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.

The Arctic Ocean receives a large supply of dissolved organic matter (DOM) from its catchment and shelf sediments, which can be traced across much of the basin’s upper waters. This signature can potentially be used as a tracer. On the shelf, the combination of river discharge and sea-ice formation, modifies water densities and mixing considerably. These waters are a source of the halocline layer that covers much of the Arctic Ocean, but also contain elevated levels of DOM. Here we demonstrate how this can be used as a supplementary tracer and contribute to evaluating ocean circulation in the Arctic. A fraction of the organic compounds that DOM consists of fluoresce and can be measured using in-situ fluorometers. When deployed on autonomous platforms these provide high temporal and spatial resolution measurements over long periods. The results of an analysis of data derived from several Ice Tethered Profilers (ITPs) offer a unique spatial coverage of the distribution of DOM in the surface 800m below Arctic sea-ice. Water mass analysis using temperature, salinity and DOM fluorescence, can clearly distinguish between the contribution of Siberian terrestrial DOM and marine DOM from the Chukchi shelf to the waters of the halocline. The findings offer a new approach to trace the distribution of Pacific waters and its export from the Arctic Ocean. Our results indicate the potential to extend the approach to separate freshwater contributions from, sea-ice melt, riverine discharge and the Pacific Ocean.

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.

Gaussian decomposition and Singular Value Decomposition combined with Non-Negative Least Squares (SVD-NNLS) method are compared and combined to estimate the concentration of 18 phytoplankton pigments (names and abbreviations are displayed in Table 2) from phytoplankton absorption spectra. Results show that both methods tend to overestimate pigment concentration. Gaussian decomposition method provides robust estimation of TChl-a, TChl-b, Chl-c1/2, PSC and PPC. The estimates of TChl-a, Fuco, Diato, $\beta$-Caro, Prasino, TChl-b, Zea, Viola and Lut from SVD-NNLS show reasonable estimation accuracy, while the other pigments are subjected to relatively large prediction errors. The estimated pigments concentrations are further exploited based on Diagnostic Pigment Analysis to derive four phytoplankton functional types, i.e. diatoms, prymnesiophytes, green algae and prokaryotes. By the application of these two methods to the particulate absorption spectra collected by underway spectrophotometry during three summer cruises in 2015 - 2017 in the Fram Strait, continuous surface phytoplankton functional types are estimated along the cruise course.