Using Optimisation Algorithms to tune Climate Models (OptClim)

OptCliM将把数学优化研究的进展带入气候模型。我们的重点是代表气候模型中未解决的物理问题的参数化过程。这些未解决的过程通过包括固定参数的方程来表示，典型的气候模型有大约100个参数。例如，雷暴不仅会产生大雨，也是水汽进入大气的一条途径。其中一个参数表示风暴中潮湿空气混合到大气中的速度。每个参数的取值范围与理论和测量结果一致，其中一些参数的变化对未来气候预测有很大影响。因此，为了充分模拟过去或未来的气候，有必要具有实际的参数值。OptCliM回应了对自动和客观方法的需求，以产生符合实际的模型。目前，气候模型中使用的值是通过手动调整其中几个值来选择的，直到模型产生对当前平均气候的可接受模拟。这一过程在人工时间上非常昂贵；它不客观，不可重现，严重依赖于个人(如果是专家的话)的判断。OptCliM将开发迭代方法，使用优化算法自动调整许多参数，使模型与观测结果一致。从允许范围内的任何一组参数值开始，优化算法确定要运行的一组初始模型配置。完成这些运行后，将模拟与观测值进行比较，并用于定义进一步运行的参数值，直到进度停止或模拟和观测之间的差异很小。将这些方法应用于气候模型的挑战来自于气候固有的噪声，以及每个模型运行的计算费用。我们将在气候建模中引入三种替代算法，以找出在使模型与一系列不同的观测保持一致并以最少的计算时间和成本实现这一目标方面，哪种算法最有效。OptCliM将：1)允许研究人员更容易地生成参数集，从而生成真实的模型，从而更好地理解过去和未来的气候变化。2)提供一种客观和透明的方法来结合模型和特定的观测结果。3)通过我们的影响计划帮助开发新的英国地球系统模型，UKESM1.4)进一步开发方法，以便更系统地探索气候建模中的不确定性，例如生成参数值集，对观测不确定性进行采样，从而产生一系列合理的模型。

OptCliM will bring into climate modelling advances from mathematical optimization research. Our focus is upon parameterised processes that represent physics that are unresolved within climate models. These unresolved processes are represented through equations that include fixed parameters, with a typical climate model having around a hundred parameters. For example, thunderstorms not only generate heavy rain but are also one route for moisture into the atmosphere. One of the parameters expresses the rate at which moist air in the storm is mixed into the atmosphere. A range of values for each parameter is consistent with theory and measurement with changes in some parameters having a dramatic effect on future climate predictions. It is therefore necessary to have realistic parameter values in order to adequately model past or future climates. OptCliM responds to the need for an automatic and objective method to produce models consistent with reality. Currently the values used in climate models are chosen by manually adjusting several of them until the model produces an acceptable simulation of the current average climate. This process is very expensive in person time; it is not objective, not reproducible, and relies heavily on individual, if expert, judgement. OptCliM will develop iterative methods that use optimisation algorithms to automatically adjust many parameters so that models are consistent with observations. Beginning from any set of parameter values within the allowed ranges, the optimisation algorithm determines an initial set of model configurations to be run. On completion of these runs, the simulations are compared against the observations, and used to define parameter values for further runs until progress halts or the difference between simulation and observations are small. The challenges in applying such methods to climate models arise from the inherent noisiness of climate, and the computational expense of each model run. We will bring into climate modelling three alternative algorithms to find which is most effective in terms of making a model consistent with a range of different observations, and achieving that goal with minimum computing time and cost.OptCliM will:1) Allow researchers to more easily generate parameter sets that produce realistic models allowing a better understanding of past and future climate change. 2) Provide an objective and transparent method to combine models and specified observations. 3) Through our impact plan contribute to the development of the new UK earth system model, UKESM1.4) Open further development of methods for a more systematic exploration of uncertainty in climate modelling, for example generating parameter value sets that sample observational uncertainty to lead to a cloud of plausible models.