使用贝叶斯模型平均法估算CMIP模式的气候敏感性
Estimating Climate Sensitivity Using Bayesian Model Averaging for CMIP Models
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中文总结 AI 辅助
该研究采用贝叶斯模型平均法（BMA），基于CMIP6的37个气候模式，结合观测数据估算TCRE，得出的TCRE范围与IPCC AR6重叠但均值更高、标准差更低，2100年变暖预测更明确且不确定性更小，参数不确定性为主要方差来源。
中文摘要 AI 辅助
瞬态气候响应对累积二氧化碳排放量（TCRE）是将温室气体排放与全球温度变化关联并为气候政策提供依据的关键指标。然而，现有的TCRE估算往往依赖主观模型选择或假定的敏感性范围，对观测数据的验证有限。我们开发了一种完全基于统计数据驱动的方法，采用贝叶斯模型平均法（BMA）估算TCRE，该方法使用了耦合模式比较计划第六阶段（CMIP6）的37个气候模式，根据其与观测温度数据的一致性对模式进行加权。与政府间气候变化专门委员会（IPCC）第六次评估报告（AR6）的TCRE估算相比，我们的BMA方法得出的很可能范围（90%区间）与AR6的范围有大量重叠，但均值更高、标准差更低。由此得出的到2100年的全球温度变化预测显示，与一些现有方法相比，变暖程度略高且不确定性更小。使用统计建模方法使该方法更易于验证，并能根据来源分解不确定性。样本外预测验证表明该方法校准良好，方差分解显示模式参数不确定性是预测方差的主要来源。
英文摘要
The Transient Climate Response to cumulative CO2 Emissions (TCRE) is a key metric for linking greenhouse gas emissions to global temperature change and informing climate policy. However, extant estimates of the TCRE often depend on subjective model selection or assumed sensitivity ranges, with limited validation against observed data. We develop a fully statistical data-driven approach using a Bayesian Model Averaging (BMA) approach to estimate the TCRE. This uses 37 climate models from the Coupled Model Intercomparison Project phase 6 (CMIP6), weighted according to their consistency with observed temperature data. Compared to the Intergovernmental Panel on Climate Change (IPCC)'s Sixth Assessment Report (AR6) TCRE estimate, our BMA approach yields a very likely range (90% interval) that overlaps substantially with that from the AR6, but with a higher mean and a lower standard deviation. The resulting projections of global temperature change to 2100 show somewhat higher warming and less uncertainty than some current methods. The use of statistical modeling methods makes it easier to validate the approach and to partition the uncertainty according to its sources. Out-of-sample predictive validation shows the method to be well calibrated. Variance decomposition shows model parameter uncertainty to be a main source of projection variance.
