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首页> 美国卫生研究院文献>Wiley-Blackwell Online Open >Estimating daily climatologies for climate indices derived from climate model data and observations
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Estimating daily climatologies for climate indices derived from climate model data and observations

机译：根据气候模型数据和观测值估算气候指数的每日气候

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摘要

Climate indices help to describe the past, present, and the future climate. They are usually closer related to possible impacts and are therefore more illustrative to users than simple climate means. Indices are often based on daily data series and thresholds. It is shown that the percentile-based thresholds are sensitive to the method of computation, and so are the climatological daily mean and the daily standard deviation, which are used for bias corrections of daily climate model data. Sample size issues of either the observed reference period or the model data lead to uncertainties in these estimations. A large number of past ensemble seasonal forecasts, called hindcasts, is used to explore these sampling uncertainties and to compare two different approaches. Based on a perfect model approach it is shown that a fitting approach can improve substantially the estimates of daily climatologies of percentile-based thresholds over land areas, as well as the mean and the variability. These improvements are relevant for bias removal in long-range forecasts or predictions of climate indices based on percentile thresholds. But also for climate change studies, the method shows potential for use.Key Points class="unordered" style="list-style-type:disc">More robust estimates of daily climate characteristicsStatistical fitting approachBased on a perfect model approach
机译：气候指数有助于描述过去，现在和未来的气候。它们通常与可能的影响密切相关，因此比简单的气候手段对用户更具说明性。指标通常基于每日数据系列和阈值。结果表明，基于百分位数的阈值对计算方法敏感，气候日均值和日标准差也对计算方法敏感，这些均值用于每日气候模型数据的偏差校正。观测参考期间或模型数据的样本量问题会导致这些估计的不确定性。大量过去的整体季节预报（称为后预报）被用于探索这些采样不确定性并比较两种不同的方法。基于完美的模型方法，结果表明，拟合方法可以显着改善陆地区域基于百分位数阈值的每日气候估计值以及均值和变异性。这些改进与远程预测或基于百分位数阈值的气候指数预测中的偏差消除有关。但对于气候变化研究，该方法也显示出了潜在的用途。要点 class =“ unordered” style =“ list-style-type：disc”> <！-list-behavior = unordered prefix-word = mark- type = disc max-label-size = 0-> 对日常气候特征的更可靠估计 统计拟合方法 基于完美模型方法

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