The under-representation of cloud formation is a long-standing bias
associated with climate simulations. Parameterisation schemes are required to
capture cloud processes within current climate models but have known biases. We
overcome these biases by embedding a Multi-Output Gaussian Process (MOGP)
trained on high resolution Unified Model simulations to represent the
variability of temperature and specific humidity within a climate model. A
trained MOGP model is coupled in-situ with a simplified Atmospheric General
Circulation Model named SPEEDY. The temperature and specific humidity profiles
of SPEEDY are perturbed at fixed intervals according to the variability
predicted from the MOGP. Ten-year predictions are generated for both control
and ML-hybrid models. The hybrid model reduces the global precipitation bias by
18\% and over the tropics by 22\%. To further understand the drivers of these
improvements, physical quantities of interest are explored, such as the
distribution of lifted index values and the alteration of the Hadley cell. The
control and hybrid set-ups are also run in a plus 4K sea-surface temperature
experiment to explore the effects of the approach on patterns relating to cloud
cover and precipitation in a warmed climate setting.

通过在气候模型中嵌入基于高分辨率 Unified Model 模拟的多输出高斯过程（MOGP）的训练，我们克服了与云形成相关的气候模拟中的长期偏见。利用 MOGP 预测的温度和比湿的变化，对简化的大气环流模型 SPEEDY 进行在位耦合。将 SPEEDY 的温度和比湿剖面按照 MOGP 预测的变化进行固定间隔的扰动。生成了控制模型和混合 ML 模型的十年预测。混合模型将全球降水偏差减少了 18％，在热带地区减少了 22％。为了进一步了解这些改进的驱动因素，我们还探索了一些感兴趣的物理量，如抬升指数值的分布和哈德利环流的改变。在温暖的气候环境中，还运行控制和混合设置的海面温度增加 4K 的实验，以探索该方法对云覆盖和降水模式的影响。