Transforming our understanding of climate shifts in the North African dust belt and upskilling Earth System Models to simulate them

随着未来几十年地球变暖，全球降雨模式将发生变化，地区结果不确定。这个问题在北非最为严重。撒哈拉沙漠是世界上最大的炎热沙漠，也是大气粉尘的最大来源，它在20世纪增长了10%。在半干旱的萨赫勒和地中海边界地区，年降雨量低，年际降雨量变化大，数千万人生活在极端的水压力中，有时会造成毁灭性的后果，就像20世纪末萨赫勒干旱期间那样。这些是理解推动北非气候过去变化的机制的主要动机。然而，仪器记录的时间很短(约150年)，地质和考古数据显示，北非气候有能力发生更极端的变化。在地球围绕太阳的轨道逐渐变化和气候系统千年尺度的变化的影响下，北非过去的气候在比今天更干燥、更尘土的条件和湿润的绿色撒哈拉时期(GSP)之间反复变化，当时沙漠变成了河流和湖泊横切的植被茂盛的地貌，居住着河马和我们的早期祖先(最近的一次是在大约14.5到5000年前的非洲湿润时期，AHP)。是什么导致了过去的变化？它们对未来意味着什么？我们对这些问题缺乏令人信服的答案，因为用于预测未来变化的计算机模型不能再现古数据所指示的极端变化。现有古数据的一个主要弱点是它们对机械控制的诊断有限。北非有两个纬度和季节不同的降雨制度，即夏季季风和冬季西风风暴路径，但我们对它们对数据中记录的湿度、干旱和植被深刻变化的时空贡献知之甚少。这些模型的一个主要弱点是，我们不知道它们缺乏技能是否反映了抑制对气候强迫的反应的过程和反馈的缺失，或者它们是否充分代表了关键过程，但无意中具有稳定性，因为不确定的参数值完全由对当代观测目标的评估确定。我们建议改变我们对北非沙尘带气候变化的理解，并提高模型的技能，通过利用我们最近取得的突破(见记录)，成功地模拟这些变化。我们将开发撒哈拉领头羊地区的替代气候记录，以梳理夏季季风和冬季风暴路径降雨量过去的变化(见目标)。我们将配置一个政府间气候变化专门委员会级别的地球系统模型(ESM)，该模型展示了绿化撒哈拉沙漠的新力量，并且在计算上足够便宜，可以对参数空间进行足够的采样，以对抗我们的新代理记录(见目标)。这将使我们能够形成对极端变化的机械性理解，并将我们的发现转化为正在开发下一代ESM的科学家，这些ESM将用于预测未来几十年的未来变化(见学术受益者)。

As Earth heats up in the coming decades, global rainfall patterns will shift with uncertain regional outcomes. Nowhere is this problem more acute than in North Africa. The world's largest hot desert and biggest source of atmospheric dust, the Sahara, grew by >10% in the 20th century. In the semi-arid Sahel and Mediterranean borderlands, where annual rainfall amounts are low and inter-annual rainfall variability is high, tens of millions of people live in extreme water stress with sometimes devastating consequences as during the late 20th century Sahel drought. These are major incentives to understand the mechanisms driving past variability in the climate of North Africa. However, the instrumental record is short (~150 yrs) and geological and archaeological data reveal a capacity for far more extreme shifts in North African climate. Paced by Earth's gradually changing orbit around the Sun and millennial-scale variability in the climate system, North Africa's past climate has shifted repeatedly between drier and dustier conditions than today and humid green Sahara periods (GSPs) when the desert was transformed into a well vegetated landscape crosscut by rivers and lakes, populated by hippopotamuses and our early ancestors (most recently during the African Humid Period, AHP, ~14.5 to 5 thousand years ago, ka).What caused these past shifts? What do they imply for the future? We lack convincing answers to these questions because the extreme shifts indicated by the palaeo-data are not reproduced by the computer models used to predict future change. A main weakness of the existing palaeo-data is their limited diagnosis of mechanistic control. North Africa has two latitudinal and seasonally distinct rainfall regimes, the summer monsoon and winter westerly storm track, but we know too little of their temporal and spatial contributions to the profound changes in humidity-aridity and vegetation recorded in the data. A main weakness in the models is that we do not know whether their lack of skill reflects missing processes and feedbacks that dampen the response to climate forcing or whether they adequately represent the key processes but are inadvertently hard-wired for stability because uncertain parameter values are determined solely by evaluation against contemporary observational targets. We propose to transform our understanding of climate shifts in the North African dust belt and to upskill models to successfully simulate them by capitalizing on recent breakthroughs that we have made (see Track Record). We will develop proxy climate records for a bellwether Saharan region to tease apart past variability in summer monsoon and winter storm-track rainfall (see Objectives). And we will configure an IPCC-class Earth System model (ESM) that shows novel power to green the Sahara and is sufficiently computationally inexpensive to permit adequate sampling of parameter space to confront our new proxy records (see Objectives). This will allow us to develop a mechanistic understanding of extreme change and to translate our findings to those scientists who are developing the next generation of ESMs that will be used to predict future change over the coming decades (see Academic Beneficiaries).