Temperature extremes in the Community Atmosphere Model with stochastic parameterizations

AMS Citation:
Tagle, F., J. Berner, M. D. Grigoriu, N. M. Mahowald, and G. Samorodnitsky, 2016: Temperature extremes in the Community Atmosphere Model with stochastic parameterizations. Journal of Climate, 29, 241-258, doi:10.1175/JCLI-D-15-0314.1.
Resource Type:article
Title:Temperature extremes in the Community Atmosphere Model with stochastic parameterizations
Abstract: 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.
Peer Review:Refereed
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OpenSky citable URL: ark:/85065/d78k7bjf
Publisher's Version: 10.1175/JCLI-D-15-0314.1
  • Felipe Tagle - NCAR/UCAR
  • Judith Berner - NCAR/UCAR
  • Mircea Grigoriu
  • Natalie Mahowald
  • Gennady Samorodnitsky
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