• Allan, R. P., K. P. Shine, A. Slingo, and J. A. Pamment, 1999: The dependence of clear-sky outgoing long-wave radiation on surface temperature and relative humidity. Quart. J. Roy. Meteor. Soc.,125, 2103–2126.

  • Arrhenius, S., 1896: On the influence of carbonic acid in the air upon the temperature of the ground. Philos. Mag. Series 5,41, 237– 276.

  • Bony, S., K.-M. Lau, and Y. C. Sud, 1997: Sea surface temperature and large-scale circulation influences on tropical greenhouse effect and cloud radiative forcing. J. Climate,10, 2055–2077.

  • Cess, R. D., 1989: Gauging water-vapour feedback. Nature,342, 736– 737.

  • ——, and Coauthors, 1990: Intercomparison and interpretation of climate feedback processes in 19 atmospheric general circulation models. J. Geophys. Res.,95, 16601–16615.

  • Chatfield, C. R., 1981: Statistics for Technology: A Course in Applied Statistics. 2d ed. Chapman and Hall, 370 pp.

  • Cox, P. M., R. A. Betts, C. B. Bunton, R. L. H. Essery, P. R. Rowntree, and J. Smith, 1999: The impact of new land surface physics on the GCM simulation of climate and climate sensitivity. Climate Dyn.,15, 183–203.

  • Del Genio, A. D., A. A. Lacis, and R. A. Ruedy, 1991: Simulations of the effect of a warmer climate on atmospheric humidity. Nature,251, 382–385.

  • Duvel, J. P., and F. M. Bréon, 1991: The clear-sky greenhouse effect sensitivity to a sea surface temperature change. J. Climate,4, 1162–1169.

  • Edwards, J. M., and A. Slingo, 1996: Studies with a flexible new radiation code. I: Choosing a configuration for a large-scale model. Quart. J. Roy. Meteor. Soc.,122, 689–719.

  • Gates, W. L., 1992: AMIP: The Atmospheric Model Intercomparison Project. Bull. Amer. Meteor. Soc.,73, 1962–1970.

  • Gordon, C., C. Cooper, C. A. Senior, H. Banks, J. M. Gregory, T. C. Johns, J. F. B. Mitchell, and R. A. Wood, 2000: The simulation of SST, sea ice extents and ocean heat transports in a coupled model without flux adjustments. Climate Dyn.,16, 147–168.

  • Gregory, D., R. Kershaw, and P. M. Inness, 1997: Parametrization of momentum transport by convection. II: Tests in single-column and general circulation models. Quart. J. Roy. Meteor. Soc.,123, 1153–1183.

  • Inamdar, A. K., and V. Ramanathan, 1998: Tropical and global scale interactions among water vapor, atmospheric greenhouse effect, and surface temperature. J. Geophys. Res.,103, 32177–32194.

  • Johns, T. C., R. E. Carnell, J. F. Crossley, J. M. Gregory, J. F. B. Mitchell, C. A. Senior, S. F. B. Tett, and R. A. Wood, 1997: The second Hadley Centre coupled ocean–atmosphere GCM: Model description, spinup and validation. Climate Dyn.,13, 103–134.

  • Lau, K.-M., C.-H. Ho, and M.-D. Chou, 1996: Water vapor and cloud feedback over the tropical oceans: Can we use ENSO as a surrogate for climate change? Geophys. Res. Lett.,23, 2971–2974.

  • Lindzen, R. S., 1990: Some coolness concerning global warming. Bull. Amer. Meteor. Soc.,71, 288–299.

  • Manabe, S., and R. T. Wetherald, 1967: Thermal equilibrium of the atmosphere with a given distribution of relative humidity. J. Atmos. Sci.,24, 241–259.

  • McNally, A. P., and M. Vesperini, 1996: Variational analysis of humidity information from TOVS radiances. Quart. J. Roy. Meteor. Soc.,122, 1521–1544.

  • Mitchell, J. F. B., T. C. Johns, and C. A. Senior, 1998: Transient response to increasing greenhouse gases using models with and without flux adjustment. Hadley Centre Tech. Note 2, Hadley Centre for Climate Prediction and Research, 15 pp. [Available from Hadley Centre, The Met. Office, Bracknell, Berkshire RG12 2SY, United Kingdom.].

  • Pope, V., M. Gallani, P. Rowntree, and R. Stratton, 2000: The impact of new physical parametrizations in the Hadley Centre climate model: HadAM3. Climate Dyn.,16, 123–146.

  • Randel, D. L., T. H. Vonder Haar, M. A. Ringerud, G. L. Stephens, T. J. Greenwald, and C. L. Combs, 1996: A new global water vapor dataset. Bull. Amer. Meteor. Soc.,77, 1233–1246.

  • Raval, A., and V. Ramanathan, 1989: Observational determination of the greenhouse effect. Nature,342, 758–761.

  • Shine, K. P., and A. Sinha, 1991: Sensitivity of the earth’s climate to height-dependent changes in the water vapour mixing ratio. Nature,354, 382–384.

  • Slingo, A., and M. J. Webb, 1997: The spectral signature of global warming. Quart. J. Roy. Meteor. Soc.,123, 293–307.

  • ——, J. A. Pamment, and M. J. Webb, 1998: A 15-year simulation of the clear-sky greenhouse effect using the ECMWF reanalyses:Fluxes and comparisons with ERBE. J. Climate,11, 690–708.

  • Soden, B. J., 1997: Variations in the tropical greenhouse effect during El Niño. J. Climate,10, 1050–1055.

  • ——, and R. Fu, 1995: A satellite analysis of deep convection, upper-tropospheric humidity, and the greenhouse effect. J. Climate,8, 2333–2351.

  • Stephens, G. L., and T. J. Greenwald, 1991: The earth’s radiation budget and its relation to atmospheric hydrology. I. Observations of the clear sky greenhouse effect. J. Geophys. Res.,96, 15311– 15324.

  • Thompson, S. L., and S. G. Warren, 1982: Parameterization of outgoing infrared radiation derived from detailed radiative calculations. J. Atmos. Sci.,39, 2667–2680.

  • Udelhofen, P. M., and D. L. Hartmann, 1995: Influence of tropical cloud systems on the relative humidity in the upper troposphere. J. Geophys. Res.,100, 7423–7440.

  • Uppala, S., 1997: ECMWF Re-Analysis Project Rep. 1, Observing system performance in ERA. European Centre for Medium-Range Weather Forecasts, Reading, United Kingdom, 261 pp.

  • Watterson, I. G., M. R. Dix, and R. A. Colman, 1999: A comparison of present and doubled CO2 climates and feedbacks simulated by three general circulation models. J. Geophys. Res.,104, 1943– 1956.

  • Yang, H., and K. K. Tung, 1998: Water vapor, surface temperature, and the greenhouse effect—A statistical analysis of tropical-mean data. J. Climate,11, 2686–2697.

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 17 17 3
PDF Downloads 11 11 2

Water Vapor Feedbacks in the ECMWF Reanalyses and Hadley Centre Climate Model

View More View Less
  • 1 Hadley Centre for Climate Prediction and Research, The Met. Office, Bracknell, Berkshire, United Kingdom
© Get Permissions
Restricted access

Abstract

Many studies have been made of the water vapor feedback, in both satellite data and climate model simulations. Most infer the magnitude of the feedback from the variability present in geographical distributions of the key variables, or from their seasonal variations, often using data only over the oceans. It is argued that a more direct measure of the feedback should come from the interannual variability of global mean quantities, because this timescale and space scale is more appropriate for such a global phenomenon. To investigate this suggestion, the feedback derived from the simulations of clear-sky longwave fluxes (CLERA), which used data from the 15-yr reanalysis project of the European Centre for Medium-Range Weather Forecasts, is compared with simulations by the latest version of the Hadley Centre climate model. Results are taken from an integration of the atmosphere-only version of the climate model with prescribed sea surface temperatures, as well as from a control and a global warming simulation by the coupled ocean–atmosphere version. There is broad consistency between the results from CLERA and the climate model as to the strength of the feedback, although there is considerable scatter in the CLERA results. The signal of changes in the well-mixed greenhouse gases is weak in CLERA but is dominant in the global warming simulation and has to be removed in order to diagnose the water vapor feedback. This result has implications for the exploitation of long time series of satellite and other data to study this and other feedbacks.

Corresponding author address: Dr. Anthony Slingo, Hadley Centre, The Met. Office, London Road, Bracknell, Berkshire RG12 2SY, United Kingdom.

Email: aslingo@meto.gov.uk

Abstract

Many studies have been made of the water vapor feedback, in both satellite data and climate model simulations. Most infer the magnitude of the feedback from the variability present in geographical distributions of the key variables, or from their seasonal variations, often using data only over the oceans. It is argued that a more direct measure of the feedback should come from the interannual variability of global mean quantities, because this timescale and space scale is more appropriate for such a global phenomenon. To investigate this suggestion, the feedback derived from the simulations of clear-sky longwave fluxes (CLERA), which used data from the 15-yr reanalysis project of the European Centre for Medium-Range Weather Forecasts, is compared with simulations by the latest version of the Hadley Centre climate model. Results are taken from an integration of the atmosphere-only version of the climate model with prescribed sea surface temperatures, as well as from a control and a global warming simulation by the coupled ocean–atmosphere version. There is broad consistency between the results from CLERA and the climate model as to the strength of the feedback, although there is considerable scatter in the CLERA results. The signal of changes in the well-mixed greenhouse gases is weak in CLERA but is dominant in the global warming simulation and has to be removed in order to diagnose the water vapor feedback. This result has implications for the exploitation of long time series of satellite and other data to study this and other feedbacks.

Corresponding author address: Dr. Anthony Slingo, Hadley Centre, The Met. Office, London Road, Bracknell, Berkshire RG12 2SY, United Kingdom.

Email: aslingo@meto.gov.uk

Save