Estimation of Ocean Biogeochemical Parameters in an Earth System Model Using the Dual One Step Ahead Smoother: A Twin Experiment

Tarkeshwar Singh,François Counillon,Mohamad El Gharamti,Jerry Tjiputra,Yiguo Wang

doi:10.3389/fmars.2022.775394

Abstract

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.

Highlights

Ocean biogeochemistry (BGC) is an important component of an Earth system model (ESM) for simulating the anthropogenic carbon sinks across the air-sea interface (e.g., Marotzke et al, 2017; Tjiputra et al, 2020)
These uncertainties are associated with empirical parameterisations of the biogeochemical inter-actions, which are linked to the complexities and imperfect descriptions of the ocean physical environment that drives the biological process, among others
We work in a perfect model framework and we are interested in how well the error of the state variables is constrained by different observation networks

Summary

Introduction

Ocean biogeochemistry (BGC) is an important component of an Earth system model (ESM) for simulating the anthropogenic carbon sinks across the air-sea interface (e.g., Marotzke et al, 2017; Tjiputra et al, 2020). The accuracy of biological and chemical process representations in ESMs is crucial for simulating the BGC state and variability as realistically as possible. The uncertainty becomes more evident at regional scales (Vancoppenolle et al, 2013), hindering their application for regional impact studies. These uncertainties are associated with empirical parameterisations of the biogeochemical inter-actions, which are linked to the complexities and imperfect descriptions of the ocean physical environment that drives the biological process, among others. Many studies have proven that resolving space and/or time varying BGC parameters is more relevant in the context of biogeochemical modeling (e.g., Losa et al, 2003; Tjiputra et al, 2007; Mattern et al, 2012; Roy et al, 2012; Doron et al, 2013)

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