To improve climate resilience for extreme fire events, researchers need to translate modelling uncertainties into useful guidance and be wary of overconfidence. If Earth system models do not capture the severity of recent Australian wildfires, development is urgently needed to assess whether we are underestimating fire risk.
This is a preview of subscription content, access via your institution
Relevant articles
Open Access articles citing this article.
-
Spatial and temporal patterns and driving factors of forest fires based on an optimal parameter-based geographic detector in the Panxi region, Southwest China
Fire Ecology Open Access 15 March 2024
-
Global increase in wildfire potential from compound fire weather and drought
npj Climate and Atmospheric Science Open Access 25 March 2022
-
Machine learning–based observation-constrained projections reveal elevated global socioeconomic risks from wildfire
Nature Communications Open Access 22 March 2022
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 print issues and online access
$209.00 per year
only $17.42 per issue
Buy this article
- Purchase on Springer Link
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
References
Giglio, L., Randerson, J. T. & van der Werf, G. R. J. Geophys. Res. Biogeosci. 118, 317–328 (2013).
University of Sydney News (8 January 2020); https://go.nature.com/3aBhTyu
Eggleton, M. National Geographic (15 November 2019); https://go.nature.com/38MEJSl
Rice, D. USA Today (8 January 2020); https://go.nature.com/36xAvfA
Mann, M. The Guardian (1 January 2020); https://go.nature.com/30WmreF
Law, T. Time (7 January 2020); https://go.nature.com/2RwgBxx
Abram, N. Scientific American Blog Network https://go.nature.com/3aNFI6o (2019).
Forkel, M. et al. Biogeosciences 16, 57–76 (2019).
Sippel, S., Meinshausen, N., Fischer, E. M., Székely, E. & Knutti, R. Nat. Clim. Change 10, 35–41 (2020).
Kirchmeier-Young, M. C., Zwiers, F. W., Gillett, N. P. & Cannon, A. J. Climatic Change 144, 365–379 (2017).
Van Wagner, C. E. & Canadian Forestry Service. Development and Structure of the Canadian Forest Fire Weather Index System. Forestry Technical Report 35 (Canadian Forestry Service, 1987).
Clarke, H. & Evans, J. P. Theor. Appl. Climatol. 136, 513–527 (2018).
Hoffmann, W. A. et al. Austr. Ecol. 37, 634–643 (2012).
Shuman, J. K. et al. Environ. Res. Lett. 12, 035003 (2017).
Thonicke, K. et al. Biogeosciences 7, 1991–2011 (2010).
Hantson, S. Biogeosciences 13, 3359–3375 (2016).
Hantson, S. et al. Geosci. Model Dev. https://doi.org/10.5194/gmd-2019-261 (2020).
Liu, Y., Goodrick, S. & Heilman, W. For. Ecol. Manag. 317, 80–96 (2014).
Arora, V. K. et al. Biogeosci. Discuss. https://doi.org/10.5194/bg-2019-473 (2019).
Norris, J., Chen, G. & Neelin, J. D. J. Clim. 32, 5397–5416 (2019).
Scheiter, S., Moncrieff, G. R., Pfeiffer, M. & Higgins, S. I. Biogeosci. Discuss. https://doi.org/10.5194/bg-2019-415 (2019).
Staver, A. C., Archibald, S. & Levin, S. A. Science 334, 230–232 (2011).
Forkel, M. et al. Environ. Res. Commun. 1, 051005 (2019).
Whitley, R. et al. Biogeosciences 14, 4711–4732 (2017).
Dahlin, K. M., Fisher, R. A. & Lawrence, P. J. Biogeosciences 12, 5061–5074 (2015).
Dahlin, K. M., Ponte, D. D., Setlock, E. & Nagelkirk, R. Ecography https://doi.org/10.1111/ecog.02443 (2017).
Williams, M., Law, B. E., Anthoni, P. M. & Unsworth, M. H. Tree Physiol. 21, 287–298 (2001).
Kauwe, M. G. D. et al. Biogeosciences 12, 7503–7518 (2015).
Whitley, R. et al. Biogeosciences 13, 3245–3265 (2016).
Teckentrup, L. et al. Biogeosciences 16, 3883–3910 (2019).
Rabin, S. S. et al. Geosci. Model Dev. 10, 1175–1197 (2017).
Neubauer, D. et al. HAMMOZ-Consortium MPI-ESM1.2-HAM model output prepared for CMIP6. https://doi.org/10.22033/ESGF/CMIP6.5016 (2019).
Seferian, R. CNRM-CERFACS CNRM-ESM2-1 model output prepared for CMIP6 CMIP. https://doi.org/10.22033/ESGF/CMIP6.1391 (2018).
EC-Earth Consortium (EC-Earth). EC-Earth-Consortium EC-Earth3-Veg model output prepared for CMIP6 ScenarioMIP. https://doi.org/10.22033/ESGF/CMIP6.727 (2019).
Danabasoglu, G. NCAR CESM2 model output prepared for CMIP6 ScenarioMIP. https://doi.org/10.22033/ESGF/CMIP6.7768 (2019).
Liu, Y. Y. et al. Nat. Clim. Change 5, 470–474 (2015).
Ge, Y., Avitabile, V., Heuvelink, G. B. M., Wang, J. & Herold, M. Int. J. Appl. Earth Obs. 31, 13–24 (2014).
Australian Government Bureau of Meteorology. Climate change — trends and extremes. http://www.bom.gov.au/climate/change (accessed 8 January 2020).
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 for producing and making available their model output, with specific contributions from MPI32, CNRM33, ECMWF34 and NCAR35. B.M.S. is supported by the French National Research Agency, ANR-17-MPGA-0016.
Author information
Authors and Affiliations
Corresponding author
Supplementary information
Supplementary Information
Supplementary Figs. 1–3
Rights and permissions
About this article
Cite this article
Sanderson, B.M., Fisher, R.A. A fiery wake-up call for climate science. Nat. Clim. Chang. 10, 175–177 (2020). https://doi.org/10.1038/s41558-020-0707-2
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41558-020-0707-2
This article is cited by
-
Spatial and temporal patterns and driving factors of forest fires based on an optimal parameter-based geographic detector in the Panxi region, Southwest China
Fire Ecology (2024)
-
Evidence and attribution of the enhanced land carbon sink
Nature Reviews Earth & Environment (2023)
-
Uncertainty in US forest carbon storage potential due to climate risks
Nature Geoscience (2023)
-
Effect of frequent bushfire on water supply reliability in Thomson Catchment, Victoria, Australia
Theoretical and Applied Climatology (2023)
-
Global increase in wildfire potential from compound fire weather and drought
npj Climate and Atmospheric Science (2022)