Atmospheric Blocking and Mean Biases in Climate Models
Atmospheric Blocking and Mean Biases in Climate Models
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DOI：
10.1175/2010jcli3728.1
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发表时间：
2010-12
影响因子：
4.9
通讯作者：
Adam A. Scaife;T. Woollings;J. Knight;G. Martin;T. Hinton
中科院分区：
文献类型：
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作者：
Adam A. Scaife;T. Woollings;J. Knight;G. Martin;T. Hinton
模式往往低估了大西洋和太平洋盆地的阻塞，这可能导致天气和气候预测出现错误。水平分辨率通常被认为是阻塞错误的罪魁祸首，这是由于分辨率较差的小尺度可变性造成的，其高级效应有助于维持阻塞。虽然这些过程对阻塞很重要，但作者表明，使用普通分析方法和当前气候模式诊断的阻塞错误中的大部分可直接归因于模式的气候偏差。这解释了最近政府间气候变化专门委员会报告中使用的模型中诊断出的大部分阻塞错误。此外，通过使用修正后的气候模式数据来解释平均模式偏差，可以大大改善统计数据。在一定程度上，低分辨率模式中的平均偏差可能得到纠正，这表明这种模式可能能够大大提高大气阻塞的水平。
Models often underestimate blocking in the Atlantic and Pacific basins and this can lead to errors in both weather and climate predictions. Horizontal resolution is often cited as the main culprit for blocking errors due to poorly resolved small-scale variability, the upscale effects of which help to maintain blocks. Although these processes are important for blocking, the authors show that much of the blocking error diagnosed using common methods of analysis and current climate models is directly attributable to the climatological bias of the model. This explains a large proportion of diagnosed blocking error in models used in the recent Intergovernmental Panel for Climate Change report. Furthermore, greatly improved statistics are obtained by diagnosing blocking using climate model data corrected to account for mean model biases. To the extent that mean biases may be corrected in low-resolution models, this suggests that such models may be able to generate greatly improved levels of atmospheric blocking.