Estimating parameters in a sea ice model using an ensemble Kalman filter

Yong-Fei Zhang,Kevin D Raeder,Timothy J Hoar,Nancy S Collins,Jeffrey L Anderson,Edward Blanchard-Wrigglesworth,Cecilia M Bitz

doi:10.5194/tc-15-1277-2021

Abstract

Abstract. Uncertain or inaccurate parameters in sea ice models influence seasonal predictions and climate change projections in terms of both mean and trend. We explore the feasibility and benefits of applying an ensemble Kalman filter (EnKF) to estimate parameters in the Los Alamos sea ice model (CICE). Parameter estimation (PE) is applied to the highly influential dry snow grain radius and combined with state estimation in a series of perfect model observing system simulation experiments (OSSEs). Allowing the parameter to vary in space improves performance along the sea ice edge but degrades in the central Arctic compared to requiring the parameter to be uniform everywhere, suggesting that spatially varying parameters will likely improve PE performance at local scales and should be considered with caution. We compare experiments with both PE and state estimation to experiments with only the latter and have found that the benefits of PE mostly occur after the data assimilation period, when no observations are available to assimilate (i.e., the forecast period), which suggests PE's relevance for improving seasonal predictions of Arctic sea ice.

Highlights

Arctic sea ice has undergone rapid decline in recent decades in all seasons (e.g., Stroeve et al, 2012; Serreze and Stroeve, 2015)
We explore the benefits of sea ice thickness (SIT) observations on sea ice parameter estimation and advocate the need to extend the data coverage of SIT observations into late spring and summer, which is possible in ICESat-2 (Kwok et al, 2020)
The default Data Assimilation Research Testbed (DART)/CICE framework is only used for state estimation; we extend DART/CICE to include parameter estimation in this study

Summary

Introduction

Arctic sea ice has undergone rapid decline in recent decades in all seasons (e.g., Stroeve et al, 2012; Serreze and Stroeve, 2015). Previous studies have demonstrated the importance of accurate initial conditions, especially SIT, in predicting Arctic sea ice extent (Day et al, 2014). Like other components of Earth system models, can suffer large uncertainties originating from uncertain parameters. Urrego-Blanco et al (2015) conducted an uncertainty quantification study of CICE5 and ranked the parameters based on the sensitivities of model predictions to a list of parameters. This work provides guidance on which parameters could be estimated using an objective method and during which seasons Their findings suggest that the estimates of the Arctic sea ice area and extent are especially sensitive to certain parameters (e.g., snow conductivity and snow grain size) in summer.

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Results

Conclusion