Clim Dyn 18:403–420. 6a, d), which means that there is no AGCM with weak mean convection and strong EAF or vice versa. For this purpose we focus here on the atmospheric components of the CGCMs and investigate how the models of the 5th phase of the Atmospheric Model Intercomparison Project (AMIP5) perform, when forced by observed SSTs, in comparison to the CGCMs. These 6 CMIP5 models are the ones with the weakest EAF (less than 0.35 of the observed strength in both zonal wind and net heat flux feedback) as shown in Fig. 12f), but is only linear related to the cloud cover response over the combined Niño3/Niño4 region in the − 0.7 K bias to − 1.6 K bias experiments (Fig. Weʼll label the more strongly convecting box “w”--a warmer surface implying more convection--and the other “c”. Same as Fig. 1998; Wang and Picaut 2004; Timmermann et al. To underpin these findings we conducted a series of experiments with a single atmosphere model, in which we added in the observed SST forcing an average equatorial SST bias with different strengths and signs, derived from the CMIP5 experiments with the weakest EAF. Composite Hoevmoeller diagrams of El Niño and La Niña events of the equatorial Pacific (averaged between 5° S and 5° N), with five month running mean Niño3.4 index > 0.5 | < − 0.5 standard deviations as selection criterion according to Trenberth (1997) for observations/reanalysis data in a sea surface temperature (SST), in b zonal wind in 10 m height (U10), in c net heat flux (Qnet) in d vertical wind in 500 hPa (omega, negative upward), in e total cloud cover; f–j same as a–e, but here for the AMIP5 sub-ensemble with STRONG atmospheric feedbacks; k–o same as a–e, but here for the AMIP5 sub-ensemble with MEDIUM atmospheric feedbacks; p–t same as a–e, but here for the AMIP5 sub-ensemble with WEAK atmospheric feedbacks; All variables are normalized with mean Niño3.4 SST 3 months before and after the maximum of the events and are centered in time on the month of the maximum of the ENSO events (lag 0). The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. Especially an equatorial cold SST bias, which is a common problem in many climate models, hampers both atmospheric feedbacks, as it shifts the PWC into a La Niña-like mean state with a too westward position of the rising branch of the PWC (Bayr et al. J Clim 22:71–92. https://doi.org/10.1038/s41586-018-0252-6, Tompkins AM (2001) On the relationship between tropical convection and sea surface temperature. 2014; Bayr et al. 2018). These authors analyzed how model physics influences the EAF strength, and cloud and convection parametrizations have been identified as a major factor of uncertainty (Lloyd et al. https://doi.org/10.1175/2008jcli2261.1, Philander S (1990) El Niño, La Niña, and the southern oscillation. 2018, 2019). The data is interpolated onto a regular 2.5° × 2.5° grid and we use all models with the required data available (see Fig. - 103.120.178.9. Biases in the AGCM physics and a biased mean state were suggested as two important factors for the too weak EAF in climate models, with the latter influenced by the former (Lloyd et al. The feedback strengths range from close to observations to only one-third of the observed strength (Bayr et al. in Dijkstra and Neelin (1995). 2011). The implemented relative SST bias in the Niño4 region is given in the list of experiments on the right. Part III: the shortwave flux feedback. 11d–f in Bayr et al. 2f–j, 3). The dashed lines mark the Niño3 and Niño4 regions and the maximum of the ENSO events in time. 2018). Figure 3 summarizes the differences in amplitude between observations and the three AMIP5 sub-ensembles. Observed precipitation for the period 1982–2016 is taken from CMAP data set (Xie and Arkin 1997) and observed total cloud cover for the period 1984–2009 from ISCCP (Rossow and Schiffer 1999). The three sub-ensembles are indicated by red (STRONG), blue (MEDIUM) and green (WEAK) color in Fig. The Walker Circulation is a convective cycle that owes its origin to the gradient of sea surface temperatures along the equator in the Pacific Ocean. For better comparison, all variables are normalized with the mean Niño3.4 SST anomalies 3 months before and after the maximum of all events. This diagram shows the Walker Circulation, a vast loop of air above the equatorial Pacific ocean. We analyze the EAF strength in the AMIP5 and CMIP5 ensembles in Sect. We acknowledge the World Climate Research Program’s Working Group on Coupled Modeling, the individual modeling groups of the Coupled Model Intercomparison Project (CMIP5) and the Atmospheric Model Intercomparison Project (AMIP5), the ECMWF and NOAA for providing the data sets. National Center for Atmospheric Research/University Corporation for Atmospheric Research. NOAA Geophysical Fluid Dynamics LaboratoryGabriel.A.Vecchi@noaa.gov
https://doi.org/10.1007/s00382-012-1475-0, Dommenget D, Haase S, Bayr T, Frauen C (2014) Analysis of the Slab Ocean El Nino atmospheric feedbacks in observed and simulated ENSO dynamics. 9b–f). 2018, 2019). The east–west pressure gradient associated with the Walker circulation undergoes an irregular interannual variation. A too weak forcing by the wind-SST feedback is compensated by a too weak damping by the heat flux feedback (Guilyardi et al. The Walker circulation is an ocean-based system of air circulation that influences weather on the Earth.. The KCM consists of the ECHAM5 AGCM (Roeckner 2003) with a resolution of T42 (~ 2.8° × 2.8°) in the horizontal and 31 vertical levels and the NEMO ocean general circulation model (Madec et al.