Appropriate statistical frameworks are required to make credible inferences about the future state of the climate from multiple climate models. The spread of projections simulated by different models is often a substantial source of uncertainty. This uncertainty can be reduced by identifying "emergent relationships" between future projections and historical simulations. Estimation of emergent relationships is hampered by unbalanced experimental designs and varying sensitivity of models to input parameters and boundary conditions. The relationship between the climate models and the Earth system is uncertain and requires careful modeling. Observation uncertainty also plays an important role when emergent relationships are exploited to constrain projections of future climate in the Earth system A new Bayesian framework is presented that can constrain projections of future climate using historical observations by exploiting robust estimates of emergent relationships while accounting for observation uncertainty. A detailed theoretical comparison with previous multi-model frameworks is provided. The proposed framework is applied to projecting surface temperature in the Arctic at the end of the 21st century. Projected temperatures in some regions are more than 2C lower when constrained by historical observations. The uncertainty about the climate response is reduced by up to 30% where strong constraints exist.

中文翻译：

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使用来自多个气候模型的观测和模拟来约束对未来气候的预测

需要适当的统计框架才能从多个气候模型中对未来的气候状态做出可信的推断。不同模型模拟的预测分布通常是不确定性的重要来源。通过识别未来预测和历史模拟之间的“紧急关系”，可以减少这种不确定性。不平衡的实验设计和模型对输入参数和边界条件的不同敏感性阻碍了紧急关系的估计。气候模型与地球系统之间的关系是不确定的，需要仔细建模。当利用紧急关系来约束地球系统中未来气候的预测时，观测不确定性也起着重要作用 提出了一个新的贝叶斯框架，该框架可以通过利用紧急关系的稳健估计同时考虑观测不确定性来约束使用历史观测的未来气候预测. 提供了与以前的多模型框架的详细理论比较。所提出的框架用于预测 21 世纪末北极的地表温度。受历史观测的限制，某些地区的预计温度会低 2 摄氏度以上。在存在强约束的情况下，气候响应的不确定性最多可减少 30%。