ORCID Profile
0000-0002-7783-9440
Current Organisations
The University of Newcastle
,
University of Technology Sydney
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Publisher: MDPI AG
Date: 20-10-2020
DOI: 10.3390/CLI8100120
Abstract: Droughts in southeastern Australia can profoundly affect the water supply to Sydney, Australia’s largest city. Increasing population, a warming climate, land surface changes and expanded agricultural use increase water demand and reduce catchment runoff. Studying Sydney’s water supply is necessary to manage water resources and lower the risk of severe water shortages. This study aims at understanding Sydney’s water supply by analysing precipitation and temperature trends across the catchment. A decreasing trend in annual precipitation was found across the Sydney catchment area. Annual precipitation also is significantly less variable, due to fewer years above the 80th percentile. These trends result from significant reductions in precipitation during spring and autumn, especially over the last 20 years. Wavelet analysis was applied to assess how the influence of climate drivers has changed over time. Attribute selection was carried out using linear regression and machine learning techniques, including random forests and support vector regression. Drivers of annual precipitation included Niño3.4, Southern Annular Mode (SAM) and DMI, and measures of global warming such as the Tasman Sea sea surface temperature anomalies. The support vector regression model with a polynomial kernel achieved correlations of 0.921 and a skill score compared to climatology of 0.721. The linear regression model also performed well with a correlation of 0.815 and skill score of 0.567, highlighting the importance of considering both linear and non-linear methods when developing statistical models. Models were also developed on autumn and winter precipitation but performed worse than annual precipitation on prediction. For ex le, the best performing model on autumn precipitation, which accounts for approximately one quarter of annual precipitation, achieved an RMSE of 418.036 mm2 on the testing data, while annual precipitation achieved an RMSE of 613.704 mm2. However, the seasonal models provided valuable insights into whether the season would be wet or dry compared to the climatology.
Publisher: MDPI AG
Date: 15-06-2022
DOI: 10.3390/CLI10060084
Abstract: In recent decades, southeast Australia has experienced both extreme drought and record-breaking rainfall, with devastating societal impacts. Variations in the Australian polar-front jet (PFJ) and the subtropical jet (STJ) determine, for ex le, the location and frequency of the cool season (April–September) weather systems influencing rainfall events and, consequently, water availability for the southern half of Australia. Changes in jet stream wind speeds also are important for aviation fuel and safety requirements. A split jet occurs when the single jet separates into the STJ and PFJ in the early cool season (April–May). This study focusses on split jet characteristics over Australian/New Zealand longitudes in recent decades. During the accelerated global warming from the mid-1990s, higher mean wind speeds were found in the PJF across the Australian region during June–September, compared to the STJ. In contrast, significant wind speed increases occur in the early cool season (April–May) at STJ latitudes, which straddle the East Coast of Australia and the adjacent Tasman Sea. These changes are linked to major changes in the mean atmospheric circulation, and they include relative vorticity and humidity, both being vital for the development of rain-bearing weather systems that affect the region.
Publisher: MDPI AG
Date: 29-09-2022
DOI: 10.3390/W14193073
Abstract: Droughts and long dry spells, interspersed with intense rainfall events, have been characteristic of the northern Murray-Darling Basin (NMDB), a major Australian agricultural region. The NMDB precipitation results from weather systems ranging from thunderstorms to larger scale events. The larger scale events exhibit high seasonal and annual rainfall variability. To detect attributes shaping the NMDB precipitation patterns, and hence net water inflows to the vast Darling River catchment area, numerous (45) possible attributes were assessed for their influence on rainfall trends. Four periods were assessed: annual, April–May (early cool-season), June–September (remaining cool-season), and October–March (warm-season). Linear and non-linear regression machine learning (ML) methods were used to identify the dominant attributes. We show the impact of climate drivers on the increasingly dry April–May months on annual precipitation and warmer temperatures since the early 1990s. The NMDB water supply was further reduced during 1992–2018 by the lack of compensating rainfall trends for the April–May decline. The identified attributes include ENSO, the Southern Annular Mode, the Indian Ocean Dipole, and both local and global sea surface temperatures. A key finding is the prominence of global warming as an attribute, both in idually and in combination with other climate drivers.
Publisher: American Meteorological Society
Date: 02-2021
Abstract: The central east coast of Australia is frequently impacted by large hail and damaging winds associated with severe convective storms, with in idual events recording damages exceeding AUD 1 billion. These storms present a significant challenge for forecasting because of their development in seemingly marginal environments. They often have been observed to intensify upon approaching the coast, with case studies and climatological analyses indicating that interactions with the sea breeze are key to this process. The relative importance of the additional lifting and vorticity along the sea-breeze front in comparison with the change to a cooler, moister air mass with stronger low-level shear behind the front has yet to be investigated. Here, the role of the sea-breeze air mass is isolated using idealized numerical simulations of storms developing in a horizontally homogeneous environment. The base-state substitution (BSS) modeling technique is utilized to introduce the sea-breeze air mass following initial storm development. Relative to a simulation without BSS, the storm is longer lived and more intense, ultimately developing supercell characteristics including increased updraft rotation, deviant motion to the left of the mean wind vector, and a strong reflectivity gradient on the inflow edge. Separately simulating the changes in the thermodynamic and wind fields reveals that the enhanced storm longevity and intensity are primarily due to the latter. The change in the low-level environmental winds slows gust-front propagation, allowing the storm to continue to ingest warm, potentially buoyant environmental air. At the same time, increased low-level shear promotes the development of persistent updraft rotation that causes the storm to make a transition from a multicell to a supercell.
Publisher: MDPI AG
Date: 11-06-2020
DOI: 10.3390/CLI8060076
Abstract: Southeast Australia is frequently impacted by drought, requiring monitoring of how the various factors influencing drought change over time. Precipitation and temperature trends were analysed for Canberra, Australia, revealing decreasing autumn precipitation. However, annual precipitation remains stable as summer precipitation increased and the other seasons show no trend. Further, mean temperature increases in all seasons. These results suggest that Canberra is increasingly vulnerable to drought. Wavelet analysis suggests that the El-Niño Southern Oscillation (ENSO) influences precipitation and temperature in Canberra, although its impact on precipitation has decreased since the 2000s. Linear regression (LR) and support vector regression (SVR) were applied to attribute climate drivers of annual precipitation and mean maximum temperature (TMax). Important attributes of precipitation include ENSO, the southern annular mode (SAM), Indian Ocean Dipole (DMI) and Tasman Sea SST anomalies. Drivers of TMax included DMI and global warming attributes. The SVR models achieved high correlations of 0.737 and 0.531 on prediction of precipitation and TMax, respectively, outperforming the LR models which obtained correlations of 0.516 and 0.415 for prediction of precipitation and TMax on the testing data. This highlights the importance of continued research utilising machine learning methods for prediction of atmospheric variables and weather pattens on multiple time scales.
Publisher: Australian Mathematical Publishing Association, Inc.
Date: 17-07-2019
DOI: 10.21914/ANZIAMJ.V60I0.13967
Abstract: Motivated by the Millennium Drought and the current drought over much of southern and eastern Australia, this detailed statistical study compares trends in annual wet season precipitation and temperature between a coastal site (Newcastle) and an inland site (Scone). Bootstrap permutation tests reveal Scone precipitation has decreased significantly over the past 40 years (p-value=0.070) whereas Newcastle has recorded little to no change (p-value=0.800). Mean maximum and minimum temperatures for Newcastle have increased over the past 40 years (p-values of 0.002 and 0.015, respectively) while the mean maximum temperature for Scone has increased (p-value = 0.058) and the mean minimum temperature has remained stable. This suggests mean temperatures during the wet season for both locations are increasing. Considering these trends along with those for precipitation, water resources in the Hunter region will be increasingly strained as a result of increased evaporation with either similar or less precipitation falling in the region. Wavelet analysis reveals that both sites have similar power spectra for precipitation and mean maximum temperature with a statistically significant signal in the two to seven year period, typically indicative of the El-Nino Southern Oscillation climate driver. The El-Nino Southern Oscillation also drives the Newcastle mean minimum temperature, whereas the Scone power spectra has no indication of a definitive driver for mean minimum temperature. References R. A., R. L. Kitching, F. Chiew, L. Hughes, P. C. D. Newton, S. S. Schuster, A. Tait, and P. Whetton. Climate change 2014: Impacts, adaptation, and vulnerability. Part B: Regional aspects. Contribution of Working Group II to the Fifth Assessment of the Intergovernmental Panel on Climate Change. Technical report, Intergovernmental Panel on Climate Change, 2014. URL www.ipcc.ch/report/ar5/wg2/. Bureau of Meteorology. Climate Glossary-Drought. URL www.bom.gov.au/climate/glossary/drought.shtml. K. M. Lau and H. Weng. Climate signal detection using wavelet transform: How to make a time series sing. B. Am. Meteorol. Soc., 76:23912402, 1995. doi:10.1175/1520-0477(1995)0762391:CSDUWT .0.CO . M. B. Richman and L. M. Leslie. Uniqueness and causes of the California drought. Procedia Comput. Sci., 61:428435, 2015. doi:10.1016/j.procs.2015.09.181. M. B. Richman and L. M. Leslie. The 20152017 Cape Town drought: Attribution and prediction using machine learning. Procedia Comput. Sci., 140:248257, 2018. doi:10.1016/j.procs.2018.10.323.
Publisher: MDPI AG
Date: 05-03-2021
DOI: 10.3390/CLI9030044
Abstract: Low pressure systems off the southeast coast of Australia can generate intense rainfall and associated flooding, destructive winds, and coastal erosion, particularly during the cool season (April–September). Impacts depend on coastal proximity, strength and latitude. Therefore, it is important to investigate changes in frequency, duration, location, and intensity of these systems. First, an existing observation-based database of these low pressure systems, for 1970–2006, is extended to 2019, focusing on April–September and using archived Australian Bureau of Meteorology MSLP charts. Second, data consistency between 1970 and 2006 and 2007 and 2019 is confirmed. Third, permutation testing is performed on differences in means and variances between the two 25-year intervals 1970–1994 and 1995–2019. Additionally, trends in positions, durations and central pressures of the systems are investigated. p-values from permutation tests reveal statistically significant increases in mean low pressure system frequencies. Specifically, a greater frequency of both total days and initial development days only, occurred in the latter period. Statistically significant lower variance for both latitude and longitude in systems that developed in both subtropical easterly and mid-latitude westerly wind regimes indicate a shift south and east in the latter period. Furthermore, statistically significant differences in variance of development location of explosive low pressure systems that develop in a low level easterly wind regime indicate a shift further south and east. These changes are consistent with fewer systems projected to impact the east coast. Finally, important changes are suggested in the large scale atmospheric dynamics of the eastern Australian/Tasman Sea region.
Publisher: Australian Mathematical Publishing Association, Inc.
Date: 23-05-2021
DOI: 10.21914/ANZIAMJ.V62.16113
Abstract: On 16 December 2015 a severe thunderstorm and associated tornado affected Sydney causing widespread damage and insured losses of $206 million. Severe impacts occurred in Kurnell, requiring repairs to Sydney's desalination plant which supplies up to 15% of Sydney water during drought, with repairs only completed at the end of 2018. Climatologically, this storm was unusual as it occurred during the morning and had developed over the ocean, rather than developing inland during the afternoon as is the case for many severe storms impacting the Sydney region. Simulations of the Kurnell storm were conducted using the Weather Research and Forecasting (WRF) model on a double nested domain using the Morrison microphysics scheme and the NSSL 2-moment 4-ice microphysics scheme. Both simulations produced severe storms that followed paths similar to the observed storm. However, the storm produced under the Morrison scheme did not have the same morphology as the observed storm. Meanwhile, the storm simulated with the NSSL scheme displayed cyclical low- and mid-level mesocyclone development, which was observed in the Kurnell storm, highlighting that the atmosphere supported the development of severe rotating thunderstorms with the potential for tornadogenesis. The NSSL storm also produced severe hail and surface winds, similar to observations. The ability of WRF to simulate general convective characteristics and a storm similar to that observed displays the applicability of this model to study the causes of severe high-impact Australian thunderstorms. References J. T. Allen and E. R. Allen. A review of severe thunderstorms in Australia. Atmos. Res., 178:347–366, 2016. doi:10.1016/j.atmosres.2016.03.011. Bureau of Meteorology. Severe Storms Archive, 2020. URL www.bom.gov.au/australia/stormarchive/. D. T. Dawson II, M. Xue, J. A. Milbrandt, and M. K. Yau. Comparison of evaporation and cold pool development between single-moment and multimoment bulk microphysics schemes in idealized simulations of tornadic thunderstorms. Month. Wea. Rev., 138:1152–1171, 2010. doi:10.1175/2009MWR2956.1. H. Hersbach, B. Bell, P. Berrisford, S. Hirahara, A. Horanyi, J. Munoz-Sabater, J. Nicolas, C. Peubey, R. Radu, D. Schepers, et al. The ERA5 global reanalysis. Quart. J. Roy. Meteor. Soc., 146:1999–2049, 2020. doi:10.1002/qj.3803. Insurance Council of Australia. Victorian bushfire losses push summer catastrophe bill past $550m, 2016. E. R. Mansell, C. L. Ziegler, and E. C. Bruning. Simulated electrification of a small thunderstorm with two-moment bulk microphysics. J. Atmos. Sci., 67:171–194, 2010. doi:10.1175/2009JAS2965.1. R. C. Miller. Notes on analysis and severe-storm forecasting procedures of the Air Force Global Weather Central, volume 200. Air Weather Service, 1972. URL ti/citations/AD0744042. H. Morrison, J. A. Curry, and V. I. Khvorostyanov. A new double-moment microphysics parameterization for application in cloud and climate models. Part I: Description. J. Atmos. Sci., 62:1665–1677, 2005. doi:10.1175/JAS3446.1. H. Morrison, G. Thompson, and V. Tatarskii. Impact of cloud microphysics on the development of trailing stratiform precipitation in a simulated squall line: Comparison of one- and two-moment schemes. Month. Wea. Rev., 137:991–1007, 2009. doi:10.1175/2008MWR2556.1. J. G. Powers, J. B. Klemp, W. C. Skamarock, C. A. Davis, J. Dudhia, D. O. Gill, J. L. Coen, D. J. Gochis, R. Ahmadov, S. E. Peckham, et al. The Weather Research and Forecasting Model: Overview, system efforts, and future directions. Bull. Am. Meteor. Soc., 98:1717–1737, 2017. doi:10.1175/BAMS-D-15-00308.1. H. Richter, A. Protat, J. Taylor, and J. Soderholm. Doppler radar and storm environment observations of a maritime tornadic supercell in Sydney, Australia. In Preprints, 28th Conf. on Severe Local Storms, Portland OR, Amer. Meteor. Soc. P, 2016. W. C. Skamarock, J. B. Klemp, J. Dudhia, D. O. Gill, Z. Liu, J. Berner, W. Wang, J. G. Powers, M. G. Duda, D. Barker, and X.-Y. Huang. A description of the advanced research WRF Model version 4. Technical report, 2019. Storm Prediction Center. The Enhanced Fujita Scale (EF Scale), 2014. URL fscale/. R. A. Warren, H. A. Ramsay, S. T. Siems, M. J. Manton, J. R. Peter, A. Protat, and A. Pillalamarri. Radar-based climatology of damaging hailstorms in Brisbane and Sydney, Australia. Quart. J. Roy. Meteor. Soc., 146:505–530, 2020. doi:10.1002/qj.3693.
Publisher: MDPI AG
Date: 26-03-2023
DOI: 10.3390/CLI11040076
Abstract: The aims of this study are to assess the impacts of accelerated climate change on summer maximum temperatures since the early 1990s in the Australian city of Sydney’s eastern coastal and western inland suburbs. Western Sydney currently experiences far more intense summer (December–March) heat waves than coastal Sydney, with maximum temperatures exceeding those of coastal Sydney by up to 10 °C. Aside from increased bushfire danger, extreme temperature days pose health and socio-economic threats to western Sydney. Permutation tests of consecutive summer periods, 1962–1991 and 1992–2021, are employed to determine the differential climate change impacts on maximum summer temperatures at two locations: Sydney and Richmond, representative of eastern and western Sydney, respectively. Attribution of observed maximum summer temperature trends in Sydney and Richmond was performed using machine learning techniques applied to known Australian region oceanic and atmospheric climate drivers. It was found that there is a marked disparity in the percentage of summer days above the 95th percentile during the accelerated climate change period (1992–2021) between Richmond (+35%) and Sydney (−24%), relative to 1962–1991. The climate drivers detected as attributes were similar in both Sydney and Richmond, but, unsurprisingly, Sydney was more affected than Richmond by the oceanic climate drivers.
No related grants have been discovered for Joshua Hartigan.