Seasonal Arctic Sea Ice Prediction Using a Newly Developed Fully Coupled Regional Model With the Assimilation of Satellite Sea Ice Observations
Abstract To increase our capability to predict Arctic sea ice and climate, we have developed a coupled atmosphere‐sea ice‐ocean model configured for the pan‐Arctic with sufficient flexibility. The Los Alamos Sea Ice Model is coupled with the Weather Research and Forecasting Model and the Regional Oc...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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American Geophysical Union (AGU)
2020-05-01
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| Series: | Journal of Advances in Modeling Earth Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1029/2019MS001938 |
| _version_ | 1818453228032884736 |
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| author | Chao‐Yuan Yang Jiping Liu Shiming Xu |
| author_facet | Chao‐Yuan Yang Jiping Liu Shiming Xu |
| author_sort | Chao‐Yuan Yang |
| collection | DOAJ |
| description | Abstract To increase our capability to predict Arctic sea ice and climate, we have developed a coupled atmosphere‐sea ice‐ocean model configured for the pan‐Arctic with sufficient flexibility. The Los Alamos Sea Ice Model is coupled with the Weather Research and Forecasting Model and the Regional Ocean Modeling System in the Coupled Ocean‐Atmosphere‐Wave‐Sediment Transport modeling system. It is well known that dynamic models used to predict Arctic sea ice at short‐term periods strongly depend on model initial conditions. Parallel Data Assimilation Framework is implemented into the new modeling system to assimilate sea ice observations and generate skillful model initialization, which aid in the prediction procedures. The Special Sensor Microwave Imager/Sounder sea ice concentration, the CyroSat‐2, and Soil Moisture and Ocean Salinity sea ice thickness are assimilated with the localized error subspace transform ensemble Kalman filter. We conduct Arctic sea ice prediction for the melting seasons of 2017 and 2018. Predictions with improved initial sea ice conditions show reasonable sea ice evolution and small biases in the minimum sea ice extent, although the ice refreezing is delayed. Our prediction experiments suggest that the use of appropriate uncertainty for the observed sea ice thickness can lead to improved spatial distribution of the initial ice thickness and thus the predicted sea ice distribution. Our new modeling system initialized by the output of the National Centers for Environmental Prediction Climate Forecast System seasonal forecasts with data assimilation can significantly increase the sea ice prediction skills in sea ice extent for the entire Arctic as well as in the Northern Sea Route compared with the predictions by the National Centers for Environmental Prediction Climate Forecast System. |
| first_indexed | 2024-12-14T21:35:39Z |
| format | Article |
| id | doaj.art-2f65f2d1901e4a1ca10c5ccd39cf3a25 |
| institution | Directory Open Access Journal |
| issn | 1942-2466 |
| language | English |
| last_indexed | 2024-12-14T21:35:39Z |
| publishDate | 2020-05-01 |
| publisher | American Geophysical Union (AGU) |
| record_format | Article |
| series | Journal of Advances in Modeling Earth Systems |
| spelling | doaj.art-2f65f2d1901e4a1ca10c5ccd39cf3a252022-12-21T22:46:34ZengAmerican Geophysical Union (AGU)Journal of Advances in Modeling Earth Systems1942-24662020-05-01125n/an/a10.1029/2019MS001938Seasonal Arctic Sea Ice Prediction Using a Newly Developed Fully Coupled Regional Model With the Assimilation of Satellite Sea Ice ObservationsChao‐Yuan Yang0Jiping Liu1Shiming Xu2South China Sea Institution and School of Atmospheric Sciences Sun Yat‐sen University Zhuhai ChinaDepartment of Atmospheric and Environmental Sciences State University of New York at Albany Albany NY USAMinistry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science Tsinghua University Beijing ChinaAbstract To increase our capability to predict Arctic sea ice and climate, we have developed a coupled atmosphere‐sea ice‐ocean model configured for the pan‐Arctic with sufficient flexibility. The Los Alamos Sea Ice Model is coupled with the Weather Research and Forecasting Model and the Regional Ocean Modeling System in the Coupled Ocean‐Atmosphere‐Wave‐Sediment Transport modeling system. It is well known that dynamic models used to predict Arctic sea ice at short‐term periods strongly depend on model initial conditions. Parallel Data Assimilation Framework is implemented into the new modeling system to assimilate sea ice observations and generate skillful model initialization, which aid in the prediction procedures. The Special Sensor Microwave Imager/Sounder sea ice concentration, the CyroSat‐2, and Soil Moisture and Ocean Salinity sea ice thickness are assimilated with the localized error subspace transform ensemble Kalman filter. We conduct Arctic sea ice prediction for the melting seasons of 2017 and 2018. Predictions with improved initial sea ice conditions show reasonable sea ice evolution and small biases in the minimum sea ice extent, although the ice refreezing is delayed. Our prediction experiments suggest that the use of appropriate uncertainty for the observed sea ice thickness can lead to improved spatial distribution of the initial ice thickness and thus the predicted sea ice distribution. Our new modeling system initialized by the output of the National Centers for Environmental Prediction Climate Forecast System seasonal forecasts with data assimilation can significantly increase the sea ice prediction skills in sea ice extent for the entire Arctic as well as in the Northern Sea Route compared with the predictions by the National Centers for Environmental Prediction Climate Forecast System.https://doi.org/10.1029/2019MS001938fully coupled modelsea icedata assimilationseasonal predictions |
| spellingShingle | Chao‐Yuan Yang Jiping Liu Shiming Xu Seasonal Arctic Sea Ice Prediction Using a Newly Developed Fully Coupled Regional Model With the Assimilation of Satellite Sea Ice Observations Journal of Advances in Modeling Earth Systems fully coupled model sea ice data assimilation seasonal predictions |
| title | Seasonal Arctic Sea Ice Prediction Using a Newly Developed Fully Coupled Regional Model With the Assimilation of Satellite Sea Ice Observations |
| title_full | Seasonal Arctic Sea Ice Prediction Using a Newly Developed Fully Coupled Regional Model With the Assimilation of Satellite Sea Ice Observations |
| title_fullStr | Seasonal Arctic Sea Ice Prediction Using a Newly Developed Fully Coupled Regional Model With the Assimilation of Satellite Sea Ice Observations |
| title_full_unstemmed | Seasonal Arctic Sea Ice Prediction Using a Newly Developed Fully Coupled Regional Model With the Assimilation of Satellite Sea Ice Observations |
| title_short | Seasonal Arctic Sea Ice Prediction Using a Newly Developed Fully Coupled Regional Model With the Assimilation of Satellite Sea Ice Observations |
| title_sort | seasonal arctic sea ice prediction using a newly developed fully coupled regional model with the assimilation of satellite sea ice observations |
| topic | fully coupled model sea ice data assimilation seasonal predictions |
| url | https://doi.org/10.1029/2019MS001938 |
| work_keys_str_mv | AT chaoyuanyang seasonalarcticseaicepredictionusinganewlydevelopedfullycoupledregionalmodelwiththeassimilationofsatelliteseaiceobservations AT jipingliu seasonalarcticseaicepredictionusinganewlydevelopedfullycoupledregionalmodelwiththeassimilationofsatelliteseaiceobservations AT shimingxu seasonalarcticseaicepredictionusinganewlydevelopedfullycoupledregionalmodelwiththeassimilationofsatelliteseaiceobservations |