03 May 2021

03 May 2021

Review status: a revised version of this preprint is currently under review for the journal OS.

Can assimilation of satellite observations improve subsurface biological properties in a numerical model? A case study for the Gulf of Mexico

Bin Wang1, Katja Fennel1, and Liuqian Yu1,2 Bin Wang et al.
  • 1Department of Oceanography, Dalhousie University, Halifax, Nova Scotia, Canada
  • 2Department of Ocean Science, The Hong Kong University of Science and Technology, Kowloon, Hong Kong

Abstract. Given current threats to ocean ecosystem health, there is a growing demand for accurate biogeochemical hindcasts, nowcasts, and predictions. Provision of such products requires data assimilation, i.e., a comprehensive strategy for incorporating observations into biogeochemical models, but current data streams of biogeochemical observations are generally considered insufficient for the operational provision of such products. This study investigates to what degree the satellite observations in combination with sparse BGC Argo profiles can improve subsurface biogeochemical properties. The multivariate Deterministic Ensemble Kalman Filter (DEnKF) has been implemented to assimilate physical and biological observations into a biogeochemical model of the Gulf of Mexico. First, the biogeochemical model component was tuned using BGC-Argo observations. Then, observations of sea surface height, sea surface temperature, and surface chlorophyll were assimilated, and profiles of both physical and biological variables were updated based on the surface information. We assessed whether this leads to improved subsurface distributions, especially of biological properties, using observations from five BGC-Argo floats that were not assimilated, but used in the a priori tuning. Results show that assimilation of the satellite data improves model representation of major circulation features, which translate into improved three-dimensional distributions of temperature and salinity. The multivariate assimilation also improves the agreement of subsurface nitrate through its tight correlation with temperature, but the improvements in subsurface chlorophyll were modest initially due to suboptimal choices of the model’s optical module. Repeating the assimilation run after adjusting light attenuation parameterization through further a priori tuning greatly improved the subsurface distribution of chlorophyll. Therefore, even sparse BGC-Argo observations can provide substantial benefits to biogeochemical prediction by enabling a priori model tuning. Given that, so far, the abundance of BGC-Argo profiles in the Gulf of Mexico and elsewhere is insufficient for sequential assimilation, updating 3D biological properties in a model that has been well calibrated is an intermediate step toward full assimilation of the new data types.

Bin Wang et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on os-2021-35', Anonymous Referee #1, 25 May 2021
  • RC2: 'Comment on os-2021-35', Anonymous Referee #2, 28 May 2021
  • RC3: 'Comment on os-2021-35', Anonymous Referee #3, 04 Jun 2021
  • RC4: 'Comment on os-2021-35', Anonymous Referee #4, 18 Jun 2021

Bin Wang et al.


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Short summary
We demonstrate that even sparse BGC Argo profiles can substantially improve biogeochemical prediction via a priori model tuning. By assimilating satellite surface chlorophyll and physical observations, subsurface distributions of physical properties and nutrients were improved immediately. The improvement of subsurface chlorophyll was modest initially but was greatly enhanced after adjusting the parameterization for light attenuation through further a priori tuning.