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Glyoxal- and formaldehyde slant column densities measured during the cruise MSM58/2 over the Atlantic Ocean using MAX-DOAS (2019)

Behrens, Lisa K ; Hilboll, Andreas ; Richter, Andreas ; [et al.]

PANGAEA

In: Supplement to: Behrens, Lisa K; Hilboll, Andreas; Richter, Andreas; Peters, Enno; Alvarado, Leonardo M A; Kalisz Hedegaard, Anna Beata; Wittrock, Folkard; Burrows, John Philipp; Vrekoussis, Mihalis (2019): Detection of outflow of formaldehyde and glyoxal from the African continent to the Atlantic Ocean with a MAX-DOAS instrument. Atmospheric Chemistry and Physics, 19(15), 10257-10278, https://doi.org/10.5194/acp-19-10257-2019

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Publication Date: 2023-03-07

Description: Trace gas maps retrieved from satellite measurements show enhanced levels of the atmospheric volatile organic compounds formaldehyde (HCHO) and glyoxal (CHOCHO) over the Atlantic Ocean. To validate the spatial distribution of this continental outflow, ship-based measurements were taken during the Continental Outflow of Pollutants towards the MArine tRoposphere (COPMAR) project. A Multi-AXis Differential Optical Absorption Spectrometer (MAX-DOAS) was operated aboard the research vessel (RV) Maria S. Merian during cruise MSM58/2. This cruise was conducted in October 2016 from Ponta Delgada (Azores) to Cape Town (South Africa), crossing between Cabo Verde and the African continent. The instrument was continuously scanning the horizon, looking towards the African continent. Enhanced levels of HCHO and CHOCHO were found in the area of expected outflow during this cruise. The observed spatial gradients of HCHO and CHOCHO along the cruise track agree with the spatial distributions from satellite measurements and the Model for OZone and Related chemical Tracers version 4 (MOZART-4) model simulations. The continental outflow from the African continent is observed in an elevated layer, higher than 1000 m, and probably originates from biogenic emissions or biomass burning according to FLEXible PARTicle dispersion model (FLEXPART) emission sensitivities.

Type: Dataset

Format: application/zip, 2 datasets

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Formaldehyde (HCHO) slant column densities measured during the cruise MSM58/2 over the Atlantic Ocean using MAX-DOAS (2019)

Behrens, Lisa K ; Hilboll, Andreas ; Richter, Andreas ; [et al.]

PANGAEA

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Publication Date: 2024-02-02

Keywords: Atlantic Ocean; Atmosphere; CT; Formaldehyde; Formaldehyde, slant column density; Formaldehyde, vertical column density; Formaldehyde air mass factor; HCHO; LATITUDE; LONGITUDE; Maria S. Merian; MAX-DOAS; MSM58/2; MSM58/2-track; Multi-AXis Differential Optical Absorption Spectrometer (MAX-DOAS); Time in days; Underway cruise track measurements

Type: Dataset

Format: text/tab-separated-values, 209 data points

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Glyoxal (CHOCHO) slant column densities measured during the cruise MSM58/2 over the Atlantic Ocean using MAX-DOAS (2019)

Behrens, Lisa K ; Hilboll, Andreas ; Richter, Andreas ; [et al.]

PANGAEA

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Publication Date: 2024-02-02

Keywords: Atlantic Ocean; Atmosphere; CHOCHO; CT; Glyoxal; Glyoxal, slant column density; Glyoxal, vertical column density; Glyoxal air mass factor; LATITUDE; LONGITUDE; Maria S. Merian; MAX-DOAS; MSM58/2; MSM58/2-track; Multi-AXis Differential Optical Absorption Spectrometer (MAX-DOAS); Time in days; Underway cruise track measurements

Type: Dataset

Format: text/tab-separated-values, 193 data points

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Mean spectral diffuse attenuation coefficients averaged for 320 nm to 338.5 nm (UVAB), 356.5 nm to 390 nm (UVA) and 390 nm to 423 nm (blue) in the Atlantic Ocean from Sentinel-5P instrument TROPOMI (2022)

Oelker, Julia ; Richter, Andreas ; Losa, Svetlana N ; [et al.]

PANGAEA

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Publication Date: 2024-04-20

Description: This data set contains the mean diffuse attenuation coefficient of the downwelling plane irradiance over the first optical depth and over three different wavelength regions: 312.5 - 338 nm (Kd-UVAB), 356.5 - 390 nm (Kd-UVA), and 390 - 423 nm (KD-blue) as retrieved from the Sentinel-5P TROPOMI sensor from 11 May to 9 June 2018 in the Atlantic Ocean. The retrieval for the products is based on Differential Optical Absorption Spectroscopy (DOAS) extended to the ocean domain (PhytoDOAS). The spectral integrated Kd are derived from the Vibrational Raman Scattering (VRS) signal of the ocean which is retrieved by DOAS fits in three different fit windows. Kd-UVAB corresponds to DOAS VRS fits in the wavelength regions of 349.5 - 382 nm, Kd-UVA to 405 - 450 nm, and Kd-blue to 450 - 493 nm. VRS fit factors in the blue fit window (450 - 493 nm) were offset-corrected (an offset of 0.186 was added to the VRS fit factor of all processed S5P ground pixels). Derived Kd-blue are otherwise unrealistically high. The offset was determined with the help of Kd data at 490 nm from the Ocean and Land Color Instrument (OLCI) onboard Sentinel-3A. Fit results from the DOAS retrieval are converted into physical quantities using look-up-tables which were established with coupled atmosphere-ocean radiative transfer modeling using the software SCIATRAN version 4.0.8 (Rozanov et al. 2017, https://www.iup.uni-bremen.de/sciatran/). Only TROPOMI data with a cloud fraction smaller 0.01 were processed by the algorithm. Output data within the Atlantic Ocean (55°N-55°S, 70°W-10°E) were gridded daily into 0.083° latitudinal/longitudinal bins. Details on the algorithm can be found in the related publication by Oelker et al. (2022).

Keywords: AC3; Arctic Amplification; AtlanticOcean; Atlantic Ocean; Binary Object; Binary Object (File Size); Binary Object (MD5 Hash); blue radiation; Differential Optical Absorption Spectroscopy; diffuse attenuation coefficient; Exploitation of Sentinel-5-P for Ocean Colour Products; FRAM; FRontiers in Arctic marine Monitoring; optical satellite data; S5POC; SAT; Satellite remote sensing; UV radiation

Type: Dataset

Format: text/tab-separated-values, 3 data points

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Phytoplankton pigment concentrations and phytoplankton groups measured on water samples collected from various expeditions in the Atlantic Ocean from 71°S to 84°N (2023)

Xi, Hongyan ; Peeken, Ilka ; Gomes, Mara ; [et al.]

PANGAEA

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Publication Date: 2024-05-28

Description: This data set composes a large amount of quality controlled in situ measurements of major pigments based on HPLC collected from various expeditions across the Atlantic Ocean spanning from 71°S to 84°N, including 11 expeditions with RV Polarstern from the North Atlantic to the Arctic Fram Strait: PS74, PSS76, PS78, PS80, PS85, PS93.2 (https://doi.org/10.1594/PANGAEA.894872), PS99.1 (https://doi.org/10.1594/PANGAEA.905502), PS99.2 ( https://doi.org/10.1594/PANGAEA.894874), PS106 (https://doi.org/10.1594/PANGAEA.899284), PS107 (https://doi.org/10.1594/PANGAEA.894860), PS121 (https://doi.org/10.1594/PANGAEA.941011), four expeditions (two with RV Polarstern and two Atlantic Meridional Transect expeditions with RRS James Clark Ross and RRS Discovery) in the trans-Atlantic Ocean: PS113 ( https://doi.org/10.1594/PANGAEA.911061), PS120, AMT28 and AMT29, and one expedition with RV Polarstern in the Southern Ocean: PS103 (https://doi.org/10.1594/PANGAEA.898941). Chlorophyll a concentration (Chl-a) of six phytoplankton functions groups (PFTs) derived from these pigments have been also included. This published data set has contributed to validate satellite PFT products available on the EU funded Copernicus Marine Service (CMEMS, https://marine.copernicus.eu/), which are derived from multi-sensor ocean colour reflectance data and sea surface temperature using an empirical orthogonal function based approach (Xi et al. 2020; 2021). Description on in situ PFT Chl-a determination from pigment data: PFT Chl-a in this data set were derived using an updated diagnostic pigment analysis (DPA) method (Soppa et al., 2014; Losa et al., 2017) with retuned coefficients by Alvarado et al (2021), that was originally developed by Vidussi et al. (2001), adapted in Uitz et al. (2006) and further refined by Hirata et al. (2011) and Brewin et al. (2015). The values of retuned DPA weighting coefficients for PFT Chl-a determination are: 1.56 for fucoxanthin, 1.53 for peridinin, 0.89 for 19'-hexanoyloxyfucoxanthin, 0.44 for 19'-butanoyloxyfucoxanthin, 1.94 for alloxanthin, 2.63 for total chlorophyll b, and 0.99 for zeaxanthin. The coefficient retuning was based on an updated global HPLC pigment data base for the open ocean (water depth 〉200 m), which was compiled based on the previously published data sets spanning from 1988 to 2012 described in Losa et al. (2017), with updates in Xi et al. (2021) and Álvarez et al. (2022), by adding other newly available HPLC pigment data collected between 2012 and 2018 mainly from SeaBASS (https://seabass.gsfc.nasa.gov/), PANGAEA, British Oceanographic Data Centre (BODC, https://www.bodc.ac.uk/), and Australian Open Access to Ocean Data (AODN, https://portal.aodn.org.au/) (as of February 2020, see Table 1 attached in the 'Additional metadata' for more details on the data sources).

Keywords: 19-Butanoyloxyfucoxanthin; 19-Hexanoyloxyfucoxanthin; AC3; Alloxanthin; AMT28; AMT28_10-33; AMT28_1-1; AMT28_11-36; AMT28_12-41; AMT28_13-44; AMT28_14-48; AMT28_15-50; AMT28_16-57; AMT28_17-58; AMT28_18-64; AMT28_19-66; AMT28_20-71; AMT28_21-73; AMT28_22-78; AMT28_23-80; AMT28_2-4; AMT28_24-85; AMT28_25-87; AMT28_27-93; AMT28_28-95; AMT28_29-100; AMT28_30-101; AMT28_31-105; AMT28_32-111; AMT28_33-112; AMT28_34-117; AMT28_35-120; AMT28_36-124; AMT28_37-126; AMT28_3-8; AMT28_38-133; AMT28_40-137; AMT28_4-11; AMT28_41-142; AMT28_43-147; AMT28_44-150; AMT28_45-155; AMT28_46-158; AMT28_47-164; AMT28_48-166; AMT28_49-174; AMT28_50-176; AMT28_51-181; AMT28_5-13; AMT28_52-183; AMT28_53-188; AMT28_54-190; AMT28_55-198; AMT28_56-199; AMT28_57-204; AMT28_58-206; AMT28_59-210; AMT28_59-212; AMT28_61-218; AMT28_6-17; AMT28_62-220; AMT28_63-226; AMT28_64-227; AMT28_65-232; AMT28_66-234; AMT28_7-21; AMT28_8-24; AMT28_9-28; AMT29; AMT29_AA; AMT29_AB; AMT29_AC; AMT29_AD; AMT29_AE; AMT29_AF; AMT29_AG; AMT29_AH; AMT29_AI; AMT29_AJ; AMT29_AK; AMT29_AL; AMT29_AM; AMT29_AN; AMT29_AO; AMT29_AP; AMT29_AQ; AMT29_AR; AMT29_AS; AMT29_AV; AMT29_AX; AMT29_BC; AMT29_BD; AMT29_BE; AMT29_BF; AMT29_BG; AMT29_BH; AMT29_BI; AMT29_BJ; AMT29_BK; AMT29_BL; AMT29_BM; AMT29_BN; AMT29_BO; AMT29_BP; AMT29_BQ; AMT29_BR; AMT29_BS; AMT29_BT; AMT29_BU; AMT29_BV; AMT29_BW; AMT29_BX; AMT29_BY; AMT29_BZ; AMT29_CA; AMT29_CB; AMT29_CC; AMT29_CD; AMT29_CE; AMT29_CF; AMT29_CG; AMT29_CH; AMT29_CJ; AMT29_CK; AMT29_CL; AMT29_CM; AMT29_CN; AMT29_CO; AMT29_CP; AMT29_CQ; AMT29_CR; AMT29_CS; AMT29_CT; AMT29_CTD_001; AMT29_CTD_002; AMT29_CTD_003; AMT29_CTD_004; AMT29_CTD_005; AMT29_CTD_006; AMT29_CTD_007; AMT29_CTD_008; AMT29_CTD_009; AMT29_CTD_010; AMT29_CTD_011; AMT29_CTD_013; AMT29_CTD_015; AMT29_CTD_016; AMT29_CTD_017; AMT29_CTD_018; AMT29_CTD_019; AMT29_CTD_020; AMT29_CTD_021; AMT29_CTD_022; AMT29_CTD_024; AMT29_CTD_025; AMT29_CTD_026; AMT29_CTD_027; AMT29_CTD_028; AMT29_CTD_029; AMT29_CTD_030; AMT29_CTD_031; AMT29_CTD_032; AMT29_CTD_034; AMT29_CTD_035; AMT29_CTD_036; AMT29_CTD_037; AMT29_CTD_038; AMT29_CTD_039; AMT29_CTD_041; AMT29_CTD_042; AMT29_CTD_043; AMT29_CTD_044; AMT29_CTD_045; AMT29_CTD_046; AMT29_CTD_047; AMT29_CTD_048; AMT29_CTD_049; AMT29_CTD_050; AMT29_CTD_051; AMT29_CTD_052; AMT29_CTD_053; AMT29_CTD_054; AMT29_CTD_055; AMT29_CU; AMT29_CV; AMT29_CW; AMT29_CX; AMT29_CY; AMT29_CZ; AMT29_DA; AMT29_DB; AMT29_DC; AMT29_DD; AMT29_DE; AMT29_DF; AMT29_DG; AMT29_DH; AMT29_DI; AMT29_DJ; AMT29_DK; AMT29_DL; AMT29_DM; AMT29_DN; AMT29_DO; AMT29_DP; AMT29_DQ; AMT29_DR; AMT29_DS; AMT29_DT; AMT29_DU; AMT29_DV; AMT29_DZ; AMT29_EB; AMT29_EC; AMT29_EE; AMT29_EF; AMT29_EG; AMT29_EI; AMT29_EK; AMT29_EL; AMT29_EM; AMT29_EO; AMT29_EQ; AMT29_ER; AMT29_ES; AMT29_ET; AMT29_EV; ANT-XXXII/2; ANT-XXXIII/4; Arctic Amplification; Arctic Ocean; ARK-XXIV/1; ARK-XXIV/2; ARK-XXIX/2.2; ARK-XXV/1; ARK-XXV/2; ARK-XXVI/1; ARK-XXVII/1; ARK-XXVII/2; ARK-XXVIII/2; ARK-XXX/1.1; ARK-XXX/1.2; ARK-XXXI/1.1,PASCAL; ARK-XXXI/1.2; ARK-XXXI/2; AWI_BioOce; Barents Sea; Biological Oceanography @ AWI; Campaign; Canarias Sea; chlorophyll; Chlorophyll a; Chlorophyll a, Diatoms; Chlorophyll a, Dinoflagellata; Chlorophyll a, Green algae; Chlorophyll a, Haptophyta; Chlorophyll a, Prochlorococcus; Chlorophyll a, Prokaryotes; Chlorophyll a + Divinyl chlorophyll a + Chlorophyllide a; Chlorophyll b + Divinyl chlorophyll b + Chlorophyllide b; Chlorophyllide a; CT; CTD, towed system; CTD/Rosette; CTD/Rosette with Underwater Vision Profiler; CTD001; CTD002; CTD003; CTD004; CTD005; CTD006; CTD007; CTD008; CTD009; CTD010; CTD011; CTD012; CTD013; CTD014; CTD015; CTD016; CTD017; CTD018; CTD019; CTD020; CTD021; CTD022; CTD023; CTD024; CTD025; CTD026; CTD027; CTD028; CTD029; CTD030; CTD031; CTD032; CTD033; CTD034; CTD035; CTD036; CTD037; CTD038; CTD039; CTD040; CTD041; CTD042; CTD043; CTD044; CTD045; CTD046; CTD047; CTD048; CTD049; CTD050; CTD051; CTD052; CTD053; CTD054; CTD055; CTD056; CTD057; CTD058; CTD059; CTD060; CTD061; CTD062; CTD063; CTD-Acoustic Doppler Current Profiler; CTD-ADCP; CTD-RO; CTD-RO_UVP; CTD-twoyo; DATE/TIME; DEPTH, water; Diagnostic Pigment Analysis (DPA); Discovery (2013); Divinyl chlorophyll a; DPA; DY110; EG_I; EG_II; EG_III; EG_IV; Event label; Exploitation of Sentinel-5-P for Ocean Colour Products; FRAM; FRontiers in Arctic marine Monitoring; Fucoxanthin; Global Long-term Observations of Phytoplankton Functional Types from Space; GLOPHYTS; Hand net; HG_I; HG_II; HG_III; HG_IV; HG_IX; HG_V; HG_VI; HG_VIII; HGIV; High Performance Liquid Chromatography (HPLC); HN; HPLC; ICE; Ice station; James Clark Ross; JR18001; Kb0; LATITUDE; Lazarev Sea; LONGITUDE; N3; N4; N5; North Greenland Sea; North Sea; Norwegian Sea; ORDINAL NUMBER; Peridinin; phytoplankton functional types; pigments; Polarstern; PORTWIMS; Project Portugal Twinning for Innovation and Excellence in Marine Science and Earth Observation; PS103; PS103_0_Underway-3; PS103_1-1; PS103_11-1; PS103_15-1; PS103_22-5; PS103_23-5; PS103_2-4; PS103_27-2; PS103_29-3; PS103_3-1; PS103_31-2; PS103_34-6; PS103_39-3; PS103_40-3; PS103_4-1; PS103_43-4; PS103_45-3; PS103_48-1; PS103_5-2; PS103_59-2; PS103_6-6; PS103_67-1; PS103_8-3; PS103_9-1; PS106_18-2; PS106_21-2; PS106_27-6; PS106_28-2; PS106_31-2; PS106_32-2; PS106_45-1; PS106_50-1; PS106_ZODIAK_170527; PS106_ZODIAK_170529; PS106_ZODIAK_170531; PS106_ZODIAK_170601; PS106_ZODIAK_170607; PS106_ZODIAK_170608; PS106_ZODIAK_170617; PS106_ZODIAK_170618; PS106_ZODIAK_170619; PS106_ZODIAK_170624; PS106_ZODIAK_170625; PS106_ZODIAK_170626; PS106_ZODIAK_170627; PS106_ZODIAK_170629; PS106_ZODIAK_170630; PS106_ZODIAK_170701; PS106_ZODIAK_170702; PS106_ZODIAK_170703; PS106_ZODIAK_170705; PS106_ZODIAK_170706; PS106_ZODIAK_170708; PS106_ZODIAK_170709; PS106_ZODIAK_170710; PS106_ZODIAK_170711; PS106_ZODIAK_170713; PS106_ZODIAK_170714; PS106_ZODIAK_170715; PS106/1; PS106/2; PS107; PS107_0_underway-9; PS107_10-4; PS107_12-3; PS107_14-1; PS107_16-3; PS107_18-3; PS107_19-1; PS107_20-8; PS107_21-1; PS107_22-6; PS107_24-1; PS107_28-1; PS107_29-1; PS107_33-6; PS107_34-5; PS107_36-1; PS107_37-1; PS107_40-2; PS107_40-3; PS107_40-4; PS107_40-5; PS107_40-6; PS107_48-1; PS107_6-8; PS107_7-1; PS107_8-1; PS113; PS113_0_underway-5; PS113_11-2; PS113_1-2; PS113_13-2; PS113_14-2; PS113_15-1; PS113_17-2; PS113_18-2; PS113_20-1; PS113_21-1; PS113_22-2; PS113_23-2; PS113_25-1; PS113_26-2; PS113_27-1; PS113_28-1; PS113_29-2; PS113_30-2; PS113_31-1; PS113_3-2; PS113_33-1; PS113_5-2; PS113_6-2; PS113_7-2; PS113_9-2; PS120; PS120_0_underway-10; PS120_11-3; PS120_15-3; PS120_19-3; PS120_20-1; PS120_21-3; PS120_24-3; PS120_3-1; PS120_5-3; PS120_8-3; PS121; PS121_0_Underway-65; PS121_1-2; PS121_12-2; PS121_15-1; PS121_16-5; PS121_24-2; PS121_25-2; PS121_27-2; PS121_28-4; PS121_29-1; PS121_32-2; PS121_33-2; PS121_34-1; PS121_35-3; PS121_36-1; PS121_38-1; PS121_39-1; PS121_40-3; PS121_43-7; PS121_44-3; PS121_45-1; PS121_52-2; PS121_52-6; PS121_5-3; PS121_7-3; PS74; PS74/104-1; PS74/107-1; PS74/108-1; PS74/112-1; PS74/119-1; PS74/120-1; PS74/127-1; PS74/128-1; PS74/132-1; PS74/133-1; PS74/134-1; PS74/1-track; PS74/2-track; PS76; PS76/001-1; PS76/002-1; PS76/005-1; PS76/007-2; PS76/009-1; PS76/017-1; PS76/020-1; PS76/025-1; PS76/026-1; PS76/030-1; PS76/034-3; PS76/039-1; PS76/041-1; PS76/044-1; PS76/049-1; PS76/051-1; PS76/057-1; PS76/058-1; PS76/062-1; PS76/064-1; PS76/068-1; PS76/072-1; PS76/080-1; PS76/082-1; PS76/094-1; PS76/098-1; PS76/102-1; PS76/109-3; PS76/110-1; PS76/111-1; PS76/120-2; PS76/121-1; PS76/122-1; PS76/124-3; PS76/129-1; PS76/132-1; PS76/134-1; PS76/135-1; PS76/136-1; PS76/138-1; PS76/139-1; PS76/157-1; PS76/159-2; PS76/166-1; PS76/167-1; PS76/170-2; PS76/173-1; PS76/174-1; PS76/175-1; PS76/176-1; PS76/178-1; PS76/179-3; PS76/181-1; PS76/182-1; PS76/184-1; PS76/185-1; PS76/194-1; PS76/200-1; PS76/201-1; PS76/203-1; PS76/204-1; PS76/208-5; PS76/210-2; PS76/211-1; PS76/216-1; PS76/220-1; PS76/223-1; PS76/224-1; PS76/227-3; PS76/229-1; PS76/231-1; PS76/233-1; PS76/235-

Type: Dataset

Format: text/tab-separated-values, 37522 data points

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Synergistic use of OLCI and TROPOMI measurements to determine phytoplankton functional types (2022)

Alvarado, Leonardo M. A. ; Oelker, Julia ; Losa, Svetlana N. ; [et al.]

In: EPIC3Living Planet Symposium 2022, Bonn, Germany, 2022-05-23-2022-05-27

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Publication Date: 2022-10-04

Description: Phytoplankton play an important role in the aquatic biogeochemical cycling such as for the formation of organic matter by photosynthetic processes through the fixation of carbon dioxide, and assimilation of macro- and micronutrients depending on their metabolic needs. These processes are common to all phytoplankton; however, some phytoplankton groups have specific needs and thus play different functional roles in the biogeochemical cycle. Information on the phytoplankton groups (PFTs) can be obtained from satellite observations such as the Ocean and Land Colour Instrument (OLCI) on board of Sentinel-3 as well as the TROPOspheric Monitoring Instrument (TROPOMI) on board the Copernicus Sentinel-5 Precursor satellite. PFTs global ocean abundance from multispectral satellites can be estimated based on the OC-PFT algorithm which is based on the assumption that a marker pigment for a specific PFT varies in dependence to the chlorophyll-a concentration. While PFTs from hyperspectral satellite measurements, as from TROPOMI, can be estimated by Differential Optical Absorption Spectroscopy (DOAS) method. In this study, chlorophyll-a concentration for three main phytoplankton functional types (diatoms, coccolithophores and cyanobacteria) are derived by combining retrievals from space-borne measurements at a high spatial resolution by the empirical OC-PFT algorithm applied to OLCI data with data retrieved from TROPOMI measurements with high spectral resolution by analytical method (Phyto-DOAS). A previous algorithm and data set based on OC-PFT retrievals applied to OC-CCI Chlorophyll-a product and Phyto-DOAS retrievals from SCIAMACHY data have shown the validity and high quality of the synergistic PFT product (Losa et al. 2017). Here, a first evaluation of the synergy of Sentinel-3 and Sentinel-5P PFT retrievals by combining OC-PFT and Phyto-DOAS is evaluated and compared to field measurements of PFT sampled during the RV Polarstern expedition PS113 in the Atlantic Ocean from May to June 2018. In addition, the adaptation of the method to enlarge the capabilities of PFT data in inland and coastal waters analytically retrieved from high spectral and high spatial data such as DESIS, EnMAP or PRISMA by synergistic use with OLCI OC-PFT data sets is discussed.

Repository Name: EPIC Alfred Wegener Institut

Type: Conference , notRev

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Retrievals of the Main Phytoplankton Groups at Lake Constance Using OLCI and Evaluated with Field Observations (2022)

Alvarado, Leonardo M. A. ; Gege, Peter ; Losa, Svetlana N. ; [et al.]

In: EPIC312th EARSeL Workshop on Imaging Spectroscopy, Potsdam, Germany, 2022-06-22-2022-06-24

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Publication Date: 2022-10-04

Description: Phytoplankton plays an important role in the aquatic biogeochemical cycling such as for the formation of organic matter by photosynthetic processes through the fixation of carbon dioxide, and assimilation of macro- and micronutrients depending on their metabolic needs. These processes are common to all phytoplankton, however some phytoplankton groups have specific needs and thus play different functional roles in the biogeochemical cycle, which are used to classify phytoplankton into different phytoplankton functional types (PFTs). Information on the phytoplankton groups can be obtained from satellite observations such as the Ocean and Land Colour Instrument (OLCI) onboard of Sentinel-3. PFTs global ocean abundance can be estimated based on the OC-PFT algorithm (Hirata et al. 2011 and related updates to it) which is based on the assumption that a marker pigment for a specific PFT varies in dependence to the chlorophyll-a concentration. In this study, OC-PFT retrieval has been developed and adapted for estimation of PFT from Lake Constance by using a large collection of in-situ HPLC data set measured since 2000 at the largest German inland water by the regional authority and further analysed to derive PFT using the diagnostic pigment analysis following Vidussi et al. (2001) with adapted coefficients for Lake Constance. The PFT retrieved from OLCI are validated using independent in situ data derived from HPLC pigment measurements from 4 field campaigns performed in 2019 and 2020 at Lake Constance. Concentrations for five phytoplankton groups (diatoms, dinoflagellates, cryptophytes, green algae, and prokaryotes) are retrieved for Lake Constance, being the dominants diatoms and cryptophytes and at lesser degree green algae. In addition, evaluation of synergistic PFT products are presented to enlarge the capabilities of PFT data in inland and coastal waters analytically retrieved from high spectral and high spatial data such as DESIS, EnMAP or PRISMA by synergistic use with OLCI OC-PFT data sets is discussed.

Repository Name: EPIC Alfred Wegener Institut

Type: Conference , notRev

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Diffuse Attenuation Of Underwater UV And Short Blue Light Obtained From TROPOMI’s High Spectral Resolution (2022)

Bracher, Astrid ; Oelker, Julia ; Alvarado, Leonardo M. A. ; [et al.]

In: EPIC312th EARSeL Workshop on Imaging Spectroscopy, Potsdam, Germany, 2022-06-22-2022-06-24

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Publication Date: 2022-10-04

Description: In this study, we exploited high spectrally reoslved Sentinel-5 Precursor’s (S5P) sensor TROPOMI’s potential to retrieve the diffuse attenuation for three bands reaching from the UV-B to the short blue wavelengths range. As a baseline, previously developed algorithms applied to similar atmospheric satellite sensors such as SCIAMACHY, GOME-2 and OMI were adapted and extended. Opposed to these precursor sensors, TROPOMI enable data acquisition due to a large swath with spatial and temporal resolution nearly as good as obtained from common open ocean color sensors, until today only multispectral. The later sensors do not enable retrievals in the UV spectral region, but are used for intercomparison to the short blue diffuse attenuation retrieved from TROPOMI. In this presentation, we provide detailed insights into the retrieval method, its uncertainty and the application to obtain data in the global ocean in open ocean and coastal regions.

Repository Name: EPIC Alfred Wegener Institut

Type: Conference , notRev

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Phytoplankton functional types observation from space in the Fram Strait (2002-2020) (2022)

Xi, Hongyan ; Peeken, Ilka ; Losa, Svetlana N. ; [et al.]

In: EPIC3Ocean Carbon From Space Workshop, Online, 2022-02-14-2022-02-18

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Publication Date: 2023-06-21

Description: Phytoplankton in the sunlit layer of the ocean act as the base of the marine food web fueling fisheries, and also regulate key biogeochemical processes. Phytoplankton composition structure varies in ocean biomes and different phytoplankton groups drive differently the marine ecosystem and biogeochemical processes. Because of this, variations in phytoplankton composition influence the entire ocean environment, specifically the ocean energy transfer and the export of organic carbon to the deep ocean. As one of the algorithms deriving phytoplankton composition from space borne data, within the framework of the EU Copernicus Marine Service (CMEMS), EOF-PFT algorithm was developed using multi-spectral satellite data collocated to an extensive in-situ PFT data set based on HPLC pigments and sea surface temperature data (Xi et al. 2020, 2021; https://marine.copernicus.eu/). By using multi-sensor merged products and Sentinel-3 OLCI data, the algorithm provides global chlorophyll a data with per-pixel uncertainty for diatoms, haptophytes, dinoflagellates, chlorophytes and prokaryotic phytoplankton spanning the period from 2002 until today. Due to different lifespans and radiometric characteristics of the ocean color sensors, the consistency of the PFTs is evaluated to provide quality-assured data for a consistent long-term monitoring of the phytoplankton community structure. As current commonly used phytoplankton carbon estimation methods rely mostly on the backscattering property of phytoplankton, which could vary dramatically for different phytoplankton taxa, as a perspective of this study, phytoplankton carbon may be better estimated in a way that accounts for phytoplankton taxonomy.

Repository Name: EPIC Alfred Wegener Institut

Type: Conference , notRev

Format: application/pdf

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