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首页> 外文期刊>Journal of Climate >High-resolution downscaled simulations of warm-season extreme precipitation events in the Colorado front range under past and future climates.
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High-resolution downscaled simulations of warm-season extreme precipitation events in the Colorado front range under past and future climates.

机译:在过去和将来的气候下,科罗拉多州前缘暖季极端降水事件的高分辨率高分辨率模拟。

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摘要

A high-resolution case-based approach for dynamically downscaling climate model data is presented. Extreme precipitation events are selected from regional climate model (RCM) simulations of past and future time periods. Each event is further downscaled using the Weather Research and Forecasting (WRF) Model to storm scale (1.3-km grid spacing). The high-resolution downscaled simulations are used to investigate changes in extreme precipitation projections from a past to a future climate period, as well as how projected precipitation intensity and distribution differ between the RCM scale (50-km grid spacing) and the local scale (1.3-km grid spacing). Three independent RCM projections are utilized as initial and boundary conditions to the downscaled simulations, and the results reveal considerable spread in projected changes not only among the RCMs but also in the downscaled high-resolution simulations. However, even when the RCM projections show an overall (i.e., spatially averaged) decrease in the intensity of extreme events, localized maxima in the high-resolution simulations of extreme events can remain as strong or even increase. An ingredients-based analysis of prestorm instability, moisture, and forcing for ascent illustrates that while instability and moisture tend to increase in the future simulations at both regional and local scales, local forcing, synoptic dynamics, and terrain-relative winds are quite variable. Nuanced differences in larger-scale and mesoscale dynamics are a key determinant in each event's resultant precipitation. Very high-resolution dynamical downscaling enables a more detailed representation of extreme precipitation events and their relationship to their surrounding environments with fewer parameterization-based uncertainties and provides a framework for diagnosing climate model errors.Digital Object Identifier http://dx.doi.org/10.1175/JCLI-D-12-00744.1
机译:提出了一种基于案例的高分辨率方法来动态缩减气候模型数据的规模。极端降水事件选自过去和未来时间段的区域气候模型(RCM)模拟。每个事件都使用天气研究和预报(WRF)模型进一步缩小规模以达到风暴规模(1.3公里网格间距)。高分辨率的降尺度模拟用于研究从过去到未来气候期间极端降水预测的变化,以及RCM尺度(50公里网格间距)和局部尺度之间预测的降水强度和分布如何不同( 1.3公里的网格间距)。三个独立的RCM投影被用作缩减模拟的初始条件和边界条件,结果表明,不仅在RCM之间,而且在缩减的高分辨率模拟中,投影变化的分布也很大。但是,即使RCM投影显示极端事件的强度总体下降(即在空间上平均),在极端事件的高分辨率模拟中局部最大值仍可以保持不变甚至增加。基于暴风雨前的不稳定性,湿度和强迫上升的基于成分的分析表明,尽管在未来的模拟中,区域和本地范围内的不稳定和湿度趋于增加,但局部强迫,天气动态和相对地形的风却变化很大。大尺度和中尺度动力学的细微差别是每个事件最终降水的关键决定因素。超高分辨率的动态降尺度功能可以用较少的基于参数化的不确定性更详细地表示极端降水事件及其与周围环境的关系,并提供诊断气候模型错误的框架。数字对象标识符http://dx.doi.org /10.1175/JCLI-D-12-00744.1

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