Reliable Climate Projections: The Final Frontier for Stochastic Parametrisation

各国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，2016年9月发布的政府国家洪水恢复力评估使用了从气候预测中计算出的风险，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。通过权衡投资成本与预测风险，政府决定投资临时防洪屏障。然而，我们目前的气候预测可能无法准确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。季节性预测中观察到的缺点也会影响气候预测。这限制了当前气候模式对决策的有用性。我建议开发一种新的技术来表示气候模式中的不确定性。这将提高我们预测不同气候结果可能性的能力。主要目标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实用性。特别是，由于气候模型的局限性，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许多相互作用的过程是一个很大的不确定性来源。大气层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。在天气和季节性预报方面，随机数被广泛用作一种工具，以帮助在模式中代表这些小尺度过程。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使天气和季节预报有了逐步的改进，但是，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。虽然这对于短距离应用是足够的，但这些方案在数学上并不严格，并且不一定在较长的时间段内保持水分、质量或能量。我的研究将提供一套新的、物理驱动的随机参数化方案。通过在源的不确定性为目标，这些计划将是有用的气候预测，以及天气和季节的时间scales.I将开发这套新的随机方案，通过比较一个典型的预测模式，高分辨率的大气模拟。我在欧洲的项目合作伙伴将提供这些最先进的模拟，其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。通过比较这两种方法，我将找出用概率方法而不是传统的确定性方法更好地表示的过程，我将首先在天气和季节预报中评估我的新方案。这提供了一种验证预测模型中快速时间尺度过程表示的方法。由于大气的非线性性质，这些过程的精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之后，我将产生一个概率气候变化预测，其中第一次以完整和物理一致的方式考虑模型的不确定性。

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.