November 3, 2 PM at Nansen (Copernicus, 1st floor).
Talk by Tarkeshwar Singh (NERSC).
Abstract
Earth System models (ESMs) are powerful tools to study the ocean’s role in the global carbon cycle and estimate the impact of climate change on marine ecosystems. The ocean biogeochemical cycle (BGC) is governed by numerous physical and biological processes that change over space and time. Accurately representing the biological dynamics in an ESM is fundamental to improve the accuracy and reliability of their projections. However, due to limitations in observational data and to reduce model complexity, BGC models utilise many poorly constrained global parameters to mimic unresolved processes. Suboptimal tuning of the parameters could contribute significantly to the errors in the simulated biogeochemical tracers. In this study, we optimise the BGC parameters in the Norwegian Earth System Model (NorESM) using an Ensemble data assimilation (DA) method. The work follows on Singh et al. (2022), which successfully demonstrated the approach in an idealised twin experiment framework. We assimilate climatological observations of physics (salinity and temperature profiles) to constrain error in ocean physics and use BGC observations (Nitrate, Phosphate, Silicate, and Oxygen) to calibrate six BGC parameters. An iterative ensemble smoother technique achieves the best results because it overcomes issues related to ensemble spread collapse. Global parameters estimation outperforms the simulation with standard parameters specifically for nutrients. Estimating spatially varying parameters allows for some further improvements in some regions but also causes large regional degradations. We proposed an efficient and flexible framework to tune model parameters.
