Abstract
We examine impacts of the Madden–Julian Oscillation (MJO) on Australian springtime temperatures and extremes, explore the mechanisms behind the teleconnections, and assess their prediction in retrospective forecasts using the Bureau of Meteorology’s ACCESS-S1 dynamical forecast system. The MJO incites strong and significant warming across southern Australia in phases 2, 3 and 4 when its active convection propagates over the Indian Ocean and Maritime Continent. The heat signal appears strongest in south-eastern Australia during MJO phases 2 and 3 in the vicinity of a deep anticyclonic anomaly which brings warmer airflow to south-western Australia while promoting shortwave radiative heating in the southeast. This occurs as part of a Rossby wave train that emanates from the Indian Ocean and disperses across the Southern Hemisphere along a great circle route towards South America, in response to MJO convective heating on the equator. Importantly, we show the wave train emerges from the divergent outflow from anomalous MJO convection, rather than from the Rossby waves that exist within the MJO's baroclinic structure. Feedbacks between transient eddies and the low frequency flow to the south of Australia and southeast of South America reinforce the wave train in phases 1–3 but act against it during its demise in phase 4. The MJO is a source of subseasonal predictability of springtime heat and cold events over southern Australia in ACCESS-S1 at lead times of 2–4 weeks, yet there remains room for improvement in the model's depiction of the MJO and its teleconnection to the Southern Hemisphere.
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Data availability
All observational and reanalysis data are publicly available. AWAP temperature data is available from https://doi.org/10.4227/166/5a8647d1c23e0, under the CC-BY-NC 4.0 International Licence. The MJO index data can be downloaded from http://www.bom.gov.au/climate/mjo/. ERAI data is available at https://apps.ecmwf.int/datasets/, and NOAA OLR data can be downloaded from https://psl.noaa.gov/data/gridded/data.interp_OLR.html. ACCESS-S1 retrospective forecast data is available to bona fide researchers upon request from the authors. The NCAR Command Language (NCL; https://www.ncl.ucar.edu) version 6.4.0 was used for data analysis and visualization of the results.
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Acknowledgements
Support for this work was provided by (i) the Forewarned is Forearmed project (FWFA), which is supported by funding from the Australian Government Department of Agriculture as part of its Rural R&D for Profit programme, and (ii) Meat and Livestock Australia, the Queensland Government through the Drought and Climate Adaptation Program, and the University of Southern Queensland through the Northern Australia Climate Program (NACP). We thank Tim Cowan and Debra Hudson for generously giving their time to help improve the overall quality of this paper. This research was undertaken with the assistance of resources from the National Computational Infrastructure Australia, a National Collaborative Research Infrastructure Strategy enabled capability supported by the Australian Government.
Funding
Support for this work was provided by (i) the Forewarned is Forearmed project (FWFA), which is supported by funding from the Australian Government Department of Agriculture as part of its Rural R&D for Profit programme, and (ii) Meat and Livestock Australia, the Queensland Government through the Drought and Climate Adaptation Program, and the University of Southern Queensland through the Northern Australia Climate Program (NACP). This research was undertaken with the assistance of resources from the National Computational Infrastructure Australia, a National Collaborative Research Infrastructure Strategy enabled capability supported by the Australian Government.
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All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by GW, HH and HL. The first draft of the manuscript was written by AM and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
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Marshall, A.G., Wang, G., Hendon, H.H. et al. Madden–Julian Oscillation teleconnections to Australian springtime temperature extremes and their prediction in ACCESS-S1. Clim Dyn 61, 431–447 (2023). https://doi.org/10.1007/s00382-022-06586-6
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DOI: https://doi.org/10.1007/s00382-022-06586-6