Assessing bloom timing and carbon, nutrient, and oxygen budgets from VOS surface, BGC-Argo profiling float, and monitoring data in the Baltic Sea

Henry C Bittig1, Laura Tuomi2, Gregor J Rehder3, Simo-Matti Siiriä2, Jens Daniel Müller3 and Bernd Schneider3, (1)Leibniz Institute for Baltic Sea Research, Department of Physical Oceanography and Instrumentation, Rostock, Germany, (2)Finnish Meteorological Institute, Marine Research Unit, Helsinki, Finland, (3)Leibniz Institute for Baltic Sea Research, Department of Marine Chemistry, Rostock, Germany
The spring phytoplankton bloom accounts for a significant portion of marine biomass production and carbon fixation. Upon termination, it causes a considerable export of organic matter below the surface mixed layer and fuels mesopelagic and benthic processes.
In the Baltic Sea, the spring bloom production depletes surface nitrate without apparent phosphate limitation, while carbon uptake and oxygen production exceed levels expected from Redfield stoichiometry. However, the exact mechanisms are still unclear. Moreover, the spring bloom is believed to set the conditions for the second, N2-fixing bloom occurring in mid-summer.
Here, we investigate the timing and subsurface progression of the spring bloom and its transition towards summer conditions in the Gotland Deep, one of the most intensively-studied areas in the central Baltic Sea. BGC-Argo profiling floats by Argo Finland provide weekly water column data of phytoplankton (Chl a fluorescence) and net community production proxies (O2), whereas the ICOS voluntary observing ship (VOS) Finnmaid gives surface CO2 observations every 2 days. These are used, together with regular monitoring data, to determine carbon, nutrient, and oxygen budgets, their vertical distribution, and elemental ratios for the spring blooms 2013 - 2019. Moreover, the high temporal resolution allows detection of similar patterns and differences in advent, evolution, and decay of the spring bloom and summer situation. The aim is to better understand mechanisms and driving factors, eventually improving regional ecosystem models, which can only be achieved by synergistic use of high-resolution surface and profiling observing systems.