Articles | Volume 23, issue 16
https://doi.org/10.5194/acp-23-9217-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-23-9217-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
22 Aug 2023
Research article |
| 22 Aug 2023
| 22 Aug 2023
Atlantic Multidecadal Oscillation modulates the relationship between El Niño–Southern Oscillation and fire weather in Australia
Guanyu Liu
Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing 100871, China
Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing 100871, China
Tong Ying
Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing 100871, China
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Cited articles
Abram, N. J., Henley, B. J., Sen Gupta, A., Lippmann, T. J. R., Clarke, H., Dowdy, A. J., Sharples, J. J., Nolan, R. H., Zhang, T. H., Wooster, M. J., Wurtzel, J. B., Meissner, K. J., Pitman, A. J., Ukkola, A. M., Murphy, B. P., Tapper, N. J., and Boer, M. M.: Connections of climate change and variability to large and extreme forest fires in southeast Australia, Communications Earth & Environment, 2, 8, https://doi.org/10.1038/s43247-020-00065-8, 2021.
An, X., Chen, W., Ma, T., Aru, H., Cai, Q., Li, C., and Sheng, L.: Key role
of Arctic sea-ice in subseasonal reversal of early and late winter PM2.5
concentration anomalies over the North China Plain, Geophys. Res. Lett, 50,
e2022GL101841, https://doi.org/10.1029/2022GL101841, 2023.
Arriagada, N. B., Palmer, A. J., Bowman, D., Morgan, G. G., Jalaludin, B.
B., and Johnston, F. H.: Unprecedented smoke-related health burden
associated with the 2019–20 bushfires in eastern Australia, Med. J. Aust.,
213, 282–283, https://doi.org/10.5694/mja2.50545, 2020.
Bradstock, R. A.: A biogeographic model of fire regimes in Australia:
current and future implications, Glob. Ecol. Biogeogr., 19, 145–158,
https://doi.org/10.1111/j.1466-8238.2009.00512.x, 2010.
Cai, W., Cowan, T., and Raupach, M.: Positive Indian Ocean Dipole events
precondition southeast Australia bushfires, Geophys. Res. Lett., 36, L19710,
https://doi.org/10.1029/2009GL039902, 2009.
Chafik, L., Hakkinen, S., England, M. H., Carton, J. A., Nigam, S.,
Ruiz-Barradas, A., Hannachi, A., and Miller, L.: Global linkages originating
from decadal oceanic variability in the subpolar North Atlantic, Geophys.
Res. Lett., 43, 10909–10919, https://doi.org/10.1002/2016GL071134, 2016.
Chen, Y., Morton, D. C., Andela, N., van der Werf, G. R., Giglio, L., and
Randerson, J. T.: A pan-tropical cascade of fire driven by El Nino/Southern
Oscillation, Nat. Clim. Change, 7, 906-911,
https://doi.org/10.1038/s41558-017-0014-8, 2017.
Chikamoto, Y., Mochizuki, T., Timmermann, A., Kimoto, M., and Watanabe, M.:
Potential tropical Atlantic impacts on Pacific decadal climate trends,
Geophys. Res. Lett., 43, 7143–7151, https://doi.org/10.1002/2016GL069544, 2016.
Chikamoto, Y., Johnson, Z. F., Wang, S. Y. S., McPhaden, M. J., and
Mochizuki, T.: El Nino-Southern Oscillation Evolution Modulated by Atlantic
Forcing, J. Geophys. Res.-Oceans, 125, e2020JC016318,
https://doi.org/10.1029/2020JC016318, 2020.
Clarke, H. and Evans, J. P.: Exploring the future change space for fire
weather in southeast Australia, Theor. Appl., 136, 513–527,
https://doi.org/10.1007/s00704-018-2507-4, 2019.
Clarke, H., Gibson, R., Cirulis, B., Bradstock, R. A., and Penman, T. D.:
Developing and testing models of the drivers of anthropogenic and
lightning-caused wildfire ignitions in south-eastern Australia, J. Environ.
Manage., 235, 34–41, https://doi.org/10.1016/j.jenvman.2019.01.055, 2019.
Clements, C. B., Zhong, S. Y., Bian, X. D., Heilman, W. E., and Byun, D. W.:
First observations of turbulence generated by grass fires, J. Geophys. Res.-Atmos., 113, D22102, https://doi.org/10.1029/2008JD010014, 2008.
Climate Analysis Section: AMO Index Data, NCAR [data set], Boulder, USA, https://climatedataguide.ucar.edu/climate-data/atlantic-multi-decadal-oscillation-amo, last access: 17 August 2022a.
Climate Analysis Section: Nino SST Indices (Nino 1+2, 3, 3.4, 4; ONI and TNI), NCAR [data set], Boulder, USA, https://climatedataguide.ucar.edu/climate-data/nino-sst-indices-nino-12-3-34-4-oni-and-tni, last access: 17 August 2022b.
Copernicus Climate Change Service: Fire danger indices historical data from the Copernicus Emergency Management Service, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.0e89c522, 2019.
Copernicus Contractor: Fire danger indices historical data from the
Copernicus Emergency Management Service, Zenodo [data set],
https://doi.org/10.5281/zenodo.5754302, 2021.
Damany-Pearce, L., Johnson, B., Wells, A., Osborne, M., Allan, J., Belcher, C., Jones, A., and Haywood, J.: Australian wildfires cause the largest stratospheric warming since Pinatubo and extends the lifetime of the Antarctic ozone hole, Sci. Rep., 12, 12665, https://doi.org/10.1038/s41598-022-15794-3, 2022.
Dowdy, A. J.: Climatological Variability of Fire Weather in Australia, J.
Appl. Meteorol. Clim., 57, 221–234, https://doi.org/10.1175/JAMC-D-17-0167.1, 2018.
Duc, H. N., Chang, L. T. C., Azzi, M., and Jiang, N. B.: Smoke aerosols
dispersion and transport from the 2013 New South Wales (Australia)
bushfires, Environ. Monit. Assess., 190, 428,
https://doi.org/10.1007/s10661-018-6810-4, 2018.
Earl, N. and Simmonds, I.: Variability, trends, and drivers of regional
fluctuations in Australian fire activity, J. Geophys. Res.-Atmos., 122,
7445–7460, https://doi.org/10.1002/2016JD026312, 2017.
Gan, R., Liu, Q., Huang, G., Hu, K. M., and Li, X. C.: Greenhouse warming and
internal variability increase extreme and central Pacific El Niño
frequency since 1980, Nat. Commun., 14, 394,
https://doi.org/10.1038/s41467-023-36053-7, 2023.
Geng, X., Zhang, W., Jin, F., Stuecker, M. F., and Levine, A. F. Z.: Modulation of the Relationship between ENSO and Its Combination Mode by the Atlantic Multidecadal Oscillation, J. Climate, 33, 4679–4695,
https://doi.org/10.1175/JCLI-D-19-0740.1, 2020.
Gent, P. R., Danabasoglu, G., Donner, L. J., Holland, M. M., Hunke, E. C.,
Jayne, S. R., Lawrence, D. M., Neale, R. B., Rasch, P. J., Vertenstein, M.,
Worley, P. H., Yang, Z., and Zhang, M.: The Community Climate System Model
Version 4, J. Climate, 24, 4973–4991, https://doi.org/10.1175/2011JCLI4083.1, 2011.
Global Modeling and Assimilation Office (GMAO): MERRA-2 tavgM_2d_flx_Nx: 2d, Monthly mean, Time-Averaged, Single-Level, Assimilation, Surface Flux Diagnostics V5.12.4, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], https://doi.org/10.5067/0JRLVL8YV2Y4, 2015a.
Global Modeling and Assimilation Office (GMAO): MERRA-2 tavgM_2d_slv_Nx: 2d, Monthly mean, Time-Averaged, Single-Level, Assimilation, Single-Level Diagnostics V5.12.4, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], https://doi.org/10.5067/AP1B0BA5PD2K, 2015b.
Harris, S., Nicholls, N., and Tapper, N.: Forecasting fire activity in
Victoria, Australia, using antecedent climate variables and ENSO indices,
Int. J. Wildland Fire, 23, 173–184, https://doi.org/10.1071/WF13024, 2014.
Hennessy, K. J., Lucas, C., Nicholls, N., Bathols, J., Suppiah, R., and
Ricketts, J. H.: Climate change impacts on fire-weather in south-east
Australia, 88 pp., ISBN: 1-921061-10-3, 2005.
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A.,
Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I.,
Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5
monthly averaged data on single levels from 1979 to present, Copernicus
Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.f17050d7, 2019.
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 hourly data on single levels from 1940 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.adbb2d47, 2023.
Huang, B., Banzon, V. F., Freeman, E., Lawrimore, J., Wei, L.,
Peterson, T. C., Smith, T. M., Thorne, P. W., Woodruff, S. D., and
Zhang, H.-M.: Extended Reconstructed Sea Surface Temperature (ERSST), Version 4, NOAA National Centers for Environmental Information [data set], https://doi.org/10.7289/V5KD1VVF, 2015.
Huang, B., Thorne, P. W., Banzon, V. F., Boyer, T., Chepurin, G., Lawrimore, J. H., Menne, M. J., Smith, T. M., Vose, R. S., and Zhang, H.-M.: NOAA Extended Reconstructed Sea Surface Temperature (ERSST), Version 5, NOAA National Centers for Environmental Information [data set], https://doi.org/10.7289/V5T72FNM, 2017.
Jain, P., Castellanos-Acuna, D., Coogan, S. C. P., Abatzoglou, J. T., and
Flannigan, M. D.: Observed increases in extreme fire weather driven by
atmospheric humidity and temperature, Nat. Clim., 12, 63–70, https://doi.org/10.1038/s41558-021-01224-1, 2021.
Japan Meteorological Agency: Characteristics of Global Sea Surface Temperature Analysis Data (COBE-SST) for Climate Use, Monthly Report on Climate System Separated, Japan Meteorological Agency, 12, 116 pp., https://psl.noaa.gov/data/gridded/data.cobe2.html (last access: 20 November 2022), 2006.
Kalnay, E., Kanamitsu, M., Kistler, R., Collins, W., Deaven, D., Gandin, L., Iredell, M., Saha, S., White, G., Woollen, J., Zhu, Y., Leetmaa, A., Reynolds, B., Chelliah, M., Ebisuzaki, W., Higgins, W., Janowiak, J., Mo,
K. C., Ropelewski, C., Wang, J., Jenne, R., and Joseph, D.: The NCEP/NCAR 40-year reanalysis project, B. Am. Meteorol. Soc., 77, 437–471, https://doi.org/10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2, 1996 (data available at: https://psl.noaa.gov/data/gridded/data.ncep.reanalysis.html, last access: 19 November 2021).
Kaplan, A., Cane, M. A., Kushnir, Y., Clement, A. C., Blumenthal, M. B., and
Rajagopalan, B.: Analyses of global sea surface temperature 1856–1991, J.
Geophys. Res.-Oceans, 103, 18567–18589, https://doi.org/10.1029/97JC01736, 1998 (data available at: https://psl.noaa.gov/data/gridded/data.kaplan_sst.html, last access: 20 November 2021).
Keeley, J. E., Brennan, T. J., and Syphard, A. D.: The effects of prolonged
drought on vegetation dieback and megafires in southern California
chaparral, Ecosphere, 13, e4203, https://doi.org/10.1002/ecs2.4203, 2022.
Koo, E., Pagni, P. J., Weise, D. R., and Woycheese, J. P.: Firebrands and
spotting ignition in large-scale fires, Int. J. Wildland Fire, 19,
818–843, https://doi.org/10.1071/WF07119, 2010.
Krawchuk, M. A., Moritz, M. A., Parisien, M. A., Van Dorn, J., and Hayhoe,
K.: Global Pyrogeography: the Current and Future Distribution of Wildfire,
PLoS One, 4, e5102, https://doi.org/10.1371/journal.pone.0005102, 2009.
Kriwoken, L. K.: Australian biodiversity and marine protected areas, Ocean
Coast. Manage., 44, 113–132, https://doi.org/10.1016/S0964-5691(96)00047-6, 1996.
Lawson, B. D. and Armitage, O. B.: Weather Guide for the Canadian Forest Fire Danger Rating System, Northern Forestry Center, Canadian Forest Service, 1–84, ISBN: 978-1-100-11565-8, 2008.
Levine, A. F. Z., McPhaden, M. J., and Frierson, D. M. W.: The impact of the
AMO on multidecadal ENSO variability, Geophys. Res. Lett., 44, 3877–3886,
https://doi.org/10.1002/2017GL072524, 2017.
Li, X. C., Holland, D. M., Gerber, E. P., and Yoo, C.: Impacts of the north
and tropical Atlantic Ocean on the Antarctic Peninsula and sea ice, Nature,
505, 538–542, https://doi.org/10.1038/nature12945, 2014.
Li, X. C., Gerber, E. P., Holland, D. M., and Yoo, C.: A Rossby Wave Bridge
from the Tropical Atlantic to West Antarctica, J. Climate, 28, 2256–2273,
https://doi.org/10.1175/JCLI-D-14-00450.1, 2015.
Li, X. C., Xie, S. P., Gille, S. T., and Yoo, C.: Atlantic-induced
pan-tropical climate change over the past three decades, Nat. Clim., 6,
275–279, https://doi.org/10.1038/nclimate2840, 2016.
Li, Y. J., Li, J. P., Jin, F. F., and Zhao, S.: Interhemispheric propagation of stationary Rossby waves in a horizontally nonuniform background flow, J.
Atmos. Sci., 72, 3233–3256, https://doi.org/10.1175/JAS-D-14-0239.1, 2015.
Li, Y. J., Feng, J., Li, J. P., and Hu, A. X.: Equatorial windows and barrier
for Stationary Rossby waves, J. Climate, 32, 6117–6135,
https://doi.org/10.1175/JCLI-D-18-0722.1, 2019.
Lin, Z. D. and Li, Y.: Remote Influence of the Tropical Atlantic on the
Variability and Trend in North West Australia Summer Precipitation, J.
Climate, 25, 2408–2420, https://doi.org/10.1175/JCLI-D-11-00020.1, 2012.
Littell, J. S., Peterson, D. L., Riley, K. L., Liu, Y. Q., and Luce, C. H.:
A review of the relationships between drought and forest fire in the United
States, Glob. Chang. Biol., 22, 2353–2369, https://doi.org/10.1111/gcb.13275, 2016.
Liu, D. Y., Zhou, C. R., Keesing, J. K., Serrano, O., Wener, A., Fang, Y., Chen, Y. J., Masque, P., Kinloch, J., Sadekov, A., and Du, Y.: Wildfires enhance phytoplankton production in tropical
oceans, Nat. Commun., 13, 1348, https://doi.org/10.1038/s41467-022-29013-0, 2022.
Liu, G. Y., Li, J., Jiang, Z. J., and Li, X. C.: Impact of Sea Surface
Temperature Variability at Different Ocean Basins on Dust Activities in the
Gobi Desert and North China, Geophys. Res. Lett., 49, e2022GL099821,
https://doi.org/10.1029/2022GL099821, 2022.
Liu, W., Huang, B., Thorne, P. W., Banzon, V. F., Zhang, H., Freeman, E., Lawrimore, J., Peterson, T. C., Smith, T. M., and Woodruff, S. D.: Extended Reconstructed Sea Surface Temperature Version 4 (ERSST.v4): Part II. Parametric and Structural Uncertainty Estimations, J. Climate, 28, 931–951, https://doi.org/10.1175/JCLI-D-14-00007.1, 2015.
Lv, Z., Yang, J.-C., Lin, X., and Zhang, Y.: Stronger North Atlantic than
Tropical Pacific Effects on North Pacific Decadal Prediction, J. Climate,
35, 5773–5785, https://doi.org/10.1175/JCLI-D-21-0957.1, 2022.
Mariani, M., Fletcher, M. S., Holz, A., and Nyman, P.: ENSO controls
interannual fire activity in southeast Australia, Geophys. Res. Lett.,
43, 10891–10900, https://doi.org/10.1002/2016GL070572, 2016.
Mariani, M., Holz, A., Veblen, T. T., Williamson, G., Fletcher, M. S., and
Bowman, D. M. J. S.: Climate Change Amplifications of Climate-Fire
Teleconnections in the Southern Hemisphere, Geophys. Res. Lett., 45,
5071–5081, https://doi.org/10.1029/2018GL078294, 2018.
McGregor, S., Timmermann, A., Stuecker, M. F., England, M. H., Merrifield,
M., Jin, F. F., and Chikamoto, Y.: Recent Walker circulation strengthening
and Pacific cooling amplified by Atlantic warming, Nat. Clim., 4,
888–892, https://doi.org/10.1038/NCLIMATE2330, 2014.
Miller, A. J., Neilson, D. J., Luther, D. S., Hendershott, M. C., Cornuelle, B. D., Worcester, P. F., Dzieciuch, M. A., Dushaw, B. D., Howe, B. M., Levin, J. C., Arango, H. G., and Haidvogel, D. B.: Barotropic Rossby wave radiation from a model Gulf
Stream, Geophys. Res. Lett., 34, L23613, https://doi.org/10.1029/2007GL031937, 2007.
Nagaraju, C., Ashok, K., Balakrishnan Nair, T. M., Guan, Z., and Cai, W.:
Potential influence of the Atlantic Multi-decadal Oscillation in modulating
the biennial relationship between Indian and Australian summer monsoons,
Int. J. Climatol., 38, 5220–5230, https://doi.org/10.1002/joc.5722, 2018.
Nguyen, H. D., Azzi, M., White, S., Salter, D., Trieu, T., Morgan, G., Rahman, M., Watt, S., Riley, M., Chang, L. T.-C., Barthelemy, X., Fuchs, D., Lieschke, K., and Nguyen, H.: The Summer 2019–2020 Wildfires in East Coast Australia and Their Impacts on Air Quality and Health in New South Wales, Australia, Int. J. Env. Res. Pub. He., 18, 3538, https://doi.org/10.3390/ijerph18073538, 2021.
Nolan, R. H., Boer, M. M., Collins, L., de Dios, V. R., Clarke, H., Jenkins,
M., Kenny, B., and Bradstock, R. A.: Causes and consequences of eastern
Australia's 2019–20 season of mega-fires, Glob. Chang. Biol., 26,
1039–1041, https://doi.org/10.1111/gcb.15125, 2020.
Power, S., Casey, T., Folland, C., Colman, A., and Mehta, V.: Inter-decadal
modulation of the impact of ENSO on Australia, Clim. Dynam., 15, 319–324,
https://doi.org/10.1007/s003820050284, 1999.
Rayner, N. A., Parker, D. E., Horton, E. B., Folland, C. K., Alexander, L.
V., Rowell, D. P., Kent, E. C., and Kaplan, A.: Global analyses of sea
surface temperature, sea ice, and night marine air temperature since the
late nineteenth century, J. Geophys. Res.-Atmos, 108, 4407,
https://doi.org/10.1029/2002JD002670, 2003 (data available at: https://www.metoffice.gov.uk/hadobs/hadisst/, last access: 20 November 2021).
Ren, H. C., Zuo J. Q., and Li, W. J.: The impact of tropical Atlantic SST
variability on the tropical atmosphere during boreal summer, J. Climate,
34, 6705–6723, https://doi.org/10.1175/JCLI-D-20-0259.1, 2021.
Ren, X. Y., Zhang, L., Cai, W. J., Li, X. C., Wang, C. Y., Jin, Y. S., and
Wu, L. X.: Influence of tropical Atlantic meridional dipole of sea surface
temperature anomalies on Antarctic autumn sea ice, Environ. Res. Lett.,
17, 094046, https://doi.org/10.1088/1748-9326/ac8f5b, 2022.
Risbey, J. S., Pook, M. J., McIntosh, P. C., Wheeler, M. C., and Hendon, H.
H.: On the Remote Drivers of Precipitation Variability in Australia, Mon.
Weather Rev., 137, 3233–3253, https://doi.org/10.1175/2009MWR2861.1, 2009.
Sardeshmukh, P. D. and Hoskins, B. J.: The Generation of Global Rotational Flow by Steady Idealized Tropical Divergence, J. Atmos. Sci., 45, 1228–1251, https://doi.org/10.1175/1520-0469(1988)045<1228:TGOGRF>2.0.CO;2, 1988.
Schneider, D. P., Deser, C., Fasullo, J., and Trenberth, K. E.: Climate Data Guide Spurs Discovery and Understanding, EOS T. Am. Geophys. Un., 94, 121, https://doi.org/10.1002/2013eo130001, 2013.
Simpkins, G. R., McGregor, S., Taschetto, A. S., Ciasto, L. M., and England,
M. H.: Tropical Connections to Climatic Change in the Extratropical Southern
Hemisphere: The Role of Atlantic SST Trends, J. Climate, 27, 4923–4936,
https://doi.org/10.1175/JCLI-D-13-00615.1, 2014.
Simpson, C. C., Pearce, H. G., Sturman, A. P., and Zawar-Reza, P.: Behaviour
of fire weather indices in the 2009–10 New Zealand wildland fire season, Int. J. Wildland Fire, 23, 1147–1164, https://doi.org/10.1071/wf12169, 2014.
Storch, H. V. and Zwiers, F. W.: Statistical Analysis in Climate Research, Cambridge University Press, Cambridge, 6–8, https://doi.org/10.2307/2669798, 2000.
Trenberth, K. E.: The Definition of El Niño, B. Am. Meteorol. Soc.,
78, 2771–2778, https://doi.org/10.1175/1520-0477(1997)078<2771:TDOENO>2.0.CO;2, 1997.
Trenberth, K. E. and Shea, D. J.: Atlantic hurricanes and natural
variability in 2005, Geophys. Res. Lett., 33, L12704, https://doi.org/10.1029/2006GL026894, 2006.
Trenberth, K. E. and Stepaniak, D. P.: Indices of El Niño Evolution, J.
Climate, 14, 1697–1701, https://doi.org/10.1175/1520-0442(2001)014<1697:LIOENO>2.0.CO;2, 2001.
van Oldenborgh, G. J., Krikken, F., Lewis, S., Leach, N. J., Lehner, F., Saunders, K. R., van Weele, M., Haustein, K., Li, S., Wallom, D., Sparrow, S., Arrighi, J., Singh, R. K., van Aalst, M. K., Philip, S. Y., Vautard, R., and Otto, F. E. L.: Attribution of the Australian bushfire risk to anthropogenic climate change, Nat. Hazards Earth Syst. Sci., 21, 941–960, https://doi.org/10.5194/nhess-21-941-2021, 2021.
Wang, B., Liu, J., Kim, H.-J., Webster, P. J., Yim, S.-Y., and Xiang, B.:
Northern Hemisphere summer monsoon intensified by mega-El Niño/southern
oscillation and Atlantic multidecadal oscillation, P. Natl. Acad. Sci.
USA, 110, 5347–5352, https://doi.org/10.1073/pnas.1219405110, 2013.
Wang, X. Y., Li, X. C., Zhu, J., and Tanajura, C. A. S.: The strengthening
of Amazonian precipitation during the wet season driven by tropical sea
surface temperature forcing, Environ. Res. Lett., 13, 094015,
https://doi.org/10.1088/1748-9326/aadbb9, 2018.
Xue, J. Q., Sun, C., Li, J. P., and Mao, J. Y.: South Atlantic Forced
Multidecadal Teleconnection to the Midlatitude South Indian Ocean, Geophys.
Res. Lett., 45, 8480–8489, https://doi.org/10.1029/2018GL078990, 2018.
Yu, J., Kao, P., Paek, H., Hsu, H., Hung, C., Lu, M., and An, S.: Linking
Emergence of the Central Pacific El Niño to the Atlantic Multidecadal
Oscillation, J. Climate, 28, 651–662, https://doi.org/10.1175/JCLI-D-14-00347.1, 2015.
Yu, P., Xu, R. B., Abramson, M. J., Li, S. S., and Guo, Y. M.: Bushfires in
Australia: a serious health emergency under climate change, Lancet Planet.
Health, 4, E7–E8, https://doi.org/10.1016/S2542-5196(19)30267-0, 2020.
Zhao, S., Li, J. P., Li, Y. J., Jin, F. F., and Zheng, J. Y.:
Interhemispheric influence of Indo-Pacific convection oscillation on
Southern Hemisphere precipitation through southward propagation of Rossby
waves, Clim. Dynam., 52, 3203–3221, https://doi.org/10.1007/s00382-018-4324-y, 2019.
Short summary
Fires in Australia are positively correlated with the El Niño–Southern Oscillation (ENSO). However, the correlation between ENSO and the Australian Fire Weather Index (FWI) increases from 0.17 to 0.70 when the Atlantic Multidecadal Oscillation (AMO) shifts from a negative to positive phase. This is explained by the teleconnection effect through which the warmer AMO generates Rossby wave trains and results in high pressures and a weather condition conducive to wildfires.
Fires in Australia are positively correlated with the El Niño–Southern Oscillation (ENSO)....
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