| Using Statistical Downscaling to Quantify the GCM-Related Uncertainty in Regional Climate Change Scenarios： A Case Study of Swedish Precipitation |
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| 引用本文： | Deliang CHEN,Christine ACHBERGER,Jouni R¨AIS¨ANEN,Cecilia HELLSTR¨OM.Using Statistical Downscaling to Quantify the GCM-Related Uncertainty in Regional Climate Change Scenarios： A Case Study of Swedish Precipitation[J].大气科学进展,2006,23(1):54-60. |
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| 作者姓名： | Deliang CHEN Christine ACHBERGER Jouni R¨AIS¨ANEN Cecilia HELLSTR¨OM |
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| 作者单位： | [1]Earth Sciences Centre, Gothenburg University, Gothenburg, Sweden [2]Laboratory for Climate Studies/National Climate Center, China Meteorological Administration, Beijing, China [3]Department of Atmospheric Sciences, Univarsity of Helsinki, Finland |
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| 基金项目： | the Chinese Academy of Sciences，the China Meteorological Administration，SMHI |
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| 摘 要： | There are a number of sources of uncertainty in regional climate change scenarios. When statistical downscaling is used to obtain regional climate change scenarios, the uncertainty may originate from the uncertainties in the global climate models used, the skill of the statistical model, and the forcing scenarios applied to the global climate model. The uncertainty associated with global climate models can be evaluated by examining the differences in the predictors and in the downscaled climate change scenarios based on a set of different global climate models. When standardized global climate model simulations such as the second phase of the Coupled Model Intercomparison Project （CMIP2） are used, the difference in the downscaled variables mainly reflects differences in the climate models and the natural variability in the simulated climates. It is proposed that the spread of the estimates can be taken as a measure of the uncertainty associated with global climate models. The proposed method is applied to the estimation of global-climate-model-related uncertainty in regional precipitation change scenarios in Sweden. Results from statistical downscaling based on 17 global climate models show that there is an overall increase in annual precipitation all over Sweden although a considerable spread of the changes in the precipitation exists. The general increase can be attributed to the increased large-scale precipitation and the enhanced westerly wind. The estimated uncertainty is nearly independent of region. However, there is a seasonal dependence. The estimates for winter show the highest level of confidence, while the estimates for summer show the least.
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| 关 键 词： | 瑞典 区域气候变化 全球气候模型 不确定性 |
| 收稿时间： | 2005-01-25 |
| 修稿时间： | 2005-08-11 |
Using statistical downscaling to quantify the GCM-related uncertainty in regional climate change scenarios: A case study of Swedish precipitation |
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| Deliang Chen,Christine Achberger,Jouni R?is?nen,Cecilia Hellstr?m.Using statistical downscaling to quantify the GCM-related uncertainty in regional climate change scenarios: A case study of Swedish precipitation[J].Advances in Atmospheric Sciences,2006,23(1):54-60. |
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| Authors: | Deliang Chen Christine Achberger Jouni Räisänen Cecilia Hellström |
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| Institution: | Earth Sciences Centre, Gothenburg University, Gothenburg, Sweden, Laboratory for Climate Studies/National Climate Center, China Meteorological Administration, Beijing, China,Earth Sciences Centre, Gothenburg University, Gothenburg, Sweden,Department of Atmospheric Sciences, University of Helsinki, Finland,Earth Sciences Centre, Gothenburg University, Gothenburg, Sweden |
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| Abstract: | There are a number of sources of uncertainty in regional climate change scenarios. When statistical downscaling is used to obtain regional climate change scenarios, the uncertainty may originate from the uncertainties in the global climate models used, the skill of the statistical model, and the forcing scenarios applied to the global climate model. The uncertainty associated with global climate models can be evaluated by examining the differences in the predictors and in the downscaled climate change scenarios based on a set of different global climate models. When standardized global climate model simulations such as the second phase of the Coupled Model Intercomparison Project (CMIP2) are used, the difference in the downscaled variables mainly reflects differences in the climate models and the natural variability in the simulated climates. It is proposed that the spread of the estimates can be taken as a measure of the uncertainty associated with global climate models. The proposed method is applied to the estimation of global-climate-model-related uncertainty in regional precipitation change scenarios in Sweden. Results from statistical downscaling based on 17 global climate models show that there is an overall increase in annual precipitation all over Sweden although a considerable spread of the changes in the precipitation exists. The general increase can be attributed to the increased large-scale precipitation and the enhanced westerly wind. The estimated uncertainty is nearly independent of region. However, there is a seasonal dependence. The estimates for winter show the highest level of confidence, while the estimates for summer show the least. |
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| Keywords: | Statistical downscaling global climate model climate change scenario uncertainty |
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