Articles | Volume 17, issue 2
https://doi.org/10.5194/gmd-17-731-2024
© 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-17-731-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model evaluation paper
| 
29 Jan 2024
Model evaluation paper |
| 29 Jan 2024
| 29 Jan 2024
Performance and process-based evaluation of the BARPA-R Australasian regional climate model version 1
Emma Howard
CORRESPONDING AUTHOR
Bureau of Meteorology, Brisbane, Australia
Chun-Hsu Su
Bureau of Meteorology, Melbourne, Australia
Christian Stassen
Bureau of Meteorology, Melbourne, Australia
Rajashree Naha
Bureau of Meteorology, Melbourne, Australia
School of Earth, Atmosphere & Environment, Monash University, Melbourne, Australia
Harvey Ye
Bureau of Meteorology, Melbourne, Australia
Acacia Pepler
Bureau of Meteorology, Sydney, Australia
Samuel S. Bell
Bureau of Meteorology, Melbourne, Australia
Andrew J. Dowdy
Bureau of Meteorology, Melbourne, Australia
Simon O. Tucker
UK Met Office, Exeter, United Kingdom
Charmaine Franklin
Bureau of Meteorology, Melbourne, Australia
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Cited articles
Arakawa, A. and Lamb, V. R.: Computational Design of the Basic Dynamical Processes of the UCLA General Circulation Model, in: General Circulation Models of the Atmosphere, edited by: Chang, J., vol. 17 of Methods in Computational Physics: Advances in Research and Applications, 173–265, Elsevier, https://doi.org/10.1016/B978-0-12-460817-7.50009-4, 1977. a
Bao, J., Sherwood, S. C., Alexander, L. V., and Evans, J. P.: Future increases in extreme precipitation exceed observed scaling rates, Nat. Clim. Change, 7, 128–132, https://doi.org/10.1038/nclimate3201, 2017. a
Beischer, T. A., Gregory, P., Dayal, K., Brown, J. R., Charles, A. N., Wang, W. X. D., and Brown, J. N.: Scope for predicting seasonal variation of the SPCZ with ACCESS-S1, Clim. Dynam., 56, 1519–1540, https://doi.org/10.1007/s00382-020-05550-6, 2021. a
Bell, S. S., Chand, S. S., Tory, K. J., and Turville, C.: Statistical Assessment of the OWZ Tropical Cyclone Tracking Scheme in ERA-Interim, J. Climate, 31, 2217–2232, https://doi.org/10.1175/JCLI-D-17-0548.1, 2018. a
Best, M. J., Pryor, M., Clark, D. B., Rooney, G. G., Essery, R. L. H., Ménard, C. B., Edwards, J. M., Hendry, M. A., Porson, A., Gedney, N., Mercado, L. M., Sitch, S., Blyth, E., Boucher, O., Cox, P. M., Grimmond, C. S. B., and Harding, R. J.: The Joint UK Land Environment Simulator (JULES), model description – Part 1: Energy and water fluxes, Geosci. Model Dev., 4, 677–699, https://doi.org/10.5194/gmd-4-677-2011, 2011. a
Binskin, M., Bennett, A., and Macintosh, A.: Royal Commission into Natural Disaster Arrangements – Report, Tech. rep., Commonwealth of Australia, Australia, ISBN 978-1-921091-45-2, 2020. a
Borowiak, A., King, A., and Lane, T.: The Link Between the Madden-Julian Oscillation and Rainfall Trends in Northwest Australia, Geophys. Res. Lett., 50, e2022GL101799, https://doi.org/10.1029/2022GL101799, 2023. a
Brown, J. R., Moise, A. F., and Colman, R. A.: The South Pacific Convergence Zone in CMIP5 simulations of historical and future climate, Clim. Dynam., 41, 2179–2197, https://doi.org/10.1007/s00382-012-1591-x, 2013. a
Bureau Of Meteorology: Australian Gridded Climate Data (AGCD) (Version 1), NCI Australia [data set], https://doi.org/10.25914/6009600304B02, 2021. a
Bureau Of Meteorology: Bureau of Meteorology Atmospheric Regional Projections for Australia (BARPA), NCI Australia [data set], https://doi.org/10.25914/Z1X6-DQ28, 2023. a
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, JSHESS, 66, 55–67, 2016. a
Clarke, J., Webb, L., and Hennessy, K.: Climate Change in Australia: Projections for Australia's NRM Regions (Chapter 2), Tech. rep., , edited by: Ekström, M., Gerbing, C., Grose, M., Bhend, J., Webb, L., and Risbey, J., CSIRO and Bureau of Meteorology 2015, Climate Change in Australia Information for Australia's Natural Resource Management Regions: Technical Report, CSIRO and Bureau of Meteorology, Australia, ISBN 9781921232947, 2015. a
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., Tech. rep., CSIRO, ISBN 978-1-76077-736-4, 2019. a
Coppola, E., Raffaele, F., Giorgi, F., Giuliani, G., Xuejie, G., Ciarlo, J. M., Sines, T. R., Torres-Alavez, J. A., Das, S., di Sante, F., Pichelli, E., Glazer, R., Müller, S. K., Abba Omar, S., Ashfaq, M., Bukovsky, M., Im, E.-S., Jacob, D., Teichmann, C., Remedio, A., Remke, T., Kriegsmann, A., Bülow, K., Weber, T., Buntemeyer, L., Sieck, K., and Rechid, D.: Climate hazard indices projections based on CORDEX-CORE, CMIP5 and CMIP6 ensemble, Clim. Dynam., 57, 1293–1383, https://doi.org/10.1007/s00382-021-05640-z, 2021. a
Corney, S. P., Katzfey, J. J., McGregor, J. L., Grose, M. R., Bennett, J. C., White, C. J., Holz, G. K., Gaynor, S. M., and Bindoff, N. L.: Climate Futures for Tasmania: climate modelling technical report. Hobart, Tasmania, Tech. rep., Climate Futures for Tasmania: climate modelling technical report, © Commonwealth of Australia (Department of Industry, Innovation, Climate Change, Science, Research and Tertiary Education) 2013, ISBN 978-0-9922764-4-7, 2010. a
Dharssi, I., Steinle, P., and Fernon, J.: Improved numerical weather predictions by using optimised urban model parameter values and satellite derived tree heights, in: MODSIM2015, 21st International Congress on Modelling and Simulation, edited by: Weber, T., McPhee, M. J., and Anderssen, R. S., Modelling and Simulation Society of Australia and New Zealand, ISBN 978-0-9872143-5-5, 2015. a
Di Virgilio, G., Evans, J. P., Blake, S. A. P., Armstrong, M., Dowdy, A. J., Sharples, J., and McRae, R.: Climate Change Increases the Potential for Extreme Wildfires, Geophys. Res. Lett., 46, 8517–8526, https://doi.org/10.1029/2019GL083699, 2019a. a, b
Di Virgilio, G., Evans, J. P., Di Luca, A., Olson, R., Argüeso, D., Kala, J., Andrys, J., Hoffmann, P., Katzfey, J. J., and Rockel, B.: Evaluating reanalysis-driven CORDEX regional climate models over Australia: model performance and errors, Clim. Dynam., 53, 2985–3005, https://doi.org/10.1007/s00382-019-04672-w, 2019b. a, b
Dowdy, A. J., Ye, H., Pepler, A., Thatcher, M., Osbrough, S. L., Evans, J. P., Di Virgilio, G., and McCarthy, N.: Future changes in extreme weather and pyroconvection risk factors for Australian wildfires, Sci. Rep., 9, 10073, https://doi.org/10.1038/s41598-019-46362-x, 2019. a
Dowdy, A. J., Brown, A., Pepler, A., Thatcher, M., Rafter, T., Evans, J., Ye, H., Su, C.-H., Bell, S., and Stassen, C.: Extreme temperature, wind and bushfire weather projections using a standardised method., Tech. rep., Australian Bureau of Meteorology, ISSN 206-3366, 2021. a
Edwards, J. M. and Slingo, A.: Studies with a flexible new radiation code. I: Choosing a configuration for a large-scale model, Q. J. Roy. Meteor. Soc., 122, 689–719, https://doi.org/10.1002/qj.49712253107, 1996. a
Evans, A., Jones, D., Smalley, R., and Lellyett, S.: An enhanced gridded rainfall analysis scheme for Australia, Tech. rep., Australian Bureau of Meteorology, ISBN 978-1-925738-12-4, 2020. a
Evans, J. P., Ji, F., Lee, C., Smith, P., Argüeso, D., and Fita, L.: Design of a regional climate modelling projection ensemble experiment – NARCliM, Geosci. Model Dev., 7, 621–629, https://doi.org/10.5194/gmd-7-621-2014, 2014. a
Evans, J. P., Di Virgilio, G., Hirsch, A. L., Hoffmann, P., Remedio, A. R., Ji, F., Rockel, B., and Coppola, E.: The CORDEX-Australasia ensemble: evaluation and future projections, Clim. Dynam., 57, 1385–1401, https://doi.org/10.1007/s00382-020-05459-0, 2021. a, b
Fowler, H. J., Lenderink, G., Prein, A. F., Westra, S., Allan, R. P., Ban, N., Barbero, R., Berg, P., Blenkinsop, S., Do, H. X., Guerreiro, S., Haerter, J. O., Kendon, E. J., Lewis, E., Schaer, C., Sharma, A., Villarini, G., Wasko, C., and Zhang, X.: Anthropogenic intensification of short-duration rainfall extremes, Nat. Rev. Earth Environ., 2, 107–122, https://doi.org/10.1038/s43017-020-00128-6, 2021. a, b, c
Gibson, P. B., Waliser, D. E., Lee, H., Tian, B., and Massoud, E.: Climate Model Evaluation in the Presence of Observational Uncertainty: Precipitation Indices over the Contiguous United States, J. Hydrometeorol., 20, 1339–1357, https://doi.org/10.1175/JHM-D-18-0230.1, 2019. a
Giorgi, F., Jones, C., and Asrar, G. R.: Addressing climate information needs at the regional level: The CORDEX framework, World Meteorological Organization Bulletin, 58, 175–183, 2009. a
Gregory, D. and Rowntree, P. R.: A Mass Flux Convection Scheme with Representation of Cloud Ensemble Characteristics and Stability-Dependent Closure, Mon. Weather Rev., 118, 1483–1506, https://doi.org/10.1175/1520-0493(1990)118<1483:AMFCSW>2.0.CO;2, 1990. a
Grose, M. R., Narsey, S., Trancoso, R., Mackallah, C., Delage, F., Dowdy, A., Di Virgilio, G., Watterson, I., Dobrohotoff, P., Rashid, H. A., Rauniyar, S., Henley, B., Thatcher, M., Syktus, J., Abramowitz, G., Evans, J. P., Su, C.-H., and Takbash, A.: A CMIP6-based multi-model downscaling ensemble to underpin climate change services in Australia, Climate Services, 30, 100368, https://doi.org/10.1016/j.cliser.2023.100368, 2023. a, b
Gupta, H. V., Kling, H., Yilmaz, K. K., and Martinez, G. F.: Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling, J. Hydrol., 377, 80–91, https://doi.org/10.1016/j.jhydrol.2009.08.003, 2009. a
Hart, N. C. G., Reason, C. J. C., and Fauchereau, N.: Building a Tropical–Extratropical Cloud Band Metbot, Mon. Weather Rev., 140, 4005–4016, https://doi.org/10.1175/MWR-D-12-00127.1, 2012. a
Hartley, A., MacBean, N., Georgievski, G., and Bontemps, S.: Uncertainty in plant functional type distributions and its impact on land surface models, Remote Sens. Environ., 203, 71–89, https://doi.org/10.1016/j.rse.2017.07.037, 2017. a
Herold, N., Downes, S. M., Gross, M. H., Ji, F., Nishant, N., Macadam, I., Ridder, N. N., and Beyer, K.: Projected changes in the frequency of climate extremes over southeast Australia, Environ. Res. Commun., 3, 011001, https://doi.org/10.1088/2515-7620/abe6b1, 2021. a
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020. a, b
Hirsch, A. L., Evans, J. P., Di Virgilio, G., Perkins-Kirkpatrick, S. E., Argüeso, D., Pitman, A. J., Carouge, C. C., Kala, J., Andrys, J., Petrelli, P., and Rockel, B.: Amplification of Australian Heatwaves via Local Land-Atmosphere Coupling, J. Geophys. Res.-Atmos., 124, 13625–13647, https://doi.org/10.1029/2019JD030665, 2019. a
Howard, E., Su, C.-H., Stassen, C., Naha, R., Ye, H., Pepler, A., Bell, S., Dowdy, A., Tucker, S., and Franklin, C.: Performance and process-based evaluation of BARPA-R (Version 1), Zenodo [data set], https://doi.org/10.5281/zenodo.8157697, 2023. a
Hurtt, G. C., Chini, L., Sahajpal, R., Frolking, S., Bodirsky, B. L., Calvin, K., Doelman, J. C., Fisk, J., Fujimori, S., Klein Goldewijk, K., Hasegawa, T., Havlik, P., Heinimann, A., Humpenöder, F., Jungclaus, J., Kaplan, J. O., Kennedy, J., Krisztin, T., Lawrence, D., Lawrence, P., Ma, L., Mertz, O., Pongratz, J., Popp, A., Poulter, B., Riahi, K., Shevliakova, E., Stehfest, E., Thornton, P., Tubiello, F. N., van Vuuren, D. P., and Zhang, X.: Harmonization of global land use change and management for the period 850–2100 (LUH2) for CMIP6, Geosci. Model Dev., 13, 5425–5464, https://doi.org/10.5194/gmd-13-5425-2020, 2020. a
Jones, C., Giorgi, F., and Asrar, G.: The coordinated regional downscaling experiment: CORDEX An International Downscaling Link to CMIP5. , CLIVAR Exchanges, 16, 34–39, 2011. a
Kim, Y., Evans, J. P., Sharma, A., and Rocheta, E.: Spatial, temporal, and multivariate bias in regional climate model simulations, Geophys. Res. Lett., 48, e2020GL092058, https://doi.org/10.1029/2020GL092058, 2021. a
Kim, Y., Evans, J. P., and Sharma, A.: Multivariate bias correction of regional climate model boundary conditions, Clim. Dynam., 61, https://doi.org/10.1007/s00382-023-06718-6, 1–17, 2023. a
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. a
Knapp, K. R., Kruk, M. C., Levinson, D. H., Diamond, H. J., and Neumann, C. J.: The International Best Track Archive for Climate Stewardship (IBTrACS): Unifying Tropical Cyclone Data, B. Am. Meteorol. Soc., 91, 363–376, https://doi.org/10.1175/2009BAMS2755.1, 2010. a
Kumar, A., Zhang, L., and Wang, W.: Sea surface temperature–precipitation relationship in different reanalyses, Mon. Weather Rev., 141, 1118–1123, 2013. a
Lee, D., Min, S.-K., Park, I.-H., Ahn, J.-B., Cha, D.-H., Chang, E.-C., and Byun, Y.-H.: Enhanced Role of Convection in Future Hourly Rainfall Extremes Over South Korea, Geophys. Res. Lett., 49, e2022GL099727, https://doi.org/10.1029/2022GL099727, 2022. a
Lock, A. P., Brown, A. R., Bush, M. R., Martin, G. M., and Smith, R. N. B.: A New Boundary Layer Mixing Scheme. Part I: Scheme Description and Single-Column Model Tests, Mon. Weather Rev., 128, 3187–3199, https://doi.org/10.1175/1520-0493(2000)128<3187:ANBLMS>2.0.CO;2, 2000. a
Luu, L. N., Vautard, R., Yiou, P., and Soubeyroux, J.-M.: Evaluation of convection-permitting extreme precipitation simulations for the south of France, Earth Syst. Dynam., 13, 687–702, https://doi.org/10.5194/esd-13-687-2022, 2022. a
Mann, G. W., Carslaw, K. S., Spracklen, D. V., Ridley, D. A., Manktelow, P. T., Chipperfield, M. P., Pickering, S. J., and Johnson, C. E.: Description and evaluation of GLOMAP-mode: a modal global aerosol microphysics model for the UKCA composition-climate model, Geosci. Model Dev., 3, 519–551, https://doi.org/10.5194/gmd-3-519-2010, 2010. a
Martin, G. M., Levine, R. C., Rodriguez, J. M., and Vellinga, M.: Understanding the development of systematic errors in the Asian summer monsoon, Geosci. Model Dev., 14, 1007–1035, https://doi.org/10.5194/gmd-14-1007-2021, 2021. a, b
McGregor, J. and Dix, M.: CCAM: Geometric aspects and dynamical formulation, Tech. rep., CSIRO Atmospheric Research, ISBN 0 643 06889 9, 2005. a
McGregor, J. L. and Dix, M. R.: An Updated Description of the Conformal-Cubic Atmospheric Model, 51–75, Springer New York, New York, NY, ISBN 978-0-387-49791-4, https://doi.org/10.1007/978-0-387-49791-4_4, 2008. a
NCI Australia: ERA5 Replicated Datasets (Version 1), NCI Australia [data set], https://doi.org/10.25914/5F48874388857, 2020. a
Pagé, C., Aoun, A., and Spinuso, A.: icclim: Calculating Climate Indices and Indicators Made Easy, ESS Open Archive, https://doi.org/10.1002/essoar.10510322.1, 2022. a
Pepler, A. and Dowdy, A.: Fewer deep cyclones projected for the midlatitudes in a warming climate, but with more intense rainfall, Environ. Res. Lett., 16, 054044, https://doi.org/10.1088/1748-9326/abf528, 2021. a
Pepler, A. S. and Dowdy, A. J.: Australia's Future Extratropical Cyclones, J. Climate, 35, 4195–4210, https://doi.org/10.1175/JCLI-D-22-0312.1, 2022. a
Pepler, A. S., Di Luca, A., Ji, F., Alexander, L. V., Evans, J. P., and Sherwood, S. C.: Projected changes in east Australian midlatitude cyclones during the 21st century, Geophys. Res. Lett., 43, 334–340, https://doi.org/10.1002/2015GL067267, 2016. a
Perkins, S. E., Pitman, A. J., Holbrook, N. J., and McAneney, J.: 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. a
Perkins-Kirkpatrick, S. E., White, C. J., Alexander, L. V., Argüeso, D., Boschat, G., Cowan, T., Evans, J. P., Ekström, M., Oliver, E. C. J., Phatak, A., and Purich, A.: Natural hazards in Australia: heatwaves, Clim. Change, 139, 101–114, https://doi.org/10.1007/s10584-016-1650-0, 2016. a
Rodríguez, J. M. and Milton, S. F.: East Asian Summer Atmospheric Moisture Transport and Its Response to Interannual Variability of the West Pacific Subtropical High: An Evaluation of the Met Office Unified Model, Atmosphere, 10, 457, https://doi.org/10.3390/atmos10080457, 2019. a
Sain, S. R., Furrer, R., and Cressie, N.: A spatial analysis of multivariate output from regional climate models, Ann. Appl. Stat., 5, 150–175, https://doi.org/10.1214/10-AOAS369, 2011. a
Simard, M., Pinto, N., Fisher, J. B., and Baccini, A.: Mapping forest canopy height globally with spaceborne lidar, J. Geophys. Res.-Biogeo., 116, G4, https://doi.org/10.1029/2011JG001708, 2011. a
Simmons, A., Hersbach, H., Munoz-Sabater, J., Nicolas, J., Vamborg, F., Berrisford, P., de Rosnay, P., Willett, K., and Woollen, J.: Low frequency variability and trends in surface air temperature and humidity from ERA5 and other datasets, ECMWF Technical Memoranda, https://doi.org/10.21957/ly5vbtbfd, 2021. a
Stassen, C., Su, C.-H., Dowdy, A. J., Franklin, C., Howard, E., and Steinle, P.: Development and Assessment of Regional Atmospheric Nudging in ACCESS, Tech. rep., Australian Bureau of Meteorology, ISBN 978-1-925738-74-2, 2023. a
Stevens, B., Fiedler, S., Kinne, S., Peters, K., Rast, S., Müsse, J., Smith, S. J., and Mauritsen, T.: MACv2-SP: a parameterization of anthropogenic aerosol optical properties and an associated Twomey effect for use in CMIP6, Geosci. Model Dev., 10, 433–452, https://doi.org/10.5194/gmd-10-433-2017, 2017. a
Stratton, R. A., Senior, C. A., Vosper, S. B., Folwell, S. S., Boutle, I. A., Earnshaw, P. D., Kendon, E., Lock, A. P., Malcolm, A., Manners, J., Morcrette, C. J., Short, C., Stirling, A. J., Taylor, C. M., Tucker, S., Webster, S., and Wilkinson, J. M.: A Pan-African Convection-Permitting Regional Climate Simulation with the Met Office Unified Model: CP4-Africa, J. Climate, 31, 3485–3508, https://doi.org/10.1175/JCLI-D-17-0503.1, 2018. a
Su, C.-H., Ryu, D., Young, R. I., Western, A. W., and Wagner, W.: Inter-comparison of microwave satellite soil moisture retrievals over the Murrumbidgee Basin, southeast Australia, Remote Sens. Environ., 134, 1–11, https://doi.org/10.1016/j.rse.2013.02.016, 2013. a
Su, C.-H., Eizenberg, N., Steinle, P., Jakob, D., Fox-Hughes, P., White, C. J., Rennie, S., Franklin, C., Dharssi, I., and Zhu, H.: BARRA v1.0: the Bureau of Meteorology Atmospheric high-resolution Regional Reanalysis for Australia, Geosci. Model Dev., 12, 2049–2068, https://doi.org/10.5194/gmd-12-2049-2019, 2019. a
Su, C.-H., Rennie, S., Dharssi, I., Torrance, J., Smith, A., Le, T., Steinle, P., Stassen, C., Warren, R. A., Wang, C., and Marshall, J. L.: BARRA2: Development of the next-generation Australian regional atmospheric reanalysis, Tech. rep., Australian Bureau of Meteorology, ISSN 2206-3366, 2022a. a
Telford, P. J., Braesicke, P., Morgenstern, O., and Pyle, J. A.: Technical Note: Description and assessment of a nudged version of the new dynamics Unified Model, Atmos. Chem. Phys., 8, 1701–1712, https://doi.org/10.5194/acp-8-1701-2008, 2008. a
Tolhurst, G., Hope, P., Osburn, L., and Rauniyar, S.: Approaches to Understanding Decadal and Long-Term Shifts in Observed Precipitation Distributions in Victoria, Australia, J. Appl. Meteorol. Climatol., 62, 13–29, https://doi.org/10.1175/JAMC-D-22-0031.1, 2023. a
Tory, K. J., Chand, S. S., Dare, R. A., and McBride, J. L.: The development and assessment of a model-, grid-, and basin-independent tropical cyclone detection scheme, J. Climate, 26, 5493–5507, 2013. a
Trancoso, R., Syktus, J., Toombs, N., Ahrens, D., Wong, K. K.-H., and Pozza, R. D.: Heatwaves intensification in Australia: A consistent trajectory across past, present and future, Sci. Total Environ., 742, 140521, https://doi.org/10.1016/j.scitotenv.2020.140521, 2020. a
Tucker, S. O., Kendon, E. J., Bellouin, N., Buonomo, E., Johnson, B., and Murphy, J. M.: Evaluation of a new 12-km regional perturbed parameter ensemble over Europe, Clim. Dynam., 58, 879–903, https://doi.org/10.1007/s00382-021-05941-3, 2022. a, b, c
Walters, D., Baran, A. J., Boutle, I., Brooks, M., Earnshaw, P., Edwards, J., Furtado, K., Hill, P., Lock, A., Manners, J., Morcrette, C., Mulcahy, J., Sanchez, C., Smith, C., Stratton, R., Tennant, W., Tomassini, L., Van Weverberg, K., Vosper, S., Willett, M., Browse, J., Bushell, A., Carslaw, K., Dalvi, M., Essery, R., Gedney, N., Hardiman, S., Johnson, B., Johnson, C., Jones, A., Jones, C., Mann, G., Milton, S., Rumbold, H., Sellar, A., Ujiie, M., Whitall, M., Williams, K., and Zerroukat, M.: The Met Office Unified Model Global Atmosphere 7.0/7.1 and JULES Global Land 7.0 configurations, Geosci. Model Dev., 12, 1909–1963, https://doi.org/10.5194/gmd-12-1909-2019, 2019. a, b
Wasko, C., Westra, S., Nathan, R., Pepler, A., Raupach, T., Dowdy, A., Johnson, F., Ho, M., McInnes, K., Jakob, D., Evans, J., Villarini, G., and Fowler, H.: A systematic review of climate change science relevant to Australian design flood estimation, Hydrol. Earth Syst. Sci. Discuss. [preprint], https://doi.org/10.5194/hess-2023-232, in review, 2023. a
Willet, M. R. and Whitall, M. A.: A Simple Prognostic based Convective Entrainment Rate for the Unified Model: Description and Tests, Tech. rep., UK Met Office, https://digital.nmla.metoffice.gov.uk/download/file/IO_3bc6ed69-58c4-463f-97b2-04a86f998578 (last access: 7 April 2022), 2017. a
Wilson, D. R. and Ballard, S. P.: A microphysically based precipitation scheme for the UK meteorological office unified model, Q. J. Roy. Meteor. Soc., 125, 1607–1636, https://doi.org/10.1002/qj.49712555707, 1999. a
Wilson, D. R., Bushell, A. C., Kerr-Munslow, A. M., Price, J. D., Morcrette, C. J., and Bodas-Salcedo, A.: PC2: A prognostic cloud fraction and condensation scheme. II: Climate model simulations, Q. J. Roy. Meteor. Soc., 134, 2109–2125, https://doi.org/10.1002/qj.332, 2008. a
Wood, N., Staniforth, A., White, A., Allen, T., Diamantakis, M., Gross, M., Melvin, T., Smith, C., Vosper, S., Zerroukat, M., and Thuburn, J.: An inherently mass-conserving semi-implicit semi-Lagrangian discretization of the deep-atmosphere global non-hydrostatic equations, Q. J. Roy. Meteor. Soc., 140, 1505–1520, https://doi.org/10.1002/qj.2235, 2014. a
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.
The BARPA-R modelling configuration has been developed to produce high-resolution climate hazard...
