Improving the multiannual, high-resolution modelling of biogeochemical cycles in the Baltic Sea by using data assimilation
2014 (English)In: Tellus. Series A, Dynamic meteorology and oceanography, ISSN 0280-6495, E-ISSN 1600-0870, Vol. 66, 24908- p.Article in journal (Refereed) Published
The impact of assimilating temperature, salinity, oxygen, phosphate and nitrate observations on marine ecosystem modelling is assessed. For this purpose, two 10-yr (1970-1979) reanalyses of the Baltic Sea are carried out using the ensemble optimal interpolation (EnOI) method and a coupled physical-biogeochemical model of the Baltic Sea. To evaluate the reanalyses, climatological data and available biogeochemical and physical in situ observations at monitoring stations are compared with results from simulations with and without data assimilation. In the first reanalysis, only observed temperature and salinity profiles are assimilated, whereas biogeochemical observations are unused. Although simulated temperature and salinity improve considerably as expected, the quality of simulated biogeochemical variables does not improve and deep water nitrate concentrations even worsen. This unexpected behaviour is explained by a lowering of the halocline in the Baltic proper due to the assimilation causing increased oxygen concentrations in the deep water and consequently altered nutrient fluxes. In the second reanalysis, both physical and biogeochemical observations are assimilated and good quality in all variables is found. Hence, we conclude that if a data assimilation method like the EnOI is applied, all available observations should be used to perform reanalyses of high quality for the Baltic Sea biogeochemical state estimates.
Place, publisher, year, edition, pages
2014. Vol. 66, 24908- p.
reanalysis, data assimilation, numerical modelling, Baltic Sea, biogeochemical simulation
Meteorology and Atmospheric Sciences
IdentifiersURN: urn:nbn:se:su:diva-112925DOI: 10.3402/tellusa.v66.24908ISI: 000346299200001OAI: oai:DiVA.org:su-112925DiVA: diva2:782174