Using Optimisation Algorithms to tune Climate Models (OptClim)

OptCliM将引入数学优化研究的气候建模进展。我们的重点是参数化过程，这些过程代表了气候模型中未解决的物理问题。这些未解决的过程通过包含固定参数的方程来表示，一个典型的气候模型大约有100个参数。例如，雷暴不仅产生大雨，而且也是水汽进入大气的途径之一。其中一个参数表示了风暴中潮湿空气混合到大气中的速率。每个参数的取值范围与理论和测量相一致，其中一些参数的变化对未来气候预测有显著影响。因此，有必要有现实的参数值，以便充分模拟过去或未来的气候。OptCliM响应了一种自动和客观的方法来生成与实际相符的模型的需求。目前，气候模式中使用的值是通过手动调整其中的几个值来选择的，直到模式对当前平均气候产生可接受的模拟。这个过程非常耗费人力；它不是客观的，不可复制的，严重依赖于个人的判断，如果专家的话。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。在这些运行完成后，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与观测值之间的差异很小。将这种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可选算法，以找出哪种算法在使模型与一系列不同的观测结果相一致方面最有效，并以最小的计算时间和成本实现这一目标。OptCliM将：1)允许研究人员更容易地生成参数集，从而产生真实的模型，从而更好地了解过去和未来的气候变化。2)提供一种客观透明的方法，将模型与特定的观测结果结合起来。3)通过我们的影响计划，促进英国地球系统新模式（UKESM1.4）的发展，进一步开发更系统地探索气候模式不确定性的方法，例如，生成采样观测不确定性的参数值集，从而形成一团合理的模式。

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.