A GCM simulation of heat waves, dry spells, and their relationships to circulation
A GCM simulation of heat waves, dry spells, and their relationships to circulation
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DOI：
10.1023/a:1005633925903
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发表时间：
2000-07-01
期刊：
影响因子：
4.8
通讯作者：
Pokorná, L
中科院分区：
文献类型：
--
作者：
Huth, R;Kysely, J;Pokorná, L
热浪和干旱期进行了分析（一）在南摩拉维亚（捷克共和国）的八个站，（二）在控制ECHAM3的GCM运行在网格点最接近的研究领域，和（三）在ECHAM3的GCM运行在相同的网格点（仅热浪）CO2浓度加倍（情景A）。GCM的输出验证对个别站的数据和区域代表性的值。在控制运行中，热浪太长，出现在一年中的晚些时候，在较高的温度下达到峰值，并且在6月和7月（8月）估计其数量。模拟的干旱期太长，其发生的年周期被扭曲。对流层中层环流、热浪和干旱期在控制气候中的联系比在观测气候中的联系要少得多。由于对流层中部环流模拟得相当成功，我们建议的假设，要么是空气质量转换和本地过程太强的模式或模拟平流太弱。在情景A气候中，热浪成为一种常见现象：夏季升温4.5摄氏度（情景A和对照气候之间的差异）导致热带天数的频率增加五倍，热浪极端情况大大增加。研究结果突出表明，需要（一）在进行气候影响研究之前，对大气环流模型的输出进行适当的验证，（二）使用降尺度和随机建模技术，将大气环流模型的大尺度信息转化为局部尺度信息，以减少大气环流模型的偏差。
Heat waves and dry spells are analyzed (i) at eight stations in south Moravia (Czech Republic), (ii) in the control ECHAM3 GCM run at the gridpoint closest to the study area, and (iii) in the ECHAM3 GCM run for doubled CO2 concentrations (scenario A) at the same gridpoint (heat waves only). The GCM outputs are validated both against individual station data and areally representative values. In the control run, the heat waves are too long, appear later in the year, peak at higher temperatures and their numbers are under- (over-) estimated in June and July (in August). The simulated dry spells are too long, and the annual cycle of their occurrence is distorted. Mid-tropospheric circulation, and heat waves and dry spells are linked much less tightly in the control climate than in the observed. Since mid-tropospheric circulation is simulated fairly successfully, we suggest the hypothesis that either the air-mass transformation and local processes are too strong in the model or the simulated advection is too weak. In the scenario A climate, the heat waves become a common phenomenon: warming of 4.5 degrees C in summer (difference between scenario A and control climates) induces a five-fold increase in the frequency of tropical days and an immense enhancement of extremity of heat waves. The results of the study underline the need for (i) a proper validation of the GCM output before a climate impact study is conducted and (ii) translation of large-scale information from GCMs into local scales using downscaling and stochastic modelling techniques in order to reduce GCMs' biases.