Detection of human influences on temperature seasonality from the nineteenth century

Abstract

It has been widely reported that anthropogenic warming is detectable with high confidence after the 1950s. However, current palaeoclimate records suggest an earlier onset of industrial-era warming. Here, we combine observational data, multiproxy palaeo records and climate model simulations for a formal detection and attribution study. Instead of the traditional approach to the annual mean temperature change, we focus on changes in temperature seasonality (that is, the summer-minus-winter temperature difference) from the regional to whole Northern Hemisphere scales. We show that the detectable weakening of temperature seasonality, which started synchronously over the northern mid–high latitudes since the late nineteenth century, can be attributed to anthropogenic forcing. Increased greenhouse gas concentrations are the main contributors over northern high latitudes, while sulfate aerosols are the major contributors over northern mid-latitudes. A reduction in greenhouse gas emissions and air pollution is expected to mitigate the weakening of temperature seasonality and its potential ecological effects.

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 temperature seasonality for 1851–2005.
Fig. 2: Regional ATCs and concentrations of CO2 and SO4.
Fig. 3: Simulated ATC magnitude driven by separate forcings for 1851–2005.
Fig. 4: Results of the detection and attribution analyses applied to the magnitude of the ATC in seven regions.

Data availability

The data that support the findings of this study are available from the corresponding author upon request.

References

  1. 1.

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

  2. 2.

    Stott, P. Attribution: weather risks in a warming world. Nat. Clim. Change 5, 516–517 (2015).

    Article  Google Scholar 

  3. 3.

    Christidis, N., Stott, P. A., Brown, S., Hegerl, G. C. & Caesar, J. Detection of changes in temperature extremes during the second half of the 20th century. Geophys. Res. Lett. 32, L20716 (2005).

    Article  Google Scholar 

  4. 4.

    Hughes, L. Biological consequences of global warming: is the signal already apparent? Trends Ecol. Evol. 15, 56–61 (2000).

    CAS  Article  Google Scholar 

  5. 5.

    Fussmann, K. E., Schwarzmuller, F., Brose, U., Jousset, A. & Rall, B. C. Ecological stability in response to warming. Nat. Clim. Change 4, 206–210 (2014).

    Article  Google Scholar 

  6. 6.

    Soh, W. K. et al. A new paleo-leaf economic proxy reveals a shift in ecosystem function in response to global warming at the onset of the triassic period. Nat. Plants 3, 17104 (2017).

    CAS  Article  Google Scholar 

  7. 7.

    Wang, G. & Dillon, M. E. Recent geographic convergence in diurnal and annual temperature cycling flattens global thermal profiles. Nat. Clim. Change 4, 988–992 (2014).

    Article  Google Scholar 

  8. 8.

    Stine, A. R., Huybers, P. & Fung, I. Y. Changes in the phase of the annual cycle of surface temperature. Nature 457, 435–440 (2009).

    CAS  Article  Google Scholar 

  9. 9.

    Mann, M. E. & Park, J. Greenhouse warming and changes in the seasonal cycle of temperature: model versus observations. Geophys. Res. Lett. 23, 1111–1114 (1996).

    CAS  Article  Google Scholar 

  10. 10.

    Wallace, C. J. & Osborn, T. J. Recent and future modulation of the annual cycle. Clim. Res. 22, 1–11 (2002).

    Article  Google Scholar 

  11. 11.

    Walther, G. R. et al. Ecological responses to recent climate change. Nature 416, 389–395 (2002).

    CAS  Article  Google Scholar 

  12. 12.

    Li, Y., Huang, Y., Bergelson, J., Nordborg, M. & Borevitz, J. O. Association mapping of local climate-sensitive quantitative trait loci in Arabidopsis thaliana. Proc. Natl Acad. Sci. USA 197, 21199–21204 (2010).

    Article  Google Scholar 

  13. 13.

    Qian, C. & Zhang, X. B. Human influences on changes in the temperature seasonality in mid- to high-latitude land areas. J. Clim. 28, 5908–5921 (2015).

    Article  Google Scholar 

  14. 14.

    Ruddiman, W. F. et al. Late Holocene climate: natural or anthropogenic? Rev. Geophys. 54, 93–118 (2016).

    Article  Google Scholar 

  15. 15.

    Abram, N. J. et al. Early onset of industrial-era warming across the oceans and continents. Nature 536, 411–418 (2016).

    CAS  Article  Google Scholar 

  16. 16.

    Duan, J. et al. Weakening of annual temperature cycle over the Tibetan Plateau since the 1870s. Nat. Commun. 8, 14008 (2017).

    CAS  Article  Google Scholar 

  17. 17.

    Duan, K. Q., Thompson, L. G., Yao, T., Davis, M. E. & Mosley-Thompson, E. A 1000 year history of atmospheric sulfate concentrations in southern Asia as recorded by a Himalayan ice core. Geophys. Res. Lett. 34, L01810 (2007).

    Article  Google Scholar 

  18. 18.

    Luterbacher, J., Dietrich, D., Xoplaki, E., Grosjean, M. & Wanner, H. European seasonal and annual temperature variability, trends, and extremes since 1500. Science 303, 1499–1503 (2004).

    CAS  Article  Google Scholar 

  19. 19.

    Jones, P. D. et al. Hemispheric and large-scale land-surface air temperature variations: an extensive revision and an update to 2010. J. Geophys. Res. Atmos. 117, D05127 (2012).

    Google Scholar 

  20. 20.

    Taylor, K. E., Stouffer, R. J. & Meehl, G. A. An overview of CMIP5 and the experiment design. Bull. Am. Meteor. Soc. 93, 485–498 (2012).

    Article  Google Scholar 

  21. 21.

    Rangwala, I., Sinsky, E. & Miller, J. R. Amplified warming projections for high altitude regions of the northern hemisphere mid-latitudes from CMIP5 models. Environ. Res. Lett. 8, 024040 (2013).

    Article  Google Scholar 

  22. 22.

    Wang, H. J., Zeng, Q. C. & Zhang, X. H. The numerical-simulation of the climatic-change caused by CO2 doubling. Sci. China Ser. B 36, 451–462 (1993).

    CAS  Google Scholar 

  23. 23.

    Shindell, D. T., Miller, R. L., Schmidt, G. A. & Pandolfo, L. Simulation of recent northern winter climate trends by greenhouse-gas forcing. Nature 399, 452–455 (1999).

    CAS  Article  Google Scholar 

  24. 24.

    Mitchell, J. F. B., Johns, T. C., Gregory, J. M. & Tett, S. F. B. Climate response to increasing levels of greenhouse gases and sulfate aerosols. Nature 376, 501–504 (1995).

    CAS  Article  Google Scholar 

  25. 25.

    Bindoff, N. L. et al. in IPCC Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) 867–931 (Cambridge Univ. Press, 2013).

  26. 26.

    Smith, S. J. et al. Anthropogenic sulfur dioxide emissions: 1850–2005. Atmos. Chem. Phys. 11, 1101–1116 (2011).

    CAS  Article  Google Scholar 

  27. 27.

    Hunter, D. E., Schwartz, S. E., Wagener, R. & Benkovitz, C. M. Seasonal, latitudinal, and secular variations in temperature trend—evidence for influence of anthropogenic sulfate. Geophys. Res. Lett. 20, 2455–2458 (1993).

    Article  Google Scholar 

  28. 28.

    Meinshausen, M. et al. Historical greenhouse gas concentrations for climate modelling (CMIP6). Geosci. Model Dev. 10, 2057–2116 (2017).

    CAS  Article  Google Scholar 

  29. 29.

    Fischer, H., Wagenbach, D. & Kipfstuhl, J. Sulfate and nitrate firn concentrations on the Greenland ice sheet—2. Temporal anthropogenic deposition changes. J. Geophys. Res. Atmos. 103, 21935–21942 (1998).

    CAS  Article  Google Scholar 

  30. 30.

    Hannig, J. & Marron, J. S. Advanced distribution theory for SiZer. J. Am. Stat. Assoc. 101, 484–499 (2006).

    CAS  Article  Google Scholar 

  31. 31.

    Sato, Y. et al. Aerosol effects on cloud water amounts were successfully simulated by a global cloud-system resolving model. Nat. Commun. 9, 985 (2018).

    Article  Google Scholar 

  32. 32.

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

    Article  Google Scholar 

  33. 33.

    Allen, M. R. & Stott, P. A. Estimating signal amplitudes in optimal fingerprinting, part I: theory. Clim. Dynam. 21, 477–491 (2003).

    Article  Google Scholar 

Download references

Acknowledgements

This research was supported by the National Key R&D Program of China (2016YFA0600404) and National Natural Science Foundation of China (41875113 and 41471035). P.W. was supported by the UK–China Research and Innovation Partnership Fund through the Met Office Climate Science for Service Partnership China as part of the Newton Fund. J.L. is supported by the Belmont Forum and JPI Climate Collaborative Research Action ‘INTEGRATE, an integrated data-model study of interactions between tropical monsoons and extratropical climate variability and extremes’. A.S. and G.H. were supported by the ERC-funded project TITAN (EC-320691), and NERC under the Belmont forum grant PacMedy (NE/P006752/1). J.D. acknowledges support from the Alexander von Humboldt Foundation. We are very grateful to K. Duan and H. Fischer for making their ice-core sulfate concentrations data available. We also thank A. Ribes for comments on the early manuscript.

Author information

Affiliations

Authors

Contributions

J.D. designed the study and performed the analyses, with support from Z.M., L.L., P.W., J.L. and E.X. J.D. drafted and revised the manuscript with input from P.W., J.L., A.S., G.H., D.G. and E.X. Y.D. and L.C. improved the figures. All authors contributed to interpreting the results and discussions.

Corresponding author

Correspondence to Jianping Duan.

Ethics declarations

Competing interests

The authors declare no competing 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 Figs. 1–12, Supplementary Tables 1–2

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Duan, J., Ma, Z., Wu, P. et al. Detection of human influences on temperature seasonality from the nineteenth century. Nat Sustain 2, 484–490 (2019). https://doi.org/10.1038/s41893-019-0276-4

Download citation

Further reading

Search

Sign up for the Nature Briefing newsletter for a daily update on COVID-19 science.
Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing