Novel approaches to observationally constrain aerosol effects in climate models

在气候模型中观测限制气溶胶效应的新方法

• 批准号: 2886996
• 金额: --
• 依托单位: University of Leeds
• 依托单位国家: 英国
• 项目类别: Studentship
• 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=studentship-2886996
• 关键词: Novel; approaches; observationally; constrain; aerosol

The aim of this project is to develop and apply novel methods to reduce the large uncertainty in aerosol radiative forcing in the Met Office climate models. The uncertainty stems from both structural model deficiencies (essentially incorrect physical processes, known as structural error) and uncertain process-related parameters in the model (parametric uncertainty). Comparison of model spread across multiple climate models and in perturbed parameter ensembles shows that these two sources of uncertainty are of comparable magnitude. Recent results from Leeds show that structural deficiencies severely limit the extent to which the overall model uncertainty can be reduced through model calibration against observations. Therefore it is vital to be able to distinguish these two sources of uncertainty. The aims of this project will be to:1) Develop methods that enable structural model errors and parametric uncertainty to be distinguished against extensive observations of aerosols, clouds and radiation2) Develop and apply methods to identify the potential causes of structural error and define approaches to improve them in the model3) Define a ranked list of model structural improvements that affect aerosol forcing to feed into the Met Office model development priorities 4) Demonstrate that separation of these two sources of uncertainty enables a narrower observationally constrained estimate of aerosol forcing through model calibration.The student will use large sets of climate model simulations (ensembles) combined with extensive observations of aerosols, clouds and radiation from surface sites, aircraft and satellite data. The ensembles, combined with model emulators, will enable essentially millions of ""variants"" of the climate model to be created and analysed against the observations.

该项目的目的是开发和应用新方法来减少气象局气候模型中气溶胶辐射强迫的巨大不确定性。不确定性源于结构模型缺陷（本质上是不正确的物理过程，称为结构误差）和模型中与过程相关的不确定参数（参数不确定性）。对多个气候模型和扰动参数集合中的模型分布进行比较表明，这两个不确定性来源的程度相当。利兹的最新结果表明，结构缺陷严重限制了通过根据观测进行模型校准来降低总体模型不确定性的程度。因此，区分这两种不确定性来源至关重要。该项目的目标是：1) 开发能够将结构模型误差和参数不确定性与气溶胶、云和辐射的广泛观测区分开的方法2) 开发和应用方法来识别结构误差的潜在原因并定义解决方法在模型中改进它们3) 定义影响气溶胶强迫的模型结构改进的排名列表，以纳入气象局模型开发优先事项4) 证明这两个不确定性来源的分离可以实现更窄的观测受限估计通过模型校准来计算气溶胶强迫。学生将使用大量的气候模型模拟（集合），并结合对气溶胶、云和来自地表站点的辐射、飞机和卫星数据的广泛观察。这些集合与模型模拟器相结合，将能够创建数以百万计的气候模型“变体”，并根据观测结果进行分析。