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首页> 外文期刊>Journal of Climate >Bias Correction of GCM Precipitation by Quantile Mapping: How Well Do Methods Preserve Changes in Quantiles and Extremes?
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Bias Correction of GCM Precipitation by Quantile Mapping: How Well Do Methods Preserve Changes in Quantiles and Extremes?

机译：用分位数映射对GCM降水进行偏差校正：方法如何保留分位数和极端值的变化？

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摘要

Quantile mapping bias correction algorithms are commonly used to correct systematic distributional biases in precipitation outputs from climate models. Although they are effective at removing historical biases relative to observations, it has been found that quantile mapping can artificially corrupt future model-projected trends. Previous studies on the modification of precipitation trends by quantile mapping have focused on mean quantities, with less attention paid to extremes. This article investigates the extent to which quantile mapping algorithms modify global climate model (GCM) trends in mean precipitation and precipitation extremes indices. First, a bias correction algorithm, quantile delta mapping (QDM), that explicitly preserves relative changes in precipitation quantiles is presented. QDM is compared on synthetic data with detrended quantile mapping (DQM), which is designed to preserve trends in the mean, and with standard quantile mapping (QM). Next, methods are applied to phase 5 of the Coupled Model Intercomparison Project (CMIP5) daily precipitation projections over Canada. Performance is assessed based on precipitation extremes indices and results from a generalized extreme value analysis applied to annual precipitation maxima. QM can inflate the magnitude of relative trends in precipitation extremes with respect to the raw GCM, often substantially, as compared to DQM and especially QDM. The degree of corruption in the GCM trends by QM is particularly large for changes in long period return values. By the 2080s, relative changes in excess of +500% with respect to historical conditions are noted at some locations for 20-yr return values, with maximum changes by DQM and QDM nearing +240% and +140%, respectively, whereas raw GCM changes are never projected to exceed +120%.
机译：分位数映射偏差校正算法通常用于校正气候模型降水量输出中的系统分布偏差。尽管它们可以有效地消除相对于观察结果的历史偏差，但已发现分位数映射可以人为地破坏未来模型预测的趋势。以前关于通过分位数制图修改降水趋势的研究都集中在平均数量上，很少关注极端情况。本文研究分位数映射算法修改平均降水量和极端降水指数的全球气候模型（GCM）趋势的程度。首先，提出了一种偏差校正算法，分位数增量映射（QDM），该算法明确保留了降水分位数的相对变化。将QDM与合成数据进行比较，采用趋势保留的分位数映射（DQM）和标准分位数映射（QM），DQM旨在保留均值趋势。接下来，将方法应用于加拿大耦合模型比较项目（CMIP5）的每日降水预测的第5阶段。基于极端降水指数和适用于年降水最大值的广义极值分析的结果对性能进行评估。与DQM尤其是QDM相比，相对于原始GCM，QM可以大大增加极端降水相对趋势的幅度。对于长期回报值的变化，QM对GCM趋势的破坏程度特别大。到2080年代，在某些位置记录了20年回报值相对于历史条件的相对变化超过+ 500％，DQM和QDM的最大变化分别接近+ 240％和+ 140％，而原始GCM更改永远不会超过+ 120％。

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