A High-Resolution Global Dataset of Extreme Sea Levels, Tides, and Storm Surges, Including Future Projections
A High-Resolution Global Dataset of Extreme Sea Levels, Tides, and Storm Surges, Including Future Projections
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DOI：
10.3389/fmars.2020.00263
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发表时间：
2020-04-29
影响因子：
3.7
通讯作者：
Verlaan, Martin
中科院分区：
文献类型：
--
作者：
Muis, Sanne;Apecechea, Maialen Irazoqui;Verlaan, Martin
由于海平面上升(SLR)，世界沿海地区面临着越来越大的沿海洪水风险。我们提出了一种新的全球极端海平面数据集--气候影响评估沿海数据集(CODEC)，该数据集可以用来准确地绘制气候变化对世界沿海地区的影响图。第三代全球潮汐和潮汐模式(GTSM)的沿海分辨率为2.5公里(欧洲为1.25公里)，用于模拟1979年至2017年ERA5气候再分析以及2040年至2100年未来气候情景的极端海平面。对观测海平面的验证显示出良好的性能，年最大值的平均偏差(MB)为-0.04m，比以前的GTSR数据集的MB低50%。到本世纪末(2071-2100年)，预计RCP4.5的10年平均水位将上升0.34米，而一些地区的水位可能上升0.5米。回归水位的变化在很大程度上是由SLR驱动的，尽管在某些地点风暴潮的变化和与潮汐的相互作用放大了SLR的影响，变化高达0.2米。通过将CODEC数据集应用于哥本哈根，我们展示了来自模拟的气候影响指标如何有助于理解局部尺度上的气候影响。此外，编解码器输出位置被设计为用作区域模型的边界条件，我们设想它们将用于动态缩小尺度。
The world's coastal areas are increasingly at risk of coastal flooding due to sea-level rise (SLR). We present a novel global dataset of extreme sea levels, the Coastal Dataset for the Evaluation of Climate Impact (CoDEC), which can be used to accurately map the impact of climate change on coastal regions around the world. The third generation Global Tide and Surge Model (GTSM), with a coastal resolution of 2.5 km (1.25 km in Europe), was used to simulate extreme sea levels for the ERA5 climate reanalysis from 1979 to 2017, as well as for future climate scenarios from 2040 to 2100. The validation against observed sea levels demonstrated a good performance, and the annual maxima had a mean bias (MB) of -0.04 m, which is 50% lower than the MB of the previous GTSR dataset. By the end of the century (2071-2100), it is projected that the 1 in 10-year water levels will have increased 0.34 m on average for RCP4.5, while some locations may experience increases of up to 0.5 m. The change in return levels is largely driven by SLR, although at some locations changes in storms surges and interaction with tides amplify the impact of SLR with changes up to 0.2 m. By presenting an application of the CoDEC dataset to the city of Copenhagen, we demonstrate how climate impact indicators derived from simulation can contribute to an understanding of climate impact on a local scale. Moreover, the CoDEC output locations are designed to be used as boundary conditions for regional models, and we envisage that they will be used for dynamic downscaling.