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实现机器辅助调整,避免低估气候变化预测中的不确定性
Frédéric Hourdin1, Brady Ferster2, Julie Deshayes2
1LMD/IPSL SU/CNRS, Paris, 75005, France.
Science advances
|July 19, 2023
概括
机器学习加速了气候模型的改进和不确定性量化. 新的方法允许自动选择模型配置,揭示与当前气候观测相一致的多种"变暖世界".
科学领域:
- 气候建模 气候建模
- 机器学习应用程序 机器学习应用程序
- 气候变化的不确定性 气候变化的不确定性
背景情况:
- 针对IPCC报告的相互比较练习旨在记录气候变化预测的不确定性.
- 目前的方法涉及繁的模型调整,可能会忽略显著的不确定性.
- 读者可能会被错误地从选定的模型配置中省略不确定性.
研究的目的:
- 通过机器学习改变气候模型调整.
- 为了同时加快模型改进和参数不确定性量化.
- 探索模型配置的自动选择如何影响气候预测.
主要方法:
- 应用最新的机器学习方法来调整气候模型.
- 基于可变的自由参数值,自动选择气候模型配置.
- 与当今气候观测相比,生成的模型配置的验证.
主要成果:
- 机器学习为气候模型调整提供了一种新的方法.
- 同时加速模型改进和不确定性量化是可以实现的.
- 不同的参数集产生了与观测一致的不同的"变暖世界".
结论:
- 机器学习可以彻底改变气候模型开发和不确定性评估.
- 自动配置选择提供了对潜在气候未来的更全面的视图.
- 这种方法提高了IPCC报告气候预测的可靠性和透明度.
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