[1] To assess the current status of climate models in simulating clouds, basic cloud climatologies from ten atmospheric general circulation models are compared with satellite measurements from the International Satellite Cloud Climatology Project (ISCCP) and the Clouds and Earth's Radiant Energy System (CERES) program. An ISCCP simulator is employed in all models to facilitate the comparison. Models simulated a four-fold difference in high-top clouds. There are also, however, large uncertainties in satellite high thin clouds to effectively constrain the models. The majority of models only simulated 30-40% of middle-top clouds in the ISCCP and CERES data sets. Half of the models underestimated low clouds, while none overestimated them at a statistically significant level. When stratified in the optical thickness ranges, the majority of the models simulated optically thick clouds more than twice the satellite observations. Most models, however, underestimated optically intermediate and thin clouds. Compensations of these clouds biases are used to explain the simulated longwave and shortwave cloud radiative forcing at the top of the atmosphere. Seasonal sensitivities of clouds are also analyzed to compare with observations. Models are shown to simulate seasonal variations better for high clouds than for low clouds. Latitudinal distribution of the seasonal variations correlate with satellite measurements at >0.9, 0.6-0.9, and À0.2-0.7 levels for high, middle, and low clouds, respectively. The seasonal sensitivities of cloud types are found to strongly depend on the basic cloud climatology in the models. Models that systematically underestimate middle clouds also underestimate seasonal variations, while those that overestimate optically thick clouds also overestimate their seasonal sensitivities. Possible causes of the systematic cloud biases in the models are discussed.

ABSTRACT:Results are presented from an intercomparison of single-column and cloud-resolving model simulations of a cold-air outbreak mixed-phase stratocumulus cloud observed during the Atmospheric Radiation Measurement (ARM) programme's Mixed-Phase Arctic Cloud Experiment. The observed cloud occurred in a well-mixed boundary layer with a cloud-top temperature of −15 • C. The average liquid water path of around 160 g m −2 was about two-thirds of the adiabatic value and far greater than the average mass of ice which when integrated from the surface to cloud top was around 15 g m −2 .Simulations of 17 single-column models (SCMs) and 9 cloud-resolving models (CRMs) are compared. While the simulated ice water path is generally consistent with observed values, the median SCM and CRM liquid water path is a factor-of-three smaller than observed. Results from a sensitivity study in which models removed ice microphysics suggest that in many models the interaction between liquid and ice-phase microphysics is responsible for the large model underestimate of liquid water path.Despite this underestimate, the simulated liquid and ice water paths of several models are consistent with observed values. Furthermore, models with more sophisticated microphysics simulate liquid and ice water paths that are in better agreement with the observed values, although considerable scatter exists. Although no single factor guarantees a good simulation, these results emphasize the need for improvement in the model representation of mixed-phase microphysics.

This study provides comprehensive insight into the notable differences in clouds and precipitation simulated by the Energy Exascale Earth System Model Atmosphere Model version 0 and version 1 (EAMv1). Several sensitivity experiments are conducted to isolate the impact of changes in model physics, resolution, and parameter choices on these differences. The overall improvement in EAMv1 clouds and precipitation is primarily attributed to the introduction of a simplified third-order turbulence parameterization Cloud Layers Unified By Binormals (along with the companion changes) for a unified treatment of boundary layer turbulence, shallow convection, and cloud macrophysics, though it also leads to a reduction in subtropical coastal stratocumulus clouds. This lack of stratocumulus clouds is considerably improved by increasing vertical resolution from 30 to 72 layers, but the gain is unfortunately subsequently offset by other retuning to reach the top-of-atmosphere energy balance. Increasing vertical resolution also results in a considerable underestimation of high clouds over the tropical warm pool, primarily due to the selection for numerical stability of a higher air parcel launch level in the deep convection scheme. Increasing horizontal resolution from 1°to 0.25°without retuning leads to considerable degradation in cloud and precipitation fields, with much weaker tropical and subtropical short-and longwave cloud radiative forcing and much stronger precipitation in the intertropical convergence zone, indicating poor scale awareness of the cloud parameterizations. To avoid this degradation, significantly different parameter settings for the low-resolution (1°) and high-resolution (0.25°) were required to achieve optimal performance in EAMv1. Plain Language Summary The Energy Exascale Earth System Model (E3SM) is a new and ongoingU.S. Department of Energy (DOE) climate modeling effort to develop a high-resolution Earth system model specifically targeting next-generation DOE supercomputers to meet the science needs of the nation and the mission needs of DOE. The increase of model resolution along with improvements in representing cloud and convective processes in the E3SM atmosphere model version 1 has led to quite significant model behavior changes from its earlier version, particularly in simulated clouds and precipitation. To understand what causes the model behavior changes, this study conducts sensitivity experiments to isolate the impact of changes in model physics, resolution, and parameter choices on these changes. Results from these sensitivity tests and discussions on the underlying physical processes provide substantial insight into the model errors and guidance for future E3SM development. Key Points:• CLUBB along with the companion changes in EAMv1 primarily account for the overall improvements in clouds and precipitation simulation • Underestimate of coastal Sc in EAMv1 is due to CLUBB and model tuning; increased vertical resolution partially offsets this degradation • The poor scale awareness of EAMv1 r...

Numerical weather prediction methods show promise for improving parameterizations in climate GCMs.C limate simulations performed with general circulation models (GCMs) are widely viewed as the principal scientific basis for developing policies to address potential future global climate change (Houghton et al. 2001). In order to reduce uncertainties in these GCM projections of future climate, there is a compelling need to improve the simulation of processes that produce the present climate. This undertaking demands close attention to systematic errors in GCM simulations.Systematic errors are persistent (average) departures of the model solution from an appropriate observational standard. For example, the GCM sys-AFFILIATIONS:

The Impact of Arctic Aerosols on Clouds During one flight leg over the water on 4 April, large chunks of ice were seen floating in the Arctic Ocean after breaking up from the ice sheet along the coastline near Barrow, Alaska. Photo by Alexei Korolev.