Laura D. Fowler

Liquid and Ice Cloud Microphysics in the CSU General Circulation Model. Part 1: Model Description and Simulated Microphysical Processes

Abstract Microphysical processes responsible for the formation and dissipation of water and ice clouds have been incorporated into the Colorado State University General Circulation Model in order to 1) yield a more physically based representation of the components of the atmospheric moisture budget, 2) link the distribution and optical properties of the model-generated clouds to the predicted cloud water and ice amounts, and 3) produce more realistic simulations of cloudiness and the earths radiation budget. The bulk cloud microphysics scheme encompasses five prognostic variables for the mass of water vapor, cloud water, cloud ice, rain, and snow. Graupel and hail are neglected. Cloud water and cloud ice are predicted to form through large-scale condensation and deposition processes and also through detrainment at the tops of cumulus towers. The production of rain and snow occur through autoconversion of cloud water and cloud ice. Rain drops falling through clouds can grow by collecting cloud water, and ...

Diurnal Variability of the Hydrologic Cycle and Radiative Fluxes: Comparisons between Observations and a GCM

The simulated diurnal cycle is in many ways an ideal test bed for new physical parameterizations. The purpose of this paper is to compare observations from the Tropical Rainfall Measurement Mission, the Earth Radiation Budget Experiment, the International Satellite Cloud Climatology Project, the Clouds and the Earth’s Radiant Energy System Experiment, and the Anglo-Brazilian Amazonian Climate Observation Study with the diurnal variability of the Amazonian hydrologic cycle and radiative energy budget as simulated by the Colorado State University general circulation model, and to evaluate improvements and deficiencies of the model physics. The model uses a prognostic cumulus kinetic energy (CKE) to relax the quasi-equilibrium closure of the Arakawa‐ Schubert cumulus parameterization. A parameter, a, is used to relate the CKE to the cumulus mass flux. This parameter is expected to vary with cloud depth, mean shear, and the level of convective activity, but up to now a single constant value for all cloud types has been used. The results of the present study show clearly that this approach cannot yield realistic simulations of both the diurnal cycle and the monthly mean climate state. Improved results are obtained using a version of the model in which a is permitted to vary with cloud depth.

Liquid and ice cloud microphysics in the CSU general circulation model. Part II: Impact on cloudiness, the earth's radiation budget, and the general circulation of the atmosphere

Abstract A prognostic equation for the mass of condensate associated with large-scale cloudiness introduces a direct coupling between the atmospheric moisture budget and the radiation budget through interactive cloud amounts and cloud optical properties. We have compared the cloudiness, the top-of-the-atmosphere and surface radiation budgets, the radiative forcing of clouds, and the atmospheric general circulation simulated with the Colorado State University general circulation model with and without such a prognostic cloud parameterization. In the EAULIQ run, the radiative effects of cloud water, cloud ice, and snow are considered; those of rain are omitted. The cloud optical depth and cloud infrared emissivity depend on the cloud water, cloud ice, and snow paths predicted by a bulk cloud microphysics parameterization. In the CONTROL run, a conventional large-scale condensation scheme is used. Cloud optical properties depend on the mean cloud temperatures. Results are presented in terms of January and Ju...

Comparison of the Seasonal Change in Cloud-Radiative Forcing from Atmospheric General Circulation Models and Satellite Observations

We compare seasonal changes in cloud-radiative forcing (CRF) at the top of the atmosphere from 18 atmospheric general circulation models, and observations from the Earth Radiation Budget Experiment (ERBE). To enhance the CRF signal and suppress interannual variability, we consider only zonal mean quantities for which the extreme months (January and July), as well as the northern and southern hemispheres, have been differenced. Since seasonal variations of the shortwave component of CRF are caused by seasonal changes in both cloudiness and solar irradiance, the latter was removed. In the ERBE data, seasonal changes in CRF are driven primarily by changes in cloud amount. The same conclusion applies to the models. The shortwave component of seasonal CRF is a measure of changes in cloud amount at all altitudes, while the longwave component is more a measure of upper level clouds. Thus important insights into seasonal cloud amount variations of the models have been obtained by comparing both components, as generated by the models, with the satellite data. For example, in 10 of the 18 models the seasonal oscillations of zonal cloud patterns extend too far poleward by one latitudinal grid. •Institute for Terrestrial and Planetary Atmospheres, Marine Sciences Research Center, State University of New York at Stony Brook. 2program for Climate Model Diagnosis and Intercomparison, Lawrence Livermore National Laboratory, Livermore, California. 3Department of Numerical Mathematics, Russian Academy of Sciences, Moscow. 4Canadian Climate Centre, Downsview, Ontario. SLaboratoire de Mdtdorologie Dynamique, Paris. 6Bureau of Meteorology Research Centre, Melbourne, Victoria, Australia. 7Department of Atmospheric Science, Colorado State University, Fort Collins. 8NASA Goddard Institute for Space Studies, New York. 9Maltrio-France, Centre National de Recherches Mdtdorologiques, Toulouse, France. •oDivision of Atmospheric Research, Commonwealth Scientific and Industrial Research Organisation, Aspendale, Victoria, Australia. •Max Planck Institute for Meteorology, Hamburg, Germany. •2National Center for Atmospheric Research, Boulder, Colorado. •3Hadley Centre for Climate Prediction and Research, U. K. Meteorological Office, Bracknell, England. •4Atmospheric Sciences Research Center, State University of New York at Albany. •SVoeikov Main Geophysical Obseratory, St. Petersburg, Russia. •6European Centre for Medium-Range Weather Forecasts, Reading, England. •7Department ofAtmospheric Sciences, University of Illinois, Urbana. 18Geophysical Fluid Dynamics Laboratory, National Oceanic and Atmospheric Administration, Princeton University, Princeton, New Jersey. Copyright 1997 by the American Geophysical Union. Paper number 97JD00927. 01480227/97/97JD-00927509.00

Liquid and Ice Cloud Microphysics in the CSU General Circulation Model. Part III: Sensitivity to Modeling Assumptions

Abstract The inclusion of cloud microphysical processes in general circulation models makes it possible to study the multiple interactions among clouds, the hydrological cycle, and radiation. The gaps between the temporal and spatial scales at which such cloud microphysical processes work and those at which general circulation models presently function force climate modelers to crudely parameterize and simplify the various interactions among the different water species (namely, water vapor, cloud water, cloud ice, rain, and snow) and to use adjustable parameters to which large-scale models can be highly sensitive. Accordingly, the authors have investigated the sensitivity of the climate, simulated with the Colorado State University general circulation model, to various aspects of the parameterization of cloud microphysical processes and its interactions with the cumulus convection and radiative transfer parameterizations. The results of 120-day sensitivity experiments corresponding to perpetual January co...

A global radiative‐convective feedback

We have investigated the sensitivity of the intensity of convective activity and atmospheric radiative cooling to radiatively thick upper-tropospheric clouds using a new version of the Colorado State University General Circulation Model (CSU GCM). The model includes a bulk cloud microphysics scheme to predict the formation of cloud water, cloud ice, rain, and snow. The cloud optical properties are interactive and dependent upon the cloud water and cloud ice paths. We find that the formation of a persistent upper tropospheric cloud ice shield leads to decreased atmospheric radiative cooling and increased static stability. Convective activity is then strongly suppressed. In this way, upper-tropospheric clouds act as regulators of the global hydrologic cycle, and provide a negative feedback between atmospheric radiative cooling and convective activity.

Interactions between Cloud Microphysics and Cumulus Convection in a General Circulation Model

In the Colorado State University general circulation model, cumulus detrainment of cloud water and cloud ice has been, up to now, the only direct coupling between convective and large-scale condensation processes. This one-way interaction from the convective to the large-scale environment parameterizes, in a highly simplified manner, the growth of anvils spreading horizontally at the tops of narrow cumulus updrafts. The reverse interaction from the large-scale to the convective updrafts, through which large-scale cloud water and cloud ice can affect microphysical processes occurring in individual convective updrafts, is missing. In addition, the effects of compensating subsidence on cloud water and cloud ice are not taken into account. A new parameterization of convection, called ‘‘EAUCUP,’’ has been developed, in which large-scale water vapor, cloud water, and cloud ice are allowed to enter the sides of the convective updrafts and can be lifted to the tops of the clouds. As the various water species are lifted, cloud microphysical processes take place, removing excess cloud water and cloud ice in the form of rain and snow. The partitioning of condensed vapor between cloud water and cloud ice, and between rain and snow, is based on temperature. The effects of compensating subsidence on the large-scale water vapor, cloud water, and cloud ice are computed separately. Convective rain is assumed to fall instantaneously to the surface. Three treatments of the convective snow are tested: 1) assuming that all snow is detrained at the tops of convective updrafts, 2) assuming that all snow falls outside of the updrafts and may evaporate, and 3) assuming that snow falls entirely inside the updrafts and melts to form rain. Including entrainment of large-scale cloud water and cloud ice inside the updrafts, large-scale compensating subsidence unifies the parameterizations of large-scale cloud microphysics and convection, but have a lesser impact than the treatment of convective snow on the simulated climate. Differences between the three alternate treatments of convective snow are discussed. Emphasis is on the change in the convective, large-scale, and radiative tendencies of temperature, and change in the convective and large-scale tendencies of water vapor, cloud water, cloud ice, and snow. Below the stratiform anvils, the change in latent heating due to the change in both convective and large-scale heatings contributes a major part to the differences in diabatic heating among the three simulations. Above the stratiform anvils, differences in the diabatic heating between the three simulations result primarily because of differences in the longwave radiative cooling. In particular, detraining convective snow at the tops of convective updrafts yields a strong increase in the longwave radiative cooling associated with increased upper-tropospheric cloudiness. The simulated climate is wetter and colder when convective snow is detrained at the tops of the updrafts than when it is detrained on the sides of the updrafts or when it falls entirely inside the updrafts. This result highlights the importance of the treatment of the ice phase and associated precipitation in the convective cloud models used in cumulus parameterizations.

Sampling strategies for the comparison of climate model calculated and satellite observed brightness temperatures

Brightness temperatures derived from polar-orbiting satellites are valuable for the evaluation of global climate models. However, the effect of orbital constraints must be taken into account to ensure valid comparisons. As part of the Atmospheric Model Intercomparison Project II climate model comparisons, this study seeks to evaluate the monthly mean simulated brightness temperature differences of possible model output sampling strategies with respect to the exact satellite sampling and whether they can be practically implemented to provide meaningful comparisons with these satellite observations. We compare various sampling strategies with a proxy satellite data set constructed from model output and actual TIROS operational vertical sounder orbital trajectories, rather than with the observations themselves. To a large extent, this enables isolation of the sampling error from errors caused by deficiencies in the modeled climate processes. Our results suggest that the traditional method of calculating brightness temperatures from monthly mean temperature and moisture profiles yields biases from both nonlinear effects and the removal of the diurnal cycle that may be unacceptable in many applications. However, we also find that a brightness temperature calculation every hour of the simulation provides substantially lower sampling biases provided that there are two or more properly aligned satellites. This is encouraging because it means that for many applications modelers need not accurately mimic actual satellite trajectories in the sampling of their simulations. If only one satellite is available for comparison with simulations, more sophisticated sampling seems necessary. For such circumstances, we introduce a simple procedure that serves as a useful approximation to the rather complex procedure required to sample a model exactly as a polar-orbiting satellite does the Earth. With all sampling methods, removal of biases associated with cloud cover is problematic and deserves further study.

Simulation of upper tropospheric clouds with the Colorado State University general circulation model

We have compared the climatology of upper tropospheric clouds simulated with the Colorado State University (CSU) general circulation model against cloud products retrieved by the International Satellite Cloud Climatology Project (ISCCP). Following the ISCCP cloud classification, upper tropospheric clouds are defined as clouds with cloud tops above 440 hPa. We refined our comparison by considering separately clouds with cloud tops above 180, 310, and 440 hPa in order to exhibit the optical characteristics of the highest clouds in the model and satellite cloud products. Four ranges of visible optical depths (τ) were used to distinguish cirrus (τ ≤ 3.6) from optically thicker cirrostratus (3.6 23) and to further differentiate between thin (0.02 < τ ≤ 1.6) and thick (1.6 < τ ≤ 3.6) cirrus. Results show that the CSU GCM simulates satisfactorily the zonally averaged distribution of upper tropospheric clouds when all values of τ are included but systematically underpredicts the frequency of occurrence of clouds with values of τ less than 3.6 when compared against ISCCP-D1 data. This result reveals that simulated total-column optical depths for columns that include upper tropospheric clouds are too large relative to satellite-derived values. The CSU GCM simulates upper tropospheric clouds in the tropics more successfully than those in the middle latitudes. In the middle latitudes the model fails to simulate upper tropospheric clouds over the continents, especially over high plateaus and mountain ranges. Discrepancies between the CSU GCM and the ISCCP cloud products can be addressed in terms of our simple formulation of the optical thickness as a function of the prognostic liquid/ice water content, the prescribed value of the effective radius, and the geometrical thickness of the upper tropospheric model layers. We investigate the impact of the vertical resolution used in the GCM on the calculation of the optical depths of single-layer clouds using estimates of the geometrical thickness of cloudy layers from the Lidar In-Space Technology Experiment.

Cloud Effects on the Ocean Surface Energy Budget

Although coupled ocean-atmosphere modeling was born about 25 years ago (Manabe and Bryan 1969), the field is just now entering (a somewhat late) adolescence, characterized by exciting new experiences (Stouffer et al. 1994), uncooperative and troublesome model behavior (Robertson et al. 1994), and dreams of future maturity (e.g. Trenberth 1992).