Reliable Climate Projections: The Final Frontier for Stochastic Parametrisation

政府和工业界需要可靠的气候预测来做出应对气候变化的决策。例如，政府于2016年9月发布的国家洪水弹性评估报告使用了根据气候预测计算的风险，以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。通过权衡投资成本和预测风险，政府决定投资于临时防洪堤。然而，我们目前的气候预测很可能不能准确地反映不同气候结果的可能性。在季节性时间尺度上，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。在季节性预报中观察到的缺陷也将影响气候预报。这限制了当前气候模型对决策的有效性。我建议开发一种新的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。主要目的是提高气候预测的可靠性，从而提高其对决策者和最终用户的有用性。我将特别针对由于气候模型的限制而导致的气候预测的不确定性。在一段计算机代码中表示大气、海洋和陆地表面的过程以及它们之间的许多相互作用是一大不确定性来源。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，其中网格点通常相距10-100公里。在天气和季节预报的情况下，随机数被广泛用作工具来帮助在模式中表示这些小范围的过程。这些所谓的“随机”参数方案提高了模型与基本物理过程的一致性。随机参数方案的引入带来了天气和季节预报的阶跃变化。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。虽然这对于短期应用来说已经足够，但这些方案在数学上并不严格，不一定在较长的时间内保持水分、质量或能量。这对于气候预测来说是非常有问题的。我的研究将提供一套新的、物理激励的随机参数方案。通过将不确定性指向源头，这些方案将对气候预测以及天气和季节性时间尺度有用。我将通过比较典型的预报模型和高分辨率的大气模拟来开发这套新的随机方案。我在欧洲各地的项目合作伙伴将提供这些最先进的模拟，在这些模拟中，小规模的过程被明确建模。在预报模式中，这些小尺度必须通过参数化方案来表示。通过比较两者，我将找出更好地用概率方式而不是传统的确定性方式表示的过程。我将初步评估我的新方案在天气和季节预报中的作用。这提供了一种方法来验证预测模型中的快速时间尺度过程的表示。由于大气的非线性性质，这些过程的准确表示对于真实的气候模拟是必要的。在以这种方式验证了我的新方法后，我将产生一个概率气候变化预测，其中第一次以完全和物理一致的方式解释了模型的不确定性。

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.