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Impacts of natural and anthropogenic forcings on historical and future changes in global-land surface air temperature in CMIP6–DAMIP simulations

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Abstract

To better understand the contributions of various external factors to past and future changes in global and regional climate, this study investigates the impacts of natural and anthropogenic forcings on historical and future changes in global land surface air temperature (GLSAT) using model simulations from the Detection and Attribution Model Intercomparison Project (DAMIP) in the Coupled Model Intercomparison Project Phase 6 (CMIP6). Results show that the anthropogenic forcing (ANT) can be robustly detected and separated from the response to the natural external forcing (NAT) since the 1970s. The observed warming changes since the 1950s are primarily attributed to the GHG forcing. ANT contributes a robust warming trend of 0.1–0.2 °C per decade for global landmass during 1951–2020 and cumulative warming by 2011–2020 (relative to 1901–1930) of 1.0–1.6 °C. These attributable warmings largely encompass the observed warming trend of ~ 0.18 °C per decade in 1951–2012 and the observed warming of 1.59 °C by 2011–2020 (relative to 1850–1900) for global landmass reported in IPCC AR5 and AR6, respectively. The anthropogenic warming is projected to increase by 3–6 °C for most global landmass under the SSP2-4.5 scenario, especially in the high latitudes Northern Hemisphere by the late twenty-first century, along with an increase in the mean and widespread flattening of the probability distribution functions (PDFs). The anthropogenic aerosol (AA) cooling effect is projected to decrease only modestly, from 0.7 °C in 2011-20 to 0.6 °C by the late 21st century, for the SSP2-4.5 scenario.

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Data Availability

The CMIP6 data were obtained from  https://esgf-node.llnl.gov/. The CRU data are available from https://data.ceda.ac.uk/badc/cru/data/cru_ts.

References

  • Allen MR, Stott PA (2003) Estimating signal amplitudes in optimal fingerprinting, Part I: Theory. Clim Dyn 21:477–491

    Google Scholar 

  • Allen MR, Tett SFB (1999) Checking for model consistency in optimal fingerprinting. Clim Dyn 15:419–434

    Google Scholar 

  • Allen MR et al (2009) Warming caused by cumulative carbon emissions towards the trillionth tonne. Nature 458:1163–1166

    ADS  CAS  PubMed  Google Scholar 

  • Arguez A, Vose RS (2011) The definition of the standard WMO climate normal: the key to deriving alternative climate normals. Bull Amer Meteor Soc 92:699–704

    ADS  Google Scholar 

  • Bindoff NL et al (2013) Detection and attribution of climate change: from global to regional. In: Stocker TF, et al. (eds.) Climate change 2013: the physical science basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. NY, USA, p 867–952

  • Bonfils CJW, Santer BD, Fyfe JC, MarvelK PTJ, Zimmerman SRH (2020) Human influence on joint changes in temperature, rainfall and continental aridity. Nat Clim Change 10:726–731

    ADS  CAS  Google Scholar 

  • Braganza K et al (2003) Simple indices of global climate variability and change: Part I-variability and correlation structure. Clim Dyn 20:491–502

    Google Scholar 

  • Braganza K, Karoly DJ, Hirst AC, Stott P, Stouffer RJ, Tett SFB (2004) Simple indices of global climate variability and change: Part II: attribution of climate change during the twentieth century. Clim Dyn 22:823–838

    Google Scholar 

  • Canty T, Mascioli NR, Smarte MD, Salawitch RJ (2013) An empirical model of global climate - Part 1: a critical evaluation of volcanic cooling. Atmos Chem Phys 13:3997–4031

    ADS  Google Scholar 

  • Chan D, Wu Q (2015) Attributing observed SST trends and subcontinental land warming to anthropogenic forcing during 1979–2005. J Clim 28:3152–3170

    ADS  Google Scholar 

  • Chen J, Dai A, Zhang Y (2019) Projected changes in daily variability and seasonal cycle of near-surface air temperature over the globe during the twenty-first century. J Climate 32:8537–8561

    ADS  Google Scholar 

  • Chen X, Tung KK (2014) Varying planetary heat sink led to global-warming slowdown and acceleration. Science 345:897–903

    ADS  CAS  PubMed  Google Scholar 

  • Cubasch U et al (2001) Projections of future climate change. In: J. T. Houghton et al. (eds) Climate change 2001: the scientific basis. Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge Univ. Press, New York. pp. 525– 582

  • Cuesta-Valero F, Garcia-Garcia A, Beltrami H et al (2021) Long-term global ground heat flux and continental heat storage from geothermal data. Clim Past 17(1):451–468

    Google Scholar 

  • Dai A, Fyfe JC, Xie S, Dai X (2015) Decadal modulation of global surface temperature by internal climate variability. Nat Clim Change 5:555–559

    ADS  Google Scholar 

  • Deser C, Phillips AS, Bourdette V, Teng H (2012) Uncertainty in climate change projections: the role of internal variability. Clim Dyn 38:527–546

    Google Scholar 

  • Dong B, Dai A (2015) The influence of the inter-decadal Pacific oscillation on temperature and precipitation over the globe. Clim Dyn 45:2667–2681

    Google Scholar 

  • England MH, McGregor S, Spence P, Meehl GA, Timmermann A, Cai W, Gupta AS, McPhaden J, Purich A, Santoso A (2014) Recent intensification of wind-driven circulation in the Pacific and the ongoing warming hiatus. Nature Clim Change 4:222–227

    ADS  Google Scholar 

  • Eyring V, Bony S, Meehl GA, Senior CA, Stevens B, Stouffer RJ, Taylor KE (2016) Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geosci Model 9:1937–1958

    Google Scholar 

  • Eyring V et al (2021) In Climate change 2021: the physical science basis. In: Masson-Delmotte V, et al. (eds.) Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, p 423–552

  • Fan X, Miao C, Duan Q, Shen C, Wu Y (2020) The performance of CMIP6 versus CMIP5 in simulating temperature extremes over the global land surface. J Geophys Res-atmos 125:e2020JD033031

  • Friedman AR et al (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 Clim 33:3487–3509

    ADS  Google Scholar 

  • Gillett NP, Zwiers FW, Weaver AJ et al (2003) Detection of human influence on sea level pressure. Nature 422:292–294

    ADS  CAS  PubMed  Google Scholar 

  • Gillett NP, Shiogama H, Funke B, Hegerl G, Knutti R, Matthes K, Santer BD, Stone D, Tebaldi C (2016) The Detection and Attribution Model Intercomparison Project (DAMIP v1.0) contribution to CMIP6. Geosci Model Dev 9:3685–3697

    ADS  Google Scholar 

  • Gillett NP, Kirchmeier-Young M, Ribes A et al (2021) Constraining human contributions to observed warming since the pre-industrial period. Nat Clim Chang 11:207–212

    ADS  Google Scholar 

  • Gulev SK etal (2021) Changing state of the climate system. In Climate change 2021: the physical science basis. In: Masson-Delmotte V, (ed) Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, p 287–422

  • Harris I, Osborn TJ, Jones P et al (2020) Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset. Sci Data 7:109

    PubMed  PubMed Central  Google Scholar 

  • Hartmann DL, coauthors (2013) Observations: atmosphere and surface. In: Stocker TF, et al. (eds.) Climate change 2013: the physical science basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA

  • Jones GS, Stott PA, Mitchell JFB (2016) Uncertainties in the attribution of greenhouse gas warming and implications for climate prediction. J Geophys Res 121(12):6969–6992

    CAS  Google Scholar 

  • Kettleborough JA, Booth BBB, Stott PA, Allen MR (2007) Estimates of uncertainty in predictions of global mean surface temperature. J Clim 20:843–855

    ADS  Google Scholar 

  • Knutson TR, Zeng F, Wittenberg AT (2013) Multi-model assessment of regional surface temperature trends. J Clim 26:8709–8743

    ADS  Google Scholar 

  • Kosaka Y, Xie SP (2013) Recent global-warming hiatus tied to equatorial Pacific surface cooling. Nature 501:403–407

    ADS  CAS  PubMed  Google Scholar 

  • Kossin JP, Mears C, Perlwitz J, and Wehner MF (2017) Detection and attribution of climate change. In: Climate Science Special Report: Fourth National Climate Assessment, Volume I. Global Change Research Program, p 114–132

  • Lee JY et al (2021) Future global climate: scenario-based projections and near-term information. In: Masson-Delmotte et al. (eds) Climate change 2021: the physical science basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 553–672

  • Li C, Zhao T, Ying K (2015) Effects of anthropogenic aerosols on temperature changes in China during the twentieth century based on CMIP5 models. Theor Appl Climatol 125:529–540

    ADS  Google Scholar 

  • Li C, Zhao T, Ying K (2017) Quantifying the contributions of anthropogenic and natural forcings to climate changes overland during 1946–2005. Clim Change 144:505–517

    ADS  CAS  Google Scholar 

  • Li C, Wang Z, Zwiers F, and Zhang X (2021) Improving the estimation of human climate influence by selecting appropriate forcing simulations. Geophys Res Lett 48(24):e2021GL095500

  • Lun Y, Liu L, Cheng L, Li X, Li H, Xu Z (2021) Assessment of GCMs simulation performance for precipitation and temperature from CMIP5 to CMIP6 over the Tibetan Plateau. Int J Climatol 41:3994–4018

    Google Scholar 

  • Marvel K, Zelinka M, Klein SA, Bonfils C, Caldwell P, Doutriaux C, Santer BD, Taylor KE (2015) External influences on modeled and observed cloud trends. J Clim 28:4820–4840

    ADS  Google Scholar 

  • Marvel K, Cook BI, Bonfils CJW, Durack PJ, Smerdon JE, Williams AP (2019) Twentieth-century hydroclimate changes consistent with human influence. Nature 569:59–65

    ADS  CAS  PubMed  Google Scholar 

  • McCarty JP (2001) Ecological consequences of recent climate change. Conserv Biol 15(2):320–331

    Google Scholar 

  • Neukom R, Gergis J, Karoly D et al (2014) Inter-hemispheric temperature variability over the past millennium. Nature Clim Change 4:362–367

    ADS  Google Scholar 

  • O’Neill BC et al (2016) The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6. Geosci Model Dev 9:3461–3482

    ADS  Google Scholar 

  • Qiao L, Zuo ZY, Xiao D, Bu L, Zhang K (2022) Variations in Eurasian surface air temperature over multiple timescales and their possible causes. Int J Climatol 1:1–20

    Google Scholar 

  • Ribes A, Planton S, Terray L (2013) Application of regularised optimal fingerprinting to attribution. Part I: method, properties and idealised analysis. Clim Dyn 41(11–12):2817–2836

  • Ribes A, Qasmi S, Gillett NP (2021) Making climate projections conditional on historical observations. Sci Adv 7(4):eabc0671

  • Ribes A, Terray L (2013) Application of regularised optimal fingerprinting to attribution Part II application to global near-surface temperature. Clim Dyn 41(11–12):2837–2853

    Google Scholar 

  • Santer BD, Po-Chedley S, Zelinka MD, Cvijanovic I, Bonfils C, Durack PJ, Fu Q, Kiehl J, Mears C, Painter J, Pallotta G, Solomon S, Wentz FJ, Zou C (2018) Human influence on the seasonal cycle of tropospheric temperature. Science 361:6399

    Google Scholar 

  • Schurer AP et al (2018) Estimating the transient climate response from observed warming. J Clim 31(20):8645–8663

    ADS  Google Scholar 

  • Seneviratne, SI, and coauthors, (2021) Weather and climate extreme events in a changing climate. In Climate change 2021: the physical science basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [eds. by Masson-Delmotte et al.]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1513–1766

  • Shiogama H et al (2016) Attributing historical changes in probabilities of record-breaking daily temperature and precipitation extreme events. SOLA 12:225–231

  • Sippel S, Meinshausen N, Fischer EM, Székely E, Knutti R (2020) Climate change now detectable from any single day of weather at global scale Nat. Clim Change 10:35–41

    ADS  Google Scholar 

  • Song YH, Chung E and Shahid S (2021) Spatiotemporal differences and uncertainties in projections of precipitation and temperature in South Korea from CMIP6 and CMIP5 general circulation models. Int J Climatol 41:5899–5199

  • SternKaufmann DI (2014) Anthropogenic and natural causes of climate change. Clim Change 122:257–269

    ADS  Google Scholar 

  • Stott PA, Jones GS, Lowe JA, Thorne P, Durman C, Johns TC, Thelen J (2006) Transient climate simulations with the HadGEM1 climate model: causes of past warming and future climate change. J Climate 19:2763–2782

    ADS  Google Scholar 

  • Stott PA et al (2016) Attribution of extreme weather and climate related events. Wires Clim Change 7:23–41

    Google Scholar 

  • Street JO, Carroll RJ, Ruppert D (1988) A note on computing robust regression estimates via iteratively reweighted least squares. Am Stat 42:152–154

    Google Scholar 

  • Sun Y, Zhang X, Zwiers FW (2014) Rapid increase in the risk of extreme summer heat in Eastern China. Nat Clim Change 4:1082–1085

    ADS  Google Scholar 

  • Sutton RT, Dong B, Gregory JM (2007) Land/sea warming ratio in response to climate change: IPCC AR4 model results and comparison with observations. Geophys Res Lett 34:L02701

    ADS  Google Scholar 

  • Taylor KE, Stouffer RJ, Meehl GA (2012) An overview of CMIP5 and the experiment design. Bull Amer Meteor Soc 93:485–498

    ADS  Google Scholar 

  • Walther GR, Post E, Convey P, Menzel A, Parmesan C, Beebee TJC, Fromentin JM, Oj H-G, Bairlein F (2002) Ecological responses to recent climate change. Nature 416:389–395

    ADS  CAS  PubMed  Google Scholar 

  • Wang YJ, Sun Y, Hu T, Qin D, Song L (2018) Attribution of temperature changes in Western China. Int J Climatol 38:742–750

    Google Scholar 

  • Wei M, Shu Q, Song Z, Song Y, Yang X, Guo Y, Li X, Qiao F (2021) Could CMIP6 climate models reproduce the early-2000s global warming slowdown? Sci China Earth Sci 64(6):853–865

    ADS  Google Scholar 

  • Williams AP, Seager R, Abatzoglou JT, Cook BI, Smerdon JE, Cook ER (2015) Contribution of anthropogenic warming to California drought during 2012–2014. Geophys Res Lett 42(16):6819–6828

    ADS  Google Scholar 

  • Yamaguchi M, Chan JCL, Moon IJ, Yoshida K, Mizuta R (2020) Global warming changes tropical cyclone translation speed. Nat Commun 11:47

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  • Zhang J, Zhao T, Dai A, Zhang W (2019) Detection and attribution of atmospheric precipitable water changes since the 1970s over China. Sci Rep 9:17609

    ADS  PubMed  PubMed Central  Google Scholar 

  • Zhao T, Li C, Zuo Z (2016) Contributions of anthropogenic and external natural forcings to climate changes over China based on CMIP5 model simulations. Sci China Earth Sci 59:503–517

    ADS  Google Scholar 

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Acknowledgements

The authors thank the climate modeling groups from CMIP6 for making their model output available (https://esgf-node.llnl.gov/). This research is supported by the National Basic Research Program of China (2020YFA0608904), the National Natural Science Foundation of China (42275185, 41975115, and 42205032), and the Natural Science Foundation of Shaanxi Province (2021JQ-166).

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Xu, C., Zhao, T., Zhang, J. et al. Impacts of natural and anthropogenic forcings on historical and future changes in global-land surface air temperature in CMIP6–DAMIP simulations. Climatic Change 177, 30 (2024). https://doi.org/10.1007/s10584-024-03686-6

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