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来减少气象局气候模式中气溶胶辐射强迫的大不确定性。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）和模型中不确定的过程相关参数（参数不确定性）。在多个气候模式和扰动参数集合的模式传播的比较表明，这两个来源的不确定性是相当的规模。利兹最近的结果表明，结构性缺陷严重限制了整体模型的不确定性，可以通过对观测模型校准减少的程度。因此，能够区分这两种不确定性来源至关重要。该项目的目标是：1）开发方法，使结构模型误差和参数不确定性能够与气溶胶的广泛观测相区分，云和辐射2）开发和应用方法来识别结构误差的潜在原因，并定义在模式中改进它们的方法3）定义影响气溶胶强迫的模式结构改进的排名列表，以输入气象局模式开发优先级4）证明这两个不确定性来源的分离可以通过模式校准实现对气溶胶强迫的更窄的观测约束估计。学生将使用大量的气候模式模拟结合对来自地面站点、飞机和卫星数据的气溶胶、云和辐射的广泛观测。这些集合与模型模拟器相结合，将能够创建和分析气候模型的数百万个“变量”。

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.