Assessing the role and consistency of satellite observation products in global physical-biogeochemical ocean reanalysis

Abstract

Abstract. As part of the European Space Agency's Climate Change Initiative, new sets of satellite observation products have been produced for Essential Climate Variables including ocean colour, sea surface temperature, sea level and sea ice. These new products have been assimilated into a global physical-biogeochemical ocean model, to create a set of 13-year reanalyses at 1° resolution and 3-year reanalyses at 1/4° resolution. In a series of experiments, the variables were assimilated individually and in combination, in order to assess their consistency from a data assimilation perspective. The satellite products, and the reanalyses assimilating them, were found to be consistent in their representation of spatial features such as fronts, sea ice extent and bloom activity. Assimilating multiple variables together often resulted in larger mean increments for a variable than assimilating it individually, revealing ways in which the model and assimilation scheme could be improved. Sea surface fugacity of carbon dioxide had lower errors against independent observations in the higher resolution simulations, and was improved by assimilating ocean colour or sea ice concentration, but degraded by assimilating sea surface temperature or sea level anomaly. Phytoplankton biomass correlated more strongly with net air-sea heat fluxes in the reanalyses than chlorophyll concentration did, and the correlation was weakened by assimilating ocean colour data, suggesting that studies of phytoplankton bloom initiation based solely on chlorophyll data may not provide a full understanding of the underlying processes.

Assimilating synthetic Biogeochemical-Argo and ocean colour observations into a global ocean model to inform observing system design

“…The reanalysis system is an upgraded version of that used in previous biogeochemical data assimilation studies at the Met Office (Ford et al, 2012;While et al, 2012;Ford and Barciela, 2017;Ford, 2019).…”

“…The data assimilation scheme used here is version 5 of NEMOVAR (Weaver et al, 2003(Weaver et al, , 2005Mogensen et al, 2009Mogensen et al, , 2012, following the implementation for assimilating physical variables into the global FOAM system (Waters et al, 2015), and for assimilating ocean colour data into HadOCC (Ford, 2019) and the European Regional Seas Ecosystem Model (ERSEM) (Skákala et al, 2018). As detailed in Waters et al (2015), this implementation of NEMOVAR uses a first guess at appropriate time (FGAT) 3D-Var methodology.…”

“…NEMOVAR is used in this study to assimilate simulated ocean colour and BGC-Argo data, as described in the following sections. NEMOVAR can be used for combined physical-biogeochemical assimilation (Ford, 2019), but physics data is not assimilated in this study.…”

“…Log-transformation is performed in order to give a more Gaussian error distribution (Campbell, 1995). The background and observation error covariances used are the same as in Ford (2019). In the horizontal, a correlation length-scale based on the first baroclinic Rossby radius is used, consistent with Waters et al (2015).…”

“…The first, which is computationally most efficient, is to calculate a set of 2D surface increments. These can then be applied equally through the mixed layer, as in Ford (2019) and Skákala et al (2018).…”