Importance of the mixed-phase cloud distribution in the control climate for assessing the response of clouds to carbon dioxide increase: a multi-model study

We have conducted a multi-model intercomparison of cloud-water in five state-of-the-art AGCMs run for control and doubled carbon dioxide climates. The most notable feature of the differences between the control and doubled carbon dioxide climates is in the distribution of cloud-water in the mixed-phase temperature band. The difference is greatest at mid and high latitudes. We found that the amount of cloud ice in the mixed phase layer in the control climate largely determines how much the cloud-water distribution changes for the doubled carbon dioxide climate. Therefore evaluation of the cloud ice distribution by comparison with data is important for future climate sensitivity studies. Cloud ice and cloud liquid both decrease in the layer below the melting layer, but only cloud liquid increases in the mixed-phase layer. Although the decrease in cloud-water below the melting layer occurs at all latitudes, the increase in cloud liquid in the mixed-phase layer is restricted to those latitudes where there is a large amount of cloud ice in the mixed-phase layer. If the cloud ice in the mixed-phase layer is concentrated at high latitudes, doubling of carbon dioxide might shift the center of cloud water distribution poleward which could decrease solar reflection because solar insolation is less at higher latitude. The magnitude of this poleward shift of cloud water appears to be larger for the higher climate sensitivity models, and it is consistent with the associated changes in cloud albedo forcing. For the control climate there is a clear relationship between the differences in cloud-water and relative humidity between the different models, for both magnitude and distribution. On the other hand the ratio of cloud ice to cloud-water follows the threshold temperature which is determined in each model. Improved measurements of relative humidity could be used to constrain the modeled representation of cloud water. At the same time, comparative analysis in global cloud resolving model simulations is necessary for further understanding of the relationships suggested in this paper.

[1]  S. Bony,et al.  Combining ERBE and ISCCP data to assess clouds in the Hadley Centre, ECMWF and LMD atmospheric climate models , 2001 .

[2]  Thomas P. Charlock,et al.  The Albedo Field and Cloud Radiative Forcing Produced by a General Circulation Model with Internally Generated Cloud Optics , 1985 .

[3]  John F. B. Mitchell,et al.  Intercomparison and interpretation of climate feedback processes in 19 atmospheric general circulation models , 1990 .

[4]  Olivier Boucher,et al.  The sulfate‐CCN‐cloud albedo effect , 1995 .

[5]  John F. B. Mitchell,et al.  Carbon Dioxide and Climate. The Impact of Cloud Parameterization , 1993 .

[6]  U. Lohmann,et al.  The sulfate-CCN-cloud albedo effect: a sensitivity study with two general circulation models , 1996 .

[7]  J. Mitchell,et al.  C02 and climate: a missing feedback? , 1989, Nature.

[8]  S. Manabe,et al.  Cloud Feedback Processes in a General Circulation Model , 1988 .

[9]  B. Ryan,et al.  On the Global Variation of Precipitating Layer Clouds , 1996 .

[10]  Jeffrey H. Yin,et al.  A consistent poleward shift of the storm tracks in simulations of 21st century climate , 2005 .

[11]  J. Kristjánsson.  Tests of a new cloud treatment in an atmospheric general circulation model , 1994 .

[12]  B. Scaillet,et al.  Correction to “Phase equilibrium constraints on the viscosity of silicic magmas: 1. Volcanic‐plutonic comparison” by Bruno Scaillet, François Holtz, and Michel Pichavant , 1999 .

[13]  I. Musat,et al.  On the contribution of local feedback mechanisms to the range of climate sensitivity in two GCM ensembles , 2006 .

[14]  B. Soden,et al.  Large-scale ice clouds in the GFDL SKYHI general circulation model , 1997 .

[15]  B. Barkstrom,et al.  Seasonal variation of cloud radiative forcing derived from the Earth Radiation Budget Experiment , 1990 .

[16]  R. Smith.  A scheme for predicting layer clouds and their water content in a general circulation model , 1990 .

[17]  S. Klein,et al.  The new GFDL global atmosphere and land model AM2-LM2: Evaluation with prescribed SST simulations , 2004 .

[18]  Leon D. Rotstayn,et al.  A physically based scheme for the treatment of stratiform clouds and precipitation in large‐scale models. I: Description and evaluation of the microphysical processes , 1997 .

[19]  J. Katzfey,et al.  A Scheme for Calculation of the Liquid Fraction in Mixed-Phase Stratiform Clouds in Large-Scale Models , 2000 .

[20]  G. Stenchikov,et al.  Radiative forcing by volcanic aerosols from 1850 to 1994 , 1999 .

[21]  D. W. Johnson,et al.  The Measurement and Parameterization of Effective Radius of Droplets in Warm Stratocumulus Clouds , 1994 .

[22]  Hilding Sundqvist,et al.  Parameterization of Condensation and Associated Clouds in Models for Weather Prediction and General Circulation Simulation , 1988 .

[23]  M. Schlesinger,et al.  Description and performance of the UIUC 24‐layer stratosphere/troposphere general circulation model , 2000 .

[24]  Hilding Sundqvist,et al.  A parameterization scheme for non-convective condensation including prediction of cloud water content , 1978 .

[25]  J. Deluisi,et al.  Impact of Clouds on the Shortwave Radiation Budget of the Surface-Atmosphere System for Snow-Covered Surfaces , 1994 .

[26]  D. Randall,et al.  Liquid and Ice Cloud Microphysics in the CSU General Circulation Model , 1996 .

[27]  Zhaoxin Li,et al.  Sensitivity of an atmospheric general circulation model to prescribed SST changes: feedback effects associated with the simulation of cloud optical properties , 1991 .

[28]  A. Korolev,et al.  Assessing Cloud-Phase Conditions , 2001 .

[29]  Ulrike Lohmann,et al.  Design and performance of a new cloud microphysics scheme developed for the ECHAM general circulation model , 1996 .

[30]  John Latham,et al.  A parametrization of the ice water content observed in frontal and convective clouds , 1996 .

[31]  Damian R. Wilson,et al.  A microphysically based precipitation scheme for the UK meteorological office unified model , 1999 .

[32]  M. Tiedtke,et al.  Representation of Clouds in Large-Scale Models , 1993 .

[33]  V. Pope,et al.  The impact of new physical parametrizations in the Hadley Centre climate model: HadAM3 , 2000 .

[34]  H. Tomita,et al.  A global cloud‐resolving simulation: Preliminary results from an aqua planet experiment , 2005 .