We use a methodological framework exploiting the power of large ensembles to evaluate how well ten coupled climate models represent the internal variability and response to external forcings in observed historical surface temperatures. This evaluation framework allows us to directly attribute discrepancies between models and observations to biases in the simulated internal variability or forced response, without relying on assumptions to separate these signals in observations. The largest discrepancies result from the overestimated forced warming in some models during recent decades. In contrast, models do not systematically over- or underestimate internal variability in global mean temperature. On regional scales, all models misrepresent surface temperature variability over the Southern Ocean, while overestimating variability over land-surface areas, such as the Amazon and South Asia, and high-latitude oceans. Our evaluation shows that MPI-GE, followed by GFDL-ESM2M and CESM-LE offer the best global and regional representation of both the internal variability and forced response in observed historical temperatures.

我们使用一种方法论框架，利用大型集合的力量来评估十个耦合气候模型在观察到的历史地表温度中代表内部变率和对外部强迫的响应的程度。该评估框架允许我们将模型和观察结果之间的差异直接归因于模拟内部可变性或强制响应中的偏差，而不依赖于在观察中分离这些信号的假设。最大的差异是由于近几十年来某些模型高估了强迫变暖。相比之下，模型不会系统地高估或低估全球平均温度的内部变化。在区域尺度上，所有模型都错误地反映了南大洋的表面温度变化，同时高估了陆地表面区域的变异性，例如亚马逊和南亚以及高纬度海洋。我们的评估表明，MPI-GE，其次是 GFDL-ESM2M 和 CESM-LE，提供了观察到的历史温度中内部变异性和强迫响应的最佳全球和区域代表性。