论文标题
使用神经网络高斯过程的多模型合奏分析
Multi-model Ensemble Analysis with Neural Network Gaussian Processes
论文作者
论文摘要
多模型集合分析将来自多个气候模型的信息整合到统一的投影中。但是，基于模型平均的现有集成方法可以稀释细节空间信息，并从重新分辨低分辨率气候模型中产生偏见。我们提出了一种使用高斯过程回归（GPR）具有无限宽的基于神经网络的协方差函数的统计方法，称为NN-GPR。 NN-GPR不需要对模型之间的关系，对通用网格的插值，没有平稳性假设，并且自动降尺度作为其预测算法的一部分。模型实验表明，通过在多个尺度上保留地理空间信号并捕获年间可变性，NN-GPR在表面温度和降水预测下可能高度熟练。我们的预测特别显示出高变异性区域中的准确性和不确定性量化技能，这使我们能够以0.44 $^\ Circ $/50 km的空间分辨率在没有区域气候模型（RCM）的情况下廉价评估尾声行为。对重新分析数据和SSP245强制气候模型的评估表明，NN-GPR与模型集合产生相似的总体气候，同时更好地捕获细节的空间模式。最后，我们将NN-GPR的区域预测与两个RCM进行比较，并表明NN-GPR可以仅使用全局模型数据作为输入与RCM的性能匹配。
Multi-model ensemble analysis integrates information from multiple climate models into a unified projection. However, existing integration approaches based on model averaging can dilute fine-scale spatial information and incur bias from rescaling low-resolution climate models. We propose a statistical approach, called NN-GPR, using Gaussian process regression (GPR) with an infinitely wide deep neural network based covariance function. NN-GPR requires no assumptions about the relationships between models, no interpolation to a common grid, no stationarity assumptions, and automatically downscales as part of its prediction algorithm. Model experiments show that NN-GPR can be highly skillful at surface temperature and precipitation forecasting by preserving geospatial signals at multiple scales and capturing inter-annual variability. Our projections particularly show improved accuracy and uncertainty quantification skill in regions of high variability, which allows us to cheaply assess tail behavior at a 0.44$^\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.
