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| DOI | 10.1016/j.atmosres.2019.04.021 |
| Improving Arctic sea ice seasonal outlook by ensemble prediction using an ice-ocean model | |
| Yang Q.; Mu L.; Wu X.; Liu J.; Zheng F.; Zhang J.; Li C. | |
| 发表日期 | 2019 |
| ISSN | 01698095 |
| 卷号 | 227 |
| 英文摘要 | An ensemble based Sea Ice Seasonal Prediction System (SISPS) is configured towards operationally predicting the Arctic summer sea ice conditions. SISPS runs as a pan-Arctic sea ice-ocean coupled model based on Massachusetts Institute of Technology general circulation model (MITgcm). A 4-month hindcast is carried out by SISPS starting from May 25, 2016. The sea ice-ocean initial fields for each ensemble member are from corresponding restart files from an ensemble data assimilation system that assimilates near-real-time Special Sensor Microwave Imager Sounder (SSMIS) sea ice concentration, Soil Moisture and Ocean Salinity (SMOS) and CryoSat-2 ice thickness. An ensemble of 11 time lagged operational atmospheric forcing from the National Center for Environmental Prediction (NCEP) climate forecast system model version 2 (CFSv2) is used to drive the ice-ocean model. Comparing with the satellite based sea ice observations and reanalysis data, the SISPS prediction shows good agreement in the evolution of sea ice extent and thickness, and performs much better than the CFSv2 operational sea ice prediction. This can be largely attributed to the initial conditions that we used in assimilating the SMOS and CryoSat-2 sea ice thickness data, thereafter reduces the initial model bias in the basin wide sea ice thickness, while in CFSv2 there is no sea ice thickness assimilation. Furthermore, comparisons with sea ice predictions driven by deterministic forcings demonstrate the importance of employing an ensemble approach to capture the large prediction uncertainty in Arctic summer. The sensitivity experiments also show that the sea ice thickness initialization that has a long-term memory plays a more important role than sea ice concentration and sea ice extent initialization on seasonal sea ice prediction. This study shows a good potential to implement Arctic sea ice seasonal prediction using the current configuration of ensemble system. © 2019 Elsevier B.V. |
| 英文关键词 | Data assimilation; Ensemble forecast; Sea ice thickness; Seasonal sea ice prediction |
| URL | https://www2.scopus.com/inward/record.uri?eid=2-s2.0-85064890389&doi=10.1016%2fj.atmosres.2019.04.021&partnerID=40&md5=ef65e4a1d4c2c60cf7aec0cb7723ccd4 |
| 语种 | 英语 |
| scopus关键词 | Climate models; Climatology; Digital storage; Forecasting; Microwave sensors; Oceanography; Soil moisture; Data assimilation; Ensemble data assimilation; Ensemble forecasts; Massachusetts Institute of Technology; National center for environmental predictions; Sea-ice thickness; Soil Moisture and Ocean Salinity (SMOS); Special sensor microwave imagers; Sea ice; data assimilation; ensemble forecasting; ice thickness; ice-ocean interaction; prediction; sea ice; seasonality; summer; Arctic Ocean |
| 来源期刊 | Atmospheric Research
 |
| 来源机构 | 中国科学院西北生态环境资源研究院 |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://gcip.llas.ac.cn/handle/2XKMVOVA/77335 |
| 推荐引用方式 GB/T 7714 | Yang Q.； Mu L.； Wu X.； Liu J.； Zheng F.； Zhang J.； Li C.. Improving Arctic sea ice seasonal outlook by ensemble prediction using an ice-ocean model[J]. 中国科学院西北生态环境资源研究院,2019,227. |
| APA | Yang Q.； Mu L.； Wu X.； Liu J.； Zheng F.； Zhang J.； Li C..(2019).Improving Arctic sea ice seasonal outlook by ensemble prediction using an ice-ocean model.Atmospheric Research,227. |
| MLA | Yang Q.； Mu L.； Wu X.； Liu J.； Zheng F.； Zhang J.； Li C.."Improving Arctic sea ice seasonal outlook by ensemble prediction using an ice-ocean model".Atmospheric Research 227(2019). |
| 条目包含的文件 | 条目无相关文件。 | |||||
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