Nowcasting the risks of wildfire

野火给人们和财产带来了越来越大的风险。有人担心，如果目前的趋势继续下去，保险和再保险公司面临的风险将变得不可持续，火灾多发地区的财产将变得无法投保。之所以出现这个问题，是因为保险定价通常依赖的历史数据不再是野火风险的可靠指南。古数据提供的证据表明，生物质燃烧，无论人类干预的程度如何，即使对微小的(摄氏1度)区域温度变化也是高度敏感的。过去一年澳大利亚东南部、加利福尼亚州和西伯利亚的严重火灾季节反映了前所未有的高温，结合特定的大气环流模式，造成了极端的火灾风险。因此，迫切需要制定一种新的办法，在空间上详细评估目前和不久的将来的野火风险，同时考虑到气候的非稳定性质，以及目前对气象、生态和人类对火灾影响的了解。该项目将展示结合气候和野火模型绘制当前和近期野火风险地图的可行性。该项目将利用大型集合和长期领先气候模式的可用性，包括英国气象局(埃克塞特)使用的最先进模式和EC-地球模式，该模式以欧洲中期天气预报中心(雷丁)预报模式为基础，在几个国家用于气候预测。这个项目不会像许多“气候影响”文献那样把重点放在长期预测上，而是着眼于当前。这一想法是基于代表气候的替代实现的模型集合，以概率方式表示当前的气候，所有这些都与目前的大气组成一致。这项工作还将量化未来5-10年后的气候。这可以相当有把握地完成，因为未来碳排放的不同情景直到20年或更长时间后才会产生明显不同的气候。这项研究的另一个关键因素是全球野火模型。目前的“基于过程的”植被火灾模型是基于对过程的有限的定量理解，并且没有达到所要求的标准。另一方面，关于火灾发生、烧毁面积和火灾辐射力的遥感数据丰富，公开可用，并在不断改进。因此，可以建立经验模型，将野火(从太空看)与其多重控制联系起来。野火模型也将是概率模型。关于实际火灾地点的信息将为基于物理、生物和人类预测因素的模型提供信息，该模型反过来将用于量化不久的将来特定地点的野火风险。利用统计和机器学习方法，改进基于遥感数据的全球野火模型，是勒维胡姆中心当前研究的一个非常活跃的领域。该项目将在这一活动的基础上，以所需的空间分辨率开发和应用适当的模型。因此，通过结合气候和野火的概率模拟，该项目有望在全球火灾模拟方面取得实质性进展；同时也为量化这一日益重要的风险的新的科学方法提供概念验证。

Wildfire represents an increasing risk to people and property. There is concern that if present trends continue, the risks to insurance and re-insurance companies will become unsustainable, and property in fire-prone regions will become uninsurable. The problem arises because historic data, on which insurance pricing generally depends, are no longer a reliable guide to wildfire risk. Palaeodata provide evidence that biomass burning, regardless of greater or lesser human intervention, is highly sensitive even to small (< 1 degree C) regional temperature shifts. Severe fire seasons during the past year in southeastern Australia, California and Siberia reflect unprecedentedly high temperatures, combining with specific atmospheric circulation patterns, to create extreme fire risks. There is thus an urgent need to develop a new approach to the spatially detailed assessment of wildfire risk in the present and the near future that takes account of the non-stationary nature of climate, together with current understanding of the meteorological, ecological and human influences on fire. The project will demonstrate the feasibility of mapping present and near-term wildfire risk using a combination of climate and wildfire models. The project will exploit the availability of large ensembles and long runs of leading climate models, including state-of-the-art models used by the UK Met Office (Exeter) and the EC-Earth model, which is based on the European Centre for Medium-range Weather Forecasts (Reading) forecast model and used for climate prediction in several countries. Instead of focusing on long-term projections, as much of the "climate impacts" literature does, this project will focus on the present. The idea is to represent the present climate probabilistically, based on model ensembles that represent alternative realizations of the climate, all consistent with the present composition of the atmosphere. This work will also quantify climate 5-10 years into the future. This can be done with reasonable confidence because different scenarios of future carbon emissions do not produce noticeably divergent climates until 20 or more years hence. The other key element of this research will be a global wildfire model. Current "process-based" vegetation-fire models are based on a still-limited quantitative understanding of the processes, and do not perform to the standard required. On the other hand, remotely sensed data on fire occurrence, burnt area and fire radiative power are abundant, publicly available, and improving. Empirical models can therefore be developed, relating wildfire (as seen from space) to its multiple controls. The wildfire model will also be probabilistic. Information on the locations of actual fires will inform a model based on physical, biological and human predictors, which in turn will be used to quantify location-specific wildfire risks for the near future. Improved global wildfire modelling based on remote-sensing data, using both statistical and machine-learning approaches, is a highly active area of current research in the Leverhulme Centre. The project will build on this activity to develop and apply a suitable model at the spatial resolution required.By combining probabilistic modelling of both climate and wildfire, this project is therefore expected to achieve a substantial advance in the state of global fire modelling; while also providing a proof-of-concept for a new scientific approach to the quantification of this increasingly important risk.