Estimating parameters in a sea ice model using an ensemble Kalman filter
| Autor: | Kevin Raeder, Timothy J. Hoar, Yong-Fei Zhang, Jeffrey L. Anderson, Nancy Collins, Edward Blanchard-Wrigglesworth, Cecilia M. Bitz |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
lcsh:GE1-350
geography geography.geographical_feature_category 010504 meteorology & atmospheric sciences Series (mathematics) Estimation theory lcsh:QE1-996.5 Climate change Radius 010502 geochemistry & geophysics Atmospheric sciences 01 natural sciences Arctic ice pack lcsh:Geology Sea ice Dry snow Environmental science Ensemble Kalman filter lcsh:Environmental sciences 0105 earth and related environmental sciences Earth-Surface Processes Water Science and Technology |
| Zdroj: | The Cryosphere, Vol 15, Pp 1277-1284 (2021) |
| ISSN: | 1994-0424 1994-0416 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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