Cited by 154Fire effects on ecosystemsClimate variability and modelsHydrology and Drought Analysis

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Understanding the variability of Australian fire weather between 1973 and 2017

Open access
Harris, Sarah, Lucas, Chris·2019·PLOS ONE

Abstract

Australian fire weather shows spatiotemporal variability on interannual and multi-decadal time scales. We investigate the climate factors that drive this variability using 39 station-based historical time series of the seasonal 90th-percentile of the McArthur Forest Fire Danger Index (FFDI) extending from 1973 through 2017. Using correlation analyses, we examine the relationship of these time series to the El Niño Southern Oscillation (ENSO), the Southern Annular Mode (SAM) and the Indian Ocean Dipole (IOD), considering both concurrent and time-lagged relationships. Additionally, longer term behaviour of the time series using linear trend analysis is discussed in the context of the climate drivers, Interdecadal Pacific Oscillation (IPO) and anthropogenic climate change. The results show that ENSO is the main driver for interannual variability of fire weather, as defined by FFDI in this study, for most of Australia. In general, El Niño-like conditions lead to more extreme fire weather, with this effect stronger in eastern Australia. However, there are significant regional variations to this general rule. In NSW, particularly along the central coast, negative SAM is a primary influence for elevated fire weather in late-winter and spring. In the southeast (VIC and TAS), the El Niño-like impact is exacerbated when positive IOD conditions are simultaneously observed. The spring conditions are key, and strongly influence what is observed during the following summer. On longer time scales (45 years), linear trends are upward at most stations; this trend is strongest in the southeast and during the spring. The positive trends are not driven by the trends in the climate drivers and they are not consistent with hypothesized impacts of the IPO, either before or after its late-1990s shift to the cold phase. We propose that anthropogenic climate change is the primary driver of the trend, through both higher mean temperatures and potentially through associated shifts in large-scale rainfall patterns. Variations from interannual factors are generally larger in magnitude than the trend effects observed to date.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1371/journal.pone.0222328

Cites

1 of 1 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Australia and Western US extreme wildfire events were directly linked to persistent drought and record-breaking temperatures.

    “Australia and Western US extreme wildfire events, among many others, were directly linked to persistent drought and record‐breaking temperatures (Harris & Lucas, 2019; van Oldenborgh et al., 2020).”

    From Scientists' warning on extreme wildfire risks to water supply (2021) · cites this paper as Harris & Lucas, 2019

    The Discussion (Interannual variability; page not available) links higher temperatures, reduced rainfall and extended drought to extreme fire weather in Australia. The paper studies fire-weather conditions, however, not specific extreme wildfire events; our copy does not establish a direct link to record-breaking temperatures or address Western US events. (LLM, conf=0.85)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Flagged first, then by citations

Research works printed but not linked: 69

  1. RA Bradstock, 2010, A biogeographic model of fire regimes in Australia: Current and future implications, Glob Ecol Biogeogr, 19, 145, 10.1111/j.1466-8238.2009.00512.x · DOI · Paper page
  2. K Hennessy, 2005, Climate change impacts on fire-weather in south-east Australia, CSIRO Mar Atmos Res, 91
  3. AJ Dowdy, 2018, Climatological variability of fire weather in Australia, J Appl Meteorol Climatol, 57, 221, 10.1175/JAMC-D-17-0167.1 · DOI
  4. H Clarke, 2013, Changes in Australian fire weather between 1973 and 2010., Int J Climatol, 33, 931, 10.1002/joc.3480 · DOI
  5. RH Luke, 1978, Bushfires in Australia. 1st ed. Bushfires in Australia
  6. R Blanchi, 2010, Meteorological conditions and wildfire-related houseloss in Australia., Int J Wildl Fire, 19, 914, 10.1071/WF08175 · DOI · Paper page
  7. R Blanchi, 2014, Environmental circumstances surrounding bushfire fatalities in Australia 1901–2011., Environ Sci Policy, 37, 192, 10.1016/j.envsci.2013.09.013 · DOI
  8. HH Hendon, 2007, Australian rainfall and surface temperature variations associated with the Southern Hemisphere annular mode, J Clim, 20, 2452, 10.1175/JCLI4134.1 · DOI
  9. JS Risbey, 2009, On the Remote Drivers of Rainfall Variability in Australia., Mon Weather Rev, 137, 3233, 10.1175/2009MWR2861.1 · DOI
  10. MJ McPhaden, 2006, ENSO as an Integrating Concept in Earth Science, Science, 314, 1740, 10.1126/science.1132588 · DOI
  11. C Wang, 2017, Coral Reefs of the Eastern Tropical Pacific, 85, 10.1007/978-94-017-7499-4_4 · DOI
  12. NH Saji, 1999, A dipole mode in the tropical Indian ocean, Nature, 401, 360, 10.1038/43854 · DOI · Paper page
  13. DWJ Thompson, 2000, Annular modes in the extratropical circulation, Part II: Trends. J Clim, 13, 1018
  14. G Love, 1986, The prediction of bushfires in central Australia, Aust Meteorol Mag, 34, 93
  15. DC Verdon, 2004, Multi-decadal variability of forest fire risk—Eastern Australia, Int J Wildl Fire, 13, 165, 10.1071/WF03034 · DOI
  16. AAJ Williams, 2001, The sensitivity of Australian fire danger to climate change, Clim Change
  17. C Lucas, 2007, Bushfire Weather in Southeast Australia: Recent Trends and Projected Climate Change Impacts, Consultancy report prepared for the Climate Institute of Australia by the Bushfire CRC and Australian Bureau of Meteorology
  18. S Harris, 2008, The relationship between the monsoonal summer rain and dry-season fire activity of northern Australia., Int J Wildl Fire, 17, 674, 10.1071/WF06160 · DOI
  19. N Nicholls, 2007, Interannual variations of area burnt in Tasmanian bushfires: Relationships with climate and predictability, Int J Wildl Fire, 16, 540, 10.1071/WF06125 · DOI
  20. L Felderhof, 2006, Comparison of fire patterns and fire frequency in two tropical savanna bioregions, Austral Ecol, 31, 736, 10.1111/j.1442-9993.2006.01645.x · DOI
  21. S Harris, 2014, Forecasting fire activity in Victoria, Australia, using antecedent climate variables and ENSO indices, Int J Wildl Fire, 23, 173, 10.1071/WF13024 · DOI
  22. M Mariani, 2016, ENSO controls interannual fire activity in southeast Australia, Geophys Res Lett, 43, 10,891, 10.1002/2016GL070572 · DOI
  23. W Cai, 2009, Positive Indian Ocean Dipole events precondition southeast Australia bushfires, Geophys Res Lett, 36, 1
  24. GJ Williamson, 2016, Bowman DMJS. Measurement of inter- and intra-annual variability of landscape fire activity at a continental scale: The Australian case, Environ Res Lett, 11
  25. M Mariani, 2016, The Southern Annular Mode determines interannual and centennial-scale fire activity in temperate southwest Tasmania, Australia, Geophys Res Lett, 43, 1702, 10.1002/2016GL068082 · DOI
  26. CSIRO BoM, 2018, State of the Climate 2018, State of the Climate
  27. S Power, 1999, Inter-decadal modulation of the impact of ENSO on Australia, Clim Dyn, 15, 319, 10.1007/s003820050284 · DOI
  28. C. Lucas, 2010, On developing a historical fire weather data-set for Australia, Aust Meteorol Oceanogr J, 60, 1, 10.22499/2.6001.001 · DOI
  29. IR Noble, 1980, McArthur’s fire‐danger meters expressed as equations, Aust J Ecol, 5, 201, 10.1111/j.1442-9993.1980.tb01243.x · DOI
  30. D. Griffiths, 1999, Improved Formula for the Drought Factor in McArthur’s Forest Fire Danger Meter., Aust For, 62, 202, 10.1080/00049158.1999.10674783 · DOI
  31. JJ Keetch, A Drought Index for Forest Fire Control, Res Pap, 35, 1968
  32. B Huang, 2017, Extended reconstructed Sea surface temperature, Version 5 (ERSSTv5): Upgrades, validations, and intercomparisons, J Clim
  33. NA Rayner, 2003, Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century, J Geophys Res, 108, 4407, 10.1029/2002JD002670 · DOI
  34. GJ Marshall, 2003, Trends in the Southern Annular Mode from observations and reanalyses, J Clim, 16, 4134, 10.1175/1520-0442(2003)016<4134:TITSAM>2.0.CO;2 · DOI
  35. BD Santer, 2000, Statistical significance of trends and trend differences in layer-average atmospheric temperature time series, J Geophys Res, 105, 7337, 10.1029/1999JD901105 · DOI
  36. MF Stuecker, 2017, Revisiting ENSO/Indian Ocean Dipole phase relationships, Geophys Res Lett, 44, 2481, 10.1002/2016GL072308 · DOI
  37. T Gong, 2010, The Impact of ENSO on Wave Breaking and Southern Annular Mode Events, J Atmos Sci, 67, 2854, 10.1175/2010JAS3311.1 · DOI
  38. B-M Kim, 2017, Amplitude-dependent relationship between the Southern Annular Mode and the El Niño Southern Oscillation in austral summer, Asia-Pacific J Atmos Sci, 53, 85, 10.1007/s13143-017-0007-6 · DOI
  39. EP Lim, 2015, Understanding and predicting the strong Southern Annular Mode and its impact on the record wet east Australian spring 2010, Clim Dyn, 44, 2807, 10.1007/s00382-014-2400-5 · DOI
  40. P Hope, 2017, A synthesis of findings Victorian Climate Initiative
  41. W Cai, 2014, Increased frequency of extreme Indian ocean dipole events due to greenhouse warming, Nature, 510, 254, 10.1038/nature13327 · DOI
  42. MP Hoerling, 1997, El Niño, La Niña, and the nonlinearity of their teleconnections, J Clim, 10, 1769, 10.1175/1520-0442(1997)010<1769:ENOLNA>2.0.CO;2 · DOI
  43. CTY Chung, 2014, Nonlinear precipitation response to El Niño and global warming in the Indo-Pacific, Clim Dyn, 42, 1837, 10.1007/s00382-013-1892-8 · DOI
  44. SB Power, 2006, The predictability of interdecadal changes in ENSO activity and ENSO teleconnections, J Clim, 19, 4755, 10.1175/JCLI3868.1 · DOI
  45. AJ Dowdy, 2017, Seasonal forecasting of fire weather based on a new global fire weather database. Proceedings for the 5th, International Fire Behaviour and Fuels Conference
  46. A Pui, 2012, Impact of the El Niño–Southern Oscillation, Indian Ocean Dipole, and Southern Annular Mode on Daily to Subdaily Rainfall Characteristics in East Australia, Mon Weather Rev, 140, 1665, 10.1175/MWR-D-11-00238.1 · DOI
  47. AK Betts, 2014, Climate coupling between temperature, humidity, precipitation, and cloud cover over the Canadian Prairies, J Geophys Res Atmos, 119, 13,305, 10.1002/2014JD022511 · DOI
  48. Y Yang, 2015, Seasonality and predictability of the Indian Ocean dipole mode: ENSO forcing and internal variability., J Clim, 28, 8021, 10.1175/JCLI-D-15-0078.1 · DOI
  49. DWJ Thompson, 2000, Annular Modes in the Extratropical Circulation. Part I: Month-to-Month Variability*, J Clim, 13, 1000, 10.1175/1520-0442(2000)013<1000:AMITEC>2.0.CO;2 · DOI
  50. P Fox-Hughes, 2014, Future fire danger climatology for Tasmania, Australia, using a dynamically downscaled regional climate model, Int J Wildl Fire, 23, 309, 10.1071/WF13126 · DOI
  51. S Evans, 2014, Variability of the Australian Monsoon and precipitation trends at Darwin, J Clim, 27, 8487, 10.1175/JCLI-D-13-00422.1 · DOI
  52. MC Wheeler, 2009, Impacts of the Madden-Julian oscillation on australian rainfall and circulation, J Clim
  53. NJ Mantua, 2002, The Pacific Decadal Oscillation, J Oceanogr
  54. EP Lim, 2017, Inter-decadal variations in the linkages between ENSO, the IOD and south-eastern Australian springtime rainfall in the past 30 years., Clim Dyn, 49, 97, 10.1007/s00382-016-3328-8 · DOI
  55. R Lin, 2018, ENSO Frequency Asymmetry and the Pacific Decadal Oscillation in Observations and 19 CMIP5 Models., Adv Atmos Sci., 35, 495, 10.1007/s00376-017-7133-z · DOI
  56. A Dai, 2015, Decadal modulation of global surface temperature by internal climate variability., Nat Clim Chang, 5, 555, 10.1038/nclimate2605 · DOI
  57. GA Meehl, 2016, Contribution of the Interdecadal Pacific Oscillation to twentieth-century global surface temperature trends., Nat Clim Chang, 6, 1005, 10.1038/nclimate3107 · DOI
  58. JG Palmer, 2015, Drought variability in the eastern Australia and New Zealand summer drought atlas (ANZDA, CE 1500–2012) modulated by the Interdecadal Paci fi c Oscillation, Environ Res Lett, 10, 124002, 10.1088/1748-9326/10/12/124002 · DOI
  59. BJ Henley, 2015, A Tripole Index for the Interdecadal Pacific Oscillation, Clim Dyn, 45, 3077, 10.1007/s00382-015-2525-1 · DOI
  60. C Lucas, 2014, The expanding tropics: A critical assessment of the observational and modeling studies, Wiley Interdiscip Rev Clim Chang, 5, 89, 10.1002/wcc.251 · DOI
  61. H Nguyen, 2015, Expansion of the Southern Hemisphere Hadley Cell in Response to Greenhouse Gas Forcing., J Clim, 28, 8067, 10.1175/JCLI-D-15-0139.1 · DOI
  62. C Lucas, 2012, An observational analysis of Southern Hemisphere tropical expansion, J Geophys Res Atmos, 117, 1, 10.1029/2011JD017033 · DOI
  63. C Lucas, 2015, Regional characteristics of tropical expansion and the role of climate variability, J Geophys Res Atmos, 120, 6809, 10.1002/2015JD023130 · DOI
  64. H Nguyen, 2017, Variability of the extent of the Hadley circulation in the southern hemisphere: a regional perspective, Clim Dyn, 0, 1
  65. HG Clarke, 2011, Regional signatures of future fire weather over eastern Australia from global climate models, Int J Wildl Fire, 20, 550, 10.1071/WF10070 · DOI
  66. MT Black, 2016, An attribution study of southeast Australian wildfire risk
  67. P Hope, 2019, On determining the impact of increasing atmospheric CO 2 on the record fire weather in eastern Australia in February 2017., Bull Am Meteorol Soc, 100, S111, 10.1175/BAMS-D-18-0135.1 · DOI
  68. JM Arblaster, 2006, Contributions of external forcings to southern annular mode trends., J Clim
  69. M Mariani, 2018, Bowman DMJS. Climate Change Amplifications of Climate-Fire Teleconnections in the Southern Hemisphere, Geophys Res Lett, 45, 5071, 10.1029/2018GL078294 · DOI

Cited by

Sorted by citations

Metadata

Authors
Harris, S., & Lucas, C.
Affiliations
  • Monash University (AU)
  • Bureau of Meteorology (AU)
Corresponding author
Harris, Sarah
Venue
PLOS ONE
Published
2019-09-19
Volume
14
Issue
9
Pages
e0222328
ISSN
1932-6203 (ISSN-L)
OpenAlex
W2974598703
Cited by
154
References
1
Citations by year
2026: 9 · 2025: 13 · 2024: 22 · 2023: 23 · 2022: 32 · 2021: 38 · 2020: 15 · 2019: 2
FWCI
11.20
Type
journal article
Language
en
Open access
Locations
  • PLoS ONE (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Figshare (submittedVersion, cc-by)
Funders
Monash University
Topics
  • Physical Sciences › Environmental Science › Global and Planetary Change › Fire effects on ecosystems
  • Physical Sciences › Environmental Science › Global and Planetary Change › Climate variability and models
  • Physical Sciences › Environmental Science › Global and Planetary Change › Hydrology and Drought Analysis
Keywords
fire weather, McArthur Forest Fire Danger Index, ENSO, Southern Annular Mode, Indian Ocean Dipole, Interdecadal Pacific Oscillation, anthropogenic climate change, interannual variability, spatiotemporal variation, Australian bushfires, rainfall patterns

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review