标题： 美国县一级气候预测的概率加权集合用于气候风险分析 显示英文标题

标题： Probability-weighted ensembles of U.S. county-level climate projections for climate risk analysis

摘要： 定量评估气候变化风险需要一种构建物理气候参数变化的概率时间序列的方法。 在这里，我们开发了两种这样的方法，替代/模型混合集合（SMME）和蒙特卡洛模式/残差（MCPR），并将它们应用于构建美国每个县在21世纪温度和降水变化的联合概率密度函数（PDF）。 这两种方法都产生$likely$ (67% 概率) 的温度和降水预测，与政府间气候变化专门委员会对等权重耦合模型比较项目5 (CMIP5) 集成的解释一致，但还提供了包括尾部估计在内的完整PDF。 例如，这两种方法表明，在代表性浓度路径 (RCP) 8.5下，连续美国地区至少升温8$^\circ$C的可能性为5%。SMME和MCPR预测的方差分解表明，在21世纪早期，背景变异性主导了不确定性，而在21世纪后半叶，强迫驱动的变化出现。 通过使用线性回归将CMIP5预测分为非强制性和强制性成分，这些方法从现有的CMIP5预测中生成非强制性变异性的估计，而无需使用单一GCM的多个实现，从而避免计算成本高昂的操作。 显示英文摘要

摘要： Quantitative assessment of climate change risk requires a method for constructing probabilistic time series of changes in physical climate parameters. Here, we develop two such methods, Surrogate/Model Mixed Ensemble (SMME) and Monte Carlo Pattern/Residual (MCPR), and apply them to construct joint probability density functions (PDFs) of temperature and precipitation change over the 21st century for every county in the United States. Both methods produce $likely$ (67% probability) temperature and precipitation projections consistent with the Intergovernmental Panel on Climate Change's interpretation of an equal-weighted Coupled Model Intercomparison Project 5 (CMIP5) ensemble, but also provide full PDFs that include tail estimates. For example, both methods indicate that, under representative concentration pathway (RCP) 8.5, there is a 5% chance that the contiguous United States could warm by at least 8$^\circ$C. Variance decomposition of SMME and MCPR projections indicate that background variability dominates uncertainty in the early 21st century, while forcing-driven changes emerge in the second half of the 21st century. By separating CMIP5 projections into unforced and forced components using linear regression, these methods generate estimates of unforced variability from existing CMIP5 projections without requiring the computationally expensive use of multiple realizations of a single GCM.