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  • PANGAEA  (3)
  • Copernicus Publications (EGU)  (2)
  • Springer
  • 2020-2024  (5)
  • 2022  (5)
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  • 2020-2024  (5)
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  • 1
    Publication Date: 2024-02-07
    Description: Large amounts of methane (CH4) could be released as a result of the gradual or abrupt thawing of Arctic permafrost due to global warming. Once available, this potent greenhouse gas is emitted into the atmosphere or transported laterally into aquatic ecosystems via hydrologic connectivity at the surface or via groundwaters. While high northern latitudes contribute up to 5 % of total global CH4 emissions, the specific contribution of Arctic rivers and streams is largely unknown. We analyzed high-resolution continuous CH4 concentrations measured between 15 and 17 June 2019 (late freshet) in a ∼120 km transect of the Kolyma River in northeast Siberia. The average partial pressure of CH4 (pCH4) in tributaries (66.8–206.8 µatm) was 2–7 times higher than in the main river channel (28.3 µatm). In the main channel, CH4 was up to 1600 % supersaturated with respect to atmospheric equilibrium. Key sites along the riverbank and at tributary confluences accounted for 10 % of the navigated transect and had the highest pCH4 (41 ± 7 µatm) and CH4 emissions (0.03 ± 0.004 ) compared to other sites in the main channel, contributing between 14 % to 17 % of the total CH4 flux in the transect. These key sites were characterized by warm waters (T〉14.5 ∘C) and low specific conductivities (κ〈88 µS cm−1). The distribution of CH4 in the river could be linked statistically to T and κ of the water and to their proximity to the shore z, and these parameters served as predictors of CH4 concentrations in unsampled river areas. The abundance of CH4-consuming bacteria and CH4-producing archaea in the river was similar to those previously detected in nearby soils and was also strongly correlated to T and κ. These findings imply that the source of riverine CH4 is closely related with sites near land. The average total CH4 flux density in the river section was 0.02 ± 0.006 , equivalent to an annual CH4 flux of 1.24×107 g CH4 yr−1 emitted during a 146 d open water season. Our study highlights the importance of high-resolution continuous CH4 measurements in Arctic rivers for identifying spatial and temporal variations, as well as providing a glimpse of the magnitude of riverine CH4 emissions in the Arctic and their potential relevance to regional CH4 budgets.
    Type: Article , PeerReviewed , info:eu-repo/semantics/article
    Format: text
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  • 2
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    Copernicus Publications (EGU)
    Publication Date: 2024-02-07
    Description: Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere in a changing climate is critical to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe and synthesize datasets and methodology to quantify the five major components of the global carbon budget and their uncertainties. Fossil CO2 emissions (E-FOS) are based on energy statistics and cement production data, while emissions from land-use change (E-LUC), mainly deforestation, are based on land use and land-use change data and bookkeeping models. Atmospheric CO2 concentration is measured directly, and its growth rate (G(ATM)) is computed from the annual changes in concentration. The ocean CO2 sink (S-OCEAN) is estimated with global ocean biogeochemistry models and observation-based data products. The terrestrial CO2 sink (S-LAND) is estimated with dynamic global vegetation models. The resulting carbon budget imbalance (B-IM), the difference between the estimated total emissions and the estimated changes in the atmosphere, ocean, and terrestrial biosphere, is a measure of imperfect data and understanding of the contemporary carbon cycle. All uncertainties are reported as +/- 1 sigma. For the first time, an approach is shown to reconcile the difference in our E-LUC estimate with the one from national greenhouse gas inventories, supporting the assessment of collective countries' climate progress. For the year 2020, E-FOS declined by 5.4 % relative to 2019, with fossil emissions at 9.5 +/- 0.5 GtC yr(-1) (9.3 +/- 0.5 GtC yr(-1) when the cement carbonation sink is included), and E-LUC was 0.9 +/- 0.7 GtC yr(-1), for a total anthropogenic CO2 emission of 10.2 +/- 0.8 GtC yr(-1) (37.4 +/- 2.9 GtCO(2)). Also, for 2020, G(ATM) was 5.0 +/- 0.2 GtC yr-1 (2.4 +/- 0.1 ppm yr(-1)), S-OCEAN was 3.0 +/- 0.4 GtC yr(-1), and S-LAND was 2.9 +/- 1 GtC yr(-1), with a B-IM of -0.8 GtC yr(-1). The global atmospheric CO2 concentration averaged over 2020 reached 412.45 +/- 0.1 ppm. Preliminary data for 2021 suggest a rebound in E-FOS relative to 2020 of +4.8 % (4.2 % to 5.4 %) globally. Overall, the mean and trend in the components of the global carbon budget are consistently estimated over the period 1959-2020, but discrepancies of up to 1 GtC yr(-1) persist for the representation of annual to semi-decadal variability in CO2 fluxes. Comparison of estimates from multiple approaches and observations shows (1) a persistent large uncertainty in the estimate of land-use changes emissions, (2) a low agreement between the different methods on the magnitude of the land CO2 flux in the northern extra-tropics, and (3) a discrepancy between the different methods on the strength of the ocean sink over the last decade. This living data update documents changes in the methods and datasets used in this new global carbon budget and the progress in understanding of the global carbon cycle compared with previous publications of this dataset (Friedlingstein et al., 2020, 2019; Le Quere et al., 2018b, a, 2016, 2015b, a, 2014, 2013). The data presented in this work are available at (Friedlingstein et al., 2021).
    Type: Article , PeerReviewed , info:eu-repo/semantics/article
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  • 3
    Publication Date: 2024-05-11
    Description: The UCTD is a CTD system that can be deployed from a moving ship, allowing for the sampling of water masses at high horizontal resolution (ranging from less than 1 km for the Rapidcast system to 10 km for deep UCTD casts) with good accuracy of the pressure, temperature, and conductivity sensors. Processing of the data involved mostly the fall-rate dependent correction of the thermal lag of the conductivity sensor and followed the approach described by Ullman and David (2014). Subsequently the corrected data was calibrated against the calibrated coincident Thermosalinograph and the calibrated nearby CTD data. The typical accuracies of the final pressure, temperature, and salinity data are 1 dbar, 0.01 °C, and 0.01 g/kg, respectively.
    Keywords: CTD, underway; CTD-UW; DATE/TIME; LATITUDE; LONGITUDE; M160; M160_0_Underway-10; M160_0_Underway-2; M160_0_Underway-4; M160_0_Underway-5; M160_0_Underway-8; Meteor (1986); Pressure, water; REEBUS; Role of Eddies for the Carbon Pump in Coastal upwelling Areas; Salinity; Sample code/label; South Atlantic Ocean; Temperature, water
    Type: Dataset
    Format: text/tab-separated-values, 67760 data points
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  • 4
    Publication Date: 2024-05-11
    Description: A Teledyne RDI Ocean Surveyor system with 38 and 75 kHz transmission frequency was used. Data was processed with a software package developed at GEOMAR following the GO-SHIP standards (Firing and Hummon, 2010). The data was subsequently averaged over one minute intervals, converted to a NetCDF based format.
    Keywords: Acoustic Doppler Current Profiler; ADCP; Binary Object; Binary Object (File Size); Binary Object (Media Type); Description; M160; M160_0_Underway-1; Meteor (1986); REEBUS; Role of Eddies for the Carbon Pump in Coastal upwelling Areas; South Atlantic Ocean
    Type: Dataset
    Format: text/tab-separated-values, 6 data points
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  • 5
    Publication Date: 2024-05-11
    Description: Seabird 911plus systems equipped with dual temperature-conductivity-oxygen sensors were employed. All systems had a 24-bottle water sampling rosette with 10 l Niskin bottles. Water sampling, processing, and calibration followed GO-SHIP recommendations (Swift, 2010; McTaggart et al., 2010; Uchida et al., 2010) and included the recommended steps Data Conversion, Sensor Time-Alignment, Creation of Bottle Files, Outlier Removal, Pressure Sensor Filtering, Conductivity Cell Thermal Mass Correction, Ship Roll Correction and Deck Offset Correction by Loop Editing, as well as Derivation of Calculated Properties. After these steps, conductivity and oxygen readings were calibrated against values determined with salinometry and Winkler titration , respectively. Finally, the downcast data was averaged over 1 dbar wide intervals. An independent upcast calibration was used to obtain calibrated CTDO values coincident with the discrete water samples.
    Keywords: CTD/Rosette; CTD-RO; DATE/TIME; Density, sigma, in situ; DEPTH, water; Event label; Fluorescence; LATITUDE; LONGITUDE; M160; M160_100-1; M160_10-1; M160_104-1; M160_105-1; M160_106-1; M160_109-1; M160_115-1; M160_117-1; M160_119-1; M160_121-1; M160_132-1; M160_134-1; M160_136-1; M160_141-1; M160_143-1; M160_148-1; M160_149-1; M160_15-1; M160_151-1; M160_165-1; M160_166-1; M160_168-1; M160_169-1; M160_17-1; M160_175-1; M160_179-1; M160_180-1; M160_184-1; M160_188-1; M160_194-1; M160_196-1; M160_198-1; M160_200-1; M160_21-1; M160_24-1; M160_25-1; M160_26-1; M160_27-1; M160_28-1; M160_29-1; M160_30-1; M160_31-1; M160_32-1; M160_33-1; M160_34-1; M160_35-1; M160_36-1; M160_37-1; M160_38-1; M160_40-1; M160_42-1; M160_43-1; M160_45-1; M160_47-1; M160_49-1; M160_5-1; M160_55-1; M160_57-1; M160_63-1; M160_65-1; M160_67-1; M160_69-1; M160_73-1; M160_75-1; M160_77-1; M160_81-1; M160_84-1; M160_86-1; M160_88-1; M160_90-1; M160_94-1; M160_96-1; M160_98-1; Meteor (1986); Nitrogen oxide; Organic matter, colored dissolved; Oxygen; Pressure, water; Radiation, photosynthetically active; Radiation, photosynthetically active, surface; REEBUS; Role of Eddies for the Carbon Pump in Coastal upwelling Areas; Salinity; Sample code/label; Sound velocity in water; South Atlantic Ocean; Temperature, water; Turbidity (Nephelometric turbidity unit)
    Type: Dataset
    Format: text/tab-separated-values, 1054976 data points
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