DOI: 10.1175/JCLI-D-14-00112.1
Scopus记录号: 2-s2.0-84961289701
论文题名: Improved seasonal prediction of temperature and precipitation over land in a high-resolution GFDL climate model
作者: Jia L. ; Yang X. ; Vecchi G.A. ; Gudgel R.G. ; Delworth T.L. ; Rosati A. ; Stern W.F. ; Wittenberg A.T. ; Krishnamurthy L. ; Zhang S. ; Msadek R. ; Kapnick S. ; Underwood S. ; Zeng F. ; Anderson W.G. ; Balaji V. ; Dixon K.
刊名: Journal of Climate
ISSN: 8948755
出版年: 2015
卷: 28, 期: 5 起始页码: 2044
结束页码: 2062
语种: 英语
Scopus关键词: Air
; Atmospheric radiation
; Atmospheric temperature
; Climatology
; Forecasting
; Climate prediction
; Forecast verification/skill
; Geophysical fluid dynamics laboratories
; High-resolution models
; Radiative forcings
; Seasonal forecasting
; Seasonal prediction
; Statistical optimization
; Climate models
; air temperature
; climate modeling
; El Nino-Southern Oscillation
; hindcasting
; precipitation assessment
; radiative forcing
; seasonal variation
; weather forecasting
英文摘要: This study demonstrates skillful seasonal prediction of 2-m air temperature and precipitation over land in a new high-resolution climatemodel developed by the Geophysical Fluid Dynamics Laboratory and explores the possible sources of the skill. The authors employ a statistical optimization approach to identify the most predictable components of seasonal mean temperature and precipitation over land and demonstrate the predictive skill of these components. First, the improved skill of the high-resolution model over the previous lowerresolution model in seasonal prediction of the Niño-3.4 index and other aspects of interest is shown. Then, the skill of temperature and precipitation in the high-resolution model for boreal winter and summer is measured, and the sources of the skill are diagnosed. Last, predictions are reconstructed using a few of the most predictable components to yield more skillful predictions than the raw model predictions. Over three decades of hindcasts, the two most predictable components of temperature are characterized by a component that is likely due to changes in external radiative forcing in borealwinter and summer and anENSO-related pattern in borealwinter. The most predictable components of precipitation in both seasons are very likely ENSO-related. These components of temperature and precipitation can be predicted with significant correlation skill at least 9 months in advance. The reconstructed predictions using only the first few predictable components from the model show considerably better skill relative to observations than raw model predictions. This study shows that the use of refined statistical analysis and a high-resolution dynamical model leads to significant skill in seasonal predictions of 2-m air temperature and precipitation over land. © 2015 American Meteorological Society. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/50461
Appears in Collections: 气候变化事实与影响
There are no files associated with this item.
作者单位: University Corporation for Atmospheric Research, Boulder, CO, United States; NOAA/Geophysical Fluid Dynamics Laboratory, Princeton, NJ, United States; Princeton University, NOAA/Geophysical Fluid Dynamics Laboratory, Princeton, NJ, United States; Dynamics Research Corporation, Andover, MA, United States
Recommended Citation:
Jia L.,Yang X.,Vecchi G.A.,et al. Improved seasonal prediction of temperature and precipitation over land in a high-resolution GFDL climate model[J]. Journal of Climate,2015-01-01,28(5)