Department of Geography and Geosciences, Salisbury University, Salisbury, MD, United States of America.
Department of Oceanography and Coastal Sciences, Louisiana State University, Baton Rouge, LA.
Sci Total Environ. 2019 Apr 15;661:326-336. doi: 10.1016/j.scitotenv.2019.01.167. Epub 2019 Jan 15.
El Yunque National Forest, situated in the Luquillo Mountains of northeast Puerto Rico, is home to a wide range of climate-sensitive ecosystems and forest types. In particular, these ecosystems are highly sensitive to changes in the hydroclimate, even on short time scales. Current global climate models (GCMs) predict coarse-scale reductions in precipitation across the Caribbean prompting the need to investigate future fine-scale hydroclimate variability in the Luquillo Mountains. This research downscales coarse-resolution GCM RCP8.5 predictions from the IPCC CMIP5 project to the local scale to better assess future rainfall variability during the most critical period of the annual hydroclimate cycle, the early rainfall season (ERS). An artificial neural network (ANN) is developed using five field variables (1000-, 850-, 700-, and 500-hPa specific humidity and 1000-700-hPa bulk wind shear) and four derived precipitation forecasting parameters from the ERA-Interim reanalysis. During the historical period (1985-2016), the ANN predicts a binary dry (<5 mm) versus wet (≥5 mm) day outcome with 92% percent accuracy. When the historical inputs are replaced with bias-corrected data from four CMIP5 GCMs, the downscaled ensemble mean indicates a 7.2% increase in ERS dry-day frequency by mid-century (2041-2060), yielding an ERS dry-day percentage of 70% by mid-century. The results presented here show that the decrease in precipitation and wet-days is, at least in part, due to an increase in 1000-700 hPa bulk wind shear and a less favorable thermodynamic environment driven by increased mid-tropospheric warming and a stronger trade wind inversion. By regressing ERS total precipitation against dry-day frequency (R = 0.95), the predicted mid-century dry-day proportion corresponds to a ~200-mm decrease in seasonal precipitation. In contrast, the ensemble predicts a dry-day frequency recovery back towards the historical climatological mean by end-century (2081-2100).
埃尔云克国家森林位于波多黎各东北部的卢奎洛山脉，是多种气候敏感生态系统和森林类型的家园。特别是，这些生态系统对水文气候的变化非常敏感，即使在短时间尺度上也是如此。当前的全球气候模式（GCMs）预测加勒比海地区的降水量会大幅减少，这促使人们需要研究卢奎洛山脉未来的精细尺度水文气候变化。这项研究将 IPCC CMIP5 项目中的粗分辨率 GCM RCP8.5 预测值下推到当地尺度，以更好地评估在年度水文气候循环中最关键的早期降雨季节（ERS）期间未来降雨的变化。使用五个现场变量（1000、850、700 和 500 hPa 比湿以及 1000-700 hPa 整层风切变）和来自 ERA-Interim 再分析的四个衍生降水预测参数，开发了一个人工神经网络（ANN）。在历史时期（1985-2016 年），ANN 以 92%的准确率预测二进制干（<5 毫米）与湿（≥5 毫米）日的结果。当用来自四个 CMIP5 GCM 的偏差校正数据替换历史输入时，下推的集合平均值表明，到本世纪中叶（2041-2060 年），ERS 干旱日的频率将增加 7.2%，到本世纪中叶，ERS 干旱日的比例将达到 70%。这里呈现的结果表明，降水和湿润日的减少至少部分是由于 1000-700 hPa 整层风切变的增加以及中层增暖导致的热力学环境的变化和更强的贸易风逆温。通过将 ERS 总降水量与干旱日频率进行回归（R=0.95），预测的本世纪中叶干旱日比例对应于季节性降水量减少约 200 毫米。相比之下，集合预测到本世纪末（2081-2100 年），干旱日频率将恢复到历史气候平均值。
