AI 中文总结
研究针对地球系统模型预测的局限，结合响应理论与生成式机器学习框架，提取低分辨率ESM场对辐射强迫的响应，引导生成模型推断每日全球高分辨率温度和降水预测，提供高时空分辨率长期气候预测，助力详细影响评估和长期气候承诺探索。
AI 中文摘要
地球系统模型（ESM）对气候系统对人为强迫响应的预测对于评估气候变化影响及为适应和缓解政策提供依据至关重要。但因其计算成本高，预测仅针对有限的标准化强迫情景且时间范围有限，时空分辨率低，不确定性难全面量化。近期数据驱动模型虽能高效准确模拟天气预测，但无法外推至未来温室气体浓度。本文结合响应理论与定制生成式机器学习框架应对挑战。提取低分辨率ESM场对辐射强迫的物理强迫响应，引导生成模型推断每日全球高分辨率温度和降水预测。概率方法可跨ESM推广，提供高时空分辨率长期、偏差校正响应，有效填补现有情景差距并将预测延伸至2300年及以后，补充ESM预测，助力详细影响评估和长期气候承诺探索。
英文摘要
Earth system model (ESM) projections of the climate system's response to anthropogenic forcing are central to assess the impacts of climate change and inform adaptation and mitigation policies. However, given their high computational cost, projections are only made for a limited set of standardized forcing scenarios with limited temporal extent, such as the Shared Socioeconomic Pathways (SSPs), the spatiotemporal resolution remains too low for direct impact assessments, and uncertainties cannot be comprehensively quantified. Recent data-driven models offer efficient and accurate high-resolution simulations for weather prediction, but cannot extrapolate to future greenhouse gas concentrations because they cannot capture the responses to unprecedented forcing, limiting their value for climate change projections. Here, we combine response theory with a tailored generative machine learning framework to address this challenge. Our approach extracts the physical forced response to radiative forcing from monthly low-resolution ESM fields, and uses this response to guide a generative model to infer consistent daily global high-resolution temperature and precipitation projections. Our probabilistic approach generalizes across ESMs and provides long-term, bias-corrected responses to radiative forcing at high spatiotemporal resolution. It efficiently generates large ensembles needed for uncertainty quantification, effectively fills the gaps between existing SSPs, and readily extends climate projections to 2300 and beyond. Our framework hence complements ESM projections by providing efficient, stable, and high spatiotemporal resolution long-term climate projection ensembles across emission scenarios, enabling detailed impact assessment and exploration of long-term climate commitment.
