Uncertainty in climate change projections: the role of internal variability
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- 作者：Clara Deser (1) cdeser@ucar.edu
Adam Phillips (1)
Vincent Bourdette (12)
Haiyan Teng (1) - 关键词：Climate change – ; Uncertainty – ; Annular modes – ; Coupled climate models – ; Climate detection and attribution
- 刊名：Climate Dynamics
- 出版时间：February 2012
- 出版年：2012
- 期刊代码：23_0930-7575
- 类别：ear
- 卷：38
- 期：3-4
- 页码：527-546
- 数据来源：sp
摘要
Uncertainty in future climate change presents a key challenge for adaptation planning. In this study, uncertainty arising from internal climate variability is investigated using a new 40-member ensemble conducted with the National Center for Atmospheric Research Community Climate System Model Version 3 (CCSM3) under the SRES A1B greenhouse gas and ozone recovery forcing scenarios during 2000–2060. The contribution of intrinsic atmospheric variability to the total uncertainty is further examined using a 10,000-year control integration of the atmospheric model component of CCSM3 under fixed boundary conditions. The global climate response is characterized in terms of air temperature, precipitation, and sea level pressure during winter and summer. The dominant source of uncertainty in the simulated climate response at middle and high latitudes is internal atmospheric variability associated with the annular modes of circulation variability. Coupled ocean-atmosphere variability plays a dominant role in the tropics, with attendant effects at higher latitudes via atmospheric teleconnections. Uncertainties in the forced response are generally larger for sea level pressure than precipitation, and smallest for air temperature. Accordingly, forced changes in air temperature can be detected earlier and with fewer ensemble members than those in atmospheric circulation and precipitation. Implications of the results for detection and attribution of observed climate change and for multi-model climate assessments are discussed. Internal variability is estimated to account for at least half of the inter-model spread in projected climate trends during 2005–2060 in the CMIP3 multi-model ensemble.