论文标题
具有多元基础图形套索的模型投影的统计缩小
Statistical Downscaling of Model Projections with Multivariate Basis Graphical Lasso
论文作者
论文摘要
我们使用多元基础图形套索（BGL）描述了一种改进的地球科学应用统计降低方法。我们使用CMIP6 Earth System模型的海面温度（SST）预测的案例研究来证明我们的方法，该模型直接应用于研究珊瑚礁漂白的多年数量投影。我们发现，对于大型数据集，BGL降尺度方法是可以计算处理的，而平方的预测误差大约比基于最新的基于最新的基于基于的统计统计降低降低方法低约8％。最后，与当前可用的大多数方法不同，BGL缩减会产生不确定性估计。我们的新方法可以应用于可用的相应高分辨率观察数据的任何模型输出变量。
We describe an improved statistical downscaling method for Earth science applications using multivariate Basis Graphical Lasso (BGL). We demonstrate our method using a case study of sea surface temperature (SST) projections from CMIP6 Earth system models, which has direct applications for studies of multi-decadal projections of coral reef bleaching. We find that the BGL downscaling method is computationally tractable for large data sets, and that mean squared predictive error is roughly 8% lower than the current state-of-the-art interpolation-based statistical downscaling method. Finally, unlike most ofthe currently available methods, BGL downscaling produces uncertainty estimates. Our novel method can be applied to any model output variable for which corresponding higher-resolution observational data is available.
