Pathways to 1.5 °C and 2 °C warming based on observational and geological constraints

Abstract

To restrict global warming to below the agreed targets requires limiting carbon emissions, the principal driver of anthropogenic warming. However, there is significant uncertainty in projecting the amount of carbon that can be emitted, in part due to the limited number of Earth system model simulations and their discrepancies with present-day observations. Here we demonstrate a novel approach to reduce the uncertainty of climate projections; using theory and geological evidence we generate a very large ensemble (3 × 104) of projections that closely match records for nine key climate metrics, which include warming and ocean heat content. Our analysis narrows the uncertainty in surface-warming projections and reduces the range in equilibrium climate sensitivity. We find that a warming target of 1.5 °C above the pre-industrial level requires the total emitted carbon from the start of year 2017 to be less than 195–205 PgC (in over 66% of the simulations), whereas a warming target of 2 °C is only likely if the emitted carbon remains less than 395–455 PgC. At the current emission rates, these warming targets are reached in 17–18 years and 35–41 years, respectively, so that there is a limited window to develop a more carbon-efficient future.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: Surface warming projections and ocean heat content anomalies.
Fig. 2: Global mean surface temperature anomaly over time from observations and model simulations.
Fig. 3: Model ensemble parameter distributions.
Fig. 4: Cumulative carbon emissions and warming projections from our observationally consistent ensemble.

References

  1. 1.

    Adoption of the Paris Agreement FCCC/CP/2015/L.9/Rev.1 (UNFCCC, 2015).

  2. 2.

    IPCC Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) (Cambridge Univ. Press, Cambridge, 2013).

  3. 3.

    Meinshausen, M. et al. Greenhouse-gas emission targets for limiting global warming to 2 °C. Nature 458, 1158–1162 (2009).

    Article  Google Scholar 

  4. 4.

    Jones, C. et al. Twenty-first-century compatible CO2 emissions and airborne fraction simulated by CMIP5 Earth system models under four representative concentration pathways. J. Clim. 26, 4398–4413 (2013).

    Article  Google Scholar 

  5. 5.

    Collins M. et al. in Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) Ch. 12 (Cambridge Univ. Press, Cambridge, 2013).

  6. 6.

    Meinshausen, M. et al. The RCP greenhouse gas concentrations and their extensions from 1765 to 2300. Clim. Change 109, 213–241 (2011).

    Article  Google Scholar 

  7. 7.

    Millar, R. J. et al. Emission budgets and pathways consistent with limiting warming to 1.5 ºC. Nat. Geosci. 10, 741–747 (2017).

    Article  Google Scholar 

  8. 8.

    Morice, C. P., Kennedy, J. J., Rayner, N. A. & Jones, P. D. Quantifying uncertainties in global and regional temperature change using an ensemble of observational estimates: the HadCRUT4 dataset. J. Geophys. Res. 117, D08101 (2012).

    Article  Google Scholar 

  9. 9.

    GISS Surface Temperature Analysis (GISTEMP) (NASA Goddard Institute for Space Studies, accessed 19 January 2017); https://data.giss.nasa.gov/gistemp/

  10. 10.

    Hansen, J., Ruedy, S., Sato, M. & Lo, K. Global surface temperature change. Rev. Geophys. 48, RG4004 (2010).

    Article  Google Scholar 

  11. 11.

    Smith, T. M., Reynolds, R. W., Peterson, T. C. & Lawrimore, J. Improvements to NOAA’s historical merged land–ocean surface temperature analysis (1880–2006). J. Clim. 21, 2283–2296 (2008).

    Article  Google Scholar 

  12. 12.

    Vose, R. S. et al. NOAA’s merged land–ocean surface temperature analysis. Bull. Am. Meteorol. Soc. 93, 1677–1685 (2012).

    Article  Google Scholar 

  13. 13.

    Levitus, S. et al. World ocean heat content and thermosteric sea level change (0–2000 m), 1955–2010. Geophys. Res. Lett. 39, 10 (2012).

    Article  Google Scholar 

  14. 14.

    Giese, B. S. & Ray, S. El Niño variability in simple ocean data assimilation (SODA), 1871–2008. J. Geophys. Res. 116, C02024 (2011).

    Article  Google Scholar 

  15. 15.

    Balmaseda, M. A., Mogensen, K. & Weaver, A. T. Evaluation of the ECMWF ocean reanalysis system ORAS4. Q. J. Roy. Meteorol. Soc. 139, 1132–1161 (2013).

  16. 16.

    Good, S. A., Martin, M. J. & Rayner, N. A. EN4: quality controlled ocean temperature and salinity profiles and monthly objective analyses with uncertainty estimates. J. Geophys. Res. Ocean. 118, 6704–6716 (2013).

    Article  Google Scholar 

  17. 17.

    Smith, D. M. et al. Earth’s energy imbalance since 1960 in observations and CMIP5 models. Geophys. Res. Lett. 42.4, 1205–1213 (2015).

    Article  Google Scholar 

  18. 18.

    Cheng, L. et al. Improved estimates of ocean heat content from 1960 to 2015. Sci. Adv. 3, e1601545 (2017).

  19. 19.

    Goodwin, P., Williams, R. G. & Ridgwell, A. Sensitivity of climate to cumulative carbon emissions due to compensation of ocean heat and carbon uptake. Nat. Geosci. 8, 29–34 (2015).

    Article  Google Scholar 

  20. 20.

    Williams, R. G., Goodwin, P., Roussenov, V. M. & Bopp, L. A framework to understand the Transient Climate Response to Emissions. Environ. Res. Lett. 11, 015003 (2016).

  21. 21.

    Goodwin, P. How historic simulation–observation discrepancy affects future warming projections in a very large model ensemble. Clim. Dyn. 47, 2219–2233 (2016).

  22. 22.

    Goodwin, P., Haigh, I. D., Rohling, E. J. & Slangen, A. A new approach to projecting 21st century sea-level changes and extremes. Earth’s Future 5, 240–253 (2017).

    Article  Google Scholar 

  23. 23.

    Rohling, E. J. et al. Making sense of palaeoclimate sensitivity. Nature 491, 683–691 (2012).

    Article  Google Scholar 

  24. 24.

    le Quéré, C. et al. Global carbon budget 2016. Earth Syst. Sci. Data 8, 605–649 (2016).

    Article  Google Scholar 

  25. 25.

    Williamson, D. et al. History matching for exploring and reducing climate model parameter space using observations and a large perturbed physics ensemble. Clim. Dyn. 41, 1703–1729 (2013).

  26. 26.

    Williamson, D., Blaker, A. T., Hampton, C. & Salter, J. Identifying and removing structural biases in climate models with history matching. Clim. Dyn. 45, 1299 (2015).

    Article  Google Scholar 

  27. 27.

    Marvel, K., Schmidt, G. A., Miller, R. L. & Nazarenko, L. S. Implications for climate sensitivity from the response to individual forcings. Nat. Clim. Change 6, 386–389 (2015).

  28. 28.

    Shindell, D. T. Inhomogeneous forcing and transient climate sensitivity. Nat. Clim. Chang. 4, 274–277 (2014).

    Article  Google Scholar 

  29. 29.

    Hansen, J. et al. Efficacy of climate forcings. J. Geophys. Res. Atmos. 110, D18104 (2005).

    Article  Google Scholar 

  30. 30.

    Winton, M., Takahashi, K. & Held, I. Importance of ocean heat uptake efficacy to transient climate change. J. Clim. 23, 2333–2344 (2010).

    Article  Google Scholar 

  31. 31.

    Armour, K. C., Bitz, C. M. & Roe, G. H. Time-varying climate sensitivity from regional feedbacks. J. Clim. 26, 4518–4534 (2013).

    Article  Google Scholar 

  32. 32.

    Gregory, J. M. & Andrews, T. Variation in climate sensitivity and feedback parameters during the historical period. Geophys. Res. Lett. 43, 3911–3920 (2016).

    Article  Google Scholar 

  33. 33.

    Armour, K. C. Energy budget constraints on climate sensitivity in light of inconstant climate feedbacks. Nat. Clim. Chang. 7, 331–335 (2017).

    Article  Google Scholar 

  34. 34.

    Rugenstein, M. A. A., Caldeira, K. & Knutti, R. Dependence of global radiative feedbacks on evolving patterns of surface heat fluxes. Geophys. Res. Lett. 43, 9877–9885 (2016).

    Article  Google Scholar 

  35. 35.

    Knutti, R. & Hergerl, G. C. The equilibrium sensitivity of the Earth’s temperature to radiation changes. Nat. Geosci. 1, 735–743 (2008).

    Article  Google Scholar 

  36. 36.

    Geoffroy, O. et al. Transient climate response in a two-layer energy-balance model. Part II: Representation of the efficacy of deep-ocean heat uptake and validation for CMIP5 AOGCMs. J. Clim. 26, 1859–1876 (2013).

    Article  Google Scholar 

  37. 37.

    Williams, R. G., Roussenov, V., Goodwin, P., Resplandy, L. & Bopp, L. Sensitivity of global warming to carbon emissions: effects of heat and carbon uptake in a suite of Earth system models. J. Clim. 30, 9343–9363 (2017).

    Article  Google Scholar 

  38. 38.

    Allen, M. R. et al. Warming caused by cumulative carbon emissions towards the trillionth tonne. Nature 458, 1163–1166 (2009).

    Article  Google Scholar 

  39. 39.

    Matthews, H. D., Gillet, N. P., Stott, P. A. & Zickfield, K. The proportionality of global warming to cumulative carbon emissions. Nature 459, 829–832 (2009).

    Article  Google Scholar 

  40. 40.

    Gillet, N. P., Arora, V. K., Matthews, D. & Allen, M. R. Constraining the ratio of global warming to cumulative CO2 emissions using CMIP5 simulations. J. Clim. 26, 6844–6858 (2013).

    Article  Google Scholar 

  41. 41.

    Rogelj, J. et al. Paris Agreement climate proposals need a boost to keep warming well below 2 °C. Nature 534, 631–639 (2016).

    Article  Google Scholar 

  42. 42.

    Rockström, J. et al. A roadmap for rapid decarbonisation. Science 355, 1269–1271 (2017).

    Article  Google Scholar 

  43. 43.

    Arora, V. K. et al. Carbon emission limits required to satisfy future representative concentration pathways of greenhouse gases. Geophys. Res. Lett. 38, L046270 (2011).

  44. 44.

    Moore, J., Lindsay, K., Doney, S., Long, M. & Misumi, K. Marine ecosystem dynamics and biogeochemical cycling in the Community Earth System Model [CESM1(BGC)]: comparison of the 1990s with the 2090s under the RCP4.5 and RCP8.5 scenarios. J. Clim. 26, 9291–9312 (2013).

    Article  Google Scholar 

  45. 45.

    Dunne, J. P. et al. GFDLs ESM2 global coupled climate carbon Earth system models. Part II: Carbon system formulation and baseline simulation characteristics. J. Clim. 26, 2247–2267 (2013).

    Article  Google Scholar 

  46. 46.

    Martin, G. M. et al. The HadGEM2 family of Met Office Unified Model climate configurations. Geosci. Model. Dev. 4, 723–757 (2011).

    Article  Google Scholar 

  47. 47.

    Jones, C. D. et al. The HadGEM2-ES implementation of CMIP5 centennial simulations. Geosci. Model. Dev. 4, 543–570 (2011).

    Article  Google Scholar 

  48. 48.

    Dufresne, J. L. et al. Climate change projections using the IPSL-CM5 Earth system model: from CMIP3 to CMIP5. Clim. Dyn. 40, 2123–2165 (2013).

    Article  Google Scholar 

  49. 49.

    Watanabe, S. et al. MIROC-ESM 2010: model description and basic results of CMIP5-20c3m experiments. Geosci. Model. Dev. 4, 845–872 (2011).

    Article  Google Scholar 

  50. 50.

    Giorgetta, M. A. et al. Climate and carbon cycle changes from 1850 to 2100 in MPI-ESM simulations for the Coupled Model Intercomparison Project phase 5: climate changes in MPI-ESM. J. Adv. Model. Earth Syst. 5, 572–597 (2013).

    Article  Google Scholar 

  51. 51.

    Tjiputra, J. F. et al. Evaluation of the carbon cycle components in the Norwegian Earth System Model (NorESM). Geosci. Model. Dev. 6, 301–325 (2013).

    Article  Google Scholar 

  52. 52.

    Kennedy, J. J., Rayner, N. A., Smith, R. O., Saunby, M. & Parker, D. E. Reassessing biases and other uncertainties in sea-surface temperature observations measured in situ since 1850. Part 2: Biases and homogenisation. J. Geophys. Res. 116, D14104 (2011).

    Article  Google Scholar 

  53. 53.

    Huang, B. et al. Extended Reconstructed Sea Surface Temperature Version 4 (ERSST.v4). Part I: Upgrades and intercomparisons. J. Clim. 28, 911–930 (2015).

    Article  Google Scholar 

  54. 54.

    Domingues, C. M. et al. Improved estimates of upper-ocean warming and multi-decadal sea-level rise. Nature 453, 1090–1093 (2008).

    Article  Google Scholar 

  55. 55.

    Ishii, M. & Kimoto, M. Reevaluation of historical ocean heat content variations with an XBT depth bias correction. J. Oceanogr. 65, 287–299 (2009).

    Article  Google Scholar 

  56. 56.

    Smith, D. M. & Murphy, J. M. An objective ocean temperature and salinity analysis using covariances from a global climate model. J. Geophys. Res. 112, C02022 (2007).

    Article  Google Scholar 

  57. 57.

    Carton, J. A. & Giese, B. S. A reanalysis of ocean climate using Simple Ocean Data Assimilation (SODA). Mon. Weather. Rev. 136, 2999–3017 (2008).

    Article  Google Scholar 

  58. 58.

    Boden, T. A., G. Marland, and R. J. Andres. Global, Regional, and National Fossil-Fuel CO 2 Emissions (Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, TN, 2016); https://doi.org/10.3334/CDIAC/00001_V2016.

  59. 59.

    Houghton, R. A. et al. Chapter G2 carbon emissions from land use and land-cover change. Biogeosciences 9, 5125–514 (2012).

    Article  Google Scholar 

  60. 60.

    Khatiwala, S. et al. Global ocean storage of anthropogenic carbon. Biogeosciences 10, 2169–2191 (2013).

    Article  Google Scholar 

Download references

Acknowledgements

We acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modelling groups (listed in Supplementary Table 1 of this paper) for producing and making available their model output. For CMIP, the US Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provides coordinating support and lead development of the software infrastructure in partnership with the Global Organization for Earth System Science Portals. This work was supported by UK Natural Environment Research Council (NERC) grants NE/P01495X/1 and NE/N009789/1. G.L.F. acknowledges support from UK NERC grants NE/D00876X/2, NE/I005595/1 and NE/P011381/1. E.J.R. acknowledges Australian Laureate Fellowship FL120100050.

Author information

Affiliations

Authors

Contributions

P.G. and R.G.W. led the writing of the manuscript, with contributions from all of the co-authors. P.G. conducted the numerical experiments, which were conceived by P.G. and G.L.F. E.J.R. provided the geological climate sensitivity distribution. V.M.R. analysed the CMIP5 Earth system model output. A.K. and R.G.W. analysed the ocean heat re-analysis records.

Corresponding author

Correspondence to Philip Goodwin.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary figures and tables.

Supplementary Data

Data used to generate Figs. 1–4 and Supplementary Figs. 1–3.

WASP_ESM_main.cpp

Main code and instructions for the WASP Earth system model.

WASP_ESM_functions.cpp

Functions and forcing scenarios and instructions for the WASP Earth system model as used to perform the experiments in this study.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Goodwin, P., Katavouta, A., Roussenov, V.M. et al. Pathways to 1.5 °C and 2 °C warming based on observational and geological constraints. Nature Geosci 11, 102–107 (2018). https://doi.org/10.1038/s41561-017-0054-8

Download citation

Further reading

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing