Articles | Volume 18, issue 3
https://doi.org/10.5194/gmd-18-703-2025
© Author(s) 2025. 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-18-703-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model evaluation paper
| 
07 Feb 2025
Model evaluation paper |
| 07 Feb 2025
| 07 Feb 2025
Evaluation of CORDEX ERA5-forced NARCliM2.0 regional climate models over Australia using the Weather Research and Forecasting (WRF) model version 4.1.2
Giovanni Di Virgilio
CORRESPONDING AUTHOR
Climate & Atmospheric Science, NSW Department of Climate Change, Energy, the Environment and Water, Sydney, Australia
Climate Change Research Centre, University of New South Wales, Sydney, Australia
Fei Ji
Climate & Atmospheric Science, NSW Department of Climate Change, Energy, the Environment and Water, Sydney, Australia
Australian Research Council Centre of Excellence for Climate Extremes, University of New South Wales, Sydney, Australia
Eugene Tam
Climate & Atmospheric Science, NSW Department of Climate Change, Energy, the Environment and Water, Sydney, Australia
Jason P. Evans
Climate Change Research Centre, University of New South Wales, Sydney, Australia
Australian Research Council Centre of Excellence for Climate Extremes, University of New South Wales, Sydney, Australia
Jatin Kala
Environmental and Conservation Sciences, Harry Butler Institute, Centre for Terrestrial Ecosystem Science and Sustainability, Murdoch University, Murdoch, WA 6150, Australia
Julia Andrys
Environmental and Conservation Sciences, Harry Butler Institute, Centre for Terrestrial Ecosystem Science and Sustainability, Murdoch University, Murdoch, WA 6150, Australia
Christopher Thomas
Climate Change Research Centre, University of New South Wales, Sydney, Australia
Dipayan Choudhury
Climate & Atmospheric Science, NSW Department of Climate Change, Energy, the Environment and Water, Sydney, Australia
Carlos Rocha
Climate & Atmospheric Science, NSW Department of Climate Change, Energy, the Environment and Water, Sydney, Australia
Yue Li
Climate & Atmospheric Science, NSW Department of Climate Change, Energy, the Environment and Water, Sydney, Australia
Matthew L. Riley
Climate & Atmospheric Science, NSW Department of Climate Change, Energy, the Environment and Water, Sydney, Australia
Related authors
Giovanni Di Virgilio, Jason P. Evans, Fei Ji, Eugene Tam, Jatin Kala, Julia Andrys, Christopher Thomas, Dipayan Choudhury, Carlos Rocha, Stephen White, Yue Li, Moutassem El Rafei, Rishav Goyal, Matthew L. Riley, and Jyothi Lingala
Geosci. Model Dev., 18, 671–702, https://doi.org/10.5194/gmd-18-671-2025, https://doi.org/10.5194/gmd-18-671-2025, 2025
Short summary
Short summary
We introduce new climate models that simulate Australia’s future climate at regional scales, including at an unprecedented resolution of 4 km for 1950–2100. We describe the model design process used to create these new climate models. We show how the new models perform relative to previous-generation models and compare their climate projections. This work is of national and international relevance as it can help guide climate model design and the use and interpretation of climate projections.
Anjana Devanand, Jason P. Evans, Andy J. Pitman, Sujan Pal, David Gochis, and Kevin Sampson
Hydrol. Earth Syst. Sci., 29, 4491–4513, https://doi.org/10.5194/hess-29-4491-2025, https://doi.org/10.5194/hess-29-4491-2025, 2025
Short summary
Short summary
Including lateral flow increases evapotranspiration near major river channels in high-resolution land surface simulations in southeast Australia, consistent with observations. The 1-km resolution model shows a widespread pattern of dry ridges that does not exist at coarser resolutions. Our results have implications for improved simulations of droughts and future water availability.
Jann Paul Mattern, Yuichiro Takeshita, Carlos Rocha, and Christopher Edwards
EGUsphere, https://doi.org/10.5194/egusphere-2025-3560, https://doi.org/10.5194/egusphere-2025-3560, 2025
This preprint is open for discussion and under review for Biogeosciences (BG).
Short summary
Short summary
We improve coastal ocean carbonate system estimates by assimilating glider pH and alkalinity data into a regional biogeochemical model. Joint assimilation with physical observations successfully improves pH estimates while maintaining physical estimates. A hybrid approach combining dynamical models with statistical methods produces accurate pH estimates without requiring biogeochemical models, offering an alternative solution for ocean acidification monitoring.
Kevin K. W. Cheung, Fei Ji, Nidhi Nishant, Jin Teng, James Bennett, and De Li Liu
Hydrol. Earth Syst. Sci., 29, 3527–3543, https://doi.org/10.5194/hess-29-3527-2025, https://doi.org/10.5194/hess-29-3527-2025, 2025
Short summary
Short summary
This study evaluates two reanalysis datasets, which are critical in climate, weather research, and water resources analysis, for the Australian region in terms of simulating daily mean precipitation and six other selected precipitation extremes. While spatial patterns of mean precipitation are well reproduced, substantial biases exist in precipitation variability, trends, and extremes. Caution in applying these datasets is thus advised in terms of the latter aspects.
Yinglin Mu, Jason Evans, Andrea Taschetto, and Chiara Holgate
EGUsphere, https://doi.org/10.5194/egusphere-2025-2833, https://doi.org/10.5194/egusphere-2025-2833, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
Lagrangian approaches have been increasingly employed due to their suitability for extreme events and climatological studies in finding moisture sources of precipitation. However, as these approaches track independent air parcels carrying moisture—rather than simulate processes based on governing physical equations—they rely on several underlying assumptions. This study tests these assumptions and refines the approaches to enhance their broader applicability.
Rajesh Kumar Sahu, Hamza Kunhu Bangalath, Suleiman Mostamandi, Jason Evans, Paul A. Kucera, and Hylke E. Beck
EGUsphere, https://doi.org/10.5194/egusphere-2025-912, https://doi.org/10.5194/egusphere-2025-912, 2025
Short summary
Short summary
This study evaluates 36 microphysics (MP) and boundary layer (BL) scheme combinations in the Weather Research and Forecasting (WRF) model for extreme rainfall over Saudi Arabia. Using Kling-Gupta Efficiency (KGE), results show YSU (BL1) and Thompson (MP8) perform best, while Morrison-MYNN (MP10_BL6) ranks lowest. The mean temporal KGE is 0.37, and the spatial KGE is 0.26, highlighting spatial prediction challenges. Findings aid model evaluation and forecasting in arid regions.
Giovanni Di Virgilio, Jason P. Evans, Fei Ji, Eugene Tam, Jatin Kala, Julia Andrys, Christopher Thomas, Dipayan Choudhury, Carlos Rocha, Stephen White, Yue Li, Moutassem El Rafei, Rishav Goyal, Matthew L. Riley, and Jyothi Lingala
Geosci. Model Dev., 18, 671–702, https://doi.org/10.5194/gmd-18-671-2025, https://doi.org/10.5194/gmd-18-671-2025, 2025
Short summary
Short summary
We introduce new climate models that simulate Australia’s future climate at regional scales, including at an unprecedented resolution of 4 km for 1950–2100. We describe the model design process used to create these new climate models. We show how the new models perform relative to previous-generation models and compare their climate projections. This work is of national and international relevance as it can help guide climate model design and the use and interpretation of climate projections.
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.
Sanaa Hobeichi, Gab Abramowitz, and Jason P. Evans
Hydrol. Earth Syst. Sci., 25, 3855–3874, https://doi.org/10.5194/hess-25-3855-2021, https://doi.org/10.5194/hess-25-3855-2021, 2021
Short summary
Short summary
Evapotranspiration (ET) links the water, energy and carbon cycle on land. Reliable ET estimates are key to understand droughts and flooding. We develop a new ET dataset, DOLCE V3, by merging multiple global ET datasets, and we show that it matches ET observations better and hence is more reliable than its parent datasets. Next, we use DOLCE V3 to examine recent changes in ET and find that ET has increased over most of the land, decreased in some regions, and has not changed in some other regions
Max Kulinich, Yanan Fan, Spiridon Penev, Jason P. Evans, and Roman Olson
Geosci. Model Dev., 14, 3539–3551, https://doi.org/10.5194/gmd-14-3539-2021, https://doi.org/10.5194/gmd-14-3539-2021, 2021
Short summary
Short summary
We present a novel stochastic approach based on Markov chains to estimate climate model weights of multi-model ensemble means. This approach showed improved performance (better correlation with observations) over existing alternatives during cross-validation and model-as-truth tests. The results of this comparative analysis should serve to motivate further studies in applications of Markov chain and other nonlinear methods to find optimal model weights for constructing ensemble means.
Peisheng Huang, Karl Hennig, Jatin Kala, Julia Andrys, and Matthew R. Hipsey
Hydrol. Earth Syst. Sci., 24, 5673–5697, https://doi.org/10.5194/hess-24-5673-2020, https://doi.org/10.5194/hess-24-5673-2020, 2020
Short summary
Short summary
Our results conclude that the climate change in the past decades has a remarkable effect on the hydrology of a large shallow lagoon with the same magnitude as that caused by the opening of an artificial channel, and it also highlighted the complexity of their interactions. We suggested that the consideration of the projected drying trend is essential in designing management plans associated with planning for environmental water provision and setting water quality loading targets.
Cited articles
Auffhammer, M., Baylis, P., and Hausman, C. H.: Climate change is projected to have severe impacts on the frequency and intensity of peak electricity demand across the United States, P. Natl. Acad. Sci. USA, 114, 1886–1891, https://doi.org/10.1073/pnas.1613193114, 2017.
Bechtold, P., Kohler, M., Jung, T., Doblas-Reyes, F., Leutbecher, M., Rodwell, M. J., Vitart, F., and Balsamo, G.: Advances in simulating atmospheric variability with the ECMWF model: From synoptic to decadal time-scales, Q. J. Roy. Meteor. Soc., 134, 1337–1351, https://doi.org/10.1002/qj.289, 2008.
Bucchignani, E., Mercogliano, P., Rianna, G., and Panitz, H. J.: Analysis of ERA-Interim-driven COSMO-CLM simulations over Middle East - North Africa domain at different spatial resolutions, Int. J. Climatol., 36, 3346–3369, https://doi.org/10.1002/joc.4559, 2016.
Bureau of Meteorology: Annual climate statement 2016, http://www.bom.gov.au/climate/current/annual/aus/2016/#:~:text=Globally,2016thewarmestyear,newrecordhasbeenset (last access: 12 February 2024), 2017.
Chou, M. D., Suarez, M. J., Liang, X. Z., and Yan, M. M. H.: A thermal infrared radiation parameterization for atmospheric studies, NASA Tech. Memo., NASA/TM-2001-104606, 19, 68 pp. https://ntrs.nasa.gov/citations/20010072848 (last access: 17 April 2023), 2001.
Christensen, O. B. and Kjellström, E.: Partitioning uncertainty components of mean climate and climate change in a large ensemble of European regional climate model projections, Clim. Dynam., 54, 4293–4308, https://doi.org/10.1007/s00382-020-05229-y, 2020.
Consortium for Small−Scale Modelling: Newsletter No. 1, https://www.cosmo-model.org/content/model/documentation/newsLetters/newsLetter01/newsLetter_01.pdf (last access: 30 January 2025), 2001.
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P., Bechtold, P., Beljaars, A. C. M., van de Berg, L., Bidlot, J., Bormann, N., Delsol, C., Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S. B., Hersbach, H., Hólm, E. V., Isaksen, L., Kållberg, P., Köhler, M., Matricardi, M., McNally, A. P., Monge-Sanz, B. M., Morcrette, J. J., Park, B. K., Peubey, C., de Rosnay, P., Tavolato, C., Thépaut, J. N., and Vitart, F.: The ERA-Interim reanalysis: configuration and performance of the data assimilation system, Q. J. Roy. Meteor. Soc., 137, 553–597, https://doi.org/10.1002/qj.828, 2011.
Dickinson, R. E., Oleson, K. W., Bonan, G., Hoffman, F., Thornton, P., Vertenstein, M., Yang, Z. L., and Zeng, X. B.: The Community Land Model and its climate statistics as a component of the Community Climate System Model, J. Climate, 19, 2302–2324, https://doi.org/10.1175/JCLI3742.1, 2006.
Di Luca, A., de Elia, R., and Laprise, R.: Potential for added value in precipitation simulated by high-resolution nested Regional Climate Models and observations, Clim. Dynam., 38, 1229–1247, https://doi.org/10.1007/s00382-011-1068-3, 2012.
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, 2019.
Di Virgilio, G., Evans, J. P., Clarke, H., Sharples, J., Hirsch, A. L., and Hart, M. A.: Climate Change Significantly Alters Future Wildfire Mitigation Opportunities in Southeastern Australia, Geophys. Res. Lett., 47, e2020GL088893, https://doi.org/10.1029/2020GL088893, 2020a.
Di Virgilio, G., Evans, J. P., Di Luca, A., Grose, M. R., Round, V., and Thatcher, M.: Realised added value in dynamical downscaling of Australian climate change, Clim. Dynam., 54, 4675–4692, https://doi.org/10.1007/s00382-020-05250-1, 2020b.
Di Virgilio, G., Ji, F., Tam, E., Evans, J. P., Kala, J., Andrys, J., Thomas, C., Choudhury, D., Rocha, C., Li, Y., and Riley, M. L.: Supporting information for “Evaluation of CORDEX ERA5-forced 'NARCliM2.0' regional climate models over Australia using the Weather Research and Forecasting (WRF) model version 4.1.2”, v1.0, Zenodo [code], https://doi.org/10.5281/zenodo.11189898, 2024.
Di Virgilio, G., Evans, J. P., Ji, F., Tam, E., Kala, J., Andrys, J., Thomas, C., Choudhury, D., Rocha, C., White, S., Li, Y., El Rafei, M., Goyal, R., Riley, M. L., and Lingala, J.: Design, evaluation, and future projections of the NARCliM2.0 CORDEX-CMIP6 Australasia regional climate ensemble, Geosci. Model Dev., 18, 671–702, https://doi.org/10.5194/gmd-18-671-2025, 2025.
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.
ESGF: CORDEX data, ESGF [data set], https://esgf-metagrid.cloud.dkrz.de/search/cordex-dkrz/, last access: 23 January 2025.
Evans, A., Jones, D., Lellyett, S., and Smalley, R.: An Enhanced Gridded Rainfall Analysis Scheme for Australia, Australian Bureau of Meteorology, http://www.bom.gov.au/research/publications/researchreports/BRR-041.pdf (last access: 22 January 2024), 2020.
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.
Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., and Taylor, K. E.: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization, Geosci. Model Dev., 9, 1937–1958, https://doi.org/10.5194/gmd-9-1937-2016, 2016.
Freidenreich, S. M. and Ramaswamy, V.: A new multiple-band solar radiative parameterization for general circulation models, J. Geophys. Res.-Atmos., 104, 31389–31409, https://doi.org/10.1029/1999JD900456, 1999.
Giorgi, F.: Regional climate modeling: Status and perspectives, J. Phys. IV, 139, 101–118, https://doi.org/10.1051/jp4:2006139008, 2006.
Giorgi, F.: Thirty Years of Regional Climate Modeling: Where Are We and Where Are We Going next?, J. Geophys. Res.-Atmos., 124, 5696–5723, https://doi.org/10.1029/2018jd030094, 2019.
Giorgi, F. and Bates, G. T.: The Climatological Skill of a Regional Model over Complex Terrain, Mon. Weather Rev., 117, 2325–2347, https://doi.org/10.1175/1520-0493(1989)117<2325:TCSOAR>2.0.CO;2, 1989.
Giorgi, F., Jones, C., and Asrar, G.: Addressing climate information needs at the regional level: The CORDEX framework, WMO Bulletin, 53, 175–183, 2009.
González Tánago, I., Urquijo, J., Blauhut, V., Villarroya, F., and De Stefano, L.: Learning from experience: a systematic review of assessments of vulnerability to drought, Nat. Hazards, 80, 951–973, https://doi.org/10.1007/s11069-015-2006-1, 2016.
Grose, M. R., Narsey, S., Delage, F. P., Dowdy, A. J., Bador, M., Boschat, G., Chung, C., Kajtar, J. B., Rauniyar, S., Freund, M. B., Lyu, K., Rashid, H., Zhang, X., Wales, S., Trenham, C., Holbrook, N. J., Cowan, T., Alexander, L., Arblaster, J. M., and Power, S.: Insights From CMIP6 for Australia's Future Climate, Earth's Future, 8, e2019EF001469, https://doi.org/10.1029/2019EF001469, 2020.
Grose, M., 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.
Gu, X. H., Zhang, Q., Li, J. F., Liu, J. Y., Xu, C. Y., and Sun, P.: The changing nature and projection of floods across Australia, J. Hydrol., 584, 124703, https://doi.org/10.1016/j.jhydrol.2020.124703, 2020.
Gutowski Jr., W. J., Giorgi, F., Timbal, B., Frigon, A., Jacob, D., Kang, H.-S., Raghavan, K., Lee, B., Lennard, C., Nikulin, G., O'Rourke, E., Rixen, M., Solman, S., Stephenson, T., and Tangang, F.: WCRP COordinated Regional Downscaling EXperiment (CORDEX): a diagnostic MIP for CMIP6, Geosci. Model Dev., 9, 4087–4095, https://doi.org/10.5194/gmd-9-4087-2016, 2016.
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.
Hoffmann, L., Günther, G., Li, D., Stein, O., Wu, X., Griessbach, S., Heng, Y., Konopka, P., Müller, R., Vogel, B., and Wright, J. S.: From ERA-Interim to ERA5: the considerable impact of ECMWF's next-generation reanalysis on Lagrangian transport simulations, Atmos. Chem. Phys., 19, 3097–3124, https://doi.org/10.5194/acp-19-3097-2019, 2019.
Hong, S. Y. and Lim, J.-O. J.: The WRF Single-Moment 6-Class Microphysics Scheme (WSM6), Asia-Pac. J. Atmos. Sci., 42, 129–151, 2006.
Hong, S.-Y., Noh, Y., and Dudhia, J.: A New Vertical Diffusion Package with an Explicit Treatment of Entrainment Processes, Mon. Weather Rev., 134, 2318–2341, https://doi.org/10.1175/MWR3199.1, 2006.
Howard, E., Su, C.-H., Stassen, C., Naha, R., Ye, H., Pepler, A., Bell, S. S., Dowdy, A. J., Tucker, S. O., and Franklin, C.: Performance and process-based evaluation of the BARPA-R Australasian regional climate model version 1, Geosci. Model Dev., 17, 731–757, https://doi.org/10.5194/gmd-17-731-2024, 2024.
Hsiang, S., Kopp, R., Jina, A., Rising, J., Delgado, M., Mohan, S., Rasmussen, D. J., Muir-Wood, R., Wilson, P., Oppenheimer, M., Larsen, K., and Houser, T.: Estimating economic damage from climate change in the United States, Science, 356, 1362–1368, https://doi.org/10.1126/science.aal4369, 2017.
Iacono, M. J., Delamere, J. S., Mlawer, E. J., Shephard, M. W., Clough, S. A., and Collins, W. D.: Radiative forcing by long-lived greenhouse gases: Calculations with the AER radiative transfer models, J. Geophys. Res.-Atmos., 113, D13103, https://doi.org/10.1029/2008JD009944, 2008.
IPCC: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, https://doi.org/10.1017/9781009157896, 2021.
Janjić, Z. I.: Comments on “Development and Evaluation of a Convection Scheme for Use in Climate Models”, J. Atmos. Sci., 57, 3686–3686, https://doi.org/10.1175/1520-0469(2000)057<3686:CODAEO>2.0.CO;2, 2000.
Jones, T. R. and Randall, D. A.: Quantifying the limits of convective parameterizations, J. Geophys. Res.-Atmos., 116, D08210, https://doi.org/10.1029/2010JD014913, 2011.
Kain, J. S.: The Kain–Fritsch convective parameterization: An update, J. Appl. Meteorol., 43, 170–181, https://doi.org/10.1175/1520-0450(2004)043<0170:TKCPAU>2.0.CO;2, 2004.
Kowalczyk, E. A., Wang, Y. P., Law, R. M., Davies, H. L., McGregor, J. L., and Abramowitz, G. S.: The CSIRO Atmosphere Biosphere Land Exchange (CABLE) model for use in climate models and as an offline model, Aspendale, Vic.: CSIRO Marine and Atmospheric Research; https://doi.org/10.4225/08/58615c6a9a51d, 2006.
Laprise, R.: Regional climate modelling, J. Comput. Phys., 227, 3641–3666, https://doi.org/10.1016/j.jcp.2006.10.024, 2008.
Ma, M. N., Ou, T. H., Liu, D. Q., Wang, S. Y., Fang, J., and Tang, J. P.: Summer regional climate simulations over Tibetan Plateau: from gray zone to convection permitting scale, Clim. Dynam., 60, 301–322, https://doi.org/10.1007/s00382-022-06314-0, 2022.
McGregor, J. L.: A new convection scheme using a simple closure. BMRC research report 93. Melbourne, Australia, https://research.csiro.au/ccam/wp-content/uploads/sites/520/2024/01/1377337417.pdf (last access 29 January 2025), 2003.
McGregor, J. L., Gordon, H. B., Watterson, I. G., Dix, M. R., and Rotstayn, L. D.: The CSIRO 9-level atmospheric general circulation model, Melbourne, CSIRO Australia, https://doi.org/10.4225/08/586550bc3318b, 1993.
McGregor, J. L. and Dix, M. R.: An updated description of the Conformal-Cubic atmospheric model, High Resolution Numerical Modelling of the Atmosphere and Ocean, Springer, New York, 51–75 pp., https://doi.org/10.1007/978-0-387-49791-4_4, 2008.
Nakanishi, M. and Niino, H.: Development of an Improved Turbulence Closure Model for the Atmospheric Boundary Layer, J. Meteorol. Soc. Jpn. Ser. II, 87, 895–912, https://doi.org/10.2151/jmsj.87.895, 2009.
Nishant, N., Evans, J. P., Di Virgilio, G., Downes, S. M., Ji, F., Cheung, K. K. W., Tam, E., Miller, J., Beyer, K., and Riley, M. L.: Introducing NARCliM1.5: Evaluating the Performance of Regional Climate Projections for Southeast Australia for 1950–2100, Earth's Future, 9, e2020EF001833, https://doi.org/10.1029/2020EF001833, 2021.
Niu, G.-Y., Yang, Z.-L., Mitchell, K. E., Chen, F., Ek, M. B., Barlage, M., Kumar, A., Manning, K., Niyogi, D., Rosero, E., Tewari, M., and Xia, Y.: The community Noah land surface model with multiparameterization options (Noah-MP): 1. Model description and evaluation with local-scale measurements, J. Geophys. Res.-Atmos., 116, D12109, https://doi.org/10.1029/2010jd015139, 2011.
Nogueira, M.: Inter-comparison of ERA-5, ERA-interim and GPCP rainfall over the last 40 years: Process-based analysis of systematic and random differences, J. Hydrol., 583, 124632, https://doi.org/10.1016/j.jhydrol.2020.124632, 2020.
NSW Government: NSW Climate Data Portal (CDP), NSW Government [data set], https://climatedata-beta.environment.nsw.gov.au/, last access: 15 August 2023.
Panitz, H.-J., Dosio, A., Büchner, M., Lüthi, D., and Keuler, K.: COSMO-CLM (CCLM) climate simulations over CORDEX-Africa domain: analysis of the ERA-Interim driven simulations at 0.44° and 0.22° resolution, Clim. Dynam., 42, 3015–3038, https://doi.org/10.1007/s00382-013-1834-5, 2014.
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.
Pleim, J. E.: A Combined Local and Nonlocal Closure Model for the Atmospheric Boundary Layer. Part I: Model Description and Testing, J. Appl. Meteorol. Climatol., 46, 1383–1395, https://doi.org/10.1175/JAM2539.1, 2007.
Reder, A., Raffa, M., Padulano, R., Rianna, G., and Mercogliano, P.: Characterizing extreme values of precipitation at very high resolution: An experiment over twenty European cities, Weather and Climate Extremes, 35, 100407, https://doi.org/10.1016/j.wace.2022.100407, 2022.
Ritter, B. and Geleyn, J.-F.: A Comprehensive Radiation Scheme for Numerical Weather Prediction Models with Potential Applications in Climate Simulations, Mon. Weather Rev., 120, 303–325, https://doi.org/10.1175/1520-0493(1992)120<0303:ACRSFN>2.0.CO;2, 1992.
Rockel, B., Will, A., and Hense, A.: The Regional Climate Model COSMO-CLM(CCLM), Meteorol. Z., 17, 347–348, https://doi.org/10.1127/0941-2948/2008/0309, 2008.
Rotstayn, L. D.: A physically based scheme for the treatment of stratiform clouds and precipitation in large-scale models. I: Description and evaluation of the microphysical processes, Q. J. Roy. Meteor. Soc., 123, 1227–1282, https://doi.org/10.1002/qj.49712354106, 1997.
Schleussner, C.-F., Lissner, T. K., Fischer, E. M., Wohland, J., Perrette, M., Golly, A., Rogelj, J., Childers, K., Schewe, J., Frieler, K., Mengel, M., Hare, W., and Schaeffer, M.: Differential climate impacts for policy-relevant limits to global warming: the case of 1.5 °C and 2 °C, Earth Syst. Dynam., 7, 327–351, https://doi.org/10.5194/esd-7-327-2016, 2016.
Seifert, A. and Beheng, K. D.: A double-moment parameterization for simulating auto conversion, accretion and self collection, Atmos. Res., 59, 265–281, https://doi.org/10.1016/S0169-8095(01)00126-0, 2001.
Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Barker, D. M., Wang, W., and Powers, J. G.: A description of the Advanced Research WRF Version 3, NCAR Tech Note NCAR/TN-475+STR, NCAR, Boulder, CO, https://doi.org/10.5065/D68S4MVH, 2008.
Stouffer, R. J., Eyring, V., Meehl, G. A., Bony, S., Senior, C., Stevens, B., and Taylor, K. E.: CMIP5 Scientific Gaps and Recommendations for CMIP6, B. Am. Meteorol. Soc., 98, 95–105, https://doi.org/10.1175/bams-d-15-00013.1, 2017.
Tebaldi, C., Arblaster, J. M., and Knutti, R.: Mapping model agreement on future climate projections, Geophys. Res. Lett., 38, L23701, https://doi.org/10.1029/2011GL049863, 2011.
Tewari, M., Wang, W., Dudhia, J., LeMone, M. A., Mitchell, K., Ek, M., Gayno, G., Wegiel, J., and Cuenca, R.: Implementation and verification of the unified Noah land surface model in the WRF model, 2165–2170, https://ams.confex.com/ams/pdfpapers/69061.pdf (last access: 23 January 2025), 2004.
Thompson, G., Field, P. R., Rasmussen, R. M., and Hall, W. D.: Explicit Forecasts of Winter Precipitation Using an Improved Bulk Microphysics Scheme. Part II: Implementation of a New Snow Parameterization, Mon. Weather Rev., 136, 5095–5115, https://doi.org/10.1175/2008MWR2387.1, 2008.
Tiedtke, M.: A Comprehensive Mass Flux Scheme for Cumulus Parameterization in Large-Scale Models, Mon. Weather Rev., 117, 1779–1800, https://doi.org/10.1175/1520-0493(1989)117<1779:acmfsf>2.0.co;2, 1989.
Torma, C., Giorgi, F., and Coppola, E.: Added value of regional climate modeling over areas characterized by complex terrain – Precipitation over the Alps, J. Geophys. Res.-Atmos., 120, 3957–3972, https://doi.org/10.1002/2014JD022781, 2015.
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.
Van de Walle, J., Thiery, W., Brousse, O., Souverijns, N., Demuzere, M., and van Lipzig, N. P. M.: A convection-permitting model for the Lake Victoria Basin: evaluation and insight into the mesoscale versus synoptic atmospheric dynamics, Clim. Dynam., 54, 1779–1799, https://doi.org/10.1007/s00382-019-05088-2, 2020.
van Oldenborgh, G. J., Krikken, F., Lewis, S., Leach, N. J., Lehner, F., Saunders, K. R., van Weele, M., Haustein, K., Li, S., Wallom, D., Sparrow, S., Arrighi, J., Singh, R. K., van Aalst, M. K., Philip, S. Y., Vautard, R., and Otto, F. E. L.: Attribution of the Australian bushfire risk to anthropogenic climate change, Nat. Hazards Earth Syst. Sci., 21, 941–960, https://doi.org/10.5194/nhess-21-941-2021, 2021.
Varga, A. J. and Breuer, H.: Sensitivity of simulated temperature, precipitation, and global radiation to different WRF configurations over the Carpathian Basin for regional climate applications, Clim. Dynam., 55, 2849–2866, https://doi.org/10.1007/s00382-020-05416-x, 2020.
Vargas Zeppetello, L. R., Raftery, A. E., and Battisti, D. S.: Probabilistic projections of increased heat stress driven by climate change, Communications Earth & Environment, 3, 183, https://doi.org/10.1038/s43247-022-00524-4, 2022.
Zhou, X., Yang, K., Ouyang, L., Wang, Y., Jiang, Y. Z., Li, X., Chen, D. L., and Prein, A.: Added value of kilometer-scale modeling over the third pole region: a CORDEX-CPTP pilot study, Clim. Dynam., 57, 1673–1687, https://doi.org/10.1007/s00382-021-05653-8, 2021.
Short summary
We evaluate the skill in simulating the Australian climate of some of the latest generation of regional climate models. We show when and where the models simulate this climate with high skill versus model limitations. We show how new models perform relative to the previous-generation models, assessing how model design features may underlie key performance improvements. This work is of national and international relevance as it can help guide the use and interpretation of climate projections.
We evaluate the skill in simulating the Australian climate of some of the latest generation of...
