Improved Bias Correction Techniques for Hydrological Simulations of Climate Change
Improved Bias Correction Techniques for Hydrological Simulations of Climate Change
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DOI：
10.1175/jhm-d-14-0236.1
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发表时间：
2015-12-01
影响因子：
3.8
通讯作者：
Hegewisch, Katherine C.
中科院分区：
文献类型：
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作者：
Pierce, David W.;Cayan, Daniel R.;Hegewisch, Katherine C.
全球气候模式（GCM）的输出通常需要进行偏差校正，然后才能用于气候变化影响研究。三个现有的偏差校正方法，并在这里开发的一个新的，适用于每日最高温度和降水量从21 GCM调查不同的方法如何改变气候变化信号的GCM。分位数映射（QM）和累积分布函数变换（CDF-t）偏差校正方法可以显着改变GCM的平均气候变化信号，月平均温度和降水的差异分别高达2摄氏度和30%。等距分位数匹配（EDCDFm）偏差校正保留GCM的变化，平均每日最高温度，但不降水。一个扩展EDCDFm称为PresRat的介绍，它通常保留的平均降水的GCM变化。另一个问题是，大气环流模型可能难以模拟作为频率函数的方差。为了解决这个问题，引入了一种频率相关的偏差校正方法，该方法在减少模型对作为频率的函数的方差的模拟中的误差方面是标准偏差校正的两倍有效，并且与标准偏差校正不同，它这样做不会使任何位置变得更糟。最后，一个预处理技术，提高了模拟的年度周期，同时仍然允许偏差校正，以考虑到整个赛季的价值一次。
Global climate model (GCM) output typically needs to be bias corrected before it can be used for climate change impact studies. Three existing bias correction methods, and a new one developed here, are applied to daily maximum temperature and precipitation from 21 GCMs to investigate how different methods alter the climate change signal of the GCM. The quantile mapping (QM) and cumulative distribution function transform (CDF-t) bias correction methods can significantly alter the GCM's mean climate change signal, with differences of up to 2 degrees C and 30% points for monthly mean temperature and precipitation, respectively. Equidistant quantile matching (EDCDFm) bias correction preserves GCM changes in mean daily maximum temperature but not precipitation. An extension to EDCDFm termed PresRat is introduced, which generally preserves the GCM changes in mean precipitation. Another problem is that GCMs can have difficulty simulating variance as a function of frequency. To address this, a frequency-dependent bias correction method is introduced that is twice as effective as standard bias correction in reducing errors in the models' simulation of variance as a function of frequency, and it does so without making any locations worse, unlike standard bias correction. Last, a preconditioning technique is introduced that improves the simulation of the annual cycle while still allowing the bias correction to take account of an entire season's values at once.