Ensemble Kalman Filter Parameter Estimation of Ocean Optical Properties for Reduced Biases in a Coupled General Circulation Model. (18th February 2021)

• Record Type: Journal Article
• Authors: Kitsios, V.; Sandery, P.; O'Kane, T. J.; Fiedler, R.
• Is Part Of: Journal of advances in modeling earth systems. Volume 13:Number 2(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-02-18
• Subjects: coupled GCM -- data assimilation -- ensemble Kalman filter -- model bias -- parameter estimation; Geological modeling -- Periodicals; Climatology -- Periodicals; Geochemical modeling -- Periodicals; 551.5011
• DOI: 10.1029/2020MS002252
• Languages: English
• ISSNs: 1942-2466

Abstract

Abstract: Coupled general circulation models (GCM), and their atmospheric, oceanic, land, and sea‐ice components have many parameters. Some parameters determine the numerics of the dynamical core, while others are based on our current understanding of the physical processes being simulated. Many of these parameters are poorly known, often globally defined, and are subject to pragmatic choices arising from a complex interplay between grid resolution and inherent model biases. To address this problem, we use an ensemble transform Kalman filter, to estimate spatiotemporally varying maps of ocean albedo and shortwave radiation e‐folding length scale in a coupled climate GCM. These parameters are designed to minimize the error between short term (3–28 days) forecasts of the climate model and a network of real world atmospheric, oceanic, and sea‐ice observations. The data assimilation system has an improved fit to observations when estimating ocean albedo and shortwave e‐folding length scale either individually or simultaneously. However, only individually estimated maps of shortwave e‐folding length scale are also shown to systematically reduce bias in longer multiyear climate forecasts during an out‐of‐sample period. The bias of the multiyear forecasts is reduced for parameter maps determined from longer DA cycle lengths. Plain Language Summary: Coupled general circulation models have many model parameters, some of which are known with little precision. This contributes to the observed biases and limits to predictability. We address this problem by using an ensemble Kalman filter to systematically and objectively determine selected ocean optical properties that minimize the difference between an ensemble of short term climate forecasts and a network of real world observations of the Earth system. These trained model parameters are shown to reduce the onset of model bias in both short term (3–28 days) forecasts and longer term multiyear forecasts of the climate. Such improved multiyear predictions would potentially enable policy makers to make better informed decisions on water, energy and agricultural infrastructure and planning. Key Points: A 96 member ensemble transform Kalman filter is used to estimate ocean optical parameters in a coupled global climate model The parameter estimation improves the fit of the data assimilation system to a network of real world observations Out‐of‐sample multiyear climate forecasts adopting estimated shortwave penetration depth have reduced model bias