Articles | Volume 5, issue 2
https://doi.org/10.5194/ascmo-5-133-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/ascmo-5-133-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
18 Jul 2019
| 18 Jul 2019
Approaches to attribution of extreme temperature and precipitation events using multi-model and single-member ensembles of general circulation models
School of Science, University of New South Wales, Canberra, ACT,
Australia
Sarah E. Perkins-Kirkpatrick
Climate Change Research Centre, University of New South Wales,
Sydney, UNSW, Australia
Andrew D. King
School of Earth Sciences, The University of Melbourne, Parkville,
Victoria, Australia
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Geert Jan van Oldenborgh, Folmer Krikken, Sophie Lewis, Nicholas J. Leach, Flavio Lehner, Kate R. Saunders, Michiel van Weele, Karsten Haustein, Sihan Li, David Wallom, Sarah Sparrow, Julie Arrighi, Roop K. Singh, Maarten K. van Aalst, Sjoukje Y. Philip, Robert Vautard, and Friederike E. L. Otto
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Southeastern Australia suffered from disastrous bushfires during the 2019/20 fire season, raising the question whether these have become more likely due to climate change. We found no attributable trend in extreme annual or monthly low precipitation but a clear shift towards more extreme heat. However, this shift is underestimated by the models. Analysing fire weather directly, we found that the chance has increased by at least 30 %, but due to the underestimation it could well be higher.
PAGES Hydro2k Consortium
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Andrew D. King, Tilo Ziehn, Matthew Chamberlain, Alexander R. Borowiak, Josephine R. Brown, Liam Cassidy, Andrea J. Dittus, Michael Grose, Nicola Maher, Seungmok Paik, Sarah E. Perkins-Kirkpatrick, and Aditya Sengupta
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The scientific community is considering new scenarios to succeed RCPs and SSPs for the next generation of Earth system model runs to project future climate change. To contribute to that effort, we reflect on relevant policy and scientific research questions and suggest categories for representative emission pathways. These categories are tailored to the Paris Agreement long-term temperature goal, high-risk outcomes in the absence of further climate policy and worlds “that could have been”.
Geert Jan van Oldenborgh, Folmer Krikken, Sophie Lewis, Nicholas J. Leach, Flavio Lehner, Kate R. Saunders, Michiel van Weele, Karsten Haustein, Sihan Li, David Wallom, Sarah Sparrow, Julie Arrighi, Roop K. Singh, Maarten K. van Aalst, Sjoukje Y. Philip, Robert Vautard, and Friederike E. L. Otto
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Southeastern Australia suffered from disastrous bushfires during the 2019/20 fire season, raising the question whether these have become more likely due to climate change. We found no attributable trend in extreme annual or monthly low precipitation but a clear shift towards more extreme heat. However, this shift is underestimated by the models. Analysing fire weather directly, we found that the chance has increased by at least 30 %, but due to the underestimation it could well be higher.
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Event attribution studies can now be performed at short notice. We document a protocol developed by the World Weather Attribution group. It includes choices of which events to analyse, the event definition, observational analysis, model evaluation, multi-model multi-method attribution, hazard synthesis, vulnerability and exposure analysis, and communication procedures. The protocol will be useful for future event attribution studies and as a basis for an operational attribution service.
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Mitchell T. Black, David J. Karoly, Suzanne M. Rosier, Sam M. Dean, Andrew D. King, Neil R. Massey, Sarah N. Sparrow, Andy Bowery, David Wallom, Richard G. Jones, Friederike E. L. Otto, and Myles R. Allen
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Fabian Lehner, Imran Nadeem, and Herbert Formayer
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Most climate time series contain annual and diurnal cycles. However, an objective criterion for deciding whether two time series have statistically equivalent annual and diurnal cycles is lacking, particularly if the residual variability is serially correlated. Such a criterion would be helpful in deciding whether a new version of a climate model better simulates such cycles. This paper derives an objective rule for such decisions based on a rigorous statistical framework.
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Alana Hough and Tony E. Wong
Adv. Stat. Clim. Meteorol. Oceanogr., 8, 117–134, https://doi.org/10.5194/ascmo-8-117-2022, https://doi.org/10.5194/ascmo-8-117-2022, 2022
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Timothy DelSole and Michael K. Tippett
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A common problem in climate studies is to decide whether a climate model is realistic. Such decisions are not straightforward because the time series are serially correlated and multivariate. Part II derived a test for deciding wether a simulation is statistically distinguishable from observations. However, the test itself provides no information about the nature of those differences. This paper develops a systematic and optimal approach to diagnosing differences between stochastic processes.
Willem Stefaan Conradie, Piotr Wolski, and Bruce Charles Hewitson
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Cape Town is situated in a small but ecologically and climatically highly diverse and vulnerable pocket of South Africa uniquely receiving its rain mostly in winter. We show complex structures in the spatial patterns of rainfall seasonality and year-to-year changes in rainfall within this domain, tied to spatial differences in the rain-bearing winds. This allows us to develop a new spatial subdivision of the region to help future studies distinguish spatially distinct climate change responses.
Willem Stefaan Conradie, Piotr Wolski, and Bruce Charles Hewitson
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The
Day Zerowater crisis affecting Cape Town after the severe 2015–2017 drought motivated renewed research interest into causes and projections of rainfall variability and change in this water-stressed region. Unusually few wet months and very wet days characterised the Day Zero Drought. Its extent expanded as it shifted gradually north-eastward, concurrent with changes in the weather systems driving drought. Our results emphasise the need to consider the interplay between drought drivers.
Erica L. Ashe, Nicole S. Khan, Lauren T. Toth, Andrea Dutton, and Robert E. Kopp
Adv. Stat. Clim. Meteorol. Oceanogr., 8, 1–29, https://doi.org/10.5194/ascmo-8-1-2022, https://doi.org/10.5194/ascmo-8-1-2022, 2022
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We develop a new technique to integrate realistic uncertainties in probabilistic models of past sea-level change. The new framework performs better than past methods (in precision, accuracy, bias, and model fit) because it enables the incorporation of previously unused data and exploits correlations in the data. This method has the potential to assess the validity of past estimates of extreme sea-level rise and highstands providing better context in which to place current sea-level change.
Katherine Dagon, Benjamin M. Sanderson, Rosie A. Fisher, and David M. Lawrence
Adv. Stat. Clim. Meteorol. Oceanogr., 6, 223–244, https://doi.org/10.5194/ascmo-6-223-2020, https://doi.org/10.5194/ascmo-6-223-2020, 2020
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Uncertainties in land model projections are important to understand in order to build confidence in Earth system modeling. In this paper, we introduce a framework for estimating uncertain land model parameters with machine learning. This method increases the computational efficiency of this process relative to traditional hand tuning approaches and provides objective methods to assess the results. We further identify key processes and parameters that are important for accurate land modeling.
Sjoukje Philip, Sarah Kew, Geert Jan van Oldenborgh, Friederike Otto, Robert Vautard, Karin van der Wiel, Andrew King, Fraser Lott, Julie Arrighi, Roop Singh, and Maarten van Aalst
Adv. Stat. Clim. Meteorol. Oceanogr., 6, 177–203, https://doi.org/10.5194/ascmo-6-177-2020, https://doi.org/10.5194/ascmo-6-177-2020, 2020
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Event attribution studies can now be performed at short notice. We document a protocol developed by the World Weather Attribution group. It includes choices of which events to analyse, the event definition, observational analysis, model evaluation, multi-model multi-method attribution, hazard synthesis, vulnerability and exposure analysis, and communication procedures. The protocol will be useful for future event attribution studies and as a basis for an operational attribution service.
Mark D. Risser and Michael F. Wehner
Adv. Stat. Clim. Meteorol. Oceanogr., 6, 115–139, https://doi.org/10.5194/ascmo-6-115-2020, https://doi.org/10.5194/ascmo-6-115-2020, 2020
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Evaluation of modern high-resolution global climate models often does not account for the geographic location of the underlying weather station data. In this paper, we quantify the impact of geographic sampling on the relative performance of climate model representations of precipitation extremes over the United States. We find that properly accounting for the geographic sampling of weather stations can significantly change the assessment of model performance.
Donald P. Cummins, David B. Stephenson, and Peter A. Stott
Adv. Stat. Clim. Meteorol. Oceanogr., 6, 91–102, https://doi.org/10.5194/ascmo-6-91-2020, https://doi.org/10.5194/ascmo-6-91-2020, 2020
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We have developed a novel and fast statistical method for diagnosing effective radiative forcing (ERF), a measure of the net effect of greenhouse gas emissions on Earth's energy budget. Our method works by inverting a recursive digital filter energy balance representation of global climate models and has been successfully validated using simulated data from UK Met Office climate models. We have estimated time series of historical ERF by applying our method to the global temperature record.
Meagan Carney and Holger Kantz
Adv. Stat. Clim. Meteorol. Oceanogr., 6, 61–77, https://doi.org/10.5194/ascmo-6-61-2020, https://doi.org/10.5194/ascmo-6-61-2020, 2020
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Extremes in weather can have lasting effects on human health and resource consumption. Studying the recurrence of these events on a regional scale can improve response times and provide insight into a changing climate. We introduce a set of clustering tools that allow for regional clustering of weather recordings from stations across Germany. We use these clusters to form regional models of summer temperature extremes and find an increase in the mean from 1960 to 2018.
Richard E. Danielson, Minghong Zhang, and William A. Perrie
Adv. Stat. Clim. Meteorol. Oceanogr., 6, 31–43, https://doi.org/10.5194/ascmo-6-31-2020, https://doi.org/10.5194/ascmo-6-31-2020, 2020
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Visibility is estimated for the 21st century using global and regional climate model output. A baseline decrease in visibility in the Arctic (10 %) is more notable than in the North Atlantic (< 5 %). We develop an adjustment that yields greater consistency among models and explore the justification of our ad hoc adjustment toward ship observations during the historical period. Baseline estimates are found to be sensitive to the representation of temperature and humidity.
Raquel Barata, Raquel Prado, and Bruno Sansó
Adv. Stat. Clim. Meteorol. Oceanogr., 5, 67–85, https://doi.org/10.5194/ascmo-5-67-2019, https://doi.org/10.5194/ascmo-5-67-2019, 2019
Matz A. Haugen, Michael L. Stein, Ryan L. Sriver, and Elisabeth J. Moyer
Adv. Stat. Clim. Meteorol. Oceanogr., 5, 37–55, https://doi.org/10.5194/ascmo-5-37-2019, https://doi.org/10.5194/ascmo-5-37-2019, 2019
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This work uses current temperature observations combined with climate models to project future temperature estimates, e.g., 100 years into the future. We accomplish this by modeling temperature as a smooth function of time both in the seasonal variation as well as in the annual trend. These smooth functions are estimated at multiple quantiles that are all projected into the future. We hope that this work can be used as a template for how other climate variables can be projected into the future.
Rasmus E. Benestad, Bob van Oort, Flavio Justino, Frode Stordal, Kajsa M. Parding, Abdelkader Mezghani, Helene B. Erlandsen, Jana Sillmann, and Milton E. Pereira-Flores
Adv. Stat. Clim. Meteorol. Oceanogr., 4, 37–52, https://doi.org/10.5194/ascmo-4-37-2018, https://doi.org/10.5194/ascmo-4-37-2018, 2018
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A new study indicates that heatwaves in India will become more frequent and last longer with global warming. Its results were derived from a large number of global climate models, and the calculations differed from previous studies in the way they included advanced statistical theory. The projected changes in the Indian heatwaves will have a negative consequence for wheat crops in India.
Alex G. Libardoni, Chris E. Forest, Andrei P. Sokolov, and Erwan Monier
Adv. Stat. Clim. Meteorol. Oceanogr., 4, 19–36, https://doi.org/10.5194/ascmo-4-19-2018, https://doi.org/10.5194/ascmo-4-19-2018, 2018
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We present new probabilistic estimates of model parameters in the MIT Earth System Model using more recent data and an updated method. Model output is compared to observed climate change to determine which sets of model parameters best simulate the past. In response to increasing surface temperatures and accelerated heat storage in the ocean, our estimates of climate sensitivity and ocean diffusivity are higher. Using a new interpolation algorithm results in smoother probability distributions.
Ralf Lindau and Victor Karel Christiaan Venema
Adv. Stat. Clim. Meteorol. Oceanogr., 4, 1–18, https://doi.org/10.5194/ascmo-4-1-2018, https://doi.org/10.5194/ascmo-4-1-2018, 2018
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Climate data contain spurious breaks, e.g., by relocation of stations, which makes it difficult to infer the secular temperature trend. Homogenization algorithms use the difference time series of neighboring stations to detect and eliminate this spurious break signal. For low signal-to-noise ratios, i.e., large distances between stations, the correct break positions may not only remain undetected, but segmentations explaining mainly the noise can be erroneously assessed as significant and true.
Erik Fraza, James B. Elsner, and Thomas H. Jagger
Adv. Stat. Clim. Meteorol. Oceanogr., 2, 105–114, https://doi.org/10.5194/ascmo-2-105-2016, https://doi.org/10.5194/ascmo-2-105-2016, 2016
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Climate influences on hurricane intensification are investigated by averaging hourly intensification rates over the period 1975–2014 in 8° by 8° latitude–longitude grid cells. The statistical effects of hurricane intensity, sea-surface temperature (SST), El Niño–Southern Oscillation (ENSO), the North Atlantic Oscillation (NAO), and the Madden–Julian Oscillation (MJO) are quantified. Intensity, SST, and NAO had a positive effect on intensification rates. The NAO effect should be further studied.
Giang T. Tran, Kevin I. C. Oliver, András Sóbester, David J. J. Toal, Philip B. Holden, Robert Marsh, Peter Challenor, and Neil R. Edwards
Adv. Stat. Clim. Meteorol. Oceanogr., 2, 17–37, https://doi.org/10.5194/ascmo-2-17-2016, https://doi.org/10.5194/ascmo-2-17-2016, 2016
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In this work, we combine the information from a complex and a simple atmospheric model to efficiently build a statistical representation (an emulator) of the complex model and to study the relationship between them. Thanks to the improved efficiency, this process is now feasible for complex models, which are slow and costly to run. The constructed emulator provide approximations of the model output, allowing various analyses to be made without the need to run the complex model again.
Cited articles
Angélil, O., Stone, D. A., Tadross, M., Tummon, F., Wehner, M., and
Knutti, R.: Attribution of extreme weather to anthropogenic greenhouse gas
emissions: Sensitivity to spatial and temporal scales, Geophys. Res. Lett.,
41, 2150–2155, https://doi.org/10.1002/2014GL059234, 2014.
Angélil, O., Stone, D., Wehner, M., Paciorek, C. J., Krishnan, H., and
Collins, W.: An independent assessment of anthropogenic attribution
statements for recent extreme temperature and rainfall events, J. Climate,
30, 5–16, https://doi.org/10.1175/JCLI-D-16-0077.1, 2017.
Bureau of Meteorology: Australia's wettest two-year period on record,
2010–2011, Spec. Clim. Statement 38, available at: http://www.bom.gov.au/climate/current/statements/scs38.pdf (last access: 16 July 2019) 10 February 2012.
Bureau of Meteorology: Australia's warmest September on record, Spec. Clim.
Statement 46, available at: http://www.bom.gov.au/climate/current/statements/scs46.pdf (last access: 16 July 2019), October 2013a.
Bureau of Meteorology: Extreme heat in January 2013, Special Climate
Statement 43, 1–19, available at:
http://www.bom.gov.au/climate/current/statements/scs43e.pdf (last access: 16 July 2019), February 2013b.
Bureau of Meteorology: Extreme Rainfall and Flooding in Coastal Queensland
and New South Wales, Spec. Clim. Statement 44, available at: http://www.bom.gov.au/climate/current/statements/scs44.pdf (last access: 16 July 2019), 18 February 2013c.
Bureau of Meteorology: Annual Climate Report 2013, available at:
http://www.bom.gov.au/climate/annual_sum/2015/Annual-Climate-Report-2015-HR.pdf (last access: 16 July 2019), 2014.
Bureau of Meteorology: Australia' s warmest autumn on record, Special
Climate Statement 52, available at: http://www.bom.gov.au/climate/current/statements/scs56.pdf (last access: 16 July 2019), 25 June 2016a.
Bureau of Meteorology: State of the Climate 2016, available at: http://www.bom.gov.au/state-of-the-climate/2016/ (last access: 16 July 2019), 2016b.
Cattiaux, J. and Ribes, A.: Defining single extreme weather events in a
climate perspective, B. Am. Meteorol. Soc., 99, 1557–1568,
https://doi.org/10.1175/BAMS-D-17-0281.1, 2018.
Christidis, N., Stott, P. A., Karoly, D. J., and Ciavarella, A.: An
Attribution Study of the Heavy Rainfall Over Eastern Australia in March
2012, B. Am. Meteorol. Soc., 94, S58–61, 2013.
Fischer, E. M., Beyerle, U., and Knutti, R.: Robust spatially aggregated
projections of climate extremes, Nat. Clim. Change, 3, 1033–1038,
https://doi.org/10.1038/nclimate2051, 2013.
Harrington, L. J., Otto, F. E. L., and Res, E.: Adapting attribution science
to the climate extremes of tomorrow, Environ. Res. Lett., 13, 123006, https://doi.org/10.1088/1748-9326/aaf4cc, 2018.
Haustein, K., Allen, M. R., Forster, P. M., Otto, F. E. L., Mitchell, D. M.,
Matthews, H. D., and Frame, D. J.: A real-time Global Warming Index, Sci.
Rep., 7, 1–6, https://doi.org/10.1038/s41598-017-14828-5, 2017.
Hawkins, E., Ortega, P., Suckling, E., Schurer, A., Hegerl, G., Jones, P.,
Joshi, M., Osborn, T. J., Masson-Delmotte, V., Mignot, J., Thorne, P., and
Van Oldenborgh, G. J.: Estimating changes in global temperature since the
preindustrial period, B. Am. Meteorol. Soc., 98, 1841–1856,
https://doi.org/10.1175/BAMS-D-16-0007.1, 2017.
Herring, S. C., Hoerling, M. P., Peterson, T. C., and Stott, P. A.:
Explaining extreme events of 2013 from a climate perspective, B. Am.
Meteorol. Soc., 95, S1–S96,
2014.
Herring, S. C., Hoerling, M. P., Kossin, J. P., Peterson, T. C., and Stott,
P. A.: Explaining extreme events of 2014 from a climate perspective, B.
Am. Meteorol. Soc., 96, S1–S172,
https://doi.org/10.1175/BAMS-ExplainingExtremeEvents2014.1, 2015.
Herring, S. C., Hoell, A., Hoerling, M. P., Kossin, J. P., III, Shreck, C. J., and
Stott, P. A.: Explaining extreme events of 2015 from a climate perspective,
B. Am. Meteorol. Soc., 97, S1–S145, https://doi.org/10.1175/BAMS-D-12-00021.1,
2016.
Herring, S. C., Christidis, N., Hoell, A., Kossin, J. P., Schreck Iii, C.
J., Stott, P. A., Quan, X. W., Hoerling, M., Smith, L., Perlwitz, J., Zhang,
T., Hoell, A., Wolter, K., and Eischeid, J.: Explaining extreme events of
2016 from a climate perspective, B. Am. Meteorol. Soc., 99, 54–59,
https://doi.org/10.1175/BAMS-D-17-0118.1, 2018.
Imada, Y., Shiogama, H., Takahashi, C., Watanabe, M., Mori, M., Kamae, Y.,
and Maeda, S.: Climate Change Increased the Likelihood of the 2016 Heat
Extremes in Asia, B. Am. Meteorol. Soc., 99, S97–S101,
https://doi.org/10.1175/BAMS-D-17-0109.1, 2018.
James, R., Washington, R., Schleussner, C. F., Rogelj, J., and Conway, D.:
Characterizing half-a-degree difference: a review of methods for identifying
regional climate responses to global warming targets, Wires
Clim. Change, 8, https://doi.org/10.1002/wcc.457, 2017.
Jones, D. A., Wang, W., and Fawcett, R.: High-quality spatial climate
data-sets for Australia, Aust. Meteorol. Ocean., 58, 233–248, 2009.
Kay, J. E., Deser, C., Phillips, A., Mai, A., Hannay, C., Strand, G.,
Arblaster, J. M., Bates, S. C., Danabasoglu, G., Edwards, J., Holland, M.,
Kushner, P., Lamarque, J.-F., Lawrence, D., Lindsay, K., Middleton, A.,
Munoz, E., Neale, R., Oleson, K., Polvani, L., and Vertenstein, M.: The
Community Earth System Model (CESM) Large Ensemble Project: A Community
Resource for Studying Climate Change in the Presence of Internal Climate
Variability, B. Am. Meteorol. Soc., 96, 1333–1349,
https://doi.org/10.1175/BAMS-D-13-00255.1, 2015.
King, A. D., Lewis, S. C., Perkins, S. E., Alexander, L. V., Donat, M. G.,
Karoly, D. J., and Black, M. T.: Limited evidence of anthropogenic influence
on the 2011–12 extreme rainfall over Southeast Australia, B. Am.
Meteorol. Soc., 94, 55–58, 2013.
King, A. D., Klingaman, N. P., Alexander, L. V., Donat, M. G., Jourdain, N.
C., and Maher, P.: Extreme rainfall variability in Australia: Patterns,
drivers, and predictability, J. Climate, 27, 6035–6050,
https://doi.org/10.1175/JCLI-D-13-00715.1, 2014.
King, A. D., Karoly, D. J., and Henley, B. J.: Australian climate extremes at
1.5 ∘C and 2 ∘C of global warming, Nat. Clim. Change,
7, 412–416, https://doi.org/10.1038/nclimate3296, 2017.
Kirchmeier-Young, M. C., Zwiers, F. W., and Gillett, N. P.: Attribution of
extreme events in Arctic Sea ice extent, J. Climate, 30, 553–571,
https://doi.org/10.1175/JCLI-D-16-0412.1, 2017.
Kirchmeier-Young, M. C., Gillett, N. P., Zwiers, F. W., Cannon, A. J., and
Anslow, F. S.: Attribution of the Influence of Human-Induced Climate Change
on an Extreme Fire Season, Earth's Future, 7, 2–10,
https://doi.org/10.1029/2018EF001050, 2019.
Knutson, T., Zeng, F., and Wittenberg, A.: Seasonal and annual mean
precipitation extremes during during 2013: A U.S. focused analysis, B.
Am. Meteorol. Soc., 95, S19–S23, 2014.
Knutson, T. R., Kam, J., Zeng, F., and Wittenberg, A. T.: CMIP5 Model-based
Assessment of Anthropogenic Influence on Record Global Warmth During 2016,
B. Am. Meteorol. Soc., 99, S11–S15, https://doi.org/10.1175/BAMS-D-17-0104.1,
2018.
Lewis, S. C. and Karoly, D. J.: Anthropogenic contributions to Australia's
record summer temperatures of 2013, Geophys. Res. Lett., 40, 3708–3709,
https://doi.org/10.1002/grl.50673, 2013.
Lewis, S. C. and Karoly, D. J.: Are estimates of anthropogenic and natural
influences on Australia's extreme 2010–2012 rainfall model-dependent?,
Clim. Dynam., 45, 679–695, https://doi.org/10.1007/s00382-014-2283-5, 2014a.
Lewis, S. C. and Karoly, D. J.: The Role of Anthropogenic Forcing in the
Record 2013 Australia-Wide Annual and Spring Temperatures, B. Am.
Meteor.ol Soc., 95, S31–S34, 2014b.
Lewis, S. C. and Mallela, J.: A Multifactor Analysis of the Record 2016
Great Barrier Reef Bleaching, B. Am. Meteorol. Soc.,
S144–S149, https://doi.org/10.1175/BAMS-D-17-0074.1, 2018.
Lewis, S. C., King, A. D., and Mitchell, D. M.: Australia's Unprecedented
Future Temperature Extremes Under Paris Limits to Warming, Geophys. Res.
Lett., 44, 9947–9956, https://doi.org/10.1002/2017GL074612, 2017a.
Lewis, S. C., Karoly, D. J., King, A. D., Perkins, S. E., and Donat, M. G.:
Mechanisms Explaining Recent Changes in Australian Climate Extremes, in:
Climate Extremes, American Geophysical Union (AGU), 249–263, 2017b.
Maher, N., Milinski, S., Suarez-Gutierrez, L., Botzet, M., Kornblueh, L.,
Takano, Y., Kröger, J., Ghosh, R., Hedemann, C., Li, C., Li, H.,
Manzini, E., Notz, D., Putrasahan, D., Boysen, L., Claussen, M., Ilyina, T.,
Olonscheck, D., Raddatz, T., Stevens, B., and Marotzke, J.: The Max Planck
Institute Grand Ensemble – enabling the exploration of climate system
variability, J. Adv. Model. Earth Sy., 11, https://doi.org/10.1029/2019MS001639, online first,
2019.
Maher, P. and Sherwood, S. C.: Disentangling the multiple sources of
large-scale variability in Australian wintertime precipitation, J. Climate,
27, 6377–6392, https://doi.org/10.1175/JCLI-D-13-00659.1, 2014.
Min, S. K., Cai, W., and Whetton, P.: Influence of climate variability on
seasonal extremes over Australia, J. Geophys. Res.-Atmos., 118, 643–654,
https://doi.org/10.1002/jgrd.50164, 2013.
Mitchell, D., AchutaRao, K., Allen, M., Bethke, I., Beyerle, U., Ciavarella, A., Forster, P. M., Fuglestvedt, J., Gillett, N., Haustein, K., Ingram, W., Iversen, T., Kharin, V., Klingaman, N., Massey, N., Fischer, E., Schleussner, C.-F., Scinocca, J., Seland, Ø., Shiogama, H., Shuckburgh, E., Sparrow, S., Stone, D., Uhe, P., Wallom, D., Wehner, M., and Zaaboul, R.: Half a degree additional warming, prognosis and projected impacts (HAPPI): background and experimental design, Geosci. Model Dev., 10, 571–583, https://doi.org/10.5194/gmd-10-571-2017, 2017.
Moss, R. H., Edmonds, J. A., Hibbard, K. A., Manning, M. R., Rose, S. K.,
van Vuuren, D. P., Carter, T. R., Emori, S., Kainuma, M., Kram, T., Meehl,
G. A., Mitchell, J. F. B., Nakicenovic, N., Riahi, K., Smith, S. J.,
Stouffer, R. J., Thomson, A. M., Weyant, J. P., and Wilbanks, T. J.: The next
generation of scenarios for climate change research and assessment, Nature,
463, 747–756, https://doi.org/10.1038/nature08823, 2010.
Nairn, J. and Fawcett, R.: Defining heatwaves: heatwave defined as a
heat-impact event servicing all communiy and business sectors in Australia,
CAWCR Tech. Rep. No 060, 84 pp., available at: https://www.cawcr.gov.au/technical-reports/CTR_060.pdf (last access: 17 July 2019), 2013.
National Academies of Sciences, Engineering, and Medicine: Attribution of Extreme Weather Events in the Context of Climate Change, Washington, DC, The National Academies Press, https://doi.org/10.17226/21852, 2016.
Paciorek, C. J., Stone, A., and Wehner, M. F.: Quantifying statistical
uncertainty in the attribution of human influence on severe weather, Weather and Climate Extremes,
20, 69–80, https://doi.org/10.1016/j.wace.2018.01.002, 2018.
Parker, H. R., Lott, F. C., Cornforth, R. J., Mitchell, D. M., Sparrow, S.,
and Wallom, D.: A comparison of model ensembles for attributing 2012 West
African rainfall, Environ. Res. Lett., 12, https://doi.org/10.1088/1748-9326/aa5386,
2017.
Perkins, S., Lewis, S., King, A., and Alexander, L.: Increased simulated risk
of the hot Australian summer of 2012/13 due to anthropogenic activity as
measured by heatwave intensity and frequency, B. Am. Meteorol. Soc.,
95, S34–37, https://doi.org/10.1175/1520-0477-95.9.s1.1, 2014.
Perkins, S. E. and Alexander, L. V.: On the measurement of heat waves, J.
Climate, 26, 4500–4517, https://doi.org/10.1175/JCLI-D-12-00383.1, 2013.
Perkins, S. E. and Fischer, E. M.: The usefulness of different realizations
for the model evaluation of regional trends in heat waves, Geophys. Res.
Lett., 40, 5793–5797, https://doi.org/10.1002/2013GL057833, 2013.
Perkins, S. E. and Gibson, P. B.: Increased risk of the 2014 Australian May
heatwave due to anthropogenic activity, B. Am. Meteorol. Soc., 96,
https://doi.org/10.1175/BAMS-D-15-00074.1, 2015.
Perkins, S. E., Pitman, A. J., Holbrook, N. J., McAneney, J., Change, C., and
Frontiers, R.: Evaluation of the AR4 climate models' simulated daily maximum
temperature, minimum temperature, and precipitation over Australia using
probability density functions, J. Climate, 20, 4356–4376,
https://doi.org/10.1175/JCLI4253.1, 2007.
Perkins, S. E., Argüeso, D. and White, C. J.: Relationships between
climate variability, soil moisture, and Australian heatwaves, J. Geophys.
Res.-Atmos., 120, 8144–8164, https://doi.org/10.1002/2015JD023592, 2015.
Perkins-Kirkpatrick, S. E. and Gibson, P. B.: Changes in regional heatwave
characteristics as a function of increasing global temperature, Sci. Rep.,
7, 1–12, https://doi.org/10.1038/s41598-017-12520-2, 2017.
Perkins-Kirkpatrick, S. E., King, A. D., Cougnon, E. A., Grose, M. R.,
Oliver, E. C. J., Holbrook, N. J., Lewis, S. C., and Pourasghar, F.: The role
of natural variability and anthropogenic climate change in the 2017/18
Tasman Sea marine heatwave, B. Am. Meteorol. Soc., 100, S105–S110,
https://doi.org/10.1175/BAMS-D-18-0116.1, 2019.
Queensland Floods Commission of Inquiry: Queensland Floods Commission of
Inquiry Final Report, 658, available at:
http://www.floodcommission.qld.gov.au/publications/final-report/ (last access: 3 April 2019), 2012.
Sanderson, B. M., Xu, Y., Tebaldi, C., Wehner, M., O'Neill, B., Jahn, A., Pendergrass, A. G., Lehner, F., Strand, W. G., Lin, L., Knutti, R., and Lamarque, J. F.: Community climate simulations to assess avoided impacts in 1.5 and 2 ∘C futures, Earth Syst. Dynam., 8, 827–847, https://doi.org/10.5194/esd-8-827-2017, 2017.
Schaller, N., Sillmann, J., Anstey, J., Fischer, E. M., Grams, C. M., and
Russo, S.: Influence of blocking on Northern European and Western Russian
heatwaves in large climate model ensembles, Environ. Res. Lett., 13, 054015,
https://doi.org/10.1088/1748-9326/aaba55, 2018.
Stone, D. A. and Allen, M. R.: The end-to-end attribution problem: From
emissions to impacts, Climatic Change, 71, 303–318,
https://doi.org/10.1007/s10584-005-6778-2, 2005.
Stott, P. A., Christidis, N., Otto, F. E. L. L., Sun, Y., Vanderlinden, J.
P., Oldenborgh, G. J. Van, Vautard, R., Storch, H. Von, Walton, P., Yiou,
P., Zwiers, F. W., van Oldenborgh, G. J., Vautard, R., von Storch, H.,
Walton, P., Yiou, P., and Zwiers, F. W.: Attribution of extreme weather and
climate-related events, Wires Clim. Change, 7, 23–41,
https://doi.org/10.1002/wcc.380, 2016.
Taylor, K. E., Stouffer, R. J., and Meehl, G. A.: An overview of CMIP5 and
the experiment design, B. Am. Meteorol. Soc., 93, 485–498,
https://doi.org/10.1175/BAMS-D-11-00094.1, 2012.
Uhe, P., Otto, F. E. L., Haustein, K., van Oldenborgh, G. J., King, A. D.,
Wallom, D. C. H., Allen, M. R., and Cullen, H.: Comparison of methods:
Attributing the 2014 record European temperatures to human influences,
Geophys. Res. Lett., 43, 8685–8693, https://doi.org/10.1002/2016GL069568, 2016.
UNFCCC: Paris Agreement, Article 2, Vol. 01415, 2016.
Van Dijk, A. I. J. M., Beck, H. E., Crosbie, R. S., De Jeu, R. A. M., Liu,
Y. Y., Podger, G. M., Timbal, B., and Viney, N. R.: The Millennium Drought in
southeast Australia (2001–2009): Natural and human causes and implications
for water resources, ecosystems, economy, and society, Water Resour. Res.,
49, 1040–1057, https://doi.org/10.1002/wrcr.20123, 2013.
Walsh, J. E., Thoman, R. L., Bhatt, U. S., Bieniek, P. A., Brettschneider,
B., Brubaker, M., Danielson, S., Lader, R., Fetterer, F., Holderied, K.,
Iken, K., Mahoney, A., McCammon, M., and Partain, J.: The High Latitude
Marine Heat Wave of 2016 and Its Impaacts on Alaska, B. Am. Meteorol.
Soc., 99, S39–S43, https://doi.org/10.1175/BAMS-D-17-0105.1, 2018.
Short summary
Extreme temperature and precipitation events in Australia have caused significant socio-economic and environmental impacts. Determining the factors contributing to these extremes is an active area of research. This paper describes a set of studies that have examined the causes of extreme climate events in recent years in Australia. Ideally, this review will be useful for the application of these extreme event attribution approaches to climate and weather extremes occurring elsewhere.
Extreme temperature and precipitation events in Australia have caused significant socio-economic...
