>Climate model response (M) and greenhouse gas emissions (S) uncertainties are consistently estimated as spreads of multi‐model and multi‐scenario climate'/> A method for investigating the relative importance of three components in overall uncertainty of climate projections
首页> 外文期刊>International Journal of Climatology: A Journal of the Royal Meteorological Society >A method for investigating the relative importance of three components in overall uncertainty of climate projections
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A method for investigating the relative importance of three components in overall uncertainty of climate projections

机译:一种研究气候投影总体不确定性三种组分的相对重要性的方法

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>Climate model response (M) and greenhouse gas emissions (S) uncertainties are consistently estimated as spreads of multi‐model and multi‐scenario climate change projections. There has been less agreement in estimating internal climate variability (V). In recent years, an initial condition ensemble (ICE) of a climate model has been developed to study V. ICE is simulated by running a climate model using an identical climate forcing but different initial conditions. Inter‐member differences of an ICE manifestly represent V. However, ICE has been barely used to investigate relative importance of climate change uncertainties. Accordingly, this study proposes a method of using ICEs, without assuming V as constant, for investigating the relative importance of climate change uncertainties and its temporal–spatial variation. Prior to investigating temporal–spatial variation in China, V estimated using ICE was compared to that using multi‐model individual time series at national scale. Results show that V using ICE is qualitatively similar to that using multi‐model individual time series for temperature. However, V is not constant for average and extreme precipitations. V and M dominate before 2050s especially for precipitation. S is dominant in the late 21st century especially for temperature. Mean temperature change is projected to be 30–70% greater than its uncertainty until 2050s, while uncertainty becomes 10–40% greater than the change in the late 21st century. Precipitation change uncertainty overwhelms its change by 70–150% throughout 21st century. Cold regions (e.g., northern China and Qinghai‐Tibetan Plateau) tend to have greater temperature change uncertainties. In dry regions (e.g., northwest China), all three uncertainties tend to be great for changes in average and extreme precipitations. This study emphasizes the importance of considering climate change uncertainty in impact studies, especially taking in
机译:

气候模型反应(M)和温室气体排放量始终如一地估计多模型和多场景气候变化预测的差价。估计内部气候变异性(V)的一致意见。近年来,已经开发了一种气候模型的初始条件集合(ICE)来研究V.通过使用相同的气候迫使但初始条件运行气候模型来模拟冰。冰的成员界明显代表V.然而,冰几乎没有用于调查气候变化不确定因素的相对重要性。因此,该研究提出了一种使用冰的方法,而不假设V作为常数,用于研究气候变化不确定性及其时间空间变化的相对重要性。在调查中国的时间空间变化之前,将使用冰估计的v与国家规模以多模型单位序列进行比较。结果表明,使用冰的V与使用多模型单个时间序列进行定性类似的温度。然而,V不是恒定的平均和极端沉淀。 V和M在2050年代之前主导,特别是降水。 S在21世纪后期占主导地位,特别是温度。平均温度变化预计比其不确定性大于2050年代的30-70%,而不确定度比21世纪晚期的变化大于10-40%。降水变化不确定性在整个21世纪整个历史上涨了70-150%。寒冷的地区(例如,中国北部和青海高原)往往具有更大的温度变化不确定性。在干燥地区(例如,中国西北),所有三个不确定性都往往是平均和极端沉淀的变化。本研究强调了考虑影响影响研究的气候变化不确定性的重要性,特别是在

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