• Alexeev, V., , P. Langen, , and J. Bates, 2005: Polar amplification of surface warming on an aquaplanet in “ghost forcing” experiments without sea ice feedback. Climate Dyn., 24, 655666, doi:10.1007/s00382-005-0018-3.

    • Search Google Scholar
    • Export Citation
  • Bengtsson, L., , V. Semenov, , and O. Johannessen, 2004: The early twentieth-century warming in the Arctic—A possible mechanism. J. Climate, 17, 40454057, doi:10.1175/1520-0442(2004)017<4045:TETWIT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Booth, B., , N. Dunstone, , P. Halloran, , T. Andrews, , and N. Bellouin, 2012: Aerosols implicated as a prime driver of twentieth-century North Atlantic climate variability. Nature, 484, 228232, doi:10.1038/nature10946.

    • Search Google Scholar
    • Export Citation
  • Boucher, O., and Coauthors, 2013: Clouds and aerosols. Climate Change 2013: The Physical Science Basis, T. F. Stocker et al., Eds., Cambridge University Press, 571–658. [Available online at https://www.ipcc.ch/pdf/assessment-report/ar5/wg1/WG1AR5_Chapter07_FINAL.pdf.]

  • Chylek, P., , C. Folland, , G. Lesins, , M. Dubey, , and M. Wang, 2009: Arctic air temperature change amplification and the Atlantic multidecadal oscillation. Geophys. Res. Lett., 36, L14801, doi:10.1029/2009GL038777.

    • Search Google Scholar
    • Export Citation
  • Chylek, P., , N. Hengartner, , G. Lesins, , J. D. Klett, , O. Humlum, , M. Wyatt, , and M. K. Dubey, 2014a: Isolating the anthropogenic component of Arctic warming. Geophys. Res. Lett., 41, 35693576, doi:10.1002/2014GL060184.

    • Search Google Scholar
    • Export Citation
  • Chylek, P., , J. D. Klett, , G. Lesins, , M. K. Dubey, , and N. Hengartner, 2014b: The Atlantic multidecadal oscillation as a dominant factor of oceanic influence on climate. Geophys. Res. Lett., 41, 16891697, doi:10.1002/2014GL059274.

    • Search Google Scholar
    • Export Citation
  • DelSole, T., , M. Tippett, , and J. Shukla, 2011: A significant component of unforced multidecadal variability in the recent acceleration of global warming. J. Climate, 24, 909926, doi:10.1175/2010JCLI3659.1.

    • Search Google Scholar
    • Export Citation
  • Flato, G., and Coauthors, 2013: Evaluation of climate models. Climate Change 2013: The Physical Science Basis, T. F. Stocker et al., Eds., Cambridge University Press, 741–866. [Available online at https://www.ipcc.ch/pdf/assessment-report/ar5/wg1/WG1AR5_Chapter09_FINAL.pdf.]

    • Search Google Scholar
    • Export Citation
  • Fomby, T., , and T. Vogelsang, 2002: The application of size-robust trend statistics to global-warming temperature series. J. Climate, 15, 117123, doi:10.1175/1520-0442(2002)015<0117:TAOSRT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Forster, P., , T. Andrews, , P. Good, , J. Gregory, , L. 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, 11391150, doi:10.1002/jgrd.50174.

    • Search Google Scholar
    • Export Citation
  • Hansen, J., , and S. Lebedeff, 1987: Global trends of measured surface air temperature. J. Geophys. Res., 92, 13 34513 372, doi:10.1029/JD092iD11p13345.

    • Search Google Scholar
    • Export Citation
  • Hansen, J., , R. Ruedy, , M. Sato, , and K. Lo, 2010: Global surface temperature change. Rev. Geophys., 48, RG4004, doi:10.1029/2010RG000345.

  • Holland, M., , and C. Bitz, 2003: Polar amplification of climate change in coupled models. Climate Dyn., 21, 221232, doi:10.1007/s00382-003-0332-6.

    • Search Google Scholar
    • Export Citation
  • IPCC, 2013: Annex II: Climate system scenario tables. Climate Change 2013: The Physical Science Basis, T. F. Stocker et al., Eds., Cambridge University Press, 1395–1446. [Available online at https://www.ipcc.ch/pdf/assessment-report/ar5/wg1/WG1AR5_AnnexII_FINAL.pdf.]

  • Kaplan, A., , M. Cane, , Y. Kushnir, , A. Clement, , M. Blumenthal, , and B. Rajagopalan, 1998: Analyses of global sea surface temperature 1856–1991. J. Geophys. Res., 103, 18 56718 589, doi:10.1029/97JC01736.

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

    • Search Google Scholar
    • Export Citation
  • Knutti, R., 2008: Why are climate models reproducing the observed global surface warming so well? Geophys. Res. Lett., 35, L18704, doi:10.1029/2008GL034932.

    • Search Google Scholar
    • Export Citation
  • Lanzante, R., 2005: A cautionary note on the use of error bars. J. Climate, 18, 36993703, doi:10.1175/JCLI3499.1.

  • Lesins, G., , T. Duck, , and J. Drummond, 2012: Surface energy balance framework for Arctic amplification of climate change. J. Climate, 25, 82778288, doi:10.1175/JCLI-D-11-00711.1.

    • Search Google Scholar
    • Export Citation
  • Massonnet, F., , T. Fichefet, , H. Goosse, , C. M. Bitz, , G. Philippon-Berthier, , M. M. Holland, , and P.-Y. Barriat, 2012: Constraining projections of summer Arctic sea ice. Cryosphere, 6, 13831394, doi:10.5194/tc-6-1383-2012.

    • Search Google Scholar
    • Export Citation
  • McKitrick, R., , and T. Vogelsang, 2014: HAC robust trend comparisons among climate series with possible level shifts. Environmetrics, 25, 528547, doi:10.1002/env.2294.

    • Search Google Scholar
    • Export Citation
  • Parker, D., , C. Folland, , A. Scaife, , J. Knight, , A. Colman, , P. Baines, , and B. Dong, 2007: Decadal to multidecadal variability and the climate change background. J. Geophys. Res., 112, D18115, doi:10.1029/2007JD008411.

    • Search Google Scholar
    • Export Citation
  • Pithan, F., , and T. Mauritsen, 2014: Arctic amplification dominated by temperature feedbacks in contemporary climate models. Nat. Geosci., 7, 181184, doi:10.1038/ngeo2071.

    • Search Google Scholar
    • Export Citation
  • Polyakov, I., , and M. Johnson, 2000: Arctic decadal and interdecadal variability. Geophys. Res. Lett., 27, 40974100, doi:10.1029/2000GL011909.

    • Search Google Scholar
    • Export Citation
  • Santer, B., and Coauthors, 2008: Consistency of modelled and observed temperature trends in the tropical troposphere. Int. J. Climatol., 28, 17031722, doi:10.1002/joc.1756.

    • Search Google Scholar
    • Export Citation
  • Screen, J., , and I. Simmonds, 2010: The central role of diminishing sea ice in recent Arctic temperature amplification. Nature, 464, 13341337, doi:10.1038/nature09051.

    • Search Google Scholar
    • Export Citation
  • Serreze, M., , and J. Francis, 2006: The Arctic amplification debate. Climatic Change, 76, 241264, doi:10.1007/s10584-005-9017-y.

  • Serreze, M., , A. Barrett, , J. Stroeve, , D. Kindig, , and M. Holland, 2009: The emergence of surface-based Arctic amplification. Cryosphere, 3, 1119, doi:10.5194/tc-3-11-2009.

    • Search Google Scholar
    • Export Citation
  • Shindell, D., 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
  • Steinman, B., , M. Mann, , and S. Miller, 2015: Atlantic and Pacific multidecadal oscillations and Northern Hemisphere temperatures. Science, 347, 988991, doi:10.1126/science.1257856.

    • Search Google Scholar
    • Export Citation
  • Taylor, P., , M. Cai, , A. Hu, , J. Meehl, , W. Washington, , and G. Zhang, 2013: A decomposition of feedback contributions to polar warming amplification. J. Climate, 26, 70237043, doi:10.1175/JCLI-D-12-00696.1.

    • Search Google Scholar
    • Export Citation
  • Trenberth, K., , and D. Shea, 2006: Atlantic hurricanes and natural variability in 2005. Geophys. Res. Lett., 33, L12704, doi:10.1029/2006GL026894.

    • Search Google Scholar
    • Export Citation
  • van der Werf, G., , and A. Dolman, 2014: Impact of Atlantic multidecadal oscillation (AMO) on deriving anthropogenic warming rates from the instrumental temperature record. Earth Syst. Dyn., 5, 375382, doi:10.5194/esd-5-375-2014.

    • Search Google Scholar
    • Export Citation
  • van Oldenborgh, G., , L. te Raa, , H. Dijkstra, , and S. Philip, 2009: Frequency- or amplitude-dependent effects of the Atlantic meridional overturning on the tropical Pacific Ocean. Ocean Sci., 5, 293301, doi:10.5194/os-5-293-2009.

    • Search Google Scholar
    • Export Citation
  • Vogelsang, T., , and P. Franses, 2005: Testing for common deterministic trend slopes. J. Econometrics, 126, 124, doi:10.1016/j.jeconom.2004.02.004.

    • Search Google Scholar
    • Export Citation
  • Wallace, J., , Q. Fu, , B. Smoliak, , P. Lin, , and C. Johanson, 2012: Simulated versus observed patterns of warming over the extratropical Northern Hemisphere continents during the cold season. Proc. Natl. Acad. Sci. USA, 109, 14 33714 342, doi:10.1073/pnas.1204875109.

    • Search Google Scholar
    • Export Citation
  • Wang, M., , J. E. Overland, , V. Kattsov, , J. E. Walsh, , X. Zhang, , and T. Pavlova, 2007: Intrinsic versus forced variation in coupled climate model simulations over the Arctic during the twentieth century. J. Climate, 20, 10931107, doi:10.1175/JCLI4043.1.

    • Search Google Scholar
    • Export Citation
  • Wang, M., and Coauthors, 2012: Constraining cloud lifetime effects of aerosols using A-Train satellite observations. Geophys. Res. Lett., 39, L15709, doi:10.1029/2012GL052204.

    • Search Google Scholar
    • Export Citation
  • Zhang, R., , T. L. Delworth, , and I. M. Held, 2007: Can the Atlantic Ocean drive the observed multidecadal variability in Northern Hemisphere mean temperature? Geophys. Res. Lett., 34, L02709, doi:10.1029/2006GL028683.

    • Search Google Scholar
    • Export Citation
  • Zhang, R., and Coauthors, 2013: Have aerosols caused the observed Atlantic multidecadal variability? J. Atmos. Sci., 70, 11351144, doi:10.1175/JAS-D-12-0331.1.

    • Search Google Scholar
    • Export Citation
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Indirect Aerosol Effect Increases CMIP5 Models’ Projected Arctic Warming

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  • 1 * Earth and Environmental Sciences, Los Alamos National Laboratory, Los Alamos, New Mexico
  • 2 Department of Economics, Michigan State University, East Lansing, Michigan
  • 3 Par Associates, Las Cruces, New Mexico
  • 4 Department of Physics, New Mexico State University, Las Cruces, New Mexico
  • 5 Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, New Mexico
  • 6 ** Virginia Bioinformatics Institute, Virginia Polytechnic Institute, Blacksburg, Virginia
  • 7 Department of Physics and Atmospheric Science, Dalhousie University, Halifax, Nova Scotia, Canada
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Abstract

Phase 5 of the Coupled Model Intercomparison Project (CMIP5) climate models’ projections of the 2014–2100 Arctic warming under radiative forcing from representative concentration pathway 4.5 (RCP4.5) vary from 0.9° to 6.7°C. Climate models with or without a full indirect aerosol effect are both equally successful in reproducing the observed (1900–2014) Arctic warming and its trends. However, the 2014–2100 Arctic warming and the warming trends projected by models that include a full indirect aerosol effect (denoted here as AA models) are significantly higher (mean projected Arctic warming is about 1.5°C higher) than those projected by models without a full indirect aerosol effect (denoted here as NAA models). The suggestion is that, within models including full indirect aerosol effects, those projecting stronger future changes are not necessarily distinguishable historically because any stronger past warming may have been partially offset by stronger historical aerosol cooling. The CMIP5 models that include a full indirect aerosol effect follow an inverse radiative forcing to equilibrium climate sensitivity relationship, while models without it do not.

Corresponding author address: Petr Chylek, Los Alamos National Laboratory, Earth and Environmental Sciences, Bikini Road, Los Alamos, NM 87545. E-mail: chylek@lanl.gov

Abstract

Phase 5 of the Coupled Model Intercomparison Project (CMIP5) climate models’ projections of the 2014–2100 Arctic warming under radiative forcing from representative concentration pathway 4.5 (RCP4.5) vary from 0.9° to 6.7°C. Climate models with or without a full indirect aerosol effect are both equally successful in reproducing the observed (1900–2014) Arctic warming and its trends. However, the 2014–2100 Arctic warming and the warming trends projected by models that include a full indirect aerosol effect (denoted here as AA models) are significantly higher (mean projected Arctic warming is about 1.5°C higher) than those projected by models without a full indirect aerosol effect (denoted here as NAA models). The suggestion is that, within models including full indirect aerosol effects, those projecting stronger future changes are not necessarily distinguishable historically because any stronger past warming may have been partially offset by stronger historical aerosol cooling. The CMIP5 models that include a full indirect aerosol effect follow an inverse radiative forcing to equilibrium climate sensitivity relationship, while models without it do not.

Corresponding author address: Petr Chylek, Los Alamos National Laboratory, Earth and Environmental Sciences, Bikini Road, Los Alamos, NM 87545. E-mail: chylek@lanl.gov
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