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Mark D. Zelinka, Stephen A. Klein, and Dennis L. Hartmann

models employed to quantify cloud feedback in early studies like those described above provide insight into potential cloud feedbacks, the cloud feedback operating in nature in response to external forcing is, as pointed out in Schneider et al. (1978) , made up of a complex mix of time, space, and radiation-weighted cloud changes. The best chance to realistically simulate the response of clouds to external forcing is with fully three-dimensional global climate models (GCMs). Inserting global mean

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Jason M. English, Jennifer E. Kay, Andrew Gettelman, Xiaohong Liu, Yong Wang, Yuying Zhang, and Helene Chepfer

( Loeb et al. 2009 ). Comparison of all-sky net TOA shortwave (SW) radiation and outgoing longwave (LW) radiation (OLR) between CAM5 and CERES-EBAF can identify overall energy balance biases in the model. In addition, comparisons of clear-sky and cloud-forcing radiative fluxes can help identify the contributions of cloud and surface albedo biases to all-sky biases. The contribution of cloud amount biases to cloud-forcing biases can be studied by comparing modeled and observed cloud amount. Recent

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Charles N. Long and Sally A. McFarlane

summertime radiative forcing by shallow cumuli at the Atmospheric Radiation Measurement Southern Great Plains site . J. Geophys. Res. , 116 , D01202 , doi:10.1029/2010JD014593 . Dorman , C. E. , 1994 : Guadalupe Island cloud trail . Mon. Wea. Rev. , 122 , 235 – 242 . Flynn , C. J. , 2004 : Vaisala ceilometer (model CT25K) handbook. U.S. DOE Tech. Rep. ARM TR-020, 17 pp. [Available online at http://www.arm.gov .] King , D. L. , and D. R. Myers , 1997 : Silicon-photodiode pyranometers

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N. Forsythe, A. J. Hardy, H. J. Fowler, S. Blenkinsop, C. G. Kilsby, D. R. Archer, and M. Z. Hashmi

correlations between CCFday and CCFnight (see the supplementary material). This means that in most cases daytime and nighttime cloud conditions are expected to be similar (in terms of sign and magnitude of anomalies), but given the physical nature of daytime (SW) and nighttime (LW) CRE the resultant temperature forcings will act in opposite directions. Which diurnal component dominates will depend upon the relative importance of SW and LW fluxes in the given season. The potential balance of diurnal CRE

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Bernhard Schulz and Juan Pedro Mellado

buoyancy flux increases with time, and hence, increases with time. We can use this relationship between time and to express the evolution of the system in terms of the nondimensional variable . Introducing this variable has the advantage that represents the scale separation between the integral scale of the in-cloud turbulence and the scale at which the radiative forcing is introduced. In addition, represents the intensity of the in-cloud turbulence, according to Eq. (4) . We reach in our

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Jeffrey Frame and Paul Markowski

simulations. The soil model is the two-layer force-restore scheme described by Noilhan and Planton (1989) . The National Aeronautics and Space Administration (NASA) Goddard Cumulus Ensemble radiative transfer model was used for both shortwave ( Chou 1990 , 1992 ; Chou et al. 1998 ) and longwave ( Tao et al. 1996 ; Chou et al. 1999 ) radiation. This model allows for the absorption, scattering, and emission of radiation by atmospheric constituents, including clouds and gases. The tilted independent

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Byung-Ju Sohn and Franklin R. Robertson

Despite the general agreement that clouds cool the earth–atmosphere, there are substantial differences in estimated magnitudes of the annual global mean of cloud radiative forcing. Recent estimates of globally averaged net cloud radiative forcing range from −2 to −27 W m−2. The reasons for these differences have not been clarified in spite of the important role of clouds in maintaining global heat balance. Here, three estimation methods [Earth Radiation Budget Experiment (ERBE), Regression I, and Regression II] are compared using the same data source and analysis period.

Intercomparison has been done for the time period of February and March 1985 over which major satellite radiation budget and cloudiness datasets (ERBE radiation budget, Nimbus-7, and ISCCP cloudiness) are contemporaneous. The global averages of five sets of net cloud radiative forcing by three independent methods agree to within 3.5 W m−2; four of five cases agree to within 1 W m−2. This suggests that differences in published global mean values of net cloud radiative forcing are mainly due to different data sources and analysis periods and a best estimated annual mean among all previous estimates appears to be the ERBE measurement, that is, −17.3 W m−2. In contrast to the close agreement in the net cloud radiative forcing estimates, both longwave and shortwave cloud radiative forcing show more dependence on the chosen method and dataset. The bias of regression-retrieved values between Nimbus-7 and ISCCP cloud climatology is largely attributed to the difference in total cloudiness between two climatologies whereas the discrepancies between the ERBE and regression method appear to be, in part, due to the conceptually different definition of clear-sky flux.

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O. P. Sharma, H. Le Treut, G. Sèze, L. Fairhead, and R. Sadourny

conditions, Sperber and Palmer (1996) found that the model reproduced the contrasting behavior in all 1987 and 1988 in each of these realizations, but in other years the all-India rainfall variability showed little or no predictability, possibly due to the internal chaotic dynamics and/or the unpredictable land surface process interaction. Thus, there is a need to examine other factors too, for example, cloud forcing, that contribute to monsoon variability because SST is not the only factor that

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James Connell

1406 JOURNAL OF APPLIED METEOROLOGY VoLma~14A Non-Thermal Mechanism for Forcing Cumulonimbus CloudUni~rsily o.[ Tgnnessee Space lnsgtule, T~dJahoma 37388 21 November 1973 and 6 ]'une 1975ABSTRACT Within the thunderstorm there is an extensive region of collision between airflows .having different transports of momentum. The inflow-updraft should interact with cloud layer environmental wind to

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Katinka Bellomo, Amy C. Clement, Joel R. Norris, and Brian J. Soden

, simulating cloud variability in climate models is challenging. While observations show clear relationships between environmental variables and cloud fraction, the simulated relationships are highly model dependent ( Clement et al. 2009 ). Moreover, in response to greenhouse gas forcing models project an increase in SST, which on its own would decrease low-level clouds ( Brient and Bony 2013 ; Sandu and Stevens 2011 ), an increase in lower tropospheric stability, which would increase low-level clouds

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