Clouds and Convective Self-Aggregation in a Multimodel Ensemble of Radiative-Convective Equilibrium Simulations.

The Radiative-Convective Equilibrium Model Intercomparison Project (RCEMIP) is an intercomparison of multiple types of numerical models configured in radiative-convective equilibrium (RCE). RCE is an idealization of the tropical atmosphere that has long been used to study basic questions in climate science. Here, we employ RCE to investigate the role that clouds and convective activity play in determining cloud feedbacks, climate sensitivity, the state of convective aggregation, and the equilibrium climate. RCEMIP is unique among intercomparisons in its inclusion of a wide range of model types, including atmospheric general circulation models (GCMs), single column models (SCMs), cloud-resolving models (CRMs), large eddy simulations (LES), and global cloud-resolving models (GCRMs). The first results are presented from the RCEMIP ensemble of more than 30 models. While there are large differences across the RCEMIP ensemble in the representation of mean profiles of temperature, humidity, and cloudiness, in a majority of models anvil clouds rise, warm, and decrease in area coverage in response to an increase in sea surface temperature (SST). Nearly all models exhibit self-aggregation in large domains and agree that self-aggregation acts to dry and warm the troposphere, reduce high cloudiness, and increase cooling to space. The degree of self-aggregation exhibits no clear tendency with warming. There is a wide range of climate sensitivities, but models with parameterized convection tend to have lower climate sensitivities than models with explicit convection. In models with parameterized convection, aggregated simulations have lower climate sensitivities than unaggregated simulations.

Nonrotating Convective Self-Aggregation in a Limited Area AGCM.

We present nonrotating simulations with the Goddard Earth Observing System (GEOS) atmospheric general circulation model (AGCM) in a square limited area domain over uniform sea surface temperature. As in previous studies, convection spontaneously aggregates into humid clusters, driven by a combination of radiative and moisture-convective feedbacks. The aggregation is qualitatively independent of resolution, with horizontal grid spacing from 3 to 110 km, with both explicit and parameterized deep convection. A budget for the spatial variance of column moist static energy suggests that longwave radiative and surface flux feedbacks help establish aggregation, while the shortwave feedback contributes to its maintenance. Mechanism-denial experiments confirm that aggregation does not occur without interactive longwave radiation. Ice cloud radiative effects help support the humid convecting regions but are not essential for aggregation, while liquid clouds have a negligible effect. Removing the dependence of parameterized convection on tropospheric humidity reduces the intensity of aggregation but does not prevent the formation of dry regions. In domain sizes less than (5,000 km)2, the aggregation forms a single cluster, while larger domains develop multiple clusters. Larger domains initialized with a single large cluster are unable to maintain them, suggesting an upper size limit. Surface wind speed increases with domain size, implying that maintenance of the boundary layer winds may limit cluster size. As cluster size increases, large boundary layer temperature anomalies develop to maintain the surface pressure gradient, leading to an increase in the depth of parameterized convective heating and an increase in gross moist stability.

Effects of explicit atmospheric convection at high CO2.

The effect of clouds on climate remains the largest uncertainty in climate change predictions, due to the inability of global climate models (GCMs) to resolve essential small-scale cloud and convection processes. We compare preindustrial and quadrupled CO2 simulations between a conventional GCM in which convection is parameterized and a "superparameterized" model in which convection is explicitly simulated with a cloud-permitting model in each grid cell. We find that the global responses of the two models to increased CO2 are broadly similar: both simulate ice-free Arctic summers, wintertime Arctic convection, and enhanced Madden-Julian oscillation (MJO) activity. Superparameterization produces significant differences at both CO2 levels, including greater Arctic cloud cover, further reduced sea ice area at high CO2, and a stronger increase with CO2 of the MJO.