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Trends in projections of standardized precipitation indices in a future climate in Poland

机译:波兰未来气候中标准化降水指数的预测趋势

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Possible future climate change effects on dryness conditions in Poland are estimated for six climate projections using the standardized precipitation index (SPI). The time series of precipitation represent six different climate model runs under the selected emission scenario for the period 1971-2099. Monthly precipitation values were used to estimate the SPI for multiple timescales (1, 3, 6, 12, and 24 months) for a spatial resolution of 25 km for the whole country. Trends in the SPI were analysed using the Mann-Kendall test with Sen's slope estimator for each grid cell for each climate model projection and aggregation scale, and results obtained for uncorrected precipitation and bias corrected precipitation were compared. Bias correction was achieved using a distribution-based quantile mapping (QM) method in which the climate model precipitation series were adjusted relative to gridded precipitation data for Poland. The results show that the spatial pattern of the trend depends on the climate model, the timescale considered and on the bias correction. The effect of change on the projected trend due to bias correction is small compared to the variability among climate models. We also summarize the mechanisms underlying the influence of bias correction on trends in precipitation and the SPI using a simple example of a linear bias correction procedure. In both cases, the bias correction by QM does not change the direction of changes but can change the slope of trend, and the influence of bias correction on SPI is much reduced. We also have noticed that the results for the same global climate model, driving different regional climate model, are characterized by a similar pattern of changes, although this behaviour is not seen at all timescales and seasons.
机译:使用标准化降水指数(SPI),对六个气候预测估计了未来气候变化对波兰干燥状况的可能影响。在选定的排放情景下,降水的时间序列代表了六种不同的气候模式,这些模式在1971-2099年期间进行。每月降水值用于估计多个时间尺度(1、3、6、12和24个月)的SPI,整个国家的空间分辨率为25 km。使用Mann-Kendall检验和Sen's斜率估计器针对每个气候模型的投影和聚集规模,对每个网格单元分析SPI的趋势,并比较未校正降水量和偏向校正降水量的结果。使用基于分布的分位数映射(QM)方法实现偏差校正,其中相对于波兰的栅格化降水数据调整了气候模型的降水序列。结果表明,趋势的空间格局取决于气候模型,所考虑的时间尺度以及偏差校正。与气候模型之间的可变性相比,由于偏差校正而导致的变化对预计趋势的影响很小。我们还使用一个简单的线性偏差校正程序示例,总结了偏差校正对降水趋势和SPI的影响的潜在机理。在这两种情况下,通过QM进行的偏差校正都不会改变变化的方向,但可以改变趋势的斜率,并且大大减小了偏差校正对SPI的影响。我们还注意到,尽管并非在所有时标和季节都可以看到这种行为,但相同的全球气候模型(驱动不同的区域气候模型)的结果具有相似的变化模式。

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