Monsoons and the Intertropical Convergence Zone in the Presence of Zonal Asymmetries

全球三分之二的人口生活在热带和亚热带地区，那里的大部分降雨是由季风提供的。这些大尺度环流主导着非洲、亚洲、澳大利亚和美洲部分地区的夏季气候，在社会、经济和生态福祉方面发挥着至关重要的作用。然而，我们对季风的理解仍然有限：大气环流模型（GCM）在当今的两个方面都表现出明显的偏差（Kitoh等人，2013年）和古气候（Boos & Korty，2016年）季风降雨模式，并且模型之间对未来变化的预测存在明显分歧（Wang等人，2020年）。重要的是，改善季风代表CMIP阶段之间是有限的：共同的模型偏差和预测中的大模型间传播持续在CMIP 6。这表明，增加模型的复杂性和分辨率本身并不能解决大的不确定性。对控制季风降雨的过程和机制以及它们对外部强迫的响应的进一步理解是至关重要的。通过最近将季风重新定义为降水现象，我们对季风的理论理解得到了推进。传统上，它们被认为是大陆尺度的陆风-海风，然而，它们现在被理解为与热带翻转环流紧密耦合，形成热带辐合带（ITCZ）季节性漂移的陆地成分（Biasutti等人，2018年）。大量的工作已经将纬向平均ITCZ的纬度与跨赤道大气能量传输联系起来，从而与全球能量约束联系起来（例如，Bischoff & Schneider，2014）。类似的工作正在进行中，以确定纬向平均ITCZ的宽度和强度（Byrne等人，此外，在浅水行星上产生了类似季风的行为，并以角动量为特征（例如Bordoni & Schneider，2008）。然而，这些理论忽视了ITCZ的显著地带性变化，这意味着它们不能很好地代表区域气候，特别是在冬至月期间（Adam等人，2016）。已经做了一些关于理解纬向不对称对ITCZ的影响的工作，既使用简单模型（例如Priv 'e & Plumb，2007年），也使用全复杂性GCM（Maroonet等人，2016），但仍有许多未解决的问题。本项目的目的是研究陆地和SST型引起的纬向不对称对ITCZ的影响，特别关注陆地上的结果，即季风区。为了实现这一点，将使用与大气环流模式的大气部分相结合的地球表面的理想化表示。将研究当前和气候变化情景（4xCO 2）。在这两种情况下，云辐射效应的作用将被考虑。CMIP 6和再分析数据将被用来通知研究问题的发展，并提供一个比较点。在该项目的后期阶段，理想化研究的结果将用于了解这些更复杂的数据集中季风的行为，从而有助于减少季风预测的不确定性。

Two-thirds of the global population lives in regions of the tropics and subtropics where the majority ofrainfall is provided by monsoons. These large-scale circulations dominate summertime climatology in partsof Africa, Asia, Australia and the Americas, playing an essential role in societal, economic and ecologicalwellbeing. However, our understanding of monsoons remains limited: general circulation models (GCMs)exhibit pronounced biases in both present-day (Kitoh et al., 2013) and paleoclimate (Boos & Korty, 2016)monsoon rainfall patterns, and there is stark disagreement between models in projections of future change(Wang et al., 2020). Importantly, improvement in monsoon representation between CMIP phases is lim-ited: common model biases and large inter-model spreads in projections persist in CMIP6. This suggeststhat increasing model complexity and resolution will not by itself resolve the large uncertainty. Improvedunderstanding of the processes and mechanisms governing monsoon rainfall, and their response to externalforcings, is vital.Our theoretical understanding of monsoons has been advanced through their recent re-framing as aglobal phenomenon. Traditionally, they were considered to be continental-scale land-sea breezes, howeverthey are now understood to be tightly coupled to the tropical overturning circulation, forming the over-land component of the seasonal excursions of the Intertropical Convergence Zone (ITCZ) (Biasutti et al.,2018). A substantial body of work has linked the latitude of the zonal-mean ITCZ to the cross-equatorialatmospheric energy transport, and consequently, global energy constraints (e.g. Bischoff & Schneider, 2014).Similar work is ongoing to characterise the zonal-mean ITCZ width and strength (Byrne et al., 2018).Furthermore, monsoon-like behaviour has been produced on a shallow aquaplanet and characterised in termsof angular momentum (e.g. Bordoni & Schneider, 2008). However, such theories disregard the significantzonal variability in the ITCZ, meaning they poorly represent regional climate, particularly during solstitialmonths (Adam et al., 2016). Some work on understanding the impact of zonal asymmetries on the ITCZhas been done, both using simple models (e.g. Priv 'e & Plumb, 2007) and full-complexity GCMs (Maroonet al., 2016), but many unresolved questions remain.The aim of this project is to study the impact of zonal asymmetries caused by land and SST patterns onthe ITCZ, with a particular focus on results over land, i.e. monsoon regions. To achieve this, an idealisedrepresentation of the Earth's surface coupled to the atmospheric component of a GCM will be used. Present-day and climate change scenarios (4xCO2) will be studied. In both cases, the role of cloud radiative effectswill be considered.CMIP6 and reanalysis data will be used to inform the development of research questions and providea point of comparison. In the later stages of the project, results from the idealised studies will be used tounderstand the behaviour of monsoons in these more complex data sets, and consequently contribute to thenarrowing of uncertainties in monsoon projections.