DOI: 10.1002/2016GL070966
论文题名: Snow data assimilation-constrained land initialization improves seasonal temperature prediction
作者: Lin P. ; Wei J. ; Yang Z.-L. ; Zhang Y. ; Zhang K.
刊名: Geophysical Research Letters
ISSN: 0094-8421
EISSN: 1944-8152
出版年: 2016
卷: 43, 期: 21 起始页码: 11423
结束页码: 11432
语种: 英语
英文关键词: GRACE
; land-atmosphere interactions
; MODIS
; seasonal climate prediction
; snow data assimilation
; Tibetan Plateau
Scopus关键词: Climatology
; Digital storage
; Forecasting
; Image reconstruction
; Radiometers
; Satellite imagery
; Data assimilation
; GRACE
; Land atmosphere interaction
; MODIS
; Seasonal climate prediction
; Tibetan Plateau
; Snow
英文摘要: 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. ©2016. American Geophysical Union. All Rights Reserved. URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84999025141&doi=10.1002%2f2016GL070966&partnerID=40&md5=ec729c5568d591c71794bc36a88b11d0
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/9384
Appears in Collections: 科学计划与规划 气候变化与战略
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作者单位: Jackson School of Geosciences, University of Texas at Austin, Austin, TX, United States
Recommended Citation:
Lin P.,Wei J.,Yang Z.-L.,et al. Snow data assimilation-constrained land initialization improves seasonal temperature prediction[J]. Geophysical Research Letters,2016-01-01,43(21).