Ensemble Kalman Filter Parameter Estimation of Ocean Optical Properties for Reduced Biases in a Coupled General Circulation Model

• Published in: Journal of Advances in Modeling Earth Systems
• Main Authors: V. Kitsios, P. Sandery, T. J. O’Kane, R. Fiedler
• Format: Article in Journal/Newspaper
• Language: English
• Published: American Geophysical Union (AGU) 2021
• Subjects: coupled GCM; data assimilation; ensemble Kalman filter; model bias; parameter estimation; Physical geography; GB3-5030; Oceanography; GC1-1581; Sea ice
• Online Access: https://doi.org/10.1029/2020MS002252; https://doaj.org/article/389735891c684f0b9d2dd369094d83a3

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.