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Improvements to Statistical Climate Downscaling Simulations by Incorporating the APHROJP Advanced Gridded Daily Precipitation Dataset

机译:通过合并APHROJP高级网格每日降水数据集来改进统计气候降尺度模拟

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In this study, we compared the daily precipitation indices derived from two types of statistical down-scaling simulations in Japan. One simulation was conducted by applying a cumulative distribution function-based downscaling method (CDFDM) to a state-of-the-art gridded daily precipitation dataset for Japan, APHROJP (Kamiguchi et al., 2010). The other simulation was conducted by applying the same downscaling method to a simple dataset, namely a set of linearly interpolated rain gauge data. We then highlighted the added values for the coarse-resolution general circulation model (GCM) outputs achieved by the incorporation of the APHROJP dataset into the statistical downscaling simulations, relative to the simulations using the simple dataset. The evaluated indices were mean precipitation, number of wet days, 90th percentile of daily precipitation, and maximum number of consecutive dry days. A comparison of the two approaches shows that the relative improvements achieved using the APHROJP dataset vs. the simple dataset are most pronounced for mean precipitation in mountainous areas, precipitation frequency on the leeward side of mountains, and intensity and frequency of heavy precipitation. These relative improvements over the regional climate change scenarios derived from the CDFDM demonstrate a significant benefit from combining advanced gridded observation datasets with statistical downscaling methods. On a regional scale, the bias of the daily precipitation indices for the simple dataset, relative to the APHROJP dataset, is occasionally comparable in amplitude to the projected change. This finding suggests the importance of higher-quality gridded observation datasets in assessing climate impacts in various fields, particularly, hydrological regimes, irrigation planning, and risk assessments of water-related disasters.
机译:在这项研究中,我们比较了日本两种类型的统计缩减模拟得出的每日降水指数。通过将基于累积分布函数的降尺度方法(CDFDM)应用于日本APHROJP的最新网格日降水量数据集,进行了一次模拟(Kamiguchi等,2010)。通过将相同的缩小比例方法应用于简单的数据集(即一组线性内插雨量计数据)来进行另一次模拟。然后,我们强调了相对于使用简单数据集的模拟,通过将APHROJP数据集并入统计缩减模拟中所获得的粗分辨率普通循环模型(GCM)输出的附加值。评估指标为平均降水量,湿天数,日降水量的90%和连续干旱天数的最大值。两种方法的比较表明,相对于简单数据集,使用APHROJP数据集获得的相对改进在山区平均降水量,山脉背风侧的降水频率以及强降水的强度和频率方面最为明显。这些源自CDFDM的区域气候变化情景的相对改进表明,将先进的网格观测数据集与统计缩减方法相结合,将带来巨大的好处。在区域尺度上,相对于APHROJP数据集,简单数据集的每日降水指数的偏差有时在幅度上与预计的变化相当。这一发现表明,高质量的网格化观测数据集在评估各个领域的气候影响,特别是水文制度,灌溉计划以及与水有关的灾害的风险评估中的重要性。

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