Novel approaches to observationally constrain aerosol effects in climate models

该项目的目的是开发和应用新的方法，以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的缺陷(本质上不正确的物理过程，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)。在多个气候模式和扰动参数集合中传播的模式的比较表明，这两个不确定性源具有相当的规模。利兹大学最近的研究结果表明，结构性缺陷严重限制了通过对照观测进行模型校准来降低总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是至关重要的。这个项目的目标是：1)开发方法，使结构模型误差和参数不确定性能够与对气溶胶、云和辐射的广泛观测区分开来。2)开发和应用方法，以确定结构误差的潜在原因，并确定在模式中改进它们的方法。3)定义影响气溶胶强迫输入英国气象局模式发展优先事项的模式结构改进的分级清单。4)证明分离这两个不确定性源能够通过模式校准对气溶胶强迫进行更狭隘的观测约束估计。学生将使用大组气候模式模拟(集合)，结合对气溶胶、云和来自地面站点的辐射的广泛观测，飞机和卫星数据。这些集合与模型仿真器相结合，基本上可以创建数百万个气候模型的“变种”，并根据观测结果进行分析。

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.