• Allan, R. P., , C. Liu, , M. Zahn, , D. A. Lavers, , E. Koukouvagias, , and A. Bodas-Salcedo, 2013: Physically consistent responses of the global atmospheric hydrological cycle in models and observations. Surv. Geophys., doi:10.1007/s10712-012-9213-z.

    • Search Google Scholar
    • Export Citation
  • Anderson, B. T., , J. R. Knight, , M. A. Ringer, , C. Deser, , A. S. Phillips, , J.-H. Yoon, , and A. Cherchi, 2010: Climate forcings and climate sensitivities diagnosed from atmospheric global circulation models. Climate Dyn., 35, 14611475, doi:10.1007/s00382-010-0798-y.

    • Search Google Scholar
    • Export Citation
  • Anderson, B. T., , J. R. Knight, , M. A. Ringer, , J. Yoon, , and A. Cherchi, 2012: Testing for the possible influence of unknown climate forcings upon global temperature increases from 1950 to 2000. J. Climate, 25, 71637172.

    • Search Google Scholar
    • Export Citation
  • Andrews, T., 2009: Forcing and response in simulated 20th and 21st century surface energy and precipitation trends. J. Geophys. Res., 114, D17110, doi:10.1029/2009JD011749.

    • Search Google Scholar
    • Export Citation
  • Andrews, T., , P. M. Forster, , O. Boucher, , N. Bellouin, , and A. Jones, 2010: Precipitation, radiative forcing and global temperature change. Geophys. Res. Lett., 27, L14701, doi:10.1029/2010GL043991.

    • Search Google Scholar
    • Export Citation
  • Andrews, T., , J. M. Gregory, , P. M. Forster, , and M. J. Webb, 2012a: Cloud adjustment and its role in CO2 radiative forcing and climate sensitivity: A review. Surv. Geophys., 33, 619635, doi:10.1007/s10712-011-9152-0.

    • Search Google Scholar
    • Export Citation
  • Andrews, T., , J. M. Gregory, , M. J. Webb, , and K. E. Taylor, 2012b: Forcing, feedbacks and climate sensitivity in CMIP5 coupled atmosphere-ocean climate models. Geophys. Res. Lett., 39, L09712, doi:10.1029/2012GL051607.

    • Search Google Scholar
    • Export Citation
  • Armour, K. C., , C. M. Bitz, , and G. H. Roe, 2013: Time-varying climate sensitivity from regional feedbacks. J. Climate, 26, 45184534.

  • Bellouin, N., , J. Rae, , A. Jones, , C. Johnson, , J. Haywood, , and O. Boucher, 2011: Aerosol forcing in the Climate Model Intercomparison Project (CMIP5) simulations by HadGEM2-ES and the role of ammonium nitrate. J. Geophys. Res., 116, D20206, doi:10.1029/2011JD016074.

    • Search Google Scholar
    • Export Citation
  • Bichet, A., , M. Wild, , D. Folini, , and C. Schär, 2011: Global precipitation response to changing forcings since 1870. Atmos. Chem. Phys., 11, 99619970.

    • Search Google Scholar
    • Export Citation
  • Bichet, A., , M. Wild, , D. Folini, , and C. Schär, 2012: Causes for decadal variations of wind speed over land: Sensitivity studies with a global climate model. Geophys. Res. Lett., 39, L11701, doi:10.1029/2012GL051685.

    • Search Google Scholar
    • Export Citation
  • Bony, S., , M. Webb, , C. Bretherton, , S. Klein, , P. Siebesma, , G. Tselioudis, , and M. Zhang, 2011: CFMIP: Towards a better evaluation and understanding of clouds and cloud feedbacks in CMIP5 models. CLIVAR Exchanges, No. 56, International CLIVAR Project Office, Southampton, United Kingdom, 20–24.

  • Bony, S., , G. Bellon, , D. Klocke, , S. Sherwood, , S. Fermepin, , and S. Denvil, 2013: Robust direct effect of carbon dioxide on tropical circulation and regional precipitation. Nat. Geosci., 6, 447–451, doi:10.1038/ngeo1799.

    • Search Google Scholar
    • Export Citation
  • Bosilovich, M. G., , S. D. Schubert, , and G. K. Walker, 2005: Global changes of the water cycle intensity. J. Climate,18, 1591–1608.

  • Butchart, N., and Coauthors, 2006: Simulations of anthropogenic change in the strength of the Brewer–Dobson circulation. Climate Dyn., 27, 727741, doi:10.1007/s00382-006-0162-4.

    • Search Google Scholar
    • Export Citation
  • Caminade, C., , and L. Terray, 2006: Influence of increased greenhouse gases and sulphate aerosols concentration upon diurnal temperature range over Africa at the end of the 20th century. Geophys. Res. Lett., 33, L15703, doi:10.1029/2006GL026381.

    • Search Google Scholar
    • Export Citation
  • Cao, L., , G. Bala, , and K. Caldeira, 2012: Climate response to changes in atmospheric carbon dioxide and solar irradiance on the time scale of days to weeks. Environ. Res. Lett., 7, 034015, doi:10.1088/1748-9326/7/3/034015.

    • Search Google Scholar
    • Export Citation
  • Deser, C., , and A. S. Phillips, 2009: Atmospheric circulation trends, 1950–2000: The relative roles of sea surface temperature forcing and direct atmospheric radiative forcing. J. Climate, 22, 396413.

    • Search Google Scholar
    • Export Citation
  • Dessler, A. E., 2010: A determination of the cloud feedback from climate variations over the past decade. Science, 330, 15231527, doi:10.1126/science.1192546.

    • Search Google Scholar
    • Export Citation
  • Dong, B., , J. M. Gregory, , and R. T. Sutton, 2009: Understanding land–sea warming contrast in response to increasing greenhouse gases. Part I: Transient adjustment. J. Climate, 22, 30793097.

    • Search Google Scholar
    • Export Citation
  • Folland, C. K., , D. M. H. Sexton, , D. J. Karoly, , C. E. Johnson, , D. P. Rowell, , and D. E. Parker, 1998: Influences of anthropogenic and oceanic forcing on recent climate change. Geophys. Res. Lett., 25 ,353356.

    • Search Google Scholar
    • Export Citation
  • Folland, C. K., , J. Shukla, , J. Kinter, , and M. J. Rodwell, 2002: The climate of the twentieth century project. CLIVAR Exchanges, No. 7, International CLIVAR Project Office, Southampton, United Kingdom, 37–39.

  • Forster, P. M., , and J. M. Gregory, 2006: The climate sensitivity and its components diagnosed from Earth Radiation Budget data. J. Climate, 19, 3952.

    • Search Google Scholar
    • Export Citation
  • Forster, P. M., , and K. Taylor, 2006: Climate forcings and climate sensitivities diagnosed from coupled climate model integrations. J. Climate, 19, 61816194.

    • Search Google Scholar
    • Export Citation
  • Forster, P. M., , R. S. Freckleton, , and K. P. Shine, 1997: On aspects of the concept of radiative forcing. Climate Dyn., 13, 547560.

  • Forster, P. M., and Coauthors, 2011: Evaluation of radiation scheme performance within chemistry climate models. J. Geophys. Res., 116, D10302, doi:10.1029/2010JD015361.

    • Search Google Scholar
    • Export Citation
  • Forster, P. M., , T. Andrews, , P. Good, , J. M. Gregory, , L. S. Jackson, , and M. Zelinka, 2013: Evaluating adjusted forcing and model spread for historical and future scenarios in the CMIP5 generation of climate models. J. Geophys. Res., 118, 1139–1150, doi:10.1002/jgrd.50174.

    • Search Google Scholar
    • Export Citation
  • Frieler, K., , M. Meinshausen, , T. Schneider von Deimling, , T. Andrews, , and P. Forster, 2011: Changes in global-mean precipitation in response to warming, greenhouse gas forcing and black carbon. Geophys. Res. Lett., 38, L04702, doi:10.1029/2010GL045953.

    • Search Google Scholar
    • Export Citation
  • Gates, W. L., 1992: AMIP: The Atmospheric Model Intercomparison Project. Bull. Amer. Meteor. Soc., 73, 19621970.

  • 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
  • Gettelman, A., and Coauthors, 2010: Multimodel assessment of the upper troposphere and lower stratosphere: Tropics and global trends. J. Geophys. Res.,115 , D00M08, doi:10.1029/2009JD013638.

    • Search Google Scholar
    • Export Citation
  • Gregory, J. M., , and P. Forster, 2008: Transient climate response estimated from radiative forcing and observed temperature change. J. Geophys. Res., 113, D23105, doi:10.1029/2008JD010405.

    • Search Google Scholar
    • Export Citation
  • Gregory, J. M., , and M. J. Webb, 2008: Tropospheric adjustment induces a cloud component in CO2 forcing. J. Climate, 21, 5871.

  • Gregory, J. M., and Coauthors, 2004: A new method for diagnosing radiative forcing and climate sensitivity. Geophys. Res. Lett., 31, L03205, doi:10.1029/2003GL018747.

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

  • Hansen, J., and Coauthors, 2002: Climate forcings in Goddard Institute for Space Studies SI2000 simulations. J. Geophys. Res., 107, 4347, doi:10.1029/2001JD001143.

    • Search Google Scholar
    • Export Citation
  • Hansen, J., and Coauthors, 2005: Efficacy of climate forcings. J. Geophys. Res., 110, D18104, doi:10.1029/2005JD005776.

  • Held, I. M., , and B. J. Soden, 2006: Robust responses of the hydrological cycle to global warming. J. Climate, 19, 56865699.

  • Held, I. M., M. Winton, K. Takahasi, T. Delworth, F. Zeng, and G. K. Vallis, 2010: Probing the fast and slow components of global warming by returning abruptly to preindustrial forcing. J. Climate, 23, 24182427.

    • Search Google Scholar
    • Export Citation
  • Jiang, J. H., and Coauthors, 2012: Evaluation of cloud and water vapor simulations in CMIP5 climate models using NASA “A-Train” satellite observations. J. Geophys. Res., 117, D14105, doi:10.1029/2011JD017237.

    • Search Google Scholar
    • Export Citation
  • Jones, C. D., and Coauthors, 2011: The HadGEM2-ES implementation of the CMIP5 centennial simulations. Geosci. Model Dev., 4, 543570, doi:10.5194/gmd-4-543-2011.

    • Search Google Scholar
    • Export Citation
  • Keeling, C. D., , R. B. Bacastow, , A. E. Bainbridge, , C. A. Ekdahl, , P. R. Guenther, , L. S. Waterman, , and J. F. S. Chin, 1976: Atmospheric carbon dioxide variations at Mauna Loa Observatory, Hawaii. Tellus, 28, 538551.

    • Search Google Scholar
    • Export Citation
  • Kiehl, J. T., 2007: Twentieth century climate model response and climate sensitivity. Geophys. Res. Lett., 34, L22710, doi:10.1029/2007GL031383.

    • Search Google Scholar
    • Export Citation
  • King, M. P., , F. Kucharski, , and F. Molteni, 2010: The roles of external forcings and internal variabilities in the Northern Hemisphere atmospheric circulation change from the 1960s to the 1990s. J. Climate, 23, 62006220.

    • Search Google Scholar
    • Export Citation
  • Knutti, R., , and J. Sedlacek, 2013: Robustness and uncertainties in the new CMIP5 climate model projections. Nat. Climate Change, 3, 369373, doi:10.1038/nclimate1716.

    • Search Google Scholar
    • Export Citation
  • Kucharski, F., and Coauthors, 2009: The CLIVAR C20C project: Skill of simulating Indian monsoon rainfall on interannual to decadal timescales. Does GHG forcing play a role? Climate Dyn., 33, 615627, doi:10.1007/s00382-008-0462-y.

    • Search Google Scholar
    • Export Citation
  • Lambert, F. H., , and M. J. Webb, 2008: Dependence of global mean precipitation on surface temperature. Geophys. Res. Lett., 35, L16706, doi:10.1029/2008GL034838.

    • Search Google Scholar
    • Export Citation
  • Loeb, N. G., , B. A. Wielicki, , D. R. Doelling, , G. Louis Smith, , D. F. Keyes, , S. Kato, , N. Manalo-Smith, , and T. Wong, 2009: Toward optimal closure of the earth’s top-of-atmosphere radiation budget. J. Climate, 22, 748766.

    • Search Google Scholar
    • Export Citation
  • Lohmann, U., and Coauthors, 2010: Total aerosol effect: Radiative forcing or radiative flux perturbation? Atmos. Chem. Phys., 10, 32353246.

    • Search Google Scholar
    • Export Citation
  • Manabe, S., , and R. T. Wetherald, 1975: The effects of doubling the CO2 concentration on the climate of a general circulation model. J. Atmos. Sci., 32, 315.

    • Search Google Scholar
    • Export Citation
  • Martin, G. M., and Coauthors, 2011: The HadGEM2 family of Met Office Unified Model Climate configurations. Geosci. Model Dev., 4, 723757, doi:10.5194/gmd-4-723-2011.

    • Search Google Scholar
    • Export Citation
  • McLandress, C., , and T. G. Shepherd, 2009: Simulated anthropogenic changes in the Brewer–Dobson circulation, including its extension to high latitudes. J. Climate, 22, 15161540.

    • Search Google Scholar
    • Export Citation
  • Meehl, G. A., , A. Hu, , J. M. Arblaster, , J. Fasullo, , and K. E. Trenberth, 2013: Externally forced and internally generated decadal climate variability associated with the interdecadal Pacific oscillation. J. Climate, 26, 72987310.

    • Search Google Scholar
    • Export Citation
  • Ming, Y., , and V. Ramaswamy, 2012: Nonlocal component of radiative flux perturbation. Geophys. Res. Lett., 39, L22706, doi:10.1029/2012GL054050.

    • Search Google Scholar
    • Export Citation
  • Ming, Y., , V. Ramaswamy, , and G. Persad, 2010: Two opposing effects of absorbing aerosols on global-mean precipitation. Geophys. Res. Lett., 37, L13701, doi:10.1029/2010GL042895.

    • Search Google Scholar
    • Export Citation
  • Mitchell, J. F. B., , C. A. Wilson, , and W. M. Cunnington, 1987: On CO2 climate sensitivity and model dependence of results. Quart. J. Roy. Meteor. Soc., 113, 293322.

    • Search Google Scholar
    • Export Citation
  • Morice, C. P., , J. J. Kennedy, , N. A. Rayner, , and P. D. Jones, 2012: Quantifying uncertainties in global and regional temperature change using an ensemble of observational estimates: The HadCRUT4 data set. J. Geophys. Res., 117, D08101, doi:10.1029/2011JD017187.

    • Search Google Scholar
    • Export Citation
  • Muller, C. J., , and P. A. O’Gorman, 2011: An energetic perspective on the regional response of precipitation to climate change. Nat. Climate Change, 1, 266271.

    • Search Google Scholar
    • Export Citation
  • Murphy, D. M., 2013: Little net clear-sky radiative forcing from recent regional redistribution of aerosols. Nat. Geosci., 6, 258262, doi:10.1038/NGEO1740.

    • Search Google Scholar
    • Export Citation
  • Murphy, D. M., , S. Solomon, , R. W. Portmann, , K. H. Rosenlof, , P. M. Forster, , and T. Wong, 2009: An observationally based energy balance for the Earth since 1950. J. Geophys. Res., 114, D17107, doi:10.1029/2009JD012105.

    • Search Google Scholar
    • Export Citation
  • Myhre, G., , E. J. Highwood, , K. P. Shine, , and F. Stordal, 1998: New estimates of radiative forcing due to well mixed greenhouse gases. Geophys. Res. Lett., 25,27152718.

    • Search Google Scholar
    • Export Citation
  • Patricola, C. M., , and K. H. Cook, 2011: Sub-Saharan northern African climate at the end of the twenty-first century: Forcing factors and climate change processes. Climate Dyn., 37, 11651188, doi:10.1007/s00382-010-0907-y.

    • Search Google Scholar
    • Export Citation
  • Rotstayn, L. D., , and J. E. Penner, 2001: Indirect aerosol forcing, quasi forcing, and climate response. J. Climate, 14, 29602975.

  • Scaife, A., and Coauthors, 2009: The CLIVAR C20C project: Selected twentieth century climate events. Climate Dyn., 33, 603614, doi:10.1007/s00382-008-0451-1.

    • Search Google Scholar
    • Export Citation
  • Seidel, D. J., , N. P. Gillett, , J. R. Lanzante, , K. P. Shine, , and P. W. Thorne, 2011: Stratospheric temperature trends: Our evolving understanding. Wiley Interdiscip. Rev. Climate Change, 2, 592616, doi:10.1002/wcc.125.

    • Search Google Scholar
    • Export Citation
  • Senior, C. A., , and J. F. B. Mitchell, 2000: The time-dependence of climate sensitivity. Geophys. Res. Lett., 17 ,26852688.

  • Sexton, D. M. H., , D. P. Rowell, , C. K. Folland, , and D. J. Karoly, 2001: Detection of anthropogenic climate change using an atmospheric GCM. Climate Dyn., 17, 669685.

    • Search Google Scholar
    • Export Citation
  • Shindell, D. T., and Coauthors, 2013: Radiative forcing in the ACCMIP historical and future climate simulations. Atmos. Chem. Phys., 13, 29392974, doi:10.5194/acp-13-2939-2013.

    • Search Google Scholar
    • Export Citation
  • Shine, K. P., , and P. M. Forster, 1999: The effect of human activity on radiative forcing of climate: A review of recent development. Global Planet. Change, 20, 202225.

    • Search Google Scholar
    • Export Citation
  • Shine, K. P., , J. Cook, , E. J. Highwood, , and M. J. Joshi, 2003a: An alternative to radiative forcing for estimating the relative importance of climate change mechanisms. Geophys. Res. Lett., 30, 2047, doi:10.1029/2003GL018141.

    • Search Google Scholar
    • Export Citation
  • Shine, K. P., and Coauthors, 2003b: A comparison of model-simulated trends in stratospheric temperatures. Quart. J. Roy. Meteor. Soc., 129, 15651588, doi:10.1256/qj.02.186.

    • Search Google Scholar
    • Export Citation
  • Skinner, C. B., , M. Ashfaq, , and N. S. Diffenbaugh, 2012: Influence of twenty-first-century atmospheric and sea surface temperature forcing on West African climate. J. Climate, 25, 527542.

    • Search Google Scholar
    • Export Citation
  • Soden, B. J., , A. J. Broccoli, , and R. S. Hemler, 2004: On the use of cloud forcing to estimate cloud feedback. J. Climate, 17, 36613665.

    • Search Google Scholar
    • Export Citation
  • Soden, B. J., , I. M. Held, , R. Colman, , K. M. Shell, , J. T. Kiehl, , and C. A. Shields, 2008: Quantifying climate feedbacks using radiative kernels. J. Climate, 21, 35043520.

    • Search Google Scholar
    • Export Citation
  • Stephens, G. L., and Coauthors, 2012: An update on Earth’s energy balance in light of the latest global observations. Nat. Geosci., 5, 691696, doi:10.1038/ngeo1580.

    • Search Google Scholar
    • Export Citation
  • Streets, D. G., and Coauthors, 2009: Anthropogenic and natural contributions to regional trends in aerosol optical depth, 1980–2006. J. Geophys. Res., 114, D00D18, doi:10.1029/2008JD011624.

    • Search Google Scholar
    • Export Citation
  • Taylor, K. E., , R. J. Stouffer, , and G. A. Meehl, 2012: An overview of CMIP5 and the experiment design. Bull. Amer. Meteor. Soc., 93, 485–498.

    • Search Google Scholar
    • Export Citation
  • Thompson, D. W. J, and Coauthors, 2012: The mystery of recent stratospheric temperature trends. Nature,491, 692–697.

  • Thorne, P. W., , J. R. Lanzante, , T. C. Peterson, , D. J. Seidel, , and K. P. Shine, 2011: Tropospheric temperature trends: History of an ongoing controversy. Wiley Interdiscip. Rev. Climate Change, 2, 6688, doi:10.1002/wcc.80.

    • Search Google Scholar
    • Export Citation
  • View in gallery

    (a) Globally annually averaged net TOA radiative flux time series in the AMIPSST,Ice,F experiment (i.e., with 1979–2008 time-varying forcing agents, as well as monthly observed SST and sea ice boundary conditions) and the AMIPSST,Ice experiment (i.e., same as above but with fixed preindustrial forcing levels). Shading equals the range across the 3-member ensemble. Red line indicates CERES–EBAF satellite-measured variations in net TOA radiative flux. (b) Time series of the effective radiative forcing (ERF) relative to the preindustrial as diagnosed from the difference in radiative flux between the AMIPSST,Ice,F and AMIPSST,Ice experiments. (c) Components of the ERF (WMGHGs, aerosol, O3, and natural). (d) Geographical distribution of the ERF averaged over the entire 1979–2008 time period.

  • View in gallery

    Comparison of (top) the effective radiative forcing of climate at 2005 (2003–07 average) relative to preindustrial and (bottom) the 1979–2008 decadal trends. Further description and numbers are given in Tables 1 and 2.

  • View in gallery

    (a) 1979–2008 average change in aerosol optical depth (AOD) at 0.55 μm relative to the preindustrial, diagnosed from an AMIP experiment with 1979–2008 aerosol emissions and one with preindustrial aerosol emissions. (b) Change in net TOA radiative flux, giving the aerosol ERF. (c) Clear-sky and (d) CRE contributions to the aerosol ERF.

  • View in gallery

    1979–2008 decadal trends in (a) aerosol AOD, (b) aerosol ERF, and (c) the clear-sky and (d) cloud radiative effect (CRECRE) contribution to the aerosol ERF trend. Quantities are calculated from a linear trend of the global annual-mean 1979–2008 time series of the difference between an AMIP experiment with 1979–2008 aerosol emissions and one with preindustrial aerosol emissions.

  • View in gallery

    (a) Relationship between the effective radiative forcing and global temperature anomaly (relative to the 1979–2008 average) in the AMIPSST,Ice,F experiment. The slope, excluding years influenced by volcanic forcing (red points), gives the climate resistance ρ = 1.7 W m−2 K−1. All points are global annual-mean data covering 1979–2008. (b) The pattern of warming/cooling during this period is determined from the 1979–2008 decadal trend in surface air temperature.

  • View in gallery

    Variations between (a) net TOA radiation, (b) LW clear-sky radiation, (c) SW clear-sky radiation, and (d) net (LW + SW) cloud radiative effect and global temperature anomaly (relative to the 1979–2008 average) in the AMIPSST,Ice experiment. In this experiment there is no external forcing, so the variation of radiative flux with ΔT measures the feedback parameter. All fluxes are defined as positive down. The correlation coefficient r is shown. All points are global annual-mean data from 1979 to 2008. Errors in the slope represent 1-σ uncertainties from the OLS regression.

  • View in gallery

    (a) Decadal trend in the global atmospheric temperature profile from 1979–2008 for AMIP experiments with and without various time-varying forcing agents as indicated. (b) Decadal trend in the zonal-mean temperature profile for the AMIPSST,Ice,F experiment and its separation into (c) an SST-driven component (from the AMIPSST,Ice experiment) and (d) a direct atmospheric response to the change in forcing agent [estimated from (b)–(c)]. The forcing-driven component is then further separated into its two largest components: (e) WMGHGs and (f) O3 (estimated from decadal trends in AMIPSST,Ice,F minus an equivalent experiment with fixed WMGHGs and O3).

  • View in gallery

    Annual-mean time series of (a) global, (b) land, (c) NH land, and (d) NH land summer (Jun–Aug) surface air temperature anomalies (relative to the 1979–2008 average) for the AMIPSST,Ice,F (i.e., with time-varying forcing agents) and AMIPSST,Ice (i.e., with fixed forcing agents) experiments. Shading equals the range across the three-member ensemble. Decadal trends are indicated. Note that panel (a) has a different scale.

  • View in gallery

    Annual-mean time series of (a) global and (b) land precipitation rate anomalies (relative to the 1979–2008 average) for the AMIPSST,Ice,F and AMIPSST,Ice experiments. Shading equals the range across the three-member ensemble. Decadal trends are indicated. Global hydrological sensitivities (dP/dT) diagnosed from (c) AMIPSST,Ice,F and (d) AMIPSST,Ice. Points are global annual means for the period 1979–2008. Note that (a) and (b) have different scales.

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 98 98 11
PDF Downloads 72 72 8

Using an AGCM to Diagnose Historical Effective Radiative Forcing and Mechanisms of Recent Decadal Climate Change

View More View Less
  • 1 Met Office Hadley Centre, Exeter, United Kingdom
© Get Permissions
Full access

Abstract

An atmospheric general circulation model is forced with observed monthly sea surface temperature and sea ice boundary conditions, as well as forcing agents that vary in time, for the period 1979–2008. The simulations are then repeated with various forcing agents, individually and in combination, fixed at preindustrial levels. The simple experimental design allows the diagnosis of the model’s global and regional time-varying effective radiative forcing from 1979 to 2008 relative to preindustrial levels. Furthermore the design can be used to (i) calculate the atmospheric model’s feedback/sensitivity parameters to observed changes in sea surface temperature and (ii) separate those aspects of climate change that are directly driven by the forcing from those driven by large-scale changes in sea surface temperature. It is shown that the atmospheric response to increased radiative forcing over the last 3 decades has halved the global precipitation response to surface warming. Trends in sea surface temperature and sea ice are found to contribute only ~60% of the global land, Northern Hemisphere, and summer land warming trends. Global effective radiative forcing is ~1.5 W m−2 in this model, with anthropogenic and natural contributions of ~1.3 and ~0.2 W m−2, respectively. Forcing increases by ~0.5 W m−2 decade−1 over the period 1979–2008 or ~0.4 W m−2 decade−1 if years strongly influenced by volcanic forcings—which are nonlinear with time—are excluded from the trend analysis. Aerosol forcing shows little global decadal trend due to offsetting regional trends whereby negative aerosol forcing weakens in Europe and North America but continues to strengthen in Southeast Asia.

Corresponding author address: Timothy Andrews, Met Office Hadley Centre, FitzRoy Road, Exeter EX1 3PB, United Kingdom. E-mail: timothy.andrews@metoffice.gov.uk

Abstract

An atmospheric general circulation model is forced with observed monthly sea surface temperature and sea ice boundary conditions, as well as forcing agents that vary in time, for the period 1979–2008. The simulations are then repeated with various forcing agents, individually and in combination, fixed at preindustrial levels. The simple experimental design allows the diagnosis of the model’s global and regional time-varying effective radiative forcing from 1979 to 2008 relative to preindustrial levels. Furthermore the design can be used to (i) calculate the atmospheric model’s feedback/sensitivity parameters to observed changes in sea surface temperature and (ii) separate those aspects of climate change that are directly driven by the forcing from those driven by large-scale changes in sea surface temperature. It is shown that the atmospheric response to increased radiative forcing over the last 3 decades has halved the global precipitation response to surface warming. Trends in sea surface temperature and sea ice are found to contribute only ~60% of the global land, Northern Hemisphere, and summer land warming trends. Global effective radiative forcing is ~1.5 W m−2 in this model, with anthropogenic and natural contributions of ~1.3 and ~0.2 W m−2, respectively. Forcing increases by ~0.5 W m−2 decade−1 over the period 1979–2008 or ~0.4 W m−2 decade−1 if years strongly influenced by volcanic forcings—which are nonlinear with time—are excluded from the trend analysis. Aerosol forcing shows little global decadal trend due to offsetting regional trends whereby negative aerosol forcing weakens in Europe and North America but continues to strengthen in Southeast Asia.

Corresponding author address: Timothy Andrews, Met Office Hadley Centre, FitzRoy Road, Exeter EX1 3PB, United Kingdom. E-mail: timothy.andrews@metoffice.gov.uk

1. Introduction

Radiative forcings have long been used to quantify and rank the drivers of climate change (e.g., Hansen et al. 1997; Shine and Forster 1999). In climate models, radiative forcings can help us understand why different models differ in their simulations of the past and future. For example, Forster et al. (2013) found the intermodel spread in the global surface temperature change across phase 5 of the Coupled Model Intercomparison Project (CMIP5) (Taylor et al. 2012) historical simulations to be primarily driven by differences in their present-day forcings. Kiehl (2007) showed an inverse relationship between near-present-day radiative forcing and climate sensitivity across older generation models, suggesting that models can capture twentieth-century warming trends with different combinations of forcings and feedbacks. Diagnosing radiative forcings in transient model simulations is therefore important for understanding their coupled responses and is a required first step to calculating their transient feedbacks.

Many different forcing definitions exist (Hansen et al. 2005) and diagnosing them in models requires either offline radiative transfer calculations (e.g., Forster et al. 1997; Myhre et al. 1998) and/or targeted climate model experiments and diagnostics (e.g., Gregory et al. 2004; Hansen et al. 2005). Diagnosing the time-evolving forcing in transient scenarios is particularly difficult because forcing and feedback evolve together. It is currently most easily achieved using a global energy budget approach, which assumes a climate feedback parameter to derive the forcing (Forster and Taylor 2006; Forster et al. 2013). This method, however, does not isolate individual forcings or provide regional information and assumes an invariant climate sensitivity parameter.

The main goal of this paper is to provide and utilize a simple and efficient experimental design, set within a clear conceptual model/definition of radiative forcing, that could be readily used to (i) diagnose a model’s historical (or at least 1979–2008) time-varying regional radiative forcing. Then, given the forcing, (ii) calculate climate sensitivity/feedback parameters in a realistic transient scenario and (iii) separate those aspects of recent historical climate change that could be usefully considered as part of the forcing (i.e., an “adjustment”; see below and section 2c) from those that are driven by large-scale changes in sea surface temperature (SST) and associated climate feedbacks.

It will be shown that a simple extension of the well-established Atmospheric Model Intercomparison Project (AMIP) (Gates 1992; Gates et al. 1999) design can provide this information. Two experiments (at least) are needed, both using 1979 to near-present observed monthly SST and sea ice boundary conditions, but one with forcing agents varying in time from 1979 to near present and one with forcing agents fixed at preindustrial levels. Note that the experimental design need not be restricted to 1979 to near present, but is here simply to exploit the already well-established AMIP design. Note also that previous studies have, to a limited extent, used similar designs for various purposes and are discussed in section 2c.

Diagnosing radiative forcing in this way is analogous to the “fixed SST” (e.g., Hansen et al. 2002; Shine et al. 2003a) definition of forcing, sometimes referred to as a “quasi forcing” (Rotstayn and Penner 2001) or “radiative flux perturbation” (e.g., Lohmann et al. 2010; Ming and Ramaswamy 2012). This definition of forcing includes not only the instantaneous radiative effect, but also any atmospheric and/or land surface adjustments that come about rapidly—days to weeks (Dong et al. 2009; Cao et al. 2012)—that are independent of large-scale changes in SST. Well-established examples of adjustments that are important for forcings include the stratospheric temperature adjustment, aerosol–cloud interactions (i.e., aerosol indirect effects) (e.g., Lohmann et al. 2010), and cloud adjustments to CO2 (e.g., Andrews et al. 2012a). Radiative forcings diagnosed this way have been shown to be one of the most appropriate definitions of forcing for predicting long-term climate change (Shine et al. 2003a; Hansen et al. 2005) and are referred to here as the effective radiative forcing (ERF).

2. Experimental design

a. Atmospheric model

The model used is the atmospheric component of the Hadley Centre Global Environmental Model, version 2 (HadGEM2-A). HadGEM2-A includes atmospheric, land surface, and hydrology processes. It has 38 vertical levels and a horizontal resolution of 1.25° latitude × 1.875° longitude. A detailed description and validation of HadGEM2-A is given in Martin et al. (2011).

b. Experiments performed

The base experiment follows the AMIP protocol for CMIP5 (Taylor et al. 2012). This involves running an AGCM with observed monthly SST and sea ice fractions from 1979 to 2008, as well as time-evolving forcing agents. Note that this differs from the original AMIP experiments that used a constant CO2 level (or CO2 equivalents) and solar constant representative of the period being simulated (Gates 1992). The base experiment is termed AMIPSST,Ice,F to indicate it includes time-varying SST, sea ice, and forcing agent boundary conditions.

The various forcing agents [including well-mixed greenhouse gases (WMGHGs), aerosols, O3, volcanoes, etc.] are shown in Table 1. The forcing datasets and how they are implemented in this model configuration are described in detail in Jones et al. (2011). Note that the forcings and model configuration is not the same as the fully coupled atmosphere–ocean historical simulation that was part of the Met Office Hadley Centre submission to CMIP5 using the Hadley Centre Global Environment Model, version 2 - Earth System (HadGEM2-ES). For example, HadGEM2-A has prescribed land surface types and O3 concentrations, whereas HadGEM2-ES has a dynamic vegetation and interactive tropospheric chemistry scheme.

Table 1.

Summary of experiments performed. All experiments use observed monthly (1979–2008) SST and sea ice fraction boundary conditions and include a three-member ensemble starting from different atmospheric states. The experiments include a 4-month spinup period from September 1978 and then are run for 30 years to cover the period of analysis, 1979–2008. A checkmark (✓) indicates that a 1979–2009 time-varying forcing species is included, and a cross (x) indicates a constant preindustrial forcing level. Forcings are CO2: carbon dioxide; CH4: methane; N2O: nitrous oxide, Halo: halocarbons; O3: ozone; SO4: sulfate aerosol; BC: black carbon aerosol; OC: organic carbon aerosol; BB: biomass burning aerosol, Sol: solar; and Vol: volcanic. Details of the forcing dataset and how they are implemented in the model are given in Jones et al. (2011).

Table 1.

The AMIPSST,Ice,F experiment is then repeated with various forcing agents, in combination and individually, set back to preindustrial (1860) levels. Table 1 indicates the various different experiments. AMIPSST,Ice indicates that the only time-varying boundary conditions are SST and sea ice; all forcing agents are fixed at preindustrial levels. The “no CO2” experiment indicates that only CO2 has been set back to preindustrial and so on. Land use over the period 1979–2008 is included in the prescribed surface types but its forcing effect on climate compared to no land use is not investigated in this study. Differencing these experiments with the AMIPSST,Ice,F experiment gives the impact of including the time-varying forcing agent/agents on the diagnostic of interest, independent of any impacts through SST.

All experiments had a 4-month spinup period from September 1978 to December 1978 and are then run for 30 years to cover the period of analysis, 1979–2008. Each experiment was run three times starting from different atmospheric initial conditions. The analysis was performed on the average of the 3-member ensemble, except where individual members are used to assess the spread across the ensemble (as indicated in the text).

c. Connection to previous studies

The experimental design is similar (though not identical) to some in previous literature. In particular, a similar design has been used in detection and attribution studies (Folland et al. 1998; Sexton et al. 2001) and the Climate of Twentieth Century (C20C) project (Folland et al. 2002). The C20C project uses an ensemble of AGCMs forced with observed SST and sea ice distributions from the 1950s onward, with various combinations of forcing agents, to study climate variability and predictability on time scales of seasons to decades (e.g., Folland et al. 2002; Scaife et al. 2009). Anderson et al. (2010, 2012) utilized the C20C design to diagnose radiative forcing in models in a manner similar to that presented here but was limited to a 1950 baseline (rather than preindustrial) and lacked the systematic set of experiments needed to diagnose the contributions of individual forcing agents and rapid adjustments.

While most studies based on the C20C project did not consider the ERF conceptual framework, some of the C20C results can be interpreted in this way. For example, Scaife et al. (2009) noted that those models that only included observed time-varying SSTs, but not time-varying forcing agents, generally underestimated the rapid recent land warming since 1970. Under the ERF framework, this implies a significant role for land temperature adjustments—independent of SST changes—in recent land warming trends.

Similar experimental frameworks (i.e., using AGCMs forced with SST changes and various forcing agent combinations) have been used to investigate mechanisms of atmospheric circulation trends (Deser and Phillips 2009; King et al. 2010), regional African climate change (e.g., Patricola and Cook 2011; Skinner et al. 2012), changes in the diurnal temperature range (Caminade and Terray 2006), perturbations to the hydrological cycle (e.g., Bosilovich et al. 2005; Bichet et al. 2011; Allan et al. 2013) variability of the Indian monsoon rainfall (Kucharski et al. 2009), and decreasing land wind speeds (e.g., Bichet et al. 2012). Most of these studies, in various ways, point to the importance of including time-varying forcing agents—not just SST changes—in simulating certain climate trends and hence motivates further examination of separating adjustments from those aspects of climate change that are mediated through large-scale changes in SST.

The design and systematic set of experiments used in this study differ somewhat from the above studies in order to maximize the utility of the experiments. For example, Folland et al. (1998) used a control experiment that had time-varying observed SSTs and sea ice from 1949, with CO2 levels (or CO2 equivalent fixed) fixed at 1949 levels. They then successively introduced different time-varying forcing agents to investigate the direct impact of these forcings on simulated climate trends. In the design proposed here, forcings are removed from the simulation (rather than introduced), and setting the forcings back to preindustrial levels additionally provides information on the ERF relative to preindustrial, rather than 1950 as in Anderson et al. (2010, 2012). This baseline is more useful, as the difference to preindustrial levels more fully defines the anthropogenic contribution to radiative forcing and is also closer to equilibrium conditions. In addition, forcing datasets and models have significantly improved since the early Folland et al. (1998) studies, so it is worth revisiting some of these issues, especially with a comprehensive model (HadGEM2-A) that compares very well against a range of observational metrics (Jiang et al. 2012).

While earlier studies have to a limited extent done similar work, none have provided a framework and systematic set of experiments to diagnose the recent time-varying ERF, including individual forcing contributions and regional variation, as well as to diagnose the models climate feedback/sensitivity parameters and adjustments as mechanisms of recent historical climate change. This paper will—for the first time—bring together and extend these ideas in detail within a single model, all set within our improved conceptual models/definitions of radiative forcing. It is not meant to be exhaustive and will highlight only certain aspects: (i) diagnosing a model’s time-varying ERF and its climate sensitivity parameters (sections 3 and 4) and (ii) diagnosing adjustments as mechanisms of atmospheric and surface temperature trends (sections 5a and 5b) and (iii) the relationship between radiative forcing and perturbations to the hydrological cycle (section 5c).

3. Effective radiative forcing

a. Method

Figure 1a shows the globally annually averaged net top-of-atmosphere (TOA) radiative flux time series (positive downward) for the AMIPSST,Ice,F and AMIPSST,Ice experiments. Shading equals the range (max to min) across the three-member ensemble. Corresponding satellite [Clouds and the Earth’s Radiant Energy System–Energy Balanced and Filled (CERES–EBAF), Loeb et al. 2009] measurements from 2000 are indicated in red purely for illustrative purposes. Stephens et al. (2012) give an observed planetary imbalance of 0.6 ± 0.4 W m−2 averaged over the decade 2000–10.

Fig. 1.
Fig. 1.

(a) Globally annually averaged net TOA radiative flux time series in the AMIPSST,Ice,F experiment (i.e., with 1979–2008 time-varying forcing agents, as well as monthly observed SST and sea ice boundary conditions) and the AMIPSST,Ice experiment (i.e., same as above but with fixed preindustrial forcing levels). Shading equals the range across the 3-member ensemble. Red line indicates CERES–EBAF satellite-measured variations in net TOA radiative flux. (b) Time series of the effective radiative forcing (ERF) relative to the preindustrial as diagnosed from the difference in radiative flux between the AMIPSST,Ice,F and AMIPSST,Ice experiments. (c) Components of the ERF (WMGHGs, aerosol, O3, and natural). (d) Geographical distribution of the ERF averaged over the entire 1979–2008 time period.

Citation: Journal of Climate 27, 3; 10.1175/JCLI-D-13-00336.1

When HadGEM2-A is forced with observed monthly SST, sea ice, and forcing agents (AMIPSST,Ice,F), the simulated global radiative fluxes are consistent with observed estimates (Fig. 1a). When the experiment is repeated with only the time-varying SST and sea ice (all forcing agents set to preindustrial, AMIPSST,Ice), the global radiation balance is strongly negative. This difference, AMIPSST,Ice,F − AMIPSST,Ice (Fig. 1b), gives the direct impact of including time-varying forcing agents relative to preindustrial levels on the global radiation balance and measures the effective radiative forcing (see also Anderson et al. 2010, 2012).

ERF diagnosed in this way can be derived through a global linearized energy budget approach as follows (see also Forster et al. 2013, their section 4.1). The net TOA radiative imbalance (N, watts per square meter) can be described by changes in forcing F (W m−2) and various climate feedback processes that are linearly proportional to global-mean surface air temperature change (ΔT), such that N = F − αΔT, where α is the climate feedback parameter (W m−2 K−1). First, let this equation represent the heat balance in the AMIPSST,Ice,F experiment and N′ = F′ − αΔT′ represent the heat balance in the AMIPSST,Ice experiment (distinguished by the primes). Defining the ERF relative to the preindustrial makes F′ = 0 by construction (since AMIPSST,Ice has preindustrial forcing levels). As both experiments have the same SSTs, ΔT ~ ΔT′ (ignoring small changes from the land surface, see section 5b), then substituting for ΔT gives F = N − N′ (as is plotted in Fig. 1b). This method of diagnosing forcing is analogous to fixed climatological SST methods (e.g., Hansen et al. 2002), but generalized to time-varying forcing agents and an evolving base state (see discussion).

An analogous calculation can be made for individual forcings; for example, differencing AMIPSST,Ice,F against the same experiments with CO2 levels fixed at preindustrial gives the CO2 ERF and so on. The analysis can be done regionally and in radiative component terms [e.g., longwave (LW), shortwave (SW), all sky (i.e., with clouds if present), and clear sky (clouds artificially set to zero)]. The difference between all-sky and clear-sky (CS) fluxes is used as a measure of the impact of clouds on the radiation balance, termed the cloud radiative effect [CRE; sometimes referred to as cloud radiative forcing (CRF) in the literature]. Note that changes in CRE can come about through changes in cloud masking of clear-sky fluxes as well as through changes in cloud properties (e.g., Soden et al. 2004, 2008).

b. Global effective radiative forcing

Figures 1b and 1c show the global annual-mean ERF time series relative to the preindustrial and its major components (WMGHGs, aerosol, O3, and natural). ERF at 2005 (defined as the 2003–07 average) and its 1979–2008 decadal trends for all experiments are tabulated in Tables 2 and 3 and compared on the same scale in Fig. 2.

Table 2.

Effective radiative forcing of climate in 2005 (2003–07 average) relative to 1860 for various forcing agents and radiative components: longwave (LW), shortwave (SW), clear sky (CS), and cloud radiative effect (CRE).

Table 2.
Table 3.

The 1979–2008 decadal trend in effective radiative forcing for various forcing agents and radiative components: LW and SW CS and CRE. Trends are determined from ordinary least squares linear regression of the 1979–2008 global annual-mean time series. Natural and all forcings are strongly influenced by nonlinear volcanic forcings (see Fig. 1c), so these trends are additionally calculated after removing years strongly influenced by volcanic forcing (1982–84 and 1991–93).

Table 3.
Fig. 2.
Fig. 2.

Comparison of (top) the effective radiative forcing of climate at 2005 (2003–07 average) relative to preindustrial and (bottom) the 1979–2008 decadal trends. Further description and numbers are given in Tables 1 and 2.

Citation: Journal of Climate 27, 3; 10.1175/JCLI-D-13-00336.1

The largest term is a positive ERF from well-mixed greenhouse gases (2.51 W m−2) partially offset by aerosol forcing (−1.38 W m−2). These forcings act on different parts of the earth’s energy flows. Greenhouse gases (GHGs) predominately act through altering LW clear-sky fluxes, while aerosol forcing acts on both SW clear-sky and SW CRE fluxes (Table 2), the latter being consistent with aerosol–cloud interactions (section 3c). Globally averaged, only WMGHGs demonstrate a large decadal trend (0.43 W m−2 decade−1), with CO2 forcing increasing by 0.27 W m−2 decade−1 between 1979 and 2008. Using annual-mean CO2 measurements from the Mauna Loa Observatory (Keeling et al. 1976) with a logarithmic formula for CO2 radiative forcing (Myhre et al. 1998) gives a similar decadal trend (~0.24 W m−2 decade−1 from 1979 to 2008) to that simulated, relative to the preindustrial CO2 level of 286 ppmv that the model uses.

Natural forcing from changes in solar activity and volcanic eruptions is strongly interrupted by large negative spikes (Figs. 1b,c). These coincide with the large volcanic eruptions of El Chichón (1982) and Mt. Pinatubo (1991) injecting sulfuric gas into the stratosphere, subsequently forming aerosols that scatter SW radiation. A linear trend analysis of the natural forcing gives a decadal trend of 0.19 W m−2 decade−1 (Table 3), but the volcanic forcing is strongly nonlinear (Fig. 1c). Removing the years most affected by large volcanic forcing (defined, somewhat arbitrarily, as years in which the natural forcing is more negative than −0.25 W m−2—i.e., years 1982–84 and 1991–94) from the natural and all forcing analysis gives a natural forcing trend much closer to zero, 0.05 W m−2 decade−1.

Forster et al. (2013) diagnosed CMIP5 historical ERF through regression techniques and assumptions on the global energy balance. The ERF presented here using the AMIP design with HadGEM2-A (~1.5 W m−2 at 2005) falls close to their multimodel mean at 2003 (~1.7 ± 0.9 W m−2). However, they report a net ERF of ~0.8 W m−2 for HadGEM2-ES. While HadGEM2-A and HadGEM2-ES share the same physical atmosphere, they are not identical models (section 2), and this study has not considered some forcings used in the fully coupled simulation, such as land use. Without an explicit comparison of the two methodologies under the same model/forcings, it is difficult to conclude whether this difference is real.

The global-mean net aerosol ERF (−1.38 W m−2) is close to the combined radiative transfer estimates of the aerosol direct effect (−0.16 W m−2) and first indirect effect (−1.3 W m−2) in HadGEM2-ES reported by Bellouin et al. (2011). This implies a relatively small role for the combined effect of other responses that may additionally be included in the ERF (such as aerosol second indirect and semidirect effects).

c. Regional forcing

The geographical distribution of the time-averaged (1979–2008) ERF (Fig. 1d) is dominated by positive forcing from WMGHGs over much of the globe. Large negative regions exist that are attributable to aerosol forcing (mostly SO4) (Fig. 3). Changes in aerosol optical depth (AOD) at 0.55 μm (Fig. 3a) relative to the preindustrial are dominated by increases over the industrialized regions of Europe, Southeast Asia, and North America. Biomass burning contributes significantly to changes over Africa.

Fig. 3.
Fig. 3.

(a) 1979–2008 average change in aerosol optical depth (AOD) at 0.55 μm relative to the preindustrial, diagnosed from an AMIP experiment with 1979–2008 aerosol emissions and one with preindustrial aerosol emissions. (b) Change in net TOA radiative flux, giving the aerosol ERF. (c) Clear-sky and (d) CRE contributions to the aerosol ERF.

Citation: Journal of Climate 27, 3; 10.1175/JCLI-D-13-00336.1

Aerosol ERF can be approximately split into direct aerosol–radiation interactions (e.g., scattering and absorption of radiation) and aerosol–cloud interactions (e.g., aerosol indirect effects, such as changes in cloud albedo and lifetime). The clear-sky and cloud radiative effect split approximates these processes, noting that it cannot, and is not meant to, be exact (Lohmann et al. 2010). The increased AOD enhances SW scattering (and to a lesser extent absorption), as seen in the negative SW clear-sky forcing (Table 3 and Fig. 3c). Off the eastern coast of continents there exist large oceanic low cloud decks. These are the regions of largest aerosol forcing (dominated by SO4 forcing) in HadGEM2-A (Fig. 3b) and is dominated by changes in SW CRE (Fig. 3d), consistent with aerosol–cloud interactions (namely the first indirect effect) that increase cloud albedo.

Despite a large aerosol forcing relative to the preindustrial, there is little to no global decadal trend over the period 1979–2008 (Table 3). Regional analysis (Fig. 4) reveals large offsetting regional trends. In Southeast Asia, AOD and forcing continue to strengthen, but it weakens in Europe and North America (Figs. 4a,b), as seen in previous modeling studies (e.g., Shindell et al. 2013) and consistent with detailed regional emission inventories derived from fuel use data and regional variations in technology (Streets et al. 2009). Murphy (2013) used recent satellite measurements and a radiative transfer model to show that a regional redistribution of aerosols has little impact on clear-sky radiative forcing. Consistent with this, the model produces no decadal trend in global clear-sky aerosol ERF from 1979 to 2008 (Table 3), despite some large regional trends (Fig. 4c).

Fig. 4.
Fig. 4.

1979–2008 decadal trends in (a) aerosol AOD, (b) aerosol ERF, and (c) the clear-sky and (d) cloud radiative effect (CRECRE) contribution to the aerosol ERF trend. Quantities are calculated from a linear trend of the global annual-mean 1979–2008 time series of the difference between an AMIP experiment with 1979–2008 aerosol emissions and one with preindustrial aerosol emissions.

Citation: Journal of Climate 27, 3; 10.1175/JCLI-D-13-00336.1

4. Climate resistance and feedback parameters

With the forcing now known it is possible to calculate the climate sensitivity parameters of the model. These are usually diagnosed from idealized coupled model experiments, such as step CO2 experiments and/or idealized AGCM experiments such as +4 K SST simulations used by the Cloud Feedback Model Intercomparison Project, version 2 (CFMIP2) (e.g., Bony et al. 2011). Diagnosing sensitivity parameters in more realistic transient scenarios is desirable if we want these parameters to be more directly comparable with observed estimates and thus would complement the idealized approach. Note that with the AGCM design the coupled response is necessarily omitted, so here the feedbacks represent the atmospheric response of the model to “perfect” SSTs.

a. Climate resistance

Gregory and Forster (2008) showed that under recent historical climate change and projections of the twenty-first century F = ρΔT is a good approximation, where ρ is the “climate resistance” (W m−2 K−1). This relationship is tested in Fig. 5. Years strongly influenced by volcanic forcing are given in red and are excluded from the regression analysis. As in Gregory and Forster (2008), linearity between F and ΔT on a multidecadal time scale is a good approximation (r = 0.78) for years not strongly influenced by volcanic forcing. The slope of the ordinary least squares (OLS) regression line gives an estimate of the climate resistance, ρ = 1.7 ± 0.29 W m−2 K−1 (1 σ uncertainty from regression), which is within the observed estimates covering a similar period by Gregory and Forster (2008). As the coupled response is necessarily omitted here, that is, the model has been forced with observed ΔT, this agreement implies that the derived estimate of F varies in a similar manor to that of Gregory and Forster.

Fig. 5.
Fig. 5.

(a) Relationship between the effective radiative forcing and global temperature anomaly (relative to the 1979–2008 average) in the AMIPSST,Ice,F experiment. The slope, excluding years influenced by volcanic forcing (red points), gives the climate resistance ρ = 1.7 W m−2 K−1. All points are global annual-mean data covering 1979–2008. (b) The pattern of warming/cooling during this period is determined from the 1979–2008 decadal trend in surface air temperature.

Citation: Journal of Climate 27, 3; 10.1175/JCLI-D-13-00336.1

b. Climate feedback parameters

Diagnosing feedbacks in transient simulations—or observations—is difficult because forcing/adjustments and feedback evolve together. Forcings first need to be removed so that they do not contaminate the feedback signal. Returning to the global linearized energy budget equation gives N − F = −αΔT. Hence, now that F has been derived, the feedback parameter −α can be diagnosed by regressing (N − F) against ΔT. Alternatively, in the AGCM framework, it is trivial to estimate the components of the feedback parameter from the AMIPSST,Ice experiment since F = 0 by definition. In some ways this is a reverse approach to the Forster and Taylor (2006) method, who first assumed α to derive F. Here F is first determined in order to derive α.

Figure 6 diagnoses a net feedback parameter of −α = −1.98 ± 0.44 W m−2 K−1 from decadal variations in the simulated radiation balance and ΔT in the AMIPSST,Ice experiment. Andrews et al. (2012b) examined these relationships in coupled atmosphere–ocean CMIP5 models on longer time scales using abrupt 4 × CO2 simulations. Unfortunately a long-term climate sensitivity experiment has not been run using the exact model physics used here, but a comparison of the response of HadGEM2-A and HadGEM2-ES might still provide some insight because they share the same physical atmospheric model. The net feedback parameter is substantially more positive (i.e., the climate more sensitive) in the long-term coupled atmosphere–ocean HadGEM2-ES simulation (−α = −0.64 W m−2 K−1). Comparing the individual feedback components [LW clear-sky ~ −2.4 (−1.7) W m−2 K−1, SW clear-sky ~ +0.7 (+0.7) W m−2 K−1, and net CRE ~ −0.3 (+0.4) W m−2 K−1; HadGEM2-ES in parenthesis] reveals that this mostly arises because of a more positive CRE and LW clear-sky feedback in the long-term coupled simulation.

Fig. 6.
Fig. 6.

Variations between (a) net TOA radiation, (b) LW clear-sky radiation, (c) SW clear-sky radiation, and (d) net (LW + SW) cloud radiative effect and global temperature anomaly (relative to the 1979–2008 average) in the AMIPSST,Ice experiment. In this experiment there is no external forcing, so the variation of radiative flux with ΔT measures the feedback parameter. All fluxes are defined as positive down. The correlation coefficient r is shown. All points are global annual-mean data from 1979 to 2008. Errors in the slope represent 1-σ uncertainties from the OLS regression.

Citation: Journal of Climate 27, 3; 10.1175/JCLI-D-13-00336.1

It cannot be ruled out that the HadGEM2-A model setup is simply a “lower sensitivity” model or that the coupled response has a large impact on the cloud radiative effect feedback in the coupled model, but another consideration is that recent decadal climate change is not a good analog for long-term (multidecadal to centennial) climate change. Figure 5b shows the decadal trend in surface warming from 1979 to 2008. As the model is forced with observed SSTs, this warming pattern closely resembles those observed (at least over the ocean). There are large parts of the Pacific where cooling has occurred during this period, consistent with the interdecadal Pacific oscillation (e.g., Meehl et al. 2013). In contrast, tropical warming patterns show no cooling in long-term climate model simulations (e.g., Knutti and Sedlacek 2013) from which feedback parameters are usually defined. Hence, it would not be surprising if the atmospheric response, especially cloud feedback, was different. This highlights the limitations of defining feedbacks relative to global-mean temperature change, as it does not account for differences in warming patterns (Senior and Mitchell 2000; Armour et al. 2013). Similar points have been made of studies that extrapolate radiative dampening rates diagnosed from interannual variability (the pattern of which is dominated by the El Niño–Southern Oscillation) to long-term externally forced change, especially for cloud feedback (e.g., Dessler 2010).

Still, linearity between N − F versus ΔT is a good approximation on the time scales of this study (Fig. 6) and would be an appropriate test for evaluating the sensitivity of atmospheric models against similarly derived observed estimates using satellite measurements of the global radiation balance (e.g., Forster and Gregory 2006; Murphy et al. 2009). To what extent linearity holds to longer time scales, giving the relationship more predictive power or the influence of the coupled response, should be a matter of further investigation.

5. Mechanisms of recent historical climate change

The experimental design is now utilized to separate the components of climate response due to adjustments (i.e., the component driven directly by the forcing agent) and SST changes. While many of the below results have been studied in greater detail elsewhere (see below and section 2c), the purpose here is to provide insight and highlight the utility of the experimental design for investigating such changes under the improved conceptual framework/forcing definition and a set of systematic experiments.

a. Decadal trends in atmospheric temperatures

Observed and modeled cooling trends in stratospheric temperatures over recent decades are well established (e.g., Seidel et al. 2011), despite recent uncertainties in the magnitude of observed cooling trends (Thompson et al. 2012). Trends in global stratospheric temperatures are almost entirely driven by the radiative effect of changes in stratospheric composition (i.e., stratospheric adjustments), predominantly from increased WMGHGs (mostly CO2) and O3 depletion. Stratospheric temperatures therefore provide an important fingerprint of anthropogenic climate change owing to the different ways in which forcing agents alter the stratospheric temperature profile (e.g., Folland et al. 1998).

The experimental design and conceptual framework presented here provides a simple way of diagnosing this and could provide a test for AGCM evaluation, complementing evaluations of radiation schemes (e.g., Forster et al. 2011), and has been used in a similar way for detection and attribution (Folland et al. 1998; Sexton et al. 2001). The global 1979–2008 decadal temperature trend profiles for the various experiments are shown in Fig. 7a. The AMIPSST,Ice,F experiment (red line) shows tropospheric warming, which is amplified aloft (see below), and large stratospheric cooling trends. The stratospheric cooling trends are absent when changes in atmospheric composition are not included (AMIPSST,Ice, blue line), as expected if the cooling trends are dominated by external changes in stratospheric composition (i.e., adjustments). The stratospheric cooling trend declines from ~0.5 K decade−1 at ~20 km in the all forcings run to ~0.4 K decade−1, when WMGHGs are fixed, and ~0.5 to ~0.0 K decade−1 just above 30 km. In other words, at ~30 km, changes in WMGHGs are entirely responsible for the global stratospheric cooling trend of ~0.5 K decade−1 in this model. The O3 depletion contributes to the cooling trend at around 20 km, declines to zero at ~30 km, and then increases again higher up (Fig. 7a).

Fig. 7.
Fig. 7.

(a) Decadal trend in the global atmospheric temperature profile from 1979–2008 for AMIP experiments with and without various time-varying forcing agents as indicated. (b) Decadal trend in the zonal-mean temperature profile for the AMIPSST,Ice,F experiment and its separation into (c) an SST-driven component (from the AMIPSST,Ice experiment) and (d) a direct atmospheric response to the change in forcing agent [estimated from (b)–(c)]. The forcing-driven component is then further separated into its two largest components: (e) WMGHGs and (f) O3 (estimated from decadal trends in AMIPSST,Ice,F minus an equivalent experiment with fixed WMGHGs and O3).

Citation: Journal of Climate 27, 3; 10.1175/JCLI-D-13-00336.1

The zonal-mean temperature trend profile in the AMIPSST,Ice,F experiment is shown in Fig. 7b. This trend is separated into a component driven through decadal increases in SST (from the AMIPSST,Ice experiment) (Fig. 7c) and a direct forcing-driven component, which is estimated as the residual (assuming linearity) between this and the trend diagnosed from the standard AMIPSST,Ice,F experiment (Fig. 7d). It confirms that the stratospheric temperature trends are largely driven by the radiative effect of changes in atmospheric composition, independent of SST and tropospheric warming, even in the zonal mean. The forcing component is further separated into its WMGHGs (which is mostly dominated by CO2) and O3 contributions (Figs. 7e and 7f, respectively). The component driven by multidecadal SST and tropospheric warming (Fig. 7c) shows a cooling trend in tropical stratospheric temperatures and a warming trend in high-latitude stratospheric temperatures. This is expected from a strengthened Brewer–Dobson circulation with climate change (e.g., Butchart et al. 2006; McLandress and Shepherd 2009), but in the global mean these SST-driven stratospheric temperature trends cancel at all levels (Fig. 7a; Shine et al. 2003b; Seidel et al. 2011; Thompson et al. 2012). Note that Folland et al. (1998) and Sexton et al. (2001) showed significant stratospheric warming in response to increased SST using an older generation model [Hadley Centre Atmosphere Model, version 2 (HadAM2a)], even in the global mean, but they attributed this to a model artifact.

Note that these model results clearly depend on the forcing datasets and model used, and no evaluation against observed estimates have been made. Changes in stratospheric water vapor could also have an impact on stratospheric temperature trends, especially in the lower stratosphere (Shine et al. 2003b; Seidel et al. 2011) where some models exhibit large trends in water vapor over recent decades (Gettelman et al. 2010), but this is not examined here.

In the troposphere, large warming is seen at high latitudes and in the upper tropical troposphere (Fig. 7b). The amplified warming aloft in the tropics is expected from moist convective processes (e.g., Manabe and Wetherald 1975), but comparisons with observations can be controversial due to large uncertainties in trends (Thorne et al. 2011). Figures 7a and 7c show that most of the tropospheric temperature trends are captured by the AMIPSST,Ice experiment. Hence, most tropospheric temperature trends are determined solely by SST changes and associated climate feedbacks; the trends in tropospheric temperature adjustments (Fig. 7d) are small compared to this term.

b. Surface temperatures

Folland et al. (1998) (among others) showed that models forced only with time-varying SSTs and sea ice underestimate land warming compared to models additionally forced with time-varying forcing agents. This implies a significant role for forcing agents in directly increasing land temperatures independent of SST changes.

This result is examined in Fig. 8. When the model is forced with time-varying forcing agents as well as observed SSTs and sea ice, the linear trend in global temperature anomaly from 1979 to 2008 is 0.172 K decade−1 (Fig. 8a, blue line). This is close to the observed estimate (0.17 K decade−1) reported by Morice et al. (2012) for the period 1979–2010 using HadCRUT4 data. When the model is forced only with changes in SST and sea ice, the trend drops to 0.135 K decade−1 (Fig. 8a, black line). Hence, assuming linearity, ~80% of the recent decadal global temperature trend in this model is driven by changes in SST and ~20% directly driven by adjustments to forcings. As the SSTs are identically prescribed in these experiments, the temperature adjustment arises predominantly from over land (Fig. 8b); the global land-warming trend of 0.304 K decade−1 has contributions of ~60% due to SST changes and ~40% directly due to forcing agents. Focusing over the Northern Hemisphere (NH) land (Fig. 8c), the warming trend in AMIPSST,Ice (0.216 K decade−1) is again ~60% of the total trend in AMIPSST,Ice,F (0.361 K decade−1), implying ~40% contribution directly from forcing agents. Winter NH land temperatures trends are difficult to isolate owing to internal atmospheric variability (not shown), but trends in NH summer (June–August) land temperatures again drop by ~40% when forcing agents are not considered, from 0.405 K decade−1 in AMIPSST,Ice,F to 0.254 K decade−1 in AMIPSST,Ice (Fig. 8d). A direct contribution from forcing agents of ~40% is slightly smaller than that reported by Folland et al. (1998), who found it contributed equally as SST changes to the global land temperature change between 1950 and 1994. This difference could be a difference in model response, but it could also be due to the different time periods considered (1950–94 compared to 1979–2008).

Fig. 8.
Fig. 8.

Annual-mean time series of (a) global, (b) land, (c) NH land, and (d) NH land summer (Jun–Aug) surface air temperature anomalies (relative to the 1979–2008 average) for the AMIPSST,Ice,F (i.e., with time-varying forcing agents) and AMIPSST,Ice (i.e., with fixed forcing agents) experiments. Shading equals the range across the three-member ensemble. Decadal trends are indicated. Note that panel (a) has a different scale.

Citation: Journal of Climate 27, 3; 10.1175/JCLI-D-13-00336.1

Given that ΔT is not identical between AMIPSST,Ice,F and AMIPSST,Ice owing to the direct effects of forcing agents on land temperatures (Fig. 8), it raises questions about the physical interpretation of effective radiative forcing and its separation from climate feedbacks. The land warming and consequent effects on the atmosphere (circulation, clouds, etc.) are included in the ERF calculation, but could be described as a climate feedback. Hansen et al. (2005) and Shine et al. (2003a) discuss an analogous effect when considering fixed-surface or fixed-SST forcing definitions. Gregory and Webb (2008) define the separation of forcing and feedback according to time scale. In their approach direct land warming acts on a similar time scale as atmospheric adjustments and thus can be usefully considered part of the forcing as it is here.

c. Precipitation

The earth’s atmospheric energy balance provides a useful constraint for understanding global (e.g., Mitchell et al. 1987) and regional (Muller and O’Gorman 2011) precipitation changes. For example, increased CO2 and black carbon directly reduce atmospheric radiative cooling, and this is largely balanced by changes in condensational heating (precipitation), independent of any surface warming that then tends to increase precipitation (e.g., Andrews et al. 2010; Ming et al. 2010; among others). A few studies (e.g., Andrews 2009; Bichet et al. 2011; Frieler et al. 2011; Allan et al. 2013; Bony et al. 2013) have looked at the direct atmospheric impact of forcing agents on precipitation in realistic transient scenarios, but most studies use idealized experiments. It is still unclear whether precipitation adjustments could be an important contributor to recent precipitation trends.

Figure 9a shows the global time series of precipitation in the AMIPSST,Ice,F and AMIPSST,Ice experiments. The increase in global precipitation when forcing agents are allowed to vary (0.003 mm day−1 decade−1) is substantially muted compared to when only SST and sea ice changes are allowed to vary (0.009 mm day−1 decade−1), consistent with the direct effect of forcing agents reducing precipitation.

Fig. 9.
Fig. 9.

Annual-mean time series of (a) global and (b) land precipitation rate anomalies (relative to the 1979–2008 average) for the AMIPSST,Ice,F and AMIPSST,Ice experiments. Shading equals the range across the three-member ensemble. Decadal trends are indicated. Global hydrological sensitivities (dP/dT) diagnosed from (c) AMIPSST,Ice,F and (d) AMIPSST,Ice. Points are global annual means for the period 1979–2008. Note that (a) and (b) have different scales.

Citation: Journal of Climate 27, 3; 10.1175/JCLI-D-13-00336.1

A commonly defined metric is the “hydrological sensitivity,” defined as global-mean precipitation change per global-mean surface temperature change (dP/dT). Models suggest this to be ~2–3% K−1 (e.g., Held and Soden 2006; Lambert and Webb 2008). Figures 9c and 9d show the hydrological sensitivity (determined from anomalies in global precipitation and surface temperature) in the AMIPSST,Ice,F and AMIPSST,Ice experiment, respectively. When only forced with increasing SST and variations in sea ice the hydrological sensitivity is ~2% K−1 in this model, but this is muted to only ~1% K−1 when the direct effects of forcing agents on precipitation are included.

Over land (Fig. 9b) the trend in precipitation is more variable and negative. In contrast to the global results, the direct influence of forcing agents on precipitation makes this negative trend less negative (i.e., it enhances precipitation). This is consistent with the direct effect of forcing agents causing land surface warming (section 5b), which tends to enhance evaporation and precipitation, in contrast to precipitation over the ocean where the atmospheric heating increases atmospheric stability and suppresses convection, moistening the boundary layer and reducing precipitation/evaporation (Cao et al. 2012).

6. Discussion

The experimental design covered the period 1979–2008 in order to exploit the already well-established AMIP experimental design that is routinely performed by modeling centers. If information on the entire historical ERF and adjustments time series was needed, then one could readily extend the design beyond the years covered, as per Held et al. (2010) and Anderson et al. (2010, 2012).

Computing transient ERFs in future scenarios would be more problematic under the AMIP design since it is not obvious what SST and sea ice boundary conditions should be used. One solution would be to use the model’s own SST and sea ice fractions from a coupled simulation. Alternatively, periodic boundary conditions could also be used. For example, Rotstayn and Penner (2001), Hansen et al. (2002), and Ming and Ramaswamy (2012) all use observed climatological boundary conditions to derive ERFs. The CMIP5 design includes a suite of AGCM experiments that use periodic preindustrial SST and sea ice climatologies representative of each models fully coupled preindustrial control (the sstClim, sstClim4×CO2, and sstClimAerosol experiments). Adding similar AGCM experiments, whether using observed or modeled climatological SST and sea ice boundary conditions, forced with transient historical and future forcings would allow a simple diagnosis of each model’s transient ERF and adjustments.

Using climatological, rather than time-evolving, SST and sea ice boundary conditions might help to reduce noise. For example, Figs. 1a and 1b indicate significant interannual variability across the three-member ensemble, which would limit analyzing year-to-year variations in ERF if one only had a single run (though large volcanic forcings are clearly seen above the variability in all ensemble members, Fig. 1b). The net ERF for all individual forcings at 2005 (2003–07 average; Table 2) for all three ensemble members fall within ±0.2 W m−2 of the ensemble mean. Without ensembles the methodology is better defined for decadal variations in ERF; the decadal trend in net ERF in Fig. 3b of all three ensemble members fall within ±0.03 W m−2 decade−1 of the ensemble-mean trend of 0.54 W m−2 decade−1.

A conceptually important point is that the AGCM design with evolving SST boundary conditions is calculating the ERF relative to an evolving base state. This may be desirable if there is a dependence of forcing on climate state; it will give a forcing closer to what the model actually felt at the time the forcing was applied. On the other hand, it will mean forcing can no longer be assumed constant as the climate evolves.

7. Summary

An atmospheric generation circulation model was forced with time-varying (1979–2008) changes in radiatively active constituents, such as greenhouse gases and atmospheric aerosols, monthly observed sea surface temperature, and sea ice boundary conditions. A simple experimental design is then proposed to diagnose the global and regional effective radiative forcing for the period 1979–2008 relative to 1860. This is done by repeating the simulations with various forcing agents, individually and in combination, fixed at 1860 levels.

While earlier studies have to a limited extent done similar work, this is the first study to show how a framework and systematic set of experiments can be used to (i) diagnose the recent time-varying regional effective radiative forcing (ERF) for individual forcing mechanisms, (ii) compute climate sensitivity parameters of an AGCM forced by observed SST changes, and (iii) examine adjustments as mechanisms of recent historical climate change, all set within our improved conceptual models/definitions of radiative forcing.

If adopted by other modeling groups the simple experimental design used here would allow us to diagnose recent historical radiative forcing in models, identify and address potential uncertainties in modeled radiative forcing, compare components of radiative feedbacks under realistic transient scenarios, and examine the relationship between radiative forcing and mechanisms of recent decadal climate change. Repeating these calculations with a model’s internally generated SSTs as boundary conditions (derived from an equivalent historical coupled atmosphere–ocean simulation) may provide a useful approach for testing the potential impact of model biases in SST on effective radiative forcing, feedbacks, climate sensitivity, and recent decadal climate trends.

Acknowledgments

I am grateful for useful discussions with Jonathan Gregory, William Ingram, Mark Ringer, Mark Webb, Karl Taylor, and Piers Forster. I thank two anonymous reviewers for helping to improve the clarity of the manuscript. This work was supported by the Joint DECC/Defra Met Office Hadley Centre Climate Programme (GA01101).

REFERENCES

  • Allan, R. P., , C. Liu, , M. Zahn, , D. A. Lavers, , E. Koukouvagias, , and A. Bodas-Salcedo, 2013: Physically consistent responses of the global atmospheric hydrological cycle in models and observations. Surv. Geophys., doi:10.1007/s10712-012-9213-z.

    • Search Google Scholar
    • Export Citation
  • Anderson, B. T., , J. R. Knight, , M. A. Ringer, , C. Deser, , A. S. Phillips, , J.-H. Yoon, , and A. Cherchi, 2010: Climate forcings and climate sensitivities diagnosed from atmospheric global circulation models. Climate Dyn., 35, 14611475, doi:10.1007/s00382-010-0798-y.

    • Search Google Scholar
    • Export Citation
  • Anderson, B. T., , J. R. Knight, , M. A. Ringer, , J. Yoon, , and A. Cherchi, 2012: Testing for the possible influence of unknown climate forcings upon global temperature increases from 1950 to 2000. J. Climate, 25, 71637172.

    • Search Google Scholar
    • Export Citation
  • Andrews, T., 2009: Forcing and response in simulated 20th and 21st century surface energy and precipitation trends. J. Geophys. Res., 114, D17110, doi:10.1029/2009JD011749.

    • Search Google Scholar
    • Export Citation
  • Andrews, T., , P. M. Forster, , O. Boucher, , N. Bellouin, , and A. Jones, 2010: Precipitation, radiative forcing and global temperature change. Geophys. Res. Lett., 27, L14701, doi:10.1029/2010GL043991.

    • Search Google Scholar
    • Export Citation
  • Andrews, T., , J. M. Gregory, , P. M. Forster, , and M. J. Webb, 2012a: Cloud adjustment and its role in CO2 radiative forcing and climate sensitivity: A review. Surv. Geophys., 33, 619635, doi:10.1007/s10712-011-9152-0.

    • Search Google Scholar
    • Export Citation
  • Andrews, T., , J. M. Gregory, , M. J. Webb, , and K. E. Taylor, 2012b: Forcing, feedbacks and climate sensitivity in CMIP5 coupled atmosphere-ocean climate models. Geophys. Res. Lett., 39, L09712, doi:10.1029/2012GL051607.

    • Search Google Scholar
    • Export Citation
  • Armour, K. C., , C. M. Bitz, , and G. H. Roe, 2013: Time-varying climate sensitivity from regional feedbacks. J. Climate, 26, 45184534.

  • Bellouin, N., , J. Rae, , A. Jones, , C. Johnson, , J. Haywood, , and O. Boucher, 2011: Aerosol forcing in the Climate Model Intercomparison Project (CMIP5) simulations by HadGEM2-ES and the role of ammonium nitrate. J. Geophys. Res., 116, D20206, doi:10.1029/2011JD016074.

    • Search Google Scholar
    • Export Citation
  • Bichet, A., , M. Wild, , D. Folini, , and C. Schär, 2011: Global precipitation response to changing forcings since 1870. Atmos. Chem. Phys., 11, 99619970.

    • Search Google Scholar
    • Export Citation
  • Bichet, A., , M. Wild, , D. Folini, , and C. Schär, 2012: Causes for decadal variations of wind speed over land: Sensitivity studies with a global climate model. Geophys. Res. Lett., 39, L11701, doi:10.1029/2012GL051685.

    • Search Google Scholar
    • Export Citation
  • Bony, S., , M. Webb, , C. Bretherton, , S. Klein, , P. Siebesma, , G. Tselioudis, , and M. Zhang, 2011: CFMIP: Towards a better evaluation and understanding of clouds and cloud feedbacks in CMIP5 models. CLIVAR Exchanges, No. 56, International CLIVAR Project Office, Southampton, United Kingdom, 20–24.

  • Bony, S., , G. Bellon, , D. Klocke, , S. Sherwood, , S. Fermepin, , and S. Denvil, 2013: Robust direct effect of carbon dioxide on tropical circulation and regional precipitation. Nat. Geosci., 6, 447–451, doi:10.1038/ngeo1799.

    • Search Google Scholar
    • Export Citation
  • Bosilovich, M. G., , S. D. Schubert, , and G. K. Walker, 2005: Global changes of the water cycle intensity. J. Climate,18, 1591–1608.

  • Butchart, N., and Coauthors, 2006: Simulations of anthropogenic change in the strength of the Brewer–Dobson circulation. Climate Dyn., 27, 727741, doi:10.1007/s00382-006-0162-4.

    • Search Google Scholar
    • Export Citation
  • Caminade, C., , and L. Terray, 2006: Influence of increased greenhouse gases and sulphate aerosols concentration upon diurnal temperature range over Africa at the end of the 20th century. Geophys. Res. Lett., 33, L15703, doi:10.1029/2006GL026381.

    • Search Google Scholar
    • Export Citation
  • Cao, L., , G. Bala, , and K. Caldeira, 2012: Climate response to changes in atmospheric carbon dioxide and solar irradiance on the time scale of days to weeks. Environ. Res. Lett., 7, 034015, doi:10.1088/1748-9326/7/3/034015.

    • Search Google Scholar
    • Export Citation
  • Deser, C., , and A. S. Phillips, 2009: Atmospheric circulation trends, 1950–2000: The relative roles of sea surface temperature forcing and direct atmospheric radiative forcing. J. Climate, 22, 396413.

    • Search Google Scholar
    • Export Citation
  • Dessler, A. E., 2010: A determination of the cloud feedback from climate variations over the past decade. Science, 330, 15231527, doi:10.1126/science.1192546.

    • Search Google Scholar
    • Export Citation
  • Dong, B., , J. M. Gregory, , and R. T. Sutton, 2009: Understanding land–sea warming contrast in response to increasing greenhouse gases. Part I: Transient adjustment. J. Climate, 22, 30793097.

    • Search Google Scholar
    • Export Citation
  • Folland, C. K., , D. M. H. Sexton, , D. J. Karoly, , C. E. Johnson, , D. P. Rowell, , and D. E. Parker, 1998: Influences of anthropogenic and oceanic forcing on recent climate change. Geophys. Res. Lett., 25 ,353356.

    • Search Google Scholar
    • Export Citation
  • Folland, C. K., , J. Shukla, , J. Kinter, , and M. J. Rodwell, 2002: The climate of the twentieth century project. CLIVAR Exchanges, No. 7, International CLIVAR Project Office, Southampton, United Kingdom, 37–39.

  • Forster, P. M., , and J. M. Gregory, 2006: The climate sensitivity and its components diagnosed from Earth Radiation Budget data. J. Climate, 19, 3952.

    • Search Google Scholar
    • Export Citation
  • Forster, P. M., , and K. Taylor, 2006: Climate forcings and climate sensitivities diagnosed from coupled climate model integrations. J. Climate, 19, 61816194.

    • Search Google Scholar
    • Export Citation
  • Forster, P. M., , R. S. Freckleton, , and K. P. Shine, 1997: On aspects of the concept of radiative forcing. Climate Dyn., 13, 547560.

  • Forster, P. M., and Coauthors, 2011: Evaluation of radiation scheme performance within chemistry climate models. J. Geophys. Res., 116, D10302, doi:10.1029/2010JD015361.

    • Search Google Scholar
    • Export Citation
  • Forster, P. M., , T. Andrews, , P. Good, , J. M. Gregory, , L. S. Jackson, , and M. Zelinka, 2013: Evaluating adjusted forcing and model spread for historical and future scenarios in the CMIP5 generation of climate models. J. Geophys. Res., 118, 1139–1150, doi:10.1002/jgrd.50174.

    • Search Google Scholar
    • Export Citation
  • Frieler, K., , M. Meinshausen, , T. Schneider von Deimling, , T. Andrews, , and P. Forster, 2011: Changes in global-mean precipitation in response to warming, greenhouse gas forcing and black carbon. Geophys. Res. Lett., 38, L04702, doi:10.1029/2010GL045953.

    • Search Google Scholar
    • Export Citation
  • Gates, W. L., 1992: AMIP: The Atmospheric Model Intercomparison Project. Bull. Amer. Meteor. Soc., 73, 19621970.

  • 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
  • Gettelman, A., and Coauthors, 2010: Multimodel assessment of the upper troposphere and lower stratosphere: Tropics and global trends. J. Geophys. Res.,115 , D00M08, doi:10.1029/2009JD013638.

    • Search Google Scholar
    • Export Citation
  • Gregory, J. M., , and P. Forster, 2008: Transient climate response estimated from radiative forcing and observed temperature change. J. Geophys. Res., 113, D23105, doi:10.1029/2008JD010405.

    • Search Google Scholar
    • Export Citation
  • Gregory, J. M., , and M. J. Webb, 2008: Tropospheric adjustment induces a cloud component in CO2 forcing. J. Climate, 21, 5871.

  • Gregory, J. M., and Coauthors, 2004: A new method for diagnosing radiative forcing and climate sensitivity. Geophys. Res. Lett., 31, L03205, doi:10.1029/2003GL018747.

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

  • Hansen, J., and Coauthors, 2002: Climate forcings in Goddard Institute for Space Studies SI2000 simulations. J. Geophys. Res., 107, 4347, doi:10.1029/2001JD001143.

    • Search Google Scholar
    • Export Citation
  • Hansen, J., and Coauthors, 2005: Efficacy of climate forcings. J. Geophys. Res., 110, D18104, doi:10.1029/2005JD005776.

  • Held, I. M., , and B. J. Soden, 2006: Robust responses of the hydrological cycle to global warming. J. Climate, 19, 56865699.

  • Held, I. M., M. Winton, K. Takahasi, T. Delworth, F. Zeng, and G. K. Vallis, 2010: Probing the fast and slow components of global warming by returning abruptly to preindustrial forcing. J. Climate, 23, 24182427.

    • Search Google Scholar
    • Export Citation
  • Jiang, J. H., and Coauthors, 2012: Evaluation of cloud and water vapor simulations in CMIP5 climate models using NASA “A-Train” satellite observations. J. Geophys. Res., 117, D14105, doi:10.1029/2011JD017237.

    • Search Google Scholar
    • Export Citation
  • Jones, C. D., and Coauthors, 2011: The HadGEM2-ES implementation of the CMIP5 centennial simulations. Geosci. Model Dev., 4, 543570, doi:10.5194/gmd-4-543-2011.

    • Search Google Scholar
    • Export Citation
  • Keeling, C. D., , R. B. Bacastow, , A. E. Bainbridge, , C. A. Ekdahl, , P. R. Guenther, , L. S. Waterman, , and J. F. S. Chin, 1976: Atmospheric carbon dioxide variations at Mauna Loa Observatory, Hawaii. Tellus, 28, 538551.

    • Search Google Scholar
    • Export Citation
  • Kiehl, J. T., 2007: Twentieth century climate model response and climate sensitivity. Geophys. Res. Lett., 34, L22710, doi:10.1029/2007GL031383.

    • Search Google Scholar
    • Export Citation
  • King, M. P., , F. Kucharski, , and F. Molteni, 2010: The roles of external forcings and internal variabilities in the Northern Hemisphere atmospheric circulation change from the 1960s to the 1990s. J. Climate, 23, 62006220.

    • Search Google Scholar
    • Export Citation
  • Knutti, R., , and J. Sedlacek, 2013: Robustness and uncertainties in the new CMIP5 climate model projections. Nat. Climate Change, 3, 369373, doi:10.1038/nclimate1716.

    • Search Google Scholar
    • Export Citation
  • Kucharski, F., and Coauthors, 2009: The CLIVAR C20C project: Skill of simulating Indian monsoon rainfall on interannual to decadal timescales. Does GHG forcing play a role? Climate Dyn., 33, 615627, doi:10.1007/s00382-008-0462-y.

    • Search Google Scholar
    • Export Citation
  • Lambert, F. H., , and M. J. Webb, 2008: Dependence of global mean precipitation on surface temperature. Geophys. Res. Lett., 35, L16706, doi:10.1029/2008GL034838.

    • Search Google Scholar
    • Export Citation
  • Loeb, N. G., , B. A. Wielicki, , D. R. Doelling, , G. Louis Smith, , D. F. Keyes, , S. Kato, , N. Manalo-Smith, , and T. Wong, 2009: Toward optimal closure of the earth’s top-of-atmosphere radiation budget. J. Climate, 22, 748766.

    • Search Google Scholar
    • Export Citation
  • Lohmann, U., and Coauthors, 2010: Total aerosol effect: Radiative forcing or radiative flux perturbation? Atmos. Chem. Phys., 10, 32353246.

    • Search Google Scholar
    • Export Citation
  • Manabe, S., , and R. T. Wetherald, 1975: The effects of doubling the CO2 concentration on the climate of a general circulation model. J. Atmos. Sci., 32, 315.

    • Search Google Scholar
    • Export Citation
  • Martin, G. M., and Coauthors, 2011: The HadGEM2 family of Met Office Unified Model Climate configurations. Geosci. Model Dev., 4, 723757, doi:10.5194/gmd-4-723-2011.

    • Search Google Scholar
    • Export Citation
  • McLandress, C., , and T. G. Shepherd, 2009: Simulated anthropogenic changes in the Brewer–Dobson circulation, including its extension to high latitudes. J. Climate, 22, 15161540.

    • Search Google Scholar
    • Export Citation
  • Meehl, G. A., , A. Hu, , J. M. Arblaster, , J. Fasullo, , and K. E. Trenberth, 2013: Externally forced and internally generated decadal climate variability associated with the interdecadal Pacific oscillation. J. Climate, 26, 72987310.

    • Search Google Scholar
    • Export Citation
  • Ming, Y., , and V. Ramaswamy, 2012: Nonlocal component of radiative flux perturbation. Geophys. Res. Lett., 39, L22706, doi:10.1029/2012GL054050.

    • Search Google Scholar
    • Export Citation
  • Ming, Y., , V. Ramaswamy, , and G. Persad, 2010: Two opposing effects of absorbing aerosols on global-mean precipitation. Geophys. Res. Lett., 37, L13701, doi:10.1029/2010GL042895.

    • Search Google Scholar
    • Export Citation
  • Mitchell, J. F. B., , C. A. Wilson, , and W. M. Cunnington, 1987: On CO2 climate sensitivity and model dependence of results. Quart. J. Roy. Meteor. Soc., 113, 293322.

    • Search Google Scholar
    • Export Citation
  • Morice, C. P., , J. J. Kennedy, , N. A. Rayner, , and P. D. Jones, 2012: Quantifying uncertainties in global and regional temperature change using an ensemble of observational estimates: The HadCRUT4 data set. J. Geophys. Res., 117, D08101, doi:10.1029/2011JD017187.

    • Search Google Scholar
    • Export Citation
  • Muller, C. J., , and P. A. O’Gorman, 2011: An energetic perspective on the regional response of precipitation to climate change. Nat. Climate Change, 1, 266271.

    • Search Google Scholar
    • Export Citation
  • Murphy, D. M., 2013: Little net clear-sky radiative forcing from recent regional redistribution of aerosols. Nat. Geosci., 6, 258262, doi:10.1038/NGEO1740.

    • Search Google Scholar
    • Export Citation
  • Murphy, D. M., , S. Solomon, , R. W. Portmann, , K. H. Rosenlof, , P. M. Forster, , and T. Wong, 2009: An observationally based energy balance for the Earth since 1950. J. Geophys. Res., 114, D17107, doi:10.1029/2009JD012105.

    • Search Google Scholar
    • Export Citation
  • Myhre, G., , E. J. Highwood, , K. P. Shine, , and F. Stordal, 1998: New estimates of radiative forcing due to well mixed greenhouse gases. Geophys. Res. Lett., 25,27152718.

    • Search Google Scholar
    • Export Citation
  • Patricola, C. M., , and K. H. Cook, 2011: Sub-Saharan northern African climate at the end of the twenty-first century: Forcing factors and climate change processes. Climate Dyn., 37, 11651188, doi:10.1007/s00382-010-0907-y.

    • Search Google Scholar
    • Export Citation
  • Rotstayn, L. D., , and J. E. Penner, 2001: Indirect aerosol forcing, quasi forcing, and climate response. J. Climate, 14, 29602975.

  • Scaife, A., and Coauthors, 2009: The CLIVAR C20C project: Selected twentieth century climate events. Climate Dyn., 33, 603614, doi:10.1007/s00382-008-0451-1.

    • Search Google Scholar
    • Export Citation
  • Seidel, D. J., , N. P. Gillett, , J. R. Lanzante, , K. P. Shine, , and P. W. Thorne, 2011: Stratospheric temperature trends: Our evolving understanding. Wiley Interdiscip. Rev. Climate Change, 2, 592616, doi:10.1002/wcc.125.

    • Search Google Scholar
    • Export Citation
  • Senior, C. A., , and J. F. B. Mitchell, 2000: The time-dependence of climate sensitivity. Geophys. Res. Lett., 17 ,26852688.

  • Sexton, D. M. H., , D. P. Rowell, , C. K. Folland, , and D. J. Karoly, 2001: Detection of anthropogenic climate change using an atmospheric GCM. Climate Dyn., 17, 669685.

    • Search Google Scholar
    • Export Citation
  • Shindell, D. T., and Coauthors, 2013: Radiative forcing in the ACCMIP historical and future climate simulations. Atmos. Chem. Phys., 13, 29392974, doi:10.5194/acp-13-2939-2013.

    • Search Google Scholar
    • Export Citation
  • Shine, K. P., , and P. M. Forster, 1999: The effect of human activity on radiative forcing of climate: A review of recent development. Global Planet. Change, 20, 202225.

    • Search Google Scholar
    • Export Citation
  • Shine, K. P., , J. Cook, , E. J. Highwood, , and M. J. Joshi, 2003a: An alternative to radiative forcing for estimating the relative importance of climate change mechanisms. Geophys. Res. Lett., 30, 2047, doi:10.1029/2003GL018141.

    • Search Google Scholar
    • Export Citation
  • Shine, K. P., and Coauthors, 2003b: A comparison of model-simulated trends in stratospheric temperatures. Quart. J. Roy. Meteor. Soc., 129, 15651588, doi:10.1256/qj.02.186.

    • Search Google Scholar
    • Export Citation
  • Skinner, C. B., , M. Ashfaq, , and N. S. Diffenbaugh, 2012: Influence of twenty-first-century atmospheric and sea surface temperature forcing on West African climate. J. Climate, 25, 527542.

    • Search Google Scholar
    • Export Citation
  • Soden, B. J., , A. J. Broccoli, , and R. S. Hemler, 2004: On the use of cloud forcing to estimate cloud feedback. J. Climate, 17, 36613665.

    • Search Google Scholar
    • Export Citation
  • Soden, B. J., , I. M. Held, , R. Colman, , K. M. Shell, , J. T. Kiehl, , and C. A. Shields, 2008: Quantifying climate feedbacks using radiative kernels. J. Climate, 21, 35043520.

    • Search Google Scholar
    • Export Citation
  • Stephens, G. L., and Coauthors, 2012: An update on Earth’s energy balance in light of the latest global observations. Nat. Geosci., 5, 691696, doi:10.1038/ngeo1580.

    • Search Google Scholar
    • Export Citation
  • Streets, D. G., and Coauthors, 2009: Anthropogenic and natural contributions to regional trends in aerosol optical depth, 1980–2006. J. Geophys. Res., 114, D00D18, doi:10.1029/2008JD011624.

    • Search Google Scholar
    • Export Citation
  • Taylor, K. E., , R. J. Stouffer, , and G. A. Meehl, 2012: An overview of CMIP5 and the experiment design. Bull. Amer. Meteor. Soc., 93, 485–498.

    • Search Google Scholar
    • Export Citation
  • Thompson, D. W. J, and Coauthors, 2012: The mystery of recent stratospheric temperature trends. Nature,491, 692–697.

  • Thorne, P. W., , J. R. Lanzante, , T. C. Peterson, , D. J. Seidel, , and K. P. Shine, 2011: Tropospheric temperature trends: History of an ongoing controversy. Wiley Interdiscip. Rev. Climate Change, 2, 6688, doi:10.1002/wcc.80.

    • Search Google Scholar
    • Export Citation
Save