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首页> 外文期刊>Hydrology and Earth System Sciences >Regional climate model data used within the SWURVE project – 2: addressing uncertainty in regional climate model data for five European case study areas
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Regional climate model data used within the SWURVE project – 2: addressing uncertainty in regional climate model data for five European case study areas

机译：SWURVE项目中使用的区域气候模型数据＆ndash; 2：解决五个欧洲案例研究区域气候模型数据中的不确定性

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摘要

To aid assessments of the impact of climate change on water related activities in the case study regions (CSRs) of the EC-funded project SWURVE, estimates of uncertainty in climate model data need to be developed. This paper compares two methods for estimating uncertainty in annual surface temperature and precipitation for the period 2070–2099. Both combine probability distribution functions for global temperature increase and for scaling variables (i.e. the change in regional temperature/precipitation per degree of global annual average temperature change) to produce a probability distribution for regional temperature and precipitation. The methods differ in terms of the distribution used for the respective probability distribution function. For scaling variables, the first method assumes a uniform distribution, whilst the second method assumes a normal distribution. For the probability distribution function of global annual average temperature change, the first method uses a uniform distribution and the second uses a log-normal approximation to a distribution derived from Wigley and Raper, 2001. Although the methods give somewhat different ranges of change, they agree on how temperature and precipitation in each of the CSRs are likely to change relative to each other. For annual surface temperature, both methods predict increases in all CSRs, although somewhat less so for NW England (5th and 95th percentiles vary between 1.1–1.9°C to 3.8–5.7°C) and about 1.7–3.1°C to 5.3–8.6°C for the others. For precipitation, most probability distributions (except for NW England) show predominantly decreasing precipitation, particularly so for the Iberian CSR (5th and 95th percentiles vary from –29.3 to –44% to –9.6 to –4%).
机译：为了帮助评估EC资助项目SWURVE的案例研究区域（CSR）中的气候变化对与水有关的活动的影响，需要建立气候模型数据不确定性的估计。本文比较了两种方法来估算2070-2099年的年表面温度和降水的不确定性。两者都结合了全球温度升高和比例变量的概率分布函数（即全球年平均温度变化每度的区域温度/降水变化）以产生区域温度和降水的概率分布。这些方法在用于各个概率分布函数的分布方面有所不同。对于缩放变量，第一种方法假定均匀分布，而第二种方法假定正态分布。对于全球年平均温度变化的概率分布函数，第一种方法使用均匀分布，第二种方法使用对数正态近似到Wigley和Raper，2001年得出的分布。尽管这些方法给出的变化范围有所不同，但它们商定每个CSR中的温度和降水如何相对变化。对于年度地表温度，这两种方法都可以预测所有CSR的增加，尽管英格兰西北部的情况要小一些（第5个和第95个百分位数在1.1-1.9°C至3.8-5.7°C之间）和约1.7-3.1°C至5.3-8.6之间其他为°C。对于降水，大多数概率分布（英格兰西北地区除外）显示降水量减少，尤其是对于伊比利亚CSR（第5和第95个百分位数在–29.3至–44％至–9.6至–4％之间）。

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