用生成式框架高效预测气候变化极端事件统计，突破传统模型计算瓶颈。
GEN2: A Generative Prediction-Correction Framework for Long-time Emulations of Spatially-Resolved Climate Extremes
- 先用高斯模拟预测，再用机器学习校正非高斯偏差。
- 仅需单个训练场景，即可准确预测多种未来气候下的极端事件统计。
- 适合气候风险评估与长期气候模拟研究者使用。
准确量化气候变化极端事件风险需要在多种排放情景下生成大量气候模拟结果，这对传统地球系统模型而言计算成本极高。本文提出GEN2，一种生成式预测-校正框架，用于高效且精准地预测极端事件统计特征。预测阶段采用条件高斯模拟器，后续通过非高斯机器学习模型进行校正。该模型在参考数据与向参考场强迫的模拟场配对上训练，确保对混沌敏感性具有鲁棒性。首先在历史ERA5数据上验证模型精度，随后展示其在多种未来气候情景下的外推能力。当仅基于单一升温情景的一个实现训练时，模型仍能准确预测不同情景下的极端事件统计，成功外推至训练数据分布之外。
原文摘要 · Abstract (English)
Accurately quantifying the increased risks of climate extremes requires generating large ensembles of climate realization across a wide range of emissions scenarios, which is computationally challenging for conventional Earth System Models. We propose GEN2, a generative prediction-correction framework for an efficient and accurate forecast of the extreme event statistics. The prediction step is constructed as a conditional Gaussian emulator, followed by a non-Gaussian machine-learning (ML) correction step. The ML model is trained on pairs of the reference data and the emulated fields nudged towards the reference, to ensure the training is robust to chaos. We first validate the accuracy of our model on historical ERA5 data and then demonstrate the extrapolation capabilities on various future climate change scenarios. When trained on a single realization of one warming scenario, our model accurately predicts the statistics of extreme events in different scenarios, successfully extrapolating beyond the distribution of training data.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。