Multi-model ensemble analysis integrates information from multiple climate models into a unified projection. However, existing integration approaches based on model averaging can dilute fine-scale spatial information and incur bias from rescaling low-resolution climate models. We propose a statistical approach, called NN-GPR, using Gaussian process regression (GPR) with an infinitely wide deep neural network based covariance function. NN-GPR requires no assumptions about the relationships between models, no interpolation to a common grid, no stationarity assumptions, and automatically downscales as part of its prediction algorithm. Model experiments show that NN-GPR can be highly skillful at surface temperature and precipitation forecasting by preserving geospatial signals at multiple scales and capturing inter-annual variability. Our projections particularly show improved accuracy and uncertainty quantification skill in regions of high variability, which allows us to cheaply assess tail behavior at a 0.44$^\circ$/50 km spatial resolution without a regional climate model (RCM). Evaluations on reanalysis data and SSP245 forced climate models show that NN-GPR produces similar, overall climatologies to the model ensemble while better capturing fine scale spatial patterns. Finally, we compare NN-GPR's regional predictions against two RCMs and show that NN-GPR can rival the performance of RCMs using only global model data as input.
翻译:然而,基于模型平均值的现有一体化方法可以冲淡微规模的空间信息,并引起低分辨率气候模型的偏差。我们建议采用称为NN-GPR(GPR)的统计方法,使用高斯进程回归(GPR),使用无限宽度的深神经网络共变量功能。NN-GPR不要求假设各种模型之间的关系,不对共同电网进行内插,不作静止假设,以及自动下调尺度作为其预测算法的一部分。模型实验表明,NNNGPR通过在多个规模上保持地理空间信号和捕捉年际变异性,对地表温度和降水量预报具有高度的技巧。我们的预测特别表明,在高变异性区域,精确性和不确定性的量化技能有所提高,使我们能够在没有区域气候模型的情况下,以0.44 ⁇ circ$50公里的空间分辨率来廉价地评估尾部行为。关于再分析数据的评价和SSP245强制气候模型表明,NNGPR能够以更精确、整体的气候模型比标,同时,我们可以更好地将RRCMS-MS-S-S-S-S-S