A. D. Del Genio

Climate simulations for 1880–2003 with GISS modelE

We carry out climate simulations for 1880–2003 with GISS modelE driven by ten measured or estimated climate forcings. An ensemble of climate model runs is carried out for each forcing acting individually and for all forcing mechanisms acting together. We compare side-by-side simulated climate change for each forcing, all forcings, observations, unforced variability among model ensemble members, and, if available, observed variability. Discrepancies between observations and simulations with all forcings are due to model deficiencies, inaccurate or incomplete forcings, and imperfect observations. Although there are notable discrepancies between model and observations, the fidelity is sufficient to encourage use of the model for simulations of future climate change. By using a fixed well-documented model and accurately defining the 1880–2003 forcings, we aim to provide a benchmark against which the effect of improvements in the model, climate forcings, and observations can be tested. Principal model deficiencies include unrealistically weak tropical El Nino-like variability and a poor distribution of sea ice, with too much sea ice in the Northern Hemisphere and too little in the Southern Hemisphere. Greatest uncertainties in the forcings are the temporal and spatial variations of anthropogenic aerosols and their indirect effects on clouds.

Interpretation of Snow-Climate Feedback as Produced by 17 General Circulation Models

Snow feedback is expected to amplify global warming caused by increasing concentrations of atmospheric greenhouse gases. The conventional explanation is that a warmer Earth will have less snow cover, resulting in a darker planet that absorbs more solar radiation. An intercomparison of 17 general circulation models, for which perturbations of sea surface temperature were used as a surrogate climate change, suggests that this explanation is overly simplistic. The results instead indicate that additional amplification or moderation may be caused both by cloud interactions and longwave radiation. One measure of this net effect of snow feedback was found to differ markedly among the 17 climate models, ranging from weak negative feedback in some models to strong positive feedback in others.

Rapid and Extensive Surface Changes Near Titan’s Equator: Evidence of April Showers

The Cassini spacecraft detected signs of recent methane rainfall on Titan’s arid equatorial surface. Although there is evidence that liquids have flowed on the surface at Titan’s equator in the past, to date, liquids have only been confirmed on the surface at polar latitudes, and the vast expanses of dunes that dominate Titan’s equatorial regions require a predominantly arid climate. We report the detection by Cassini’s Imaging Science Subsystem of a large low-latitude cloud system early in Titan’s northern spring and extensive surface changes (spanning more than 500,000 square kilometers) in the wake of this storm. The changes are most consistent with widespread methane rainfall reaching the surface, which suggests that the dry channels observed at Titan’s low latitudes are carved by seasonal precipitation.

Cassini Imaging of Saturn: Southern Hemisphere Winds and Vortices

High-resolution images of Saturns southern hemisphere acquired by the Cassini Imaging Science Subsystem between February and October 2004 are used to create maps of cloud morphology at several wavelengths, to derive zonal winds, and to characterize the distribution, frequency, size, morphology, color, behavior, and lifetime of vortices. Nonequatorial wind measurements display only minor differences from those collected since 1981 and reveal a strong, prograde flow near the pole. The region just southward of the velocity minimum at 40.7°S is especially active, containing numerous vortices, some generated in the proximity of convective storms. The two eastward jets nearest the pole display periodicity in their longitudinal structure, but no direct analogs to the northern hemispheres polar hexagon or ribbon waves were observed. Characteristics of winds and vortices are compared with those of Saturns northern hemisphere and Jupiters atmosphere.

Intercomparison and interpretation of surface energy fluxes in atmospheric general circulation models

We have analyzed responses of the surface energy budgets and hydrologic cycles of 19 atmospheric general circulation models to an imposed, globally uniform sea surface temperature perturbation of 4 K. The responses of the simulated surface energy budgets are extremely diverse and are closely linked to the responses of the simulated hydrologic cycles. The response of the net surface energy flux is not controlled by cloud effects; instead, it is determined primarily by the response of the latent heat flux. The prescribed warming of the oceans leads to major increases in the atmospheric water vapor content and the rates of evaporation and precipitation. The increased water vapor amount drastically increases the downwelling infrared radiation at the Earths surface, but the amount of the change varies dramatically from one model to another.

Uncertainties in Carbon Dioxide Radiative Forcing in Atmospheric General Circulation Models

Global warming caused by an increase in the concentrations of greenhouse gases, is the direct result of greenhouse gas—induced radiative forcing. When a doubling of atmospheric carbon dioxide is considered, this forcing differed substantially among 15 atmospheric general circulation models. Although there are several potential causes, the largest contributor was the carbon dioxide radiation parameterizations of the models.

Comparisons of EOS MLS Cloud Ice Measurements with ECMWF analyses and GCM Simulations: Initial Results

To assess the status of global climate models (GCMs) in simulating upper-tropospheric ice water content (IWC), a new set of IWC measurements from the Earth Observing Systems Microwave Limb Sounder (MLS) are used. Comparisons are made with ECMWF analyses and simulations from several GCMs, including two with multi-scale-modeling framework. For January 2005 monthly and daily mean values, the spatial agreement between MLS and ECMWF is quite good, although MLS estimates are higher by a factor of 2-3 over the Western Pacific, tropical Africa and South America. For the GCMs, the model-data agreement is within a factor of 2-4 with larger values of disagreement occurring over Eastern Pacific and Atlantic ITCZs, tropical Africa and South America. The implications arising from sampling and uncertainties in the observations, the modeled values and their comparison are discussed. These initial results demonstrate the potential usefulness of this data set for evaluating GCM performance and guiding development efforts.

Analysis of snow feedbacks in 14 general circulation models

Snow feedbacks produced by 14 atmospheric general circulation models have been analyzed through idealized numerical experiments. Included in the analysis is an investigation of the surface energy budgets of the models. Negative or weak positive snow feedbacks occurred in some of the models, while others produced strong positive snow feedbacks. These feedbacks are due not only to melting snow, but also to increases in boundary temperature, changes in air temperature, changes in water vapor, and changes in cloudiness. As a result, the net response of each model is quite complex. We analyze in detail the responses of one model with a strong positive snow feedback and another with a weak negative snow feedback. Some of the models include a temperature dependence of the snow albedo, and this has significantly affected the results.

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

Implementation of Subgrid Cloud Vertical Structure inside a GCM and Its Effect on the Radiation Budget

Abstract The GISS (Goddard Institute for Space Studies) GCM (general circulation model) predicts stratiform and convective cloud cover and optical thickness at nine atmospheric levels in horizontal grid boxes of 4° lat × 5° long. Until now, the radiative fluxes were calculated once per grid box, assuming clear sky or a complete cloud cover. Here, a refinement of the radiative flux calculation is explored by introducing a horizontal subgrid cloud overlap scheme in which cloud blocks are formed by adjacent cloud layers using maximum overlap. Different cloud blocks are separated by an atmospheric level of clear sky and are assumed to overlap randomly inside the grid box. This subgrid cloud structure allows determination of the occurrence probabilities of columns with different vertical structures inside each horizontal grid box. Then, radiative fluxes are calculated for each of these columns. The radiative fluxes of each horizontal grid box are obtained as the occurrence probability weighted sum of the colum...