Antarctic phytoplankton in response to environmental change studied by a synergistic approach using multi- and hyper-spectral satellite data (PhySyn)

Project funded by the DFG within SPP "Antarctic" (2015-2018)

The project focuses on the assessment of the impact of environmental change in the Southern Ocean on phytoplankton. Phytoplankton is the key organism determining the functioning of the marine ecosystem and biogeochemical cycle and it can be detected from space. In this study analytical bio-optical retrieval techniques are to be used to develop generic methods, which extract unique global long-term information on phytoplankton composition. The methods will be based on using all available high-resolution optical satellite data that are complemented by in-situ and multi-spectral satellite data. Combined with modeling studies, this information will be used to attribute the relative importance of anthropogenic activity and natural phenomena on the marine ecosystem and biogeochemical cycling of the Southern Oceans during the last decades.

Adaptation of satellite-based phytoplankton functional types retrieval for the Southern Ocean

The PhytoDOAS method (Bracher et al. 2009, Sadeghi et al. 2012a) developed within the Helmholtz University Young Investigator PHYTOOPTICS Group will be further improved in order to adapt the algorithm for the regions with high solar zenith angle (SZA) and thin clouds and therefore to improve the retrievals of chlorophyll “a” concentrations of several phytoplankton functional types (PFTs) of the Southern Ocean. The algorithm will be also augmented by considering a number of hyper-spectral satellite sensors (see Fig.1). First results to adapt PhytoDOAS to OMI/Aura data show very promising results (Oelker et al., 2016). Merging all the PFT products from hyper-spectral data with the small pixel total Chl “a” information from various multispectral data sets will allow to construct Antarctic PFTs data set with improved spatial and temporal resolution. 

Modeling PFTs

A version of the Darwin ocean biogeochemical model coupled to the MITgcm general circulation model (Follows et al., 2007, Dutkiewicz et al., 2015) is used to simulate the dynamics of various phytoplankton functional types: Analogues of diatoms, other larger eukaryotes, Synechococcus , high and low light Prochlorococcus, nitrogen fixing Trichodesmium, unicellular diazotrophs, small eukaryotes and cocolithophores (see Fig. 2).

Following Taylor et al. (2013) we use the circulation model configuration based on a cubedsphere grid (Menemenlis et al. 2008) with mean horizontal spacing of ~18 km and 50 vertical levels with the resolution ranging from 10 m near the surface to ~450 m in the deep ocean. The model is forced by 6houly atmospheric conditions from the NCEP Climate Forecast System Reanalysis (CFSR). An example of preliminary PFT distribution after a 2 months run with the model is shown in Fig. 3.



Bracher A, Dinter, T., Vountas, M., Burrows, J. P., Röttgers, R., Peeken, I. (2009) Quantitative observation of cyanobacteria and diatoms from space using PhytoDOAS on SCIAMACHY data. Biogeosciences, 6, 751–764.

Dutkiewicz, S., Hickman, A. E., Jahn, O., Gregg, W. W., C. B. Mouw, C. B., and M. J. Follows (2015) Capturing optically important constituent and properties 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.

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.

Oelker J., Dinter T., Richter A., Burrows J. P., Bracher A. (2016) Towards improved spatial resolution of hyper-spectral phytoplankton functional type products. Oral presentation at Colour and Light in the Ocean from Earth Observation (CLEO) Relevance and Applications Products from Space and Perspectives from Models, ESA-ESRIN, Frascati, Italy, 7 Sep 2016

Sadeghi, A., Dinter, T., Vountas, M., Taylor, B. B., Altenburg-Soppa, M., Peeken, I., Bracher, A. (2012) Improvement to the PhytoDOAS method for identification of coccolithophores using hyper-spectral satellite data. Ocean Science, 8, 1055–1070.

Taylor, M. H., Losch, M., Bracher, A. (2013) On the drivers of phytoplankton blooms in the Antarctic seasonal ice zone: a modelling approach. J. Geophys. Res.–Oceans 188: 63‐75.

Fig. 1: Number of cloud-free pixels for January 2009 used to derive PhytoDOAS PFT products. Left panel: current retrieval-from SCIAMACHY data with solar zenith angle (SZA) <60°, middle panel: for the Southern Ocean specialized retrieval- from SCIAMACHY with SZA <80° and loser cloud-criterion, right panel: for the Southern Ocean specialized retrieval- from GOME-2 (on Metop-A) data.

Fig. 2: The schematic diagram of the DARWIN biogeochemical model (produced in accordance with the model description by Dutkiewicz et al., 2015).

Fig. 3: Spatial distribution of the model PFTs after 2 months of Darwin‐based model integration started from the same initial conditions.