This Study is led by David Ford from the Met Office. An additional contributor to this Study is Pablo Ortega from BSC.
The main CCI ECVs used in this Study are Sea Surface Temperature, Sea Surface Salinity, Sea Ice, Sea Level, and Ocean Colour.
It is estimated that this Study will run from April 2024 until July 2025.
The models EC-Earth3-CC and GloSea6/MEDUSA are used. This Study has a tentative start date of April 2024 and comprises of two main parts with a third optional part. The first main part is the assimilation of ESA CCI variables to produce forced ocean/sea-ice reconstructions with EC-Earth3-CC and GloSea6/MEDUSA predictions systems. This includes the assimilation of physical variables: Sea Surface Temperature, Sea Ice Concentration and 3D ocean temperatures from EN4 below the ocean mixed layer. Then additional assimilation of Ocean Colour to determine the role of non-physical variables to BGC predictability and then additional assimilation of Sea Surface Salinity, Sea Surface Height and 3D ocean salinity from EN4 (GloSea6/MEDUSA). The second main part is exploring the impact of assimilation choices of these reconstructions on physical and biogeochemical properties such as evaluating physical properties of reconstructions and identifying a best strategy to reconstruct ocean biogeochemistry. The third, optional, part is to explore the impact of assimilation choices of these reconstructions on seasonal predictions.
Recent progress in Earth System Models (ESM), in particular the incorporation of biogeochemistry in the ocean models, has enabled the use of ESMs for predicting changes in key biogeochemical variables that act as ecosystem drivers (e.g., pH, oxygen, net primary production, chlorophyll) at seasonal to decadal time scales (Park et al., 2019). Such ESM-based predictions have the potential to be used for predicting variations in fish populations and yields, and provide useful information to aquaculture, fishers and policy makers (Tommasi et al., 2017). Seasonal predictions are commonly initialized from reanalyses that assimilate observations into the dynamical forecasting systems (J C Acosta et al., 2022). Assimilation of CCI Sea Ice Concentration (WP3.8 in the previous phase of CMUG) demonstrated added value on summer prediction in the Northern Hemisphere (J C Acosta et al., 2022). Mean state wind stress correction leads to a modest but significant improvement in predictive skill in ecosystem drivers (SST, Chlorophyll, PP). Correcting the full field leads to large predictive skill, demonstrating the dominant role of the wind in ocean BGC.
- What is the value of assimilating physical (e.g., SST, SSS) and biogeochemical (OC or OC-derived) CCI ocean ECVs in seasonal predictions of ocean biogeochemistry?
- What is the dominant factor at initialization (the physical or the biogeochemical state) in determining the ocean biogeochemistry predictive skill at global and regional scales?
- What is the best strategy for constraining initial conditions in order to achieve the highest prediction skill in ocean biogeochemistry?
- Park, J.-Y et al. (2019) Science, 365, 284-288
- Tommasi, D. et al. (2017) Ecological Applications, 27(2), 378-388
- J C Acosta Navarro et al. (2022) Environ. Res. Lett. 17, 064008
Results and conclusions
Results and conclusions will be provided once the Study is complete.