Climate variability in the Norwegian Sea was investigated in terms of ocean heat and freshwater contents of Atlantic water above a reference surface, using hydrographic data during spring 1951–2010. The main processes acting on this variability were examined and then quantified. The area-averaged water mass cooled and freshened, but a deepening of the reference surface resulted in a positive trend in the heat content of 0.3 W m−2. Air-sea heat fluxes explained about half of the interannual variability in heat content. The effect of the advection of Atlantic and Arctic waters on the variability varied with time, apparently due to large-scale changes in the ocean circulation. The data are consistent with the explanation that changing wind patterns caused buffering and then release of Arctic water in the Iceland Sea during the late 1960s to early 1970s, and this caused large hydrographic changes in the Norwegian Sea.

We assembled a dataset of 14C-based productivity measurements to understand the critical variables required for accurate assessment of daily depth-integrated phytoplankton carbon fixation (PP(PPeu)u) from measurements of sea surface pigment concentrations (Csat)(Csat). From this dataset, we developed a light-dependent, depth-resolved model for carbon fixation (VGPM) that partitions environmental factors affecting primary production into those that influence the relative vertical distribution of primary production (Pz)z) and those that control the optimal assimilation efficiency of the productivity profile (P(PBopt). The VGPM accounted for 79% of the observed variability in Pz and 86% of the variability in PPeu by using measured values of PBopt. Our results indicate that the accuracy of productivity algorithms in estimating PPeu is dependent primarily upon the ability to accurately represent variability in Pbopt. We developed a temperature-dependent Pbopt model that was used in conjunction with monthly climatological images of Csat sea surface temperature, and cloud-corrected estimates of surface irradiance to calculate a global annual phytoplankton carbon fixation (PPannu) rate of 43.5 Pg C yr‒1. The geographical distribution of PPannu was distinctly different than results from previous models. Our results illustrate the importance of focusing Pbopt model development on temporal and spatial, rather than the vertical, variability.

The SDC_ARC_CLIM_TS_V1 product contains Temperature and Salinity Climatologies for Arctic Ocean including seasonal and monthly fields for 6 decades starting from 1955 to 2014. The climatological fields were computed from merged Arctic Ocean data that combines data extracted from the 2 major sources: 1) SeaDataNet infrastructure and 2) World Ocean Database (2018) for reanalysis. The computation was done with the DIVA Classic (Data-Interpolating Variational Analysis), version 4.7.2.

The SeaDataCloud Temperature and Salinity Climatologies have been processed from the historical Data Collection of the Arctic Ocean (from 56°N to 83°N and from 45°W to 70°E) including temperature and salinity in situ data of the water column for the period 1955 – 2019. The analysis was run in six decadal periods (1955-1964/1965-1974/1975-1984/1985-1994/1995-2004/2005-2014). A subset of the World Ocean Database 2018 (WOD18, https://www.nodc.noaa.gov/OC5/WOD/pr_wod.html) dataset has been integrated as external data-source to improve data availability for analysis.

Moving 6-year analysis of phosphate in the Arctic Ocean, for each season in the period 1965-2024. Every year of the time dimension corresponds to the 6-year centered average for each season. Winter: December-February, Spring: March-May, Summer: June-August, Autumn: September-November. Depth range (IODE standard depths): 0, 5, 10, 20, 30, 40, 50, 75, 100, 125, 150, 200, 250, 300, 400, ..., 1500, 1750, 2000, 2500m. Units: umol/l. Description of DIVA analysis: The computation was done with DIVAnd (Data-Interpolating Variational Analysis in n dimensions), version 2.7.13, using GEBCO 30sec topography for the spatial connectivity of water masses. The horizontal resolution of the produced DIVAnd maps grids is 0.125 degrees. Signal-to-noise ratio was fixed to 3.0, horizontal correlation length varying from 45 km near the coast to 150 km, and vertical correlation length varying between 25 and 1000 m. Logarithmic transformation is applied to the data prior to the analysis. Background field: analysis with signal-to-noise ratio = 20, horizontal correlation length 50-200 km, and vertical correlation length 25-1000 m.

Moving 6-year analysis of silicate in the Arctic Ocean, for each season in the period 1970-2024. Every year of the time dimension corresponds to the 6-year centered average for each season. Winter: December-February, Spring: March-May, Summer: June-August, Autumn: September-November. Depth range (IODE standard depths): 0, 5, 10, 20, 30, 40, 50, 75, 100, 125, 150, 200, 250, 300, 400, ..., 1500, 1750, 2000, 2500m. Units: umol/l. Description of DIVA analysis: The computation was done with DIVAnd (Data-Interpolating Variational Analysis in n dimensions), version 2.7.13, using GEBCO 30sec topography for the spatial connectivity of water masses. The horizontal resolution of the produced DIVAnd maps grids is 0.125 degrees. Signal-to-noise ratio was fixed to 3.0, horizontal correlation length varying from 45 km near the coast to 150 km, and vertical correlation length varying between 25 and 1000 m. Logarithmic transformation is applied to the data prior to the analysis. Background field: analysis with signal-to-noise ratio = 20, horizontal correlation length 50-200 km, and vertical correlation length 25-1000 m.

SeaDataCloud Temperature and Salinity historical data collection for the Arctic Ocean, including revised quality flags after quality control with ODV. The dataset format is ODV binary collections. You can read, analyse and export from the ODV application provided by Alfred Wegener institute at http://odv.awi.de/. For data access please register at http://www.marine-id.org/.

SeaDataCloud Temperature and Salinity historical data collection for the Arctic Ocean, including revised quality flags after quality control with ODV. The dataset format is ODV binary collections. You can read, analyse and export from the ODV application provided by Alfred Wegener institute at http://odv.awi.de/. For data access please register at http://www.marine-id.org/.