基于气候敏感性的气候模型的贝叶斯权重
Elias C Massoud1, Hugo K Lee2, Adam Terando3,4
1Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN USA.
概括
贝叶斯平均模型通过加权气候模型来改进气候预测,避免偏差.
科学领域:
- 气候科学是气候科学.
- 地球系统科学 地球系统科学
- 气候建模 气候建模
背景情况:
- 气候模型组合可以产生偏差的预测,这是由于具有高气候灵敏度的"热模型".
- 现有的方法,如模型模拟器或杀,在解决这种偏差方面存在局限性.
研究的目的:
- 实施贝叶斯模型平均化 (BMA) 以解决"热模型"问题而不排除模型.
- 使用多个证据线来生成模型权重的不偏见的后置概率分布.
主要方法:
- 使用贝叶斯模型平均 (BMA) 作为统计框架.
- 基于地球气候敏感性的多个证据线构建模型重量.
主要成果:
- 根据BMA的最新预测,本世纪末全球平均地表温度将增加2°C (SSP1-2.6) 和5°C (SSP5-8.5).
- 这些BMA衍生的估计值低于CMIP6整体的简单多模型平均值.
- 该方法对低概率模型保留了一些权重,承认了潜在的极端气候敏感度值.
结论:
- 贝叶斯平均模型为客观的气候变化预测提供了一个强大的框架.
- 这种方法有效地整合了科学证据,提供了更可靠的未来气候场景.
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