The ability of eight climate models to simulate the Madden-Julian oscillation (MJO) is examined using diagnostics developed by the U.S. Climate Variability and Predictability (CLIVAR) MJO Working Group. Although the MJO signal has been extracted throughout the annual cycle, this study focuses on the boreal winter (November-April) behavior. Initially, maps of the mean state and variance and equatorial space-time spectra of 850-hPa zonal wind and precipitation are compared with observations. Models best represent the intraseasonal space-time spectral peak in the zonal wind compared to that of precipitation. Using the phasespace representation of the multivariate principal components (PCs), the life cycle properties of the simulated MJOs are extracted, including the ability to represent how the MJO evolves from a given subphase and the associated decay time scales. On average, the MJO decay (e-folding) time scale for all models is shorter (;20-29 days) than observations (;31 days). All models are able to produce a leading pair of multivariate principal components that represents eastward propagation of intraseasonal wind and precipitation anomalies, although the fraction of the variance is smaller than observed for all models. In some cases, the dominant time scale of these PCs is outside of the 30-80-day band.Several key variables associated with the model's MJO are investigated, including the surface latent heat flux, boundary layer (925 hPa) moisture convergence, and the vertical structure of moisture. Low-level moisture convergence ahead (east) of convection is associated with eastward propagation in most of the models. A few models are also able to simulate the gradual moistening of the lower troposphere that precedes observed MJO convection, as well as the observed geographical difference in the vertical structure of moisture associated with the MJO. The dependence of rainfall on lower tropospheric relative humidity and the fraction of rainfall that is stratiform are also discussed, including implications these diagnostics have for MJO simulation. Based on having the most realistic intraseasonal multivariate empirical orthogonal functions, principal component power spectra, equatorial eastward propagating outgoing longwave radiation (OLR), latent heat flux, low-level moisture convergence signals, and vertical structure of moisture over the Eastern Hemisphere, the superparameterized Community Atmosphere Model (SPCAM) and the ECHAM4/ Ocean Isopycnal Model (OPYC) show the best skill at representing the MJO.

This study compares two models that differ primarily in their cloud parameterizations and produce extremely different simulations of the Madden-Julian oscillation (MJO). The Community Atmosphere Model (CAM) version 3.0 from NCAR uses the Zhang-McFarlane scheme for deep convection and does not produce an MJO. The ''superparameterized'' version of the CAM (SP-CAM) replaces the cloud parameterizations with a two-dimensional cloud-resolving model (CRM) in each grid column and produces an extremely vigorous MJO.This analysis shows that the CAM is unable to produce high-humidity regions in the mid-to lower troposphere because of a lack of coupling between parameterized convection and environmental relative humidity. The SP-CAM produces an overly moist column due in part to excessive near-surface winds and evaporation during strong convective events. In the real tropics and the SP-CAM, convection within a highhumidity environment produces intense latent heating, which excites the large-scale circulation that is the signature of the MJO. The authors suggest that a model must realistically represent convective processes that moisten the entire tropical troposphere in order to produce a simulation of the MJO.

Abstract.Most global climate models parameterize separate cloud types using separate parameterizations. This approach has several disadvantages, including obscure interactions between parameterizations and inaccurate triggering of cumulus parameterizations.Alternatively, a unified cloud parameterization uses one equation set to represent all cloud types. Such cloud types include stratiform liquid and ice cloud, shallow convective cloud, and deep convective cloud. Vital to the success of a unified parameterization is a general interface between clouds and microphysics. One such interface involves drawing Monte Carlo samples of subgrid variability of temperature, water vapor, cloud liquid, and cloud ice, and feeding the sample points into a microphysics scheme.This study evaluates a unified cloud parameterization and a Monte Carlo microphysics interface that has been implemented in the Community Atmosphere Model (CAM) version 5.3. Model computational expense is estimated, and sensitivity to the number of subcolumns is investigated. Results describing the mean climate and tropical variability from global simulations are presented. The new model shows a degradation in precipitation skill but improvements in shortwave cloud forcing, liquid water path, long-wave cloud forcing, precipitable water, and tropical wave simulation.

Cloud processes play a central role in the dynamics of the tropical atmosphere, but for many years the shortcomings of cloud parameterizations have limited our ability to simulate and understand important tropical weather systems such as the Madden–Julian oscillation. Since about 2001, “superparameterization” has emerged as a new path forward, complementing but not replacing studies based on conventional parameterizations. This chapter provides an overview of work with superparameterization, including a discussion of the method itself and a summary of key results.