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Evaluation of uncertainties in Downscaling precipitation due to Climate change scenarios

机译:应气候变化情景引起的降水中的不确定性评估

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There are considerable uncertainties in precipitation downscaling especially in considering climate change scenario effects. Evaluation of these uncertainties plays an important role in Integrated Water Resources Management (IWRM). Global Climate Models (GCMs) are the primary tools for climate change. There is considerable uncertainty in GCM simulations of climate change associated with: (i) uncertainty in future green house gas emissions and cycles that are usually simulated 'off-line' (ii) uncertainty in the GCM response to model structure, parameterization, and spatial resolution. In this paper, the effects of using different climate change scenarios for precipitation downscaling are evaluated. Uncertainties in the downscaling model are also dependent on the input data and available observations for model calibration. For assessment of this aspect of uncertainties in precipitation downscaling, different periods of available data are used for model calibration. After developing 100 sets of ensemble data of downscaled precipitation for one hundred years, the probability distributions of downscaled values are determined and compared with the observed values. The results show that there are considerable uncertainties associated with the climate change scenarios and the input data of the model.
机译:特别是在考虑气候变化情景效应时,降水量较低,具有相当大的不确定性。评估这些不确定性在综合水资源管理(IWRM)中起着重要作用。全球气候模型(GCMS)是气候变化的主要工具。 GCM模拟与:(i)未来绿色房屋气体排放和循环的不确定性,通常在GCM响应模型结构,参数化和空间模拟“脱机”(ii)的不确定性中,存在相当多的不确定性解析度。在本文中,评估了使用不同气候变化场景进行降水缩小的效果。缩小模型中的不确定性也取决于输入数据和用于模型校准的可用观察。为了评估降水缩小的不确定性的这种方面,可用数据的不同时间用于模型校准。在开发100套次要降水的集合数据一百年后,确定并将较低值的概率分布与观察到的值进行比较。结果表明,与气候变化场景和模型的输入数据有相当大的不确定性。

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