Sequential learning of climate change via a physical-parameter-based state-space model and Bayesian inference

IF 4.8 2区环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES

Climatic Change Pub Date : 2024-06-01 DOI:10.1007/s10584-024-03739-w

Yuchuan Lai, Matteo Pozzi

引用次数: 0

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通过基于物理参数的状态空间模型和贝叶斯推理对气候变化进行连续学习

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Climatic Change 环境科学-环境科学

CiteScore

10.20

自引率

4.20%

发文量

180

审稿时长

7.5 months

期刊介绍： Climatic Change is dedicated to the totality of the problem of climatic variability and change - its descriptions, causes, implications and interactions among these. The purpose of the journal is to provide a means of exchange among those working in different disciplines on problems related to climatic variations. This means that authors have an opportunity to communicate the essence of their studies to people in other climate-related disciplines and to interested non-disciplinarians, as well as to report on research in which the originality is in the combinations of (not necessarily original) work from several disciplines. The journal also includes vigorous editorial and book review sections.