通过贝叶斯方法精准反推火山气溶胶注入，量化误差与不确定性。
Stratospheric aerosol source inversion: Noise, variability, and uncertainty quantification
- 基于贝叶斯近似误差法，融合气候模型噪声与内部变率。
- 利用E3SM模型生成合成数据，准确反演气溶胶源强度与位置。
- 适合气候建模、火山气候效应研究者参考。
平流层气溶胶在地球系统中扮演重要角色，可影响数月到数年的气候。然而，对部分观测的气溶胶注入（如火山喷发）特征进行估计时存在显著不确定性。本文提出一种平流层气溶胶源反演框架，通过贝叶斯近似误差方法考虑背景气溶胶噪声与地球系统内部变率。利用能源百亿亿级地球系统模型（E3SM）设计的模拟数据，构建了涵盖数据生成、处理、降维、算子学习与贝叶斯反演的全流程框架，各环节针对全球尺度平流层建模挑战而优化。通过合成观测数据开展数值实验，严格评估了该方法在反演气溶胶源及其不确定性方面的性能。
原文摘要 · Abstract (English)
Stratospheric aerosols play an important role in the earth system and can affect the climate on timescales of months to years. However, estimating the characteristics of partially observed aerosol injections, such as those from volcanic eruptions, is fraught with uncertainties. This article presents a framework for stratospheric aerosol source inversion which accounts for background aerosol noise and earth system internal variability via a Bayesian approximation error approach. We leverage specially designed earth system model simulations using the Energy Exascale Earth System Model (E3SM). A comprehensive framework for data generation, data processing, dimension reduction, operator learning, and Bayesian inversion is presented where each component of the framework is designed to address particular challenges in stratospheric modeling on the global scale. We present numerical results using synthesized observational data to rigorously assess the ability of our approach to estimate aerosol sources and associate uncertainty with those estimates.
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