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Snow data assimilation‐constrained land initialization improves seasonal temperature prediction
Author(s) -
Lin Peirong,
Wei Jiangfeng,
Yang ZongLiang,
Zhang Yongfei,
Zhang Kai
Publication year - 2016
Publication title -
geophysical research letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.007
H-Index - 273
eISSN - 1944-8007
pISSN - 0094-8276
DOI - 10.1002/2016gl070966
Subject(s) - snow , environmental science , climatology , northern hemisphere , data assimilation , initialization , latitude , moderate resolution imaging spectroradiometer , high latitude , seasonality , climate model , atmospheric sciences , climate change , meteorology , satellite , geology , geography , aerospace engineering , statistics , oceanography , mathematics , geodesy , computer science , engineering , programming language
Abstract We present the first systematic study to quantify the impact of land initialization on seasonal temperature prediction in the Northern Hemisphere, emphasizing the role of land snow data assimilation (DA). Three suites of ensemble seasonal integrations are conducted for coupled land‐atmosphere runs. The land component is initialized using datasets from (1) no DA, (2) assimilating Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover fraction (SCF), and (3) assimilating both MODIS SCF and Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage. Results show that snow DA improves temperature predictions especially in the Tibetan Plateau (by 5–20%) and high latitudes. Improvements at low latitudes are seen immediately and last up to 60 days, whereas improvements at high latitudes only appear later in transitional seasons. At high latitudes, assimilating GRACE data results in marked and prolonged improvements (by ~25%) due to large initial snow mass changes. This study has great implications for future land DA and seasonal climate prediction studies.