Articles | Volume 19, issue 3
https://doi.org/10.5194/gmd-19-1367-2026
© Author(s) 2026. 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-19-1367-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
12 Feb 2026
Development and technical paper |
| 12 Feb 2026
| 12 Feb 2026
Refining the Lagrangian approach for moisture source identification through sensitivity testing of assumptions using BTrIMS1.1
Climate Change Research Centre, University of New South Wales, Sydney, 2052, Australia
ARC Centre of Excellence for 21st Century Weather, Melbourne, Australia
Jason P. Evans
Climate Change Research Centre, University of New South Wales, Sydney, 2052, Australia
ARC Centre of Excellence for 21st Century Weather, Melbourne, Australia
Andréa S. Taschetto
Climate Change Research Centre, University of New South Wales, Sydney, 2052, Australia
ARC Centre of Excellence for 21st Century Weather, Melbourne, Australia
Chiara Holgate
ARC Centre of Excellence for 21st Century Weather, Melbourne, Australia
Research School of Earth Sciences, Australia National University, 0200, Canberra, Australia
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Rajesh Kumar Sahu, Hamza Kunhu Bangalath, Suleiman Mostamandi, Jason Evans, Paul A. Kucera, and Hylke E. Beck
Nat. Hazards Earth Syst. Sci., 26, 21–40, https://doi.org/10.5194/nhess-26-21-2026, https://doi.org/10.5194/nhess-26-21-2026, 2026
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This study tests 36 combinations of microphysics and boundary layer schemes in the Weather Research and Forecasting model for extreme rainfall over Saudi Arabia. Using the Kling–Gupta Efficiency, the Yonsei University boundary layer with the Thompson microphysics performs best; the Morrison microphysics with the Mellor–Yamada–Nakanishi–Niino boundary layer ranks lowest. Mean temporal efficiency is 0.37, spatial efficiency is 0.26, revealing spatial prediction challenges in arid regions.
Youngil Kim and Jason Evans
EGUsphere, https://doi.org/10.5194/egusphere-2025-6411, https://doi.org/10.5194/egusphere-2025-6411, 2026
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
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Climate models used to study future climate often contain systematic errors that affect high-resolution simulations. This study presents a new open-source tool that reduces these errors before regional climate simulations are run. By correcting multiple atmospheric variables together and at short time scales, the method improves realism and consistency in simulated climate patterns. This leads to more reliable regional projections, particularly for extreme weather events.
Wilma G. C. Huneke, Andrew McC. Hogg, Martin Dix, Daohua Bi, Arnold Sullivan, Shayne McGregor, Chiara M. Holgate, Siobhan P. O'Farrell, and Micael J. T. Oliveira
Geosci. Model Dev., 18, 9991–10015, https://doi.org/10.5194/gmd-18-9991-2025, https://doi.org/10.5194/gmd-18-9991-2025, 2025
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A new configuration of the Australian Community Climate and Earth System Simulator coupled model, ACCESS-CM2, with a higher resolution ocean-sea ice component is introduced. The new version of the coupled climate model was designed to better capture smaller-scale ocean motions. While this configuration improves the representation of many aspects of the climate system, some biases from the existing lower-resolution version persist.
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
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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.
Ingo Richter, Ping Chang, Ping-Gin Chiu, Gokhan Danabasoglu, Takeshi Doi, Dietmar Dommenget, Guillaume Gastineau, Zoe E. Gillett, Aixue Hu, Takahito Kataoka, Noel S. Keenlyside, Fred Kucharski, Yuko M. Okumura, Wonsun Park, Malte F. Stuecker, Andréa S. Taschetto, Chunzai Wang, Stephen G. Yeager, and Sang-Wook Yeh
Geosci. Model Dev., 18, 2587–2608, https://doi.org/10.5194/gmd-18-2587-2025, https://doi.org/10.5194/gmd-18-2587-2025, 2025
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Tropical ocean basins influence each other through multiple pathways and mechanisms, referred to here as tropical basin interaction (TBI). Many researchers have examined TBI using comprehensive climate models but have obtained conflicting results. This may be partly due to differences in experiment protocols and partly due to systematic model errors. The Tropical Basin Interaction Model Intercomparison Project (TBIMIP) aims to address this problem by designing a set of TBI experiments that will be performed by multiple models.
Giovanni Di Virgilio, Fei Ji, Eugene Tam, Jason P. Evans, Jatin Kala, Julia Andrys, Christopher Thomas, Dipayan Choudhury, Carlos Rocha, Yue Li, and Matthew L. Riley
Geosci. Model Dev., 18, 703–724, https://doi.org/10.5194/gmd-18-703-2025, https://doi.org/10.5194/gmd-18-703-2025, 2025
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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.
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
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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
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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
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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
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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.
Nicholas King-Hei Yeung, Laurie Menviel, Katrin J. Meissner, Andréa S. Taschetto, Tilo Ziehn, and Matthew Chamberlain
Clim. Past, 17, 869–885, https://doi.org/10.5194/cp-17-869-2021, https://doi.org/10.5194/cp-17-869-2021, 2021
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The Last Interglacial period (LIG) is characterised by strong orbital forcing compared to the pre-industrial period (PI). This study compares the mean climate state of the LIG to the PI as simulated by the ACCESS-ESM1.5, with a focus on the southern hemispheric monsoons, which are shown to be consistently weakened. This is associated with cooler terrestrial conditions in austral summer due to decreased insolation, and greater pressure and subsidence over land from Hadley cell strengthening.
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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.
Lagrangian approaches have been increasingly employed due to their suitability for extreme...
