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Regional and general

机译:区域和一般

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This study demonstrates that long-term climate model solutions can be efficiently converted to storm surge time series at points of interest (POIs) for the future. The all-source Green's function (ASGF) regression model is used for this conversion. In addition to being data assimilative, the ASGF regression model can also simulate storm surges at a POI faster than the traditional modelling approach by orders of magnitude. This is demonstrated using the tidal gauge at Sept-Iles (Quebec, Canada) in the Gulf of St. Lawrence as the POI. First the ASGF regression model is used to assimilate 32 years of tidal gauge data, producing a continuous hindcast of storm surges and a set of best-estimate regression parameters. Second, the ASGF regression model with the best-estimate parameters is used to convert a Canadian Regional Climate Model solution (CRCM/AHJ) to an hourly time series of storm surges from 1961 to 2100. Gumbel's extreme value analysis (EVA) is then applied to the time series as a whole and also to tri-decadal segments. The tri-decadal approach is used to investigate whether there is any progressive shortening or lengthening of storm surge return periods as a result of future climate change. A method for correcting for bias due to the forcing field at the EVA level is also demonstrated.
机译:这项研究表明,长期的气候模型解决方案可以在未来的关注点(POI)上有效地转换为风暴潮时间序列。全源格林函数(ASGF)回归模型用于此转换。除了具有数据同化作用外,ASGF回归模型还可以比传统建模方法快几个数量级的POI来模拟风暴潮。使用位于圣劳伦斯湾的Sept-Iles(加拿大魁北克)的潮汐仪作为POI进行了证明。首先,ASGF回归模型用于吸收32年的潮汐量表数据,从而连续产生风暴潮的后遗症和一组最佳估计的回归参数。其次,使用具有最佳估计参数的ASGF回归模型将加拿大区域气候模型解决方案(CRCM / AHJ)转换为1961年至2100年每小时的风暴潮时间序列。然后应用Gumbel的极值分析(EVA)整个时间序列,以及三个十年的时间段。三十年方法用于调查由于未来气候变化而导致的风暴潮重现期是否有任何逐渐缩短或延长的趋势。还演示了一种用于校正由于EVA级别的强制场而引起的偏差的方法。

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  • 来源
    《Oceanographic Literature Review》 |2016年第2期|249-250|共2页
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