A trend-preserving bias correction - the ISI-MIP approach
A trend-preserving bias correction - the ISI-MIP approach
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DOI：
10.5194/esd-4-219-2013
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发表时间：
2013-01-01
影响因子：
7.3
通讯作者：
Piontek, F.
中科院分区：
文献类型：
--
作者：
Hempel, S.;Frieler, K.;Piontek, F.
统计偏差校正通常应用于气候影响建模，以校正气候模型数据中模拟历史数据与观测数据的系统偏差。方法基于生成的传递函数，将模拟历史数据的分布映射到观察值的分布。这些数据随后被用于纠正未来的预测。在这里，我们提出了偏差校正方法，在ISI-MIP，第一个跨部门影响模型相互比较项目。ISI-MIP旨在综合不同全球变暖水平下农业、水、生物群落、卫生和基础设施部门的影响预测。用于影响模拟的偏差校正气候数据只能在陆地区域提供。为了确保与全球（陆地+海洋）温度信息的一致性，偏差校正方法必须保留变暖信号。在这里，我们提出了应用的方法，保留了月温度的绝对变化，以及月降水量和ISI-MIP所需的其他变量的相对变化。所提出的方法是一个修改的传递函数的方法中应用的水模型相互比较项目（水MIP）。月平均值的校正之后是对月平均值的日变率的校正，除了ISI-MIP方法的一般思想和技术细节之外，我们还展示和讨论了应用偏差校正的潜力和局限性。特别是，虽然趋势和长期平均值得到了很好的体现，但对变异性调整的局限性仍然存在，这可能会影响到，G.小尺度特征或极端。
Statistical bias correction is commonly applied within climate impact modelling to correct climate model data for systematic deviations of the simulated historical data from observations. Methods are based on transfer functions generated to map the distribution of the simulated historical data to that of the observations. Those are subsequently applied to correct the future projections. Here, we present the bias correction method that was developed within ISI-MIP, the first Inter-Sectoral Impact Model Intercomparison Project. ISI-MIP is designed to synthesise impact projections in the agriculture, water, biome, health, and infrastructure sectors at different levels of global warming.Bias-corrected climate data that are used as input for the impact simulations could be only provided over land areas. To ensure consistency with the global (land + ocean) temperature information the bias correction method has to preserve the warming signal. Here we present the applied method that preserves the absolute changes in monthly temperature, and relative changes in monthly values of precipitation and the other variables needed for ISI-MIP. The proposed methodology represents a modification of the transfer function approach applied in the Water Model Intercomparison Project (Water-MIP). Correction of the monthly mean is followed by correction of the daily variability about the monthly mean.Besides the general idea and technical details of the ISI-MIP method, we show and discuss the potential and limitations of the applied bias correction. In particular, while the trend and the long-term mean are well represented, limitations with regards to the adjustment of the variability persist which may affect, e. g. small scale features or extremes.