• Adler, R. F., and Coauthors, 2018: The Global Precipitation Climatology Project (GPCP) monthly analysis (new version 2.3) and a review of 2017 global precipitation. Atmosphere, 9, 138, https://doi.org/10.3390/atmos9040138.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Barnes, E. A., J. W. Hurrell, I. Ebert-Uphoff, C. Anderson, and D. Anderson, 2019: Viewing forced climate patterns through an AI lens. Geophys. Res. Lett., 46, 13 38913 398, https://doi.org/10.1029/2019GL084944.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bellouin, N., and Coauthors, 2020: Bounding global aerosol radiative forcing of climate change. Rev. Geophys., 58, e2019RG000660, https://doi.org/10.1029/2019RG000660.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Beusch, L., L. Gudmundsson, and S. I. Seneviratne, 2020: Emulating Earth system model temperatures with MESMER: From global mean temperature trajectories to grid-point-level realizations on land. Earth Syst. Dyn., 11, 139159, https://doi.org/10.5194/esd-11-139-2020.

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

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chung, E., and B. Soden, 2017: Hemispheric climate shifts driven by anthropogenic aerosol–cloud interactions. Nat. Geosci., 10, 566571, https://doi.org/10.1038/ngeo2988.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Deng, J., A. Dai, and H. Xu, 2020: Nonlinear climate responses to increasing CO2 and anthropogenic aerosols simulated by CESM1. J. Climate, 33, 281301, https://doi.org/10.1175/JCLI-D-19-0195.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Deser, C., A. S. 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.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Deser, C., L. Terray, and A. S. Phillips, 2016: Forced and internal components of winter air temperature trends over North America during the past 50 years: Mechanisms and implications. J. Climate, 29, 22372258, https://doi.org/10.1175/JCLI-D-15-0304.1.

    • Crossref
    • 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.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • DiNezio, P. N., A. C. Clement, G. A. Vecchi, B. J. Soden, B. P. Kirtman, and S. Lee, 2009: Climate response of the equatorial Pacific to global warming. J. Climate, 22, 48734892, https://doi.org/10.1175/2009JCLI2982.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dittus, A. J., E. Hawkins, L. J. Wilcox, R. Sutton, C. J. Smith, M. B. Andrews, and P. M. Forster, 2020: Sensitivity of historical climate simulations to uncertain aerosol forcing. Geophys. Res. Lett., 47, e2019GL085806, https://doi.org/10.1029/2019GL085806.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dong, B., R. T. Sutton, E. Highwood, and L. Wilcox, 2014: The impacts of European and Asian anthropogenic sulfur dioxide emissions on Sahel rainfall. J. Climate, 27, 70007017, https://doi.org/10.1175/JCLI-D-13-00769.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dong, Y., C. Proistosescu, K. C. Armour, and D. S. Battisti, 2019: Attributing historical and future evolution of radiative feedbacks to regional warming patterns using a Green’s function approach: The preeminence of the western Pacific. J. Climate, 32, 54715491, https://doi.org/10.1175/JCLI-D-18-0843.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fischer, E., and R. Knutti, 2015: Anthropogenic contribution to global occurrence of heavy-precipitation and high-temperature extremes. Nat. Climate Change, 5, 560564, https://doi.org/10.1038/nclimate2617.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Friedman, A. R., Y. Hwang, J. C. H. Chiang, and D. M. W. Frierson, 2013: Interhemispheric temperature asymmetry over the twentieth century and in future projections. J. Climate, 26, 54195433, https://doi.org/10.1175/JCLI-D-12-00525.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Friedman, A. R., G. C. Hegerl, A. P. Schurer, S. Lee, W. Kong, W. Cheng, and J. C. H. Chiang, 2020: Forced and unforced decadal behavior of the interhemispheric SST contrast during the instrumental period (1881–2012): Contextualizing the late 1960s–early 1970s shift. J. Climate, 33, 34873509, https://doi.org/10.1175/JCLI-D-19-0102.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ganguly, D., P. J. Rasch, H. Wang, and J.-H. Yoon, 2012: Climate response of the South Asian monsoon system to anthropogenic aerosols. J. Geophys. Res., 117, D13209, https://doi.org/10.1029/2012JD017508.

    • Search Google Scholar
    • Export Citation
  • Gettelman, A., and Coauthors, 2010: Global simulations of ice nucleation and ice supersaturation with an improved cloud scheme in the Community Atmosphere Model. J. Geophys. Res., 115, D18216, https://doi.org/10.1029/2009JD013797.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gettelman, A., L. Lin, B. Medeiros, and J. Olson, 2016: Climate feedback variance and the interaction of aerosol forcing and feedbacks. J. Climate, 29, 66596675, https://doi.org/10.1175/JCLI-D-16-0151.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ghan, S., and Coauthors, 2016: Challenges in constraining anthropogenic aerosol effects on cloud radiative forcing using present-day spatiotemporal variability. Proc. Natl. Acad. Sci. USA, 113, 58045811, https://doi.org/10.1073/pnas.1514036113.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Giannini, A., and A. Kaplan, 2019: The role of aerosols and greenhouse gases in Sahel drought and recovery. Climatic Change, 152, 449466, https://doi.org/10.1007/s10584-018-2341-9.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gryspeerdt, E., and Coauthors, 2020: Surprising similarities in model and observational aerosol radiative forcing estimates. Atmos. Chem. Phys., 20, 613623, https://doi.org/10.5194/acp-20-613-2020.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hawkins, E., and R. Sutton, 2009: The potential to narrow uncertainty in regional climate predictions. Bull. Amer. Meteor. Soc., 90, 10951108, https://doi.org/10.1175/2009BAMS2607.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Haywood, J. M., A. Jones, N. Bellouin, and D. Stephenson, 2013: Asymmetric forcing from stratospheric aerosols impacts Sahelian rainfall. Nat. Climate Change, 3, 660665, https://doi.org/10.1038/nclimate1857.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hegerl, G. C., H. von 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.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Held, I. M., and B. J. Soden, 2006: Robust responses of the hydrological cycle to global warming. J. Climate, 19, 56865699, https://doi.org/10.1175/JCLI3990.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Huang, B., and Coauthors, 2017: Extended Reconstructed Sea Surface Temperature, version 5 (ERSSTv5): Upgrades, validations, and intercomparisons. J. Climate, 30, 81798205, https://doi.org/10.1175/JCLI-D-16-0836.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hwang, Y. T., D. M. Frierson, and S. M. Kang, 2013: Anthropogenic sulfate aerosol and the southward shift of tropical precipitation in the late 20th century. Geophys. Res. Lett., 40, 28452850, https://doi.org/10.1002/grl.50502.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jiménez-de-la-Cuesta, D., and T. Mauritsen, 2009: Emergent constraints on Earth’s transient and equilibrium response to doubled CO2 from post-1970s global warming. Nat. Geosci., 12, 902905, https://doi.org/10.1038/s41561-019-0463-y.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kang, S. M., Y. Shin, and S.-P. Xie, 2018: Extratropical forcing and tropical rainfall distribution: Energetics framework and ocean Ekman advection. npj Climate Atmos. Sci., 1, 20172, https://doi.org/10.1038/s41612-017-0004-6.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kang, S. M., and Coauthors, 2019: Extratropical–Tropical Interaction Model Intercomparison Project (ETIN-MIP): Protocol and initial results. Bull. Amer. Meteor. Soc., 100, 25892606, https://doi.org/10.1175/BAMS-D-18-0301.1.

    • Crossref
    • 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.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lamarque, J.-F., and Coauthors, 2010: Historical (1850–2000) gridded anthropogenic and biomass burning emissions of reactive gases and aerosols: Methodology and application. Atmos. Chem. Phys., 10, 70177039, https://doi.org/10.5194/acp-10-7017-2010.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lehner, F., C. Deser, N. Maher, J. Marotzke, E. Fischer, L. Brunner, R. Knutti, and E. Hawkins, 2020: Partitioning climate projection uncertainty with multiple large ensembles and CMIP5/6. Earth Syst. Dyn., 11, 491508, https://doi.org/10.5194/esd-11-491-2020.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, X., M. Ting, and D. E. Lee, 2018: Fast adjustments of the Asian summer monsoon to anthropogenic aerosols. Geophys. Res. Lett., 45, 10011010, https://doi.org/10.1002/2017GL076667.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, X., and Coauthors, 2012: Toward a minimal representation of aerosols in climate models: Description and evaluation in the Community Atmosphere Model CAM5. Geosci. Model Dev., 5, 709739, https://doi.org/10.5194/gmd-5-709-2012.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Maher, N., and Coauthors, 2019: The Max Planck Institute Grand Ensemble: Enabling the exploration of climate system variability. J. Adv. Model. Earth Syst., 11, 20502069, https://doi.org/10.1029/2019MS001639.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Manabe, S., and R. Stouffer, 1993: Century-scale effects of increased atmospheric CO2 on the ocean–atmosphere system. Nature, 364, 215218, https://doi.org/10.1038/364215a0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McKinnon, K. A., and C. Deser, 2018: Internal variability and regional climate trends in an observational large ensemble. J. Climate, 31, 67836802, https://doi.org/10.1175/JCLI-D-17-0901.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McKinnon, K. A., A. Poppick, E. Dunn-Sigouin, and C. Deser, 2017: An “observational large ensemble” to compare observed and modeled temperature trend uncertainty due to internal variability. J. Climate, 30, 75857598, https://doi.org/10.1175/JCLI-D-16-0905.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Milinski, S., N. Maher, and D. Olonscheck, 2019: How large does a large ensemble need to be? Earth Syst. Dyn. Discuss., https://doi.org/10.5194/esd-2019-70.

    • Search Google Scholar
    • Export Citation
  • Ming, Y., and V. Ramaswamy, 2009: Nonlinear climate and hydrological responses to aerosol effects. J. Climate, 22, 13291339, https://doi.org/10.1175/2008JCLI2362.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Morrison, H., and A. Gettelman, 2008: A new two-moment bulk stratiform cloud microphysics scheme in the NCAR Community Atmosphere Model (CAM3), Part I: Description and numerical tests. J. Climate, 21, 36423659, https://doi.org/10.1175/2008JCLI2105.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Myhre, G., and Coauthors, 2013: Anthropogenic and natural radiative forcing. Climate Change 2013: The Physical Science Basis, T. F. Stocker et al., Eds., Cambridge University Press, 659–740.

  • Myhre, G., and Coauthors, 2017: PDRMIP: A precipitation driver and response model intercomparison project—Protocol and preliminary results. Bull. Amer. Meteor. Soc., 98, 11851198, https://doi.org/10.1175/BAMS-D-16-0019.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Neale, R. B., and Coauthors, 2012: Description of the NCAR Community Atmosphere Model (CAM 5.0). NCAR Tech. Note NCAR/TN-486+STR, 268 pp., https://doi.org/10.5065/D6N877R0.

    • Crossref
    • Export Citation
  • Neelin, J. D., C. Chou, and H. Su, 2003: Tropical drought regions in global warming and El Niño teleconnections. Geophys. Res. Lett., 30, 2275, https://doi.org/10.1029/2003GL018625.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pendergrass, A. G., F. Lehner, B. M. Sanderson, and Y. Xu, 2015: Does extreme precipitation intensity depend on the emissions scenario? Geophys. Res. Lett., 42, 87678774, https://doi.org/10.1002/2015GL065854.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Persad, G. G., and K. Caldeira, 2018: Divergent global-scale temperature effects from identical aerosols emitted in different regions. Nat. Commun., 9, 3289, https://doi.org/10.1038/s41467-018-05838-6.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rayner, N. A., D. E. Parker, E. B. Horton, C. K. Folland, L. V. Alexander, D. P. Rowell, E. C. Kent, and A. Kaplan, 2003: Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J. Geophys. Res., 108, 4407, https://doi.org/10.1029/2002JD002670.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rohde, R., and Coauthors, 2013: Berkeley Earth temperature averaging process. Geoinfor. Geostat.: Overview, 1, 20100, https://doi.org/10.4172/2327-4581.1000103.

    • Search Google Scholar
    • Export Citation
  • Rotstayn, L. D., and U. Lohmann, 2002: Tropical rainfall trends and the indirect aerosol effect. J. Climate, 15, 21032116, https://doi.org/10.1175/1520-0442(2002)015<2103:TRTATI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Samset, B. H., M. T. Lund, M. Bollasina, G. Myhre, and L. Wilcox, 2019: Emerging Asian aerosol patterns. Nat. Geosci., 12, 582584, https://doi.org/10.1038/s41561-019-0424-5.

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

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schneider, U., A. Becker, P. Finger, A. Meyer-Christoffer, B. Rudolf, and M. Ziese, 2015: GPCC full data reanalysis version 6.0 at 0.5°: Monthly land-surface precipitation from rain-gauges built on GTS-based and historic data. Global Precipitation Climatology Centre, accessed October 2019, https://doi.org/10.5676/DWD_GPCC/FD_M_V7_050.

    • Crossref
    • Export Citation
  • Seo, J., S. M. Kang, and D. M. Frierson, 2014: Sensitivity of intertropical convergence zone movement to the latitudinal position of thermal forcing. J. Climate, 27, 30353042, https://doi.org/10.1175/JCLI-D-13-00691.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sippel, S., N. Meinshausen, A. Merrifield, F. Lehner, A. G. Pendergrass, E. Fischer, and R. Knutti, 2019: Uncovering the forced climate response from a single ensemble member using statistical learning. J. Climate, 32, 56775699, https://doi.org/10.1175/JCLI-D-18-0882.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Soden, B., and E. Chung, 2017: The large-scale dynamical response of clouds to aerosol forcing. J. Climate, 30, 87838794, https://doi.org/10.1175/JCLI-D-17-0050.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Solomon, A., L. M. Polvani, K. L. Smith, and R. P. Abernathey, 2015: The impact of ozone depleting substances on the circulation, temperature, and salinity of the Southern Ocean: An attribution study with CESM1(WACCM). Geophys. Res. Lett., 42, 55475555, https://doi.org/10.1002/2015GL064744.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stevens, B., 2015: Rethinking the lower bound on aerosol radiative forcing. J. Climate, 28, 47944819, https://doi.org/10.1175/JCLI-D-14-00656.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stevenson, S., A. Capotondi, J. Fasullo, and B. Otto-Bliesner, 2019: Forced changes to twentieth century ENSO diversity in a last millennium context. Climate Dyn., 52, 73597374, https://doi.org/10.1007/s00382-017-3573-5.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stuecker, M. F., and Coauthors, 2020: Strong remote control of future equatorial warming by off-equatorial forcing. Nat. Climate Change, 10, 124129, https://doi.org/10.1038/s41558-019-0667-6.

    • Crossref
    • 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.

    • Crossref
    • 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.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tebaldi, C., and J. M. Arblaster, 2014: Pattern scaling: Its strengths and limitations, and an update on the latest model simulations. Climatic Change, 122, 459471, https://doi.org/10.1007/s10584-013-1032-9.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Thompson, D. W. J., E. A. Barnes, C. Deser, W. E. Foust, and A. S. Phillips, 2015: Quantifying the role of internal climate variability in future climate trends. J. Climate, 28, 64436456, https://doi.org/10.1175/JCLI-D-14-00830.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ting, M., Y. Kushnir, R. Seager, and C. Li, 2009: Forced and internal twentieth-century SST trends in the North Atlantic. J. Climate, 22, 14691481, https://doi.org/10.1175/2008JCLI2561.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tomas, R. A., C. Deser, and L. Sun, 2016: The role of ocean heat transport in the global climate response to projected Arctic sea ice loss. J. Climate, 29, 68416859, https://doi.org/10.1175/JCLI-D-15-0651.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Undorf, S., D. Polson, M. A. Bollasina, Y. Ming, A. Schurer, and G. C. Hegerl, 2018a: Detectable impact of local and remote anthropogenic aerosols on the 20th century changes of West African and South Asian monsoon precipitation. J. Geophys. Res. Atmos., 123, 48714889, https://doi.org/10.1029/2017jd027711.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Undorf, S., M. A. Bollasina, and G. C. Hegerl, 2018b: Impacts of the 1900–1974 increase in anthropogenic aerosol emissions from North America and Europe on Eurasian summer climate. J. Climate, 31, 83818399, https://doi.org/10.1175/JCLI-D-17-0850.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • van Marle, M. J. E., and Coauthors, 2017: Historic global biomass burning emissions for CMIP6 (BB4CMIP) based on merging satellite observations with proxies and fire models (1750–2015). Geosci. Model Dev., 10, 33293357, https://doi.org/10.5194/gmd-10-3329-2017.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, C.-C., W.-L. Lee and C. Chou, 2019: Climate effects of anthropogenic aerosol forcing on tropical precipitation and circulations. J. Climate, 32, 52755287, https://doi.org/10.1175/JCLI-D-18-0641.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, H., S. Xie, and Q. Liu, 2016: Comparison of climate response to anthropogenic aerosol versus greenhouse gas forcing: Distinct patterns. J. Climate, 29, 51755188, https://doi.org/10.1175/JCLI-D-16-0106.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, K., C. Deser, L. Sun, and R. A. Tomas, 2018: Fast response of the tropics to an abrupt loss of Arctic sea ice via ocean dynamics. Geophys. Res. Lett., 45, 42644272, https://doi.org/10.1029/2018gl077325.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • White, R. H., A. A. McFarlane, D. M. W. Frierson, S. M. Kang, Y. Shin, and M. Friedman, 2018: Tropical precipitation and cross-equatorial heat transport in response to localized heating: Basin and hemisphere dependence. Geophys. Res. Lett., 45, 11 94911 958, https://doi.org/10.1029/2018GL078781.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wilcox, L. J., E. J. Highwood, B. B. B. Booth, and K. S. Carslaw, 2015: Quantifying sources of inter-model diversity in the cloud albedo effect. Geophys. Res. Lett., 42, 15681575, https://doi.org/10.1002/2015GL063301.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wilcox, L. J., and Coauthors, 2020: Accelerated increases in global and Asian summer monsoon precipitation from future aerosol reductions. Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2019-1188.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wills, R. C. J., D. S. Battisti, K. C. Armour, T. Schneider, and C. Deser, 2020: Pattern recognition methods to separate forced responses from internal variability in climate model ensembles and observations. J. Climate, https://doi.org/10.1175/JCLI-D-19-0855.1, in press.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xie, S.-P., C. Deser, G. A. Vecchi, J. Ma, H. Teng, and A. T. Wittenberg, 2010: Global warming pattern formation: Sea surface temperature and rainfall. J. Climate, 23, 966986, https://doi.org/10.1175/2009JCLI3329.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xie, S.-P., B. Lu, and B. Xiang, 2013: Similar spatial patterns of climate responses to aerosol and greenhouse gas changes. Nat. Geosci., 6, 828832, https://doi.org/10.1038/ngeo1931.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xu, Y., J. Lamarque, and B. M. Sanderson, 2018: The importance of aerosol scenarios in projections of future heat extremes. Climatic Change, 146, 393406, https://doi.org/10.1007/s10584-015-1565-1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yeager, S. G., and Coauthors, 2018: Predicting near-term changes in the Earth system: A large ensemble of initialized decadal prediction simulations using the Community Earth System Model. Bull. Amer. Meteor. Soc., 99, 18671886, https://doi.org/10.1175/BAMS-D-17-0098.1.

    • Crossref
    • 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.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhao, A., D. S. Stevenson, and M. A. Bollasina, 2019: Climate forcing and response to greenhouse gases, aerosols, and ozone in CESM1. J. Geophys. Res. Atmos., 124, 13 87613 894, https://doi.org/10.1029/2019JD030769.

    • Crossref
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Isolating the Evolving Contributions of Anthropogenic Aerosols and Greenhouse Gases: A New CESM1 Large Ensemble Community Resource

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  • 1 Climate and Global Dynamics Division, NCAR, Boulder, Colorado
  • 2 The University of Texas at Austin, Austin, Texas
  • 3 University of California at Santa Barbara, Santa Barbara, California
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Abstract

The evolving roles of anthropogenic aerosols (AER) and greenhouse gases (GHG) in driving large-scale patterns of precipitation and SST trends during 1920–2080 are studied using a new set of “all-but-one-forcing” initial-condition large ensembles (LEs) with the Community Earth System Model version 1 (CESM1), which complement the original “all-forcing” CESM1 LE (ALL). The large number of ensemble members (15–20) in each of the new LEs enables regional impacts of AER and GHG to be isolated from the noise of the model’s internal variability. Our analysis approach, based on running 50-yr trends, accommodates geographical and temporal changes in patterns of forcing and response. AER are shown to be the primary driver of large-scale patterns of externally forced trends in ALL before the late 1970s, and GHG to dominate thereafter. The AER and GHG forced trends are spatially distinct except during the 1970s transition phase when aerosol changes are mainly confined to lower latitudes. The transition phase is also characterized by a relative minimum in the amplitude of forced trend patterns in ALL, due to a combination of reduced AER and partially offsetting effects of AER and GHG. Internal variability greatly limits the detectability of AER- and GHG-forced trend patterns in individual realizations based on pattern correlation metrics, especially during the historical period, highlighting the need for LEs. We estimate that <20% of the spatial variances of observed precipitation and SST trends are attributable to AER and GHG forcing, although model biases in patterns of forced response and signal-to-noise may affect this estimate.

Denotes content that is immediately available upon publication as open access.

Corresponding author: Dr. Clara Deser, cdeser@ucar.edu

Abstract

The evolving roles of anthropogenic aerosols (AER) and greenhouse gases (GHG) in driving large-scale patterns of precipitation and SST trends during 1920–2080 are studied using a new set of “all-but-one-forcing” initial-condition large ensembles (LEs) with the Community Earth System Model version 1 (CESM1), which complement the original “all-forcing” CESM1 LE (ALL). The large number of ensemble members (15–20) in each of the new LEs enables regional impacts of AER and GHG to be isolated from the noise of the model’s internal variability. Our analysis approach, based on running 50-yr trends, accommodates geographical and temporal changes in patterns of forcing and response. AER are shown to be the primary driver of large-scale patterns of externally forced trends in ALL before the late 1970s, and GHG to dominate thereafter. The AER and GHG forced trends are spatially distinct except during the 1970s transition phase when aerosol changes are mainly confined to lower latitudes. The transition phase is also characterized by a relative minimum in the amplitude of forced trend patterns in ALL, due to a combination of reduced AER and partially offsetting effects of AER and GHG. Internal variability greatly limits the detectability of AER- and GHG-forced trend patterns in individual realizations based on pattern correlation metrics, especially during the historical period, highlighting the need for LEs. We estimate that <20% of the spatial variances of observed precipitation and SST trends are attributable to AER and GHG forcing, although model biases in patterns of forced response and signal-to-noise may affect this estimate.

Denotes content that is immediately available upon publication as open access.

Corresponding author: Dr. Clara Deser, cdeser@ucar.edu
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