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

极端海平面事件对人类社会和生态系统的破坏性可能比平均海平面缓慢上升更大。如果海平面极端事件的频率在人为气候变化的影响下发生变化，它可能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，还容易受到台风和热带外气旋的影响。然而，目前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，以及内部变率的幅度及其发生频率可能有多大。亚洲-洪水将分析一系列全球和区域气候模拟，在过去的千年中进行不同的空间分辨率，现在的气候和未来的情景，目的是确定东南亚地区的大气天气模式（这里定义为包括台风和热带外风暴），最有效地引起沿海洪水，由于风暴洪水或由于极端的大陆降雨或两者的组合。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局部极端降水进行大量模拟的成本很高，因此我们将采用统计降尺度方法。在这种方法中，代表大气动力学的大尺度预测器使用观测数据集与当地气候进行统计联系。一旦这些统计模型得到校准，它们就可以应用于过去和未来的全球和区域气候模拟，以估计这类极端事件发生频率的变化。在亚洲洪水中，我们将使用基于分类回归树和随机森林的统计模型。这些都是合适的（虽然相当复杂）分类（天气类型）方案，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。为了校正统计模式，我们将使用来自气象再分析产品和气候模拟的网格观测数据，一边是同化数据，另一边是当地的日海平面和日降水观测记录。这些结果将与该SPP中的另外两个建议联系起来。在过去几个世纪的模拟（古气候模拟）的情况下，结果将与该SPP中根据代理数据调查洪水频率的其他项目进行比较。在情景模拟的情况下，结果将用于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.