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, providing information about model biases and compensating errors which could be addressed in the future development of the model and assimilation scheme. 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 it was 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 sparsity of in situ observations, espe-cially for variables such as phytoplankton and zooplankton biomass, has always made it difficult to validate results or compare conclusions from different studies. Many studies have used inherently multivariate assimilation methods such as the ensemble Kalman filter (Evensen, 2003), while others have employed balance relationships (Ford et al, 2012;Rousseaux and Gregg, 2012;Teruzzi et al, 2014;Skákala et al, 2018). This study used a form of the latter, with phytoplankton biomass variables updated to maintain background stoichiometric ratios.…”

“…An alternative approach could be to use the nitrogen balancing scheme of Hemmings et al (2008), which explicitly updates several model state variables to try and account for differing errors in phytoplankton growth and loss processes. This has been successfully used in previous ocean colour assimilation studies (Ford et al, 2012;Ford and Barciela, 2017;Ford, 2020) with the HadOCC model (Palmer and Totterdell, 2001). It was originally designed and tuned for use with HadOCC, so it requires further development and tuning for use with the more complex MEDUSA, but an initial implementation for MEDUSA has been developed.…”

“…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, 2020).…”

Marine data assimilation in the UK: the past, the present and the vision for the future

“…In addition, a scheme was developed for assimilation of in situ pCO2 data (While et al, 2012). These schemes have since been applied with 3DVar using the NEMOVAR assimilation framework (Ford, 2020) and in Ford (2021) they were applied with the Model for ecosystem dynamics, nutrient Utilisation, Sequestration and Acidification (MEDUSA, Yool et al, 2013). The latter study introduced assimilation of multivariate in situ profiles as might be obtained from BGC-Argo data in an observing system simulation experiment using synthetic profiles.…”

Deep chlorophyll maximum and nutricline in the Mediterranean Sea: emerging properties from a multi-platform assimilated biogeochemical model experiment

“…In ocean biogeochemical modelling, the assimilation of satellite ocean-colour observations has been successfully applied in research and operational applications at both global and regional scales (Fennel et al, 2019;Groom et al, 2019). Chlorophyll concentration is the most commonly assimilated variable since the first applications of ocean biogeochemical DA (Ciavatta et al, 2016;Dorofeyev and Sukhikh, 2018;Ford and Barciela, 2017;Ford, 2020a;Gehlen et al, 2015;Mattern et al, 2017;Pradhan et al, 2019;Ratheesh et al, 2016;Santana-Falcón et al, 2020;Song et al, 2016;Teruzzi et al, 2018;Tsiaras et al, 2017). However, assimilation of the ocean-colour diffuse attenuation coefficient, phytoplankton functional types, particulate organic carbon and inherent optical properties has been suggested as promising alternative to chlorophyll assimilation (Ciavatta et al, 2019(Ciavatta et al, , 2018(Ciavatta et al, , 2014Jones et al, 2016;Pradhan et al, 2020;Shulman et al, 2013;Skákala et al, 2018;Xiao and Friedrichs, 2014).…”