Program for Climate Model Diagnosis and Intercomparison (PCMDI), Lawrence Livermore National Laboratory, Livermore, CA 94550, USA.
Proc Natl Acad Sci U S A. 2013 Jan 2;110(1):26-33. doi: 10.1073/pnas.1210514109. Epub 2012 Nov 29.
We perform a multimodel detection and attribution study with climate model simulation output and satellite-based measurements of tropospheric and stratospheric temperature change. We use simulation output from 20 climate models participating in phase 5 of the Coupled Model Intercomparison Project. This multimodel archive provides estimates of the signal pattern in response to combined anthropogenic and natural external forcing (the fingerprint) and the noise of internally generated variability. Using these estimates, we calculate signal-to-noise (S/N) ratios to quantify the strength of the fingerprint in the observations relative to fingerprint strength in natural climate noise. For changes in lower stratospheric temperature between 1979 and 2011, S/N ratios vary from 26 to 36, depending on the choice of observational dataset. In the lower troposphere, the fingerprint strength in observations is smaller, but S/N ratios are still significant at the 1% level or better, and range from three to eight. We find no evidence that these ratios are spuriously inflated by model variability errors. After removing all global mean signals, model fingerprints remain identifiable in 70% of the tests involving tropospheric temperature changes. Despite such agreement in the large-scale features of model and observed geographical patterns of atmospheric temperature change, most models do not replicate the size of the observed changes. On average, the models analyzed underestimate the observed cooling of the lower stratosphere and overestimate the warming of the troposphere. Although the precise causes of such differences are unclear, model biases in lower stratospheric temperature trends are likely to be reduced by more realistic treatment of stratospheric ozone depletion and volcanic aerosol forcing.
我们进行了一项多模式检测和归因研究，使用气候模型模拟输出和基于卫星的对流层和平流层温度变化测量结果。我们使用参与耦合模式比较计划第五阶段的 20 个气候模型的模拟输出。这个多模式档案提供了对人为和自然外部强迫综合响应的信号模式（指纹）以及内部产生的变异性噪声的估计。利用这些估计值，我们计算了信号与噪声（S/N）比，以量化观测结果中指纹相对于自然气候噪声指纹强度的强度。对于 1979 年至 2011 年之间的低层平流层温度变化，S/N 比在 26 到 36 之间变化，具体取决于观测数据集的选择。在对流层下部，观测到的指纹强度较小，但 S/N 比仍在 1%或更高水平显著，范围从 3 到 8。我们没有发现这些比率因模型变异性误差而被虚假夸大的证据。在去除所有全球平均信号后，在涉及对流层温度变化的 70%测试中，模型指纹仍然可以识别。尽管模型和观测到的大气温度变化的地理模式在大尺度特征上存在这种一致性，但大多数模型都无法复制观测到的变化规模。平均而言，分析的模型低估了观测到的低层平流层冷却，高估了对流层变暖。尽管造成这种差异的确切原因尚不清楚，但对平流层臭氧消耗和火山气溶胶强迫的更现实处理可能会降低模型在低层平流层温度趋势上的偏差。
