Arctic Oceanography - Oceanography: Atmosphere-Ocean Exchange, Biogeochemistry & Physics

The Arctic Ocean is, on average, the shallowest of Earth’s oceans. Its vast continental shelf areas, which account for approximately half of the Arctic Ocean’s total area, are heavily influenced by the surrounding land masses through river run-off and coastal erosion. As a main area of deep water formation, the Arctic is one of the main «engines» of global ocean circulation, due to large freshwater inputs, it is also strongly stratified. The Arctic Ocean’s complex oceanographic configuration is tightly linked to the atmosphere, the land, and the cryosphere. The physical dynamics not only drive important climate and global circulation patterns, but also control biogeochemical cycles and ecosystem dynamics. Current changes in Arctic sea-ice thickness and distribution, air and water temperatures, and water column stability are resulting in measurable shifts in the properties and functioning of the ocean and its ecosystems. The Arctic Ocean is forecast to shift to a seasonally ice-free ocean resulting in changes to physical, chemical, and biological processes. These include the exchange of gases across the atmosphere-ocean interface, the wind-driven ciruclation and mixing regimes, light and nutrient availability for primary production, food web dynamics, and export of material to the deep ocean. In anticipation of these changes, extending our knowledge of the present Arctic oceanography and these complex changes has never been more urgent.

Catlin Arctic Survey 2010 Environmental data

The citation text is: Findlay H.S. (2015). Catlin Arctic Survey 2010 Environmental data. British Oceanographic Data Centre - Natural Environment Research Council, UK. doi:10/767.

An assessment of phytoplankton primary productivity in the Arctic Ocean from satellite ocean color/in situ chlorophyll-abased models

We investigated 32 net primary productivity (NPP) models by assessing skills to reproduce integrated NPP in the Arctic Ocean. The models were provided with two sources each of surface chlorophyll-a concentration (chlorophyll), photosynthetically available radiation (PAR), sea surface temperature (SST), and mixed-layer depth (MLD). The models were most sensitive to uncertainties in surface chlorophyll, generally performing better with in situ chlorophyll than with satellite-derived values. They were much less sensitive to uncertainties in PAR, SST, and MLD, possibly due to relatively narrow ranges of input data and/or relatively little difference between input data sources. Regardless of type or complexity, most of the models were not able to fully reproduce the variability of in situ NPP, whereas some of them exhibited almost no bias (i.e., reproduced the mean of in situ NPP). The models performed relatively well in low-productivity seasons as well as in sea ice-covered/deep-water regions. Depth-resolved models correlated more with in situ NPP than other model types, but had a greater tendency to overestimate mean NPP whereas absorption-based models exhibited the lowest bias associated with weaker correlation. The models performed better when a subsurface chlorophyll-a maximum (SCM) was absent. As a group, the models overestimated mean NPP, however this was partly offset by some models underestimating NPP when a SCM was present. Our study suggests that NPP models need to be carefully tuned for the Arctic Ocean because most of the models performing relatively well were those that used Arctic-relevant parameters.