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Seasonal streamflow forecast: a GCM multi-model downscaling approach

机译：季节性流量预测：GCM多模型降级方法

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This work investigates the predictability of seasonal to inter-annual streamflow over several river basins in Norway through the use of multi-model ensembles. As general circulation models (GCMs) do not explicitly simulate streamflow, a statistical link is made between GCM-forecast fields generated in December and average streamflow in the melting season May-June. By using the Climate Predictability Tool (CPT) three models were constructed and from these a multi-model was built. The multi-model forecast is tested against climatology to determine the quality of the forecast. Results from the forecasts show that the multi-model performs better than the individual models and that this method shows improved forecast skills if compared to previous studies conducted in the same basins. The highest forecast skills are found for basins located in the southwest of Norway. The physical interpretation for this is that stations on the windward side of the Scandinavian mountains are exposed to the prevailing winds from the Atlantic Ocean, a principal source of predictive information from the atmosphere on this timescale.
机译：这项工作通过使用多模型集合调查了挪威几个流域的季节性至年际流量的可预测性。由于一般环流模型（GCM）并未明确模拟流量，因此在12月生成的GCM预测场与5月至6月融化季节的平均流量之间建立了统计联系。通过使用气候可预测性工具（CPT），构建​​了三个模型，并由此建立了一个多模型。针对气候条件对多模型预测进行测试，以确定预测的质量。预测结果表明，与以前在同一盆地进行的研究相比，该多模型的性能优于单个模型，并且该方法显示出提高的预测技能。发现位于挪威西南部盆地的预报技能最高。对此的物理解释是，斯堪的纳维亚山脉的上风侧的气象站暴露于大西洋的盛行风中，而大西洋是该时间尺度上大气预测信息的主要来源。

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