Pierce David W, Barnett Tim P, Santer Benjamin D, Gleckler Peter J
Division of Climate, Atmospheric Sciences, and Physical Oceanography, Scripps Institution of Oceanography, La Jolla, CA 92093, USA.
Proc Natl Acad Sci U S A. 2009 May 26;106(21):8441-6. doi: 10.1073/pnas.0900094106. Epub 2009 May 13.
Regional or local climate change modeling studies currently require starting with a global climate model, then downscaling to the region of interest. How should global models be chosen for such studies, and what effect do such choices have? This question is addressed in the context of a regional climate detection and attribution (D&A) study of January-February-March (JFM) temperature over the western U.S. Models are often selected for a regional D&A analysis based on the quality of the simulated regional climate. Accordingly, 42 performance metrics based on seasonal temperature and precipitation, the El Nino/Southern Oscillation (ENSO), and the Pacific Decadal Oscillation are constructed and applied to 21 global models. However, no strong relationship is found between the score of the models on the metrics and results of the D&A analysis. Instead, the importance of having ensembles of runs with enough realizations to reduce the effects of natural internal climate variability is emphasized. Also, the superiority of the multimodel ensemble average (MM) to any 1 individual model, already found in global studies examining the mean climate, is true in this regional study that includes measures of variability as well. Evidence is shown that this superiority is largely caused by the cancellation of offsetting errors in the individual global models. Results with both the MM and models picked randomly confirm the original D&A results of anthropogenically forced JFM temperature changes in the western U.S. Future projections of temperature do not depend on model performance until the 2080s, after which the better performing models show warmer temperatures.
区域或局部气候变化建模研究目前需要从全球气候模型开始，然后向下缩放至感兴趣的区域。对于此类研究，应如何选择全球模型，以及这些选择会产生什么影响？在美国西部1月至2月至3月（JFM）气温的区域气候检测与归因（D&A）研究背景下探讨了这个问题。在区域D&A分析中，模型通常根据模拟区域气候的质量来选择。因此，构建了基于季节温度和降水、厄尔尼诺/南方涛动（ENSO）以及太平洋年代际涛动的42个性能指标，并将其应用于21个全球模型。然而，在指标上模型的得分与D&A分析结果之间未发现强相关性。相反，强调了进行具有足够实现次数的集合运行以减少自然内部气候变率影响的重要性。此外，在研究平均气候的全球研究中已发现的多模型集合平均（MM）相对于任何单个模型的优越性，在这项包括变率测量的区域研究中也是如此。有证据表明，这种优越性很大程度上是由各个全球模型中抵消误差的消除所导致的。MM和随机选择的模型的结果均证实了美国西部JFM温度变化人为强迫的原始D&A结果。直到2080年代，温度的未来预测都不依赖于模型性能，在此之后，表现较好的模型显示温度更高。
