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首页> 外文期刊>Journal of Climate >A Multivariate Quantile-Matching Bias Correction Approach with Auto- and Cross-Dependence across Multiple Time Scales: Implications for Downscaling
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A Multivariate Quantile-Matching Bias Correction Approach with Auto- and Cross-Dependence across Multiple Time Scales: Implications for Downscaling

机译：在多个时间尺度上具有自动和交叉相关性的多元分位数匹配偏差校正方法：降级的含义

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摘要

A novel multivariate quantile-matching nesting bias correction approach is developed to remove systematic biases in general circulation model (GCM) outputs over multiple time scales. This is a significant advancement over typical quantile-matching alternatives available for bias correction, as they implicitly assume that correction of individual variable attributes will lead to correction of dependence biases between multiple variables. Furthermore, existing approaches perform bias correction at a given time scale (e.g., daily), whereas applications often require biases to be addressed at more than one time scale (such as annual in the case of most water resources planning projects). The proposed approach addresses all these issues, and additionally attempts to correct for lag-1 dependence (and cross-dependence) attributes across multiple time scales. The approach is called multivariate recursive quantile nesting bias correction (MRQNBC). The fidelity of the approach is demonstrated by applying it to a vector of CSIRO Mk3 GCM atmospheric variables and comparing the results with the commonly used quantile-matching approach. Following this, the implications of the approach in hydrology-and water resources-related applications are demonstrated by feeding the bias-corrected data to a rainfall downscaling model and comparing the downscaled rainfall attributes for current and future climate. The proposed approach is shown to represent the variability and persistence related attributes better and can thus be expected to have important consequences for the simulation of occurrence and intensity of extreme events such as floods and droughts in downscaled simulations, of importance in various climate impact assessment applications.
机译：开发了一种新颖的多元分位数匹配嵌套偏差校正方法，以消除多个时间范围内通用循环模型（GCM）输出中的系统偏差。与可用于偏差校正的典型分位数匹配替代方案相比，这是一项重大进步，因为它们隐含地假设对单个变量属性的校正将导致对多个变量之间的依赖偏差进行校正。此外，现有方法在给定的时间尺度（例如每天）上执行偏差校正，而应用程序通常需要在一个以上的时间尺度上解决偏差（例如，对于大多数水资源规划项目而言，是每年一次）。所提出的方法解决了所有这些问题，并且还尝试在多个时间范围内校正lag-1依赖性（和交叉依赖性）属性。该方法称为多元递归分位数嵌套偏差校正（MRQNBC）。通过将其应用于CSIRO Mk3 GCM大气变量矢量并将结果与​​常用分位数匹配方法进行比较，可以证明该方法的保真度。然后，通过将偏差校正后的数据输入降雨缩减模型并比较当前和未来气候的缩减降雨属性，证明了该方法在与水文和水资源相关的应用中的意义。结果表明，所提出的方法可以更好地表示与变异性和持久性相关的属性，因此可以预期对模拟极端事件的发生和强度产生重大影响，例如缩减规模的模拟中的洪水和干旱，在各种气候影响评估应用中都具有重要意义。

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