The Fractional Energy Balance Equation, macroweather forecasts and climate projections

分数能量平衡方程、宏观天气预报和气候预测

• 批准号: RGPIN-2022-03377
• 负责人: Lovejoy, Shaun
• 金额: $ 3.13万
• 依托单位: McGill University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=762522
• 关键词: Fractional; Energy; Balance; Equation; macroweather

It is increasingly recognized that new approaches to monthly and seasonal forecasting and to multidecadal climate projections are needed. The proposed is to continue a multidecadal research program that I initiated at the end of the 1970's aiming to apply and develop stochastic, scaling, (turbulent, multifractal) approaches to atmospheric modelling. Over the last years, it has spawned state-of-the-art monthly, seasonal forecasts and (low uncertainty) multidecadal projections. In the next decade, this new paradigm will be developed and extended in various ways. At the moment, supercomputer atmospheric models (GCMs) - extended weather models - are the only tools for making monthly and seasonal (macroweather) forecasts and multidecadal climate projections. Their continued development assumes that realism requires the inclusion of as many details and processes as possible. Yet, any structures smaller than the model resolution (currently tens of kilometers) may grow explosively (the "butterfly effect") so that beyond ten days, GCM outputs become effectively random. A consequence is unacceptably large projection uncertainties, e.g. the famous 1.5 to 4.5 oC range for CO2 doubling. GCMs use continuum mechanics that assume molecular level details are irrelevant yet once again, most of the details of the collective behavior of a huge number of interacting vortices are irrelevant and new, even higher level turbulent laws emerge. This research program builds models directly at macroweather (e.g. monthly) scales using the physical principles of scale invariance and energy conservation that is possible due to the new discovery that when a famous energy balance model is updated it yields a unified model based on the new fractional energy balance equation (FEBE). The FEBE is both more physically based and yields the scaling macroweather and climate models as respectively high and low frequency approximations. For temperatures, these monthly scale models already prove to be comparable or more skillful than GCMs while multidecadal projections have less than half the uncertainties of the GCM ensemble ([1], [2]; it requires a million times fewer computations. When used directly for climate projections, the FEBE fully supports the latest IPCC projections while reducing the uncertainty by over 50%. This research program will develop and extend the FEBE for regional forecasts and projections and for understanding past (paleo) climates. Challenges include theoretical, and mathematical issues needed to resolve new (fractional) operators as well as to consolidate the physical basis of the theory. It involves developing new numerical techniques and models. Its foundations will be studied by using satellite and other observations of the earth's radiative exchanges. If successful, it could change the way we think about atmospheric processes while providing better forecasts and projections, impacting humanities efforts to avoid climate catastrophe.

人们越来越认识到，需要对月度和季节预报以及对十年气候预测采取新的办法。这项提议是为了继续我在20世纪70年代末发起的一项数十年研究计划，该计划旨在将随机、尺度(湍流、多重分形)方法应用和发展到大气模拟中。在过去的几年里，它催生了最先进的月度、季节性预测和(低不确定性)数十年预测。在接下来的十年里，这一新的范式将以各种方式得到发展和推广。目前，超级计算机大气模型(GCM)(扩展的天气模型)是做出月度和季节(宏观天气)预测以及数十年气候预测的唯一工具。它们的继续发展假设现实主义需要包括尽可能多的细节和过程。然而，任何小于模式分辨率(目前为数十公里)的结构可能会爆炸式增长(“蝴蝶效应”)，因此超过10天后，GCM的输出实际上是随机的。其结果是不可接受的巨大预测不确定性，例如著名的1.5到4.5 oC的二氧化碳倍增范围。GCM使用连续介质力学，假设分子水平的细节再次无关，大量相互作用的涡旋的集体行为的大多数细节是无关的，新的、甚至更高水平的湍流定律出现。这项研究计划使用尺度不变性和能量守恒的物理原理直接在宏观天气(例如每月)尺度上建立模型，这是因为新的发现，当一个著名的能量平衡模型更新时，它会产生一个基于新的分数能量平衡方程(FEBE)的统一模型。FEBE更多地以物理为基础，将尺度宏观天气和气候模式分别作为高频和低频近似值。对于温度，这些月度尺度模型已经被证明与GCM相当或更熟练，而数十年预测的不确定性不到GCM集合的一半([1]，[2]；它需要的计算量少一百万倍。当直接用于气候预测时，FEDE完全支持IPCC的最新预测，同时将不确定性降低了50%以上。这项研究计划将开发和扩展区域预报和预测以及了解过去(古)气候的FEBE。挑战包括解决新的(分数)运算符以及巩固理论的物理基础所需的理论、和数学问题。它涉及开发新的数值技术和模型。它的基础将通过卫星和其他对地球辐射交换的观测来研究。如果成功，它可能会改变我们对大气过程的看法，同时提供更好的预测和预测，影响避免气候灾难的人文努力。