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Assessing the Uncertainty in Projecting Local Mean Sea Level from Global Temperature

机译:从全球温度估算局部平均海平面的不确定性评估

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摘要

The process of moving from an ensemble of global climate model temperature projections to local sea level projections requires several steps. Sea level was estimated in Olympia, Washington (a city that is very concerned with sea level rise because parts of downtown are barely above mean highest high tide), by relating global mean temperature to global sea level; relating global sea level to sea levels at Seattle, Washington; and finally relating Seattle to Olympia. There has long been a realization that accurate assessment of the precision of projections is needed for science-based policy decisions. When a string of statistical and/or deterministic models is connected, the uncertainty of each individual model needs to be accounted for. Here the uncertainty is quantified for each model in the described system and the total uncertainty is assessed in a cascading effect throughout the system. The projected sea level rise over time and its total estimated uncertainty are visualized simultaneously for the years 2000-2100, the increased uncertainty due to each of the component models at a particular projection year is identified, and estimates of the time at which a certain sea level rise will first be reached are made.
机译:从全球气候模型整体温度预测向本地海平面预测转变的过程需要几个步骤。通过将全球平均温度与全球海平面相关联,估计了华盛顿州奥林匹亚的海平面(该城市非常关注海平面上升,因为市区的部分地区仅略高于平均最高涨潮);将全球海平面与华盛顿西雅图的海平面相关;最后将西雅图与奥林匹亚联系起来。长期以来一直认识到,基于科学的政策决策需要对预测精度进行准确的评估。当连接一连串统计和/或确定性模型时,需要考虑每个单独模型的不确定性。在此,对所描述系统中每个模型的不确定性进行量化,并以整个系统的级联效应评估总不确定性。同时显示2000年至2100年的预计海平面随时间的上升及其总的不确定性,确定了由于特定预测年份的每个分量模型而导致的不确定性增加,并估算了特定海域的时间会首先达到水平上升。

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