MSB-FRA: Scaling fire size from local process to continental pattern

火是地球系统的一个基本过程，它决定了植被结构，改变了大气动力学和碳循环，破坏了人类的基础设施和生计。人们只需要想想最近在美国西海岸发生的极端火灾季节，就可以知道火灾的影响有多严重。预测火灾发生的地点和强度对未来的人类和生态系统都至关重要。全球生物圈模型中的火灾模块可以预测火灾的频率和强度。然而，将火有效而准确地纳入全球生物圈模型仍然很困难，因为全球模型运行的尺度相对于许多大气、海洋和生态过程运行的尺度而言很粗糙。缺少的关键因素之一是真实的火灾灭绝。目前，大多数全球模型迫使火灾在一段预先确定的间隔后熄灭，而不是让它们燃烧几天到几个月。极端的火灾造成了地球系统的大部分影响，捕捉它们的影响对有用的预测至关重要。在全球火灾模型中提高对灭火机制的理解和表征可以改善对地球系统火灾发生的预测。该项目的更广泛影响将是更广泛地改善现场和大草原的火灾管理，以及培训博士后助理。对火灾扑灭的不完全理解迫使全球火灾模型要么依赖于火灾模式的统计表示，要么依赖于关于火灾行为的局部无比例假设。因此，火灾模型在预测地球系统过去、现在和未来的火灾分布时，彼此之间以及与现实情况大相径庭。在这个项目中，研究人员问为什么火灾会熄灭，首先是在稀树草原生态系统中，那里火灾频繁，因此数据丰富，然后扩展到其他易燃系统（包括北美西部的针叶树和地中海系统）。这些数据提供给灭火的理论概括（阶段1）。该团队预测，火灾范围将由燃料/景观的随机异质性与潜在地形、天气或景观中的人为模式之间的相互作用造成。然后，他们将把这项工作的结果应用到全球火灾模型中，从CLM4-DGVM和SPITFIRE-JSBACH开始，然后通过与火灾模型相互比较项目（第二阶段）的合作进行更广泛的研究。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Fire is a fundamental process in the Earth system, determining vegetation structure, changing atmospheric dynamics and carbon cycling, and damaging human infrastructure and livelihoods. One need only think of the recent, extreme fire season on the US West Coast for an example of how serious the impacts of fire can be. Predicting where fire will occur and how intense it will be is critical for both human and ecological systems in the future. Fire modules in global biosphere models allow forecasts of fire frequency and intensity. However, incorporating fire into global biosphere models efficiently and accurately remains difficult because global models run at a coarse scale relative to that at which many atmospheric, oceanic, and ecological processes operate. One of the key missing components is realistic fire extinctions. Currently, most global models force fires to go out after some pre-determined interval instead of allowing them to burn for days to months. The extreme fires contribute the majority of the impacts in the Earth system, and capturing their effects is critical to useful predictions. Improving the understanding and representation of fire extinction mechanisms in global fire models may improve predictions of fire occurrence in the Earth system. The broader impacts of this project will be improvements in fire management at field sites and savannas more broadly, and training of postdoctoral associates.An incomplete understanding of fire extinction forces global fire models to rely either on statistical representations of fire pattern or on local, unscaled assumptions about fire behavior. As a result, fire models diverge wildly, from each other and from reality, in their predictions for past, current, and future fire distributions in the Earth system. In this project, the researcher ask why fires go out, first in savanna ecosystems, where fires are frequent and therefore data plentiful, then expanding into other flammable systems (including coniferous and Mediterranean systems like those in Western North America). Such data feeds into theoretical generalizations of fire extinction (Phase 1). The team predicts that fire extent will result from interactions between random heterogeneity in fuels/landscapes and underlying topographic, weather, or anthropogenic patterns in the landscape. They will then implement the results of this work into global fire models, beginning with CLM4-DGVM and SPITFIRE-JSBACH and then proceeding more broadly via collaborations with the Fire Model Intercomparison Project (Phase 2).This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.