The simulations from climate models require bias correction prior to use in impact assessments or when used as predictors in statistical or dynamic downscaling models. Recent works have sought to address each of these limitations and the results are the Multivariate Recursive Nesting Bias Correction (MRNBC) and Multivariate recursive Quantile-matching Nested Bias Correction (MRQNBC) methods. The model was applied to a mountain region of Heihe River. A comparison of the historical and generated statistics shows that the model preserves all the important characteristics of meteorological variables at daily, monthly, seasonally and annual time scales. This study has documented the performance of Multivariate Recursive Nesting Bias Correction to remove the discrepancy between the predictors in the simulated GCM and the reanalysis NCEP data and assess the projected future precipitation accuracy in the headwater region of Heihe River. A relatively high spatial resolution GCM outputs—ACCESS1-3—from the CMIP5 Earth System Models (ESMs) was employed to downscale for the historical 1960–2005 and the future period 2010–2100 under the scenarios of Representative Concentration Pathways RCP4.5 and RCP8.5. The MRNBC method can dramatically increase the performance of the simulated precipitation data. Verified by statistical score metrics applied for evaluation of the results, the developed method appears to be an important statistical tool in the correction of the bias between the GCM output and the reanalysis data, leading to significant improvements in the predictive performance accuracy of the precipitation projections. The projected precipitation under RCP8.5 appeared to exhibit the significant increasing trend relative to the RCP4.5 scenario in the headwater region of Heihe River. Future precipitation will increasing by 8% and 20% for near and long term period under RCP4.5 and increasing 14% and 37% for near and long term period, under RCP8.5, respectively.

中文翻译：

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使用多元偏差校正软件包校正黑河中的气候模型模拟

气候模型的模拟在用于影响评估之前或在统计或动态缩减模型中用作预测变量之前，需要进行偏差校正。最近的工作试图解决这些局限性，结果是采用多元递归嵌套偏倚校正（MRNBC）和采用多元递归分位数匹配嵌套偏倚校正（MRQNBC）方法。该模型已应用于黑河山区。历史数据和生成的统计数据的比较表明，该模型在每日，每月，季节性和年度时间尺度上都保留了气象变量的所有重要特征。这项研究已经证明了多元递归嵌套偏差校正的性能，可以消除模拟GCM中的预测因子与再分析NCEP数据之间的差异，并评估黑河源头地区未来的预测降水精度。在代表性浓度路径RCP4.5和RCP8的情况下，采用了CMIP5地球系统模型（ESMs）相对较高的空间分辨率GCM输出（ACCESS1-3）来缩小1960-2005年历史和2010-2100年未来的尺度。 .5。MRNBC方法可以大大提高模拟降水数据的性能。通过用于评估结果的统计评分指标进行验证，开发的方法似乎是校正GCM输出和再分析数据之间偏差的重要统计工具，从而显着提高了降水预测的预测性能精度。与RCP4.5情景相比，RCP8.5下的预计降水量在黑河源头地区表现出明显的增加趋势。在RCP4.5下，近期和长期的未来降水量将分别增长8％和20％，在RCP8.5下，近期和长期的未来降水量将分别增长14％和37％。黑河源区的5种情景。在RCP4.5下，近期和长期的未来降水量将分别增长8％和20％，在RCP8.5下，近期和长期的未来降水量将分别增长14％和37％。黑河源区的5种情景。在RCP4.5下，近期和长期的未来降水量将分别增长8％和20％，在RCP8.5下，近期和长期的未来降水量将分别增长14％和37％。