Description: An Arctic sea ice thickness record covering from 2010 to 2016 is generated by assimilating satellite thickness from CryoSat-2 and Soil Moisture and Ocean Salinity (SMOS). The model is based on the Massachusetts Institute of Technology general circulation model (MITgcm) and the assimilation is performed by a local Error Subspace Transform Kalman filter (LESTKF) coded in the Parallel Data Assimilation Framework (PDAF).

Description: This data set provides the collocated data of remote sensing reflectance (Rrs) at 9 bands extracted from the merged ocean color products from GlobColour archive (https://www.globcolour.info/), satellite sea surface temperature from CMEMS (https://marine.copernicus.eu/), and chlorophyll a concentrations (Chl-a) derived from a global database of in situ HPLC pigment data collected from 2002 to 2012. The total Chl-a, Chl-a of six phytoplankton functional types (PFTs) that are diatoms, dinoflagellates, haptophytes, green algae, prokaryotes and Prochlorococcus, and two fractions of prokaryotes and Prochlorococcus are included in this data set. PFT Chl-a and fractions are derived using an updated diagnostic pigment analysis (DPA) method (Soppa et al., 2014; Losa et al., 2017), 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). Matchups of satellite Rrs to in situ PFT data (which were also matchups to SST) were extracted from global 4-km daily merged products. Extraction and averaging protocol including quality control were described in detail in Xi et al. (2020).

Description: We derive the chlorophyll a concentration (Chla)for three main phytoplankton functional types (PFTs)-- diatoms, coccolithophores and cyanobacteria- by combining satellite multispectral-based information, being of a high spatial and temporal resolution, with retrievals based on high resolution of PFT absorption properties derived from hyperspectral measurements. The multispectral-based PFT Chla retrievals are based on a revised version of the empirical OC-PFT algorithm (Hirata et al. 2011) applied to the Ocean Colour Climate Change Initiative (OC-CCI) total Chla product. The PhytoDOAS analytical algorithm (Bracher et al. 2009, Sadeghi et al. 2012) is used with some modifications to derive PFT Chla from SCIAMACHY hyperspectral measurements. To combine synergistically these two PFT products (OC-PFT and PhytoDOAS), an optimal interpolation is performed for each PFT in every OC-PFT sub-pixel within a PhytoDOAS pixel, given its Chla and its a priori error statistics. The synergistic product (SynSenPFT) is presented for the period of August 2002 ? March 2012 and evaluated against in situ HPLC pigment data and satellite information on phytoplankton size classes (PSC) (Brewin et al. 2010, Brewin et al. 2015) and the size fraction (Sf) by Ciotti and Bricaud (2006. The most challenging aspects of the SynSenPFT algorithm implementation are discussed. Perspectives on SynSenPFT product improvements and prolongation of the time series over the next decades by adaptation to Sentinel multi- and hyperspectral instruments are highlighted.

Description: The SynSenPFT product is presented as chlorophyll "a" concentrations (Chla) for diatoms, coccolithophores and cyanobacteria (some of the phytoplankton functional types, PFT) obtained globally over the World Ocean on a 4 km sinusoidal grid on a daily basis over the period of August 2002 - March 2012. The SynSenPFT is a synergistic combination of the PFT products of initial-input OC-PFT (Hirata et al., 2011, Soppa et al., 2014) applied to total chlorophyll "a" (TChla) data of Ocean Colour Climate Change Initiative (OC-CCI, Version 2, ESA) and PhytoDOAS (Bracher et al., 2009, Sadeghi et al., 2012) version 3.3 available at doi:10.1594/PANGAEA.870486 with an optimal interpolation (OI). The OI method is applied to OC-PFT and PhytoDOAS Chla products of diatoms, cyanobacteria (called prokarytoes by the OC-PFT method) and haptophytes (for OC-PFT) and coccolithophores (for PhytoDOAS). Note that OC-PFT retrieves haptophytes while PhytoDOAS retrieves coccolithophores, a (often dominating) sub-group of haptophytes. Algorithmically, the SynSenPFT is an update of OC-PFT Chla with PhytoDOAS Chla values weighted in accordance to our degree of belief to both initial-input data products. Within the current version of SynSenPFT algorithm the update is done for every sub-pixel of OC-PFT within a PhytoDOAS pixel. Thus, SynSenPFT in every OC-PFT sub-pixel on average is nudged towards PhytoDOAS values as close as allowed by the prescribed PhytoDOAS and OC-PFT error statistics.

Description: This data set is composed of model output from Darwin-MITgcm, of chlorophyll-a concentration, coloured dissolved organic matter absorption, sea-ice concentration, sea surface and subsurface temperature, surface heat flux, ice-covered days, mixed layer depth, meridional advection of temperature. It covers a time period from January 2007 to January 2017, while some fields cover only parts of the summer of 2012.

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).

Description: This phytoplankton group (PFT) concentration a (Chl a) data are output from the algorithm PhytoDOAS version 3.3 applied to SCIAMACHY data from 2 Aug 2002 to 8 Apr 2012. Data have been gridded monthly on 0.5° latitude to 0.5°. For cyanobacteria (includes all prokaryotic phytoplankton) and diatoms the PhytoDOAS PFT retrieval algorithm by Bracher et al. (2009) and for coccolithophores the algorithm by Sadeghi et al. (2012) have been used. However, these methods have slightly been improved which includes: - Data during SCIAMACHY instrument decontamination are excluded in the analysis. - SCIAMACHY level-1b input data for PhytoDOAS are now version 7.04 data (instead of version 6.0). - The wavelength window for all three phytoplankton groups (PFTs) fit factor starts at 427.5 nm (instead of 429 nm). - Coccolithophores fit factors are retrieved in a retrieval fitting simultaneously diatoms and coccolithophores (instead of a triple fit with also fitting dinoflagellates as in Sadeghi et al. 2012). - Vibrational Raman Scattering (VRS) is now fitted directly in the blue spectrum (450 to 495 nm), following Dinter et al. (2015), (instead of in the UV—A region as in Vountas et al. 2007) except that here the daily solar background spectrum measured by SCIAMACHY and the VRS pseudo absorption spectrum calculated based on a SCIAMACHY solar spectrum following Vountas et al. (2007) was used in order to correct for the variation of instrumental effects over time (this is not achieved when using the RTM simulated background spectrum as done in Dinter et al. 2015). - The PFT Chl a are derived from the ratio of the PFT fit factor to the VRS fit factor multiplied by a LUT (Look Up Table). The LUT is based on radiative transfer model (RTM) SCIATRAN simulations (see Rozanov et al. 2014) accounting also for changing solar zenith angle (SZA).

Description: The presence of optically active water constituents is known to attenuate the light penetration in the ocean and impact the ocean heat content. Here, we investigate the influence of colored dissolved organic matter (CDOM) and total suspended matter (TSM) on the radiative heating of the Laptev Sea shelf waters. The Laptev Sea region is heavily influenced by the Lena River, one of the largest river systems in the Arctic region. We simulate the radiative heating by using a coupled atmosphere-ocean radiative transfer model (RTM) and in situ measurements from the TRANSDRIFT XVII expedition carried out in September 2010. The results indicate that CDOM and TSM have significant influence on the energy budget of the Laptev Sea shelf waters, absorbing most of the solar energy in the first 2 m of the water column. In the station with the highest CDOM absorption (aCDOM(443) = 1.77 m−1) ~43% more energy is absorbed in the surface layer compared to the station with the lowest aCDOM(443) (~0.2 m−1), which translates to an increased radiative heating of ~0.6°C/day. The increased absorbed energy by the water constituents also implies increased sea ice melt rate and changes in the surface heat fluxes to the atmosphere. By using satellite remote sensing and RTM we quantify the spatial distribution of the radiative heating in the Laptev Sea for a typical summer day. The combined use of satellite remote sensing, RT modeling and in situ observations can be used to improve parameterization schemes in atmosphere-ocean circulation models to assess the role of the ocean in the effect of Arctic amplification.

Description: The impact of assimilating sea ice thickness data derived from ESA's Soil Moisture and Ocean Salinity (SMOS) satellite together with Special Sensor Microwave Imager/Sounder (SSMIS) sea ice concentration data of the National Snow and Ice Data Center (NSIDC) in a coupled sea ice-ocean model is examined. A period of 3 months from 1 November 2011 to 31 January 2012 is selected to assess the forecast skill of the assimilation system. The 24 h forecasts and longer forecasts are based on the Massachusetts Institute of Technology general circulation model (MITgcm), and the assimilation is performed by a localized Singular Evolutive Interpolated Kalman (LSEIK) filter. For comparison, the assimilation is repeated only with the SSMIS sea ice concentrations. By running two different assimilation experiments, and comparing with the unassimilated model, independent satellite-derived data, and in situ observation, it is shown that the SMOS ice thickness assimilation leads to improved thickness forecasts. With SMOS thickness data, the sea ice concentration forecasts also agree better with observations, although this improvement is smaller.

Description: The coupled ocean circulation‐ecosystem model MITgcm‐REcoM2 is used to simulate biogeochemical variables in a global configuration. The ecosystem model REcoM2 simulates two phytoplankton groups, diatoms and small phytoplankton, using a quota formulation with variable carbon, nitrogen, and chlorophyll contents of the cells. To improve the simulation of the phytoplankton variables, chlorophyll‐a data from the European Space Agency Ocean‐Color Climate Change Initiative (OC‐CCI) for 2008 and 2009 are assimilated with an ensemble Kalman filter. Utilizing the multivariate cross covariances estimated by the model ensemble, the assimilation constrains all model variables describing the two phytoplankton groups. Evaluating the assimilation results against the satellite data product SynSenPFT shows an improvement of total chlorophyll and more importantly of individual phytoplankton groups. The assimilation improves both phytoplankton groups in the tropical and midlatitude regions, whereas the assimilation has a mixed response in the high‐latitude regions. Diatoms are most improved in the major ocean basins, whereas small phytoplankton show small deteriorations in the Southern Ocean. The improvement of diatoms is larger when the multivariate assimilation is computed using the ensemble‐estimated cross covariances between total chlorophyll and the phytoplankton groups than when the groups are updated so that their ratio to total chlorophyll is preserved. The comparison with in situ observations shows that the correlation of the simulated chlorophyll of both phytoplankton groups with these data is increased whereas the bias and error are decreased. Overall, the multivariate assimilation of total chlorophyll modifies the two phytoplankton groups separately, even though the sum of their individual chlorophyll concentrations represents the total chlorophyll.