SST and ENSO variability and change simulated in historical experiments of CMIP5 models Bhaskar Jha • Zeng-Zhen Hu • Arun Kumar Received: 14 December 2012 / Accepted: 8 May 2013 Ó Springer-Verlag Berlin Heidelberg 2013 Abstract This work documents the diversity in Coupled Model Inter-comparison Project Phase 5 (CMIP5) models in simulating different aspects of sea surface temperature (SST) variability, particularly those associated with the El Nin ˜o–Southern Oscillation (ENSO), as well as the impact of low-frequency variations on the ENSO variability and its global teleconnection. The historical simulations (1870–2005) include 10 models with ensemble member ranging from 3 to 10 that are forced with observed atmo- spheric composition changes reflecting both natural and anthropogenic forcings. It is shown that the majority of the CMIP5 models capture the relative large SST anomaly variance in the tropical central and eastern Pacific, as well as in North Pacific and North Atlantic. The frequency of ENSO is not well captured by almost all models, particu- larly for the period of 5–6 years. The low-frequency vari- ations in SST caused by external forcings affect the SST variability and also modify the global teleconnection of ENSO. The models reproduce the global averaged SST low-frequency variations, particularly since 1970s. How- ever, majority of the models are unable to correctly sim- ulate the spatial pattern of the observed SST trends. These results suggest that it is still a challenge to reproduce the features of global historical SST variations with the state- of-the-art coupled general circulation model. 1 Introduction Sea surface temperature (SST), particularly in the tropical Pacific Ocean associated with the El Nin ˜o–Southern Oscillation (ENSO), is closely connected with global cli- mate variations on seasonal and longer time scales (Ras- musson and Carpenter 1982; Ropelewski and Halpert 1987; Philander 1990; Glantz 2000; Hoerling and Kumar 2002; McPhaden et al. 2006; Tippett and Barnston 2008). ENSO associated SST anomaly (SSTA) in the tropical Pacific is also the major source for climate predictability over the global lands and oceans (National Research Council 2010; Wang et al. 2010; reference therein). Thus, to understand regionalization of climate trends under the influence of changing atmospheric composition, it is important that SST variability, particularly in the tropical Pacific associated with ENSO, is simulated well by climate models. There has been enormous progress in understanding the physical mechanisms of SST and ENSO variabilities and their impact on climate, but still, some aspects remain less understood and real-time prediction skill of ENSO remains low. For example, Barnston et al. (2012) indicate that ENSO real-time prediction skills averaged in 2002–2011 for 12 dynamical and 8 statistical models was somewhat lower than that for 1980s and 1990s. Also, there is no consensus about the impact of long-term climate variation on ENSO variability (Meehl and Washington 1996; Jin et al. 2001; Vecchi and Wittenberg 2010; Collins et al. 2010; Hu et al. 2012; references therein). As changes in ENSO variability has the potential to be one of the key B. Jha (&) Á Z.-Z. Hu Á A. Kumar Climate Prediction Center, NCEP/NWS/NOAA, 5830 University Research Court, College Park, MD 20740, USA e-mail: bhaskar.jha@noaa.gov B. Jha WYLE Science, Technology and Engineering Group, Houston, TX, USA 123 Clim Dyn DOI 10.1007/s00382-013-1803-z