Assimilation of Global Total Chlorophyll OC‐CCI Data and Its Impact on Individual Phytoplankton Fields. Issue 1 (21st January 2019)

• Record Type: Journal Article
• Authors: Pradhan, H. K.; Völker, C.; Losa, S. N.; Bracher, A.; Nerger, L.
• Is Part Of: Journal of geophysical research. Volume 124:Issue 1(2019)
• Page Start: 470
• Page End: 490
• Publication Date: 2019-01-21
• Subjects: multivariate assimilation -- biogeochemistry -- total chlorophyll -- cross covariance -- OC‐CCI -- SynSenPFT; Oceanography -- Periodicals; 551.4605
• DOI: 10.1029/2018JC014329
• Languages: English
• ISSNs: 2169-9275

Abstract

Abstract: The coupled ocean circulation‐ecosystem model MITgcm‐REcoM2 is used to simulate biogeochemical variables in a global configuration. The ecosystem model REcoM2 simulates two phytoplankton groups, diatoms and small phytoplankton, using a quota formulation with variable carbon, nitrogen, and chlorophyll contents of the cells. To improve the simulation of the phytoplankton variables, chlorophyll‐a data from the European Space Agency Ocean‐Color Climate Change Initiative (OC‐CCI) for 2008 and 2009 are assimilated with an ensemble Kalman filter. Utilizing the multivariate cross covariances estimated by the model ensemble, the assimilation constrains all model variables describing the two phytoplankton groups. Evaluating the assimilation results against the satellite data product SynSenPFT shows an improvement of total chlorophyll and more importantly of individual phytoplankton groups. The assimilation improves both phytoplankton groups in the tropical and midlatitude regions, whereas the assimilation has a mixed response in the high‐latitude regions. Diatoms are most improved in the major ocean basins, whereas small phytoplankton show small deteriorations in the Southern Ocean. The improvement of diatoms is larger when the multivariate assimilation is computed using the ensemble‐estimated cross covariances between total chlorophyll and the phytoplankton groups than when the groups are updated so that their ratio to total chlorophyll is preserved. The comparison with in situ observations shows that the correlation of the simulated chlorophyll of both phytoplankton groups with these data is increased whereas the bias and error are decreased. Overall, the multivariate assimilation of total chlorophyll modifies the two phytoplankton groups separately, even though the sum of their individual chlorophyll concentrations represents the total chlorophyll. Plain Language Summary: Different types of plankton are simulated globally with ocean ecosystem models. To further increase their prediction quality, we combine the model with satellite observations of chlorophyll using modern methods called data assimilation. This method allows us not only to improve the modeled total chlorophyll but also the simulation of the different plankton types. Further, we can fill gaps in the satellite data that results, for example, from clouds. Thus, we are able to better predict the ocean ecosystem, which in turn helps to understand climate change patterns and carbon cycle processes. Key Points: Multivariate assimilation of satellite chlorophyll data improves simulation of phytoplankton functional groups and influences them differently Small phytoplankton is weakly deteriorated in the Southern Ocean, while diatoms are improved globally Regional variability of assimilation leads to stronger improvement at midlatitudes and equator than at high latitudes