• Allan, R. P., 2000: Evaluation of simulated clear-sky longwave radiation using ground-based observations. J. Climate, 13 , 19511964.

  • Alpert, P., , P. Kishcha, , Y. J. Kaufman, , and R. Schwarzbard, 2005: Global dimming or local dimming?: Effect of urbanization on sunlight availability. Geophys. Res. Lett., 32 .L17802, doi:10.1029/2005GL023320.

    • Search Google Scholar
    • Export Citation
  • Arking, A., 1996: Absorption of solar energy in the atmosphere: Discrepancy between model and observations. Science, 273 , 779782.

  • Barker, H. W., , and Z. Li, 1995: Improved simulation of clear-sky shortwave radiative transfer in the CCC-GCM. J. Climate, 8 , 22132223.

    • Search Google Scholar
    • Export Citation
  • Bolle, H-J., and Coauthors, 2006: Mediterranean Land-Surface Processes Assessed from Space. Springer-Verlag, 760 pp.

  • Bony, S., , J-L. Dufresne, , H. Le Treut, , J-J. Morcrette, , and C. A. Senior, 2004: On dynamic and thermodynamic components of cloud changes. Climate Dyn., 22 , 7186. doi:10.1007/s00382-003-0369-6.

    • Search Google Scholar
    • Export Citation
  • Cess, R. D., and Coauthors, 1995: Absorption of solar-radiation by clouds: Observations versus models. Science, 267 , 496499.

  • Cess, R. D., , M. Zhang, , P-H. Wang, , and B. A. Wielicki, 2001: Cloud structure anomalies over the tropical Pacific during the 1997/98 El Niño. Geophys. Res. Lett., 28 , 45474550.

    • Search Google Scholar
    • Export Citation
  • Chevallier, F., , and J-J. Morcrette, 2000: Comparison of model fluxes with surface and top-of-the-atmosphere observations. Mon. Wea. Rev., 128 , 38393852.

    • Search Google Scholar
    • Export Citation
  • Clough, S. A., , M. J. Iacono, , and J-L. Moncet, 1992: Line-by-line calculations of atmospheric fluxes and cooling rates: Application to water vapor. J. Geophys. Res., 97 , D14. 15 76115 785.

    • Search Google Scholar
    • Export Citation
  • Cusack, S., , A. Slingo, , J. M. Edwards, , and M. Wild, 1998: The radiative impact of a simple aerosol climatology on the Hadley Centre Atmospheric GCM. Quart. J. Roy. Meteor. Soc., 124 , 25172526. doi:10.1002/qj.49712455117.

    • Search Google Scholar
    • Export Citation
  • Darnell, W. L., , W. F. Staylor, , S. K. Gupta, , N. A. Ritchey, , and A. C. Wilber, 1992: Seasonal variation of surface radiation budget derived from International Satellite Cloud Climatology Project C1 data. J. Geophys. Res., 97 , 15 74115 760.

    • Search Google Scholar
    • Export Citation
  • 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 , 689720.

    • Search Google Scholar
    • Export Citation
  • Essery, R. L. H., , M. Best, , and P. Cox, 2001: MOSES 2.2 technical documentation. Hadley Centre Tech. Note 30, Hadley Centre, Met Office, Fitzroy Road, Exeter, United Kingdom, 30 pp.

  • Essery, R. L. H., , M. J. Best, , R. A. Betts, , P. M. Cox, , and C. M. Taylor, 2003: Explicit representation of subgrid heterogeneity in a GCM land-surface scheme. J. Hydrometeor., 4 , 530543.

    • Search Google Scholar
    • Export Citation
  • Garrat, J. R., 1994: Incoming shortwave fluxes at the surface—A comparison of GCM results with observations. J. Climate, 7 , 7280.

  • Gates, W. L., and Coauthors, 1999: An overview of the results of the Atmospheric Model Intercomparison Project (AMIP I). Bull. Amer. Meteor. Soc., 80 , 2955.

    • Search Google Scholar
    • Export Citation
  • Gilgen, H., , M. Wild, , and A. Ohmura, 1998: Means and trends of shortwave irradiance data at the surface estimated from global energy balance archive data. J. Climate, 11 , 20422061.

    • Search Google Scholar
    • Export Citation
  • Gleckler, P. J., 2005: Surface energy balance errors in AGCMs: Implications for ocean-atmosphere model coupling. Geophys. Res. Lett., 32 .L15708, doi:10.1029/2005GL023061.

    • Search Google Scholar
    • Export Citation
  • Gupta, S. K., , W. L. Darnell, , and A. C. Wilber, 1992: A parameterization for longwave surface radiation from satellite data: Recent improvements. J. Appl. Meteor., 31 , 13611367.

    • Search Google Scholar
    • Export Citation
  • Gupta, S. K., , N. A. Ritchey, , A. C. Wilber, , C. H. Whitlock, , G. G. Gibson, , and P. W. Stackhouse, 1999: A climatology of surface radiation budget derived from satellite data. J. Climate, 12 , 26912710.

    • Search Google Scholar
    • Export Citation
  • Hansen, J., , M. Sato, , and R. Reudy, 1997: Radiative forcing and climate response. J. Geophys. Res., 102 , D6. 68316864.

  • Hansen, M. C., , R. S. Defries, , J. R. G. Townshend, , and R. Sohlberg, 2000: Global land cover classification at 1 km spatial resolution using a classification tree approach. Int. J. Remote Sens., 21 , 13311364. doi:10.1080/014311600210209.

    • Search Google Scholar
    • Export Citation
  • Harrison, E. P., , P. Minnis, , B. R. Barkstrom, , V. Ramanathan, , R. D. Cess, , and G. G. Gibson, 1990: Seasonal variation of cloud radiative forcing derived from the Earth Radiation Budget Experiment. J. Geophys. Res., 95 , 18 68718 703.

    • Search Google Scholar
    • Export Citation
  • Jin, Y., , C. B. Schaaf, , F. Gao, , X. Li, , A. H. Strahler, , X. Zeng, , and R. E. Dickinson, 2002: How does snow impact the albedo of vegetated land surfaces as analyzed with MODIS data? Geophys. Res. Lett., 29 .1374, doi:10.1029/2001GL014132.

    • Search Google Scholar
    • Export Citation
  • Johns, T. C., and Coauthors, 2006: The new Hadley Centre climate model HadGEM1: Evaluation of coupled simulations. J. Climate, 19 , 13271353.

    • Search Google Scholar
    • Export Citation
  • Kristjánsson, J. E., , J. M. Edwards, , and D. L. Mitchell, 2000: Impact of a new scheme for optical properties of ice crystals on climates of two GCM’s. J. Geophys. Res., 105 , D8. 10 06310 079.

    • Search Google Scholar
    • Export Citation
  • Li, Z., 1995: Intercomparison between two satellite-based products of net surface shortwave radiation. J. Geophys. Res., 100 , 32213232.

    • Search Google Scholar
    • Export Citation
  • Li, Z., , and H. G. Leighton, 1993: Global climatologies of solar radiation budgets at the surface and in the atmosphere from 5 years of ERBE data. J. Geophys. Res., 98 , 49194930.

    • Search Google Scholar
    • Export Citation
  • Li, Z., , H. W. Barker, , and L. Moreau, 1995: The variable effect of clouds on atmospheric absorption of solar-radiation. Nature, 376 , 486490.

    • Search Google Scholar
    • Export Citation
  • Li, Z., , L. Moreau, , and A. Arking, 1997: On solar energy disposition: A perspective from observation and modeling. Bull. Amer. Meteor. Soc., 78 , 5370.

    • Search Google Scholar
    • Export Citation
  • Liepert, B. G., 2002: Observed reductions of surface solar radiation at sites in the United States and worldwide from 1961 to 1990. Geophys. Res. Lett., 29 .1421, doi:10.1029/2002GL014910.

    • Search Google Scholar
    • Export Citation
  • Liepert, B. G., , J. Feichter, , U. Lohmann, , and E. Roeckner, 2004: Can aerosols spin down the water cycle in a warmer and moister world? Geophys. Res. Lett., 31 .L06207, doi:10.1029/2003GL019060.

    • Search Google Scholar
    • Export Citation
  • Lu, R. Y., , B. W. Dong, , R. D. Cess, , and G. L. Potter, 2004: The 1997/1998 El Niño: A test for climate models. Geophys. Res. Lett., 31 .L12216, doi:10.1029/2004GL019956.

    • Search Google Scholar
    • Export Citation
  • Martin, G. M., , M. A. Ringer, , V. D. Pope, , A. Jones, , C. Dearden, , and T. J. Hinton, 2006: The physical properties of the atmosphere in the new Hadley Centre Global Environmental Model (HadGEM1). Part I: Model description and global climatology. J. Climate, 19 , 12741301.

    • Search Google Scholar
    • Export Citation
  • Moody, E. G., , M. D. King, , S. Platnick, , C. B. Schaaf, , and F. Gao, 2005: Spatially complete global spectral surface albedos: Value-added datasets derived from Terra MODIS land products. IEEE Trans. Geosci. Remote Sens., 43 , 144158. doi:10.1109/TGRS.2004.838359.

    • Search Google Scholar
    • Export Citation
  • Ohmura, A., and Coauthors, 1998: Baseline Surface Radiation Network (BSRN/WCRP): New precision radiometry for climate research. Bull. Amer. Meteor. Soc., 79 , 21152136.

    • Search Google Scholar
    • Export Citation
  • Pinker, R. T., , and I. Laszlo, 1992: Modeling surface solar irradiance for satellite applications on a global scale. J. Appl. Meteor., 31 , 194211.

    • Search Google Scholar
    • Export Citation
  • Pinker, R. T., , B. Zhang, , and E. G. Dutton, 2005: Do satellites detect trends in surface solar radiation? Science, 308 , 850854.

  • Ramanathan, V., 1987: The role of Earth Radiation Budget studies in climate and general circulation research. J. Geophys. Res., 92 , 40754095.

    • Search Google Scholar
    • Export Citation
  • Ramanathan, V., , R. D. Cess, , E. F. Harrison, , P. Minnis, , B. R. Barkstrom, , E. Ahmad, , and D. Hartmann, 1989: Cloud radiative forcing and climate: Results from the Earth Radiation Budget Experiment. Science, 243 , 5763.

    • Search Google Scholar
    • Export Citation
  • Ramanathan, V., , B. Subasilar, , G. J. Zhang, , W. Conant, , R. D. Cess, , J. T. Kiehl, , H. Grassl, , and L. Shi, 1995: Warm pool heat-budget and shortwave cloud forcing: A missing physics. Science, 267 , 499503.

    • Search Google Scholar
    • Export Citation
  • 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 , 12331246.

    • Search Google Scholar
    • Export Citation
  • Raschke, E., , A. Ohmura, , W. B. Rossow, , B. E. Carlson, , Y-C. Zhang, , C. Stubenrauch, , M. Kottek, , and M. Wild, 2005: Cloud effects on the radiation budget based on ISCCP data (1991 to 1995). Int. J. Climatol., 25 , 11031125. doi:10.1002/joc.1157.

    • Search Google Scholar
    • Export Citation
  • Ringer, M. A., , and R. P. Allan, 2004: Evaluating climate model simulations of tropical cloud. Tellus, 56 , 308327.

  • Ringer, M. A., and Coauthors, 2006: The physical properties of the atmosphere in the new Hadley Centre Global Environmental Model (HadGEM1). Part II: Aspects of variability and regional climate. J. Climate, 19 , 13021326.

    • Search Google Scholar
    • Export Citation
  • Rossow, W. B., , and Y. C. Zhang, 1995: Calculation of surface and top of atmosphere radiative fluxes from physical quantities based on ISCCP data sets. 2: Validation and first results. J. Geophys. Res., 100 , D1. 11671197.

    • Search Google Scholar
    • Export Citation
  • Rossow, W. B., , and R. A. Schiffer, 1999: Advances in understanding clouds from ISCCP. Bull. Amer. Meteor. Soc., 80 , 22612287.

  • Rothman, L. S., and Coauthors, 2003: The hitran molecular spectroscopic database: Edition of 2000 including updates through 2001. J. Quant. Spectrosc. Radiat. Transfer, 82 , 544. doi:10.1016/S0022-4073(03)00146-8.

    • Search Google Scholar
    • Export Citation
  • Schaaf, C. B., and Coauthors, 2002: First operational BRDF, albedo nadir reflectance products from MODIS. Remote Sens. Environ., 83 , 135148.

    • Search Google Scholar
    • Export Citation
  • Sellers, P. J., , S. O. Los, , C. J. Tucker, , C. O. Justice, , D. A. Dazlich, , G. J. Collatz, , and D. A. Randall, 1996: A revised land surface parameterization (SiB2) for atmospheric GCMS. Part II: The generation of global fields of terrestrial biophysical parameters from satellite data. J. Climate, 9 , 706737.

    • Search Google Scholar
    • Export Citation
  • Smith, G. L., , A. C. Wilber, , S. K. Gupta, , and P. W. Stackhouse Jr., 2002: Surface radiation budget and climate classification. J. Climate, 15 , 11751188.

    • Search Google Scholar
    • Export Citation
  • Smith, S. J., , R. Andres, , E. Conception, , and J. Lurz, 2004: Historical sulfur dioxide emissions 1850–2000: Methods and results. PNNL Rep. 14537, Joint Global Change Research Institute, College Park, MD, 16 pp.

  • Stackhouse Jr, P. W., , S. J. Cox, , S. K. Gupta, , R. C. DiPasquale, , and D. E. Brown, 1999: The WCRP/GEWEX Surface Radiation Budget Project Release 2: First results at 1 degree resolution. Preprints. 10th Conf. on Atmospheric Radiation: A Symp. with Tributes to the Works of Verner E. Suomi, Madison, WI, Amer. Meteor. Soc., 520–523.

    • Search Google Scholar
    • Export Citation
  • Stanhill, G., , and S. Cohen, 2001: Global dimming: A review of the evidence for a widespread and significant reduction in global radiation with discussion of its probable causes and possible agricultural consequences. Agric. For. Meteor., 107 , 255278.

    • Search Google Scholar
    • Export Citation
  • Stanhill, G., , and S. Moreshet, 2002: Global radiation climate changes: The world network. Climatic Change, 21 , 5775.

  • Stephens, G. L., 1996: How much solar radiation do clouds absorb? Science, 271 , 11311133.

  • Stephens, G. L., 2005: Cloud feedbacks in the climate system: A critical review. J. Climate, 18 , 237273.

  • Stephens, G. L., and Coauthors, 2002: The CloudSat mission and the A-Train. Bull. Amer. Meteor. Soc., 83 , 17711790.

  • Streets, D. G., , Y. Wu, , and M. Chin, 2006: Two-decadal aerosol trends as a likely explanation of the global dimming/brightening transition. Geophys. Res. Lett., 33 .L15806, doi:10.1029/2006GL026471.

    • Search Google Scholar
    • Export Citation
  • Trewartha, G. T., , and L. H. Horn, 1980: An Introduction to Climate. 5th ed. McGraw-Hill, 416 pp.

  • Wang, Z., , X. Zeng, , M. Barlage, , R. E. Dickinson, , F. Gao, , and C. B. Schaaf, 2004: Using MODIS BRDF and albedo data to evaluate global model land surface albedo. J. Hydrometeor., 5 , 314.

    • Search Google Scholar
    • Export Citation
  • Whitlock, C. H., and Coauthors, 1995: First global WCRP shortwave surface radiation budget data set. Bull. Amer. Meteor. Soc., 76 , 905922.

    • Search Google Scholar
    • Export Citation
  • Wild, M., 2005: Solar radiation budgets in atmospheric model intercomparisons from a surface perspective. Geophys. Res. Lett., 32 .L07704, doi:10.1029/2005GL022421.

    • Search Google Scholar
    • Export Citation
  • Wild, M., , A. Ohmura, , H. Gilgen, , and E. Roeckner, 1995: Validation of general circulation model radiative fluxes using surface observations. J. Climate, 8 , 13091324.

    • Search Google Scholar
    • Export Citation
  • Wild, M., , A. Ohmura, , H. Gilgen, , E. Roeckner, , M. Giorgetta, , and J-J. Morcrette, 1998: The disposition of radiative energy in the global climate system: GCM-calculated versus observational estimates. Climate Dyn., 14 , 853869. doi:10.1007/s003820050260.

    • Search Google Scholar
    • Export Citation
  • Wild, M., , A. Ohmura, , H. Gilgen, , J. J. Morcrette, , and A. Slingo, 2001: Evaluation of downward longwave radiation in general circulation models. J. Climate, 14 , 32273239.

    • Search Google Scholar
    • Export Citation
  • Wild, M., , A. Ohmura, , H. Gilgen, , and D. Rosenfeld, 2004: On the consistency of trends in radiation and temperature records and implications for the global hydrological cycle. Geophys. Res. Lett., 31 .L11201, doi:10.1029/2003GL019188.

    • Search Google Scholar
    • Export Citation
  • Wild, M., and Coauthors, 2005: From dimming to brightening: Decadal changes in solar radiation at the earth’s surface. Science, 308 , 847850.

    • Search Google Scholar
    • Export Citation
  • Wild, M., , C. N. Long, , and A. Ohmura, 2006: Evaluation of clear-sky solar fluxes in GCMs participating in AMIP and IPCC-AR4 from a surface perspective. J. Geophys. Res., 111 .D01104, doi:10.1029/2005JD006118.

    • Search Google Scholar
    • Export Citation
  • Wittmeyer, I. L., , and T. H. Vonder Haar, 1994: Analysis of the global ISCCP TOVS water vapor climatology. J. Climate, 7 , 325333.

  • Zhang, Y., , W. B. Rossow, , and A. A. Lacis, 1995: Calculation of surface and top of atmosphere radiative fluxes from physical quantities based on ISCCP datasets 1. Method and sensitivity to input data uncertainties. J. Geophys. Res., 100 , 11491165.

    • Search Google Scholar
    • Export Citation
  • Zhang, Y., , W. B. Rossow, , A. A. Lacis, , V. Oinas, , and M. I. Mishchenko, 2004: Calculation of radiative fluxes from the surface to top of atmosphere based on ISCCP and other global data sets: Refinements of the radiative transfer model and input data. J. Geophys. Res., 109 .D19105, doi:10.1029/2003JD004457.

    • Search Google Scholar
    • Export Citation
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    Annual means of (a), (d), (g) Ss,d and (b), (e), (h) Ss,n. Differences are HadGAM1 minus ISCCP-FD. Units are W m−2.

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    Annual means of (a), (d), (g) Ls,d (W m−2); (b), (e), (h) precipitable water content (kg m−2); and (c), (f), (i) surface temperature (K) for HadGAM1 and ISCCP-FD. Differences are HadGAM1 minus ISCCP-FD.

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    Hemispheric, seasonal cycle of surface radiation budget. Anomalies with respect to annual mean values are represented. Units are W m−2, except for the albedo, expressed in %. (a) Surface downward SW radiation, (b) surface downward LW radiation, (c) surface albedo, (d) surface upward LW radiation, (e) surface SW net radiation, and (f) surface LW net radiation. Gray shades in HadGAM1 curves show the range of variability from the five-member ensemble of model runs.

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    Zonal means of the (left) radiation budget and (right) cloud radiative forcing at TOA, within the atmosphere (ATM) and at the surface (SFC). CSW, CLW, and CTOT are the SW, LW, and total cloud forcing, respectively. Solid lines with symbols are ISCCP-FD results, and nonsolid lines are HadGAM1 simulations.

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    Scatterplots of monthly mean SW downward radiation at BSRN sites against HadGAM1. See Table 2 for details on the sites.

  • View in gallery

    As in Fig. 5, but for downwelling LW radiation.

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    Scatterplot of annual means of SW and LW downward radiation as observed at BSRN sites and calculated by HadGAM1. Observational and model points are connected by solid line segments. The end of the segment with the number corresponds to the observed values; the end with the diamond represents the values computed by HadGAM1. The legend shows the labels of BSRN stations, as listed in Table 2.

  • View in gallery

    Comparison of mean annual cycle of SW downward flux as observed at BSRN sites (solid) and computed by HadGAM1 (dashed), and ISCCP-FD (dot–dashed). See Table 2 for details about the sites.

  • View in gallery

    As in Fig. 8, but for downwelling LW radiation.

  • View in gallery

    Hovmoeller plots of the tropical Pacific (10°S, 10°N) as derived from satellite data and simulated by HadGAM1. (a) Ss,d from ISCCP-FD, (b) Ss,d from HadGAM1, (c) Ls,d from ISCCP-FD, and (d) Ls,d from HadGAM1.

  • View in gallery

    Surface radiative fluxes climatology for February 1984–2000 and anomalies for February 1998 over the tropical Pacific as derived from satellite data and simulated by HadGAM1. (a) Ss,d climatology from HadGAM1, (b) February 1998 Ss,d anomaly from HadGAM1, (c) Ss,d climatology from ISCCP-FD, (d) February 1998 Ss,d anomaly from ISCCP-FD, (e) Ls,d climatology from HadGAM1, (f) February 1998 Ls,d anomaly from HadGAM1, (g) Ls,d climatology from ISCCP-FD, and (h) February 1998 Ls,d anomaly from ISCCP-FD.

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    Regional means of land surface albedo for January and July for HadGAM1 and MODIS. Maps for HadGAM1 correspond to 20-yr averages, whereas MODIS are 2-yr averages. White areas are water surfaces or missing data points.

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    Surface albedo over Europe in January. (a) HadGAM1 monthly albedo aver aged over 20 yr, (b) HadGAM1 monthly snow amount (kg m−2) averaged over 20 yr, (c) white-sky albedo from MODIS MOD43C1 for the first 16 days of 2001 and 2002, (d) HadGAM1 minus MODIS MOD43C1, (e) white-sky albedo from MODIS spatially complete product for the first 16 days of 2000–04, and (f) HadGAM1 minus MODIS spatially complete. White areas are water surfaces or missing data points.

  • View in gallery

    Seasonal cycle of the surface albedo over three different regions: (a) Iberian Peninsula from (38°N, 8°W) to (43°N, 1°W); (b) Southeast United States from (31°N, 95°W) to (36°N, 85°W); (c) eastern Europe from (50°N, 25°E) to (55°N, 30°E). Solid line shows the model results, whereas dashed–dotted line shows the observations from the MODIS spatially complete dataset. The asterisks are the values for January and July from MOD43C1. The dashed line shows ISCCP-FD.

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    Decadal means of difference in incoming surface shortwave radiation as simulated by HadGEM1. Difference between (a) the 1950s and the 1980s and (b) the 1980s and the 2000s. Units are W m−2.

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Evaluation of the Surface Radiation Budget in the Atmospheric Component of the Hadley Centre Global Environmental Model (HadGEM1)

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  • 1 Met Office, Hadley Centre, Exeter, United Kingdom
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Abstract

The partitioning of the earth radiation budget (ERB) between its atmosphere and surface components is of crucial interest in climate studies as it has a significant role in the oceanic and atmospheric general circulation. An analysis of the present-day climate simulation of the surface radiation budget in the atmospheric component of the new Hadley Centre Global Environmental Model version 1 (HadGEM1) is presented, and the simulations are assessed by comparing the results with fluxes derived from satellite data from the International Satellite Cloud Climatology Project (ISCCP) and ground measurements from the Baseline Surface Radiation Network (BSRN).

Comparisons against radiative fluxes from satellite and ground observations show that the model tends to overestimate the surface incoming solar radiation (Ss,d). The model simulates Ss,d very well over the polar regions. Consistency in the comparisons against BSRN and ISCCP-FD suggests that the ISCCP-FD database is a good test for the performance of the surface downwelling solar radiation in climate model simulations. Overall, the simulation of downward longwave radiation is closer to observations than its shortwave counterpart. The model underestimates the downward longwave radiation with respect to BSRN measurements by 6.0 W m−2.

Comparisons of land surface albedo from the model and estimates from the Moderate Resolution Imaging Spectroradiometer (MODIS) show that HadGEM1 overestimates the land surface albedo over deserts and over midlatitude landmasses in the Northern Hemisphere in January. Analysis of the seasonal cycle of the land surface albedo in different regions shows that the amplitude and phase of the seasonal cycle are not well represented in the model, although a more extensive validation needs to be carried out.

Two decades of coupled model simulations of the twentieth-century climate are used to look into the model’s simulation of global dimming/brightening. The model results are in line with the conclusions of the studies that suggest that global dimming is far from being a uniform phenomenon across the globe.

Corresponding author address: Dr. A. Bodas-Salcedo, Met Office, Hadley Centre, FitzRoy Rd., Exeter EX1 3PB, United Kingdom. Email: alejandro.bodas@metoffice.gov.uk

Abstract

The partitioning of the earth radiation budget (ERB) between its atmosphere and surface components is of crucial interest in climate studies as it has a significant role in the oceanic and atmospheric general circulation. An analysis of the present-day climate simulation of the surface radiation budget in the atmospheric component of the new Hadley Centre Global Environmental Model version 1 (HadGEM1) is presented, and the simulations are assessed by comparing the results with fluxes derived from satellite data from the International Satellite Cloud Climatology Project (ISCCP) and ground measurements from the Baseline Surface Radiation Network (BSRN).

Comparisons against radiative fluxes from satellite and ground observations show that the model tends to overestimate the surface incoming solar radiation (Ss,d). The model simulates Ss,d very well over the polar regions. Consistency in the comparisons against BSRN and ISCCP-FD suggests that the ISCCP-FD database is a good test for the performance of the surface downwelling solar radiation in climate model simulations. Overall, the simulation of downward longwave radiation is closer to observations than its shortwave counterpart. The model underestimates the downward longwave radiation with respect to BSRN measurements by 6.0 W m−2.

Comparisons of land surface albedo from the model and estimates from the Moderate Resolution Imaging Spectroradiometer (MODIS) show that HadGEM1 overestimates the land surface albedo over deserts and over midlatitude landmasses in the Northern Hemisphere in January. Analysis of the seasonal cycle of the land surface albedo in different regions shows that the amplitude and phase of the seasonal cycle are not well represented in the model, although a more extensive validation needs to be carried out.

Two decades of coupled model simulations of the twentieth-century climate are used to look into the model’s simulation of global dimming/brightening. The model results are in line with the conclusions of the studies that suggest that global dimming is far from being a uniform phenomenon across the globe.

Corresponding author address: Dr. A. Bodas-Salcedo, Met Office, Hadley Centre, FitzRoy Rd., Exeter EX1 3PB, United Kingdom. Email: alejandro.bodas@metoffice.gov.uk

1. Introduction

The earth radiation budget (ERB) is the ensemble of radiative fluxes entering and leaving the earth–atmosphere system, which drives the earth’s climate. Therefore, measurements of this balance are needed to improve our knowledge of the earth’s climate and climate change (Ramanathan 1987; Ramanathan et al. 1989). Since the 1970s, great efforts have been made to measure this budget globally and with sufficient accuracy by means of broadband sensors aboard satellites. All these measurements provide invaluable information regarding the top-of-the-atmosphere (TOA) radiation budget, and they have been extensively used as validation tools for climate models (Li et al. 1997; Chevallier and Morcrette 2000; Bony et al. 2004; Ringer and Allan 2004). However, TOA ERB measurements do not provide a complete constraint on the atmosphere’s radiative properties. This means that the partitioning of the ERB between the atmosphere (ATM) and surface (SFC) components is of crucial interest in climate studies. Gleckler (2005) showed that this partitioning has a significant role in the oceanic and atmospheric general circulation. In addition, the balance between longwave radiative cooling and latent heating establishes a link between radiative processes and the hydrological cycle. Therefore, any changes in the radiative budget of the atmosphere will have an impact on the response of the hydrological cycle (Stephens 2005). These two reasons highlight the importance of knowing both the surface and the atmospheric radiation budgets (SARB), not only for their direct impact on the general circulation, but also for their role in climate feedback problems.

As satellites provide TOA measurements, the SRB has to be modeled from those measurements, together with information about the state of the atmosphere and the surface. A global perspective of the surface radiation budget, both in the shortwave and longwave parts of the spectrum, can only be obtained from satellites. Several studies have used the International Satellite Cloud Climatology Project (ISCCP) C1 data to provide a global perspective of the surface radiation budget (Pinker and Laszlo 1992; Darnell et al. 1992; Whitlock et al. 1995; Zhang et al. 1995; Gupta et al. 1999). Li and Leighton (1993) computed a global climatology of the solar radiation budget using data from the Earth Radiation Budget Experiment (ERBE), not relying on ISCCP data. Li (1995) intercompared the net surface shortwave radiation (NSSR) as derived from ERBE against the NSSR derived from ISCCP by Whitlock et al. (1995). More recently, these datasets have been upgraded by using ISCCP-D1 data and improved algorithms (Stackhouse et al. 1999; Zhang et al. 2004). In addition, these satellite-based databases, along with surface observations, have been used to evaluate the performance of the simulations of the surface radiation budget by climate models (Garrat 1994; Li et al. 1997; Wild et al. 1998; Wild 2005). However, interest in the SRB is not confined to global scales. Smith et al. (2002) showed that the regional climate and surface radiation are related, and Bolle et al. (2006) used the surface net radiation as a possible indicator of changes in the Mediterranean region.

Note that we usually refer to the atmospheric component of the Hadley Centre Global Environmental Model version 1 (HadGEM1) as the Hadley Centre Global Atmospheric Model (HadGAM1), and we will use this notation throughout the paper. This paper is organized as follows: The data used in this study and a brief description of the model are presented in section 2. Section 3 compares the climatologies of SRB provided by HadGAM1 and ISCCP-FD. The perfomance of HadGAM1’s simulation of the surface radiation budget is evaluated against surface observations in section 4. Section 5 looks at the representation of the interannual variability of surface incoming radiation over the tropical Pacific, and section 6 compares HadGAM1’s land surface albedo with that from the Moderate Resolution Imaging Spectroradiometer (MODIS). Two decades of coupled model simulations of the twentieth-century climate are used to look into the model’s simulation of global dimming/brightening in section 7. Conclusions are presented in section 8.

2. Model description and experimental design

We use present-day climate simulations from the atmosphere-only version of the new Hadley Centre climate model, HadGEM1, referred to as HadGAM1. HadGAM1 uses a horizontal resolution of 1.25° latitude by 1.875° longitude, and has 38 vertical levels, the top level being at around 39 km. The simulations used are from a five-member ensemble of model runs of HadGAM1, forced with observed sea surface temperatures (SSTs) from the second Atmospheric Model Intercomparison Project (AMIP-II; Gates et al. 1999), each member using different initial conditions. The runs start on December 1978, and we use a 20-yr climatology from 1981 to 2000. We use monthly mean diagnostics of the different components of the SARB as well as other diagnostics that help the interpretation of the results (e.g., cloud cover and precipitable water content). Here we give some details of the physical processes relevant for the simulation of the surface radiation budget, but a more detailed description of HadGAM1, and its performance in terms of global climatology, variability, and regional climate can be explored in Martin et al. (2006) and Ringer et al. (2006).

The radiation code is that of Edwards and Slingo (1996) used in the third climate configuration of the Met Office Unified Model (HadCM3; HadAM3 for the atmospheric component), with some developments. The longwave band from 1200 to 1500 cm−1 has been split at 1330 cm−1 in order to better represent the overlap between CH4 and N2O; gaseous absorption is based on the updated High-Resolution Transmission (HITRAN) 2000 database (Rothman et al. 2003); the water vapor continuum is version 2.4 of the Clough–Kneizys–Davies (CKD) formulation (Clough et al. 1992) and has been included in the shortwave region; ice crystal sizes are determined using the parameterization by Kristjánsson et al. (2000); the sea surface albedo is based on the functional form of Barker and Li (1995), modified in the light of aircraft data; and the land surface albedo is described by Essery et al. (2003).

The simple aerosol climatology used previously (Cusack et al. 1998) has largely been superseded in HadGAM1 by schemes to interactively simulate sulfate, fossil-fuel black carbon, biomass-burning and sea-salt aerosols, as detailed in Martin et al. (2006). Only the stratospheric sulphuric acid aerosol component of the earlier climatology has been retained. The direct radiative effect (scattering and absorption of radiation) of all aerosols is included; this means that the “semi-direct” effect (the impact on clouds of the warming caused by absorbing aerosols; Hansen et al. 1997) is also included. Parameterizations of both first and second indirect aerosol effects (impact on cloud droplet size and on precipitation efficiency, respectively) are also included, with sulfate, biomass-burning, and sea-salt aerosols being considered cloud condensation nuclei; black carbon aerosols are assumed to be hydrophobic and so do not have indirect effects in HadGAM1.

As a global representation of the observed surface radiation budget, we use the ISCCP-FD database (Zhang et al. 2004). The ISCCP-FD dataset contains radiative fluxes at the top of the atmosphere, the surface, and at three levels in the atmosphere (680, 440, and 100 hPa). They are calculated using ISCCP-D cloud products (Rossow and Schiffer 1999) as the main input, along with information from other sources in order to complete the radiative description of the atmosphere and surface. An assessment of the quality of the ISCCP-FD fluxes can be consulted in Zhang et al. (2004) and Raschke et al. (2005). Previous versions of the methodology are documented in Zhang et al. (1995) and Rossow and Zhang (1995).

We also use ground measurements at 28 sites belonging to the Baseline Surface Radiation Network (BSRN; Ohmura et al. 1998). The BSRN is a project of the World Climate Research Program (WCRP) that aims to provide radiation ground measurements to validate satellite-derived products and climate models and to detect long-term climate variations. Standard BSRN radiation measurements are provided at 1-min temporal resolution, although we degrade the time series to a monthly mean resolution in order to match the temporal resolution of the model diagnostics. In addition, owing to the disparity of the spatial resolutions between the model grid and the ground measurements, we expect this time-averaging process to reduce the errors introduced by this disparity in resolutions.

In addition to ISCCP and BSRN data, other databases have been used as sources of independent information on surface and atmospheric properties: 16-day averages of surface albedo data from the MODIS instrument onboard the Terra satellite (Schaaf et al. 2002).

3. Climatology

In this section results of the comparisons of the 20-yr SRB climatologies between HadGAM1 and ISCCP-FD are presented. We also examine the representation of surface properties in HadGAM1. We will use the following notation for the radiative fluxes throughout this paper: the first (capital) letter denotes the spectral interval (i.e., S for shortwave, L for longwave, and T for total), the first subscript denotes the level (i.e., t for TOA, a for atmosphere, and s for surface), and the second subscript denotes the direction (i.e., u for upwelling, d for downwelling, and n for net). For instance, St,u is the TOA shortwave upwelling radiation, and Ts,n is the net total surface radiation. Although we use the terms overestimation and underestimation in the comparison between model simulations and ISCCP-FD estimates, one should not forget the inherent limitations in the ISCCP-FD dataset, so these terms have to be understood as higher or lower than the ISCCP-FD values.

a. Regional means

Figure 1 shows the geographical distributions of the annual means of Ss,d, Ss,n, and total cloud amount for HadGAM1 and ISCCP-FD. HadGAM1 generally overestimates Ss,d over landmasses, this overestimation being more severe in the summer hemisphere (not shown). A positive bias is also observed in the eastern Pacific intertropical convergence zone (ITCZ) and South Pacific convergence zone (SPCZ). Although the representation of cloud has notably improved with respect to the previous Hadley Centre model (Martin et al. 2006), HadGAM1 shows a general lack of cloud almost everywhere, which is particularly severe over the regions where Ss,d is overestimated (Figs. 1c,f,i). The maps of Ss,n (Figs. 1b,e,h) show an overall picture very similar to that of Ss,d, but there are some relevant differences that highlight differences in the surface albedo. For instance, in spite of the severe overestimation of Ss,d over the Sahara Desert and over most of the landmasses, the difference maps of Ss,n show a mixture of positive and negative biases over those regions, which indicate higher albedos than those used by ISCCP-FD. These results show relevant differences in the surface albedo between HadGAM1 and ISCCP-FD that deserve more attention. We analyze surface albedo in greater detail in section 6.

The regional distribution pattern of Ls,d (Figs. 2a,d,g) is highly correlated to the distribution of precipitable water content (PWC; Figs. 2b,e,h) and surface temperature (Figs. 2c,f,i). HadGAM1 shows a negative bias in Ls,d over most land regions, particularly over the desert regions of the Northern Hemisphere (NH). These errors in Ls,d are correlated with errors in the distribution of PWC over land regions. Although still present, this correlation is not as strong over ocean. It can be observed how positive biases in PWC over the Indian Ocean are not linked to biases in downward LW radiation. A possible explanation is the nonlinear response of Ls,d with respect to PWC. The actual amount of PWC over that region is high (greater than 40 kg m−2), making the lower troposphere already very opaque to LW radiation. Therefore, the response of Ls,d to a change in PWC is almost saturated. Changes in PWC do not seem to explain the differences in Ls,d in the stratocumulus regions in the eastern basins of the subtropical oceans. Negative biases in PWC are observed in these regions, but an impact in Ls,d is observed only in regions close to the coast. In these regions, differences in low cloud may be compensating for errors in PWC. Model SSTs are prescribed, and therefore free of errors introduced by model physics. Over land this is not the case, and errors in PWC are usually associated with errors in surface temperature (Figs. 2c,f,i), with both contributing to errors in Ls,d. Over the ocean, differences in PWC and cloud are the main contributors to the differences in Ls,d, so it appears that the sensitivity to errors in PWC over the ocean is smaller than over land. Over the ocean, the greatest differences in surface temperature are observed in midlatitudes, particularly in the winter hemisphere (not shown). Over subtropical deserts HadGAM1 underestimates land surface temperature with respect to ISCCP-FD. The opposite behavior occurs in tropical rain forests, where HadGAM1 overestimates land surface temperature, especially over the Amazon. In midlatitude land regions, the differences are seasonally dependent, and HadGAM1 shows a positive bias in summer and a negative bias in winter (not shown).

b. Global means and seasonal cycle

Global and hemispheric values of the SRB for DJF, JJA and annual means are given in Table 1. The largest differences between the model and ISCCP-FD are found in the NH, both in absolute and relative values. Differences between members of the ensemble are negligible (not shown), and therefore differences between simulations and ISCCP-FD cannot be explained by the model variability. As seen above, HadGAM1 generally overestimates Ss,d in both seasons, globally and in both hemispheres.

The overestimation of Ss,d has been a consistent bias for many years in previous generations of models, not only in all-sky conditions, but also in cloudless atmospheres (Wild et al. 1995; Li et al. 1997; Wild et al. 1998; Chevallier and Morcrette 2000). Possible origins of this error are the underestimated water vapor absorption in the near infrared and the crude representation of aerosols. Improvements in the radiative transfer codes of several models have led to a better representation of the global mean energy distribution between the atmosphere and the surface under cloud-free conditions (Wild et al. 2006). However, global mean values of Ss,d in all-sky conditions still show a large discrepancies among the current generation of GCMs (Wild 2005). HadGAM1 shows a clear-sky atmospheric absorption of 69.6 W m−2, very close to 70 W m−2, quoted by Wild et al. (2006) as the most likely value of solar radiation absorbed in the cloud-free atmosphere. The underestimation of cloud amount shown in Fig. 1i is not entirely responsible for the overestimation of Ss,d. We have compared long-term averages of model clear-sky Ss,d over the 17 BSRN locations used by Wild et al. (2006). The model shows an overestimation of 8.2 W m−2 with respect to the ground observations, which is ≈50% of the all-sky bias (see section 4). This contrasts with the negligible bias shown by HadAM3 in clear-sky Ss,d (Wild et al. 2006), aerosols being the main cause for this overestimation. The aerosol species missing in HadGAM1, and the fact that HadAM3 uses an aerosol climatology (Cusack et al. 1998), means HadAM3 has a more accurate (altough less physically based) representation of the climatological radiative impact of aerosols.

Figures 3a–f show the hemispheric annual cycle of the surface radiation budget. Values are anomalies with respect to the annual mean values shown in Table 1. Downward SW flux, surface albedo, and net SW flux are shown, as well as downward, upward, and net LW fluxes. Figure 3a shows a strong annual cycle in Ss,d, with amplitudes of more than 50 Wm−2 in both hemispheres. The Southern Hemisphere (SH) shows a greater amplitude, which is consistent with the fact that the sun–Earth distance is a minimum during summer in the SH, thereby adding the effects of more vertical illumination and less sun–Earth distance (Gupta et al. 1999). In addition, the NH has a stronger annual cycle of precipitable water content than the SH, with high values in summer, which also helps reduce the amplitude of the annual cycle of Ss,d (Randel et al. 1996; Wittmeyer and Vonder Haar 1994). The model reproduces the annual cycle reasonably well, although it overestimates the amplitude in the NH and underestimates it in the SH. Annual values are overestimated in HadGAM1, particularly in the NH where the amount of SW radiation reaching the surface is 6% more than that obtained by ISCCD-FD.

The seasonal cycle of upward SW fluxes (Ss,u; not shown) is one order of magnitude smaller than that of the downward fluxes (typical hemispheric albedos of ≈0.1). Surface albedos are shown in Fig. 3c. HadGAM1 shows small differences in amplitude and phase with respect to ISCCP-FD in the NH. However, the SH shows big differences both in amplitude and phase. HadGAM1 shows a well-defined seasonal cycle, with a maximum during winter consistent with greater solar zenith angles, whereas ISCCP-FD shows a very weak seasonal cycle with maximum in September–October. Also note the difference in the annual mean values, with HadGAM1 showing surface albedos greater than ISCCP-FD in both hemispheres (Table 1). Figure 3e shows the results for the SW net flux, which are similar to those of the downward flux owing to the big differences in the amplitudes of the seasonal cycles of Ss,d and Ss,u. The overestimation in the surface albedo partly compensates for the overestimation in Ss,d, making the relative differences in annual means in Ss,n smaller than those in Ss,d.

The downward LW flux (Ls, d) is presented in Fig. 3b. Because of the greater amplitude of the annual cycle of surface temperature over landmasses and the coupling between the surface and the lower troposphere, the NH shows an amplitude twice as large as the SH (Gupta et al. 1999). The model compares resonably well with ISCCP-FD, although overestimating slightly the amplitude of the cycle in the NH. A similar behavior is exhibited by Ls,u (Fig. 3d), although with bigger differences in the NH, where HadGAM1 shows a greater amplitude of the seasonal cycle. These differences in Ls,u cause the amplitude of the NH seasonal cycle of Ls,n (Fig. 3f) to be significantly smaller in HadGAM1 than in ISCCP-FD, although with the correct phase. In the SH, the amplitude and phase are reasonably well captured by HadGAM1. It should be noted that, in the LW, there is no compensation of errors between downward and upward fluxes in the annual mean values. HadGAM1 underestimates the downward LW radiation and overestimates the upward LW radiation, which makes the surface LW cooling to be overestimated by ≈18%.

c. Energy distribution

The derivation of the surface radiation budget from satellite data also allows us to obtain the atmospheric radiation budget as a residual from the TOA and SFC fluxes. Both the atmosphere and the surface budgets are critical in the cloud feedback problem: the energy balance of the atmosphere occurs as a balance between the radiative cooling of the atmosphere and latent heating associated with precipitation, and therefore cloud feedbacks that affect the radiative heating of the atmosphere will influence the response of the hydrological cycle (Stephens 2005).

Figure 4 shows the comparisons for the zonal means of the radiation budget (left-hand column) and cloud radiative forcing (right-hand column) at TOA, ATM, and SFC. The scales are not intended to provide accurate detail, but to provide a global picture of the energy distribution. At TOA, St,n, Lt,n, and Tt,n are well represented by the model, although with some compensation between errors in the SW and LW in the tropics. This shows the well-known distribution of Tt,n, with positive values between 40°S and 40°N, and negative values poleward. The atmosphere shows a consistent radiative cooling at all latitudes (∼100 W m−2), caused by greater LW cooling than SW heating. Both SW heating and LW cooling are greater in the tropics than in midlatitudes. The surface shows net heating at all latitudes, dominated by SW heating with a strong latitudinal dependence. The LW cooling has less latitudinal dependence, with maxima in the subtropics (deserts). It is observed how the global balance at TOA is achieved mainly by SW heating at the surface and LW cooling in the atmosphere. These quantities define the basic energy distribution driving the global circulation, and so we expect the models to simulate them correctly.

The zonal pattern of cloud radiative forcing (Figs. 4d–f) is also well captured by HadGAM1 at TOA, showing a well-known result from TOA measurements of SW and LW radiation (Ramanathan et al. 1989; Harrison et al. 1990): clouds produce net cooling of the earth–atmosphere system as a result of two opposite effects: SW cooling and LW heating. Differences between HadGAM1 and ISCCP-FD are mainly caused by an underestimation of the impact of clouds in the SW in HadGAM1, although there are nonnegligible differences in LW in the tropics and Southern Hemisphere. In the atmosphere, the SW cloud forcing CSW is small, showing a small SW heating of the atmosphere due to clouds, although the magnitude of this has been discussed in the literature (Cess et al. 1995; Ramanathan et al. 1995; Li et al. 1995; Stephens 1996; Arking 1996). Therefore, the net cloud forcing (CNET) is controlled by the LW component (CLW). ISCCP-FD data show that clouds produce LW effective heating (reduced cooling) in the tropics and LW cooling in the subtropics and midlatitudes. In the tropics, the LW effective heating is due to the increase of infrared absorption and emission at colder temperatures than for clear skies. In the extratropics, the mechanism seems to be more subtle: because of lower PWC than in the tropics, clouds increase atmospheric emissivity (particularly in the water vapor “window” region) and therefore increase the outgoing LW radiation. This increase in emissivity seems to offset the effect of the decrease in the effective emission temperature owing to the presence of clouds (Rossow and Zhang 1995). HadGAM1 captures this behavior, although the region with net effective heating effect is larger, comprising all latitudes from 50°S to 50°N. Therefore, although clouds have almost no net effect globally in the atmosphere (2.4 W m−2 from 60°S to 60°N), they may enhance the latitudinal gradient in LW cooling in the tropics, thereby reinforcing the meridional gradient of the forcing of the atmospheric circulation (Rossow and Zhang 1995; Stephens 2005). The understanding of the impact of clouds in the atmospheric radiation budget will be improved with a better knowledge of the vertical distribution of clouds provided by current satellite missions carrying active instruments (Stephens et al. 2002). Finally, as can be observed in the lower plot, the surface is dominated by a net cooling effect, produced by SW cooling. This cooling is partially offset by LW heating, mainly in the extratropics. This seems to confirm the previous argument regarding CLW the atmosphere. In the tropics, the atmosphere is generally very opaque to LW radiation because of the high PWC, and therefore clouds have little impact on the LW flux reaching the surface.

4. Comparison against ground measurements

For this study, we use all-sky downwelling SW and LW fluxes at the surface measured at the BSRN stations listed in Table 2, where for each station, its geographical position and period used in the comparison are shown. Although the BSRN fluxes are provided at a very high temporal resolution (1 min), we use monthly means as the standard model diagnostics have that time resolution. The BSRN monthly means have been computed by first obtaining a mean monthly diurnal cycle to avoid any bias due to missing data.

Figure 5 shows the comparisons of observed and computed SW downward fluxes at the selected sites. In addition to the scatterplot, each plot shows the bias (model minus observed) and standard deviation of the differences in watts per meters squared, and number of points used in the comparison. Generally, HadGAM1 shows a tendency to overestimate Ss,d, which is consistent with our previous comparisons against ISCCP fluxes, implying that the ISCCP-FD database is a good test for the performance of the incoming solar radiation in climate model simulations. The overestimation in Ss,d does not seem to be regionally dependent. An interesting exception are those stations located in polar regions (e.g., gvn, nya, spo, syo). They are the only ones that show negative biases (HadGAM1 underestimating Ss,d). The bias (standard deviation) for all the monthly means included in the comparison (1810 samples) is 16.2 (28.0) W m−2.

In the case of the Antarctic sites, the agreement is remarkable, with a negligible bias, and a relatively low standard deviation despite the high values of surface insolation. This is not caused by persistent cloudless conditions in this site, as the seasonal cycle of simulated cloud amount over the South Pole shows values between 20% and 60% over the months with sunlight. Therefore, the agreement is achieved despite the nonnegligible cloud amount. This is consistent with the seasonal cycle of clear-sky insolation for Georg von Neumayer, Antartica (Wild et al. 2006), which shows maximum values around 50 W m−2 higher than the ones for all-sky presented here.

Figure 6 is similar to Fig. 5, but for Ls,d. The overall bias (standard deviation) of all the monthly means (1783 samples) included in the comparison is −6.0 (19.6) W m−2. This is again consistent with the negative bias with respect to ISCCP-FD shown in Table 1 for the globe. Although HadGAM1 shows a negative bias for most of the sites, this behavior is not as general as the positive bias shown in Ss,d. There are 12 sites where HadGAM1 shows a positive bias, although for 6 of them the bias is less than 2 W m−2. The comparisons at polar sites do not show any distinct behavior, being similar to other sites at lower latitudes. This contrasts with comparisons of Ls,d against surface measurements in previous generations of models, which showed an underestimation of downwelling LW radiation at high latitudes, and no biases or even slight overestimations at low latitudes (Wild et al. 2001). This behavior was also found for clear-sky fluxes (Allan 2000). The global mean value from HadGAM1 is 339 W m−2, greater than the value of 333 W m−2 from HadAM3, and closer to the best estimate of 344 W m−2 given by Wild et al. (2001). This increase in global mean Ls,d with respect to HadAM3 is mainly due to improvements in the simulation of low cloud over the oceans (Martin et al. 2006).

From these general comparisons we conclude that, overall, the simulation of Ls,d is better than that of Ss,d. This can also be seen in Fig. 7, which shows a scatterplot of long-term annual means of Ss,d against Ls,d as observed at BSRN sites and calculated by HadGAM1. The segments show the distance in the (Ls,d,Ss,d) space between simulations and observations. It is clearly observed that there is a privileged direction, with most of the segments pointing toward the top of the plot. This means that the positive bias in Ss,d dominates over the low or positive biases in Ls,d.

In Fig. 8 we compare the mean annual cycles of Ss,d at the selected locations as observed by BSRN (solid), simulated by HadGAM1 (dashed), and derived by ISCCP-FD (dot–dashed). Note that the scales are different, being adapted to the amplitude of the annual cycle. The annual cycle of surface insolation is mainly controlled by orbital geometry, and is modulated by cloud, aerosols, and water vapor variations. Stations located in the NH midlatitudes show a maximum insolation around June, and a minimum around December, with a typical amplitude of ∼200 W m−2. Obviously, a similar cycle is observed in the SH midlatitudes (e.g., Lauder, New Zealand), but with opposite phase. At polar latitudes this cycle is exaggerated, with amplitudes of more than 200 W m−2, and a few months with no insolation during the polar winter. At tropical sites (e.g., Ilorin, Nigeria; Kwajalein, Marshall Islands; Momote, Papua New Guinea; and Nauru Island), the annual cycle has an amplitude smaller than 100 W m−2, and contains a semiannual harmonic, as over these sites the solar declination coincides with their latitude (overhead sun) twice a year. The modulation of the mean annual cycle of Ss,d by clouds at tropical sites can be very strong, and indeed can dominate the orbital effect. This is clearly observed in Ilorin, where a minimum of Ss,d is observed in August, when the insolation at TOA is maximum. This is caused by the strong annual cycle in cloud cover, which is positively correlated with the variation of TOA insolation. Overall, HadGAM1 captures the shape of the annual cycle of Ss,d quite well at all the sites, although it shows a general tendency to overestimate Ss,d, mainly due to lack of cloud. At midlatitude sites, the general overestimation is usually present throughout the year, being more prominent during summer months. As was highlighted in Fig. 5, polar sites show a remarkably good agreement, capturing the mean annual cycle with great accuracy. The annual cycles from ISCCP-FD are close to the observations by BSRN, and generally are in better agreement with the observations than the simulations. This gives confidence on the use of the ISCCP-FD shortwave fluxes for model evaluation.

Similarly, Fig. 9 shows the mean annual cycles of Ls,d from BSRN (solid), HadGAM1 (dashed), and ISCCP-FD (dot–dashed). Midlatitude sites show a mean annual cycle with amplitude around 100 W m−2, a maximum in the summer months and a minimum in winter, lagging by approximately 1 month the mean annual cycle in Ss,d. The polar regions follow a similar pattern, but with a smaller amplitude, whereas the tropical sites show no annual cycle in Ls,d. The general characteristics of the annual cycle are well captured by HadGAM1, although it shows a tendency to produce smaller fluxes. Particularly severe are the cases of sites in desert regions (e.g., Alice Springs, Australia; Solar Village, Riyadh, Saudi Arabia; and Tamanrasset, Algeria). The longwave fluxes from ISCCP do not show a better agreement with BSRN fluxes than the model simulations. HadGAM1 tends to underestimate Ls,d, whereas ISCCP-FD shows an overestimation of the same magnitude. The bias (standard deviation) from all the monthly means analyzed is 6.3 (28.8) W m−2 for ISCCP-FD, as compared to −6.0 (19.6) W m−2 for HadGAM1. This means that the usability of ISCCP-FD longwave fluxes for model evaluation is less than that of shortwave fluxes.

5. Interannual variability

In this section we focus our attention on the interannual variability of surface incoming radiation over the tropical Pacific (TP; 10°S–10°N), as El Niño is the main mode of variability of that region and provides a useful means to test cloud–climate interactions in climate models (Lu et al. 2004). Here we analyze the impact of El Niño on interannual variability from a surface radiation perspective, and show HadGAM1 performance to capture the impact of El Niño events on the surface radiation budget. Because SSTs are prescribed in atmosphere-only experiments, we look at the response of the atmosphere to changes in SSTs under El Niño conditions. The weakening of the gradient in SST between the tropical eastern and western Pacific under El Niño conditions causes the Walker circulation to weaken, or even collapse in strong events (Cess et al. 2001). This collapse of the Walker circulation is well captured by HadGAM1 (Johns et al. 2006). Clouds respond to these changes in the tropical circulation, with higher-than-average cloud amount in the tropical eastern Pacific and lower-than-average amounts in the tropical western Pacific (Cess et al. 2001).

Figure 10 shows Hovmoeller plots of Ss,d and Ls,d for the tropical Pacific as derived from satellite data and simulated by HadGAM1. It shows the time series of the anomalies with respect to the mean annual cycle of the overlapping months of the ISCCP database and HadGAM 1 run. The anomaly patterns of Ss,d (Figs. 10a,b) in ISCCP and HadGAM1 are very similar, with HadGAM1 capturing very well the negative anomalies in the tropical central Pacific in Los Niños of 1986/87, 1991/92, and 1997/98. The opposite effect of La Niña can be observed over (roughly) the same areas in 1988/89 and 1999/2000, with a high anomaly in Ss,d. The impact over the tropical eastern Pacific is less strong, and only the strong 1997/98 event shows a high anomaly in that region. This anomaly extends farther west in HadGAM1 than in ISCCP. The impact of El Niño/La Niña events is also clearly visible in Ls,d (Figs. 10c,d), showing noticeable differences. The downward longwave radiation at the surface is dominated by the emission coming from the lower levels of the troposphere, and indeed this is correlated with surface temperature (Gupta et al. 1992). Therefore, Ls,d anomalies are correlated with the SST anomalies (not shown), with cloud variations playing a secondary role. During Los Niños of 1986/87, 1991/92, and 1997/98, the central and eastern Pacific shows a positive anomaly in Ls,d due to the warm anomaly in SST over the same regions. During La Niña events, the cold anomalies are correlated with smaller Ls,d.

Figure 11 shows the spatial patterns of the February climatology (1984–2000) of surface radiative fluxes over the tropical Pacific (left-hand side column), as well as the anomalies for the strong 1998 El Niño (right-hand side column). The shortwave incoming radiation is shown in Figs. 1a–d, and it is observed that HadGAM1 reproduces reasonably well the climatological pattern, with minima over deep convective regions and in the stratocumulus area off the coast of Chile. The main differences are the excess of radiation reaching the ground in several regions, such as the western part of the Maritime Continent and Australia. A good simulation of the mean state does not necessarily imply a good simulation of the variability. Figures 1b,d show that the simulation of the anomaly pattern of Ss,d is also very well captured by HadGAM1, with a dipolar anomaly in the equatorial Pacific. A high anomaly is observed over the warm pool, where a reduction in the convective intensity causes cloud radiative impact to decrease and hence increase in Ss,d. By contrast, a low anomaly is observed in the central equatorial Pacific, where convection intensifies, produces more cloud and decreases the amount of solar radiation reaching the ground. The longwave counterpart is shown in Figs. 11e–h. To first order, the climatological pattern of Ls,d shows a picture similar to that of Ss,d, but with maxima where we had minima before and vice versa. The Ls,d anomalies also show the correlation with changes in cloud and water vapor, with a low anomaly in Ls,d over the warm pool, and a high anomaly over the equatorial central Pacific, extending eastward to the coast of South America.

6. Land surface albedo

As shown in section 3, comparisons of surface albedo between HadGAM1 and ISCCP-FD highlighted some differences. However, as ISCCP-FD surface albedo is based on the GISS GCM surface albedo, modified with a procedure similar to that explained in Zhang et al. (1995), to some extent we are comparing the albedos of two GCMs. To obtain a better picture of the representation of the surface albedo with an independent source of information, we have chosen version 4 of the MODIS 16-day 0.05° global albedo product (MOD43C1; Schaaf et al. 2002). The MOD43C1 is a level 3 climate modeling grid product that provides measures of earth’s “white-sky” (only diffuse radiation) and “black-sky” (only direct radiation) albedo in 16-day averages with a 0.05° spatial resolution. It provides spectral albedo in seven MODIS bands, and in three broad bands: visible, near infrared, and total solar spectrum. As HadGAM1 does not have any spectral dependence in the specification of the land surface albedo, we only use the albedo from MODIS in the whole solar spectrum. The MODIS white-sky albedo is generally a good approximation to the monthly-average albedo (Wang et al. 2004), and therefore we use this product to compare directly with the model albedo.

HadGAM1 uses the Met Office Surface Exchange Scheme version 2 (MOSES 2; Essery et al. 2001, 2003), which includes a tiled representation of heterogeneous surfaces. It represents each grid box as a mixture of five vegetation types (i.e., broadleaf trees, needleleaf trees, temperate grass, tropical grass, and shrubs) and four nonvegetated surface types (i.e., urban, inland water, soil, and ice). Vegetation distributions are obtained from observations of 14 land cover classes at 1-km resolution derived from the Advanced Very High Resolution Radiometer (AVHRR) data (Hansen et al. 2000), and mapped onto the MOSES 2 surface types (Essery et al. 2003). A leaf area index for each vegetation tile is read from maps based on those used by the second Simple Biosphere Model (Sellers et al. 1996). A constant land surface albedo for all spectral bands is then computed for each grid box using the surface and leaf area index distributions, and the simulated snow amount (Essery et al. 2001).

Figure 12 presents comparisons of the regional means of surface albedo from HadGAM1 with white-sky albedo from MODIS and shows maps for January and July of the 20-yr averages for HadGAM1 and 2-yr averages for MODIS. Similar comparisons have been made with each of the 2 yr used in the MODIS average (not shown), and significant differences with the 2-yr mean have only been found in a few regions. The accuracy of the white-sky albedo MODIS retrieval is reported to be within 0.02 in magnitude (Wang et al. 2004); the color scale in the maps showing the differences has been selected to show in light green those regions that lie within that interval. It is observed that HadGAM1 overestimates the surface albedo over deserts in southern Africa, Australia, and South America. This overestimation is also evident in the deserts of the NH, although with some differences. The Sahara Desert and the Arabian Peninsula show a mixture of positive and negative biases, caused by a higher spatial variability in the albedo as obtained by MODIS than in HadGAM1. HadGAM1 shows a very small spatial variability, with albedos greater that 0.35 everywhere in North Africa and the Arabian Peninsula, which is not the case in MODIS. Another important difference in Africa is the Sahel region, where HadGAM1 underestimates the surface albedo. This is caused by a lack of a transition region between the Sahara Desert and central Africa in HadGAM1. While MODIS shows values of 0.25 to 0.35 that represent the transition between the higher values over the Sahara (typically >0.35) and the lower values in central Africa (typically <0.20), HadGAM1 does not show that transition, going quite abruptly from the higher values to the lower ones. Finally, there is also a very high bias over midlatitude landmasses in the NH in January. This overestimation is not present in July, which indicates the existence of seasonal-dependent errors.

To explore these errors in more detail, Fig. 13 shows the regional surface albedo over Europe in January. It shows the model monthly means averaged over 20 yr, as well as two MODIS products: the MOD43C1 product already used in Fig. 12 and the spatially complete albedo. The spatially complete albedo is a “value-added” product, which uses an ecosystem-dependent temporal interpolation technique to fill missing or seasonally snow-covered data in the official MOD43B3 product (from which MOD43C1 is obtained; Moody et al. 2005). This figure also shows the HadGAM1 monthly snow amount (kilograms per meter squared) averaged over 20 yr (Fig. 13b). As can be seen, the presence of snow in HadGAM1 increases the albedo over central and northern Europe, and explains the differences over the areas that are not actually covered by snow in the MODIS dataset. By comparing Figs. 13b,c,e, it can be deduced that the area covered by snow is much wider in HadGAM1 than in MODIS, although the difference might not be significant as we are comparing a 20-yr climatology against a 2-yr average from MODIS. Although the snow cover is responsible for the differences in those regions, the presence of snow is negligible in many regions of France, the United Kingdom, and the Iberian Peninsula, which indicates an overestimation in the model snow-free surface albedo over those regions.

Figure 14a shows the seasonal cycle of the surface albedo averaged over the Iberian Peninsula [(38°N, 8°W) to (43°N, 1°W)]. The fact that the snow-cover impact on the surface albedo over this area is negligible is shown by the fact that the value of MOD43C1 is very close to the spatially complete product. It is obvious that seasonal cycle of the surface albedo is not well represented in HadGAM1 over that region. The mean annual value is slightly too high, whereas the amplitude of the oscillation is too small. In addition, the phases of both oscillations do not coincide: HadGAM1 reaches the maximum value in April/May, and the minimum in September, whereas MODIS peaks in August, and reaches its minimum value in December/January. The representation of the seasonal cycle of ISCCP-FD is also very poor, with opposite behavior to MODIS.

Figure 14b shows a similar plot for the Southeast United States [(31°N, 95°W) to (36°N, 85°W)], with a negligible influence of snow during the winter months. This plot shares some features with the Iberian Peninsula, in the sense that HadGAM1 shows a consistent negative bias, and a poor representation of the seasonal cycle. The errors are particularly relevant during the winter months, when MODIS measures the lowest albedos, and HadGAM1 shows a quasi-constant albedo throughout all the year, close to the value measured by MODIS in the summer months. Again, the representation of the seasonal cycle of ISCCP-FD is also very poor as compared to MODIS.

Figure 14c shows the same information as Fig. 14a, but for eastern Europe [(50°N, 25°E) to (55°N, 30°E)]. From May to October, HadGAM1 follows quite well the seasonal cycle of surface albedo shown by MODIS, although showing greater values. This result is also obtained in other regions in the NH midlatitudes, such as central Asia (not shown). The impact of the snow cover in the surface albedo is evident between November and March. This can be seen in the MOD43C1 albedo in January, which shows a value of ∼0.28 as compared to ∼0.11 given by MODIS-SC. The differences from HadGAM1 with respect to MOD43C1 in January are large, with HadGAM1 showing a surface albedo that is too high. This may indicate an excess in the amount of snow simulated, but also deficiencies in the snow albedo parameterization. However, MODIS data are less reliable for snow-covered regions, particularly for nonforested regions (Jin et al. 2002). This, and the fact that ISCCP shows similar values to HadGAM1 in January, may indicate that MODIS is underestimating the surface albedo of snow. A comprehensive intercomparison of the different surface albedo databases is needed in order to understand their quality and differences.

7. Global dimming

Numerous studies have noted an observed long-term trend in incoming surface shortwave radiation over the late twentieth century, a phenomenon known as “global dimming” (e.g., Stanhill and Cohen 2001; Liepert 2002; Wild et al. 2004). These studies have used a variety of surface-based radiometer data, such as those derived from the world radiation network set up for the International Geophysical Year (Stanhill and Moreshet 2002) and those from the Global Energy Balance Archive (GEBA) of ETH Zurich (Gilgen et al. 1998). The trend originally observed was negative (hence, the name given to the phenomenon), and an increase in the concentrations of anthropogenic aerosols, acting via both direct and indirect effects on clouds, was ascribed as the most probable cause (Stanhill and Cohen 2001). The amount of reduction in global-mean surface solar radiation varies between different studies and the time periods chosen. For example, Stanhill and Cohen (2001) suggest a reduction of 20 W m−2 over the period 1958–92, whereas Liepert (2002) suggests a decrease of 7 W m−2 for the period 1961–90.

More recent observations (Wild et al. 2005) have since shown a reversal of the trend in some areas of the world, beginning sometime in the late 1980s. This reversal is attributed to a decrease in anthropogenic aerosol emissions caused by the implementation of air quality legislation in areas such as Europe and the United States and by the collapse of the former Soviet Union, and to a recovery from the emissions caused by major volcanic eruptions such as El Chichón and Pinatubo. This is corroborated by the study of Streets et al. (2006), which analyzes the changes in anthropogenic aerosol emissions over the period from 1980 to 2000, and concludes that these changes are likely to be the cause of the transition from dimming to brightening.

The HadGAM1 simulations analyzed in the previous sections of this paper are not ideal for evaluating the model’s simulation of global dimming/brightening as they only cover the 20-yr period up to 2000. We therefore use decadal-mean data from simulations using the full atmosphere–ocean coupled model HadGEM1 (Johns et al. 2006). These simulations cover the period 1860–2010 and include the effects of anthropogenic changes in greenhouse gases, aerosols and land use, as well as natural changes from volcanic eruptions and solar variations. Time-varying historical emissions of anthropogenic aerosols (or aerosol precursors) were used: the emission data of Smith et al. (2004) were used for SO2, and those compiled by T. Nozawa (National Institute for Environmental Studies, Japan, 2003, personal communication) for biomass-burning and fossil-fuel black carbon. Figure 15a shows the difference in incoming surface shortwave radiation between the 1950s and the 1980s. This shows dimming of up to −10 W m−2 over Europe and the United States, in reasonable agreement with Liepert (2002). There are also areas with much larger decreases, with values of −15 to −20 W m−2 over parts of Africa, India, and China. Figure 15b shows the subsequent change between the 1980s and the 2000s. This shows that, in agreement with Wild et al. (2005), areas such as Europe and the United States show an increase in surface shortwave radiation. However, both the dimming between the 1950s and the 1980s (Fig. 15a) and the brightening between the 1980s and the 2000s (Fig. 15b) are by no means global, with strong spatial heterogeneity evident in both cases. Indeed the brightening over parts of Europe and the United States shown in Fig. 15b is more than offset by the dimming from increases in biomass-burning aerosol emissions in Africa and industrial emissions over Southeast Asia.

It is apparent, however, that in global-mean terms the amount of dimming shown in Fig. 15a is markedly less, at −2 W m−2, than the values given by the observational studies of Liepert (2002) and Stanhill and Cohen (2001). This result is similar to that from some other modeling studies of global dimming, such as Liepert et al. (2004) with the ECHAM4 model, who obtained a reduction of −3.8 W m−2 over the century from the 1880s to the 1980s. While there are undoubtedly shortcomings in the models and their treatment of aerosols and their effects, these model results are more in line with the conclusions of Alpert et al. (2005). Their examination of GEBA data over the period 1964–89 suggests that global dimming is a highly variable phenomenon, dominated by the pollution generated by increasing urbanization. A lower value for the global-mean change in surface shortwave radiation is also supported by the study of Pinker et al. (2005), who use ISCCP data to analyze the change in surface solar radiation over the whole globe for the period 1983–2001. They conclude that the tendencies in surface solar radiation observed at a global scale are much smaller than those obtained from ground-based observations.

8. Conclusions

The surface radiation budget from HadGAM1 in a 20-yr present-day climate simulation has been compared with the surface radiation budget derived from satellite data and measured with ground stations, and we summarize the main conclusions.

  • HadGAM1 generally overestimates Ss,d over landmasses. The bias (standard deviation) with respect to ground measurements is 17.2 (28.6) W m−2. HadGAM1 simulates Ss,d very well over the polar regions. Although regional differences of Ss,d are correlated with errors in cloud amount or cloud optical thickness, this overestimation is also present under clear skies, consistent with a low aerosol optical thickness compared with observations. This contrasts with the negligible bias shown by HadAM3 in clear-sky Ss,d (Wild et al. 2006). Certain missing aerosol species in HadGAM1, and the fact that HadAM3 uses an aerosol climatology (Cusack et al. 1998), means HadAM3 provides a more accurate (although less physically based) representation of the climatological radiative impact of aerosols.
  • HadGAM1 tends to underestimate Ls,d. The bias (standard deviation) compared with ground measurements is −6.0 (19.6) W m−2. The global mean Ls,d is closer to observations than HadAM3. This is mainly due to improvements in the simulation of low cloud.
  • HadGAM1 overestimates the surface albedo over deserts in South Africa, Australia, and South America. This overestimation is also evident in the deserts of the NH, although mixed with underestimations over some regions. HadGAM1 severely underestimates the surface albedo over the Sahel region. Also important is the positive bias over midlatitude landmasses in the NH in January. A more detailed analysis of the land surface albedo for different regions has shown that the amplitude and phase of the seasonal cycle is not well represented in HadGAM1, although a more extensive validation needs to be carried out. Our results also suggest that a comprehensive intercomparison of the different surface albedo databases is needed in order to understand their quality and differences.
  • Global dimming: in global-mean terms the amount of dimming simulated by HadGEM1 is less than the values given by the studies of Liepert (2002) and Stanhill and Cohen (2001). The model results are more in line with the conclusions of Alpert et al. (2005), suggesting that global dimming is far from being a uniform phenomenon across the globe.

Acknowledgments

This work was supported by the UK Department for Environment, Food, and Rural Affairs under Contract PECD 7/12/37. The ISCCP-FD data were obtained from the International Satellite Cloud Climatology Project Web site (http://isccp.giss.nasa.gov) maintained by the ISCCP research group at the NASA Goddard Institute for Space Studies. The World Radiation Monitoring Center is acknowledged for the release of BSRN data. The MODIS MOD43C1 data are distributed by the Land Processes Distributed Active Archive Center (LP DAAC), located at the U.S. Geological Survey (USGS) Center for Earth Resources Observation and Science (EROS) Web site (http://LPDAAC.usgs.gov). We also thank the comments of two anonymous reviewers.

REFERENCES

  • Allan, R. P., 2000: Evaluation of simulated clear-sky longwave radiation using ground-based observations. J. Climate, 13 , 19511964.

  • Alpert, P., , P. Kishcha, , Y. J. Kaufman, , and R. Schwarzbard, 2005: Global dimming or local dimming?: Effect of urbanization on sunlight availability. Geophys. Res. Lett., 32 .L17802, doi:10.1029/2005GL023320.

    • Search Google Scholar
    • Export Citation
  • Arking, A., 1996: Absorption of solar energy in the atmosphere: Discrepancy between model and observations. Science, 273 , 779782.

  • Barker, H. W., , and Z. Li, 1995: Improved simulation of clear-sky shortwave radiative transfer in the CCC-GCM. J. Climate, 8 , 22132223.

    • Search Google Scholar
    • Export Citation
  • Bolle, H-J., and Coauthors, 2006: Mediterranean Land-Surface Processes Assessed from Space. Springer-Verlag, 760 pp.

  • Bony, S., , J-L. Dufresne, , H. Le Treut, , J-J. Morcrette, , and C. A. Senior, 2004: On dynamic and thermodynamic components of cloud changes. Climate Dyn., 22 , 7186. doi:10.1007/s00382-003-0369-6.

    • Search Google Scholar
    • Export Citation
  • Cess, R. D., and Coauthors, 1995: Absorption of solar-radiation by clouds: Observations versus models. Science, 267 , 496499.

  • Cess, R. D., , M. Zhang, , P-H. Wang, , and B. A. Wielicki, 2001: Cloud structure anomalies over the tropical Pacific during the 1997/98 El Niño. Geophys. Res. Lett., 28 , 45474550.

    • Search Google Scholar
    • Export Citation
  • Chevallier, F., , and J-J. Morcrette, 2000: Comparison of model fluxes with surface and top-of-the-atmosphere observations. Mon. Wea. Rev., 128 , 38393852.

    • Search Google Scholar
    • Export Citation
  • Clough, S. A., , M. J. Iacono, , and J-L. Moncet, 1992: Line-by-line calculations of atmospheric fluxes and cooling rates: Application to water vapor. J. Geophys. Res., 97 , D14. 15 76115 785.

    • Search Google Scholar
    • Export Citation
  • Cusack, S., , A. Slingo, , J. M. Edwards, , and M. Wild, 1998: The radiative impact of a simple aerosol climatology on the Hadley Centre Atmospheric GCM. Quart. J. Roy. Meteor. Soc., 124 , 25172526. doi:10.1002/qj.49712455117.

    • Search Google Scholar
    • Export Citation
  • Darnell, W. L., , W. F. Staylor, , S. K. Gupta, , N. A. Ritchey, , and A. C. Wilber, 1992: Seasonal variation of surface radiation budget derived from International Satellite Cloud Climatology Project C1 data. J. Geophys. Res., 97 , 15 74115 760.

    • Search Google Scholar
    • Export Citation
  • 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 , 689720.

    • Search Google Scholar
    • Export Citation
  • Essery, R. L. H., , M. Best, , and P. Cox, 2001: MOSES 2.2 technical documentation. Hadley Centre Tech. Note 30, Hadley Centre, Met Office, Fitzroy Road, Exeter, United Kingdom, 30 pp.

  • Essery, R. L. H., , M. J. Best, , R. A. Betts, , P. M. Cox, , and C. M. Taylor, 2003: Explicit representation of subgrid heterogeneity in a GCM land-surface scheme. J. Hydrometeor., 4 , 530543.

    • Search Google Scholar
    • Export Citation
  • Garrat, J. R., 1994: Incoming shortwave fluxes at the surface—A comparison of GCM results with observations. J. Climate, 7 , 7280.

  • Gates, W. L., and Coauthors, 1999: An overview of the results of the Atmospheric Model Intercomparison Project (AMIP I). Bull. Amer. Meteor. Soc., 80 , 2955.

    • Search Google Scholar
    • Export Citation
  • Gilgen, H., , M. Wild, , and A. Ohmura, 1998: Means and trends of shortwave irradiance data at the surface estimated from global energy balance archive data. J. Climate, 11 , 20422061.

    • Search Google Scholar
    • Export Citation
  • Gleckler, P. J., 2005: Surface energy balance errors in AGCMs: Implications for ocean-atmosphere model coupling. Geophys. Res. Lett., 32 .L15708, doi:10.1029/2005GL023061.

    • Search Google Scholar
    • Export Citation
  • Gupta, S. K., , W. L. Darnell, , and A. C. Wilber, 1992: A parameterization for longwave surface radiation from satellite data: Recent improvements. J. Appl. Meteor., 31 , 13611367.

    • Search Google Scholar
    • Export Citation
  • Gupta, S. K., , N. A. Ritchey, , A. C. Wilber, , C. H. Whitlock, , G. G. Gibson, , and P. W. Stackhouse, 1999: A climatology of surface radiation budget derived from satellite data. J. Climate, 12 , 26912710.

    • Search Google Scholar
    • Export Citation
  • Hansen, J., , M. Sato, , and R. Reudy, 1997: Radiative forcing and climate response. J. Geophys. Res., 102 , D6. 68316864.

  • Hansen, M. C., , R. S. Defries, , J. R. G. Townshend, , and R. Sohlberg, 2000: Global land cover classification at 1 km spatial resolution using a classification tree approach. Int. J. Remote Sens., 21 , 13311364. doi:10.1080/014311600210209.

    • Search Google Scholar
    • Export Citation
  • Harrison, E. P., , P. Minnis, , B. R. Barkstrom, , V. Ramanathan, , R. D. Cess, , and G. G. Gibson, 1990: Seasonal variation of cloud radiative forcing derived from the Earth Radiation Budget Experiment. J. Geophys. Res., 95 , 18 68718 703.

    • Search Google Scholar
    • Export Citation
  • Jin, Y., , C. B. Schaaf, , F. Gao, , X. Li, , A. H. Strahler, , X. Zeng, , and R. E. Dickinson, 2002: How does snow impact the albedo of vegetated land surfaces as analyzed with MODIS data? Geophys. Res. Lett., 29 .1374, doi:10.1029/2001GL014132.

    • Search Google Scholar
    • Export Citation
  • Johns, T. C., and Coauthors, 2006: The new Hadley Centre climate model HadGEM1: Evaluation of coupled simulations. J. Climate, 19 , 13271353.

    • Search Google Scholar
    • Export Citation
  • Kristjánsson, J. E., , J. M. Edwards, , and D. L. Mitchell, 2000: Impact of a new scheme for optical properties of ice crystals on climates of two GCM’s. J. Geophys. Res., 105 , D8. 10 06310 079.

    • Search Google Scholar
    • Export Citation
  • Li, Z., 1995: Intercomparison between two satellite-based products of net surface shortwave radiation. J. Geophys. Res., 100 , 32213232.

    • Search Google Scholar
    • Export Citation
  • Li, Z., , and H. G. Leighton, 1993: Global climatologies of solar radiation budgets at the surface and in the atmosphere from 5 years of ERBE data. J. Geophys. Res., 98 , 49194930.

    • Search Google Scholar
    • Export Citation
  • Li, Z., , H. W. Barker, , and L. Moreau, 1995: The variable effect of clouds on atmospheric absorption of solar-radiation. Nature, 376 , 486490.

    • Search Google Scholar
    • Export Citation
  • Li, Z., , L. Moreau, , and A. Arking, 1997: On solar energy disposition: A perspective from observation and modeling. Bull. Amer. Meteor. Soc., 78 , 5370.

    • Search Google Scholar
    • Export Citation
  • Liepert, B. G., 2002: Observed reductions of surface solar radiation at sites in the United States and worldwide from 1961 to 1990. Geophys. Res. Lett., 29 .1421, doi:10.1029/2002GL014910.

    • Search Google Scholar
    • Export Citation
  • Liepert, B. G., , J. Feichter, , U. Lohmann, , and E. Roeckner, 2004: Can aerosols spin down the water cycle in a warmer and moister world? Geophys. Res. Lett., 31 .L06207, doi:10.1029/2003GL019060.

    • Search Google Scholar
    • Export Citation
  • Lu, R. Y., , B. W. Dong, , R. D. Cess, , and G. L. Potter, 2004: The 1997/1998 El Niño: A test for climate models. Geophys. Res. Lett., 31 .L12216, doi:10.1029/2004GL019956.

    • Search Google Scholar
    • Export Citation
  • Martin, G. M., , M. A. Ringer, , V. D. Pope, , A. Jones, , C. Dearden, , and T. J. Hinton, 2006: The physical properties of the atmosphere in the new Hadley Centre Global Environmental Model (HadGEM1). Part I: Model description and global climatology. J. Climate, 19 , 12741301.

    • Search Google Scholar
    • Export Citation
  • Moody, E. G., , M. D. King, , S. Platnick, , C. B. Schaaf, , and F. Gao, 2005: Spatially complete global spectral surface albedos: Value-added datasets derived from Terra MODIS land products. IEEE Trans. Geosci. Remote Sens., 43 , 144158. doi:10.1109/TGRS.2004.838359.

    • Search Google Scholar
    • Export Citation
  • Ohmura, A., and Coauthors, 1998: Baseline Surface Radiation Network (BSRN/WCRP): New precision radiometry for climate research. Bull. Amer. Meteor. Soc., 79 , 21152136.

    • Search Google Scholar
    • Export Citation
  • Pinker, R. T., , and I. Laszlo, 1992: Modeling surface solar irradiance for satellite applications on a global scale. J. Appl. Meteor., 31 , 194211.

    • Search Google Scholar
    • Export Citation
  • Pinker, R. T., , B. Zhang, , and E. G. Dutton, 2005: Do satellites detect trends in surface solar radiation? Science, 308 , 850854.

  • Ramanathan, V., 1987: The role of Earth Radiation Budget studies in climate and general circulation research. J. Geophys. Res., 92 , 40754095.

    • Search Google Scholar
    • Export Citation
  • Ramanathan, V., , R. D. Cess, , E. F. Harrison, , P. Minnis, , B. R. Barkstrom, , E. Ahmad, , and D. Hartmann, 1989: Cloud radiative forcing and climate: Results from the Earth Radiation Budget Experiment. Science, 243 , 5763.

    • Search Google Scholar
    • Export Citation
  • Ramanathan, V., , B. Subasilar, , G. J. Zhang, , W. Conant, , R. D. Cess, , J. T. Kiehl, , H. Grassl, , and L. Shi, 1995: Warm pool heat-budget and shortwave cloud forcing: A missing physics. Science, 267 , 499503.

    • Search Google Scholar
    • Export Citation
  • 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 , 12331246.

    • Search Google Scholar
    • Export Citation
  • Raschke, E., , A. Ohmura, , W. B. Rossow, , B. E. Carlson, , Y-C. Zhang, , C. Stubenrauch, , M. Kottek, , and M. Wild, 2005: Cloud effects on the radiation budget based on ISCCP data (1991 to 1995). Int. J. Climatol., 25 , 11031125. doi:10.1002/joc.1157.

    • Search Google Scholar
    • Export Citation
  • Ringer, M. A., , and R. P. Allan, 2004: Evaluating climate model simulations of tropical cloud. Tellus, 56 , 308327.

  • Ringer, M. A., and Coauthors, 2006: The physical properties of the atmosphere in the new Hadley Centre Global Environmental Model (HadGEM1). Part II: Aspects of variability and regional climate. J. Climate, 19 , 13021326.

    • Search Google Scholar
    • Export Citation
  • Rossow, W. B., , and Y. C. Zhang, 1995: Calculation of surface and top of atmosphere radiative fluxes from physical quantities based on ISCCP data sets. 2: Validation and first results. J. Geophys. Res., 100 , D1. 11671197.

    • Search Google Scholar
    • Export Citation
  • Rossow, W. B., , and R. A. Schiffer, 1999: Advances in understanding clouds from ISCCP. Bull. Amer. Meteor. Soc., 80 , 22612287.

  • Rothman, L. S., and Coauthors, 2003: The hitran molecular spectroscopic database: Edition of 2000 including updates through 2001. J. Quant. Spectrosc. Radiat. Transfer, 82 , 544. doi:10.1016/S0022-4073(03)00146-8.

    • Search Google Scholar
    • Export Citation
  • Schaaf, C. B., and Coauthors, 2002: First operational BRDF, albedo nadir reflectance products from MODIS. Remote Sens. Environ., 83 , 135148.

    • Search Google Scholar
    • Export Citation
  • Sellers, P. J., , S. O. Los, , C. J. Tucker, , C. O. Justice, , D. A. Dazlich, , G. J. Collatz, , and D. A. Randall, 1996: A revised land surface parameterization (SiB2) for atmospheric GCMS. Part II: The generation of global fields of terrestrial biophysical parameters from satellite data. J. Climate, 9 , 706737.

    • Search Google Scholar
    • Export Citation
  • Smith, G. L., , A. C. Wilber, , S. K. Gupta, , and P. W. Stackhouse Jr., 2002: Surface radiation budget and climate classification. J. Climate, 15 , 11751188.

    • Search Google Scholar
    • Export Citation
  • Smith, S. J., , R. Andres, , E. Conception, , and J. Lurz, 2004: Historical sulfur dioxide emissions 1850–2000: Methods and results. PNNL Rep. 14537, Joint Global Change Research Institute, College Park, MD, 16 pp.

  • Stackhouse Jr, P. W., , S. J. Cox, , S. K. Gupta, , R. C. DiPasquale, , and D. E. Brown, 1999: The WCRP/GEWEX Surface Radiation Budget Project Release 2: First results at 1 degree resolution. Preprints. 10th Conf. on Atmospheric Radiation: A Symp. with Tributes to the Works of Verner E. Suomi, Madison, WI, Amer. Meteor. Soc., 520–523.

    • Search Google Scholar
    • Export Citation
  • Stanhill, G., , and S. Cohen, 2001: Global dimming: A review of the evidence for a widespread and significant reduction in global radiation with discussion of its probable causes and possible agricultural consequences. Agric. For. Meteor., 107 , 255278.

    • Search Google Scholar
    • Export Citation
  • Stanhill, G., , and S. Moreshet, 2002: Global radiation climate changes: The world network. Climatic Change, 21 , 5775.

  • Stephens, G. L., 1996: How much solar radiation do clouds absorb? Science, 271 , 11311133.

  • Stephens, G. L., 2005: Cloud feedbacks in the climate system: A critical review. J. Climate, 18 , 237273.

  • Stephens, G. L., and Coauthors, 2002: The CloudSat mission and the A-Train. Bull. Amer. Meteor. Soc., 83 , 17711790.

  • Streets, D. G., , Y. Wu, , and M. Chin, 2006: Two-decadal aerosol trends as a likely explanation of the global dimming/brightening transition. Geophys. Res. Lett., 33 .L15806, doi:10.1029/2006GL026471.

    • Search Google Scholar
    • Export Citation
  • Trewartha, G. T., , and L. H. Horn, 1980: An Introduction to Climate. 5th ed. McGraw-Hill, 416 pp.

  • Wang, Z., , X. Zeng, , M. Barlage, , R. E. Dickinson, , F. Gao, , and C. B. Schaaf, 2004: Using MODIS BRDF and albedo data to evaluate global model land surface albedo. J. Hydrometeor., 5 , 314.

    • Search Google Scholar
    • Export Citation
  • Whitlock, C. H., and Coauthors, 1995: First global WCRP shortwave surface radiation budget data set. Bull. Amer. Meteor. Soc., 76 , 905922.

    • Search Google Scholar
    • Export Citation
  • Wild, M., 2005: Solar radiation budgets in atmospheric model intercomparisons from a surface perspective. Geophys. Res. Lett., 32 .L07704, doi:10.1029/2005GL022421.

    • Search Google Scholar
    • Export Citation
  • Wild, M., , A. Ohmura, , H. Gilgen, , and E. Roeckner, 1995: Validation of general circulation model radiative fluxes using surface observations. J. Climate, 8 , 13091324.

    • Search Google Scholar
    • Export Citation
  • Wild, M., , A. Ohmura, , H. Gilgen, , E. Roeckner, , M. Giorgetta, , and J-J. Morcrette, 1998: The disposition of radiative energy in the global climate system: GCM-calculated versus observational estimates. Climate Dyn., 14 , 853869. doi:10.1007/s003820050260.

    • Search Google Scholar
    • Export Citation
  • Wild, M., , A. Ohmura, , H. Gilgen, , J. J. Morcrette, , and A. Slingo, 2001: Evaluation of downward longwave radiation in general circulation models. J. Climate, 14 , 32273239.

    • Search Google Scholar
    • Export Citation
  • Wild, M., , A. Ohmura, , H. Gilgen, , and D. Rosenfeld, 2004: On the consistency of trends in radiation and temperature records and implications for the global hydrological cycle. Geophys. Res. Lett., 31 .L11201, doi:10.1029/2003GL019188.

    • Search Google Scholar
    • Export Citation
  • Wild, M., and Coauthors, 2005: From dimming to brightening: Decadal changes in solar radiation at the earth’s surface. Science, 308 , 847850.

    • Search Google Scholar
    • Export Citation
  • Wild, M., , C. N. Long, , and A. Ohmura, 2006: Evaluation of clear-sky solar fluxes in GCMs participating in AMIP and IPCC-AR4 from a surface perspective. J. Geophys. Res., 111 .D01104, doi:10.1029/2005JD006118.

    • Search Google Scholar
    • Export Citation
  • Wittmeyer, I. L., , and T. H. Vonder Haar, 1994: Analysis of the global ISCCP TOVS water vapor climatology. J. Climate, 7 , 325333.

  • Zhang, Y., , W. B. Rossow, , and A. A. Lacis, 1995: Calculation of surface and top of atmosphere radiative fluxes from physical quantities based on ISCCP datasets 1. Method and sensitivity to input data uncertainties. J. Geophys. Res., 100 , 11491165.

    • Search Google Scholar
    • Export Citation
  • Zhang, Y., , W. B. Rossow, , A. A. Lacis, , V. Oinas, , and M. I. Mishchenko, 2004: Calculation of radiative fluxes from the surface to top of atmosphere based on ISCCP and other global data sets: Refinements of the radiative transfer model and input data. J. Geophys. Res., 109 .D19105, doi:10.1029/2003JD004457.

    • Search Google Scholar
    • Export Citation

Fig. 1.
Fig. 1.

Annual means of (a), (d), (g) Ss,d and (b), (e), (h) Ss,n. Differences are HadGAM1 minus ISCCP-FD. Units are W m−2.

Citation: Journal of Climate 21, 18; 10.1175/2008JCLI2097.1

Fig. 2.
Fig. 2.

Annual means of (a), (d), (g) Ls,d (W m−2); (b), (e), (h) precipitable water content (kg m−2); and (c), (f), (i) surface temperature (K) for HadGAM1 and ISCCP-FD. Differences are HadGAM1 minus ISCCP-FD.

Citation: Journal of Climate 21, 18; 10.1175/2008JCLI2097.1

Fig. 3.
Fig. 3.

Hemispheric, seasonal cycle of surface radiation budget. Anomalies with respect to annual mean values are represented. Units are W m−2, except for the albedo, expressed in %. (a) Surface downward SW radiation, (b) surface downward LW radiation, (c) surface albedo, (d) surface upward LW radiation, (e) surface SW net radiation, and (f) surface LW net radiation. Gray shades in HadGAM1 curves show the range of variability from the five-member ensemble of model runs.

Citation: Journal of Climate 21, 18; 10.1175/2008JCLI2097.1

Fig. 4.
Fig. 4.

Zonal means of the (left) radiation budget and (right) cloud radiative forcing at TOA, within the atmosphere (ATM) and at the surface (SFC). CSW, CLW, and CTOT are the SW, LW, and total cloud forcing, respectively. Solid lines with symbols are ISCCP-FD results, and nonsolid lines are HadGAM1 simulations.

Citation: Journal of Climate 21, 18; 10.1175/2008JCLI2097.1

Fig. 5.
Fig. 5.

Scatterplots of monthly mean SW downward radiation at BSRN sites against HadGAM1. See Table 2 for details on the sites.

Citation: Journal of Climate 21, 18; 10.1175/2008JCLI2097.1

Fig. 6.
Fig. 6.

As in Fig. 5, but for downwelling LW radiation.

Citation: Journal of Climate 21, 18; 10.1175/2008JCLI2097.1

Fig. 7.
Fig. 7.

Scatterplot of annual means of SW and LW downward radiation as observed at BSRN sites and calculated by HadGAM1. Observational and model points are connected by solid line segments. The end of the segment with the number corresponds to the observed values; the end with the diamond represents the values computed by HadGAM1. The legend shows the labels of BSRN stations, as listed in Table 2.

Citation: Journal of Climate 21, 18; 10.1175/2008JCLI2097.1

Fig. 8.
Fig. 8.

Comparison of mean annual cycle of SW downward flux as observed at BSRN sites (solid) and computed by HadGAM1 (dashed), and ISCCP-FD (dot–dashed). See Table 2 for details about the sites.

Citation: Journal of Climate 21, 18; 10.1175/2008JCLI2097.1

Fig. 9.
Fig. 9.

As in Fig. 8, but for downwelling LW radiation.

Citation: Journal of Climate 21, 18; 10.1175/2008JCLI2097.1

Fig. 10.
Fig. 10.

Hovmoeller plots of the tropical Pacific (10°S, 10°N) as derived from satellite data and simulated by HadGAM1. (a) Ss,d from ISCCP-FD, (b) Ss,d from HadGAM1, (c) Ls,d from ISCCP-FD, and (d) Ls,d from HadGAM1.

Citation: Journal of Climate 21, 18; 10.1175/2008JCLI2097.1

Fig. 11.
Fig. 11.

Surface radiative fluxes climatology for February 1984–2000 and anomalies for February 1998 over the tropical Pacific as derived from satellite data and simulated by HadGAM1. (a) Ss,d climatology from HadGAM1, (b) February 1998 Ss,d anomaly from HadGAM1, (c) Ss,d climatology from ISCCP-FD, (d) February 1998 Ss,d anomaly from ISCCP-FD, (e) Ls,d climatology from HadGAM1, (f) February 1998 Ls,d anomaly from HadGAM1, (g) Ls,d climatology from ISCCP-FD, and (h) February 1998 Ls,d anomaly from ISCCP-FD.

Citation: Journal of Climate 21, 18; 10.1175/2008JCLI2097.1

Fig. 12.
Fig. 12.

Regional means of land surface albedo for January and July for HadGAM1 and MODIS. Maps for HadGAM1 correspond to 20-yr averages, whereas MODIS are 2-yr averages. White areas are water surfaces or missing data points.

Citation: Journal of Climate 21, 18; 10.1175/2008JCLI2097.1

Fig. 13.
Fig. 13.

Surface albedo over Europe in January. (a) HadGAM1 monthly albedo aver aged over 20 yr, (b) HadGAM1 monthly snow amount (kg m−2) averaged over 20 yr, (c) white-sky albedo from MODIS MOD43C1 for the first 16 days of 2001 and 2002, (d) HadGAM1 minus MODIS MOD43C1, (e) white-sky albedo from MODIS spatially complete product for the first 16 days of 2000–04, and (f) HadGAM1 minus MODIS spatially complete. White areas are water surfaces or missing data points.

Citation: Journal of Climate 21, 18; 10.1175/2008JCLI2097.1

Fig. 14.
Fig. 14.

Seasonal cycle of the surface albedo over three different regions: (a) Iberian Peninsula from (38°N, 8°W) to (43°N, 1°W); (b) Southeast United States from (31°N, 95°W) to (36°N, 85°W); (c) eastern Europe from (50°N, 25°E) to (55°N, 30°E). Solid line shows the model results, whereas dashed–dotted line shows the observations from the MODIS spatially complete dataset. The asterisks are the values for January and July from MOD43C1. The dashed line shows ISCCP-FD.

Citation: Journal of Climate 21, 18; 10.1175/2008JCLI2097.1

Fig. 15.
Fig. 15.

Decadal means of difference in incoming surface shortwave radiation as simulated by HadGEM1. Difference between (a) the 1950s and the 1980s and (b) the 1980s and the 2000s. Units are W m−2.

Citation: Journal of Climate 21, 18; 10.1175/2008JCLI2097.1

Table 1.

Global and hemispherical means of the different components of the SRB in HadGAM1. Values in parentheses are for the ISCCP-FD climatology. Units are W m−2.

Table 1.
Table 2.

List of BSRN stations used in this study. They have been classified in five climate groups according to the climate classification by Trewartha and Horn (1980). BSRN label, longitude, latitude, and period used in the comparison are shown.

Table 2.
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