Asia-Floods - Extreme sea-level events along South-East Asian coast: past, present and future

与平均海平面缓慢上升相比，极端海平面事件对人类社会和生态系统的破坏可能更大。如果海平面极端的频率在人为气候变化的影响下发生变化，可能会对气候变化影响的估计产生深远的影响，从而对计划的适应措施产生深远的影响。东南亚除了是世界上人口最多的地区之一外，还受到台风和热带气旋的影响。然而，尚不清楚风暴洪水和极端降雨的频率如何取决于外部辐射强迫，以及内部变化的幅度和发生频率可能有多大。亚洲洪水将分析过去千年来进行的一系列具有不同空间分辨率的全球和区域气候模拟、当前气候和未来情景，目的是确定东南亚区域(这里定义为包括台风和热带以外风暴)最有效地造成沿海洪水的大气天气模式，这些模式要么是由于风暴洪水，要么是由于极端大陆降雨，或者两者兼而有之。由于全球和区域气候模式的空间分辨率使得对暴雨洪水和极端局地降水进行大量模拟的成本很高，我们将采用统计降尺度方法。在这种方法中，代表大气动态的大规模预报器使用观测数据集在统计上与当地气候联系在一起。一旦这些统计模型被校准，它们就可以应用于过去和未来的全球和区域气候模拟，以估计这些类型的极端事件频率的变化。在亚洲洪灾中，我们将使用基于分类和回归树和随机森林的统计模型。这些都是合适的分类(天气分类)方案，虽然相当复杂，但可以针对规定的目标进行优化，在这种情况下是风暴潮和极端沿海降水。为了校准统计模型，我们将使用来自气象再分析产品的网格观测和气候模拟，一方面是数据同化，另一方面是每日海平面和每日降水量的本地观测。这些结果将与本SPP中的另外两项建议相联系。在过去几个世纪的模拟(古气候模拟)的情况下，结果将与SPP内的其他项目进行比较，这些项目根据代理数据调查洪水的频率。在情景模拟的情况下，结果将被用来估计沿海洪水造成的经济成本增加。

Extreme sea-level events may be more disruptive for human societies and ecosystems than a slowly rising mean sea-level. If the frequency of sea-level extremes changes under the influence of anthropogenic climate change, it may have profound consequences in the estimation of climate change impacts and therefore on the planned adaptation measures. South East Asia, in addition to being one of the most populated regions in the world, is exposed to the impacts of typhoons and extra-tropical cyclones. It is however not yet clear how the frequency of storm floods and extreme rainfall may depend on the external radiative forcing and how large the amplitude of the internal variability and their frequency of occurrence may be.Asia-Floods will analyze a series of global and regional climate simulations, with different spatial resolutions, conducted over the past millennium, present climate and future scenarios, with the objective of identifying the atmospheric weather patterns in the South East Asia region (defined here as including typhoons and extra-tropical storms) that are most effective in causing coastal flooding either due to storm floods or due to extreme continental rainfall or a combination of both. Since the spatial resolution of global and regional climate models makes it costly to perform a large number of simulations of storm floods and extreme localized precipitation, we will apply a statistical downscaling approach. Within this approach, large-scale predictors that represent the atmospheric dynamics are statistically linked to local climate using observational data sets. Once these statistical models are calibrated they can be applied to past and future global and regional climate simulations to estimate changes in the frequency of these types of extreme events. In Asia-Floods we will use statistical models based on Classification and Regression Trees and Random Forest. These are suitable, though rather sophisticated, classification (weather typing) schemes that can be optimized towards a prescribed target, in this case storm surges and extreme coastal precipitation. To calibrate the statistical models we will use gridded observations from meteorological reanalysis products and climate simulations with data assimilation on one side, and local observations of daily sea-level and daily precipitation records on the other side.These results will be linked to two other proposals within this SPP. In the case of the simulations over the past centuries (paleoclimate simulations), the results will be compared to other projects within this SPP that investigate the frequency of flooding from proxy data. In the case of the scenario simulations, the results will be used to estimate the increase in economic costs from coastal flooding.