STRAT-MSB - Quantifying the Role of Stratospheric Mean State Bias on Representations of Climate

STRAT-MSB - 量化平流层平均状态偏差对气候表征的作用

• 批准号: EP/Y029623/1
• 负责人: Paulo Ceppi
• 金额: $ 23.84万
• 依托单位: Imperial College London
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FY029623%2F1
• 关键词: STRAT; MSB; Quantifying; Role; Stratospheric

The proposed work will seek to quantify the role of stratospheric mean state bias in driving inter-model spread in stratospheric andtropospheric sensitivity to temperature perturbations in the lower stratosphere. Previous literature has highlighted majorinadequacies in the ability of global circulation models to capture processes in the upper troposphere and lower stratosphere,including the transport of water vapor into the stratosphere. This has major implications for the radiative budget of the entireatmosphere and feeds back on stratospheric circulation and tropospheric dynamical variability. This project will leverage the outputsof a novel model inter-comparison project in which a temperature tendency has been fixed in the lower stratosphere of five state ofthe art climate models. Early results indicate that in spite of an equivalent perturbation, each model exhibits unique thermalresponses which result in differing surface climate responses. Motivated by these findings, this project will extend analysis tocharacterize both the stratospheric and tropospheric inter-model differences, including changes to water vapor transport and theoverturning circulation. The source of these differences will be explored using a novel idealized stratospheric model to identify whatfraction of the response may be attributed to a model's stratospheric mean state. Results will be constrained by observations of thestratospheric profile. Outcomes of this work will guide model development by quantifying the importance of mean state bias ascompared to process level parameterizations, or numerical methods. Understanding sources of uncertainty in model behaviorenables improved interpretation of current future warming projections with implications for national and global scale adaptation andmitigation planning.

拟议的工作将旨在量化平流层平均状态偏差在驱动平流层和对流层敏感性对较低平流层中温度扰动的敏感性中的作用。以前的文献强调了全球循环模型捕获对流层和下层平流层的过程的能力，包括水蒸气进入平流层的能力。这对全面的辐射预算具有重大影响，并以平流层循环和对流层动力学变化为基础。该项目将利用一个新型模型间比较项目的输出，在该项目中，温度趋势已固定在艺术气候模型的五个状态的平流层中。早期结果表明，尽管有同等的扰动，但每个模型都表现出独特的热反应，从而导致表面气候不同。在这些发现的激励下，该项目将扩展分析，以使平流层和对流层间模型之间的差异表达，包括对水蒸气运输和循环循环的变化。这些差异的来源将使用新型理想的平流层模型来探索，以识别响应的哪些曲线可能归因于模型的平流层平均状态。结果将受到theStrosposper剖面的观测来限制。这项工作的结果将通过量化平均状态偏差与过程级别参数化或数值方法的重要性来指导开发。了解模型可行能力中不确定性的来源改善了对当前未来变暖预测的解释，对国家和全球规模的适应和防备计划的影响。