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Variability and Change as Simulated by CMIP5 Models
Reference
Jha, B., Hu, Z.-Z. and Kumar, A. 2014. SST and ENSO variability and change simulated in historical experiments of CMIP5 models. Climate Dynamics 42: 2113-2124.

Background
The authors write that "sea surface temperature (SST), particularly in the tropical Pacific Ocean associated with the El Niņo-Southern Oscillation (ENSO), is closely connected with global climate variations on seasonal and longer time scales," and they note that the ENSO-associated SST anomaly (SSTA) in the tropical Pacific "is also the major source of climate predictability over the global lands and oceans," citing the National Research Council (2010) and Wang et al. (2010). And they therefore say "it is important that SST variability, particularly in the tropical Pacific associated with ENSO, is simulated well by climate models."

What was done
Jha et al. used historical simulations (1870-2005) derived from ten CMIP5 models forced with observed atmospheric composition changes, reflecting both natural and anthropogenic sources, in order to provide an assessment of the realism of the model-simulated global historical SST and ENSO variability, and to also examine the impact of low-frequency variations on ENSO and its global teleconnections.

What was learned
The three U.S. researchers report that (1) there are "numerous differences in details among the models," that (2) "the amplitude of Niņo3.4 SST variability is overestimated in most of the models," that (3) "some models show smaller amplitudes as compared to the observations," that (4) the "frequency of ENSO at the period of 5-6 years is not well simulated by almost all models," that (5) the "majority of the models are unable to capture the spatial pattern of the observed linear trend over the entire analysis period (136 years)," that (6) "low-frequency variations before 1970 are not well captured by the models," that (7) the "majority of the models are too cold during 1870-1895, but too warm during 1905-1950," and that (8) the "majority of the models are unable to correctly simulate the spatial pattern of the observed SST trends."

What it means
In the concluding sentence of their paper's abstract, Jha et al. say their results suggest that "it is still a challenge to reproduce the features of global historical SST variations with state-of-the-art coupled general circulation models."

References
National Research Council. 2010. Assessment of Intraseasonal to Interannual Climate Prediction and Predictability. The National Academies Press, Washington, DC, USA, p. 192.

Wang, W., Chen, M. and Kumar, A. 2010. An assessment of the CFS real-time seasonal forecasts. Weather Forecasting 25: 950-969.

Reviewed 9 July 2014