The Earth has humans, so why don’t our climate models?

Abstract

While climate models have rapidly advanced in sophistication over recent decades, they lack dynamic representation of human behavior and social systems despite strong feedbacks between social processes and climate. The impacts of climate change alter perceptions of risk and emissions behavior that, in turn, influence the rate and magnitude of climate change. Addressing this deficiency in climate models requires a substantial interdisciplinary effort to couple models of climate and human behavior. We suggest a multi-model approach that considers a range of theories and implementations of human behavior and social systems, similar to the multi-model approach that has been used to explore the physical climate system. We describe the importance of linking social factors with climate processes and identify four priorities essential to advancing the development of coupled social-climate models.

This is a preview of subscription content, log in to check access.

Fig. 1

Data availability

N/A

References

  1. Ajzen I (1991) The theory of planned behavior. Organ Behav Hum Decis Process 50:179–211. https://doi.org/10.1016/0749-5978(91)90020-T

    Article  Google Scholar 

  2. Beckage B, Gross LJ, Kauffman S (2011) The limits to prediction in ecological systems. Ecosphere 2:1–12

    Article  Google Scholar 

  3. Beckage B, Gross LJ, Lacasse K et al (2018) Linking models of human behaviour and climate alters projected climate change. Nat Clim Chang 8:79–84. https://doi.org/10.1038/s41558-017-0031-7

    Article  Google Scholar 

  4. Bonan GB, Doney SC (2018) Climate, ecosystems, and planetary futures: the challenge to predict life in Earth system models. Science 359:eaam8328. https://doi.org/10.1126/science.aam8328

    Article  Google Scholar 

  5. Bury TM, Bauch CT, Anand M (2019) Charting pathways to climate change mitigation in a coupled socio-climate model. PLoS Comput Biol:15

  6. Calvin K, Bond-Lamberty B (2018) Integrated human-earth system modeling—state of the science and future directions. Environ Res Lett 13:063006. https://doi.org/10.1088/1748-9326/aac642

    Article  Google Scholar 

  7. Carslaw KS, Lee LA, Regayre LA, Johnson JS (2018) Climate models are uncertain, but we can do something about it. Eos 99. https://doi.org/10.1029/2018EO093757

  8. Clarke L, Eom J, Marten EH et al (2018) Effects of long-term climate change on global building energy expenditures. Energy Econ 72:667–677. https://doi.org/10.1016/j.eneco.2018.01.003

    Article  Google Scholar 

  9. Creutzig F, Fernandez B, Haberl H et al (2016) Beyond technology: demand-side solutions for climate change mitigation. Annu Rev Environ Resour 41:173–198. https://doi.org/10.1146/annurev-environ-110615-085428

    Article  Google Scholar 

  10. Demski C, Capstick S, Pidgeon N et al (2017) Experience of extreme weather affects climate change mitigation and adaptation responses. Clim Chang 140:149–164. https://doi.org/10.1007/s10584-016-1837-4

    Article  Google Scholar 

  11. Dickinson E, Henderson-Sellers A, Kennedy J (1993) Biosphere-atmosphere transfer scheme (BATS) version 1e as Coupled to the NCAR Community Climate Model (No. NCAR/TN-387+STR). University Corporation for Atmospheric Research. https://doi.org/10.5065/D67W6959

  12. Field CB, Lobell DB, Peters HA, Chiariello NR (2007) Feedbacks of terrestrial ecosystems to climate change. Annu Rev Environ Resour 32:1–29. https://doi.org/10.1146/annurev.energy.32.053006.141119

    Article  Google Scholar 

  13. Filatova T (2015) Empirical agent-based land market: integrating adaptive economic behavior in urban land-use models. Comput Environ Urban Syst 54:397–413. https://doi.org/10.1016/j.compenvurbsys.2014.06.007

    Article  Google Scholar 

  14. Füssel H-M (2010) How inequitable is the global distribution of responsibility, capability, and vulnerability to climate change: a comprehensive indicator-based assessment. Glob Environ Chang 20:597–611. https://doi.org/10.1016/j.gloenvcha.2010.07.009

    Article  Google Scholar 

  15. Gifford R (2011) The dragons of inaction: psychological barriers that limit climate change mitigation and adaptation. Am Psychol 66:290

    Article  Google Scholar 

  16. Hargreaves T (2011) Practice-ing behaviour change: applying social practice theory to pro-environmental behaviour change. J Consum Cult 11:79–99

    Article  Google Scholar 

  17. Hoffman AJ (2010) Climate change as a cultural and behavioral issue: addressing barriers and implementing solutions. Social Science Research Network, Rochester

  18. Kiehl J (2006) Geoengineering climate change: treating the symptom over the cause? Clim Chang 77:227

    Article  Google Scholar 

  19. Lorenz EN (2006) Predictability-a problem partly solved. In: Palmer T, Hagedorn R (eds) Predictability of weather and climate. Cambridge University Press, pp 40–58

  20. Lu X, Wrathall DJ, Sundsøy PR et al (2016) Detecting climate adaptation with mobile network data in Bangladesh: anomalies in communication, mobility and consumption patterns during cyclone Mahasen. Clim Chang 138:505–519. https://doi.org/10.1007/s10584-016-1753-7

    Article  Google Scholar 

  21. Lynch P (2006) The Emergence of Numerical Weather Prediction: Richardson’s Dream. Chapter 10: The ENIAC integrations. Cambridge Univ Press.

  22. Manabe S, Bryan K (1969) Climate calculations with a combined ocean-atmosphere model. J Atmos Sci 26:786–789

    Article  Google Scholar 

  23. McCright AM, Dunlap RE, Xiao C (2013) Increasing influence of party identification on perceived scientific agreement and support for government action on climate change in the United States, 2006–12. Wea Climate Soc 6:194–201. https://doi.org/10.1175/WCAS-D-13-00058.1

    Article  Google Scholar 

  24. Meadows DH, Meadows DL, Randers J, Behrens WW (1972) The limits to growth. N Y 102:27

  25. Meadows D, Randers J, Meadows D (2004) Limits to growth: the 30-year update. Chelsea Green Publishing

  26. Müller-Hansen F, Schlüter M, Mäs M et al (2017) Towards representing human behavior and decision making in earth system models – an overview of techniques and approaches. Earth Syst Dyn 8:977–1007. https://doi.org/10.5194/esd-8-977-2017

    Article  Google Scholar 

  27. Niamir L, Filatova T, Voinov A, Bressers H (2018) Transition to low-carbon economy: assessing cumulative impacts of individual behavioral changes. Energy Policy 118:325–345. https://doi.org/10.1016/j.enpol.2018.03.045

    Article  Google Scholar 

  28. Niamir L, Kiesewetter G, Wagner F et al (2020) Assessing the macroeconomic impacts of individual behavioral changes on carbon emissions. Clim Chang 158:141–160. https://doi.org/10.1007/s10584-019-02566-8

    Article  Google Scholar 

  29. Nordhaus W (2018) Evolution of modeling of the economics of global warming: Changes in the DICE model, 1992-2017. 22

  30. Nordhaus W (2019) Climate change: the ultimate challenge for economics. Am Econ Rev 109:1991–2014. https://doi.org/10.1257/aer.109.6.1991

    Article  Google Scholar 

  31. Palmer PI, Smith MJ (2014) Earth systems: model human adaptation to climate change. Nature News 512:365. https://doi.org/10.1038/512365a

    Article  Google Scholar 

  32. Penna CCR, Geels FW (2015) Climate change and the slow reorientation of the American car industry (1979–2012): an application and extension of the Dialectic Issue LifeCycle (DILC) model. Res Policy 44:1029–1048. https://doi.org/10.1016/j.respol.2014.11.010

    Article  Google Scholar 

  33. Phillips NA (1956) The general circulation of the atmosphere: a numerical experiment. Q J R Meteorol Soc 82:123–164. https://doi.org/10.1002/qj.49708235202

    Article  Google Scholar 

  34. Plattner G-K, Joos F, Stocker TF, Marchal O (2001) Feedback mechanisms and sensitivities of ocean carbon uptake under global warming. Tellus Ser B Chem Phys Meteorol 53:564–592. https://doi.org/10.3402/tellusb.v53i5.16637

    Article  Google Scholar 

  35. Prodhomme C, Batté L, Massonnet F et al (2016) Benefits of increasing the model resolution for the seasonal forecast quality in EC-Earth. J Clim 29:9141–9162. https://doi.org/10.1175/JCLI-D-16-0117.1

    Article  Google Scholar 

  36. Richardson LF (1922) Weather prediction by numerical methods. Cambridge University Press, London

  37. Schlüter M, Mcallister RRJ, Arlinghaus R et al (2012) New horizons for managing the environment: a review of coupled social-ecological systems modeling. Nat Resour Model 25:219–272. https://doi.org/10.1111/j.1939-7445.2011.00108.x

    Article  Google Scholar 

  38. Schlüter M, Baeza A, Dressler G et al (2017) A framework for mapping and comparing behavioural theories in models of social-ecological systems. Ecol Econ 131:21–35. https://doi.org/10.1016/j.ecolecon.2016.08.008

    Article  Google Scholar 

  39. Sellers PJ, Mintz Y, Sud YC, Dalcher A (1986) A simple biosphere model (SiB) for use within general circulation models. J Atmos Sci 43:505–531

    Article  Google Scholar 

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

    Article  Google Scholar 

  41. Thornton PE, Calvin K, Jones AD et al (2017) Biospheric feedback effects in a synchronously coupled model of human and Earth systems. Nat Clim Chang 7:496–500. https://doi.org/10.1038/nclimate3310

    Article  Google Scholar 

  42. Vaughan NE, Lenton TM (2011) A review of climate geoengineering proposals. Clim Chang 109:745–790. https://doi.org/10.1007/s10584-011-0027-7

    Article  Google Scholar 

  43. Vavrus S, Walsh JE, Chapman WL, Portis D (2006) The behavior of extreme cold air outbreaks under greenhouse warming. Int J Climatol 26:1133–1147. https://doi.org/10.1002/joc.1301

    Article  Google Scholar 

  44. Visschers VHM, Shi J, Siegrist M, Arvai J (2017) Beliefs and values explain international differences in perception of solar radiation management: insights from a cross-country survey. Clim Chang 142:531–544. https://doi.org/10.1007/s10584-017-1970-8

    Article  Google Scholar 

  45. Walsh K, Lavender S, Scoccimarro E, Murakami H (2013) Resolution dependence of tropical cyclone formation in CMIP3 and finer resolution models. Clim Dyn 40:585–599. https://doi.org/10.1007/s00382-012-1298-z

    Article  Google Scholar 

  46. Weber EU (2010) What shapes perceptions of climate change? WIREs Clim Chang 1:332–342. https://doi.org/10.1002/wcc.41

    Article  Google Scholar 

  47. Wigley TML (2006) A combined mitigation/Geoengineering approach to climate stabilization. Science 314:452–454. https://doi.org/10.1126/science.1131728

    Article  Google Scholar 

Download references

Funding

This work resulted from a working group jointly supported by both the National Institute for Mathematical Biological Synthesis, a synthesis center supported by the National Science Foundation through NSF Award DBI-1300426 with additional support from the University of Tennessee, Knoxville, and the National Socio-Environmental Synthesis Center (SESYNC) under funding received from the National Science Foundation DBI-1052875. BB, JW, and AZ additionally acknowledge support from NSF through VT EPSCoR Grant Nos. EPS-1101317 and OIA-1556770. FH additionally acknowledges support from the RUBISCO SFA, which is supported by the Regional and Global Model Analysis program activity in the Biological and Environmental Research office in the U.S. Department of Energy's Office of Science. Oak Ridge National Laboratory is managed by UT-Battelle, LLC, for the U.S. Department of Energy under Contract No. DE-AC05-00OR22725.

Author information

Affiliations

Authors

Contributions

Manuscript was conceptualized at working group meetings including all authors. BB, KL, JW, LG, NF, and FH were all involved in writing the initial manuscript; BB, KL, JW, and LG revised the final manuscript. All authors commented on previous versions of the manuscript and approved of final manuscript.

Corresponding author

Correspondence to Brian Beckage.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Code availability

N/A

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Beckage, B., Lacasse, K., Winter, J.M. et al. The Earth has humans, so why don’t our climate models?. Climatic Change 163, 181–188 (2020). https://doi.org/10.1007/s10584-020-02897-x

Download citation

Keywords

  • Coupled social-climate models
  • Natural-human systems
  • Climate change
  • Behavioral theory