Biogenic aerosol precursors from phytoplankton production can affect cloud properties, especially in remote regions such as the Arctic Ocean. Reliable estimates on variability and trend of these precursors are required as extensive measurements in the Arctic are still scarce. We present a setup of the coupled ocean biogeochemical model FESOM2.1-REcoM3 where we integrated dissolved carboxylic acid containing polysaccharides (PCHO) and Transparent Exopolymer Particles (TEP) to describe these precursors in the upper ocean. We define PCHO as one part of the excreted organic carbon, which can then aggregate to form larger particles, TEP. Compared to observations, the simulation provides a valid TEP estimate with mean concentrations of 200-400 µg C L-1 on the continental shelves and 10-50 µg C L-1 in the central basins (0-30 m depth range). Further, the simulation for 1990-2019 reveals a significant positive trend of TEP of 0.5-3 µg C L-1 yr-1 during July-September in the Amerasian Basin (+3.5% yr-1), the Canadian Archipelago (+1.2% yr-1) and the Kara Sea (+0.8% yr-1), in contrast to the eastern Fram Strait (-0.4% yr-1), the Barents Sea (-0.3% yr-1), and parts of the Eurasian Basin with a significant decrease of -0.5-2 µg C L-1 yr-1. Our study provides for the first time an integration of TEP formation, aggregation and remineralization processes into a global ocean biogeochemical model. This simulation assembles valuable data on biogenic aerosol precursors, and as such, fills a gap on which Earth System Models can greatly benefit to improve the understanding of aerosol feedbacks within the Arctic climate.

Proper Arctic sea-ice forecasting for the melt season is still a major challenge because of the recent lack of reliable pan-Arctic summer sea-ice thickness (SIT) data. A new summer CryoSat-2 SIT observation data set based on an artificial intelligence algorithm may alleviate this situation. We assess the impact of this new data set on the initialization of sea-ice forecasts in the melt seasons of 2015 and 2016 in a coupled sea ice-ocean model with data assimilation. We find that the assimilation of the summer CryoSat-2 SIT observations can reduce the summer ice-edge forecast error. Further, adding SIT observations to an established forecast system with sea-ice concentration assimilation leads to more realistic short-term summer ice-edge forecasts in the Arctic Pacific sector. The long-term Arctic-wide SIT prediction is also improved. In spite of remaining uncertainties, summer CryoSat-2 SIT observations have the potential to improve Arctic sea-ice forecast on multiple time scales.

Firstly, we re-tune an algorithm based on empirical orthogonal functions (EOF) for globally retrieving the chlorophyll a concentration (Chl-a) of phytoplankton functional types (PFTs) from multi-sensor merged ocean color (OC) products. The re-tuned algorithm, namely EOF-SST hybrid algorithm, is improved by: (i) using 30% more matchups between the updated global in situ pigment database and satellite remote sensing reflectance (Rrs) products, and (ii) including sea surface temperature (SST) as an additional input parameter. In addition to the Chl-a of the six PFTs (diatoms, haptophytes, dinoflagellates, green algae, prokaryotes and Prochlorococcus), the fractions of prokaryotes and Prochlorococcus Chl-a to total Chl-a (TChl-a), are also retrieved by the EOF-SST hybrid algorithm. Matchup data are further separated for low and high temperature regimes based on different PFT dependences on SST, to establish the SST-separated hybrid algorithms which further shows improved performance as compared to the EOF-SST hybrid algorithm. The per-pixel uncertainty of the retrieved TChl-a and PFT products is estimated by taking into account the uncertainties from both input data and model parameters through Monte Carlo simulations and analytical error propagation. The uncertainty assessment provided within this study sets the ground to extend the long-term continuous satellite observations of global PFT products by transferring the algorithm and its method to determine uncertainties to similar OC products until today. Satellite PFT uncertainty is also essential to evaluate and improve coupled ecosystem-ocean models which simulate PFTs, and furthermore can be used to directly improve these models via data assimilation.

A new version of the AWI Coupled Prediction System is developed based on the Alfred Wegener Institute Climate Model v3.0. Both the ocean and the atmosphere models are upgraded or replaced, reducing the computation time by a factor of 5 at a given resolution. This allowed us to increase the ensemble size from 12 to 30, maintaining a similar resolution in both model components. The online coupled data assimilation scheme now additionally utilizes sea-surface salinity and sea-level anomaly as well as temperature and salinity profile observations. Results from the data assimilation demonstrate that the sea-ice and ocean states are reasonably constrained. In particular, the temperature and salinity profile assimilation has mitigated systematic errors in the deeper ocean, although issues remain over polar regions where strong atmosphere-ocean-ice interaction occurs. One-year-long sea-ice forecasts initialized on January 1st, April 1st, July 1st and October 1st from 2003 to 2019 are described. To correct systematic forecast errors, sea-ice concentration from 2011 to 2019 is calibrated by trend-adjusted quantile mapping using the preceding forecasts from 2003 to 2010. The sea-ice edge raw forecast skill is within the range of operational global subseasonal-to-seasonal forecast systems, outperforming a climatological benchmark for about two weeks in the Arctic and about three weeks in the Antarctic. The calibration is much more effective in the Arctic: Calibrated sea-ice edge forecasts outperform climatology for about 45 days in the Arctic but only 27 days in the Antarctic. Both the raw and the calibrated forecast skill exhibit strong seasonal variations.