Arctic rivers are intricate water networks that chemically and biologically process carbon before releasing it as carbon dioxide (CO2) into the atmosphere or carrying it to the ocean. Primary producers use inorganic carbon to build biomass at the basis of the trophic chain. Little is known about how Arctic rivers adapt to climate warming, changes in hydrology and biogeochemical properties. To quantify net and gross biological productivity we measured the dissolved oxygen-to-argon (O2/Ar) ratios and O2 triple isotopologue composition in the river Kolyma and in its tributary Ambolikha during late freshet (June) and base-flow conditions (August) in 2019. We found that hydrological factors restricted river productivity. The river system released CO2 into the atmosphere in June and August, however August emissions were only 6 % of late freshet emissions. Also, the Ambolikha tributary emitted twice as much CO2 per area than the main Kolyma channel in June. Due to higher river flow and turbidity in June, river production was reduced, while lower flows in August permitted more light penetration and a phytoplankton bloom at the confluence of tributary and main Kolyma channel. Total CO2 emissions per area during June and August amounted to (5±11) % of the gross carbon uptake estimated at the bloom site. Thus, in-stream metabolism can exceed riverine CO2 emissions under specific flow and light conditions. Arctic climate change may promote biological productivity in particular locations and increase its contribution to carbon budgets in Arctic rivers as flow slows during longer open water periods.

A document by Colette LaMonica Kelly. Click on the document to view its contents.

Oceanography has entered an era of new observing platforms, such as biogeochemical Argo floats and gliders, some of which will provide three-dimensional maps of essential ecosystem variables on the North-West European (NWE) Shelf. In a foreseeable future operational centres will use multi-platform assimilation to integrate those valuable data into ecosystem reanalyses and forecast systems. Here we address some important questions related to glider biogeochemical data assimilation and introduce multi-platform data assimilation in a (pre)operational model of the NWE Shelf-sea ecosystem. We test the impact of the different multi-platform system components (glider vs satellite, physical vs biogeochemical) on the simulated biogeochemical variables. To characterize the model performance we focus on the period around the phytoplankton spring bloom, since the bloom is a major ecosystem driver on the NWE Shelf. We found that the timing and magnitude of the phytoplankton bloom is insensitive to the physical data assimilation, which is explained in the study. To correct the simulated phytoplankton bloom one needs to assimilate chlorophyll observations from glider or satellite Ocean Color (OC) into the model. Although outperformed by the glider chlorophyll assimilation, we show that OC assimilation has mostly desirable impact on the sub-surface chlorophyll. Since the OC assimilation updates chlorophyll only in the mixed layer, the impact on the sub-surface chlorophyll is the result of the model dynamical response to the assimilation. We demonstrate that the multi-platform assimilation combines the advantages of its components and always performs comparably to its best performing component.

We use a recently developed spectrally resolved bio-optical module to better represent the interaction between the incoming irradiance and the heat fluxes in the upper ocean within the (pre-)operational physical-biogeochemical model on the North-West European (NWE) Shelf. The module attenuates light based on the simulated biogeochemical tracer concentrations, and thus introduces a two-way coupling between the biogeochemistry and physics. We demonstrate that in the late spring-summer the two-way coupled model heats up the upper oceanic layer, shallows the mixed layer depth and influences the mixing in the upper ocean. The increased heating in the upper oceanic layer reduces the convective mixing and improves by ~5 days the timing of the late phytoplankton bloom of the ecosystem model. This improvement is relatively small compared with the existing model bias in bloom timing, but sufficient to have a visible impact on model skill. We show that the changes to the model temperature and salinity introduced by the module have mixed impact on the physical model skill, but the skill can be improved by assimilating the observations of temperature, salinity and chlorophyll concentrations into the model. However, in the situations where we improved the simulation of temperature, either via the bio-optical module, or via assimilation of temperature and salinity, we have shown that we also improved the simulated oxygen concentration as a result of the changes in the simulated air-sea gas flux. Overall, comparing different 1-year experiments showed that the best model skill is achieved with joint physical-biogeochemical assimilation into the two-way coupled model.