhances otolith growth in young fish. Science, 324(5935), 1683, https://doi.org/10.1126/science.1169806
    Publication Date: 2024-06-04
    Description: A large fraction of the carbon dioxide added to the atmosphere by human activity enters the sea, causing ocean acidification. We show that otoliths (aragonite ear bones) of young fish grown under high CO2 (low pH) conditions are larger than normal, contrary to expectation. We hypothesize that CO2 moves freely through the epithelium around the otoliths in young fish, accelerating otolith growth while the local pH is controlled. This is the converse of the effect commonly reported for structural biominerals.
    Keywords: Alkalinity, total; Animalia; Aragonite saturation state; Atractoscion nobilis; Atractoscion nobilis, dry mass; Atractoscion nobilis, larval age; Atractoscion nobilis, orientation; Atractoscion nobilis, otolith area; Behaviour; Bicarbonate ion; Biomass/Abundance/Elemental composition; Bottles or small containers/Aquaria (〈20 L); Calcite saturation state; Calculated using CO2SYS; Calculated using seacarb after Nisumaa et al. (2010); Carbon, inorganic, dissolved; Carbonate ion; Carbonate system computation flag; Carbon dioxide; Checkley_etal_09; Chordata; EPOCA; EUR-OCEANS; European network of excellence for Ocean Ecosystems Analysis; European Project on Ocean Acidification; EXP; Experiment; Experimental treatment; Fugacity of carbon dioxide (water) at sea surface temperature (wet air); Growth/Morphology; Image analysis NIH ImageJ; Laboratory experiment; Laboratory strains; Light:Dark cycle; Measured; Nekton; Not applicable; OA-ICC; Ocean Acidification International Coordination Centre; Otolith; Partial pressure of carbon dioxide (water) at sea surface temperature (wet air); Pelagos; pH; Salinity; Single species; Temperature, water
    Type: Dataset
    Format: text/tab-separated-values, 4392 data points
    Location Call Number Expected Availability
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  • 48
    facet.materialart.
    Unknown
    PANGAEA
    In:  Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven
    Publication Date: 2024-06-04
    Description: Raw data acquired by GPS1 position sensors on board research aircraft Polar 6 during the campaign P6_244_ANT_23_24 were processed to receive a validated master track which can be used as reference of further expedition data. Novatel FlexPak6 GPS receiver was used as navigation sensors during the campaign. Data were downloaded from DAVIS SHIP data base (https://dship.awi.de) with a resolution of 1 sec. Processed data are provided as a master track with 1 sec resolution and a generalized track with a reduced set of the most significant positions of the master track. A detailed report on processing is also available for each flight.
    Type: Dataset
    Format: application/zip, 16 datasets
    Location Call Number Expected Availability
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  • 49
    Publication Date: 2024-06-04
    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
    Location Call Number Expected Availability
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  • 50
    Publication Date: 2024-06-04
    Description: ASCII file with Data from the SeaBird Glider-Payload CTD (GPCTD) of the following format: 1 Header line: [SPOT.ON general serial format version 1] followed by datalines: [2016.10.01T08.22.49.317 | 1.54, -2.2081,-0.00001, 3199.84] [Timestamp | Pressure(db), Temperature(°C), Conductivity(S/m), DissolvedOxygenFrequency(Hz)]
    Keywords: Arctic Ocean; BEAST; DATE/TIME; Event label; GPCTD; LATITUDE; LONGITUDE; MOSAiC; MOSAiC20192020; 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_24-97; PS122/2_25-104; PS122/2_25-44; Remotely operated sensor platform BEAST; Sea-bird SBE Glider Payload CTD; Uniform resource locator/link to raw data file
    Type: Dataset
    Format: text/tab-separated-values, 17 data points
    Location Call Number Expected Availability
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  • 51
    Publication Date: 2024-06-04
    Description: The data set contains daily files of atmospheric radiation measured during zenith (mwr00) and boundary layer (mwrBL00) mode by the HATPRO microwave radiometer (see Rose et al., 2005) onboard the Polarstern during cruise PS122 (MOSAiC expedition). The data covers the range October 2019 to October 2020. The atmospheric radiation measurements are given as brightness temperatures in seven K band (22.24 - 31.4 GHz) and seven V band (51.26 - 58 GHz) channels. The elevation scans have been perfomed approximately every 30 minutes while zenith measurements (elevation angle at 90 degrees) fill the remaining time. The brightness temperatures are provided for all available times so that it is up to the user to decide whether or not to use the values if quality flags are set. Additionally included are temperature, pressure and humidity measurements at the instrument location as well as quality flags characterizing the instrument and retrieval performance.
    Keywords: AC3; Arctic; Arctic Amplification; Arctic Ocean; ATMOBS; Atmospheric Observatory; Binary Object; Binary Object (File Size); brightness temperatures; Comment; DATE/TIME; Event label; Hatpro; LATITUDE; LONGITUDE; microwave radiometer; Mosaic; MOSAiC; MOSAiC20192020; Multidisciplinary drifting Observatory for the Study of Arctic Climate; North Greenland Sea; Other event; Polarstern; PS122; PS122/1; PS122/1_1-38; PS122/2; PS122/2_14-18; PS122/3; PS122/3_28-6; PS122/4; PS122/4_43-11; PS122/4_43-145; PS122/5; PS122/5_58-3; remote sensing
    Type: Dataset
    Format: text/tab-separated-values, 1392 data points
    Location Call Number Expected Availability
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  • 52
    Publication Date: 2024-06-04
    Description: The data set contains daily files of atmospheric radiation measured by the MiRAC-P (or LHUMPRO-243-340) microwave radiometer (see Mech et al., 2019) onboard the Polarstern during cruise PS122 (MOSAiC expedition). The data covers the range October 2019 to October 2020. The atmospheric radiation measurements are given as brightness temperatures in six double side band averaged G band (183.31 +/- 0.6 to 183.31 +/- 7.5 GHz) and two higher frequency (243 and 340 GHz) channels. The brightness temperatures are provided for all available times so that it is up to the user to decide whether or not to use the values if quality flags are set. Additionally included are temperature, pressure and humidity measurements at the instrument location as well as quality flags characterizing the instrument and retrieval performance.
    Keywords: AC3; Arctic; Arctic Amplification; Arctic Ocean; ATMOBS; Atmospheric Observatory; Binary Object; Binary Object (File Size); brightness temperature; DATE/TIME; Event label; LATITUDE; LONGITUDE; microwave radiometer; Mosaic; MOSAiC; MOSAiC20192020; Multidisciplinary drifting Observatory for the Study of Arctic Climate; North Greenland Sea; Other event; Polarstern; PS122; PS122/1; PS122/1_1-38; PS122/2; PS122/2_14-18; PS122/3; PS122/3_28-6; PS122/4; PS122/4_43-11; PS122/4_43-145; PS122/5; PS122/5_58-3; remote sensing
    Type: Dataset
    Format: text/tab-separated-values, 346 data points
    Location Call Number Expected Availability
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  • 53
    Publication Date: 2024-06-04
    Description: The provided dataset contains surface water samples from lakes and ponds, streams and inflows in the Lucky Lake catchment (lake center coordinates: 72°17'56.1N; 126°10'29.7E) in the south of Kurungnakh Island, Lena River Delta, Russia. It includes concentrations of dissolved organic carbon (DOC) as well as stable isotopes of oxygen (δ¹⁸O) and hydrogen (δD). Samples of this dataset were collected during the Russian-German LENA expeditions in July and August 2013, June to September 2014, and in July 2016. For DOC measurements, we used the Shimadzu TOC-VCPH high-temperature catalytic combustion. Stable isotopes of oxygen (δ¹⁸O) and hydrogen (δD) were measured with a Finnigan MAT Delta-S mass spectrometer at the ISOLAB Isotope Facility AWI Potsdam.
    Keywords: aquatic carbon cycle; Arctic lakes; AWI_Envi; AWI_Perma; AWI Arctic Land Expedition; Carbon, organic, dissolved; DATE/TIME; DEPTH, water; Deuterium excess; Event label; KUR16_W_13; KUR16_W_14; KUR16_W_15; KUR16_W_16; KUR16_W_23; LATITUDE; LD13_A_01; LD13_A_02; LD13_A_04; LD13_A_05; LD13_A_06; LD13_A_07; LD13_A_08; LD13_A_09; LD13_A_10; LD13_A_11; LD13_A_12; LD13_A_13; LD13_A_14; LD13_A_15; LD13_A_35; LD13_A_36; LD13_A_37; LD13_A_38; LD13_A_39; LD13_A_40; LD13_A_41; LD13_A_42; LD13_A_43; LD13_A_44; LD13_A_51; LD13_A_52; LD13_A_53; LD13_A_54; LD13_A_55; LD13_A_56; LD13_A_57; LD13_A_58; LD13_A_61; LD13_A_62; LD13_S_01; LD13_S_03; LD13_S_06; LD13_S_09; LD13_S_10; LD13_S_15; LD13_S_27; LD13_S_30; LD13_S_37; LD13_S_40; LD13_S_41; LD13_S_42; LD13_S_49; LD13_S_52; LD13_S_53; LD14_45; LD14_46; LD14_47; LD14_48; LD14_49; LD14_50; LD14_A_01; LD14_A_02; LD14_A_03; LD14_A_04; LD14_A_05; LD14_A_06; LD14_A_07; LD14_A_08; LD14_A_09; LD14_A_10; LD14_A_11; LD14_A_13; LD14_A_14; LD14_A_39; LD14_A_41; LD14_A_68; LD14_A_69; LD14_A_70; LD14_A_71; LD14_A_72; LD14_A_73; LD14_A_74; LD14_A_75; LD14_A_76; LD14_A_77; LD14_A_78; LD14_A_79; LD14_A_81; LD14_A_83; LD14_B_01; LD14_B_21; LD14_B_22; LD14_T_13; LD14_T_14; LD14_T_15; LD14_T_16; LD14_T_18; LD14_T_19; LD14_T_20; LD14_T_21; LD14_T_22; LD14_T_24; LD14_T_25; LD14_T_27; Lena2013; Lena2016_spring, Lena2016_summer; Lena Delta, Siberia, Russia; Lena River Delta, Russia; LONGITUDE; Mass spectrometer Finnigan MAT Delta-S (ISOLAB); MULT; Multiple investigations; Permafrost; Permafrost Research; PETA-CARB; Polar Terrestrial Environmental Systems @ AWI; Rapid Permafrost Thaw in a Warming Arctic and Impacts on the Soil Organic Carbon Pool; RU-Land_2013_Lena; RU-Land_2014_Lena; RU-Land_2016_Lena; Shimadzu TOC-VCPH total organic carbon analyzer SN H51304730370CS (ISOLAB); Siberia; thermokarst lakes; δ18O, standard deviation; δ18O, water; δ Deuterium, standard deviation; δ Deuterium, water
    Type: Dataset
    Format: text/tab-separated-values, 599 data points
    Location Call Number Expected Availability
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  • 54
    Publication Date: 2024-06-04
    Description: The dataset comprises stable water isotopes and conductitities of a lead case study during leg 5 of the MOSAiC campaign. Samples have been taken from different water and ice types for this lead case study. Discrete water samples were taken using a peristaltic pump (Masterflex E/S Portable Sampler, Masterflex, USA) through a 2 m long PTFE tube (L/S Pump Tubing, Masterflex, USA). 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). Snow on the sea ice was sampled with a polyethylene shovel (GL Science Inc., Tokyo, Japan) and placed into a polyethylene zip-loc bag. Ice in the lead was collected and a 0.25 m ' 0.25 m ice block was cut with a hand saw and placed into a zip-lock bag. Ice temperature at the surface was measured with a needle-type temperature sensor (Testo 110 NTC, Brandt Instruments, Inc., USA). Two ice cores from the bottom of a melt pond were collected, using an ice corer with an inner diameter of 0.09 m (Mark II coring system, KOVACS Enterprises, Inc., USA). The cores were cut with a stainless steel saw into 0.1 m thick sections and stored in plastic bags for subsequent salinity and δ18O measurements. Snow and ice samples were immediately placed in a cooler box along with refrigerants to keep their temperature low and to minimize brine drainage. Onboard Polarstern, ice samples were transferred into ice melting bags (Smart bags PA, AAK 5L, GL Sciences Inc., Japan) and melted in the dark at +4°C. After the ice melted, the meltwater was placed in a 30-mL glass screw-cap vial for later stable water isotope measurement and into a 100-mL polypropylene bottle (I-Boy, AS ONE Corporation, Japan) for later salinity measurement. These samples were stored at +4°C in the dark until analysis. Under-ice water samples (from about 10 m depth) were collected via R/V Polarstern's underway water sampling system during leg 5. Samples were placed into 250-mL glass vials (Duran Co. Ltd, Germany) for later δ18O and salinity measurements. Salinity of collected samples was determined with a same conductivity sensor used on sea ice (Cond 315i, WTW GmbH, Germany). Oxygen and hydrogen isotope analyses were carried out at the ISOLAB Facility at AWI Potsdam (hdl:10013/sensor.ddc92f54-4c63-492d-81c7-696260694001) with mass spectrometers (DELTA-S Finnigan MAT, USA): hdl:10013/sensor.af148dea-fe65-4c87-9744-50dc4c81f7c9 and hdl: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; AWI_Envi; AWI_Perma; Calculated after Dansgaard (1964); Chamber for gas sampling; CHAMGAS; Comment; Conductivity sensor Cond 315i, WTW GmbH, Germany; DATE/TIME; DEPTH, ice/snow; DEPTH, water; Deuterium excess; Event label; freshwater; IC; Ice corer; Latitude of event; leads; Leg 5; Longitude of event; Mass spectrometer Finnigan MAT Delta-S (ISOLAB); Mosaic; MOSAiC; MOSAiC20192020; Multidisciplinary drifting Observatory for the Study of Arctic Climate; Permafrost Research; Polarstern; Polar Terrestrial Environmental Systems @ AWI; PS122/5; PS122/5_59-343; PS122/5_59-389; PS122/5_59-392; PS122/5_59-446; PS122/5_59-447; PS122/5_60-130; PS122/5_60-133; PS122/5_60-146; PS122/5_60-16; PS122/5_60-260; PS122/5_60-61; PS122/5_61-126; PS122/5_61-205; PS122/5_61-206; PS122/5_62-117; PS122/5_62-120; PS122/5_62-35; PS122/5_62-40; PS122/5_62-42; PS122/5_62-5; Salinity; Sample code/label; Sample ID; Sample type; Sea ice; snow; SNOW; Snow/ice sample; Station label; Water sample; WS; δ18O, water; δ Deuterium, water
    Type: Dataset
    Format: text/tab-separated-values, 838 data points
    Location Call Number Expected Availability
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  • 55
    Publication Date: 2024-06-04
    Description: Snow samples were collected from several locations on the main MOSAiC ice floe on weekly basis. Snow samples for measurement of stable water isotopes (δ18O, δD,) were collected in three different layers (top, middle, bottom) using a metal density cutter. At first, samples were stored in sealed plastic bags and the air was squeezed out before closing the bags. At later stages of the expedition, samples were stored in plastic cups with lids. Later the samples were thawed completely at room temperature and poured into 20 ml glass vials and sealed with parafilm tape and stored at 4°C. 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); Comment; DATE/TIME; DEPTH, water; Deuterium excess; Event label; Height, relative, from ice/snow line, maximum; Height, relative, from ice/snow line, minimum; IC; Ice corer; isotopes; Layer description; Mass spectrometer Finnigan MAT Delta-S (ISOLAB); Mosaic; MOSAiC; MOSAiC20192020; Multidisciplinary drifting Observatory for the Study of Arctic Climate; Polarstern; PS122/1; PS122/1_10-35; PS122/1_10-38; PS122/1_10-5; PS122/1_11-23; PS122/1_4-10; PS122/1_5-5; PS122/1_5-92; PS122/1_5-93; PS122/1_5-95; PS122/1_6-10; PS122/1_6-136; PS122/1_6-140; PS122/1_6-34; PS122/1_6-6; PS122/1_6-61; PS122/1_7-105; PS122/1_7-106; PS122/1_7-12; PS122/1_7-89; PS122/1_8-110; PS122/1_8-24; PS122/1_8-33; PS122/1_8-79; PS122/1_9-23; PS122/1_9-31; PS122/1_9-39; PS122/1_9-65; PS122/2; PS122/2_16-9; PS122/2_17-109; PS122/2_17-16; PS122/2_18-17; PS122/2_18-66; PS122/2_19-144; PS122/2_19-28; PS122/2_19-9; PS122/2_19-92; PS122/2_20-36; PS122/2_20-4; PS122/2_20-80; PS122/2_20-83; PS122/2_21-14; PS122/2_21-15; PS122/2_21-96; PS122/2_22-5; PS122/2_22-6; PS122/2_22-73; PS122/2_23-2; PS122/2_23-34; PS122/2_23-73; PS122/2_23-74; PS122/2_23-9; PS122/2_24-14; PS122/2_24-15; PS122/2_24-35; PS122/2_24-86; PS122/2_25-22; PS122/2_25-80; PS122/2_25-81; PS122/3; PS122/3_29-28; PS122/3_29-29; PS122/3_29-9; PS122/3_30-17; PS122/3_30-25; PS122/3_31-44; PS122/3_31-55; PS122/3_31-64; PS122/3_31-65; PS122/3_31-91; PS122/3_32-5; PS122/3_32-88; PS122/3_32-92; PS122/3_33-53; PS122/3_33-65; PS122/3_33-66; PS122/3_34-34; PS122/3_34-45; PS122/3_35-23; PS122/3_35-53; PS122/3_35-56; PS122/3_36-14; PS122/3_36-178; PS122/3_36-35; PS122/3_36-99; PS122/3_37-129; PS122/3_37-41; PS122/3_37-57; PS122/3_38-1; PS122/3_38-141; PS122/3_38-4; PS122/3_38-51; PS122/3_39-46; PS122/3_39-48; PS122/3_39-88; PS122/3_39-92; PS122/4; PS122/4_44-157; PS122/4_44-215; PS122/4_44-216; PS122/4_44-47; PS122/4_46-32; PS122/4_46-50; PS122/4_47-23; PS122/4_48-142; PS122/4_48-143; PS122/4_48-144; PS122/4_48-145; PS122/4_48-146; PS122/5; PS122/5_59-204; PS122/5_59-313; PS122/5_60-2; PS122/5_60-91; PS122/5_61-25; PS122/5_62-124; PS122/5_62-44; Sample code/label; Sample ID; Sample type; snow; SNOWPIT; Snow pit; Snow sampler metal; SSM; Station label; δ18O, water; δ Deuterium, water
    Type: Dataset
    Format: text/tab-separated-values, 2717 data points
    Location Call Number Expected Availability
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  • 56
    Publication Date: 2024-06-04
    Description: Underway seawater samples have been taken from underneath the research vessel Polarstern through a pipe installed on the ship. The valve had been open for about 2 minutes before collecting the samples to avoid possible contaminations. Water samples for measurement of stable water isotopes (δ18O, δD,) were collected in narrow-mouth low-density polyethylene 20- or 30-mL plastic bottles (VWR international LLC, Germany), sealed with Parafilm M and stored at +4 °C from the end of the expedition until the measurement. Average daily salinity values were extracted from dship portal (https://dship.awi.de/). 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); Comment; DATE/TIME; DEPTH, water; Deuterium excess; Event label; isotopes; Mass spectrometer Finnigan MAT Delta-S (ISOLAB); MOSAiC; MOSAiC20192020; MOSAiC expedition; Multidisciplinary drifting Observatory for the Study of Arctic Climate; North Greenland Sea; Polarstern; PS122/1; PS122/1_10-111; PS122/1_10-2; PS122/1_10-25; PS122/1_10-32; PS122/1_10-52; PS122/1_10-77; PS122/1_10-93; PS122/1_11-15; PS122/1_11-28; PS122/1_11-4; PS122/1_11-42; PS122/1_7-111; PS122/1_7-95; PS122/1_8-100; PS122/1_8-119; PS122/1_8-15; PS122/1_8-26; PS122/1_8-4; PS122/1_8-84; PS122/1_9-103; PS122/1_9-15; PS122/1_9-29; PS122/1_9-45; PS122/1_9-56; PS122/1_9-92; PS122/2; PS122/2_15-6; PS122/2_15-8; PS122/2_16-14; PS122/2_16-29; PS122/2_16-37; PS122/2_16-53; PS122/2_16-61; PS122/2_16-8; PS122/2_17-17; PS122/2_17-2; PS122/2_17-25; PS122/2_17-42; PS122/2_17-67; PS122/2_17-75; PS122/2_17-97; PS122/2_18-15; PS122/2_18-2; PS122/2_18-23; PS122/2_18-39; PS122/2_18-58; PS122/2_18-75; PS122/2_18-90; PS122/2_19-114; PS122/2_19-17; PS122/2_19-2; PS122/2_19-33; PS122/2_19-58; PS122/2_19-80; PS122/2_19-90; PS122/2_20-1; PS122/2_20-111; PS122/2_20-13; PS122/2_20-29; PS122/2_20-50; PS122/2_20-75; PS122/2_20-99; PS122/2_21-11; PS122/2_21-112; PS122/2_21-123; PS122/2_21-50; PS122/2_21-69; PS122/2_21-85; PS122/2_22-14; PS122/2_22-2; PS122/2_22-31; PS122/2_22-64; PS122/2_22-84; PS122/2_22-95; PS122/2_23-1; PS122/2_23-11; PS122/2_23-33; PS122/2_23-49; PS122/2_23-66; PS122/2_23-86; PS122/2_24-20; PS122/2_24-30; PS122/2_24-41; PS122/2_24-48; PS122/2_24-5; PS122/2_24-71; PS122/2_24-82; PS122/2_25-101; PS122/2_25-29; PS122/2_25-43; PS122/2_25-5; PS122/2_25-56; PS122/2_25-75; PS122/2_25-88; PS122/3; PS122/3_29-21; PS122/3_29-36; PS122/3_29-51; PS122/3_29-6; PS122/3_29-60; PS122/3_29-7; PS122/3_29-81; PS122/3_30-18; PS122/3_30-23; PS122/3_30-35; PS122/3_30-52; PS122/3_30-6; PS122/3_30-66; PS122/3_30-83; PS122/3_31-13; PS122/3_31-16; PS122/3_31-28; PS122/3_31-46; PS122/3_31-54; PS122/3_31-60; PS122/3_31-78; PS122/3_32-1; PS122/3_32-21; PS122/3_32-37; PS122/3_32-48; PS122/3_32-60; PS122/3_32-72; PS122/3_33-15; PS122/3_33-35; PS122/3_33-50; PS122/3_33-64; PS122/3_33-79; PS122/3_33-8; PS122/3_33-93; PS122/3_34-1; PS122/3_34-12; PS122/3_34-26; PS122/3_34-35; PS122/3_34-47; PS122/3_34-61; PS122/3_34-74; PS122/3_35-103; PS122/3_35-17; PS122/3_35-3; PS122/3_35-35; PS122/3_35-52; PS122/3_35-75; PS122/3_35-89; PS122/3_36-1; PS122/3_36-110; PS122/3_36-13; PS122/3_36-135; PS122/3_36-34; PS122/3_36-55; PS122/3_36-72; PS122/3_37-112; PS122/3_37-12; PS122/3_37-2; PS122/3_37-23; PS122/3_37-42; PS122/3_37-67; PS122/3_37-90; PS122/3_38-113; PS122/3_38-22; PS122/3_38-26; PS122/3_38-37; PS122/3_38-48; PS122/3_38-67; PS122/3_38-88; PS122/3_39-1; PS122/3_39-14; PS122/3_39-29; PS122/3_39-49; PS122/3_39-68; PS122/3_39-76; PS122/3_39-85; PS122/3_40-1; PS122/3_40-13; PS122/3_40-22; PS122/3_40-30; PS122/3_40-35; PS122/3_40-45; PS122/3_40-7; PS122/3_41-13; PS122/3_41-20; PS122/3_41-27; PS122/3_41-38; PS122/3_41-4; PS122/3_41-42; PS122/3_41-48; PS122/3_42-1; PS122/3_42-12; PS122/3_42-21; PS122/3_42-27; PS122/3_42-33; PS122/3_42-43; PS122/3_42-52; PS122/3_42-58; PS122/3_42-6; PS122/3_42-67; PS122/4; PS122/4_44-123; PS122/4_44-131; PS122/4_44-146; PS122/4_44-160; PS122/4_44-175; PS122/4_44-19; PS122/4_44-194; PS122/4_44-28; PS122/4_44-34; PS122/4_44-54; PS122/4_44-64; PS122/4_44-80; PS122/4_44-85; PS122/4_44-96; PS122/4_45-111; PS122/4_45-133; PS122/4_45-14; PS122/4_45-6; PS122/4_45-65; PS122/4_45-90; PS122/4_46-124; PS122/4_46-22; PS122/4_46-5; PS122/4_46-51; PS122/4_46-67; PS122/4_46-96; PS122/4_47-105; PS122/4_47-118; PS122/4_47-24; PS122/4_47-37; PS122/4_47-5; PS122/4_47-70; PS122/4_47-88; PS122/4_48-119; PS122/4_48-149; PS122/4_48-156; PS122/4_48-18; PS122/4_48-48; PS122/4_48-68; PS122/4_49-23; PS122/4_49-24; PS122/4_49-28; PS122/4_49-58; PS122/4_49-67; PS122/4_49-82; PS122/4_49-96; PS122/4_50-12; PS122/4_50-22; PS122/4_50-3; PS122/4_50-34; PS122/4_50-44; PS122/4_50-53; PS122/4_50-59; PS122/4_50-62; PS122/5; PS122/5_59-11; PS122/5_59-133; PS122/5_59-151; PS122/5_59-167; PS122/5_59-179; PS122/5_59-199; PS122/5_59-220; PS122/5_59-247; PS122/5_59-26; PS122/5_59-266; PS122/5_59-287; PS122/5_59-323; PS122/5_59-341; PS122/5_59-36; PS122/5_59-361; PS122/5_59-379; PS122/5_59-4; PS122/5_59-59; PS122/5_59-65; PS122/5_60-11; PS122/5_60-116; PS122/5_60-140; PS122/5_60-169; PS122/5_60-35; PS122/5_60-50; PS122/5_60-73; PS122/5_61-106; PS122/5_61-141; PS122/5_61-184; PS122/5_61-208; PS122/5_61-244; PS122/5_61-3; PS122/5_61-34; PS122/5_62-111; PS122/5_62-133; PS122/5_62-150; PS122/5_62-171; PS122/5_62-63; PS122/5_62-8; PS122/5_62-86; PS122/5_63-115; PS122/5_63-123; PS122/5_63-134; PS122/5_63-144; PS122/5_63-27; PS122/5_63-52; PS122/5_63-6; PS122/5_63-66; PS122/5_63-7; PS122/5_63-78; PS122/5_63-93; Salinity; Sample code/label; Sample ID; Sample type; seawater; see abstract; Station label; Surface water sample; SWS; Tap; TAP; Water sample; WS; δ18O, water; δ Deuterium, water
    Type: Dataset
    Format: text/tab-separated-values, 2375 data points
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  • 57
    Publication Date: 2024-06-04
    Keywords: AC; Aircraft; Flight 14; P6_244_ANT_23_24_2312101401; P6-244_ANT_23_24; POLAR 6
    Type: Dataset
    Format: application/zip, 302.4 kBytes
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  • 58
    Publication Date: 2024-06-04
    Keywords: Calculated average/mean values; ChRM, Declination; ChRM, Inclination; ChRM, Polarity; Comment; Geological profile sampling; GEOPRO; Horizon; Kulyumbe river, Siberia, Russia; Kulyumbe-section; Outcrop ID; POINT DISTANCE from start; Sample code/label; δ13C; δ18O
    Type: Dataset
    Format: text/tab-separated-values, 9284 data points
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  • 59
    facet.materialart.
    Unknown
    PANGAEA
    In:  Institute of Geology, Komi Scientific Cener, Ural Division, Russian Academy of Sciences
    Publication Date: 2024-06-04
    Keywords: Archive of Ocean Data; ARCOD; Bolshezemelskaya Tundra; Event label; Horizon; Latitude of event; Limestone; Longitude of event; Metamorphite; Outcrop ID; Quartzite; ShRV-1; ShRV-10; ShRV-11; ShRV-12; ShRV-13; ShRV-2; ShRV-3; ShRV-4; ShRV-6; ShRV-7; ShRV-8; Terrigenous
    Type: Dataset
    Format: text/tab-separated-values, 144 data points
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  • 60
    facet.materialart.
    Unknown
    PANGAEA
    In:  Institute of Geology, Komi Scientific Cener, Ural Division, Russian Academy of Sciences
    Publication Date: 2024-06-04
    Keywords: Amphibole; Apatite; Archive of Ocean Data; ARCOD; Bolshezemelskaya Tundra; Counting, Stereo Microscope; Epidote; Event label; Garnet; Heavy minerals; Hematite, Fe2O3; Horizon; Ilmenite; Kyanite; Latitude of event; Leucoxene; Limonite; Longitude of event; Magnetite; Minerals; Outcrop ID; Pyrite, FeS2; Pyroxene; Rutile; Separation with use of heavy (2.9) liquid; ShRV-10; ShRV-11; ShRV-13; ShRV-4; ShRV-7; ShRV-9; Siderite; Staurolite; Titanite; Titanium minerals; Tourmaline; Zircon
    Type: Dataset
    Format: text/tab-separated-values, 138 data points
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  • 61
    Publication Date: 2024-06-04
    Description: This dataset contains measured dissolved trace element concentrations (Fe, Mn, Co, Ni, Cu, Zn, Cd and Pb) of station depth profiles sampled in Fram Strait (North Greenland Sea) during GEOTRACES expedition GN05 (PS100) between 21 July and 1 September 2016. Samples were collected strictly following GEOTRACES guidelines (Cutter et al., 2017; https://www.geotraces.org) and analysed exactly as per Rapp et al., 2017 ( Anal. Chim. Acta; doi:10.1016/j.aca.2017.05.008). Concentrations were intercalibrated with GEOTRACES reference materials SAFe S and GSC (Bruland Research Lab), with exception of dissolved Cd data. Information on the analytical procedure including reference materials and limits of detection can be found in related published manuscripts, the PhD thesis of Stephan Krisch (Christian-Albrechts-Universität zu Kiel) or can be obtained from the authors upon request. Table caption: Measured concentrations of dissolved trace elements in Fram Strait sampled during GEOTRACES expedition GN05 (PS100) between 21 July-1 September 2006. Uncertainty is calculated as one standard deviation (1σ, STD) to replicate measurements via ICP-MS. ND = no data. Use of quality flags (QF) according to GEOTRACES policy (https://www.geotraces.org/geotraces-quality-flag-policy/). Plesae note, dissolved Cd data is not quality controlled. Somes samples were pooled (indicated in column "Bottle") from different bottles at one depth; the concentrations reflects the mean and the corresponding uncertainty is calculated as the standard deviation to replicate measurements. Trace metal concentrations at station 24 may show larger variations between different bottles at one specific depth. Because station 24 is located at Dijmphna Sund entrance sill, we associate these discrepancies to the water column's strong lateral and vertical turbulence (see ucCTD physical oceanography data) (e.g. Mortensen et al. 2011, 2013, Carroll et al. 2017) that goes in hand with localized TM aggregation-dissolution and sediment resuspension processes, thus affecting TM fractionation (e.g. Homoky et al. 2012).
    Keywords: Arctic; ARK-XXX/2, GN05; Bottle number; Cadmium, dissolved; Cadmium, dissolved, standard deviation; calculated, 1 sigma; Cobalt, dissolved; Cobalt, dissolved, standard deviation; Copper, dissolved; Copper, dissolved, standard deviation; Cruise/expedition; CTD/Rosette, ultra clean; CTD-UC; Date/Time of event; DEPTH, water; Elevation of event; Event label; Fram Strait; GEOTRACES; Global marine biogeochemical cycles of trace elements and their isotopes; GN05; Greenland Sea; Inductively coupled plasma - mass spectrometry (ICP-MS); Iron, dissolved; Iron, dissolved, standard deviation; Latitude of event; Lead, dissolved; Lead, standard deviation; Longitude of event; Manganese, dissolved; Manganese, dissolved, standard deviation; micronutrients; Nickel, dissolved; Nickel, dissolved, standard deviation; North Greenland Sea; Polarstern; PS100; PS100/013-1; PS100/015-1; PS100/021-1; PS100/028-1; PS100/033-1; PS100/037-1; PS100/042-1; PS100/044-1; PS100/053-2; PS100/056-1; PS100/074-1; PS100/082-1; PS100/090-1; PS100/094-1; PS100/101-1; PS100/102-1; PS100/103-2; PS100/135-1; PS100/165-1; PS100/189-1; PS100/202-1; PS100/214-1; PS100/241-1; PS100/262-1; PS100/274-2; PS100/280-1; PS100/288-1; Quality flag; Seadatanet flag: Data quality control procedures according to SeaDataNet (2010); Standard deviation, relative; Station label; trace elements; trace metals; Zinc, dissolved; Zinc, dissolved, standard deviation
    Type: Dataset
    Format: text/tab-separated-values, 16511 data points
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  • 62
    Publication Date: 2024-06-04
    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: 20200125_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_21-122; Remote Sensing of the Seasonal Evolution of Climate-relevant Sea Ice Properties; Sea ice; Surface Elevation
    Type: Dataset
    Format: text/tab-separated-values, 178 data points
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  • 63
    Publication Date: 2024-06-04
    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
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  • 64
    Publication Date: 2024-06-04
    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; PS122/3; PS122/3_37-66; Remote Sensing of the Seasonal Evolution of Climate-relevant Sea Ice Properties; Sea ice; Surface Elevation
    Type: Dataset
    Format: text/tab-separated-values, 322 data points
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  • 65
    Publication Date: 2024-06-04
    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; 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_2_45_2019092801; PS122/1; PS122/1_2-45; Remote Sensing of the Seasonal Evolution of Climate-relevant Sea Ice Properties; Sea ice; Surface Elevation
    Type: Dataset
    Format: text/tab-separated-values, 4 data points
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  • 66
    Publication Date: 2024-06-04
    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; PS122/2; 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, 2 data points
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  • 67
    Publication Date: 2024-06-04
    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: 20200202_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_22-97; 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
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  • 68
    facet.materialart.
    Unknown
    PANGAEA
    In:  Japan Meteorological Agency, Tokyo
    Publication Date: 2024-06-04
    Keywords: Anemometer; BARO; Barometer; Baseline Surface Radiation Network; BSRN; Cosmonauts Sea; DATE/TIME; Dew/frost point; Horizontal visibility; HYGRO; Hygrometer; Monitoring station; MONS; Pressure, atmospheric; SYO; Syowa; Temperature, air; Thermometer; Visibility sensor; Wind direction; Wind speed
    Type: Dataset
    Format: text/tab-separated-values, 248711 data points
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  • 69
    facet.materialart.
    Unknown
    PANGAEA
    In:  Japan Meteorological Agency, Tokyo
    Publication Date: 2024-06-04
    Keywords: Anemometer; BARO; Barometer; Baseline Surface Radiation Network; BSRN; Cosmonauts Sea; DATE/TIME; Dew/frost point; Horizontal visibility; HYGRO; Hygrometer; Monitoring station; MONS; Pressure, atmospheric; SYO; Syowa; Temperature, air; Thermometer; Visibility sensor; Wind direction; Wind speed
    Type: Dataset
    Format: text/tab-separated-values, 267726 data points
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  • 70
    Publication Date: 2024-06-04
    Keywords: AC; Aircraft; Flight 16; P6_244_ANT_23_24_2312111602; P6-244_ANT_23_24; POLAR 6
    Type: Dataset
    Format: application/zip, 800 kBytes
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  • 71
    Publication Date: 2024-06-04
    Keywords: AC; Aircraft; Flight 15; P6_244_ANT_23_24_2312111501; P6-244_ANT_23_24; POLAR 6
    Type: Dataset
    Format: application/zip, 832.1 kBytes
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  • 72
    Publication Date: 2024-06-04
    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; 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_2-57; Remote Sensing of the Seasonal Evolution of Climate-relevant Sea Ice Properties; Sea ice; Surface Elevation
    Type: Dataset
    Format: text/tab-separated-values, 16 data points
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  • 73
    Publication Date: 2024-06-04
    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: 20191206_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_10-78; Remote Sensing of the Seasonal Evolution of Climate-relevant Sea Ice Properties; Sea ice; Surface Elevation
    Type: Dataset
    Format: text/tab-separated-values, 26 data points
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  • 74
    Publication Date: 2024-06-04
    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: 20191228_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_17-101; 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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  • 75
    Publication Date: 2024-06-04
    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: 20200107_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_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, 38 data points
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  • 76
    facet.materialart.
    Unknown
    PANGAEA
    In:  Institute of Geology, Komi Scientific Cener, Ural Division, Russian Academy of Sciences
    Publication Date: 2024-06-04
    Keywords: Amphibole; Apatite; Archive of Ocean Data; ARCOD; Bolshezemelskaya Tundra; Counting, Stereo Microscope; Epidote; Event label; Garnet; Heavy minerals; Hematite, Fe2O3; Ilmenite; Kyanite; Latitude of event; Limonite; Longitude of event; Magnetite; Minerals; Outcrop ID; Pyrite, FeS2; Pyroxene; Rock type; Rutile; Separation with use of heavy (2.9) liquid; ShRV-13; ShRV-6; ShRV-7; Siderite; Sillimanite; Staurolite; Titanite; Titanium minerals; Tourmaline; Zircon
    Type: Dataset
    Format: text/tab-separated-values, 115 data points
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  • 77
    Publication Date: 2024-06-04
    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: 20191105_01; Airborne laser scanning; Arctic; 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; PS122/1; 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, 48 data points
    Location Call Number Expected Availability
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  • 78
    Publication Date: 2024-06-04
    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: 20191029_01; Airborne laser scanning; Arctic; 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; PS122/1; PS122/1_5-9; Remote Sensing of the Seasonal Evolution of Climate-relevant Sea Ice Properties; Sea ice; Surface Elevation
    Type: Dataset
    Format: text/tab-separated-values, 172 data points
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  • 79
    Publication Date: 2024-06-04
    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: 20191112_02; Airborne laser scanning; Arctic; 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; PS122/1; 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, 50 data points
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  • 80
    Publication Date: 2024-06-04
    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: 20191119_01; Airborne laser scanning; Arctic; 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; PS122/1; PS122/1_8-23; Remote Sensing of the Seasonal Evolution of Climate-relevant Sea Ice Properties; Sea ice; Surface Elevation
    Type: Dataset
    Format: text/tab-separated-values, 102 data points
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  • 81
    Publication Date: 2024-06-04
    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: 20191130_01; Airborne laser scanning; Arctic; 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; PS122/1; PS122/1_9-98; Remote Sensing of the Seasonal Evolution of Climate-relevant Sea Ice Properties; Sea ice; Surface Elevation
    Type: Dataset
    Format: text/tab-separated-values, 44 data points
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  • 82
    Publication Date: 2024-06-04
    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: 20191228_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_17-101; Remote Sensing of the Seasonal Evolution of Climate-relevant Sea Ice Properties; Sea ice; Surface Elevation
    Type: Dataset
    Format: text/tab-separated-values, 282 data points
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  • 83
    Publication Date: 2024-06-04
    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: 20191224_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_17-98; Remote Sensing of the Seasonal Evolution of Climate-relevant Sea Ice Properties; Sea ice; Surface Elevation
    Type: Dataset
    Format: text/tab-separated-values, 390 data points
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  • 84
    Publication Date: 2024-06-04
    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: 20200125_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_21-122; 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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  • 85
    Publication Date: 2024-06-04
    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: 20200108_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_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, 166 data points
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  • 86
    Publication Date: 2024-06-04
    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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  • 87
    Publication Date: 2024-06-04
    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: 20200108_04; 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_19-53; Remote Sensing of the Seasonal Evolution of Climate-relevant Sea Ice Properties; Sea ice; Surface Elevation
    Type: Dataset
    Format: text/tab-separated-values, 178 data points
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  • 88
    Publication Date: 2024-06-04
    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: 20200116_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_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, 362 data points
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  • 89
    Publication Date: 2024-06-04
    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_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_21-77; 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
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  • 90
    Publication Date: 2024-06-04
    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: 20200128_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_22-16; 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
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  • 91
    Publication Date: 2024-06-04
    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
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  • 92
    Publication Date: 2024-06-04
    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; Calculated; DATE/TIME; DEPTH, water; Digital precision altimeter, Tritech, PA500; Distance, relative, X; Distance, relative, Y; Distance to sea ice bottom; FRAM; FRontiers in Arctic marine Monitoring; MOSAiC; MOSAiC20192020; MOSAiC expedition; Multidisciplinary drifting Observatory for the Study of Arctic Climate; Polarstern; PS122/2; PS122/2_25-44; Quality flag, position; Remotely operated sensor platform BEAST; Remotely operated vehicle (ROV); Sea ice; Sea ice draft; Sea-ice draft; Sea Ice Physics @ AWI; Survey ID
    Type: Dataset
    Format: text/tab-separated-values, 726 data points
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  • 93
    Publication Date: 2024-06-04
    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; Calculated; DATE/TIME; DEPTH, water; Digital precision altimeter, Tritech, PA500; Distance, relative, X; Distance, relative, Y; Distance to sea ice bottom; FRAM; FRontiers in Arctic marine Monitoring; MOSAiC; MOSAiC20192020; MOSAiC expedition; Multidisciplinary drifting Observatory for the Study of Arctic Climate; Polarstern; PS122/3; PS122/3_29-14; Quality flag, position; Remotely operated sensor platform BEAST; Remotely operated vehicle (ROV); Sea ice; Sea ice draft; Sea-ice draft; Sea Ice Physics @ AWI; Survey ID
    Type: Dataset
    Format: text/tab-separated-values, 32922 data points
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  • 94
    Publication Date: 2024-06-04
    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; Calculated; DATE/TIME; DEPTH, water; Digital precision altimeter, Tritech, PA500; Distance, relative, X; Distance, relative, Y; Distance to sea ice bottom; FRAM; FRontiers in Arctic marine Monitoring; MOSAiC; MOSAiC20192020; MOSAiC expedition; Multidisciplinary drifting Observatory for the Study of Arctic Climate; Polarstern; PS122/3; PS122/3_29-65; Quality flag, position; Remotely operated sensor platform BEAST; Remotely operated vehicle (ROV); Sea ice; Sea ice draft; Sea-ice draft; Sea Ice Physics @ AWI; Survey ID
    Type: Dataset
    Format: text/tab-separated-values, 17220 data points
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  • 95
    Publication Date: 2024-06-04
    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; Calculated; DATE/TIME; DEPTH, water; Digital precision altimeter, Tritech, PA500; Distance, relative, X; Distance, relative, Y; Distance to sea ice bottom; FRAM; FRontiers in Arctic marine Monitoring; MOSAiC; MOSAiC20192020; MOSAiC expedition; Multidisciplinary drifting Observatory for the Study of Arctic Climate; Polarstern; PS122/3; PS122/3_31-17; Quality flag, position; Remotely operated sensor platform BEAST; Remotely operated vehicle (ROV); Sea ice; Sea ice draft; Sea-ice draft; Sea Ice Physics @ AWI; Survey ID
    Type: Dataset
    Format: text/tab-separated-values, 6306 data points
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  • 96
    Publication Date: 2024-06-04
    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; Calculated; DATE/TIME; DEPTH, water; Digital precision altimeter, Tritech, PA500; Distance, relative, X; Distance, relative, Y; Distance to sea ice bottom; FRAM; FRontiers in Arctic marine Monitoring; MOSAiC; MOSAiC20192020; MOSAiC expedition; Multidisciplinary drifting Observatory for the Study of Arctic Climate; Polarstern; PS122/3; PS122/3_31-75; Quality flag, position; Remotely operated sensor platform BEAST; Remotely operated vehicle (ROV); Sea ice; Sea ice draft; Sea-ice draft; Sea Ice Physics @ AWI; Survey ID
    Type: Dataset
    Format: text/tab-separated-values, 59178 data points
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  • 97
    Publication Date: 2024-06-04
    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; Calculated; DATE/TIME; DEPTH, water; Digital precision altimeter, Tritech, PA500; Distance, relative, X; Distance, relative, Y; Distance to sea ice bottom; FRAM; FRontiers in Arctic marine Monitoring; MOSAiC; MOSAiC20192020; MOSAiC expedition; Multidisciplinary drifting Observatory for the Study of Arctic Climate; Polarstern; PS122/3; PS122/3_30-69; Quality flag, position; Remotely operated sensor platform BEAST; Remotely operated vehicle (ROV); Sea ice; Sea ice draft; Sea-ice draft; Sea Ice Physics @ AWI; Survey ID
    Type: Dataset
    Format: text/tab-separated-values, 8400 data points
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  • 98
    Publication Date: 2024-06-04
    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; PS122/3; PS122/3_35-49; 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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  • 99
    Publication Date: 2024-06-04
    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: 20191228_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_17-101; 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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  • 100
    Publication Date: 2024-06-04
    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: 20200107_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_19-44; 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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