Reliable Climate Projections: The Final Frontier for Stochastic Parametrisation

可靠的气候预测：随机参数化的最终前沿

• 批准号: NE/P018238/1
• 负责人: Hannah Christensen
• 金额: $ 68.37万
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FP018238%2F1
• 关键词: Reliable; Climate; Projections; Final; Frontier

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.

政府和行业需要可靠的气候预测来应对气候变化。例如，2016年9月发表的政府国家洪水弹性评论使用了从气候预测计算出的风险来评估如何最好地保护该国免受极端天气和洪水事件的侵害。通过权衡投资成本与预测的风险，政府决定投资暂时的保护性洪水障碍。但是，我们目前的气候预测可能并不能准确地代表不同气候结果的可能性。在我们可以测试预测的熟练性的季节性时间表上，我们可以看到我们当前的模型无法可靠地预测不同事件的可能性。在季节性预测中观察到的缺点也将感染气候预测。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的可能性的能力。关键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。尤其是，由于气候模型的局限性，我将针对气候预测的不确定性。代表大气，海洋和土地表面及其在计算机代码中的许多相互作用的过程是很大的不确定性来源。大气中的某些过程发生在太小的刻度上，无法在计算机模型使用的网格上明确表示，那里的网格点通常相距10-100 km。取而代之的是，这些过程被解释为使用“参数化方案”。在天气和季节性预测的情况下，随机数被广泛用作工具，以帮助表示模型中的这些小规模过程。这些所谓的“随机”参数化方案提高了模型与潜在物理过程的一致性。随机参数化方案的引入产生了天气和季节性预测的逐步改变。但是，当前的随机方案并不能直接针对不确定性的物理来源。相反，它们只是针对不确定性对预测的影响。尽管这足以用于短期应用，但这些方案在数学上并不严格，并且不一定在较长时间内保留水分，质量或能量。对于气候预测，这是非常有问题的。我的研究将提供一组新的，身体动机的随机参数化方案。通过将不确定性靶向来源，这些方案将对气候投影以及天气和季节性时间表有用。我将通过将典型的预测模型与高分辨率大气模拟进行比较，从而开发这套新的随机方案。我的整个欧洲项目合作伙伴将提供这些最先进的模拟，在这些模拟中，小规模流程被明确建模。在预测模型中，这些小尺度必须通过参数化方案表示。通过比较两者，我将确定以概率方式而不是以传统的确定性方式更好地表示的过程。我最初将评估我在天气和季节性预测中的新方案。这提供了一种方法来验证预测模型中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现实的气候模拟是必要的。以这种方式验证了我的新方法后，我将产生一个概率的气候变化投影，在这种情况下，第一次以完整且身体一致的方式解释了模型不确定性。

项目成果

Scale-Aware Space-Time Stochastic Parameterization of Subgrid-Scale Velocity Enhancement of Sea Surface Fluxes

海面通量亚网格尺度速度增强的尺度感知时空随机参数化

• DOI: 10.1029/2020ms002367
• 发表时间: 2021
• 期刊: Journal of Advances in Modeling Earth Systems
• 影响因子: 6.8
• 作者: Bessac J

Oxford Research Encyclopedia of Climate Science

牛津研究气候科学百科全书

• 发表时间: 2017
• 作者: Steven Yearley

The Value of Initialization on Decadal Timescales: State-Dependent Predictability in the CESM Decadal Prediction Large Ensemble

十年时间尺度上初始化的价值：CESM 十年预测大型集合中的状态相关可预测性

• DOI: 10.1175/jcli-d-19-0571.1
• 发表时间: 2020
• 期刊: Journal of Climate
• 影响因子: 4.9
• 作者: Christensen H

From reliable weather forecasts to skilful climate response: A dynamical systems approach

从可靠的天气预报到熟练的气候响应：动力系统方法

• DOI: 10.1002/qj.3476
• 发表时间: 2019
• 期刊: Quarterly Journal of the Royal Meteorological Society
• 影响因子: 8.9
• 作者: Christensen H

Does ENSO Regularity Increase in a Warming Climate?

• DOI: 10.1175/jcli-d-19-0545.1
• 发表时间: 2020-02-01
• 期刊: JOURNAL OF CLIMATE
• 影响因子: 4.9
• 作者: Berner, Judith;Christensen, Hannah M.;Sardeshmukh, Prashant D.
• 通讯作者: Sardeshmukh, Prashant D.