Downscaled_CMIP3_Temperature_Precipitation_Projections_over_a_hydroclimaic_transect_of_Michigan

Climate change will affect global temperatures and the distribution and amount of precipitation, which are expected to impact regional hydrology and water resources in many parts of the world. It is therefore vital to quantify characteristics of the change and the corresponding uncertainty. A substantial amount of recent research has relied on climate projections obtained with General Circulation Models (GCMs) to assess climate change. However, such modeling results typically carry biases that must be reduced in some optimal fashion before any conclusions about robustness of climate change can be drawn. To minimize model- and scenario-specific biases, we combined information provided by the 3rd phase of the Coupled Model Intercomparison Project database with a Bayesian Weighted Averaging method. Specifically, the results of 12 GCMs for three emission scenarios B1, A1B, and A2 were downscaled for mid- (2046–2065) and end-century (2081–2100) intervals, at six WebMET locations that represent a hydroclimatic transect of Michigan. Furthermore, hourly results of future climate are generated by an advanced weather generator using the information from the combine GCMs ensemble.

气候变化将对全球温度以及降水的分布与量产生影响，这些影响预计将对世界许多地区的区域水文和水资源造成冲击。因此，量化变化的特征及其相应的不确定性至关重要。大量近期研究依赖通用环流模型（GCMs）获得的气候预测来评估气候变化。然而，此类模型结果通常存在偏差，这些偏差必须在某种优化方式下得到降低，才能得出关于气候变化稳健性的任何结论。为了最小化模型和情景特定的偏差，我们结合了耦合模型互评项目数据库第三阶段提供的信息，并采用贝叶斯加权平均法。具体而言，对三个排放情景B1、A1B和A2下的12个GCMs的结果进行了降尺度处理，适用于中期（2046–2065）和世纪末（2081–2100）时段，以及六个WebMET地点，这些地点代表了密歇根州的水文气候横断面。此外，通过利用综合GCMs集合的信息，由先进的天气发生器生成了未来气候的每小时结果。