singh – Bjerknes Climate Prediction Unit

Singh, T., Counillon, F., Tjiputra, J., Wang, Y. 2025: A novel ensemble-based parameter estimation for improving ocean biogeochemistry in an Earth system model. JAMES. https://doi.org/10.1029/2024MS004237

Summary: Estimating ocean biogeochemistry (BGC) parameters in Earth System Models is challenging due to multiple error sources and interlinked parameter sensitivities. Reducing the temperature and salinity 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 tuned to compensate for the physical ocean model biases. We successfully apply the iterative ensemble smoother data assimilation technique to estimate BGC parameters in NorESM with reduced bias in its physical ocean component. We estimate BGC parameters based on the monthly climatological error of nitrate, phosphate, and oxygen in a coupled reanalysis of NorESM that assimilates observed monthly climatology of temperature and salinity. First, we compare the performance of globally uniform and spatially varying parameter estimations. Both approaches reduce BGC bias obtained with default parameters, even for variables not assimilated in the parameter estimation (e.g., 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 assess the performance of global estimated parameters in a 30-year coupled reanalysis produced by assimilating 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.

Link to publication. You are most welcome to contact us or the corresponding author(s) directly, if you have questions.

Singh, T., Counillon, F., Tjiputra, J., Wang Y., El Gharamti, M. 2022: Estimation of Ocean Biogeochemical Parameters in an Earth System Model Using the Dual One Step Ahead Smoother: A Twin Experiment. Front. Mar. Sci. https://doi.org/10.3389/fmars.2022.775394 .

For an easy-to-understand overview of this publication, produced in collaboration with the TRIATLAS project, we recommend starting with this neat article written by Henrike Wilborn, at NERSC: “Making climate models more accurate by improving their tuning“.

Summary: Ocean biogeochemical (BGC) models utilise a large number of poorly-constrained global parameters to mimic unresolved processes and reproduce the observed complex spatio-temporal patterns. Large model errors stem primarily from inaccuracies in these parameters whose optimal values can vary both in space and time. This study aims to demonstrate the ability of ensemble data assimilation (DA) methods to provide high-quality and improved BGC parameters within an Earth system model in an idealized perfect twin experiment framework. We use the Norwegian Climate Prediction Model (NorCPM), which combines the Norwegian Earth System Model with the Dual-One-Step ahead smoothing-based Ensemble Kalman Filter (DOSA-EnKF). We aim to estimate five spatially varying BGC parameters by assimilating salinity and temperature profiles and surface BGC (Phytoplankton, Nitrate, Phosphate, Silicate, and Oxygen) observations in a strongly coupled DA framework—i.e., jointly updating ocean and BGC state-parameters during the assimilation. We show how BGC observations can effectively constrain error in the ocean physics and vice versa. The method converges quickly (less than a year) and largely reduces the errors in the BGC parameters. Some parameter error remains, but the resulting state variable error using the estimated parameters for a free ensemble run and for a reanalysis performs nearly as well as with true parameter values. Optimal parameter values can also be recovered by assimilating climatological BGC observations or sparse observational networks. The findings of this study demonstrate the applicability of the DA approach for tuning the system in a real framework.

Link to publication. You are most welcome to contact us or the corresponding author(s) directly, if you have questions.