globalchange  > 气候变化事实与影响
DOI: 10.1175/JCLI-D-15-0314.1
Scopus记录号: 2-s2.0-84957647699
论文题名:
Temperature extremes in the community Atmosphere Model with stochastic parameterizations
作者: Tagle F.; Berner J.; Grigoriu M.D.; Mahowald N.M.; Samorodnitsky G.
刊名: Journal of Climate
ISSN: 8948755
出版年: 2016
卷: 29, 期:1
起始页码: 241
结束页码: 258
语种: 英语
Scopus关键词: Atmospheric temperature ; Kinetic energy ; Kinetics ; Parameterization ; Stochastic systems ; Temperature ; Community atmosphere model ; Distribution of temperature ; Model evaluation/performance ; Near surface temperature ; Northern Hemispheres ; Stochastic kinetics ; Sub-grid scale process ; Temperature extremes ; Stochastic models ; air temperature ; annual variation ; atmospheric modeling ; climate modeling ; extreme event ; kinetic energy ; Northern Hemisphere ; parameterization ; performance assessment ; stochasticity ; surface temperature
英文摘要: This paper evaluates the performance of the NCAR Community Atmosphere Model, version 4 (CAM4), in simulating observed annual extremes of near-surface temperature and provides the first assessment of the impact of stochastic parameterizations of subgrid-scale processes on such performance. Two stochastic parameterizations are examined: The stochastic kinetic energy backscatter scheme and the stochastically perturbed parameterization tendency scheme. Temperature extremes are described in terms of 20-yr return levels and compared to those estimated from ERA-Interim and the Hadley Centre Global Climate Extremes Index 2 (HadEX2) observational dataset. CAM4 overestimates warm and cold extremes over land regions, particularly over the Northern Hemisphere, when compared against reanalysis. Similar spatial patterns, though less spatially coherent, emerge relative to HadEX2. The addition of a stochastic parameterization generally produces a warming of both warm and cold extremes relative to the unperturbed configuration; however, neither of the proposed parameterizations meaningfully reduces the biases in the simulated temperature extremes of CAM4. Adjusting warm and cold extremes by mean conditions in the respective annual extremes leads to good agreement between the models and reanalysis; however, adjusting for the bias in mean temperature does not help to reduce the observed discrepancies. Based on the behavior of the annual extremes, this study concludes that the distribution of temperature in CAM4 exhibits too much variability relative to that of reanalysis, while the stochastic parameterizations introduce a systematic bias in its mean rather than alter its variability. © 2016 American Meteorological Society.
资助项目: NSF, National Science Foundation
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/50151
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作者单位: Cornell University, Ithaca, NY, United States; National Center for Atmospheric Research, Boulder, CO, United States

Recommended Citation:
Tagle F.,Berner J.,Grigoriu M.D.,et al. Temperature extremes in the community Atmosphere Model with stochastic parameterizations[J]. Journal of Climate,2016-01-01,29(1)
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