Climate Model Biases and Modification of the Climate Change Signal by Intensity-Dependent Bias Correction
Climate Model Biases and Modification of the Climate Change Signal by Intensity-Dependent Bias Correction
复制标题
气候模型偏差和通过强度相关偏差校正对气候变化信号的修正
作者：
M. Ivanov;J. Luterbacher;S. Kotlarski
气候变化影响研究和风险评估需要准确估计气候变化信号。原始气候模型数据包括系统性偏差，这些偏差影响了日降水量和风速等高影响变量的CCS。本文提出了一种新的，一般的，和可扩展的分析理论的影响，这些偏见的CCS的分布均值和分位数。该理论表明，歪曲模型强度和概率的非零（积极）事件有可能扭曲原始模型CCS估计。我们测试的分析描述在一个具有挑战性的应用程序中的偏差校正和降尺度的日降水量在高山地形，15个区域气候模型（RCMs）的输出减少到当地气象站。理论上预测的CCS修改以及近似的偏差校正方法的修改，即使是最大的绝对修改站-RCM组合。这些结果表明，CCS修正偏差校正是消除模型偏差的直接结果。因此，只要强度依赖性偏差校正的应用在科学上是适当的，CCS修改应该是一个理想的效果。分析理论可以用作工具，以1）检测具有高可能性的模型偏差，以扭曲CCS和2）有效地生成新的，改进的CCS数据集。后者与制定适当的气候变化适应、减缓和复原战略高度相关。未来的研究需要集中在开发基于过程的偏差校正，依赖于模拟强度，而不是保留原始模型CCS。
Climate change impact research and risk assessment require accurate estimates of the climate change signal (CCS). Raw climate model data include systematic biases that affect the CCS of high-impact variables such as daily precipitation and wind speed. This paper presents a novel, general, and extensible analytical theory of the effect of these biases on the CCS of the distribution mean and quantiles. The theory reveals that misrepresented model intensities and probability of nonzero (positive) events have the potential to distort raw model CCS estimates. We test the analytical description in a challenging application of bias correction and downscaling to daily precipitation over alpine terrain, where the output of 15 regional climate models (RCMs) is reduced to local weather stations. The theoretically predicted CCS modification well approximates the modification by the bias correction method, even for the station–RCM combinations with the largest absolute modifications. These results demonstrate that the CCS modification by bias correction is a direct consequence of removing model biases. Therefore, provided that application of intensity-dependent bias correction is scientifically appropriate, the CCS modification should be a desirable effect. The analytical theory can be used as a tool to 1) detect model biases with high potential to distort the CCS and 2) efficiently generate novel, improved CCS datasets. The latter are highly relevant for the development of appropriate climate change adaptation, mitigation, and resilience strategies. Future research needs to focus on developing process-based bias corrections that depend on simulated intensities rather than preserving the raw model CCS.
区域气候模型评估背景下绩效测量的现场意义第 1 部分：温度
影响因子：
3.4
作者：
Ivanov;Warrach-Sagi;Wulfmeyer
通讯作者：
Wulfmeyer
影响因子：
4.4
作者：
Sillmann, J.;Kharin, V. V.;Bronaugh, D.
通讯作者：
Bronaugh, D.
影响因子：
5.1
作者：
Kotlarski, S.;Keuler, K.;Wulfmeyer, V.
通讯作者：
Wulfmeyer, V.