Reliable Climate Projections: The Final Frontier for Stochastic Parametrisation

政府和行业需要可靠的气候预测来做出应对气候变化的决策。例如，2016年9月发布的政府《国家抗洪能力评估》（National Flood Resilience Review）利用气候预测计算出的风险来评估如何最好地保护国家免受极端天气和洪水事件的影响。经过对投资成本和预测风险的权衡，政府决定投资临时防洪堤。然而，我们目前的气候预测很可能不能准确地代表不同气候结果的可能性。在季节性的时间尺度上，我们可以测试我们预测的技巧，我们可以看到，我们目前的模型无法可靠地预测不同事件的可能性。在季节预报中观察到的缺点也将影响气候预报。这限制了当前气候模型对决策的有用性。我建议开发一种新技术来表示气候模型中的不确定性。这将提高我们预测不同气候结果可能性的能力。关键目标是提高气候预测的可靠性，从而提高它们对决策者和最终用户的有用性。特别是，由于气候模式的局限性，我将针对气候预测中的不确定性。在一段计算机代码中表示大气、海洋和陆地表面及其相互作用的过程是不确定性的一大来源。大气中的一些过程发生的尺度太小，无法在计算机模型使用的网格上明确表示，网格点通常相距10-100公里。相反，这些过程是用“参数化方案”来解释的。在天气和季节预报的情况下，随机数被广泛用作一种工具来帮助表示模型中的这些小尺度过程。这些所谓的“随机”参数化方案提高了模型与底层物理过程的一致性。随机参数化方案的引入在天气和季节预报方面产生了逐步变化的改进。然而，目前的随机方案并不直接针对不确定性的物理来源。相反，他们只是针对不确定性对预测的影响。虽然这对于短程应用来说是足够的，但这些方案在数学上并不严格，并且不一定能在较长时间内保持水分、质量或能量。这对气候预测来说是个很大的问题。我的研究将提供一套新的，物理动机的随机参数化方案。通过从源头上确定不确定性，这些方案将有助于气候预测以及天气和季节时间尺度。我将通过比较典型的预报模式和高分辨率的大气模拟来发展这组新的随机方案。我在欧洲的项目伙伴将提供这些最先进的模拟，其中小规模的过程被明确地建模。在预测模型中，这些小尺度必须通过参数化方案来表示。通过对两者的比较，我将识别出用概率方式而不是传统的确定性方式更好地表示的过程。我将首先在天气和季节预报方面评估我的新方案。这提供了一种在预测模型中验证快速时间尺度过程表示的方法。由于大气的非线性性质，这些过程的准确表示对于现实的气候模拟是必要的。在以这种方式验证了我的新方法之后，我将产生一个概率气候变化预测，在这个预测中，模型的不确定性第一次以一种完整的、物理上一致的方式被考虑在内。

Reliable climate projections are needed by governments and industry to make decisions in response to climate change. For example, the governmental National Flood Resilience Review, published in September 2016, used risks calculated from climate projections to evaluate how to best protect the country from extreme weather and flood events. By weighing up the cost of investment against the predicted risks, the government decided to invest in temporary protective flood barriers.However, it is likely that our current climate projections do not accurately represent the possibility of different climate outcomes. On seasonal timescales, where we can test the skilfulness of our predictions, we can see that our current models are unable to reliably predict the likelihood of different events. The shortcomings observed in seasonal forecasts will also infect climate prediction. This limits the usefulness of current climate models for decision-making.I propose to develop a new technique for representing uncertainty in climate models. This will improve our ability to predict the likelihood of different climate outcomes. The key aim is to improve the reliability of climate projections, and therefore their usefulness to decision-makers and end users.In particular, I will target uncertainty in climate prediction due to the limitations of the climate model. The process of representing the atmosphere, oceans and land-surface and their many interactions in a piece of computer code is a large source of uncertainty. Some processes in the atmosphere take place on too small a scale to be explicitly represented on the grid used by a computer model, where grid points are typically 10-100 km apart. Instead, these processes are accounted for using "parametrisation schemes".In the case of weather and seasonal forecasting, random numbers are widely used as a tool to help represent these small-scale processes in the model. These so called "stochastic" parametrisation schemes improve the consistency of the model with the underlying physical processes. The introduction of stochastic parametrisation schemes produced a step-change improvement in weather and seasonal forecasts.However, current stochastic schemes do not directly target the physical source of uncertainty. Instead they simply target the impact of the uncertainty on the forecast. While this is sufficient for short-range applications, these schemes are not mathematically rigorous, and do not necessarily conserve moisture, mass or energy over longer time periods. This is very problematic for climate prediction.My research will provide a set of new, physically motivated stochastic parametrisation schemes. By targeting uncertainty at the source, these schemes will be useful for climate projections as well as on weather and seasonal timescales.I will develop this set of new stochastic schemes by comparing a typical forecast model to a high-resolution atmospheric simulation. My project partners across Europe will provide these state-of-the-art simulations in which small-scale processes are explicitly modelled. In the forecast model, these small-scales must be represented through parametrisation schemes. By comparing the two, I will identify processes that are better represented in a probabilistic manner instead of in the traditional deterministic manner.I will initially evaluate my new schemes in weather and seasonal forecasts. This provides a way to validate the representation of fast timescale processes in the forecast model. Accurate representation of these processes is necessary for realistic climate simulation, due to the non-linear nature of the atmosphere. Having validated my new approach in this way, I will produce a probabilistic climate change projection where, for the first time, model uncertainty is accounted for in a complete and physically consistent manner.