• AchutaRao, K., and K. R. Sperber, 2002: Simulation of the El Niño Southern Oscillation: Results from the Coupled Model Intercomparison Project. Climate Dyn., 19, 191209, https://doi.org/10.1007/s00382-001-0221-9.

    • Search Google Scholar
    • Export Citation
  • Allen, M. R., and S. F. B. Tett, 1999: Checking for model consistency in optimal fingerprinting. Climate Dyn., 15, 419434, https://doi.org/10.1007/s003820050291.

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

    • Search Google Scholar
    • Export Citation
  • Bandoro, J., S. Solomon, A. Donohoe, D. W. J. Thompson, and B. D. Santer, 2014: Influences of the Antarctic ozone hole on Southern Hemisphere summer climate change. J. Climate, 27, 62456264, https://doi.org/10.1175/JCLI-D-13-00698.1.

    • Search Google Scholar
    • Export Citation
  • Barnett, T. P., D. Pierce, K. AchutaRao, P. Gleckler, B. D. Santer, J. Gregory, and W. Washington, 2005: Penetration of human-induced warming signal into the world’s oceans. Science, 309, 284287, https://doi.org/10.1126/science.1112418.

    • Search Google Scholar
    • Export Citation
  • Bindoff, N. L., and Coauthors, 2013: Detection and attribution of climate change: From global to regional. Climate Change 2013: The Physical Science Basis, T. F. Stocker et al., Eds., Cambridge University Press, 867952.

    • Search Google Scholar
    • Export Citation
  • Bintanja, R., and E. C. van der Linden, 2013: The changing seasonal climate in the Arctic. Sci. Rep., 3, 1556, https://doi.org/10.1038/srep01556.

    • Search Google Scholar
    • Export Citation
  • Bonfils, C., B. D. Santer, T. J. Phillips, K. Marvel, L. R. Leung, C. Doutriaux, and A. Capotondi, 2015: Relative contributions of mean-state shifts and ENSO-driven variability to precipitation changes in a warming climate. J. Climate, 28, 999710 013, https://doi.org/10.1175/JCLI-D-15-0341.1.

    • Search Google Scholar
    • Export Citation
  • Bonfils, C., B. D. Santer, J. C. Fyfe, K. Marvel, T. J. Phillips, and S. R. H. Zimmerman, 2020: Human influence on joint changes in temperature, rainfall and continental aridity. Nat. Climate Change, 10, 726731, https://doi.org/10.1038/s41558-020-0821-1.

    • Search Google Scholar
    • Export Citation
  • Brogli, R., N. Kröner, S. L. Sørland, D. Lüthi, and C. Schär, 2019: The role of Hadley circulation and lapse-rate changes for the future European summer climate. J. Climate, 32, 385404, https://doi.org/10.1175/JCLI-D-18-0431.1.

    • Search Google Scholar
    • Export Citation
  • Carslaw, K. S., and Coauthors, 2013: Large contribution of natural aerosols to uncertainty in indirect forcing. Nature, 503, 6771, https://doi.org/10.1038/nature12674.

    • Search Google Scholar
    • Export Citation
  • Cheung, A. H., M. E. Mann, B. A. Steinman, L. M. Frankcombe, M. H. England, and S. K. Miller, 2017: Reply to “Comment on comparison of low-frequency internal climate variability in CMIP5 models and observations.” J. Climate, 30, 97739782, https://doi.org/10.1175/JCLI-D-17-0531.1.

    • Search Google Scholar
    • Export Citation
  • Cohen, J. M., M. J. Lajeunesse, and J. R. Rohr, 2018: A global synthesis of animal phenological responses to climate change. Nat. Climate Change, 8, 224228, https://doi.org/10.1038/s41558-018-0067-3.

    • Search Google Scholar
    • Export Citation
  • Curry, J., and P. Webster, 2011: Climate science and the uncertainty monster. Bull. Amer. Meteor. Soc., 92, 16671682, https://doi.org/10.1175/2011BAMS3139.1.

    • Search Google Scholar
    • Export Citation
  • Deser, C., A. Phillips, V. Bourdette, and H. Teng, 2012: Uncertainty in climate change projections: The role of internal variability. Climate Dyn., 38, 527546, https://doi.org/10.1007/s00382-010-0977-x.

    • Search Google Scholar
    • Export Citation
  • Deser, C., A. Phillips, M. A. Alexander, and B. V. Smoliak, 2014: Projecting North American climate over the next 50 years: Uncertainty due to internal variability. J. Climate, 27, 22712296, https://doi.org/10.1175/JCLI-D-13-00451.1.

    • Search Google Scholar
    • Export Citation
  • Deser, C., and Coauthors, 2020: Insights from Earth system model initial-condition large ensembles and future prospects. Nat. Climate Change, 10, 277286, https://doi.org/10.1038/s41558-020-0731-2.

    • Search Google Scholar
    • Export Citation
  • Donohoe, A., and D. S. Battisti, 2013: The seasonal cycle of atmospheric heating and temperature. J. Climate, 26, 49624980, https://doi.org/10.1175/JCLI-D-12-00713.1.

    • Search Google Scholar
    • Export Citation
  • Douville, H., and M. Plazzotta, 2017: Midlatitude summer drying: An underestimated threat in CMIP5 models? Geophys. Res. Lett., 44, 99679975, https://doi.org/10.1002/2017GL075353.

    • Search Google Scholar
    • Export Citation
  • Duan, J., and Coauthors, 2019: Detection of human influences on temperature seasonality from the nineteenth century. Nat. Sustain., 2, 484490, https://doi.org/10.1038/s41893-019-0276-4.

    • Search Google Scholar
    • Export Citation
  • Dwyer, J. G., M. Biasutti, and A. H. Sobel, 2012: Projected changes in the seasonal cycle of surface temperature. J. Climate, 25, 63596374, https://doi.org/10.1175/JCLI-D-11-00741.1.

    • Search Google Scholar
    • Export Citation
  • Enfield, D. B., A. M. Mestas-Nuñez, and P. J. Trimble, 2001: The Atlantic multidecadal oscillation and its relation to rainfall and river flows in the continental U.S. Geophys. Res. Lett., 28, 20772080, https://doi.org/10.1029/2000GL012745.

    • Search Google Scholar
    • Export Citation
  • England, M. H., and Coauthors, 2014: Recent intensification of wind–driven circulation in the Pacific and the ongoing warming hiatus. Nat. Climate Change, 4, 222227, https://doi.org/10.1038/nclimate2106.

    • Search Google Scholar
    • Export Citation
  • Eyring, V., and Coauthors, 2013: Long-term ozone changes and associated climate impacts in CMIP5 simulations. J. Geophys. Res. Atmos., 118, 50295060, https://doi.org/10.1002/jgrd.50316.

    • Search Google Scholar
    • Export Citation
  • Eyring, V., S. Bony, G. A. Meehl, C. A. Senior, B. Stevens, R. J. Stouffer, and K. E. Taylor, 2016: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev., 9, 19371958, https://doi.org/10.5194/gmd-9-1937-2016.

    • Search Google Scholar
    • Export Citation
  • Fasullo, J. T., J. F. Lamarque, C. Hannay, N. Rosenblum, S. Tilmes, P. DeRepentigny, A. Jahn, and C. Deser, 2022: Spurious late historical-era warming in CESM2 driven by prescribed biomass burning emissions. Geophys. Res. Lett., 49, e2021GL097420, https://doi.org/10.1029/2021GL097420.

    • Search Google Scholar
    • Export Citation
  • Feldl, N., and T. M. Merlis, 2021: Polar amplification in idealized climates: The role of ice, moisture, and seasons. Geophys. Res. Lett., 48, e2021GL094130, https://doi.org/10.1029/2021GL094130.

    • Search Google Scholar
    • Export Citation
  • Feldl, N., S. Bordoni, and T. M. Merlis, 2017: Coupled high-latitude climate feedbacks and their impact on atmospheric heat transport. J. Climate, 30, 189201, https://doi.org/10.1175/JCLI-D-16-0324.1.

    • Search Google Scholar
    • Export Citation
  • Feldl, N., S. Po-Chedley, H. K. A. Singh, S. Hay, and P. J. Kushner, 2020: Sea ice and atmospheric circulation shape the high-latitude lapse rate feedback. npj Climate Atmos. Sci., 3, 41, https://doi.org/10.1038/s41612-020-00146-7.

    • Search Google Scholar
    • Export Citation
  • Frankcombe, L. M., M. H. England, M. E. Mann, and B. A. Steinman, 2015: Separating internal variability from the externally forced climate response. J. Climate, 28, 81848202, https://doi.org/10.1175/JCLI-D-15-0069.1.

    • Search Google Scholar
    • Export Citation
  • Frierson, D. M. W., 2006: Robust increases in midlatitude static stability in simulations of global warming. Geophys. Res. Lett., 33, L24816, https://doi.org/10.1029/2006GL027504.

    • Search Google Scholar
    • Export Citation
  • Frierson, D. M. W., J. Lu, and G. Chen, 2007: Width of the Hadley cell in simple and comprehensive general circulation models. Geophys. Res. Lett., 34, L18804, https://doi.org/10.1029/2007GL031115.

    • Search Google Scholar
    • Export Citation
  • Fu, Q., and C. M. Johanson, 2004: Stratospheric influences on MSU-derived tropospheric temperature trends: A direct error analysis. J. Climate, 17, 46364640, https://doi.org/10.1175/JCLI-3267.1.

    • Search Google Scholar
    • Export Citation
  • Fu, Q., C. M. Johanson, S. G. Warren, and D. J. Seidel, 2004: Contribution of stratospheric cooling to satellite-inferred tropospheric temperature trends. Nature, 429, 5558, https://doi.org/10.1038/nature02524.

    • Search Google Scholar
    • Export Citation
  • Fu, Q., C. M. Johanson, J. M. Wallace, and T. Reichler, 2006: Enhanced mid-latitude tropospheric warming in satellite measurements. Science, 312, 1179, https://doi.org/10.1126/science.1125566.

    • Search Google Scholar
    • Export Citation
  • Fyfe, J. C., and Coauthors, 2016: Making sense of the early-2000s warming slowdown. Nat. Climate Change, 6, 224228, https://doi.org/10.1038/nclimate2938.

    • Search Google Scholar
    • Export Citation
  • Fyfe, J. C., and Coauthors, 2017: Large near-term projected snowpack loss over the western United States. Nat. Commun., 8, 14996, https://doi.org/10.1038/ncomms14996.

    • Search Google Scholar
    • Export Citation
  • Fyfe, J. C., V. Kharin, B. D. Santer, R. N. S. Cole, and N. P. Gillett, 2021: Significant impact of forcing uncertainty in a large ensemble of climate model simulations. Proc. Natl. Acad. Sci. USA, 118, e2016549118, https://doi.org/10.1073/pnas.2016549118.

    • Search Google Scholar
    • Export Citation
  • Gillett, N. P., F. W. Zwiers, A. J. Weaver, and P. A. Stott, 2003: Detection of human influence on sea-level pressure. Nature, 422, 292294, https://doi.org/10.1038/nature01487.

    • Search Google Scholar
    • Export Citation
  • Gillett, N. P., B. D. Santer, and A. J. Weaver, 2004: Stratospheric cooling and the troposphere. Nature, 432, 1, https://doi.org/10.1038/nature03209.

    • Search Google Scholar
    • Export Citation
  • Hasselmann, K., 1979: On the signal-to-noise problem in atmospheric response studies. Meteorology over the Tropical Oceans, D. B. Shaw, Ed., Royal Meteorological Society, 251259.

    • Search Google Scholar
    • Export Citation
  • Hawkins, E., and R. Sutton, 2012: Time of emergence of climate signals. Geophys. Res. Lett., 39, L01702, https://doi.org/10.1029/2011GL050087.

    • Search Google Scholar
    • Export Citation
  • He, J., and B. J. Soden, 2017: A re-examination of the projected subtropical precipitation decline. Nat. Climate Change, 7, 5357, https://doi.org/10.1038/nclimate3157.

    • Search Google Scholar
    • Export Citation
  • Hegerl, G. C., H. Storch, K. Hasselmann, B. D. Santer, U. Cubasch, and P. D. Jones, 1996: Detecting greenhouse-gas-induced climate change with an optimal fingerprint method. J. Climate, 9, 22812306, https://doi.org/10.1175/1520-0442(1996)009<2281:DGGICC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Hegerl, G. C., and Coauthors, 2007: Understanding and attributing climate change. Climate Change 2007: The Physical Science Basis, S. Solomon et al., Eds., Cambridge University Press, 663745.

    • Search Google Scholar
    • Export Citation
  • Held, I. M., and Coauthors, 2000: The general circulation of the atmosphere. 70 pp., https://www.gfdl.noaa.gov/wp-content/uploads/files/user_files/ih/lectures/woods_hole.pdf.

    • Search Google Scholar
    • Export Citation
  • Henley, B. J., J. Gergis, D. J. Karoly, S. Power, J. Kennedy, and C. K. Folland, 2015: A tripole index for the Interdecadal Pacific Oscillation. Climate Dyn., 45, 30773090, https://doi.org/10.1007/s00382-015-2525-1.

    • Search Google Scholar
    • Export Citation
  • Henley, B. J., and Coauthors, 2017: Spatial and temporal agreement in climate model simulations of the Interdecadal Pacific Oscillation. Environ. Res. Lett., 12, 044011, https://doi.org/10.1088/1748-9326/aa5cc8.

    • Search Google Scholar
    • Export Citation
  • Hersbach, H., and Coauthors, 2020: The ERA5 global reanalysis. Quart. J. Roy. Meteor. Soc., 146, 19992049, https://doi.org/10.1002/qj.3803.

    • Search Google Scholar
    • Export Citation
  • Hu, Y. Y., and Q. Fu, 2007: Observed poleward expansion of the Hadley circulation since 1979. Atmos. Chem. Phys., 7, 52295236, https://doi.org/10.5194/acp-7-5229-2007.

    • Search Google Scholar
    • Export Citation
  • IPCC, 2021: Summary for policymakers. Climate Change 2021: The Physical Sciences Basis, V. Masson-Delmotte et al., Eds., Cambridge University Press, 332.

    • Search Google Scholar
    • Export Citation
  • Kajtar, J. B., M. Collins, L. M. Frankcombe, M. H. England, T. J. Osborn, and M. Juniper, 2019: Global mean surface temperature response to large-scale patterns of variability in observations and CMIP5. Geophys. Res. Lett., 46, 22322241, https://doi.org/10.1029/2018GL081462.

    • Search Google Scholar
    • Export Citation
  • Kalnay, E., and Coauthors, 1996: The NCEP/NCAR 40-Year Reanalysis Project. Bull. Amer. Meteor. Soc., 77, 437471, https://doi.org/10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Kamae, Y., H. Shiogama, M. Watanabe, M. Ishii, H. Ueda, and M. Kimoto, 2015: Recent slowdown of tropical upper tropospheric warming associated with Pacific climate variability. Geophys. Res. Lett., 42, 29953003, https://doi.org/10.1002/2015GL063608.

    • Search Google Scholar
    • Export Citation
  • Kang, S. M., and J. Liu, 2012: Expansion of the Hadley cell under global warming: Winter versus summer. J. Climate, 25, 83878393, https://doi.org/10.1175/JCLI-D-12-00323.1.

    • Search Google Scholar
    • Export Citation
  • Kang, S. M., S.-P. Xie, C. Deser, and B. Xiang, 2021: Zonal mean and shift modes of historical climate response to evolving aerosol distribution. Sci. Bull., 66, 24052411, https://doi.org/10.1016/j.scib.2021.07.013.

    • Search Google Scholar
    • Export Citation
  • Kay, J. E., and Coauthors, 2015: The Community Earth System Model (CESM) Large Ensemble Project: A community resource for studying climate change in the presence of internal climate variability. Bull. Amer. Meteor. Soc., 96, 13331349, https://doi.org/10.1175/BAMS-D-13-00255.1.

    • Search Google Scholar
    • Export Citation
  • Kirchmeier-Young, M. C., F. W. Zwiers, and N. P. Gillett, 2017: Attribution of extreme events in Arctic sea ice extent. J. Climate, 30, 553571, https://doi.org/10.1175/JCLI-D-16-0412.1.

    • Search Google Scholar
    • Export Citation
  • Kosaka, Y., and S.-P. Xie, 2013: Recent global-warming hiatus tied to equatorial Pacific surface cooling. Nature, 501, 403407, https://doi.org/10.1038/nature12534.

    • Search Google Scholar
    • Export Citation
  • Kravtsov, S., 2017: Comment on “Comparison of low-frequency internal climate variability in CMIP5 models and observations.” J. Climate, 30, 97639772, https://doi.org/10.1175/JCLI-D-17-0438.1.

    • Search Google Scholar
    • Export Citation
  • Lienert, F., J. C. Fyfe, and W. J. Merryfield, 2011: Do climate models capture the tropical influences on North Pacific sea surface temperature variability? J. Climate, 24, 62036209, https://doi.org/10.1175/JCLI-D-11-00205.1.

    • Search Google Scholar
    • Export Citation
  • Maher, N., S. McGregor, M. H. England, and A. S. Gupta, 2015: Effects of volcanism on tropical variability. Geophys. Res. Lett., 42, 60246033, https://doi.org/10.1002/2015GL064751.

    • Search Google Scholar
    • Export Citation
  • Mahlstein, I., G. Hegerl, and S. Solomon, 2012: Emerging local warming signals in observational data. Geophys. Res. Lett., 39, L21711, https://doi.org/10.1029/2012GL053952.

    • Search Google Scholar
    • Export Citation
  • Manabe, S., R. T. Wetherald, and R. J. Stouffer, 1981: Summer dryness due to an increase of atmospheric CO2 concentration. Climatic Change, 3, 347386, https://doi.org/10.1007/BF02423242.

    • Search Google Scholar
    • Export Citation
  • Mann, M. E., and K. A. Emanuel, 2006: Atlantic hurricane trends linked to climate change. Eos, Trans. Amer. Geophys. Union, 87, 233241, https://doi.org/10.1029/2006EO240001.

    • Search Google Scholar
    • Export Citation
  • Mantsis, D. F., and A. C. Clement, 2009: Simulated variability in the mean atmospheric meridional circulation over the 20th century. Geophys. Res. Lett., 36, L06704, https://doi.org/10.1029/2008GL036741.

    • Search Google Scholar
    • Export Citation
  • Marvel, K., and C. Bonfils, 2013: Identifying external influences on global precipitation. Proc. Natl. Acad. Sci. USA, 110, 19 30119 306, https://doi.org/10.1073/pnas.1314382110.

    • Search Google Scholar
    • Export Citation
  • Marvel, K., M. Biasutti, C. Bonfils, K. E. Taylor, Y. Kushnir, and B. I. Cook, 2017: Observed and projected changes to the precipitation annual cycle. J. Climate, 30, 49834995, https://doi.org/10.1175/JCLI-D-16-0572.1.

    • Search Google Scholar
    • Export Citation
  • Mears, C., and F. J. Wentz, 2017: A satellite-derived lower-tropospheric atmospheric temperature dataset using an optimized adjustment for diurnal effects. J. Climate, 30, 76957718, https://doi.org/10.1175/JCLI-D-16-0768.1.

    • Search Google Scholar
    • Export Citation
  • Mears, C., F. J. Wentz, P. Thorne, and D. Bernie, 2011: Assessing uncertainty in estimates of atmospheric temperature changes from MSU and AMSU using a Monte-Carlo estimation technique. J. Geophys. Res. Atmos., 116, D08112, https://doi.org/10.1029/2010JD014954.

    • Search Google Scholar
    • Export Citation
  • Meehl, G. A., J. M. Arblaster, J. T. Fasullo, A. Hu, and K. E. Trenberth, 2011: Model-based evidence of deep-ocean heat uptake during surface-temperature hiatus periods. Nat. Climate Change, 1, 360364, https://doi.org/10.1038/nclimate1229.

    • Search Google Scholar
    • Export Citation
  • Meehl, G. A., A. Hu, B. D. Santer, and S.-P. Xie, 2016: Contribution of the Interdecadal Pacific Oscillation to twentieth-century global surface temperature trends. Nat. Climate Change, 6, 10051008, https://doi.org/10.1038/nclimate3107.

    • Search Google Scholar
    • Export Citation
  • Meinshausen, M., and Coauthors, 2011: The RCP greenhouse gas concentrations and their extensions from 1765 to 2300. Climatic Change, 109, 213241, https://doi.org/10.1007/s10584-011-0156-z.

    • Search Google Scholar
    • Export Citation
  • Min, S. K., X. Zhang, F. W. Zwiers, and T. Agnew, 2008: Human influence on Arctic sea ice detectable from early 1990s onwards. Geophys. Res. Lett., 35, L21701, https://doi.org/10.1029/2008GL035725.

    • Search Google Scholar
    • Export Citation
  • Min, S. K., X. Zhang, F. W. Zwiers, P. Friederichs, and A. Hense, 2009: Signal detectability in extreme precipitation changes assessed from twentieth century climate simulations. Climate Dyn., 32, 95111, https://doi.org/10.1007/s00382-008-0376-8.

    • Search Google Scholar
    • Export Citation
  • Mitchell, J. F. B., and D. J. Karoly, 2001: Detection of climate change and attribution of causes. Climate Change 2001: The Scientific Basis, J. T. Houghton et al., Eds., Cambridge University Press, 695738.

    • 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. Atmos., 117, D08101, https://doi.org/10.1029/2011JD017187.

    • Search Google Scholar
    • Export Citation
  • North, G. R., K. Y. Kim, S. S. P. Shen, and J. W. Hardin, 1995: Detection of forced climate signals. Part 1: Filter theory. J. Climate, 8, 401408, https://doi.org/10.1175/1520-0442(1995)008<0401:DOFCSP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • O’Reilly, C. H., D. J. Befort, A. Weisheimer, T. Woollings, A. Ballinger, and G. Hegerl, 2021: Projections of Northern Hemisphere extratropical climate underestimate internal variability and associated uncertainty. Commun. Earth Environ., 2, 194, https://doi.org/10.1038/s43247-021-00268-7.

    • Search Google Scholar
    • Export Citation
  • Pallotta, J., and B. D. Santer, 2020: Multi-frequency analysis of simulated versus observed variability in tropospheric temperature. J. Climate, 33, 10 38310 402, https://doi.org/10.1175/JCLI-D-20-0023.1.

    • Search Google Scholar
    • Export Citation
  • Parmesan, C., and G. Yohe, 2003: A globally coherent fingerprint of climate change impacts across natural systems. Nature, 421, 3742, https://doi.org/10.1038/nature01286.

    • Search Google Scholar
    • Export Citation
  • Pierce, D., P. J. Gleckler, T. P. Barnett, B. D. Santer, and P. J. Durack, 2012: The fingerprint of human-induced changes in the ocean’s salinity and temperature fields. Geophys. Res. Lett., 39, L21704, https://doi.org/10.1029/2012GL053389.

    • Search Google Scholar
    • Export Citation
  • Po-Chedley, S., T. J. Thorsen, and Q. Fu, 2015: Removing diurnal cycle contamination in satellite-derived tropospheric temperatures: Understanding tropical tropospheric trend discrepancies. J. Climate, 28, 22742290, https://doi.org/10.1175/JCLI-D-13-00767.1.

    • Search Google Scholar
    • Export Citation
  • Po-Chedley, S., B. D. Santer, S. Fueglistaler, M. D. Zelinka, P. Cameron-Smith, J. F. Painter, and Q. Fu, 2021: Natural variability contributes to model–satellite differences in tropical tropospheric warming. Proc. Natl. Acad. Sci. USA, 118, e2020962118, https://doi.org/10.1073/pnas.2020962118.

    • Search Google Scholar
    • Export Citation
  • Qian, C., and X. Zhang, 2015: Human influences on changes in the temperature seasonality in mid-to high-latitude land areas. J. Climate, 28, 59085921, https://doi.org/10.1175/JCLI-D-14-00821.1.

    • Search Google Scholar
    • Export Citation
  • Quan, X.-W., M. P. Hoerling, J. Perlwitz, H. F. Diaz, and T. Xu, 2014: How fast are the tropics expanding? J. Climate, 27, 19992013, https://doi.org/10.1175/JCLI-D-13-00287.1.

    • Search Google Scholar
    • Export Citation
  • Randel, W. J., L. Polvani, F. Wu, D. E. Kinnison, C.-Z. Zou, and C. Mears, 2017: Troposphere–stratosphere temperature trends derived from satellite data compared with ensemble simulations from WACCM. J. Geophys. Res. Atmos., 122, 96519667, https://doi.org/10.1002/2017JD027158.

    • Search Google Scholar
    • Export Citation
  • Riahi, K., and Coauthors, 2017: The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview. Global Environ. Change, 42, 153168, https://doi.org/10.1016/j.gloenvcha.2016.05.009.

    • Search Google Scholar
    • Export Citation
  • Risser, M. D., and M. F. Wehner, 2017: Attributable human-induced changes in the likelihood and magnitude of the observed extreme precipitation during Hurricane Harvey. Geophys. Res. Lett., 44, 12 45712 464, https://doi.org/10.1002/2017GL075888.

    • Search Google Scholar
    • Export Citation
  • Rodgers, K. B., J. Lin, and T. L. Frölicher, 2015: Emergence of multiple ocean ecosystem drivers in a large ensemble suite with an Earth system model. Biogeosciences, 12, 33013320, https://doi.org/10.5194/bg-12-3301-2015.

    • Search Google Scholar
    • Export Citation
  • Rodgers, K. B., and Coauthors, 2021: Ubiquity of human-induced changes in climate variability. Earth Syst. Dyn., 12, 13931411, https://doi.org/10.5194/esd-12-1393-2021.

    • Search Google Scholar
    • Export Citation
  • Root, T. L., D. P. MacMynowski, M. D. Mastrandrea, and S. H. Schneider, 2005: Human-modified temperatures induce species changes: Joint attribution. Proc. Natl. Acad. Sci. USA, 102, 74657469, https://doi.org/10.1073/pnas.0502286102.

    • Search Google Scholar
    • Export Citation
  • Santer, B. D., W. Brüggemann, U. Cubasch, K. Hasselmann, H. Höck, E. Maier-Reimer, and U. Mikolajewicz, 1994: Signal-to-noise analysis of time-dependent greenhouse warming experiments. Climate Dyn., 9, 267285, https://doi.org/10.1007/BF00204743.

    • Search Google Scholar
    • Export Citation
  • Santer, B. D., T. M. L. Wigley, T. P. Barnett, and E. Anyamba, 1995: Detection of climate change and attribution of causes. Climate Change 1995: The Science of Climate Change, J. T. Houghton et al., Eds., Cambridge University Press, 407443.

    • Search Google Scholar
    • Export Citation
  • Santer, B. D., and Coauthors, 1996: A search for human influences on the thermal structure of the atmosphere. Nature, 382, 3946, https://doi.org/10.1038/382039a0.

    • Search Google Scholar
    • Export Citation
  • Santer, B. D., and Coauthors, 2003: Influence of satellite data uncertainties on the detection of externally forced climate change. Science, 300, 12801284, https://doi.org/10.1126/science.1082393.

    • Search Google Scholar
    • Export Citation
  • Santer, B. D., and Coauthors, 2009: Incorporating model quality information in climate change detection and attribution studies. Proc. Natl. Acad. Sci. USA, 106, 14 77814 783, https://doi.org/10.1073/pnas.0901736106.

    • Search Google Scholar
    • Export Citation
  • Santer, B. D., and Coauthors, 2018: Human influence on the seasonal cycle of tropospheric temperature. Science, 361, eaas8806, https://doi.org/10.1126/science.aas8806.

    • Search Google Scholar
    • Export Citation
  • Santer, B. D., J. Fyfe, S. Solomon, J. Painter, C. Bonfils, G. Pallotta, and M. Zelinka, 2019: Quantifying stochastic uncertainty in detection time of human-caused climate signals. Proc. Natl. Acad. Sci. USA, 116, 19 82119 827, https://doi.org/10.1073/pnas.1904586116.

    • Search Google Scholar
    • Export Citation
  • Santer, B. D., and Coauthors, 2021: Using climate model simulations to constrain observations. J. Climate, 34, 62816301, https://doi.org/10.1175/JCLI-D-20-0768.1.

    • Search Google Scholar
    • Export Citation
  • Seidel, D. J., and W. J. Randel, 2007: Recent widening of the tropical belt: Evidence from tropopause observations. J. Geophys. Res. Atmos., 112, D20113, https://doi.org/10.1029/2007JD008861.

    • Search Google Scholar
    • Export Citation
  • Serreze, M. C., and R. G. Barry, 2011: Processes and impacts of Arctic amplification: A research synthesis. Global Planet. Change, 77, 8596, https://doi.org/10.1016/j.gloplacha.2011.03.004.

    • Search Google Scholar
    • Export Citation
  • Simmons, A., and Coauthors, 2020: Global stratospheric temperature bias and other stratospheric aspects of ERA5 and ERA5.1. Tech. Memo 859, European Centre for Medium-Range Weather Forecasts, 40 pp.

    • Search Google Scholar
    • Export Citation
  • Sippel, S., N. Meinshausen, E. M. Fischer, E. Székely, and R. Knutti, 2020: Climate change now detectable from any single day of weather at global scale. Nat. Climate Change, 10, 3541, https://doi.org/10.1038/s41558-019-0666-7.

    • Search Google Scholar
    • Export Citation
  • Sippel, S., N. Meinshausen, E. Székely, E. Fischer, A. G. Pendergrass, F. Lehner, and R. Knutti, 2021: Robust detection of forced warming in the presence of potentially large climate variability. Sci. Adv., 7, eabh4429, https://doi.org/10.1126/sciadv.abh4429.

    • Search Google Scholar
    • Export Citation
  • Smith, R. D., J. K. Dukowicz, and R. C. Malone, 1992: Parallel ocean general circulation modeling. Physica D, 60, 3861, https://doi.org/10.1016/0167-2789(92)90225-C.

    • Search Google Scholar
    • Export Citation
  • Solomon, S., J. S. Daniel, R. R. Neely, J.-P. Vernier, E. G. Dutton, and L. W. Thomason, 2011: The persistently variable “background” stratospheric aerosol layer and global climate change. Science, 333, 866870, https://doi.org/10.1126/science.1206027.

    • Search Google Scholar
    • Export Citation
  • Solomon, S., P. J. Young, and B. Hassler, 2012: Uncertainties in the evolution of stratospheric ozone and implications for recent temperature changes in the tropical lower stratosphere. Geophys. Res. Lett., 39, L17706, https://doi.org/10.1029/2012GL052723.

    • Search Google Scholar
    • Export Citation
  • Solomon, S., and Coauthors, 2017: Mirrored changes in Antarctic ozone and stratospheric temperature in the late 20th versus early 21st centuries. J. Geophys. Res. Atmos., 122, 89408950, https://doi.org/10.1002/2017JD026719.

    • Search Google Scholar
    • Export Citation
  • Spencer, R. W., J. R. Christy, and W. D. Braswell, 2017: UAH version 6 global satellite temperature products: Methodology and results. Asia-Pac. J. Atmos. Sci., 53, 121130, https://doi.org/10.1007/s13143-017-0010-y.

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

    • Search Google Scholar
    • Export Citation
  • Stine, A. R., and P. Huybers, 2012: Changes in the seasonal cycle of temperature and atmospheric circulation. J. Climate, 25, 73627380, https://doi.org/10.1175/JCLI-D-11-00470.1.

    • Search Google Scholar
    • Export Citation
  • Stott, P. A., S. F. B. Tett, G. S. Jones, M. R. Allen, J. F. B. Mitchell, and G. J. Jenkins, 2000: External control of 20th century temperature by natural and anthropogenic forcings. Science, 290, 21332137, https://doi.org/10.1126/science.290.5499.2133.

    • Search Google Scholar
    • Export Citation
  • Stott, P. A., D. A. Stone, and M. R. Allen, 2004: Human contribution to the European heatwave of 2003. Nature, 432, 610614, https://doi.org/10.1038/nature03089.

    • Search Google Scholar
    • Export Citation
  • Stott, P. A., and Coauthors, 2016: Attribution of extreme weather and climate-related events. J. Atmos. Sci., 7, 2341, https://doi.org/10.1002/wcc.380.

    • Search Google Scholar
    • Export Citation
  • Stouffer, R. J., G. Hegerl, and S. Tett, 2000: A comparison of surface air temperature variability in three 1000-yr coupled ocean–atmosphere model integrations. J. Climate, 13, 513537, https://doi.org/10.1175/1520-0442(2000)013<0513:ACOSAT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Suárez-Gutiérrez, L., C. Li, P. W. Thorne, and J. Marotzke, 2017: Internal variability in simulated and observed tropical tropospheric temperature trends. Geophys. Res. Lett., 44, 57095719, https://doi.org/10.1002/2017GL073798.

    • Search Google Scholar
    • Export Citation
  • Swart, N. C., S. T. Gille, J. C. Fyfe, and N. P. Gillett, 2018: Recent Southern Ocean warming and freshening driven by greenhouse gas emissions and ozone depletion. Nat. Geosci., 11, 836841, https://doi.org/10.1038/s41561-018-0226-1.

    • Search Google Scholar
    • Export Citation
  • Swart, N. C., and Coauthors, 2019: The Canadian Earth System Model version 5 (CanESM5.0.3). Geosci. Model Dev., 12, 48234873, https://doi.org/10.5194/gmd-12-4823-2019.

    • Search Google Scholar
    • Export Citation
  • Tatebe, H., and Coauthors, 2019: Description and basic evaluation of simulated mean state, internal variability, and climate sensitivity in MIROC6. Geosci. Model Dev., 12, 27272765, https://doi.org/10.5194/gmd-12-2727-2019.

    • 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, 485498, https://doi.org/10.1175/BAMS-D-11-00094.1.

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

    • Search Google Scholar
    • Export Citation
  • Tett, S. F. B., J. F. B. Mitchell, D. E. Parker, and M. R. Allen, 1996: Human influence on the atmospheric vertical temperature structure: Detection and observations. Science, 274, 11701173, https://doi.org/10.1126/science.274.5290.1170.

    • Search Google Scholar
    • Export Citation
  • Tett, S. F. B., T. C. Johns, and J. F. B. Mitchell, 1997: Global and regional variability in a coupled AOGCM. Climate Dyn., 13, 303323, https://doi.org/10.1007/s003820050168.

    • Search Google Scholar
    • Export Citation
  • Thompson, D. W. J., J. J. Kennedy, J. M. Wallace, and P. D. Jones, 2008: A large discontinuity in the mid-twentieth century in observed global-mean surface temperature. Nature, 453, 646649, https://doi.org/10.1038/nature06982.

    • Search Google Scholar
    • Export Citation
  • Thompson, D. W. J., S. Solomon, P. J. Kushner, M. H. England, K. M. Grise, and D. J. Karoly, 2011: Signatures of the Antarctic ozone hole in Southern Hemisphere surface climate change. Nat. Geosci., 4, 741749, https://doi.org/10.1038/ngeo1296.

    • Search Google Scholar
    • Export Citation
  • Thorne, P. W., and Coauthors, 2002: Assessing the robustness of zonal mean climate change detection. Geophys. Res. Lett., 29, 1920, https://doi.org/10.1029/2002GL015717.

    • Search Google Scholar
    • Export Citation
  • Trenberth, K. E., 2015: Has there been a hiatus? Science, 349, 691692, https://doi.org/10.1126/science.aac9225.

  • Wetherald, R. T., and S. Manabe, 1995: The mechanisms of summer dryness induced by greenhouse warming. J. Climate, 8, 30963108, https://doi.org/10.1175/1520-0442(1995)008<3096:TMOSDI>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Wilks, D. S., 1995: Statistical Methods in the Atmospheric Sciences. Academic Press, 467 pp.

  • Willett, K. M., N. P. Gillett, P. D. Jones, and P. W. Thorne, 2007: Attribution of observed surface humidity changes to human influence. Nature, 449, 710712, https://doi.org/10.1038/nature06207.

    • Search Google Scholar
    • Export Citation
  • Yettella, V., and M. R. England, 2018: The role of internal variability in twenty-first-century projections of the seasonal cycle of Northern Hemisphere surface temperature. J. Geophys. Res. Atmos., 123, 13 14913 167, https://doi.org/10.1029/2018JD029066.

    • Search Google Scholar
    • Export Citation
  • Zelinka, M. D., T. Andrews, P. M. Forster, and K. E. Taylor, 2014: Quantifying components of aerosol–cloud–radiation interactions in climate models. J. Geophys. Res. Atmos., 119, 75997615, https://doi.org/10.1002/2014JD021710.

    • Search Google Scholar
    • Export Citation
  • Zelinka, M. D., T. A. Myers, D. T. McCoy, S. Po-Chedley, P. M. Caldwell, P. Ceppi, S. A. Klein, and K. E. Taylor, 2020: Causes of higher climate sensitivity in CMIP6 models. Geophys. Res. Lett., 47, e2019GL085782, https://doi.org/10.1029/2019GL085782.

    • Search Google Scholar
    • Export Citation
  • Zhang, X., F. W. Zwiers, G. C. Hegerl, F. H. Lambert, N. P. Gillett, S. Solomon, P. A. Stott, and T. Nozawa, 2007: Detection of human influence on twentieth-century precipitation trends. Nature, 448, 461465, https://doi.org/10.1038/nature06025.

    • Search Google Scholar
    • Export Citation
  • Zou, C.-Z., and W. Wang, 2011: Inter-satellite calibration of AMSU-A observations for weather and climate applications. J. Geophys. Res. Atmos., 116, D23113, https://doi.org/10.1029/2011JD016205.

    • Search Google Scholar
    • Export Citation
  • Zou, C.-Z., M. D. Goldberg, and X. Hao, 2018: New generation of U.S. satellite microwave sounder achieves high radiometric stability performance for reliable climate change detection. Sci. Adv., 4, eaau0049, https://doi.org/10.1126/sciadv.aau0049.

    • Search Google Scholar
    • Export Citation
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Robust Anthropogenic Signal Identified in the Seasonal Cycle of Tropospheric Temperature

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  • 1 aProgram for Climate Model Diagnosis and Intercomparison, Lawrence Livermore National Laboratory, Livermore, California
  • | 2 bJoint Institute for Regional Earth System Science and Engineering, University of California at Los Angeles, Los Angeles, California
  • | 3 cDepartment of Earth and Planetary Sciences, University of California at Santa Cruz, Santa Cruz, California
  • | 4 dCanadian Centre for Climate Modelling and Analysis, Environment and Climate Change Canada, Victoria, British Columbia, Canada
  • | 5 eDepartment of Atmospheric Sciences, University of Washington, Seattle, Washington
  • | 6 fMassachusetts Institute of Technology, Earth, Atmospheric, and Planetary Sciences, Cambridge, Massachusetts
  • | 7 gCenter for Climate Physics, Institute for Basic Science, Busan, South Korea
  • | 8 hPusan National University, Busan, South Korea
  • | 9 iDepartment of Oceanography and International Pacific Research Center, School of Ocean and Earth Science and Technology, University of Hawai‘i at Mānoa, Honolulu, Hawaii
  • | 10 jRemote Sensing Systems, Santa Rosa, California
  • | 11 kCenter for Satellite Applications and Research, NOAA/NESDIS, Camp Springs, Maryland
  • | 12 lNational Center for Atmospheric Research, Boulder, Colorado
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Abstract

Previous work identified an anthropogenic fingerprint pattern in TAC(x, t), the amplitude of the seasonal cycle of mid- to upper-tropospheric temperature (TMT), but did not explicitly consider whether fingerprint identification in satellite TAC(x, t) data could have been influenced by real-world multidecadal internal variability (MIV). We address this question here using large ensembles (LEs) performed with five climate models. LEs provide many different sequences of internal variability noise superimposed on an underlying forced signal. Despite differences in historical external forcings, climate sensitivity, and MIV properties of the five models, their TAC(x, t) fingerprints are similar and statistically identifiable in 239 of the 240 LE realizations of historical climate change. Comparing simulated and observed variability spectra reveals that consistent fingerprint identification is unlikely to be biased by model underestimates of observed MIV. Even in the presence of large (factor of 3–4) intermodel and inter-realization differences in the amplitude of MIV, the anthropogenic fingerprints of seasonal cycle changes are robustly identifiable in models and satellite data. This is primarily due to the fact that the distinctive, global-scale fingerprint patterns are spatially dissimilar to the smaller-scale patterns of internal TAC(x, t) variability associated with the Atlantic multidecadal oscillation and El Niño–Southern Oscillation. The robustness of the seasonal cycle detection and attribution results shown here, taken together with the evidence from idealized aquaplanet simulations, suggest that basic physical processes are dictating a common pattern of forced TAC(x, t) changes in observations and in the five LEs. The key processes involved include GHG-induced expansion of the tropics, lapse-rate changes, land surface drying, and sea ice decrease.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Benjamin D. Santer, bensanter1289@gmail.com

Abstract

Previous work identified an anthropogenic fingerprint pattern in TAC(x, t), the amplitude of the seasonal cycle of mid- to upper-tropospheric temperature (TMT), but did not explicitly consider whether fingerprint identification in satellite TAC(x, t) data could have been influenced by real-world multidecadal internal variability (MIV). We address this question here using large ensembles (LEs) performed with five climate models. LEs provide many different sequences of internal variability noise superimposed on an underlying forced signal. Despite differences in historical external forcings, climate sensitivity, and MIV properties of the five models, their TAC(x, t) fingerprints are similar and statistically identifiable in 239 of the 240 LE realizations of historical climate change. Comparing simulated and observed variability spectra reveals that consistent fingerprint identification is unlikely to be biased by model underestimates of observed MIV. Even in the presence of large (factor of 3–4) intermodel and inter-realization differences in the amplitude of MIV, the anthropogenic fingerprints of seasonal cycle changes are robustly identifiable in models and satellite data. This is primarily due to the fact that the distinctive, global-scale fingerprint patterns are spatially dissimilar to the smaller-scale patterns of internal TAC(x, t) variability associated with the Atlantic multidecadal oscillation and El Niño–Southern Oscillation. The robustness of the seasonal cycle detection and attribution results shown here, taken together with the evidence from idealized aquaplanet simulations, suggest that basic physical processes are dictating a common pattern of forced TAC(x, t) changes in observations and in the five LEs. The key processes involved include GHG-induced expansion of the tropics, lapse-rate changes, land surface drying, and sea ice decrease.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Benjamin D. Santer, bensanter1289@gmail.com

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