References

GMD

Geoscientific Model Development

GMD

Geosci. Model Dev.

1991-9603

Copernicus Publications

Göttingen, Germany

10.5194/gmd-6-1705-2013

Intercomparison of temperature trends in IPCC CMIP5 simulations with observations, reanalyses and CMIP3 models

¹ A. M. Powell Jr.

² Zhao

¹ ³

Environmental Science and Technological Center, College of Science, George Mason University, Fairfax, Virginia, USA

NOAA/NESDIS/STAR, Camp Springs, Maryland, USA

Nanjing University of Information Science and Technology, Nanjing, China

21 10 2013

6 5 1705 1714

2013

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On the basis of the fifth Coupled Model Intercomparison Project (CMIP5) and the climate model simulations covering 1979 through 2005, the temperature trends and their uncertainties have been examined to note the similarities or differences compared to the radiosonde observations, reanalyses and the third Coupled Model Intercomparison Project (CMIP3) simulations. The results show noticeable discrepancies for the estimated temperature trends in the four data groups (radiosonde, reanalysis, CMIP3 and CMIP5), although similarities can be observed. Compared to the CMIP3 model simulations, the simulations in some of the CMIP5 models were improved. The CMIP5 models displayed a negative temperature trend in the stratosphere closer to the strong negative trend seen in the observations. However, the positive tropospheric trend in the tropics is overestimated by the CMIP5 models relative to CMIP3 models. While some of the models produce temperature trend patterns more highly correlated with the observed patterns in CMIP5, the other models (such as CCSM4 and IPSL_CM5A-LR) exhibit the reverse tendency. The CMIP5 temperature trend uncertainty was significantly reduced in most areas, especially in the Arctic and Antarctic stratosphere, compared to the CMIP3 simulations. Similar to the CMIP3, the CMIP5 simulations overestimated the tropospheric warming in the tropics and Southern Hemisphere and underestimated the stratospheric cooling. The crossover point where tropospheric warming changes into stratospheric cooling occurred near 100 hPa in the tropics, which is higher than in the radiosonde and reanalysis data. The result is likely related to the overestimation of convective activity over the tropical areas in both the CMIP3 and CMIP5 models. Generally, for the temperature trend estimates associated with the numerical models including the reanalyses and global climate models, the uncertainty in the stratosphere is much larger than that in the troposphere, and the uncertainty in the Antarctic is the largest. In addition, note that the reanalyses show the largest uncertainty in the lower tropical stratosphere, and the CMIP3 simulations show the largest uncertainty in both the south and north polar regions.

References 1

Christy, J. R. and Norris, W. B.: Satellite and VIZ–radiosonde intercomparisons for diagnosis of nonclimatic influences, J. Atmos. Oceanic Technol., 23, 1181–1194, 2006.

Kanamitsu, M., Ebisuzaki, W., Woollen, J., Yang, S. K., Hnilo, J. J., Fiorino, M., and Potter, G. L.: NCEP–DOE AMIP-II Reanalysis (R-2), Bull. Amer. Meteor. Soc., 83, 1631–1643, 2002.

Lanzante, J. R., Klein, S. A., and Seidel, D. J.: Temporal homogenization of monthly radiosonde temperature data, Part I: Methodology, J. Climate, 16, 224–240, 2003.

Meehl, G. A., Covey, C., Delworth, T., Latif, M., McAvaney, B., Mitchell, J. F. B., Stouffer, R. J., and Taylor, K. E.: The WCRP CMIP3 multimodel dataset: A new era in climate change research, Bull. Amer. Meteor. Soc., 88, 1383–1394, 2007.

Sakmoto, M. and Christy, J.: The influences of TOVS radiance assimilation on temperature and moisture tendencies in JRA-25 and ERA-40, J. Atmos. Oceanic Technol., 26, 1435–1455, <a href="http://dx.doi.org/10.1175/2009JTECHA1193.1">https://doi.org/10.1175/2009JTECHA1193.1</a>, 2009.

Santer, B. D., Hnilo, J. J., Wigley, T. M. L., Boyle, J. S., Doutriaux, C., Fiorino, M., Parker, D. E., and Taylor, K. E.: Uncertainties in observationally based estimates of temperature change in the free atmosphere, J. Geophys. Res., 104, 6305–6333, 1999.

Seidel, D. J., Angell, J. K., Christy, J., Free, M., Klein, S. A., Lanzante, J. R., Mears, C., Parker, D., Schabel, M., Spencer, R., Sterin, A., Thorne, P., and Wentz, F.: Uncertainty in signals of large scale climate variations in radiosonde and satellite upper air temperature data sets, J. Climate, 17, 2225–2240, <a href="http://dx.doi.org/10.1175/1520-0442">https://doi.org/10.1175/1520-0442</a>, 2004.

Taylor, K. E., Stouffer, R. J., and Meehl, G. A.: An Overview of CMIP5 and the Experiment Design, Bull. Amer. Meteor. Soc., 93, 4850–498, <a href="http://dx.doi.org/10.1175/BAMS-D-11-00094.1">https://doi.org/10.1175/BAMS-D-11-00094.1</a>, 2012.

Trenberth, K. E., Stepaniak, D. P., Hurrell, J. W., and Fiorino, M.: Quality of Reanalyses in the tropics, J. Climate, 14, 1499–1510, 2001.

Xu, J. and Powell, A.: Ensemble Spread and Its Implication for the Evaluation of Temperature Trends from Multiple Radiosondes and Reanalyses Products, Geophy. Res. Lett., 37, L17704, <a href="http://dx.doi.org/10.1029/2010GL044300">https://doi.org/10.1029/2010GL044300</a>, 2010.

Xu, J. and Powell, A.: Uncertainty estimation of the global temperature trends for multiple radiosondes, reanalyses, and CMIP3/IPCC climate model simulations, Theor. Appl. Climatol., 108, 505–518, <a href="http://dx.doi.org/10.1007/s00704-011-0548-z">https://doi.org/10.1007/s00704-011-0548-z</a>, 2012.