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Simulating precipitation and temperature in the Lake Champlain basin using a regional climate model: limitations and uncertainties

机译:使用区域气候模型模拟尚普兰湖盆地的降水和温度:局限性和不确定性

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The Lake Champlain Basin has socioeconomic and ecological significance for the Northeastern United States and Quebec, Canada. Temperatures and extreme precipitation events have been increasing across this region over the past three decades. Accurate, high-resolution climate simulations are critical to assessing potential climate change risk in the Lake Champlain Basin. We evaluate the performance of a regional climate model, the Weather Research and Forecasting (WRF) model, to downscale ERA-Interim reanalysis data to 4 km for the Lake Champlain Basin. Specifically, we compare an ensemble of five WRF experiments with different physics configurations using a one-way, triple-nested domain (36, 12, and 4 km) over three 5-year periods (1980-1984, 1995-1999, and 2010-2014) to Daymet, a gridded observational dataset. We find that WRF simulations of the Lake Champlain Basin generally reproduce the observed temperature and precipitation seasonal cycles, but have cold and wet biases. The simulation of mean temperature by WRF is most sensitive to the choice of radiation scheme, while the simulation of mean precipitation is most sensitive to the choice of radiation, cumulus, and microphysics scheme. We find that turning the cumulus scheme on improves the simulation of the precipitation seasonal cycle at a 4 km resolution, but also substantially enhances the wet bias. Using a coarser resolution (36 km) produces smaller regionally averaged precipitation biases, but not improved correlations between simulated and observed monthly precipitation. Both spatial resolution and turning the cumulus scheme off have minor effects on simulated temperature.
机译:尚普兰湖盆地对美国东北部和加拿大魁北克具有社会经济和生态意义。在过去的三十年中,该地区的温度和极端降水事件一直在增加。准确,高分辨率的气候模拟对于评估尚普兰湖盆地潜在的气候变化风险至关重要。我们评估了区域气候模型,天气研究和预报(WRF)模型的性能,以将尚普兰湖盆地的ERA中期再分析数据缩减至4 km。具体来说,我们比较了三个物理五年(1980-1984年,1995-1999年和2010年)中使用单向三嵌套域(36、12和4 km)的五个具有不同物理配置的WRF实验的整体-2014年)到Daymet(一个网格化的观测数据集)。我们发现尚普兰湖盆地的WRF模拟通常重现了观测到的温度和降水季节周期,但具有冷和湿偏差。 WRF对平均温度的模拟对辐射方案的选择最敏感,而平均降水对模拟辐射,积云和微物理方案的选择最敏感。我们发现,启用积云方案可以改善4 km分辨率下降水季节周期的模拟,但也可以大大提高湿偏度。使用较粗糙的分辨率(36 km)会产生较小的区域平均降水量偏差,但不会改善模拟和观测的月度降水之间的相关性。空间分辨率和关闭积云方案都对模拟温度影响很小。

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