基于卫星校正的高分辨率和高精度全球地表月平均太阳辐射数据集（1850-2100）

对未来太阳表面辐射（SSR）的准确预估对于评估气候变化的影响和太阳能潜力具有重要意义。然而，来自耦合模式比对项目第6阶段（CMIP6）的模拟显示SSR预估存在显著的不确定性。本研究利用贝叶斯线性回归（BLR）方法综合CMIP6模式预估和ISCCP-ITP-CNN卫星反演数据，建立1850-2100年高质量的SSR月度数据集。选择了5个CMIP6模型，利用BLR方法逐格为每个模型分配权重，计算生成校正后的长期SSR数据集。基于地面观测（1960-2017）的评价表明，合成的SSR优于单个CMIP6模型及其原始多模型均值。校正后的地表太阳辐射空间格局与ISCCP-ITP-CNN（1983-2018）吻合较好。高分辨率（0.1°×0.1°）合成的SSR数据集提供了1850-2100年历史和4种未来情景（SSP126、SSP245、SSP370、SSP585）的月度预测，代表了未来地表太阳辐射变化及其相关的气候影响，该数据集将支持模拟气候变化下的地表过程和太阳能应用。

Accurate projections of future Surface Solar Radiation (SSR) are of great significance for assessing the impacts of climate change and solar energy potential. However, simulations from the Coupled Model Intercomparison Project Phase 6 (CMIP6) reveal significant uncertainties in SSR projections. In this study, we utilized the Bayesian Linear Regression (BLR) method to integrate CMIP6 model projections and ISCCP-ITP-CNN satellite-retrieved data, and developed a high-quality monthly SSR dataset spanning 1850–2100. Five CMIP6 models were selected, and weights were assigned to each model grid-by-grid using the BLR method to generate a corrected long-term SSR dataset. Evaluations based on ground-based observations (1960–2017) demonstrate that the synthesized SSR outperforms individual CMIP6 models and their original multi-model ensemble mean. The spatial pattern of the corrected surface solar radiation shows good consistency with ISCCP-ITP-CNN data (1983–2018). The high-resolution (0.1° × 0.1°) synthesized SSR dataset provides monthly projections for both the historical period (1850–2100) and four future scenarios (SSP126, SSP245, SSP370, SSP585). It represents future changes in surface solar radiation and their associated climate impacts, and will support simulations of terrestrial processes under climate change and solar energy applications.

搜集汇总

背景与挑战

背景概述

该数据集是一个高分辨率（0.1°×0.1°）和高精度的全球地表月平均太阳辐射数据集，覆盖1850年至2100年，包含历史和四种未来情景（SSP126、SSP245、SSP370、SSP585）的月度预测。数据通过贝叶斯线性回归方法综合CMIP6模型和卫星反演数据生成，优于原始模型数据，适用于气候变化影响评估和太阳能潜力研究。