区域气候模式在中国西北地区气温和降水长时间序列模拟的误差分析

doi:10.7522/j.isnn.1000-0240.2016.0009

摘要/Abstract

摘要： 气温和降水微小的变化都可能引起中国西北地区脆弱生态环境空间格局剧烈变化.由于西北地区气象站点分布稀疏,站点观测结果难于完全代表西北地区复杂地貌的气象要素空间分布格局.区域气候模式模拟可以弥补现有观测资料的不足,但模式模拟存在的误差,往往制约着西北区域气候变化评估工作.本文在CN05格点数据和站点数据基础上,从空间格局、极值模拟等方面对高分辨率WRF模式数据气温和降水模拟精度进行评估,假定偏差恒定基础上,使用分类回归树模型建立偏差预测模型.结果表明:CN05数据和WRF模式多年年平均气温和多年平均年降水数据空间格局基本一致,但WRF模式结果空间格局更加细致.WRF模式结果在山区和湖泊地区降水偏高估,气温和降水距平在新疆地区相对于其他地区变化较为一致;95%分位数的降水在甘肃和新疆地区模拟结果好于宁夏、青海和陕西;5%和95%分位数气温,WRF模式大部分站点表现为暖偏差,而CN05数据表现为冷偏差;不同的地貌单元区域气温和降水的偏差时间序列存在一定的相关性.在西北五省区气温和降水的分类树模型中,由于高程和地形复杂度对偏差预测模型影响较大,无法在整个西北地区建立统一的误差预测模型.

关键词: WRF, 气温, 降水, 偏差, 地貌, CART算法

Abstract: In Northwest China, a little change in temperature and precipitation would lead into a dramatically adverse disaster in the vulnerable ecological environment. However, due to the scarcity and uneven distribution of the meteorological stations in Northwest China, it is difficult to accurately assess the regional climate change in time and space. The Regional Climate Model (RCM) in state of art provides a powerful tool to simulate with highly horizontal resolution, which may supplement the existing observation. However, the poor performance of the model has slowed down the progress of regional climate change research in Northwest China. With the gridded dataset (CN05) and observation in situ, in this paper, simulation ability is assessed of the High Resolution WRF model's output in the spatial pattern, extremes, and anomaly and so on. On the assumption that the temperature and precipitation's biases keep in constant, the data in situ are used to train the Classification Regression Tree Model (CART) for the Bias Predication Model. The results show that the spatial patterns of annual mean temperature and annual precipitation from CN05 dataset consist with WRF model's output, and WRF's outputs illustrate more features in spatial pattern. However, the WRF overestimates precipitation in alpine and lakes. The temperature and precipitation anomalies in the Xinjiang perform more homogenously as compared with other regions. WRF's 95% of the simulated precipitation in Gansu and Xinjiang is better than that in Ningxia, Qinghai and Shaanxi. For 5% and 95% points of the temperature, the WRF simulation has warm bias in most sites, while the CN05 simulation has cold bias. Temperature and precipitation's bias have a certain relation among the five different geomorphic units. It is impossible to establish a uniform Bias Predication Model to apply in whole Northwest China, because of the height and terrain complexity, which has a large weight in Bias Predication Model.

Key words: WRF model, air temperature, precipitation, bias, topography, CART

中图分类号:

P435

马奕, 白磊, 李倩, 殷刚, 赵鑫, 李兰海. 区域气候模式在中国西北地区气温和降水长时间序列模拟的误差分析[J]. 冰川冻土, 2016, 38(1): 77-88.

MA Yi, BAI Lei, LI Qian, YIN Gang, ZHAO Xin, LI Lanhai. The error analysis of the long term air temperature and precipitation in Northwest China simulated by WRF model[J]. JOURNAL OF GLACIOLOGY AND GEOCRYOLOGY, 2016, 38(1): 77-88.

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