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
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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.
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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.
Anjana Devanand, Jason Evans, Andy Pitman, Sujan Pal, David Gochis, and Kevin Sampson
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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.
Kevin K. W. Cheung, Fei Ji, Nidhi Nishant, Jin Teng, James Bennett, and De Li Liu
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-286, https://doi.org/10.5194/hess-2024-286, 2024
Revised manuscript accepted for HESS
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This study evaluates two reanalysis datasets, which are critical in climate, weather research and water resources analysis, for the Australian region in simulating mean precipitation and six 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.
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.
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
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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.
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Hydrol. Earth Syst. Sci., 23, 851–870, https://doi.org/10.5194/hess-23-851-2019, https://doi.org/10.5194/hess-23-851-2019, 2019
Stephen Blenkinsop, Hayley J. Fowler, Renaud Barbero, Steven C. Chan, Selma B. Guerreiro, Elizabeth Kendon, Geert Lenderink, Elizabeth Lewis, Xiao-Feng Li, Seth Westra, Lisa Alexander, Richard P. Allan, Peter Berg, Robert J. H. Dunn, Marie Ekström, Jason P. Evans, Greg Holland, Richard Jones, Erik Kjellström, Albert Klein-Tank, Dennis Lettenmaier, Vimal Mishra, Andreas F. Prein, Justin Sheffield, and Mari R. Tye
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Measurements of sub-daily (e.g. hourly) rainfall totals are essential if we are to understand short, intense bursts of rainfall that cause flash floods. We might expect the intensity of such events to increase in a warming climate but these are poorly realised in projections of future climate change. The INTENSE project is collating a global dataset of hourly rainfall measurements and linking with new developments in climate models to understand the characteristics and causes of these events.
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Hydrol. Earth Syst. Sci., 22, 1317–1336, https://doi.org/10.5194/hess-22-1317-2018, https://doi.org/10.5194/hess-22-1317-2018, 2018
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Yanan Fan, Roman Olson, and Jason P. Evans
Geosci. Model Dev., 10, 2321–2332, https://doi.org/10.5194/gmd-10-2321-2017, https://doi.org/10.5194/gmd-10-2321-2017, 2017
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We develop a novel and principled Bayesian statistical approach to computing model weights in climate change projection ensembles of regional climate models. The approach accounts for uncertainty in model bias, trend and internal variability. The weights are easily interpretable and the ensemble weighted models are shown to provide the correct coverage and improve upon existing methods in terms of providing narrower confidence intervals for climate change projections.
Jason P. Evans, Xianhong Meng, and Matthew F. McCabe
Hydrol. Earth Syst. Sci., 21, 409–422, https://doi.org/10.5194/hess-21-409-2017, https://doi.org/10.5194/hess-21-409-2017, 2017
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Hoori Ajami, Ashish Sharma, Lawrence E. Band, Jason P. Evans, Narendra K. Tuteja, Gnanathikkam E. Amirthanathan, and Mohammed A. Bari
Hydrol. Earth Syst. Sci., 21, 281–294, https://doi.org/10.5194/hess-21-281-2017, https://doi.org/10.5194/hess-21-281-2017, 2017
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M. Decker, A. Pitman, and J. Evans
Hydrol. Earth Syst. Sci., 19, 3433–3447, https://doi.org/10.5194/hess-19-3433-2015, https://doi.org/10.5194/hess-19-3433-2015, 2015
J. Teng, N. J. Potter, F. H. S. Chiew, L. Zhang, B. Wang, J. Vaze, and J. P. Evans
Hydrol. Earth Syst. Sci., 19, 711–728, https://doi.org/10.5194/hess-19-711-2015, https://doi.org/10.5194/hess-19-711-2015, 2015
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H. Ajami, J. P. Evans, M. F. McCabe, and S. Stisen
Hydrol. Earth Syst. Sci., 18, 5169–5179, https://doi.org/10.5194/hess-18-5169-2014, https://doi.org/10.5194/hess-18-5169-2014, 2014
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J. Kala, J. P. Evans, A. J. Pitman, C. B. Schaaf, M. Decker, C. Carouge, D. Mocko, and Q. Sun
Geosci. Model Dev., 7, 2121–2140, https://doi.org/10.5194/gmd-7-2121-2014, https://doi.org/10.5194/gmd-7-2121-2014, 2014
J. P. Evans, F. Ji, C. Lee, P. Smith, D. Argüeso, and L. Fita
Geosci. Model Dev., 7, 621–629, https://doi.org/10.5194/gmd-7-621-2014, https://doi.org/10.5194/gmd-7-621-2014, 2014
D. Argüeso, J. P. Evans, and L. Fita
Hydrol. Earth Syst. Sci., 17, 4379–4388, https://doi.org/10.5194/hess-17-4379-2013, https://doi.org/10.5194/hess-17-4379-2013, 2013
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We describe, calibrate and test the Danish Center for Earth System Science (DCESS) II model, a new, broad, adaptable and fast Earth system model. DCESS II is designed for global simulations over timescales of years to millions of years using limited computer resources like a personal computer. With its flexibility and comprehensive treatment of the global carbon cycle, DCESS II is a useful, computationally friendly tool for simulations of past climates as well as for future Earth system projections.
Gang Tang, Zebedee Nicholls, Alexander Norton, Sönke Zaehle, and Malte Meinshausen
Geosci. Model Dev., 18, 2193–2230, https://doi.org/10.5194/gmd-18-2193-2025, https://doi.org/10.5194/gmd-18-2193-2025, 2025
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We studied carbon–nitrogen coupling in Earth system models by developing a global carbon–nitrogen cycle model (CNit v1.0) within the widely used emulator MAGICC. CNit effectively reproduced the global carbon–nitrogen cycle dynamics observed in complex models. Our results show persistent nitrogen limitations on plant growth (net primary production) from 1850 to 2100, suggesting that nitrogen deficiency may constrain future land carbon sequestration.
Ngoc Thi Nhu Do, Kengo Sudo, Akihiko Ito, Louisa K. Emmons, Vaishali Naik, Kostas Tsigaridis, Øyvind Seland, Gerd A. Folberth, and Douglas I. Kelley
Geosci. Model Dev., 18, 2079–2109, https://doi.org/10.5194/gmd-18-2079-2025, https://doi.org/10.5194/gmd-18-2079-2025, 2025
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Understanding historical isoprene emission changes is important for predicting future climate, but trends and their controlling factors remain uncertain. This study shows that long-term isoprene trends vary among Earth system models mainly due to partially incorporating CO2 effects and land cover changes rather than to climate. Future models that refine these factors’ effects on isoprene emissions, along with long-term observations, are essential for better understanding plant–climate interactions.
Gang Tang, Zebedee Nicholls, Chris Jones, Thomas Gasser, Alexander Norton, Tilo Ziehn, Alejandro Romero-Prieto, and Malte Meinshausen
Geosci. Model Dev., 18, 2111–2136, https://doi.org/10.5194/gmd-18-2111-2025, https://doi.org/10.5194/gmd-18-2111-2025, 2025
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We analyzed carbon and nitrogen mass conservation in data from various Earth system models. Our findings reveal significant discrepancies between flux and pool size data, where cumulative imbalances can reach hundreds of gigatons of carbon or nitrogen. These imbalances appear primarily due to missing or inconsistently reported fluxes – especially for land-use and fire emissions. To enhance data quality, we recommend that future climate data protocols address this issue at the reporting stage.
Florian Börgel, Sven Karsten, Karoline Rummel, and Ulf Gräwe
Geosci. Model Dev., 18, 2005–2019, https://doi.org/10.5194/gmd-18-2005-2025, https://doi.org/10.5194/gmd-18-2005-2025, 2025
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Forecasting river runoff, which is crucial for managing water resources and understanding climate impacts, can be challenging. This study introduces a new method using convolutional long short-term memory (ConvLSTM) networks, a machine learning model that processes spatial and temporal data. Focusing on the Baltic Sea region, our model uses weather data as input to predict daily river runoff for 97 rivers.
Tao Zhang, Cyril Morcrette, Meng Zhang, Wuyin Lin, Shaocheng Xie, Ye Liu, Kwinten Van Weverberg, and Joana Rodrigues
Geosci. Model Dev., 18, 1917–1928, https://doi.org/10.5194/gmd-18-1917-2025, https://doi.org/10.5194/gmd-18-1917-2025, 2025
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Earth system models (ESMs) struggle with the uncertainties associated with parameterizing subgrid physics. Machine learning (ML) algorithms offer a solution by learning the important relationships and features from high-resolution models. To incorporate ML parameterizations into ESMs, we develop a Fortran–Python interface that allows for calling Python functions within Fortran-based ESMs. Through two case studies, this interface demonstrates its feasibility, modularity, and effectiveness.
Camilla Mathison, Eleanor J. Burke, Gregory Munday, Chris D. Jones, Chris J. Smith, Norman J. Steinert, Andy J. Wiltshire, Chris Huntingford, Eszter Kovacs, Laila K. Gohar, Rebecca M. Varney, and Douglas McNeall
Geosci. Model Dev., 18, 1785–1808, https://doi.org/10.5194/gmd-18-1785-2025, https://doi.org/10.5194/gmd-18-1785-2025, 2025
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We present PRIME (Probabilistic Regional Impacts from Model patterns and Emissions), which is designed to take new emissions scenarios and rapidly provide regional impact information. PRIME allows large ensembles to be run on multi-centennial timescales, including the analysis of many important variables for impact assessments. Our evaluation shows that PRIME reproduces the climate response for known scenarios, providing confidence in using PRIME for novel scenarios.
Katherine M. Smith, Alice M. Barthel, LeAnn M. Conlon, Luke P. Van Roekel, Anthony Bartoletti, Jean-Christophe Golaz, Chengzhu Zhang, Carolyn Branecky Begeman, James J. Benedict, Gautam Bisht, Yan Feng, Walter Hannah, Bryce E. Harrop, Nicole Jeffery, Wuyin Lin, Po-Lun Ma, Mathew E. Maltrud, Mark R. Petersen, Balwinder Singh, Qi Tang, Teklu Tesfa, Jonathan D. Wolfe, Shaocheng Xie, Xue Zheng, Karthik Balaguru, Oluwayemi Garuba, Peter Gleckler, Aixue Hu, Jiwoo Lee, Ben Moore-Maley, and Ana C. Ordoñez
Geosci. Model Dev., 18, 1613–1633, https://doi.org/10.5194/gmd-18-1613-2025, https://doi.org/10.5194/gmd-18-1613-2025, 2025
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Version 2.1 of the U.S. Department of Energy's Energy Exascale Earth System Model (E3SM) adds the Fox-Kemper et al. (2011) mixed-layer eddy parameterization, which restratifies the ocean surface layer through an overturning streamfunction. Results include surface layer bias reduction in temperature, salinity, and sea ice extent in the North Atlantic; a small strengthening of the Atlantic meridional overturning circulation; and improvements to many atmospheric climatological variables.
Huilin Huang, Yun Qian, Gautam Bisht, Jiali Wang, Tirthankar Chakraborty, Dalei Hao, Jianfeng Li, Travis Thurber, Balwinder Singh, Zhao Yang, Ye Liu, Pengfei Xue, William J. Sacks, Ethan Coon, and Robert Hetland
Geosci. Model Dev., 18, 1427–1443, https://doi.org/10.5194/gmd-18-1427-2025, https://doi.org/10.5194/gmd-18-1427-2025, 2025
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We integrate the E3SM Land Model (ELM) with the WRF model through the Lightweight Infrastructure for Land Atmosphere Coupling (LILAC) Earth System Modeling Framework (ESMF). This framework includes a top-level driver, LILAC, for variable communication between WRF and ELM and ESMF caps for ELM initialization, execution, and finalization. The LILAC–ESMF framework maintains the integrity of the ELM's source code structure and facilitates the transfer of future ELM model developments to WRF-ELM.
Michael Nole, Jonah Bartrand, Fawz Naim, and Glenn Hammond
Geosci. Model Dev., 18, 1413–1425, https://doi.org/10.5194/gmd-18-1413-2025, https://doi.org/10.5194/gmd-18-1413-2025, 2025
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Safe carbon dioxide (CO2) storage is likely to be critical for mitigating some of the most severe effects of climate change. We present a simulation framework for modeling CO2 storage beneath the seafloor, where CO2 can form a solid. This can aid in permanent CO2 storage for long periods of time. Our models show what a commercial-scale CO2 injection would look like in a marine environment. We discuss what would need to be considered when designing a subsea CO2 injection.
Reyk Börner, Jan O. Haerter, and Romain Fiévet
Geosci. Model Dev., 18, 1333–1356, https://doi.org/10.5194/gmd-18-1333-2025, https://doi.org/10.5194/gmd-18-1333-2025, 2025
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The daily cycle of sea surface temperature (SST) impacts clouds above the ocean and could influence the clustering of thunderstorms linked to extreme rainfall and hurricanes. However, daily SST variability is often poorly represented in modeling studies of how clouds cluster. We present a simple, wind-responsive model of upper-ocean temperature for use in atmospheric simulations. Evaluating the model against observations, we show that it performs significantly better than common slab models.
Malcolm J. Roberts, Kevin A. Reed, Qing Bao, Joseph J. Barsugli, Suzana J. Camargo, Louis-Philippe Caron, Ping Chang, Cheng-Ta Chen, Hannah M. Christensen, Gokhan Danabasoglu, Ivy Frenger, Neven S. Fučkar, Shabeh ul Hasson, Helene T. Hewitt, Huanping Huang, Daehyun Kim, Chihiro Kodama, Michael Lai, Lai-Yung Ruby Leung, Ryo Mizuta, Paulo Nobre, Pablo Ortega, Dominique Paquin, Christopher D. Roberts, Enrico Scoccimarro, Jon Seddon, Anne Marie Treguier, Chia-Ying Tu, Paul A. Ullrich, Pier Luigi Vidale, Michael F. Wehner, Colin M. Zarzycki, Bosong Zhang, Wei Zhang, and Ming Zhao
Geosci. Model Dev., 18, 1307–1332, https://doi.org/10.5194/gmd-18-1307-2025, https://doi.org/10.5194/gmd-18-1307-2025, 2025
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HighResMIP2 is a model intercomparison project focusing on high-resolution global climate models, that is, those with grid spacings of 25 km or less in the atmosphere and ocean, using simulations of decades to a century in length. We are proposing an update of our simulation protocol to make the models more applicable to key questions for climate variability and hazard in present-day and future projections and to build links with other communities to provide more robust climate information.
Jordi Buckley Paules, Simone Fatichi, Bonnie Warring, and Athanasios Paschalis
Geosci. Model Dev., 18, 1287–1305, https://doi.org/10.5194/gmd-18-1287-2025, https://doi.org/10.5194/gmd-18-1287-2025, 2025
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We present and validate enhancements to the process-based T&C model aimed at improving its representation of crop growth and management practices. The updated model, T&C-CROP, enables applications such as analysing the hydrological and carbon storage impacts of land use transitions (e.g. conversions between crops, forests, and pastures) and optimizing irrigation and fertilization strategies in response to climate change.
Sébastien Masson, Swen Jullien, Eric Maisonnave, David Gill, Guillaume Samson, Mathieu Le Corre, and Lionel Renault
Geosci. Model Dev., 18, 1241–1263, https://doi.org/10.5194/gmd-18-1241-2025, https://doi.org/10.5194/gmd-18-1241-2025, 2025
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This article details a new feature we implemented in the popular regional atmospheric model WRF. This feature allows for data exchange between WRF and any other model (e.g. an ocean model) using the coupling library Ocean–Atmosphere–Sea–Ice–Soil Model Coupling Toolkit (OASIS3-MCT). This coupling interface is designed to be non-intrusive, flexible and modular. It also offers the possibility of taking into account the nested zooms used in WRF or in the models with which it is coupled.
Axel Lauer, Lisa Bock, Birgit Hassler, Patrick Jöckel, Lukas Ruhe, and Manuel Schlund
Geosci. Model Dev., 18, 1169–1188, https://doi.org/10.5194/gmd-18-1169-2025, https://doi.org/10.5194/gmd-18-1169-2025, 2025
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Earth system models are important tools to improve our understanding of current climate and to project climate change. Thus, it is crucial to understand possible shortcomings in the models. New features of the ESMValTool software package allow one to compare and visualize a model's performance with respect to reproducing observations in the context of other climate models in an easy and user-friendly way. We aim to help model developers assess and monitor climate simulations more efficiently.
Ulrich G. Wortmann, Tina Tsan, Mahrukh Niazi, Irene A. Ma, Ruben Navasardyan, Magnus-Roland Marun, Bernardo S. Chede, Jingwen Zhong, and Morgan Wolfe
Geosci. Model Dev., 18, 1155–1167, https://doi.org/10.5194/gmd-18-1155-2025, https://doi.org/10.5194/gmd-18-1155-2025, 2025
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The Earth Science Box Modeling Toolkit (ESBMTK) is a user-friendly Python library that simplifies the creation of models to study earth system processes, such as the carbon cycle and ocean chemistry. It enhances learning by emphasizing concepts over programming and is accessible to students and researchers alike. By automating complex calculations and promoting code clarity, ESBMTK accelerates model development while improving reproducibility and the usability of scientific research.
Florian Zabel, Matthias Knüttel, and Benjamin Poschlod
Geosci. Model Dev., 18, 1067–1087, https://doi.org/10.5194/gmd-18-1067-2025, https://doi.org/10.5194/gmd-18-1067-2025, 2025
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CropSuite is a new open-source crop suitability model. It provides a GUI and a wide range of options, including a spatial downscaling of climate data. We apply CropSuite to 48 staple and opportunity crops at a 1 km spatial resolution in Africa. We find that climate variability significantly impacts suitable areas but also affects optimal sowing dates and multiple cropping potential. The results provide valuable information for climate impact assessments, adaptation, and land-use planning.
Kerstin Hartung, Bastian Kern, Nils-Arne Dreier, Jörn Geisbüsch, Mahnoosh Haghighatnasab, Patrick Jöckel, Astrid Kerkweg, Wilton Jaciel Loch, Florian Prill, and Daniel Rieger
Geosci. Model Dev., 18, 1001–1015, https://doi.org/10.5194/gmd-18-1001-2025, https://doi.org/10.5194/gmd-18-1001-2025, 2025
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The ICOsahedral Non-hydrostatic (ICON) model system Community Interface (ComIn) library supports connecting third-party modules to the ICON model. Third-party modules can range from simple diagnostic Python scripts to full chemistry models. ComIn offers a low barrier for code extensions to ICON, provides multi-language support (Fortran, C/C++, and Python), and reduces the migration effort in response to new ICON releases. This paper presents the ComIn design principles and a range of use cases.
Daniel Ries, Katherine Goode, Kellie McClernon, and Benjamin Hillman
Geosci. Model Dev., 18, 1041–1065, https://doi.org/10.5194/gmd-18-1041-2025, https://doi.org/10.5194/gmd-18-1041-2025, 2025
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Machine learning has advanced research in the climate science domain, but its models are difficult to understand. In order to understand the impacts and consequences of climate interventions such as stratospheric aerosol injection, complex models are often necessary. We use a case study to illustrate how we can understand the inner workings of a complex model. We present this technique as an exploratory tool that can be used to quickly discover and assess relationships in complex climate data.
Bo Dong, Paul Ullrich, Jiwoo Lee, Peter Gleckler, Kristin Chang, and Travis A. O'Brien
Geosci. Model Dev., 18, 961–976, https://doi.org/10.5194/gmd-18-961-2025, https://doi.org/10.5194/gmd-18-961-2025, 2025
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A metrics package designed for easy analysis of atmospheric river (AR) characteristics and statistics is presented. The tool is efficient for diagnosing systematic AR bias in climate models and useful for evaluating new AR characteristics in model simulations. In climate models, landfalling AR precipitation shows dry biases globally, and AR tracks are farther poleward (equatorward) in the North and South Atlantic (South Pacific and Indian Ocean).
Panagiotis Adamidis, Erik Pfister, Hendryk Bockelmann, Dominik Zobel, Jens-Olaf Beismann, and Marek Jacob
Geosci. Model Dev., 18, 905–919, https://doi.org/10.5194/gmd-18-905-2025, https://doi.org/10.5194/gmd-18-905-2025, 2025
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In this paper, we investigated performance indicators of the climate model ICON (ICOsahedral Nonhydrostatic) on different compute architectures to answer the question of how to generate high-resolution climate simulations. Evidently, it is not enough to use more computing units of the conventionally used architectures; higher memory throughput is the most promising approach. More potential can be gained from single-node optimization rather than simply increasing the number of compute nodes.
Kangari Narender Reddy, Somnath Baidya Roy, Sam S. Rabin, Danica L. Lombardozzi, Gudimetla Venkateswara Varma, Ruchira Biswas, and Devavat Chiru Naik
Geosci. Model Dev., 18, 763–785, https://doi.org/10.5194/gmd-18-763-2025, https://doi.org/10.5194/gmd-18-763-2025, 2025
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The study aimed to improve the representation of wheat and rice in a land model for the Indian region. The modified model performed significantly better than the default model in simulating crop phenology, yield, and carbon, water, and energy fluxes compared to observations. The study highlights the need for global land models to use region-specific crop parameters for accurately simulating vegetation processes and land surface processes.
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.
Nathan P. Gillett, Isla R. Simpson, Gabi Hegerl, Reto Knutti, Dann Mitchell, Aurélien Ribes, Hideo Shiogama, Dáithí Stone, Claudia Tebaldi, Piotr Wolski, Wenxia Zhang, and Vivek K. Arora
EGUsphere, https://doi.org/10.5194/egusphere-2024-4086, https://doi.org/10.5194/egusphere-2024-4086, 2025
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Climate model simulations of the response to human and natural influences together, natural climate influences alone, and greenhouse gases alone, among others, are key to quantifying human influence on the climate. The last set of such coordinated simulations underpinned key findings in the last Intergovernmental Panel on Climate Change (IPCC) report. Here we propose a new set of such simulations to be used in the next generation of attribution studies, and to underpin the next IPCC report.
Jiawang Feng, Chun Zhao, Qiuyan Du, Zining Yang, and Chen Jin
Geosci. Model Dev., 18, 585–603, https://doi.org/10.5194/gmd-18-585-2025, https://doi.org/10.5194/gmd-18-585-2025, 2025
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In this study, we improved the calculation of how aerosols in the air interact with radiation in WRF-Chem. The original model used a simplified method, but we developed a more accurate approach. We found that this method significantly changes the properties of the estimated aerosols and their effects on radiation, especially for dust aerosols. It also impacts the simulated weather conditions. Our work highlights the importance of correctly representing aerosol–radiation interactions in models.
Eduardo Moreno-Chamarro, Thomas Arsouze, Mario Acosta, Pierre-Antoine Bretonnière, Miguel Castrillo, Eric Ferrer, Amanda Frigola, Daria Kuznetsova, Eneko Martin-Martinez, Pablo Ortega, and Sergi Palomas
Geosci. Model Dev., 18, 461–482, https://doi.org/10.5194/gmd-18-461-2025, https://doi.org/10.5194/gmd-18-461-2025, 2025
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We present the high-resolution model version of the EC-Earth global climate model to contribute to HighResMIP. The combined model resolution is about 10–15 km in both the ocean and atmosphere, which makes it one of the finest ever used to complete historical and scenario simulations. This model is compared with two lower-resolution versions, with a 100 km and a 25 km grid. The three models are compared with observations to study the improvements thanks to the increased resolution.
Catherine Guiavarc'h, David Storkey, Adam T. Blaker, Ed Blockley, Alex Megann, Helene Hewitt, Michael J. Bell, Daley Calvert, Dan Copsey, Bablu Sinha, Sophia Moreton, Pierre Mathiot, and Bo An
Geosci. Model Dev., 18, 377–403, https://doi.org/10.5194/gmd-18-377-2025, https://doi.org/10.5194/gmd-18-377-2025, 2025
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The Global Ocean and Sea Ice configuration version 9 (GOSI9) is the new UK hierarchy of model configurations based on the Nucleus for European Modelling of the Ocean (NEMO) and available at three resolutions. It will be used for various applications, e.g. weather forecasting and climate prediction. It improves upon the previous version by reducing global temperature and salinity biases and enhancing the representation of Arctic sea ice and the Antarctic Circumpolar Current.
Andy Richling, Jens Grieger, and Henning W. Rust
Geosci. Model Dev., 18, 361–375, https://doi.org/10.5194/gmd-18-361-2025, https://doi.org/10.5194/gmd-18-361-2025, 2025
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The performance of weather and climate prediction systems is variable in time and space. It is of interest how this performance varies in different situations. We provide a decomposition of a skill score (a measure of forecast performance) as a tool for detailed assessment of performance variability to support model development or forecast improvement. The framework is exemplified with decadal forecasts to assess the impact of different ocean states in the North Atlantic on temperature forecast.
Maria R. Russo, Sadie L. Bartholomew, David Hassell, Alex M. Mason, Erica Neininger, A. James Perman, David A. J. Sproson, Duncan Watson-Parris, and Nathan Luke Abraham
Geosci. Model Dev., 18, 181–191, https://doi.org/10.5194/gmd-18-181-2025, https://doi.org/10.5194/gmd-18-181-2025, 2025
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Observational data and modelling capabilities have expanded in recent years, but there are still barriers preventing these two data sources from being used in synergy. Proper comparison requires generating, storing, and handling a large amount of data. This work describes the first step in the development of a new set of software tools, the VISION toolkit, which can enable the easy and efficient integration of observational and model data required for model evaluation.
Bijan Fallah, Masoud Rostami, Emmanuele Russo, Paula Harder, Christoph Menz, Peter Hoffmann, Iulii Didovets, and Fred F. Hattermann
Geosci. Model Dev., 18, 161–180, https://doi.org/10.5194/gmd-18-161-2025, https://doi.org/10.5194/gmd-18-161-2025, 2025
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We tried to contribute to a local climate change impact study in central Asia, a region that is water-scarce and vulnerable to global climate change. We use regional models and machine learning to produce reliable local data from global climate models. We find that regional models show more realistic and detailed changes in heavy precipitation than global climate models. Our work can help assess the future risks of extreme events and plan adaptation strategies in central Asia.
Manuel Schlund, Bouwe Andela, Jörg Benke, Ruth Comer, Birgit Hassler, Emma Hogan, Peter Kalverla, Axel Lauer, Bill Little, Saskia Loosveldt Tomas, Francesco Nattino, Patrick Peglar, Valeriu Predoi, Stef Smeets, Stephen Worsley, Martin Yeo, and Klaus Zimmermann
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-236, https://doi.org/10.5194/gmd-2024-236, 2025
Revised manuscript accepted for GMD
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The Earth System Model Evaluation Tool (ESMValTool) is a community diagnostics and performance metrics tool for the evaluation of Earth system models. Here, we describe recent significant improvements of ESMValTool’s computational efficiency including parallel, out-of-core, and distributed computing. Evaluations with the enhanced version of ESMValTool are faster, use less computational resources, and can handle input data larger than the available memory.
Thomas Rackow, Xabier Pedruzo-Bagazgoitia, Tobias Becker, Sebastian Milinski, Irina Sandu, Razvan Aguridan, Peter Bechtold, Sebastian Beyer, Jean Bidlot, Souhail Boussetta, Willem Deconinck, Michail Diamantakis, Peter Dueben, Emanuel Dutra, Richard Forbes, Rohit Ghosh, Helge F. Goessling, Ioan Hadade, Jan Hegewald, Thomas Jung, Sarah Keeley, Lukas Kluft, Nikolay Koldunov, Aleksei Koldunov, Tobias Kölling, Josh Kousal, Christian Kühnlein, Pedro Maciel, Kristian Mogensen, Tiago Quintino, Inna Polichtchouk, Balthasar Reuter, Domokos Sármány, Patrick Scholz, Dmitry Sidorenko, Jan Streffing, Birgit Sützl, Daisuke Takasuka, Steffen Tietsche, Mirco Valentini, Benoît Vannière, Nils Wedi, Lorenzo Zampieri, and Florian Ziemen
Geosci. Model Dev., 18, 33–69, https://doi.org/10.5194/gmd-18-33-2025, https://doi.org/10.5194/gmd-18-33-2025, 2025
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Detailed global climate model simulations have been created based on a numerical weather prediction model, offering more accurate spatial detail down to the scale of individual cities ("kilometre-scale") and a better understanding of climate phenomena such as atmospheric storms, whirls in the ocean, and cracks in sea ice. The new model aims to provide globally consistent information on local climate change with greater precision, benefiting environmental planning and local impact modelling.
Yilin Fang, Hoang Viet Tran, and L. Ruby Leung
Geosci. Model Dev., 18, 19–32, https://doi.org/10.5194/gmd-18-19-2025, https://doi.org/10.5194/gmd-18-19-2025, 2025
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Hurricanes may worsen water quality in the lower Mississippi River basin (LMRB) by increasing nutrient runoff. We found that runoff parameterizations greatly affect nitrate–nitrogen runoff simulated using an Earth system land model. Our simulations predicted increased nitrogen runoff in the LMRB during Hurricane Ida in 2021, albeit less pronounced than the observations, indicating areas for model improvement to better understand and manage nutrient runoff loss during hurricanes in the region.
Giovanni Seijo-Ellis, Donata Giglio, Gustavo Marques, and Frank Bryan
Geosci. Model Dev., 17, 8989–9021, https://doi.org/10.5194/gmd-17-8989-2024, https://doi.org/10.5194/gmd-17-8989-2024, 2024
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A CESM–MOM6 regional configuration of the Caribbean Sea was developed in response to the rising need for high-resolution models for climate impact studies. The configuration is validated for the period 2000–2020 and improves significant errors in a low-resolution model. Oceanic properties are well represented. Patterns of freshwater associated with the Amazon River are well captured, and the mean flows of ocean waters across multiple passages in the Caribbean Sea agree with observations.
Yan Bo, Hao Liang, Tao Li, and Feng Zhou
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-212, https://doi.org/10.5194/gmd-2024-212, 2024
Revised manuscript accepted for GMD
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This study proposed an advancing framework for modeling regional rice production, water use, and greenhouse gas emissions. The framework integrated a process-based soil-crop model with key physiological effects, a novel model upscaling method, and the NSGA-II multi-objective optimization algorithm at a parallel computing platform. The framework provides a valuable tool for irrigation optimization to deliver co-benefits of ensuring food production, reducing water use and greenhouse gas emissions.
Elizabeth J. Drenkard, Charles A. Stock, Andrew C. Ross, Yi-Cheng Teng, Theresa Morrison, Wei Cheng, Alistair Adcroft, Enrique Curchitser, Raphael Dussin, Robert Hallberg, Claudine Hauri, Katherine Hedstrom, Albert Hermann, Michael G. Jacox, Kelly A. Kearney, Remi Pages, Darren J. Pilcher, Mercedes Pozo Buil, Vivek Seelanki, and Niki Zadeh
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-195, https://doi.org/10.5194/gmd-2024-195, 2024
Preprint under review for GMD
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We made a new regional ocean model to assist fisheries and ecosystem managers make decisions in the Northeast Pacific Ocean (NEP). We found that the model did well simulating past ocean conditions like temperature, and nutrient and oxygen levels, and can even reproduce metrics used by and important to ecosystem managers.
Deifilia To, Julian Quinting, Gholam Ali Hoshyaripour, Markus Götz, Achim Streit, and Charlotte Debus
Geosci. Model Dev., 17, 8873–8884, https://doi.org/10.5194/gmd-17-8873-2024, https://doi.org/10.5194/gmd-17-8873-2024, 2024
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Pangu-Weather is a breakthrough machine learning model in medium-range weather forecasting that considers 3D atmospheric information. We show that using a simpler 2D framework improves robustness, speeds up training, and reduces computational needs by 20 %–30 %. We introduce a training procedure that varies the importance of atmospheric variables over time to speed up training convergence. Decreasing computational demand increases the accessibility of training and working with the model.
Fang Li, Xiang Song, Sandy P. Harrison, Jennifer R. Marlon, Zhongda Lin, L. Ruby Leung, Jörg Schwinger, Virginie Marécal, Shiyu Wang, Daniel S. Ward, Xiao Dong, Hanna Lee, Lars Nieradzik, Sam S. Rabin, and Roland Séférian
Geosci. Model Dev., 17, 8751–8771, https://doi.org/10.5194/gmd-17-8751-2024, https://doi.org/10.5194/gmd-17-8751-2024, 2024
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This study provides the first comprehensive assessment of historical fire simulations from 19 Earth system models in phase 6 of the Coupled Model Intercomparison Project (CMIP6). Most models reproduce global totals, spatial patterns, seasonality, and regional historical changes well but fail to simulate the recent decline in global burned area and underestimate the fire response to climate variability. CMIP6 simulations address three critical issues of phase-5 models.
Seung H. Baek, Paul A. Ullrich, Bo Dong, and Jiwoo Lee
Geosci. Model Dev., 17, 8665–8681, https://doi.org/10.5194/gmd-17-8665-2024, https://doi.org/10.5194/gmd-17-8665-2024, 2024
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We evaluate downscaled products by examining locally relevant co-variances during precipitation events. Common statistical downscaling techniques preserve expected co-variances during convective precipitation (a stationary phenomenon). However, they dampen future intensification of frontal precipitation (a non-stationary phenomenon) captured in global climate models and dynamical downscaling. Our study quantifies a ramification of the stationarity assumption underlying statistical downscaling.
Emmanuel Nyenah, Petra Döll, Daniel S. Katz, and Robert Reinecke
Geosci. Model Dev., 17, 8593–8611, https://doi.org/10.5194/gmd-17-8593-2024, https://doi.org/10.5194/gmd-17-8593-2024, 2024
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Research software is vital for scientific progress but is often developed by scientists with limited skills, time, and funding, leading to challenges in usability and maintenance. Our study across 10 sectors shows strengths in version control, open-source licensing, and documentation while emphasizing the need for containerization and code quality. We recommend workshops; code quality metrics; funding; and following the findable, accessible, interoperable, and reusable (FAIR) standards.
Chris Smith, Donald P. Cummins, Hege-Beate Fredriksen, Zebedee Nicholls, Malte Meinshausen, Myles Allen, Stuart Jenkins, Nicholas Leach, Camilla Mathison, and Antti-Ilari Partanen
Geosci. Model Dev., 17, 8569–8592, https://doi.org/10.5194/gmd-17-8569-2024, https://doi.org/10.5194/gmd-17-8569-2024, 2024
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Climate projections are only useful if the underlying models that produce them are well calibrated and can reproduce observed climate change. We formalise a software package that calibrates the open-source FaIR simple climate model to full-complexity Earth system models. Observations, including historical warming, and assessments of key climate variables such as that of climate sensitivity are used to constrain the model output.
Jingwei Xie, Xi Wang, Hailong Liu, Pengfei Lin, Jiangfeng Yu, Zipeng Yu, Junlin Wei, and Xiang Han
Geosci. Model Dev., 17, 8469–8493, https://doi.org/10.5194/gmd-17-8469-2024, https://doi.org/10.5194/gmd-17-8469-2024, 2024
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We propose the concept of mesoscale ocean direct numerical simulation (MODNS), which should resolve the first baroclinic deformation radius and ensure the numerical dissipative effects do not directly contaminate the mesoscale motions. It can be a benchmark for testing mesoscale ocean large eddy simulation (MOLES) methods in ocean models. We build an idealized Southern Ocean model using MITgcm to generate a type of MODNS. We also illustrate the diversity of multiscale eddy interactions.
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...
