Articles | Volume 17, issue 7
https://doi.org/10.5194/gmd-17-2755-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-17-2755-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Continental-scale bias-corrected climate and hydrological projections for Australia
Justin Peter
Australian Bureau of Meteorology, GPO Box 1289K, Melbourne, VIC 3001, Australia
Centre for Applied Climate Sciences, University of Southern Queensland, Toowoomba, QLD 4350, Australia
Elisabeth Vogel
Australian Bureau of Meteorology, GPO Box 1289K, Melbourne, VIC 3001, Australia
Water Research Centre, School of Civil and Environmental Engineering, The University of New South Wales, Sydney, NSW 2052, Australia
Wendy Sharples
Australian Bureau of Meteorology, GPO Box 1289K, Melbourne, VIC 3001, Australia
Ulrike Bende-Michl
CORRESPONDING AUTHOR
Australian Bureau of Meteorology, GPO Box 1289K, Melbourne, VIC 3001, Australia
Louise Wilson
Australian Bureau of Meteorology, GPO Box 1289K, Melbourne, VIC 3001, Australia
Met Office, International Climate Services, Exeter, United Kingdom
Pandora Hope
Australian Bureau of Meteorology, GPO Box 1289K, Melbourne, VIC 3001, Australia
Andrew Dowdy
Australian Bureau of Meteorology, GPO Box 1289K, Melbourne, VIC 3001, Australia
Greg Kociuba
Australian Bureau of Meteorology, GPO Box 1289K, Melbourne, VIC 3001, Australia
Sri Srikanthan
Australian Bureau of Meteorology, GPO Box 1289K, Melbourne, VIC 3001, Australia
Vi Co Duong
Australian Bureau of Meteorology, GPO Box 1289K, Melbourne, VIC 3001, Australia
Jake Roussis
Australian Bureau of Meteorology, GPO Box 1289K, Melbourne, VIC 3001, Australia
Vjekoslav Matic
Australian Bureau of Meteorology, GPO Box 1289K, Melbourne, VIC 3001, Australia
Zaved Khan
Australian Bureau of Meteorology, GPO Box 1289K, Melbourne, VIC 3001, Australia
CSIRO Environment, GPO Box 1700, Canberra, ACT 2601, Australia
Alison Oke
Australian Bureau of Meteorology, GPO Box 1289K, Melbourne, VIC 3001, Australia
Margot Turner
Australian Bureau of Meteorology, GPO Box 1289K, Melbourne, VIC 3001, Australia
Stuart Baron-Hay
Australian Bureau of Meteorology, GPO Box 1289K, Melbourne, VIC 3001, Australia
Fiona Johnson
Water Research Centre, School of Civil and Environmental Engineering, The University of New South Wales, Sydney, NSW 2052, Australia
Raj Mehrotra
Water Research Centre, School of Civil and Environmental Engineering, The University of New South Wales, Sydney, NSW 2052, Australia
Ashish Sharma
Water Research Centre, School of Civil and Environmental Engineering, The University of New South Wales, Sydney, NSW 2052, Australia
Marcus Thatcher
CSIRO Marine and Atmospheric Research, Aspendale, VIC 3195, Australia
Ali Azarvinand
Australian Bureau of Meteorology, GPO Box 1289K, Melbourne, VIC 3001, Australia
Steven Thomas
Australian Bureau of Meteorology, GPO Box 1289K, Melbourne, VIC 3001, Australia
Ghyslaine Boschat
Australian Bureau of Meteorology, GPO Box 1289K, Melbourne, VIC 3001, Australia
Chantal Donnelly
Australian Bureau of Meteorology, GPO Box 1289K, Melbourne, VIC 3001, Australia
Robert Argent
Australian Bureau of Meteorology, GPO Box 1289K, Melbourne, VIC 3001, Australia
Related authors
No articles found.
Christopher A. Pickett-Heaps, Patrick Sunter, Wendy Sharples, Michael Pegios, Catherine Wilson, Alex Cornish, Richard Laugesen, and Elisabetta Carrara
EGUsphere, https://doi.org/10.5194/egusphere-2025-1379, https://doi.org/10.5194/egusphere-2025-1379, 2025
Short summary
Short summary
This study evaluates seasonal forecast skill of river discharge from (1) a gridded hydrological model coupled with statistical post-processing and (2) a locally calibrated statistical hydrological model dependant on recent hydrological observations. Results indicate a similar level of forecast skill. The statistical post-processor is not dependant on recent observations to maintain forecast skill, a finding that will have a positive impact on operational hydrological forecasting.
Matthew O. Grant, Anna M. Ukkola, Elisabeth Vogel, Sanaa Hobeichi, Andy J. Pitman, Alex Raymond Borowiak, and Keirnan Fowler
EGUsphere, https://doi.org/10.5194/egusphere-2024-4024, https://doi.org/10.5194/egusphere-2024-4024, 2025
Short summary
Short summary
Australia is regularly subjected to severe and widespread drought. By using multiple drought indicators, we show that while there have been widespread decreases in droughts since the beginning of the 20th century. However, many regions have seen an increase in droughts in more recent decades. Despite these changes, our analysis shows that they remain within the range of observed variability and are not unprecedented in the context of past droughts.
Michelle Ho, Declan O'Shea, Conrad Wasko, Rory Nathan, and Ashish Sharma
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-403, https://doi.org/10.5194/hess-2024-403, 2025
Revised manuscript under review for HESS
Short summary
Short summary
There is unequivocal evidence that climate change will impact the risk profile of dams, which are critical for water supply and flood mitigation. We project changes in the overtopping risk for 18 large dams in Australia in response to global warming. We consider the impacts of climate change on rainfall depth, rainfall temporal pattern, and rainfall losses. Under 4 °C of global warming, the risk of overtopping floods was 2.4–17 times that of historical conditions.
Phuong Loan Nguyen, Lisa V. Alexander, Marcus J. Thatcher, Son C. H. Truong, Rachael N. Isphording, and John L. McGregor
Geosci. Model Dev., 17, 7285–7315, https://doi.org/10.5194/gmd-17-7285-2024, https://doi.org/10.5194/gmd-17-7285-2024, 2024
Short summary
Short summary
We use a comprehensive approach to select a subset of CMIP6 models for dynamical downscaling over Southeast Asia, taking into account model performance, model independence, data availability and the range of future climate projections. The standardised benchmarking framework is applied to assess model performance through both statistical and process-based metrics. Ultimately, we identify two independent model groups that are suitable for dynamical downscaling in the Southeast Asian region.
Anna M. Ukkola, Steven Thomas, Elisabeth Vogel, Ulrike Bende-Michl, Steven Siems, Vjekoslav Matic, and Wendy Sharples
EGUsphere, https://doi.org/10.31223/X56110, https://doi.org/10.31223/X56110, 2024
Short summary
Short summary
Future drought changes in Australia –the driest inhabited continent on Earth– have remained stubbornly uncertain. We assess future drought changes in Australia using projections from climate and hydrological models. We show an increasing probability of drought over highly-populated and agricultural regions of Australia in coming decades, suggesting potential impacts on agricultural activities, ecosystems and urban water supply.
Wendy Sharples, Katayoon Bahramian, Kesav Unnithan, Christoph Rüdiger, Jiawei Hou, Christopher Pickett-Heaps, and Elisabetta Carrara
Proc. IAHS, 386, 237–249, https://doi.org/10.5194/piahs-386-237-2024, https://doi.org/10.5194/piahs-386-237-2024, 2024
Short summary
Short summary
Two flood events occurred in the Hawkesbury-Nepean valley in 2020 and 2021, however, the impact of each of those events was different in terms of lives lost (2 fatalities compared to none) and economic losses (more than 2 billion compared to less than 1 billion AUD). Reasons for the variation in impacts are explored by determining the inundation extents, and examining antecedent and climatic conditions. We found that antecedent conditions exerted a major control on the size of the impact.
Conrad Wasko, Seth Westra, Rory Nathan, Acacia Pepler, Timothy H. Raupach, Andrew Dowdy, Fiona Johnson, Michelle Ho, Kathleen L. McInnes, Doerte Jakob, Jason Evans, Gabriele Villarini, and Hayley J. Fowler
Hydrol. Earth Syst. Sci., 28, 1251–1285, https://doi.org/10.5194/hess-28-1251-2024, https://doi.org/10.5194/hess-28-1251-2024, 2024
Short summary
Short summary
In response to flood risk, design flood estimation is a cornerstone of infrastructure design and emergency response planning, but design flood estimation guidance under climate change is still in its infancy. We perform the first published systematic review of the impact of climate change on design flood estimation and conduct a meta-analysis to provide quantitative estimates of possible future changes in extreme rainfall.
Emma Howard, Chun-Hsu Su, Christian Stassen, Rajashree Naha, Harvey Ye, Acacia Pepler, Samuel S. Bell, Andrew J. Dowdy, Simon O. Tucker, and Charmaine Franklin
Geosci. Model Dev., 17, 731–757, https://doi.org/10.5194/gmd-17-731-2024, https://doi.org/10.5194/gmd-17-731-2024, 2024
Short summary
Short summary
The BARPA-R modelling configuration has been developed to produce high-resolution climate hazard projections within the Australian region. When using boundary driving data from quasi-observed historical conditions, BARPA-R shows good performance with errors generally on par with reanalysis products. BARPA-R also captures trends, known modes of climate variability, large-scale weather processes, and multivariate relationships.
Bibi S. Naz, Wendy Sharples, Yueling Ma, Klaus Goergen, and Stefan Kollet
Geosci. Model Dev., 16, 1617–1639, https://doi.org/10.5194/gmd-16-1617-2023, https://doi.org/10.5194/gmd-16-1617-2023, 2023
Short summary
Short summary
It is challenging to apply a high-resolution integrated land surface and groundwater model over large spatial scales. In this paper, we demonstrate the application of such a model over a pan-European domain at 3 km resolution and perform an extensive evaluation of simulated water states and fluxes by comparing with in situ and satellite data. This study can serve as a benchmark and baseline for future studies of climate change impact projections and for hydrological forecasting.
Nicky M. Wright, Claire E. Krause, Steven J. Phipps, Ghyslaine Boschat, and Nerilie J. Abram
Clim. Past, 18, 1509–1528, https://doi.org/10.5194/cp-18-1509-2022, https://doi.org/10.5194/cp-18-1509-2022, 2022
Short summary
Short summary
The Southern Annular Mode (SAM) is a major mode of climate variability. Proxy-based SAM reconstructions show changes that last millennium climate simulations do not reproduce. We test the SAM's sensitivity to solar forcing using simulations with a range of solar values and transient last millennium simulations with large-amplitude solar variations. We find that solar forcing can alter the SAM and that strong solar forcing transient simulations better match proxy-based reconstructions.
Philippa A. Higgins, Jonathan G. Palmer, Chris S. M. Turney, Martin S. Andersen, and Fiona Johnson
Clim. Past, 18, 1169–1188, https://doi.org/10.5194/cp-18-1169-2022, https://doi.org/10.5194/cp-18-1169-2022, 2022
Short summary
Short summary
We studied eight New Zealand tree species and identified differences in their responses to large volcanic eruptions. The response is dependent on the species and how well it can tolerate stress, but substantial within-species differences are also observed depending on site factors, including altitude and exposure. This has important implications for tree-ring temperature reconstructions because site selection and compositing methods can change the magnitude of observed volcanic cooling.
Roseanna C. McKay, Julie M. Arblaster, and Pandora Hope
Weather Clim. Dynam., 3, 413–428, https://doi.org/10.5194/wcd-3-413-2022, https://doi.org/10.5194/wcd-3-413-2022, 2022
Short summary
Short summary
Understanding what makes it hot in Australia in spring helps us better prepare for harmful impacts. We look at how the higher latitudes and tropics change the atmospheric circulation from early to late spring and how that changes maximum temperatures in Australia. We find that the relationship between maximum temperatures and the tropics is stronger in late spring than early spring. These findings could help improve forecasts of hot months in Australia in spring.
Xia Wu, Lucy Marshall, and Ashish Sharma
Hydrol. Earth Syst. Sci., 26, 1203–1221, https://doi.org/10.5194/hess-26-1203-2022, https://doi.org/10.5194/hess-26-1203-2022, 2022
Short summary
Short summary
Decomposing parameter and input errors in model calibration is a considerable challenge. This study transfers the direct estimation of an input error series to their rank estimation and develops a new algorithm, i.e., Bayesian error analysis with reordering (BEAR). In the context of a total suspended solids simulation, two synthetic studies and a real study demonstrate that the BEAR method is effective for improving the input error estimation and water quality model calibration.
Danlu Guo, Camille Minaudo, Anna Lintern, Ulrike Bende-Michl, Shuci Liu, Kefeng Zhang, and Clément Duvert
Hydrol. Earth Syst. Sci., 26, 1–16, https://doi.org/10.5194/hess-26-1-2022, https://doi.org/10.5194/hess-26-1-2022, 2022
Short summary
Short summary
We investigate the impact of baseflow contribution on concentration–flow (C–Q) relationships across the Australian continent. We developed a novel Bayesian hierarchical model for six water quality variables across 157 catchments that span five climate zones. For sediments and nutrients, the C–Q slope is generally steeper for catchments with a higher median and a greater variability of baseflow contribution, highlighting the key role of variable flow pathways in particulate and solute export.
Siyuan Tian, Luigi J. Renzullo, Robert C. Pipunic, Julien Lerat, Wendy Sharples, and Chantal Donnelly
Hydrol. Earth Syst. Sci., 25, 4567–4584, https://doi.org/10.5194/hess-25-4567-2021, https://doi.org/10.5194/hess-25-4567-2021, 2021
Short summary
Short summary
Accurate daily continental water balance predictions are valuable in monitoring and forecasting water availability and land surface conditions. A simple and robust method was developed for an operational water balance model to constrain model predictions temporally and spatially with satellite soil moisture observations. The improved soil water storage prediction can provide constraints in model forecasts that persist for several weeks.
Cited articles
Alexander, L. V. and Arblaster, J. M.: Assessing trends in observed and modelled climate extremes over Australia in relation to future projections, Int. J. Climatol., 29, 417–435, https://doi.org/10.1002/JOC.1730, 2009.
Azarnivand, A., Sharples, W., Bende-michl, U., Shokri, A., and Srikanthan, S.: Analysing the uncertainty of modelling hydrologic states of AWRA-L – understanding impacts from parameter uncertainty for the National Hydrological Projections, Bureau Research Report No. 060, 39 pp., 2022.
Bureau of Meteorology: The Bureau of Meteorology’s National Hydrological Projection data collection on changes to Australia’s hydrological water balance, NCI Australia [data set], https://doi.org/10.25914/6130680DC5A51, 2021.
Chiew, F. H. S.: Estimation of rainfall elasticity of streamflow in Australia, Hydrolog. Sci. J., 51, 613–625, https://doi.org/10.1623/HYSJ.51.4.613, 2006.
Chiew, F. H. S., Zheng, H., and Potter, N. J.: Rainfall-Runoff modelling considerations to predict streamflow characteristics in ungauged catchments and under climate change, Water, 10, 7–9, https://doi.org/10.3390/w10101319, 2018.
Chubb, T. H., Manton, M. J., Siems, S. T., and Peace, A. D.: Evaluation of the AWAP daily precipitation spatial analysis with an independent gauge network in the Snowy Mountains, J. South. Hemisph. Earth Syst. Sci., 66, 55–67, 2016.
Clarke, J., Grose, M., Thatcher, M., Hernaman, V., Heady, C., Round, V., Rafter, T., Trenham, C., and Wilson, L.: Victorian Climate Projections 2019 Technical Report, ISBN 978-1-76077-735-7, 2019.
Crosbie, R., McCallum, J., and Harrington, G.: Diffuse groundwater recharge modelling across northern Australia. A report to the Australian Government from the CSIRO Northern Australia Sustainable Yields Project, 56 pp., 2009.
CSIRO: Climate change projections and impacts on runoff for Tasmania: CSIRO Tasmania Sustainable Yields Project, Report two of seven to the Australian Government, 1–18, https://doi.org/10.4225/08/58557f3814539, 2009.
CSIRO and Bureau of Meteorology: Climate Change in Australia Projections for Australia's Natural Resource Management Regions, Technical Report, ISBN 9781921232947, 2015.
CSIRO and Bureau of Meteorology: State of the Climate 2022, http://www.bom.gov.au/state-of-the-climate/2022/documents/2022-state-of-the-climate-web.pdf (last access: 10 April 2022), 2022.
Dey, R., Lewis, S. C., Arblaster, J. M., and Abram, N. J.: A review of past and projected changes in Australia's rainfall, Wires Clim. Change, 10, 1–23, https://doi.org/10.1002/wcc.577, 2019.
Dix, M., Vohralik, P., Bi, D., Rashid, H., Marsland, S., O'Farrell, S., Uotila, P., Hirst, T., Kowalczyk, E., Sullivan, A., Yan, H., Franklin, C., Sun, Z., Watterson, I., Collier, M., Noonan, J., Rotstayn, L., Stevens, L., Uhe, P., and Puri, K.: The ACCESS coupled model: Documentation of core CMIP5 simulations and initial results, Aust. Meteorol. Oceanogr. J., 63, 83–99, https://doi.org/10.22499/2.6301.006, 2013.
Dowdy, A.: Quantile Matching for Extremes code, Zenodo [code], https://doi.org/10.5281/zenodo.7939660, 2023.
Dowdy, A.: A bias correction method designed for weather and climate extremes, Bureau Research Report No. 087, Bureau of Meteorology, 65 pp., ISBN 9781925738759, 2023.
Dowdy, A. J.: Seamless climate change projections and seasonal predictions for bushfires in Australia, J. South. Hemisph. Earth Syst. Sci., 70, 120–138, https://doi.org/10.1071/ES20001, 2020.
Dunne, J. P., John, J. G., Adcroft, A. J., Griffies, S. M., Hallberg, R. W., Shevliakova, E., Stouffer, R. J., Cooke, W., Dunne, K. A., Harrison, M. J., Krasting, J. P., Malyshev, S. L., Milly, P. C. D., Phillipps, P. J., Sentman, L. T., Samuels, B. L., Spelman, M. J., Winton, M., Wittenberg, A. T., and Zadeh, N.: GFDL's ESM2 global coupled climate-carbon earth system models. Part I: Physical formulation and baseline simulation characteristics, J. Climate, 25, 6646–6665, https://doi.org/10.1175/JCLI-D-11-00560.1, 2012.
Ekström, M., Grose, M. R., and Whetton, P. H.: An appraisal of downscaling methods used in climate change research, Wires Clim. Change, 6, 301–319, https://doi.org/10.1002/wcc.339, 2015.
Evans, A., Jones, D., Smalley, R., and Lellyett, S.: An enhanced gridded rainfall dataset scheme for Australia, Bureau Research Report No. 41, 41 pp., ISBN 978-1-925738-12-4, 2020.
Fowler, H. J., Blenkinsop, S., and Tebaldi, C.: Linking climate change modelling to impacts studies: recent advances in downscaling techniques for hydrological modelling, Int. J. Climatol., 27, 1547–1578, https://doi.org/10.1002/joc.1556, 2007.
Frost, A. J. and Wright, D. P.: Evaluation of the Australian Landscape Water Balance model : AWRA-L v6. A comparison of AWRA-L v6 against Observed Hydrological Data and Peer Models, Bureau Technical Report, 79 pp., 2018.
Frost, A. J., Ramchurn A., and Smith A.: The Australian Landscape Water Balance model (AWRA-L v6) Technical Description of the Australian Water Resources Assessment Landscape model version 6, http://www.bom.gov.au/other/copyright.shtml (last access: 1 June 2021), 2018.
Garratt, J. R.: The atmospheric boundary layer, Cambridge University Press, Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, https://doi.org/10.5359/jawe.2006.693, 1992.
Grant, I., Jones, D., Wang, W., Fawcett, R., and Barratt, D.: Meteorological and Remotely Sensed Datasets for Hydrological Modelling: A Contribution to the Australian Water Availability Project, in: Catchment-scale Hydrological Modelling & Data Assimilation (CAHMDA-3) International Workshop on Hydrological Prediction: Modelling, Observation and Data Assimilation, 1–4, 2008.
Hempel, S., Frieler, K., Warszawski, L., Schewe, J., and Piontek, F.: A trend-preserving bias correction – the ISI-MIP approach, Earth Syst. Dynam., 4, 219–236, https://doi.org/10.5194/esd-4-219-2013, 2013.
Heo, J. H., Ahn, H., Shin, J. Y., Kjeldsen, T. R., and Jeong, C.: Probability distributions for a quantile mapping technique for a bias correction of precipitation data: A case study to precipitation data under climate change, Water, 11, 1475, https://doi.org/10.3390/w11071475, 2019.
Hewitson, B. C., Daron, J., Crane, R. G., Zermoglio, M. F., and Jack, C.: Interrogating empirical-statistical downscaling, Climatic Change, 122, 539–554, https://doi.org/10.1007/s10584-013-1021-z, 2014.
Hoffmann, P., Katzfey, J. J., McGregor, J. L., and Thatcher, M.: Bias and variance correction of sea surface temperatures used for dynamical downscaling, J. Geophys. Res.-Atmos., 121, 12877–12890, https://doi.org/10.1002/2016JD025383, 2016.
IPCC: IPCC Special Report on Emissions Scenarios, Prepared by Working Group III of the Intergovernmental Panel on Climate Change, Cambridge, United Kingdom, and New York, NY, USA, https://www.ipcc.ch/site/assets/uploads/2018/03/emissions_scenarios-1.pdf (last access: 15 September 2021), 2000.
Johnson, F. and Sharma, A.: A nesting model for bias correction of variability at multiple time scales in general circulation model precipitation simulations, Water Resour. Res., 48, W01504, https://doi.org/10.1029/2011WR010464, 2012.
Jones, D. A., Wang, W., and Fawcett, R.: AWAP_Jones_2009, Aust. Meteorol. Oceanogr. J., 58, 58, 233–248, 2009.
Kharin, V. V., Zwiers, F. W., Zhang, X., and Hegerl, G. C.: Changes in temperature and precipitation extremes in the IPCC ensemble of global coupled model simulations, J. Climate, 20, 1419–1444, https://doi.org/10.1175/JCLI4066.1, 2007.
King, A. D., Alexander, L. V., and Donat, M. G.: The efficacy of using gridded data to examine extreme rainfall characteristics: a case study for Australia, Int. J. Climatol., 33, 2376–2387, https://doi.org/10.1002/JOC.3588, 2013.
Leander, R. and Buishand, T. A.: Resampling of regional climate model output for the simulation of extreme river flows, J. Hydrol., 332, 487–496, https://doi.org/10.1016/j.jhydrol.2006.08.006, 2007.
Lenderink, G., Buishand, A., and van Deursen, W.: Estimates of future discharges of the river Rhine using two scenario methodologies: direct versus delta approach, Hydrol. Earth Syst. Sci., 11, 1145–1159, https://doi.org/10.5194/hess-11-1145-2007, 2007.
Maraun, D.: Bias correction, quantile mapping, and downscaling: Revisiting the inflation issue, J. Climate, 26, 2137–2143, https://doi.org/10.1175/JCLI-D-12-00821.1, 2013.
Maraun, D.: Bias Correcting Climate Change Simulations – a Critical Review, Curr. Clim. Chang. Rep., 2, 211–220, https://doi.org/10.1007/s40641-016-0050-x, 2016.
Maraun, D. and Widmann, M.: Statistical Downscaling and Bias Correction for Climate Research, Cambridge University Press, https://doi.org/10.1017/9781107588783, 2018.
Maraun, D., Shepherd, T. G., Widmann, M., Zappa, G., Walton, D., Gutiérrez, J. M., Hagemann, S., Richter, I., Soares, P. M. M., Hall, A., and Mearns, L. O.: Towards process-informed bias correction of climate change simulations, Nat. Clim. Change, 7, 764–773, https://doi.org/10.1038/nclimate3418, 2017.
McArthur, A. G.: Fire behaviour in eucalypt forests, Forestry and Timber Bureau, Canberra, 1967.
McGregor, J. L.: C-CAM geometric aspects and dynamical formulation, Technical Report 70, CSIRO Atmospheric Research, 43 pp., 2005.
McGregor, J. L. and Dix, M. R.: An Updated Description of the Conformal-Cubic Atmospheric Model, High Resolut. Numer. Model. Atmos. Ocean, 51–75, https://doi.org/10.1007/978-0-387-49791-4_4, 2008.
McVicar, T. R., Van Niel, T. G., Li, L. T., Roderick, M. L., Rayner, D. P., Ricciardulli, L., and Donohue, R. J.: Wind speed climatology and trends for Australia, 1975–2006: Capturing the stilling phenomenon and comparison with near-surface reanalysis output, Geophys. Res. Lett., 35, 1–6, https://doi.org/10.1029/2008GL035627, 2008.
Mehrotra, R. and Sharma, A.: An improved standardization procedure to remove systematic low frequency variability biases in GCM simulations, Water Resour. Res., 48, W12601, https://doi.org/10.1029/2012WR012446, 2012.
Mehrotra, R. and Sharma, A.: Correcting for systematic biases in multiple raw GCM variables across a range of timescales, J. Hydrol., 520, 214–223, https://doi.org/10.1016/j.jhydrol.2014.11.037, 2015.
Milly, P. C. D. and Dunne, K. A.: Potential evapotranspiration and continental drying, Nat. Clim. Change, 610, 946–949, https://doi.org/10.1038/nclimate3046, 2016.
Nash, J. E. and Sutcliffe, J. V.: River flow forecasting through conceptual models part I – A discussion of principles, J. Hydrol., 10, 282–290, https://doi.org/10.1016/0022-1694(70)90255-6, 1970.
Naumann, G., Alfieri, L., Wyser, K., Mentaschi, L., Betts, R. A., Carrao, H., Spinoni, J., Vogt, J., and Feyen, L.: Global Changes in Drought Conditions Under Different Levels of Warming, Geophys. Res. Lett., 45, 3285–3296, https://doi.org/10.1002/2017GL076521, 2018.
NCI Data Catalogue: NCI Data Catalogue, https://doi.org/10.25914/6130680dc5a51, https://geonetwork.nci.org.au/geonetwork/srv/eng/catalog.search#/metadata/f6683_9441_8676_1139, last access: 9 April 2024.
Pascolini-Campbell, M., Reager, J. T., Chandanpurkar, H. A., and Rodell, M.: A 10 per cent increase in global land evapotranspiration from 2003 to 2019, Nature, 593, 543–547, https://doi.org/10.1038/s41586-021-03503-5, 2021.
Perkins, S. E., Moise, A., Whetton, P., and Katzfey, J.: Regional changes of climate extremes over Australia – a comparison of regional dynamical downscaling and global climate model simulations, Int. J. Climatol., 34, 3456–3478, https://doi.org/10.1002/JOC.3927, 2014.
Perrin, C., Michel, C., and Andréassian, V.: Improvement of a parsimonious model for streamflow simulation, J. Hydrol., 279, 275–289, https://doi.org/10.1016/S0022-1694(03)00225-7, 2003.
Peter, J.: JustinRPeter/isimip-bias-correction: v1.0 (v1.0), Zenodo [code], https://doi.org/10.5281/zenodo.7839687, 2023a.
Peter, J.: JustinRPeter/mrnbc_zenodo: v1.0.0 (v1.0.0), Zenodo [code], https://doi.org/10.5281/zenodo.8046380, 2023b.
Peter, J.: JustinRPeter/nhp_mrnbc_stitching: v1.0.0 (v1.0.0), Zenodo [code], https://doi.org/10.5281/zenodo.7873637, 2023c.
Peter, J.: JustinRPeter/nhp_mrnbc_stitching: v1.0.0 (v1.0.0), Zenodo [code], https://doi.org/10.5281/zenodo.7873637, 2023d.
Peter, J.: JustinRPeter/nhp_transform_wind_grids: v1.0.0 (v1.0.0), Zenodo [code], https://doi.org/10.5281/zenodo.7873409, 2023e.
Peter, J.: AusClimateService/NHP_evaluation: nhp_evaluation (v1.0), Zenodo [code], https://doi.org/10.5281/zenodo.7844885, 2023f.
Peter, J.: JustinRPeter/nhp_extremes_plots: v1.0 (v1.0), Zenodo [code], https://doi.org/10.5281/zenodo.7869921, 2023g.
Peters, G. P. and Hausfather, Z.: Emissions – the “business as usual” story is misleading, Nature, 577, 618–620, 2020.
Piani, C., Haerter, J. O., and Coppola, E.: Statistical bias correction for daily precipitation in regional climate models over Europe, Theor. Appl. Climatol., 99, 187–192, https://doi.org/10.1007/s00704-009-0134-9, 2010a.
Piani, C., Weedon, G. P., Best, M., Gomes, S. M., Viterbo, P., Hagemann, S., and Haerter, J. O.: Statistical bias correction of global simulated daily precipitation and temperature for the application of hydrological models, J. Hydrol., 395, 199–215, https://doi.org/10.1016/j.jhydrol.2010.10.024, 2010b.
Potter, N. J., Chiew, F. H. S., Charles, S. P., Fu, G., Zheng, H., and Zhang, L.: Bias in dynamically downscaled rainfall characteristics for hydroclimatic projections, Hydrol. Earth Syst. Sci., 24, 2963–2979, https://doi.org/10.5194/hess-24-2963-2020, 2020.
Prestele, R., Arneth, A., Bondeau, A., de Noblet-Ducoudré, N., Pugh, T. A. M., Sitch, S., Stehfest, E., and Verburg, P. H.: Current challenges of implementing anthropogenic land-use and land-cover change in models contributing to climate change assessments, Earth Syst. Dynam., 8, 369–386, https://doi.org/10.5194/esd-8-369-2017, 2017.
Roderick, M. L., Greve, P., and Farquhar, G. D.: On the assessment of aridity with changes in atmospheric CO2, Water Resour. Res., 51, 5450–5463, https://doi.org/10.1002/2015WR017031, 2015.
Scheff, J. and Frierson, D. M. W.: Terrestrial aridity and its response to greenhouse warming across CMIP5 climate models, J. Climate, 28, 5583–5600, https://doi.org/10.1175/JCLI-D-14-00480.1, 2015.
Schwalm, C. R., Glendon, S., and Duffy, P. B.: RCP8.5 tracks cumulative CO2 emissions, P. Natl. Acad. Sci. USA, 117, 19656–19657, https://doi.org/10.1073/PNAS.2007117117, 2020.
Shepherd, T. G., Boyd, E., Calel, R. A., Chapman, S. C., Dessai, S., Dima-West, I. M., Fowler, H. J., James, R., Maraun, D., Martius, O., Senior, C. A., Sobel, A. H., Stainforth, D. A., Tett, S. F. B., Trenberth, K. E., van den Hurk, B. J. J. M., Watkins, N. W., Wilby, R. L., and Zenghelis, D. A.: Storylines: an alternative approach to representing uncertainty in physical aspects of climate change, Climatic Change, 151, 555–571, https://doi.org/10.1007/s10584-018-2317-9, 2018.
Sherwood, S. C., Roca, R., Weckwerth, T. M., and Andronova, N. G.: Tropospheric water vapor, convection, and climate, Rev. Geophys., 48, 2001, https://doi.org/10.1029/2009RG000301, 2010.
Srikanthan, R. and Pegram, G. G. S.: A nested multisite daily rainfall stochastic generation model, J. Hydrol., 371, 142–153, https://doi.org/10.1016/j.jhydrol.2009.03.025, 2009.
Stocker, T. F., Qin, D., Plattner, G. K., Tignor, M. M. B., Allen, S. K., Boschung, J., Nauels, A., Xia, Y., Bex, V., and Midgley, P. M.: Climate change 2013 the physical science basis: Working Group I contribution to the fifth assessment report of the intergovernmental panel on climate change, https://doi.org/10.1017/CBO9781107415324, 2013.
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.
Teutschbein, C. and Seibert, J.: Bias correction of regional climate model simulations for hydrological climate-change impact studies: Review and evaluation of different methods, J. Hydrol., 456–457, 12–29, https://doi.org/10.1016/j.jhydrol.2012.05.052, 2012.
tha051 and Dix, M.: JustinRPeter/ccam_vicdelwp2018: ccam_vicdelwp2018 (v1.0), Zenodo [code], https://doi.org/10.5281/zenodo.7884565, 2023.
Timbal, B., Arblaster, J. M., and Power, S.: Attribution of the late-twentieth-century rainfall decline in southwest Australia, J. Climate, 19, 2046–2062, https://doi.org/10.1175/JCLI3817.1, 2006.
Trewin, B., Braganza, K., Fawcett, R., Grainger, S., Jovanovic, B., Jones, D., Martin, D., Smalley, R., and Webb, V.: An updated long-term homogenized daily temperature data set for Australia, Geosci. Data J., 7, 149–169, https://doi.org/10.1002/gdj3.95, 2020.
Vogel, E., Johnson, F., Marshall, L., Bende-Michl, U., Wilson, L., Peter, J. R., Wasko, C., Srikanthan, S., Sharples, W., Dowdy, A., Hope, P., Khan, Z., Mehrotra, R., Sharma, A., Matic, V., Oke, A., Turner, M., Thomas, S., Donnelly, C., and Duong, V. C.: An evaluation framework for downscaling and bias correction in climate change impact studies, J. Hydrol., 622, 129693, https://doi.org/10.1016/J.JHYDROL.2023.129693, 2023.
Voldoire, A., Sanchez-Gomez, E., Salas y Mélia, D., Decharme, B., Cassou, C., Sénési, S., Valcke, S., Beau, I., Alias, A., Chevallier, M., Déqué, M., Deshayes, J., Douville, H., Fernandez, E., Madec, G., Maisonnave, E., Moine, M. P., Planton, S., Saint-Martin, D., Szopa, S., Tyteca, S., Alkama, R., Belamari, S., Braun, A., Coquart, L., and Chauvin, F.: The CNRM-CM5.1 global climate model: Description and basic evaluation, Clim. Dynam., 40, 2091–2121, https://doi.org/10.1007/s00382-011-1259-y, 2013.
Wasko, C. and Nathan, R.: Influence of changes in rainfall and soil moisture on trends in flooding, J. Hydrol., 575, 432–441, https://doi.org/10.1016/J.JHYDROL.2019.05.054, 2019.
Wasko, C. and Sharma, A.: Global assessment of flood and storm extremes with increased temperatures, Sci. Rep., 71, 1–8, https://doi.org/10.1038/s41598-017-08481-1, 2017.
Wasko, C., Nathan, R., and Peel, M. C.: Trends in global flood and streamflow timing based on local water year, Water Resour. Res., 56, e2020WR027233, https://doi.org/10.1029/2020WR027233, 2020.
Wasko, C., Shao, Y., Vogel, E., Wilson, L., Wang, Q. J., Frost, A., and Donnelly, C.: Understanding trends in hydrologic extremes across Australia, J. Hydrol., 593, 125877, https://doi.org/10.1016/j.jhydrol.2020.125877, 2021.
Watanabe, M., Suzuki, T., O'Ishi, R., Komuro, Y., Watanabe, S., Emori, S., Takemura, T., Chikira, M., Ogura, T., Sekiguchi, M., Takata, K., Yamazaki, D., Yokohata, T., Nozawa, T., Hasumi, H., Tatebe, H., and Kimoto, M.: Improved climate simulation by MIROC5: Mean states, variability, and climate sensitivity, J. Climate, 23, 6312–6335, https://doi.org/10.1175/2010JCLI3679.1, 2010.
Watterson, I. G., Chua, Z. W., and Hope, P. K.: Extreme monthly rainfall over Australia in a changing climate, J. South. Hemisph. Earth Syst. Sci., 66, 402–423, https://doi.org/10.22499/3.6604.003, 2017.
Wilson, L., Bende-Michl, U., Sharples, W., Vogel, E., Peter, J., Srikanthan, S., Khan, Z., Matic, V., Oke, A., Turner, M., Co Duong, V., Loh, S., Baron-Hay, S., Roussis, J., Kociuba, G., Hope, P., Dowdy, A., Donnelly, C., Argent, R., Thomas, S., Kitsios, A., and Bellhouse, J.: A national hydrological projections service for Australia, Clim. Serv., 28, 100331, https://doi.org/10.1016/J.CLISER.2022.100331, 2022.
Yang, Y., Roderick, M. L., Zhang, S., McVicar, T. R., and Donohue, R. J.: Hydrologic implications of vegetation response to elevated CO2 in climate projections, Nat. Clim. Change, 9, 44–48, https://doi.org/10.1038/s41558-018-0361-0, 2019.
Zhang, X. S., Amirthanathan, G. E., Bari, M. A., Laugesen, R. M., Shin, D., Kent, D. M., MacDonald, A. M., Turner, M. E., and Tuteja, N. K.: How streamflow has changed across Australia since the 1950s: evidence from the network of hydrologic reference stations, Hydrol. Earth Syst. Sci., 20, 3947–3965, https://doi.org/10.5194/hess-20-3947-2016, 2016.
Zheng, H., Yang, Z. L., Lin, P., Wei, J., Wu, W. Y., Li, L., Zhao, L., and Wang, S.: On the Sensitivity of the Precipitation Partitioning Into Evapotranspiration and Runoff in Land Surface Parameterizations, Water Resour. Res., 55, 95–111, https://doi.org/10.1029/2017WR022236, 2019a.
Zheng, H., Chiew, F. H. S., Potter, N. J., and Kirono, D. G. C.: Projections of water futures for Australia: An update, in: 23rd International Congress on Modelling and Simulation – Supporting Evidence-Based Decision Making: The Role of Modelling and Simulation, MODSIM 2019, 1000–1006, https://doi.org/10.36334/modsim.2019.k7.zhengh, 2019.
Short summary
We detail the production of datasets and communication to end users of high-resolution projections of rainfall, runoff, and soil moisture for the entire Australian continent. This is important as previous projections for Australia were for small regions and used differing techniques for their projections, making comparisons difficult across Australia's varied climate zones. The data will be beneficial for research purposes and to aid adaptation to climate change.
We detail the production of datasets and communication to end users of high-resolution...