Calibrating ocean biogeochemistry (BGC) parameters in Earth System Models is challenging because there are multiple sources of error, and the parameters' sensitivities are interlinked. Reducing the bias in the ocean physical component of the Norwegian Earth System Model (NorESM) diminishes the BGC state bias at intermediate depth but leads to a greater bias increase near the surface. This suggests that BGC parameters are currently tuned to compensate for the ocean physics biases. We successfully apply the iterative ensemble smoother data assimilation technique to re-calibrate BGC parameters in NorESM with reduced bias in its ocean physics component.  We calibrated BGC parameters from the monthly climatological error of nitrate, phosphate, and oxygen in a coupled reanalysis of NorESM that assimilates monthly climatology of temperature and salinity.  First, we compare the performance of globally and spatially varying parameter estimations. Both approaches reduce BGC bias obtained with default parameters, even for variables not assimilated in the parameter estimation (such as CO2 fluxes and primary production). While spatial parameter estimation performs locally best, it also increases biases in areas with few observations, and overall performs poorer than global parameter estimation. A second iteration further reduces the bias in the near-surface BGC with global parameter estimation. Finally, we verify the global estimated parameters in a 30-year coupled reanalysis, which assimilates time-varying temperature and salinity observations. This reanalysis reduces error by  10-20% for phosphate, nitrate, oxygen, and dissolved inorganic carbon compared to a reanalysis done with default parameters.  

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.