Four decades have passed since Madden and Julian made the pioneering discovery of a 40-50-day oscillation in the zonal winds in the tropics (Madden and Julian 1971, 1972). This discovery has led to numerous studies of a phenomenon now aptly called the Madden-Julian oscillation (MJO). Although MJO dynamics are still not fully understood (Madden and Julian 1994; Zhang 2005), MJO is known to interact with a panoply of climate phenomena across different spatial and temporal scales (Lau and Waliser 2005). Examples of MJO interactions with some of these phenomena include its feedbacks with El Nino events (e.
g., Marshall et al. 2009; Hendon et al. 2007; Zavala-Garay et al. 2005; Bergman et al. 2001; Kessler 2001; Takayabu et al. 1999), its feedbacks with the North Atlantic Oscillation (Cassou 2008), its impact on the onset and break of the Indian and Australian summermonsoons (e.g.
, Yasunari 1979;Wheeler andMcBride 2005), its impact on the formation of tropical cyclones (e.g., Liebmann et al. 1994; Maloney and Hartmann 2000a,b), and its impact on the mean climate state (Sardeshmukh and Sura 2007). A better understanding and simulation of the MJO in models would help in studying these various climate phenomena, modeling them, and being able to predict these climate events better (Slingo and Inness 2005; Waliser 2006b).To fully understand these important components of earth’s climate, we need better knowledge of how the MJO interacts with these components at various temporal and spatial scales (Lau andWaliser 2005). Yet, current climate models still have difficulty representing the MJO realistically.
Numerous model intercomparison studies of their ability to capture theMJOhave been published (Slingo et al. 1996; Waliser et al. 2003; Lin et al. 2006; Zhang et al. 2006; Sperber and Annamalai 2008; Kim et al. 2009), revealing how GCMs continue to struggle to represent MJO.Slingo et al.
(1996) showed in their study of the tropical intraseasonal variability using atmospheric GCM simulations forced by observedmonthly-mean sea surface temperature (SST) that the Atmospheric Model Intercomparison Project (AMIP) models were unable to simulate the observed spectral peak in the 30-70-day-period band of the global (zonal wavenumber 1) equatorial 200-hPa velocity potential. Lin et al. (2006) analyzed MJO variability in 14 Coupled Model Intercomparison Project phase 3 (CMIP3) models, elucidating that only 2 models had MJO variance comparable to observations but that many other MJO features were lacking realism even in these models. Kim et al. (2009) studied a recent set of globalmodels and noted that only two of them, the super-parameterized Community Atmosphere Model (SPCAM) and ECHAM4/Ocean Isopycnal Model (OPYC), yielded a respectable representation of MJO.The aforementioned multimodel studies attempted to provide insight into what is important forMJO simulation by comparing the different physical parameterizations employed by models of differing MJO verisimilitude.
A common theme throughout these studies is that a good MJO representation is influenced by the convection parameterization employed in the model, although many other factors come into play. As established global climate models continue to be improved with improved physics, they need to be validated for their performance in representing MJO variability because of its importance in influencing other climate phenomena.Here we document how parameterizations in the National Center for Atmospheric Research (NCAR) Community Climate SystemModel (CCSM) affect themodeled MJO activity in long-term climate simulations.
The latest version, CCSM4, has a novel deep convective momentum transport scheme, which profoundly alters the behavior of MJO events in the model. Our primary goal is to quantify the characteristics of MJO activity in CCSM4 according to the set of community diagnostics that has been developed to compare MJO simulations in climate models with observations developed by the Climate Variability and Predictability (CLIVAR)Madden-JulianOscillationWorking Group 2008 (CL-MJOWG08); Waliser et al. 2009).Recent work by Zhou et al. (2012) with a modified version of CCSM3 showed thatMJOs weremore realistic when including a convective momentum transport term and a dilute plume approximation (DPA) in the convective parameterization scheme. Inclusion of the DPA improves the correlation between intraseasonal convective heating and intraseasonal temperature, which is critical for the buildup of the available potential energy.
More realistic low-level background zonal winds over the Indo-Pacific warmpool improves the propagation speeds of intraseasonal variability in the convecting systems. We will show that theMJOis further improved in CCSM4 with 18 horizontal resolution compared to the previous versions of uncoupled CCSM studied by Zhang (2003) with the NCAR CCM3 using the modified convection parameterization scheme of Zhang andMcFarlane (1995) at 38 resolution with prescribed SST; by Kim et al. (2009) with an uncoupled Community Atmosphere Model, version 3 (CAM3.5) at 28 resolution with prescribed SST; and by Zhou et al. (2012) with CCSM3.5 at 28 resolution.Since the MJO is found here to be well represented in CCSM4, we also explore its interaction with other climate phenomena on interannual time scales. These include ENSO, the Indian monsoon, and the Indian Ocean zonal mode (IOZM) of SST.
We demonstrate that the simulated MJO has key similarities with observed MJO for each of these climate modes. Since the record lengths are short, further analysis will be required to solidify this relationship in terms of causal linkages.In section 2 we briefly describe the CCSM4 model used here, indicating the primary changes in the model setup from its previous version. The various observational datasets used in this study for the comparison of MJO in the model with those of nature are also presented. Section 3 contains the analysis of the model MJO, focusing on the structure and propagating features of theMJO in the model as compared to observations. In section 4 we identify relations between the model MJO and several climate indices from the model and observations.
Section 5 summarizes the results.