First Rains: Fast-tracking multiscale prediction of rainfall onset across tropical and subtropical regional climates

初雨：热带和亚热带区域气候降雨发生的快速多尺度预测

• 批准号: MR/W011379/1
• 负责人: Neil Hart
• 金额: $ 169.11万
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FW011379%2F1
• 关键词: First; Rains; Fast; tracking; multiscale

When will the rains start? Vast regions of Earth's surface experience months-long dry periods before the start of the rainy season. Onset of these rains has defined the start of agricultural calendars for millennia, however, the rapid rate of climate change is upending cen-turies of local knowledge about the arrival of the first rains. Pre-onset heat extremes are amplifying and the risk of delayed onset is increasing as the planet warms to current CO2 levels; these are risks already committed to irrespective of future CO2 emission. Dire impacts on water, food, health and energy systems accompany such delays. First Rains sets out a research programme to fast-track advances in onset prediction and make the breakthroughs integral to unlocking robust climate adaptation in the face of fickle first rains.Rainfall onset is a dramatic feature of (sub)tropical climates signalling a rapid regime switch from desiccated soils and skies to rain-filled atmospheres. This sharp switch between seasons is heralded by arrival of large thunderstorms. Timing of this arrival is critical for agricultural economies and yet it has rarely been a sole focus of prediction research programmes for over a decade. This lack in focus partly reflects numerical models that, until now, only estimated tropical thunderstorms. And yet, results from recent global monsoon theory advances point to increased delays of onset. Projections of delayed rainfall are most stark in southern Africa, the least studied of the regional monsoons. Critically, little research has engaged local forecast experts here in efforts to regionalise global theory. Gaps in both prediction science and dynamical theory continue to prevent provision of urgently needed decision-relevant onset metrics to climate adaptation efforts. However, cutting-edge new atmospheric models that directly simulate thunderstorms are now available, state-of-the-art observations provide the most comprehensive estimates the Earth System to date, and machine learning (ML) tools are providing powerful new ways to explore these data. These are the tools needed to close onset research gaps and deliver the urgently needed advance in onset prediction.First Rains will pursue this goal from two fronts. New convective-scale atmospheric models will be rigorously trialled, in close collaboration with modelling centres, to determine new-found capabilities in predicting onset days to weeks in advance. Identified model weaknesses will be fed back to model developers. Careful diagnosis of convective-scale regional dynamics and predictability will ensure maximum benefit to the most at-risk countries. The second line of research will focus on improving characterisation of the spatio-temporal statistics of the first rains, which are more important for operational decisions than a single defined onset date. Innovative use of statistical ML algorithms will aid this onset characterisation in observations and models. Application of ML methods will also provide powerful ways to determine the most important sources of onset predictability in these data. These analyses of state-of-the-art Earth observations and convective-scale models will help determine prediction skill across forecast lead-times from days to months and point to targets for improving this skill further. Advancing the dynamical theory of regional to local-scale onset will unify the convective-scale modelling and observational analysis approaches.The resulting breakthrough in fundamental prediction research will succeed in close collaboration with experts from countries most exposed to fickle first rains. The FLF +3 years will support uptake of the prediction advances into existing in-country climate adaptation and dissemination networks across the food-water-health nexus. First Rains will solve a fundamental prediction science problem and meet a long-standing and urgent societal need: generating climate information to enable effective adaptation to a warmer world.

什么时候开始下雨？在雨季开始之前，地球表面的广大地区会经历长达数月的干旱期。几千年来，这些降雨的到来定义了农业日历的开始，然而，气候变化的快速速度正在颠覆几个世纪以来当地关于第一场降雨到来的知识。随着地球变暖至目前的二氧化碳水平，爆发前的极端高温正在加剧，延迟爆发的风险也在增加；无论未来二氧化碳排放量如何，这些都是已经承担的风险。这种延误对水、食品、健康和能源系统造成了严重影响。 “初雨”制定了一项研究计划，旨在快速推进降雨爆发预测方面的进展，并在面对变化无常的初雨时实现强有力的气候适应所不可或缺的突破。降雨的到来是（亚）热带气候的一个显着特征，标志着快速的气候变化从干燥的土壤和天空到充满雨水的大气。大雷暴的到来预示着季节之间的急剧转变。这种到来的时机对于农业经济至关重要，但十多年来它很少成为预测研究项目的唯一焦点。这种缺乏重点的部分原因是数值模型迄今为止仅估计了热带雷暴。然而，最近全球季风理论进展的结果表明，季风爆发的延迟有所增加。南部非洲降雨延迟的预测最为明显，这是对区域季风研究最少的地区。关键的是，很少有研究让当地的预测专家参与到全球理论的区域化工作中。预测科学和动力学理论的差距继续阻碍为气候适应工作提供迫切需要的与决策相关的起始指标。然而，现在可以直接模拟雷暴的尖端新大气模型，最先进的观测提供了迄今为止地球系统最全面的估计，机器学习（ML）工具正在提供强大的新方法来探索这些数据。这些是缩小发病研究差距和提供发病预测方面迫切需要的进展所需的工具。First Rains 将从两个方面实现这一目标。新的对流规模大气模型将与建模中心密切合作进行严格试验，以确定新发现的提前数天至数周预测爆发的能力。识别出的模型弱点将反馈给模型开发人员。对对流规模区域动态和可预测性的仔细诊断将确保风险最高的国家获得最大利益。第二线研究将侧重于改进首次降雨的时空统计特征，这对于操作决策比单一定义的开始日期更重要。统计机器学习算法的创新使用将有助于观察和模型中的这种起始表征。机器学习方法的应用还将提供强大的方法来确定这些数据中发病可预测性的最重要来源。这些对最先进的地球观测和对流规模模型的分析将有助于确定从几天到几个月的预测提前期的预测技能，并指出进一步提高这项技能的目标。将区域尺度爆发的动力学理论推进到地方尺度将统一对流尺度建模和观测分析方法。基础预测研究的突破将通过与来自首场降雨变化最严重的国家的专家的密切合作取得成功。 FLF+3 年将支持将预测进展纳入国内现有的气候适应和传播网络，涵盖食品与水健康的关系。 “初雨”将解决一个基本的预测科学问题，并满足长期而紧迫的社会需求：生成气候信息，以有效适应变暖的世界。

项目成果

A quasi-geostrophic analysis of summertime southern African linear-regime westerly waves

夏季南部非洲线性型西风波的准地转分析

• DOI: 10.1007/s00382-023-07067-0
• 发表时间: 2024
• 期刊: Climate Dynamics
• 影响因子: 4.6
• 作者: Ndarana T

Characteristics of tropical-extratropical cloud bands over tropical and subtropical South America simulated by BAM-1.2 and HadGEM3-GC3.1

BAM-1.2和HadGEM3-GC3.1模拟的南美洲热带和副热带云带特征

• DOI: 10.1002/qj.4470
• 发表时间: 2023
• 期刊: Quarterly Journal of the Royal Meteorological Society
• 影响因子: 8.9
• 作者: Zilli M