The need to understand differences among general circulation model projections of CO2-induced climatic change has motivated the present study, which provides an intercomparison and interpretation of climate feedback processes in 19 atmospheric general circulation models. This intercomparison uses sea surface temperature change as a surrogate for climate change. The interpretation of cloud-climate interactions is given special attention. A roughly threefold variation in one measure of global climate sensitivity is found among the 19 models. The important conclusion is that most of this variation is attributable to differences in the models' depiction of cloud feedback, a result that emphasizes the need for improvements in the treatment of clouds in these models if they are ultimately to be used as reliable climate predictors. It is further emphasized that cloud feedback is the consequence of all interacting physical and dynamical processes in a general circulation model. The result of these processes is to produce changes in temperature, moisture distribution, and clouds which are integrated into the radiative response termed cloud feedback. INTRODUCTIONProjected increases in the concentration of atmospheric carbon dioxide and other greenhouse gases are expected to have an important impact on climate. The most comprehensive way to infer future climatic change associated with this perturbation of atmospheric composition is by means of three-dimensional general circulation models (GCMs). Schlesinger and Mitchell [1987] have, however, demonstrated that several existing GCMs simulate climate responses to increasing CO2 that differ considerably. Cess and Potter [1988], following a suggestion by Speltnan and Manabe [1984], indicate that differences in global-mean warming, The global-mean direct radiative forcing G of the surfaceatmosphere system is evaluated by holding all other climate parameters fixed. It is this quantity that induces the ensuing climate change, and physically, it represents a change in the net (solar plus infrared) radiative flux at the top of the atmosphere (TOA). For an increase in the CO2 concentration of the atmosphere, to cite one example, G is the reduction in the emitted TOA infrared flux resulting solely from the CO2 increase, and this reduction results in a heating of the surface-atmosphere system. The response process is the change in climate that is then necessary to restore the TOA radiation balance, such that that is either too warm or too cold, then it will respectively produce a climate sensitivity parameter that is too small or too large, and clearly, the intercomparison simulation had to be designed to eliminate this effect. There was also a practical constraint: the CO2 simulations require large amounts of computer time for equilibration of the rather primitive ocean models that have been used in these numerical experiments.An attractive alternative that eliminated both of the above mentioned difficulties was to adopt +_2øK sea surface temperature ( The perpetual July simulation e...

[1] The radiative effects from increased concentrations of well-mixed greenhouse gases (WMGHGs) represent the most significant and best understood anthropogenic forcing of the climate system. The most comprehensive tools for simulating past and future climates influenced by WMGHGs are fully coupled atmosphere-ocean general circulation models (AOGCMs). Because of the importance of WMGHGs as forcing agents it is essential that AOGCMs compute the radiative forcing by these gases as accurately as possible. We present the results of a radiative transfer model intercomparison between the forcings computed by the radiative parameterizations of AOGCMs and by benchmark line-by-line (LBL) codes. The comparison is focused on forcing by CO 2 , CH 4 , N 2 O, CFC-11, CFC-12, and the increased H 2 O expected in warmer climates. The models included in the intercomparison include several LBL codes and most of the global models submitted to the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4). In general, the LBL models are in excellent agreement with each other. However, in many cases, there are substantial discrepancies among the AOGCMs and between the AOGCMs and LBL codes. In some cases this is because the AOGCMs neglect particular absorbers, in particular the near-infrared effects of CH 4 and N 2 O, while in others it is due to the methods for modeling the radiative processes. The biases in the AOGCM forcings are generally largest at the surface level. We quantify these differences and discuss the implications for interpreting variations in forcing and response across the multimodel ensemble of AOGCM simulations assembled for the IPCC AR4.Citation: Collins, W. D., et al., (2006), Radiative forcing by well-mixed greenhouse gases: Estimates from climate models in the

Six years ago, we compared the climate sensitivity of 19 atmospheric general circulation models and found a roughly threefold variation among the models; most of this variation was attributed to differences in the models' depictions of cloud feedback. In an update of this comparison, current models showed considerably smaller differences in net cloud feedback, with most producing modest values. There are, however, substantial differences in the feedback components, indicating that the models still have physical disagreements

The primary purpose of this study is to assess the performance of 1D solar radiative transfer codes that are used currently both for research and in weather and climate models. Emphasis is on interpretation and handling of unresolved clouds. Answers are sought to the following questions: (i) How well do 1D solar codes interpret and handle columns of information pertaining to partly cloudy atmospheres? (ii) Regardless of the adequacy of their assumptions about unresolved clouds, do 1D solar codes perform as intended? One clear-sky and two plane-parallel, homogeneous (PPH) overcast cloud cases serve to elucidate 1D model differences due to varying treatments of gaseous transmittances, cloud optical properties, and basic radiative transfer. The remaining four cases involve 3D distributions of cloud water and water vapor as simulated by cloud-resolving models. Results for 25 1D codes, which included two line-by-line (LBL) models (clear and overcast only) and four 3D Monte Carlo (MC) photon transport algorithms, were submitted by 22 groups. Benchmark, domain-averaged irradiance profiles were computed by the MC codes. For the clear and overcast cases, all MC estimates of top-of-atmosphere albedo, atmospheric absorptance, and surface absorptance agree with one of the LBL codes to within Ϯ2%. Most 1D codes underestimate atmospheric absorptance by typically 15-25 W m Ϫ2 at overhead sun for the standard tropical atmosphere regardless of clouds. Depending on assumptions about unresolved clouds, the 1D codes were partitioned into four genres: (i) horizontal variability, (ii) exact overlap of PPH clouds, (iii) maximum/random overlap of PPH clouds, and (iv) random overlap of PPH clouds. A single MC code was used to establish conditional benchmarks applicable to each genre, and all MC codes were used to establish the full 3D benchmarks. There is a tendency for 1D codes to cluster near their respective conditional benchmarks, though intragenre variances typically exceed those for the clear and overcast cases. The majority of 1D codes fall into the extreme category of maximum/random overlap of PPH clouds and thus generally disagree with full 3D benchmark values. Given the fairly limited scope of these tests and the inability of any one code to perform extremely well for all cases begs the question that a paradigm shift is due for modeling 1D solar fluxes for cloudy atmospheres.

No abstract