Improved ocean biogeochemistry (BGC) parameters in Earth System Models can enhance the representation of the global carbon cycle. We aim to demonstrate the potential of parameter estimation (PE) using an ensemble data assimilation method to optimise five key BGC parameters within the Norwegian Earth System Model (NorESM). The optimal BGC parameter values are estimated with an iterative ensemble smoother technique, applied a-posteriori to the error of monthly climatological estimates of nitrate, phosphate and oxygen produced by a coupled reanalysis that assimilates monthly ocean physical observed climatology. Reducing the ocean physics biases while keeping the default parameters (DP) initially reduces BGC state bias in the intermediate depth but deteriorates near the surface, suggesting that the DP are tuned to compensate for physical biases. Globally uniform and spatially varying estimated parameters from the first iteration effectively mitigate the deterioration and reduce BGC errors compared to DP, also for variables not used in the PE (such as C0$_2$ fluxes and primary production). While spatial PE performs superior in specific regions, global PE performs best overall. A second iteration can further improve the performance of global PE for near-surface BGC variables. Finally, we assess the performance of the global estimated parameters in a 30-year coupled reanalysis, assimilating time-varying temperature and salinity observations. It reduces error by 20\%, 18\%, 7\%, and 27\% for phosphate, nitrate, oxygen, and dissolved inorganic carbon, respectively, compared to the default version of NorESM. The proposed PE approach is a promising innovative tool to calibrate ESM in the future.

A document by sebastien_barthelemy. Click on the document to view its contents.

Initialization is essential for accurate seasonal-to-decadal (S2D) climate predictions. The initialization schemes used differ on the component initialized, the Data Assimilation (DA) method, or the technique. We compare five popular schemes within NorCPM following the same experimental protocol: reanalysis from 1980–2010 and seasonal and decadal predictions initialized from the reanalysis. We compare atmospheric initialization—Newtonian relaxation (nudging)—against ocean initialization—Ensemble Kalman Filter—(ODA). On the atmosphere, we explore the benefit of full-field (NudF-UVT) or anomaly (NudA-UVT) nudging of horizontal winds and temperature (U, V, and T) observations. The scheme NudA-UV nudges horizontal winds to disentangle the role of wind-driven variability. The scheme ODA+NudA-UV provides a first attempt at joint initialization of the ocean and atmospheric components. During the reanalysis, atmospheric nudging leads to atmosphere and land components best synchronized with observations. Conversely, ODA best synchronizes the ocean component with observations. The atmospheric nudging schemes are better at reproducing specific events, such as the rapid North Atlantic subpolar gyre (SPG) shift. An abrupt climatological change using the NudA-UV scheme demonstrates that energy conservation is crucial when only assimilating winds. ODA outperforms atmospheric-initialized versions for S2D global predictions, while atmospheric nudging is preferable for accurately initializing phenomena in specific regions, with the technique’s benefit depending on the prediction’s temporal scale. For instance, atmospheric full-field initialization benefits the tropical Atlantic Niño at one-month lead time, and atmospheric anomaly initialization benefits longer lead times, reducing hindcast drift. Combining atmosphere and ocean initialization yields sub-optimal results, as sustaining the ensemble’s reliability—required for ODA’s performance—is challenging with atmospheric nudging.

A document by Akhilesh S. Nair. Click on the document to view its contents.

A supermodel connects different models interactively so that their systematic errors compensate and achieve a model with superior performance. It differs from the standard non-interactive multi-model ensembles (NI), which combines model outputs a-posteriori. We formulate the first supermodel framework for Earth System Models (ESMs) and use data assimilation to synchronise models. The ocean of three ESMs is synchronised every month by assimilating pseudo sea surface temperature (SST) observations generated from them. Discrepancies in grid and resolution are handled by constructing the synthetic pseudo-observations on a common grid. We compare the performance of two supermodel approaches to that of the NI for 1980—2006. In the first (EW), the models are connected to the equal-weight multi-model mean, while in the second (SINGLE), they are connected to a single model. Both versions achieve synchronisation in locations where the ocean drives the climate variability. The time variability of the supermodel multi-model mean SST is reduced compared to the observed variability; most where synchronisation is not achieved and is bounded by NI. The damping is larger in EW than in SINGLE because EW yields additional damping of the variability in the individual models. Hence, under partial synchronisation, the part of variability that is not synchronised gets damped in the multi-model average pseudo-observations, causing a deflation during the assimilation. The SST bias in individual models of EW is reduced compared to that of NI, and so is its multi-model mean in the synchronised regions. The performance of a trained supermodel remains to be tested.