Traditionally, the effects of clouds in GCMs have been represented by semiempirical parameterizations. Recently, a cloud-resolving model (CRM) was embedded into each grid column of a realistic GCM, the NCAR Community Atmosphere Model (CAM), to serve as a superparameterization (SP) of clouds. Results of the standard CAM and the SP-CAM are contrasted, both using T42 resolution (2.8° × 2.8° grid), 26 vertical levels, and up to a 500-day-long simulation. The SP was based on a two-dimensional (2D) CRM with 64 grid columns and 24 levels collocated with the 24 lowest levels of CAM. In terms of the mean state, the SP-CAM produces quite reasonable geographical distributions of precipitation, precipitable water, top-of-the-atmosphere radiative fluxes, cloud radiative forcing, and high-cloud fraction for both December–January–February and June–July–August. The most notable and persistent precipitation bias in the western Pacific, during the Northern Hemisphere summer of all the SP-CAM runs with 2D SP, seems to go away through the use of a small-domain three-dimensional (3D) SP with the same number of grid columns as the 2D SP, but arranged in an 8 × 8 square with identical horizontal resolution of 4 km. Two runs with the 3D SP have been carried out, with and without explicit large-scale momentum transport by convection. Interestingly, the double ITCZ feature seems to go away in the run that includes momentum transport. The SP improves the diurnal variability of nondrizzle precipitation frequency over the standard model by precipitating most frequently during late afternoon hours over the land, as observed, while the standard model maximizes its precipitation frequency around local solar noon. Over the ocean, both models precipitate most frequently in the early morning hours as observed. The SP model also reproduces the observed global distribution of the percentage of days with nondrizzle precipitation rather well. In contrast, the standard model tends to precipitate more frequently, on average by about 20%–30%. The SP model seems to improve the convective intraseasonal variability over the standard model. Preliminary results suggest that the SP produces more realistic variability of such fields as 200-mb wind and OLR, relative to the control, including the often poorly simulated Madden–Julian oscillation (MJO).

Aimed at reducing deficiencies in representing the Madden-Julian oscillation (MJO) in general circulation models (GCMs), a global model evaluation project on vertical structure and physical processes of the MJO was coordinated. In this paper, results from the climate simulation component of this project are reported. It is shown that the MJO remains a great challenge in these latest generation GCMs. The systematic eastward propagation of the MJO is only well simulated in about one fourth of the total participating models. The observed vertical westward tilt with altitude of the MJO is well simulated in good MJO models but not in the poor ones. Damped Kelvin wave responses to the east of convection in the lower troposphere could be responsible for the missing MJO preconditioning process in these poor MJO models. Several process-oriented diagnostics were conducted to discriminate key processes for realistic MJO simulations. While large-scale rainfall partition and low-level mean zonal winds over the Indo-Pacific in a model are not found to be closely associated with its MJO skill, two metrics, including the low-level relative humidity difference between high-and low-rain events and seasonal mean gross moist stability, exhibit statistically significant correlations with the MJO performance. It is further indicated that increased cloud-radiative feedback tends to be associated with reduced amplitude of intraseasonal variability, which is incompatible with the radiative instability theory previously proposed for the MJO. Results in this study confirm that inclusion of air-sea interaction can lead to significant improvement in simulating the MJO.

The Madden-Julian oscillation (MJO) is a convectively coupled 30-70 day (intraseasonal) tropical atmospheric mode that drives variations in global weather but which is poorly simulated in most atmospheric general circulation models. Over the past two decades, field campaigns and modeling experiments have suggested that tropical atmosphere-ocean interactions may sustain or amplify the pattern of enhanced and suppressed atmospheric convection that defines the MJO and encourage its eastward propagation through the Indian and Pacific Oceans. New observations collected during the past decade have advanced our understanding of the ocean response to atmospheric MJO forcing and the resulting intraseasonal sea surface temperature fluctuations. Numerous modeling studies have revealed a considerable impact of the mean state on MJO ocean-atmosphere coupled processes, as well as the importance of resolving the diurnal cycle of atmosphere-upper ocean interactions. New diagnostic methods provide insight to atmospheric variability and physical processes associated with the MJO but offer limited insight on the role of ocean feedbacks. Consequently, uncertainty remains concerning the role of the ocean in MJO theory. Our understanding of how atmosphere-ocean coupled processes affect the MJO can be improved by collecting observations in poorly sampled regions of MJO activity, assessing oceanic and atmospheric drivers of surface fluxes, improving the representation of upper ocean mixing in coupled model simulations, designing model experiments that minimize mean state differences, and developing diagnostic tools to evaluate the nature and role of coupled ocean-atmosphere processes over the MJO cycle.

The Colorado State University (CSU) Multiscale Modeling Framework (MMF) is a new type of general circulation model (GCM) that replaces the conventional parameterizations of convection, clouds, and boundary layer with a cloud-resolving model (CRM) embedded into each grid column. The MMF has been used to perform a 19-yr-long Atmospheric Model Intercomparison Project-style simulation using the 1985-2004 sea surface temperature (SST) and sea ice distributions as prescribed boundary conditions. Particular focus has been given to the simulation of the interannual and subseasonal variability.The annual mean climatology is generally well simulated. Prominent biases include excessive precipitation associated with the Indian and Asian monsoon seasons, precipitation deficits west of the Maritime Continent and over Amazonia, shortwave cloud effect biases west of the subtropical continents due to insufficient stratocumulus clouds, and longwave cloud effect biases due to overestimation of high cloud amounts, especially in the tropics. The geographical pattern of the seasonal cycle of precipitation is well reproduced, although the seasonal variance is considerably overestimated mostly because of the excessive monsoon precipitation mentioned above. The MMF does a good job of reproducing the interannual variability in terms of the spatial structure and magnitude of major anomalies associated with El Niño-Southern Oscillation (ENSO).The subseasonal variability of tropical climate associated with the Madden-Julian oscillation (MJO) and equatorially trapped waves are particular strengths of the simulation. The wavenumber-frequency power spectra of the simulated outgoing longwave radiation (OLR), precipitation rate, and zonal wind at 200 and 850 mb for time scales in the range of 2-96 days compare very well to the spectra derived from observations, and show a robust MJO and Kelvin and Rossby waves with phase speeds similar to those observed. The geographical patterns of the MJO and Kelvin wave-filtered OLR variance for summer and winter seasons are well simulated; however, the variances tend to be overestimated by as much as 50%. The observed seasonal and interannual variations of the strength of the MJO are also well reproduced.The physical realism of the simulated marine stratocumulus clouds is demonstrated by an analysis of the composite diurnal cycle of cloud water content, longwave (IR) cooling, vertical velocity variance, rainfall, and subcloud vertical velocity skewness. The relationships between vertical velocity variance, IR cooling, and negative skewness all suggest that, despite the coarse numerical grid of the CRM, the simulated clouds behave in a manner consistent with the understanding of the stratocumulus dynamics. In the stratocumulusto-cumulus transition zone, the diurnal cycle of the inversion layer as simulated by the MMF also bears a remarkable resemblance to in situ observations. It is demonstrated that in spite of the coarse spacing of the CRM grid used in the current version of MMF, the bulk of vertica...

A model evaluation approach is proposed in which weather and climate prediction models are analyzed along a Pacific Ocean cross section, from the stratocumulus regions off the coast of California, across the shallow convection dominated trade winds, to the deep convection regions of the ITCZ-the Global Energy and Water Cycle Experiment Cloud System Study/Working Group on Numerical Experimentation (GCSS/ WGNE) Pacific Cross-Section Intercomparison (GPCI). The main goal of GPCI is to evaluate and help understand and improve the representation of tropical and subtropical cloud processes in weather and climate prediction models. In this paper, a detailed analysis of cloud regime transitions along the cross section from the subtropics to the tropics for the season June-July-August of 1998 is presented. This GPCI study confirms many of the typical weather and climate prediction model problems in the representation of clouds: underestimation of clouds in the stratocumulus regime by most models with the corresponding consequences in terms of shortwave radiation biases; overestimation of clouds by the 40-yr ECMWF Re-Analysis (ERA-40) in the deep tropics (in particular) with the corresponding impact in the outgoing longwave radiation; large spread between the different models in terms of cloud cover, liquid water path and shortwave radiation; significant differences between the models in terms of vertical cross sections of cloud properties (in particular), vertical velocity, and relative humidity. An alternative analysis of cloud cover mean statistics is proposed where sharp gradients in cloud cover along the GPCI transect are taken into account. This analysis shows that the negative cloud bias of some models and ERA-40 in the stratocumulus regions [as compared to the first International Satellite Cloud Climatology Project (ISCCP)] is associated not only with lower values of cloud cover in these regimes, but also with a stratocumulus-to-cumulus transition that occurs too early along the trade wind Lagrangian trajectory. Histograms of cloud cover along the cross section differ significantly between models. Some models exhibit a quasi-bimodal structure with cloud cover being either very large (close to 100%) or very small, while other models show a more continuous transition. The ISCCP observations suggest that reality is in-between these two extreme examples. These different patterns reflect the diverse nature of the cloud, boundary layer, and convection parameterizations in the participating weather and climate prediction models.

Since its discovery in the early 1970s, the crucial role of the Madden‐Julian Oscillation (MJO) in the global hydrological cycle and its tremendous influence on high‐impact climate and weather extremes have been well recognized. The MJO also serves as a primary source of predictability for global Earth system variability on subseasonal time scales. The MJO remains poorly represented in our state‐of‐the‐art climate and weather forecasting models, however. Moreover, despite the advances made in recent decades, theories for the MJO still disagree at a fundamental level. The problems of understanding and modeling the MJO have attracted significant interest from the research community. As a part of the AGU's Centennial collection, this article provides a review of recent progress, particularly over the last decade, in observational, modeling, and theoretical study of the MJO. A brief outlook for near‐future MJO research directions is also provided.

Precipitation variability is analyzed in two versions of the Community Atmospheric Model (CAM), the standard model, CAM, and a "multiscale modeling framework" (MMF), in which the cumulus parameterization has been replaced with a cloud-resolving model. Probability distribution functions (PDFs) of daily mean rainfall in three geographic locations [the Amazon Basin and western Pacific in December-February (DJF) and the North American Great Plains in June-August (JJA)] indicate that the CAM produces too much light-moderate rainfall (10 ϳ 20 mm day Ϫ1 ), and not enough heavy rainfall, compared to observations. The MMF underestimates rain contributions from the lightest rainfall rates but correctly simulates more intense rainfall events. These differences are not always apparent in seasonal mean rainfall totals.Analysis of 3-6-hourly rainfall and sounding data in the same locations reveals that the CAM produces moderately intense rainfall as soon as the boundary layer energizes. Precipitation is also concurrent with tropospheric relative humidity and lifted parcel buoyancy increases. In contrast, the MMF and observations are characterized by a lag of several hours between boundary layer energy buildup and precipitation, and a gradual increase in the depth of low-level relative humidity maximum prior to rainfall.The environmental entrainment rate selection in the CAM cumulus parameterization influences CAM precipitation timing and intensity, and may contribute to the midlevel dry bias in that model. The resulting low-intensity rainfall in the CAM leads to rainfall-canopy vegetation interactions that are different from those simulated by the MMF. The authors present evidence suggesting that this interaction may artificially inflate North American Great Plains summertime rainfall totals in the CAM.

The interaction of ocean coupling and model physics in the simulation of the intraseasonal oscillation (ISO) is explored with three general circulation models: the Community Atmospheric Model, versions 3 and 4 (CAM3 and CAM4), and the superparameterized CAM3 (SPCAM3). Each is integrated coupled to an ocean model, and as an atmosphere-only model using sea surface temperatures (SSTs) from the coupled SPCAM3, which simulates a realistic ISO. For each model, the ISO is best simulated with coupling. For each SST boundary condition, the ISO is best simulated in SPCAM3.Near-surface vertical gradients of specific humidity, Dq (temperature, DT), explain ;20% (50%) of tropical Indian Ocean latent (sensible) heat flux variance, and somewhat less of west Pacific variance. In turn, local SST anomalies explain ;5% (25%) of Dq (DT) variance in coupled simulations, and less in uncoupled simulations. Ergo, latent and sensible heat fluxes are strongly controlled by wind speed fluctuations, which are largest in the coupled simulations, and represent a remote response to coupling. The moisture budget reveals that wind variability in coupled simulations increases east-of-convection midtropospheric moistening via horizontal moisture advection, which influences the direction and duration of ISO propagation.These results motivate a new conceptual model for the role of ocean feedbacks on the ISO. Indian Ocean surface fluxes help developing convection attain a magnitude capable of inducing the circulation anomalies necessary for downstream moistening and propagation. The ''processing'' of surface fluxes by model physics strongly influences the moistening details, leading to model-dependent responses to coupling.