Bracher, Astrid

Dinter, Tilman

Dutkiewicz, Stephanie

Heim, Birgit

Losa, Svetlana N.

Losch, Martin

Oelker, Julia

Pefanis, Vasileios

Rozanov, Vladimir V.

Soppa, Mariana A.

Electronic Publication Information Center

BREMERHAVENAm Handelshafen 1227570 BremerhavenTelefon 0471 4831-0www.awi.deAbstractIn our study we focus on improving our understanding of possible interactions between the open water and sea ice and the surface ocean biogeochemistryunder the recently observed sea ice decline in the Arctic. In particular, the analysis of changes in phytoplankton functional types (PFTs) based on long-termtime series of satellite retrievals and supported by a modeling study is presented. The phytoplankton dynamics as well as phytoplankton diversity inresponse to Arctic Amplification is simulated with the DARWIN biogeochemical model (Follows et al., 2007, Dutkiewicz et al., 2015) coupled to theMassachusetts Institute of Technology general circulation model (MITgcm) with a configuration based on a cubed-sphere grid (Menemenlis et al. 2008). Thebiogeochemocal module is coupled to an optical/radiative transfer model (RTM) that allows to consider explicitly phytoplankton and CDOM as oceanicoptical constituents and, therefore, to investigate possible feedbacks between ocean – oceanic biota – sea ice – atmosphere and evaluate satellite oceancolour (phytoplankton chlorophyll “a” and CDOM) data products.Satellite retrievalsTwo algorithms based on the Neural Networks – Case 2 Regional Coast Colour(C2RCC) and Extreme Case 2 Waters (C2X, trained for extreme ranges of scatteringand absorption) – are considered for retrieving CDOM absorption and total chlorophyll“a” (Chla), the evaluation has been done for the Laptev Sea.Additionally, SynSenPFT (Losa et al. 2017) Chla product for diatoms, cyanobacteriaand coccolithophores is used for evaluating the coupled sea-ice – ocean –biogeochemical simulations and obtaining long-term time series on the Arcticphytoplankton diversity (based on combined information).Comments/OutlookThe model simulations show that CDOM alters remineralisation processes affectingthe nutrients distribution and therefore the spatial and temporal distribution of PFTscompeting for the available resources. Amount and distribution of CDOM impactsPFTs dynamics as well by impacting strongly the light penetration and availability forthe photosynthesis.More Darwin-based numerical simulations (with increased model resolution) are to beperformed to complement satellite retrievals and in situ measurements whenunderstanding the role of CDOM (phytoplankton/Chla) in the radiative heating in theshelf waters (the Laptev Sea).ModelA version of the Darwin ocean biogeochemical model coupled to the MITgcmgeneral circulation model is used to simulate the dynamics of CDOM and 6 variousphytoplankton functional types: Analogues of diatoms, other large eukaryotes,picophytoplankton Prochlorococcus, other picophytoplankton, nitrogen fixers, andcoccolithophores. Following Taylor et al. (2013) we use the circulation modelconfiguration based on a cubed-sphere grid (Menemenlis et al. 2008) with meanhorizontal spacing of ~18 km and 50 vertical levels with the resolution ranging from10 m near the surface to ~450 m in the deep ocean. The model is forced by 6-houlyatmospheric conditions from the NCEP Climate Forecast System Reanalysis(CFSR).Ccccolithophores are considered with corrected physiology to account for highaffinity for nutrients and ability to escape grazing control (Losa et al. 2006). Diatomsrepresent two different size classes, other large include two distinct life stages ofPhaeocystis (Popova et al. 2007).Figure: Spatial distribution of the Arctic CDOM for August and September 2003. Modeling the Arctic coloured dissolved organic matter (CDOM) and phytoplankton diversity in/with support to/from satellite retrievalsSvetlana N. Losa1, Mariana A. Soppa1, Vasileios Pefanis1, Martin Losch1, Birgit Heim2, Julia Oelker3, Stephanie Dutkiewicz4, Tilman Dinter1, Vladimir V. Rozanov3, Astrid Bracher1,31Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research, Bremerhaven, Germany2Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research, Potsdam, Germany3Institute of Environmental Physics (IUP), University of Bremen, Bremen, Germany4Massachusetts Institute of Technology, Cambridge, Massachusetts, USAReferences: Dutkiewicz, S., Hickman, A. E., Jahn, O., Gregg, W. W., C. B. Mouw, C. B., and M. J. Follows (2015) Capturing optically important constituent andproperties in a marine biogeochemical and ecosystem model, Biogeosciences, 12, 4447-4481. Follows, M. J., Dutkiewicz, S., Grant, S., and Chisholm, S. W. (2007)Emergent Biogeography Of Microbial Communities In A Model Ocean, Science, 315, 1843–1846. Losa, S. N., Soppa, M. A., Dinter, T., Wolanin, A., Brewin, R. J. W.,Bricaud, A., Oelker, J., Peeken, I., Gentili, B., Rozanov, V. and Bracher, A. (2017). Synergistic exploitation of hyper- and multispectral precursor Sentinelmeasurements to determine Phytoplankton Functional Types (SynSenPFT). Frontiers in Marine Science, 4, 203, doi: 10.3389/fmars.2017.00203. Menemenlis, D.,Campin, J.-M., Heimbach, P., Hill, C., Lee, T., Nguyen, A., Schodlock, M., and H. Zhang (2008). High resolution global ocean and sea ice data synthesis (2008)Mercator Ocean Quartely Newsletter, 31, 13–21. Taylor, M. H., Losch, M., Bracher, A. (2013) On the drivers of phytoplankton blooms in the Antarctic seasonal icezone: a modelling approach. J. Geophys. Res.–Oceans 188: 63-75.Acknowledgement: This work was supported by by the Helmholtz Climate Initiative REKLIM (Regional Climate Change)and National Grant no. TR 172 (AC)3 „Arctic Amplification“ subproject C03. The coupled model simulations wereperformed with resources provided by the North-German Supercomputing Alliance (HLRN). The C2RCC and C2Xalgorithms are available via ESA’s Sentinel Toolbox SNAP.Schematic representation of the Darwin biogeochemical cycling (produced in accordance with the model description by Dutkiewiczet al., 2015)MERIS CDOM absorption aCDOM(443) (04.08.2010)MERIS Chlorophyll “a” Chla (04.08.2010)C2RCCC2RCCC2XC2XFigure: Spatial distribution of the model simulated Arctic chlorophyll “a”  for diatoms, other large phytoplankton) and coccolithophores in August 2005. In collaboration with  Jens Hölemann, Sebastian Hellmann, Fedor Martynov, Markus JanoutCDOM &  radiative budget in the Laptev SeaStation Chl_2m (mg*m-3)TSM_2m (gm-3)a_CDOM443_2m (m-1)S02 2.49 1 1.66S16 0.84 7.2 1.08S19 0.01 0.8 1.03S22 0.32 0.4 0.39S40 0.17 0.4 0.2Figure: Spatial distribution of the total chlorophyll “a” concentration in the Laptev Sea retrieved with C2RCC and C2X on 4 August 2010. Figure: Spatial distribution of the CDOM absorption in the Laptev Sea retrieved with C2RCC and C2X on 4 August 2010. Laptev Sea Laptev Seaincreased heating rates (max. 0.79°C/day) due to TSM and CDOM absorptionSCIATRAN 1D RTM – based estimates of the energy  absorbed by CDOM&TSM  / total heat flux differences04.08.2010            ∆Qtot

Modeling the Arctic coloured dissolved organic matter (CDOM) and phytoplankton diversity in/with support to/from satellite retrievals

BREMERHAVENAm Handelshafen 1227570 BremerhavenTelefon 0471 4831-0www.awi.deAbstractIn our study we focus on improving our understanding of possible interactions between the open water and sea ice and the surface ocean biogeochemistryunder the recently observed sea ice decline in the Arctic. In particular, the analysis of changes in phytoplankton functional types (PFTs) based on long-termtime series of satellite retrievals and supported by a modeling study is presented. The phytoplankton dynamics as well as phytoplankton diversity inresponse to Arctic Amplification is simulated with the DARWIN biogeochemical model (Follows et al., 2007, Dutkiewicz et al., 2015) coupled to theMassachusetts Institute of Technology general circulation model (MITgcm) with a configuration based on a cubed-sphere grid (Menemenlis et al. 2008). Thebiogeochemocal module is coupled to an optical/radiative transfer model (RTM) that allows to consider explicitly phytoplankton and CDOM as oceanicoptical constituents and, therefore, to investigate possible feedbacks between ocean – oceanic biota – sea ice – atmosphere and evaluate satellite oceancolour (phytoplankton chlorophyll “a” and CDOM) data products.Satellite retrievalsTwo algorithms based on the Neural Networks – Case 2 Regional Coast Colour(C2RCC) and Extreme Case 2 Waters (C2X, trained for extreme ranges of scatteringand absorption) – are considered for retrieving CDOM absorption and total chlorophyll“a” (Chla), the evaluation has been done for the Laptev Sea.Additionally, SynSenPFT (Losa et al. 2017) Chla product for diatoms, cyanobacteriaand coccolithophores is used for evaluating the coupled sea-ice – ocean –biogeochemical simulations and obtaining long-term time series on the Arcticphytoplankton diversity (based on combined information).Comments/OutlookThe model simulations show that CDOM alters remineralisation processes affectingthe nutrients distribution and therefore the spatial and temporal distribution of PFTscompeting for the available resources. Amount and distribution of CDOM impactsPFTs dynamics as well by impacting strongly the light penetration and availability forthe photosynthesis (still to be investigated in more details).More Darwin-based numerical simulations (with increased model resolution) are to beperformed to complement satellite retrievals and in situ measurements whenunderstanding the role of CDOM (phytoplankton/Chla) in the radiative heating in theshelf waters (the Laptev Sea).ModelA version of the Darwin ocean biogeochemical model coupled to the MITgcmgeneral circulation model is used to simulate the dynamics of CDOM and 6 variousphytoplankton functional types: Analogues of diatoms, other large eukaryotes,picophytoplankton Prochlorococcus, other picophytoplankton, nitrogen fixers, andcoccolithophores. Following Taylor et al. (2013) we use the circulation modelconfiguration based on a cubed-sphere grid (Menemenlis et al. 2008) with meanhorizontal spacing of ~18 km and 50 vertical levels with the resolution ranging from10 m near the surface to ~450 m in the deep ocean. The model is forced by 6-houlyatmospheric conditions from the NCEP Climate Forecast System Reanalysis(CFSR).Figure: Spatial distribution of the Arctic CDOM for August and September 2003. Modeling the Arctic coloured dissolved organic matter (CDOM) and phytoplankton diversity in/with support to satellite retrievalsSvetlana N. Losa1, Mariana A. Soppa1, Vasileios Pefanis1, Martin Losch1, Birgit Heim2, Julia Oelker3, Stephanie Dutkiewicz4, Tilman Dinter1, Vladimir V. Rozanov3, Astrid Bracher1,31Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research, Bremerhaven, Germany2Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research, Potsdam, Germany3Institute of Environmental Physics (IUP), University of Bremen, Bremen, Germany4Massachusetts Institute of Technology, Cambridge, Massachusetts, USAReferences: Dutkiewicz, S., Hickman, A. E., Jahn, O., Gregg, W. W., C. B. Mouw, C. B., and M. J. Follows (2015) Capturing optically important constituent andproperties in a marine biogeochemical and ecosystem model, Biogeosciences, 12, 4447-4481. Follows, M. J., Dutkiewicz, S., Grant, S., and Chisholm, S. W. (2007)Emergent Biogeography Of Microbial Communities In A Model Ocean, Science, 315, 1843–1846. Losa, S. N., Soppa, M. A., Dinter, T., Wolanin, A., Brewin, R. J. W.,Bricaud, A., Oelker, J., Peeken, I., Gentili, B., Rozanov, V. and Bracher, A. (2017). Synergistic exploitation of hyper- and multispectral precursor Sentinelmeasurements to determine Phytoplankton Functional Types (SynSenPFT). Frontiers in Marine Science, 4, 203, doi: 10.3389/fmars.2017.00203. Menemenlis, D.,Campin, J.-M., Heimbach, P., Hill, C., Lee, T., Nguyen, A., Schodlock, M., and H. Zhang (2008). High resolution global ocean and sea ice data synthesis (2008)Mercator Ocean Quartely Newsletter, 31, 13–21. Taylor, M. H., Losch, M., Bracher, A. (2013) On the drivers of phytoplankton blooms in the Antarctic seasonal icezone: a modelling approach. J. Geophys. Res.–Oceans 188: 63-75.Acknowledgement: This work was supported by by the Helmholtz Climate Initiative REKLIM (Regional Climate Change)and National Grant no. TR 172 (AC)3 „Arctic Amplification“ subproject C03. The coupled model simulations wereperformed with resources provided by the North-German Supercomputing Alliance (HLRN). The C2RCC and C2Xalgorithms are available via ESA’s Sentinel Toolbox SNAP.Schematic representation of the Darwin biogeochemical cycling (produced	in	accordance	with	the	model	description	by	Dutkiewiczet	al.,	2015)MERIS CDOM absorption aCDOM(443) (04.08.2010)MERIS Chlorophyll “a” Chla (04.08.2010)C2RCCC2RCCC2XC2XFigure: Spatial distribution of the model simulated Arctic chlorophyll “a”  for diatoms, other large phytoplankton, pico-phytoplankton (including small diatoms) and coccolithophores in September 2003. In	collaboration	with		Jens	Hölemann,	Sebastian	Hellmann,	Fedor Martynov,	Markus	JanoutCDOM &  radiative budget in the Laptev SeaStation Chl_2m (mg*m-3)TSM_2m (gm-3)a_CDOM443_2m (m-1)S02 2.49 1 1.66S16 0.84 7.2 1.08S19 0.01 0.8 1.03S22 0.32 0.4 0.39S40 0.17 0.4 0.2Figure: Spatial distribution of the total chlorophyll “a” concentration in the Laptev Sea retrieved with C2RCC and C2X on 4 August 2010. Figure: Spatial distribution of the CDOM absorption in the Laptev Sea retrieved with C2RCC and C2X on 4 August 2010. Laptev Sea Laptev Seaincreased	heating	rates	(max.	0.3°C/h)	due	to	TSM	and	CDOM	absorptionSCIATRAN 1D RTM –based estimates of the energy absorbed by CDOM&TSM at some stations

Modeling the Arctic coloured dissolved organic matter (CDOM) and phytoplankton diversity in/with support to satellite retrievals

https://epic.awi.de/id/eprint/48398/1/Poster_FAMOS_2018_Losa_et_al.pdf

Modeling the Arctic coloured dissolved organic matter (CDOM) and phytoplankton diversity in/with support to/from satellite retrievals

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