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Reducing Uncertainty on Global Precipitation Projections

机译：减少全球降水预测的不确定性

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In order to study the future of freshwater availability, reliable precipitation projections are required. Potential future changes in global precipitation are investigated by analyzing the Global Climate Models’ projections. However, these projections cannot be used in their native form on climate change impact studies, due to the high systematic errors and biases that they feature, limiting the applicability of these projections. Various methodologies have been developed to correct the precipitation bias, including dynamical and statistical methods. Here we present a global precipitation ensemble projection for the 21st century. We use a multi-segment statistical bias correction method that radically reduces the correction-induced uncertainty to the precipitation. The ensemble consist of results from three different global climate models for A2 and B1 emission scenarios, in order to reduce the uncertainty related to the model selection. The results show significant changes in areal mean and extreme precipitation during the 21st century for the A2 and B1 emission scenarios. For all simulations, the results show that the global mean and extreme precipitation will increase under both scenarios, indicating a more intense forthcoming global water cycle.
机译：为了研究淡水的未来，需要可靠的降水预测。通过分析全球气候模式的预测，可以研究全球降水未来的潜在变化。但是，由于这些预测具有很高的系统性误差和偏见，因此不能以其原始形式用于气候变化影响研究，从而限制了这些预测的适用性。已经开发出各种方法来校正降水偏差，包括动态和统计方法。在这里，我们介绍了21世纪的全球降水总量预测。我们使用多段统计偏差校正方法，从根本上减少了校正引起的降水不确定性。为了减少与模型选择有关的不确定性，该集合由针对A2和B1排放情景的三种不同全球气候模型的结果组成。结果表明，在21世纪，A2和B1排放情景的平均面积和极端降水发生了显着变化。对于所有模拟，结果表明，在两种情况下，全球平均降水量和极端降水量都会增加，表明即将到来的全球水循环将更加强烈。

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