Uncertainty analysis of statistical downscaling models using Hadley Centre Coupled Model
Uncertainty analysis of statistical downscaling models using Hadley Centre Coupled Model
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DOI：
10.1007/s00704-013-0844-x
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发表时间：
2013-03
影响因子：
3.4
通讯作者：
S. Samadi;C. Wilson;H. Moradkhani
中科院分区：
文献类型：
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作者：
S. Samadi;C. Wilson;H. Moradkhani
基于回归的统计降尺度是一种广泛用于解决大气环流模式空间分辨率的方法。然而，评估与气候变量有关的不确定性对气候影响研究至关重要。本研究提出了一个程序，以表征在回归为基础的统计降尺度的日降水量和温度在伊朗西部的一个高度脆弱的地区（半干旱集水区）的不确定性，基于两个降尺度模型：统计降尺度模型（SDSM）和人工神经网络（ANN）模型。在平均值，方差和湿/干的时间段的偏差估计为缩小规模的数据，使用有力的统计检验30年的观测和缩小规模的每日降水量和温度数据从国家环境预测中心再分析预测的1961年至1990年。在日温度的情况下，不确定性是通过在95%的置信水平下比较缩小尺度和观测日数据的月平均值和方差来估计的。在日降水量，降尺度的不确定性进行了评估，比较月平均干，湿的拼写长度和它们的置信区间，累积频率分布的月平均日降水量，和分布的月干湿天观测和模拟的日降水量。结果表明，降水降尺度模拟的不确定性较高，但日气温模拟能较准确地再现极端事件。最后，这项研究表明，SDSM是最熟练的模型在再现各种统计特性的观测数据在95%的置信水平，而人工神经网络模型是最不能够在这方面。本研究试图测试的不确定性回归为基础的统计降尺度技术在半干旱地区，因此有助于提高预测质量的气候变化影响评估在这种类型的地区。
Regression-based statistical downscaling is a method broadly used to resolve the coarse spatial resolution of general circulation models. Nevertheless, the assessment of uncertainties linked with climatic variables is essential to climate impact studies. This study presents a procedure to characterize the uncertainty in regression-based statistical downscaling of daily precipitation and temperature over a highly vulnerable area (semiarid catchment) in the west of Iran, based on two downscaling models: a statistical downscaling model (SDSM) and an artificial neural network (ANN) model. Biases in mean, variance, and wet/dry spells are estimated for downscaled data using vigorous statistical tests for 30 years of observed and downscaled daily precipitation and temperature data taken from the National Center for Environmental Prediction reanalysis predictors for the years of 1961 to 1990. In the case of daily temperature, uncertainty is estimated by comparing monthly mean and variance of downscaled and observed daily data at a 95 % confidence level. In daily precipitation, downscaling uncertainties were evaluated from comparing monthly mean dry and wet spell lengths and their confidence intervals, cumulative frequency distributions of monthly mean of daily precipitation, and the distributions of monthly wet and dry days for observed and modeled daily precipitation. Results showed that uncertainty in downscaled precipitation is high, but simulation of daily temperature can reproduce extreme events accurately. Finally, this study shows that the SDSM is the most proficient model at reproducing various statistical characteristics of observed data at a 95 % confidence level, while the ANN model is the least capable in this respect. This study attempts to test uncertainties of regression-based statistical downscaling techniques in a semiarid area and therefore contributes to an improvement of the quality of predictions of climate change impact assessment in regions of this type.