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颖的方法，以减少气体辐射强迫在大都会办公室气候模型中的巨大不确定性。不确定性源于结构模型缺陷（本质上是错误的物理过程，称为结构误差）和模型中与过程相关的参数不确定（参数不确定性）。在多个气候模型和扰动参数集合中扩散的模型的比较表明，这两个不确定性来源具有可比的幅度。利兹的最新结果表明，结构缺陷严重限制了通过模型校准对观测值降低整体模型不确定性的程度。因此，能够区分这两个不确定性来源至关重要。该项目的目的将是：1）开发方法，使结构模型错误和参数不确定性可以与广泛观察到气溶胶，云和辐射的广泛观察区分，以区别于云和辐射2）开发和应用方法来确定结构性错误的潜在原因，并确定方法来确定方法以改进模型的模型效果的分离列表，该方法是在模型中列出模型的高级列表。这两种不确定性来源使通过模型校准的气溶胶强迫的观察限制估计更窄。学生将使用大量的气候模型模拟（Ensembles）以及对气雾剂，云，云和辐射的广泛观察，来自地表地点，飞机和卫星数据。结合模型仿真器的合奏将使气候模型的基本上数百万“变体”能够与观测值创建和分析。