Articles | Volume 12, issue 5
https://doi.org/10.5194/gmd-12-2049-2019
© Author(s) 2019. 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-12-2049-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model experiment description paper
| 
24 May 2019
Model experiment description paper |
| 24 May 2019
| 24 May 2019
BARRA v1.0: the Bureau of Meteorology Atmospheric high-resolution Regional Reanalysis for Australia
Chun-Hsu Su
CORRESPONDING AUTHOR
Bureau of Meteorology, Docklands, Victoria 3008, Australia
Nathan Eizenberg
Bureau of Meteorology, Docklands, Victoria 3008, Australia
Peter Steinle
Bureau of Meteorology, Docklands, Victoria 3008, Australia
Dörte Jakob
Bureau of Meteorology, Docklands, Victoria 3008, Australia
Paul Fox-Hughes
Bureau of Meteorology, Hobart, Tasmania 7000, Australia
Christopher J. White
Department of Civil and Environmental Engineering, University of Strathclyde, Glasgow, Scotland, UK
Antarctic Climate and Ecosystems Cooperative Research Centre, Hobart, Australia
Susan Rennie
Bureau of Meteorology, Docklands, Victoria 3008, Australia
Charmaine Franklin
Bureau of Meteorology, Docklands, Victoria 3008, Australia
Imtiaz Dharssi
Bureau of Meteorology, Docklands, Victoria 3008, Australia
Hongyan Zhu
Bureau of Meteorology, Docklands, Victoria 3008, Australia
Related authors
Mike Bush, David L. A. Flack, Huw W. Lewis, Sylvia I. Bohnenstengel, Chris J. Short, Charmaine Franklin, Adrian P. Lock, Martin Best, Paul Field, Anne McCabe, Kwinten Van Weverberg, Segolene Berthou, Ian Boutle, Jennifer K. Brooke, Seb Cole, Shaun Cooper, Gareth Dow, John Edwards, Anke Finnenkoetter, Kalli Furtado, Kate Halladay, Kirsty Hanley, Margaret A. Hendry, Adrian Hill, Aravindakshan Jayakumar, Richard W. Jones, Humphrey Lean, Joshua C. K. Lee, Andy Malcolm, Marion Mittermaier, Saji Mohandas, Stuart Moore, Cyril Morcrette, Rachel North, Aurore Porson, Susan Rennie, Nigel Roberts, Belinda Roux, Claudio Sanchez, Chun-Hsu Su, Simon Tucker, Simon Vosper, David Walters, James Warner, Stuart Webster, Mark Weeks, Jonathan Wilkinson, Michael Whitall, Keith D. Williams, and Hugh Zhang
Geosci. Model Dev., 18, 3819–3855, https://doi.org/10.5194/gmd-18-3819-2025, https://doi.org/10.5194/gmd-18-3819-2025, 2025
Short summary
Short summary
RAL configurations define settings for the Unified Model atmosphere and Joint UK Land Environment Simulator. The third version of the Regional Atmosphere and Land (RAL3) science configuration for kilometre- and sub-kilometre-scale modelling represents a major advance compared to previous versions (RAL2) by delivering a common science definition for applications in tropical and mid-latitude regions. RAL3 has more realistic precipitation distributions and an improved representation of clouds and visibility.
Emma Howard, Chun-Hsu Su, Christian Stassen, Rajashree Naha, Harvey Ye, Acacia Pepler, Samuel S. Bell, Andrew J. Dowdy, Simon O. Tucker, and Charmaine Franklin
Geosci. Model Dev., 17, 731–757, https://doi.org/10.5194/gmd-17-731-2024, https://doi.org/10.5194/gmd-17-731-2024, 2024
Short summary
Short summary
The BARPA-R modelling configuration has been developed to produce high-resolution climate hazard projections within the Australian region. When using boundary driving data from quasi-observed historical conditions, BARPA-R shows good performance with errors generally on par with reanalysis products. BARPA-R also captures trends, known modes of climate variability, large-scale weather processes, and multivariate relationships.
Chun-Hsu Su, Nathan Eizenberg, Dörte Jakob, Paul Fox-Hughes, Peter Steinle, Christopher J. White, and Charmaine Franklin
Geosci. Model Dev., 14, 4357–4378, https://doi.org/10.5194/gmd-14-4357-2021, https://doi.org/10.5194/gmd-14-4357-2021, 2021
Short summary
Short summary
The Bureau of Meteorology Atmospheric Regional Reanalysis for Australia (BARRA) has produced a very high-resolution reconstruction of Australian historical weather from 1990 to 2018. This paper demonstrates the added weather and climate information to supplement coarse- or moderate-resolution regional and global reanalyses. The new climate data can allow greater understanding of past weather, including extreme events, at very local kilometre scales.
Suwash Chandra Acharya, Rory Nathan, Quan J. Wang, Chun-Hsu Su, and Nathan Eizenberg
Hydrol. Earth Syst. Sci., 24, 2951–2962, https://doi.org/10.5194/hess-24-2951-2020, https://doi.org/10.5194/hess-24-2951-2020, 2020
Short summary
Short summary
BARRA is a high-resolution reanalysis dataset over the Oceania region. This study evaluates the performance of sub-daily BARRA precipitation at point and spatial scales over Australia. We find that the dataset reproduces some of the sub-daily characteristics of precipitation well, although it exhibits some spatial displacement errors, and it performs better in temperate than in tropical regions. The product is well suited to complement other estimates derived from remote sensing and rain gauges.
Suwash Chandra Acharya, Rory Nathan, Quan J. Wang, Chun-Hsu Su, and Nathan Eizenberg
Hydrol. Earth Syst. Sci., 23, 3387–3403, https://doi.org/10.5194/hess-23-3387-2019, https://doi.org/10.5194/hess-23-3387-2019, 2019
Short summary
Short summary
BARRA is a novel regional reanalysis for Australia. Our research demonstrates that it is able to characterize a rich spatial variation in daily precipitation behaviour. In addition, its ability to represent large rainfalls is valuable for the analysis of extremes. It is a useful complement to existing precipitation datasets for Australia, especially in sparsely gauged regions.
Mike Bush, David L. A. Flack, Huw W. Lewis, Sylvia I. Bohnenstengel, Chris J. Short, Charmaine Franklin, Adrian P. Lock, Martin Best, Paul Field, Anne McCabe, Kwinten Van Weverberg, Segolene Berthou, Ian Boutle, Jennifer K. Brooke, Seb Cole, Shaun Cooper, Gareth Dow, John Edwards, Anke Finnenkoetter, Kalli Furtado, Kate Halladay, Kirsty Hanley, Margaret A. Hendry, Adrian Hill, Aravindakshan Jayakumar, Richard W. Jones, Humphrey Lean, Joshua C. K. Lee, Andy Malcolm, Marion Mittermaier, Saji Mohandas, Stuart Moore, Cyril Morcrette, Rachel North, Aurore Porson, Susan Rennie, Nigel Roberts, Belinda Roux, Claudio Sanchez, Chun-Hsu Su, Simon Tucker, Simon Vosper, David Walters, James Warner, Stuart Webster, Mark Weeks, Jonathan Wilkinson, Michael Whitall, Keith D. Williams, and Hugh Zhang
Geosci. Model Dev., 18, 3819–3855, https://doi.org/10.5194/gmd-18-3819-2025, https://doi.org/10.5194/gmd-18-3819-2025, 2025
Short summary
Short summary
RAL configurations define settings for the Unified Model atmosphere and Joint UK Land Environment Simulator. The third version of the Regional Atmosphere and Land (RAL3) science configuration for kilometre- and sub-kilometre-scale modelling represents a major advance compared to previous versions (RAL2) by delivering a common science definition for applications in tropical and mid-latitude regions. RAL3 has more realistic precipitation distributions and an improved representation of clouds and visibility.
Christopher J. White, Mohammed Sarfaraz Gani Adnan, Marcello Arosio, Stephanie Buller, YoungHwa Cha, Roxana Ciurean, Julia M. Crummy, Melanie Duncan, Joel Gill, Claire Kennedy, Elisa Nobile, Lara Smale, and Philip J. Ward
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2024-178, https://doi.org/10.5194/nhess-2024-178, 2024
Revised manuscript under review for NHESS
Short summary
Short summary
Indicators contain observable and measurable characteristics to understand the state of a concept or phenomenon and/or monitor it over time. There have been limited efforts to understand how indicators are being used in multi-hazard and multi-risk contexts. We find most of existing indicators do not include the interactions between hazards or risks. We propose 12 recommendations to enable the development and uptake of multi-hazard and multi-risk indicators.
Lou Brett, Christopher J. White, Daniela I.V. Domeisen, Bart van den Hurk, Philip Ward, and Jakob Zscheischler
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2024-182, https://doi.org/10.5194/nhess-2024-182, 2024
Revised manuscript accepted for NHESS
Short summary
Short summary
Compound events, where multiple weather or climate hazards occur together, pose significant risks to both society and the environment. These events, like simultaneous wind and rain, can have more severe impacts than single hazards. Our review of compound event research from 2012–2022 reveals a rise in studies, especially on events that occur concurrently, hot and dry events and compounding flooding. The review also highlights opportunities for research in the coming years.
Conrad Wasko, Seth Westra, Rory Nathan, Acacia Pepler, Timothy H. Raupach, Andrew Dowdy, Fiona Johnson, Michelle Ho, Kathleen L. McInnes, Doerte Jakob, Jason Evans, Gabriele Villarini, and Hayley J. Fowler
Hydrol. Earth Syst. Sci., 28, 1251–1285, https://doi.org/10.5194/hess-28-1251-2024, https://doi.org/10.5194/hess-28-1251-2024, 2024
Short summary
Short summary
In response to flood risk, design flood estimation is a cornerstone of infrastructure design and emergency response planning, but design flood estimation guidance under climate change is still in its infancy. We perform the first published systematic review of the impact of climate change on design flood estimation and conduct a meta-analysis to provide quantitative estimates of possible future changes in extreme rainfall.
Emma Howard, Chun-Hsu Su, Christian Stassen, Rajashree Naha, Harvey Ye, Acacia Pepler, Samuel S. Bell, Andrew J. Dowdy, Simon O. Tucker, and Charmaine Franklin
Geosci. Model Dev., 17, 731–757, https://doi.org/10.5194/gmd-17-731-2024, https://doi.org/10.5194/gmd-17-731-2024, 2024
Short summary
Short summary
The BARPA-R modelling configuration has been developed to produce high-resolution climate hazard projections within the Australian region. When using boundary driving data from quasi-observed historical conditions, BARPA-R shows good performance with errors generally on par with reanalysis products. BARPA-R also captures trends, known modes of climate variability, large-scale weather processes, and multivariate relationships.
Ivana Čavlina Tomašević, Paul Fox-Hughes, Kevin Cheung, Višnjica Vučetić, Jon Marsden-Smedley, Paul Beggs, and Maja Telišman Prtenjak
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2023-210, https://doi.org/10.5194/nhess-2023-210, 2024
Manuscript not accepted for further review
Short summary
Short summary
We have analyzed a severe wildfire event in Tasmania, Australia that also developed thunderstorm clouds. The drivers of this compound hazard were highly complex, which included climatic factors (above normal heavy rain seasons followed by heatwave), weather systems (fronts and high winds) to heighten fire severity and unstable atmosphere to develop thunderstorm clouds, all in coincidence. Such event has demonstrated the difficulty to assess wildfire risk in a warming climate.
Mike Bush, Ian Boutle, John Edwards, Anke Finnenkoetter, Charmaine Franklin, Kirsty Hanley, Aravindakshan Jayakumar, Huw Lewis, Adrian Lock, Marion Mittermaier, Saji Mohandas, Rachel North, Aurore Porson, Belinda Roux, Stuart Webster, and Mark Weeks
Geosci. Model Dev., 16, 1713–1734, https://doi.org/10.5194/gmd-16-1713-2023, https://doi.org/10.5194/gmd-16-1713-2023, 2023
Short summary
Short summary
Building on the baseline of RAL1, the RAL2 science configuration is used for regional modelling around the UM partnership and in operations at the Met Office. RAL2 has been tested in different parts of the world including Australia, India and the UK. RAL2 increases medium and low cloud amounts in the mid-latitudes compared to RAL1, leading to improved cloud forecasts and a reduced diurnal cycle of screen temperature. There is also a reduction in the frequency of heavier precipitation rates.
Ivana Čavlina Tomašević, Kevin K. W. Cheung, Višnjica Vučetić, Paul Fox-Hughes, Kristian Horvath, Maja Telišman Prtenjak, Paul J. Beggs, Barbara Malečić, and Velimir Milić
Nat. Hazards Earth Syst. Sci., 22, 3143–3165, https://doi.org/10.5194/nhess-22-3143-2022, https://doi.org/10.5194/nhess-22-3143-2022, 2022
Short summary
Short summary
One of the most severe and impactful urban wildfire events in Croatian history has been reconstructed and analyzed. The study identified some important meteorological influences related to the event: the synoptic conditions of the Azores anticyclone, cold front, and upper-level shortwave trough all led to the highest fire weather index in 2017. A low-level jet, locally known as bura wind that can be explained by hydraulic jump theory, was the dynamic trigger of the event.
Enrico Tubaldi, Christopher J. White, Edoardo Patelli, Stergios Aristoteles Mitoulis, Gustavo de Almeida, Jim Brown, Michael Cranston, Martin Hardman, Eftychia Koursari, Rob Lamb, Hazel McDonald, Richard Mathews, Richard Newell, Alonso Pizarro, Marta Roca, and Daniele Zonta
Nat. Hazards Earth Syst. Sci., 22, 795–812, https://doi.org/10.5194/nhess-22-795-2022, https://doi.org/10.5194/nhess-22-795-2022, 2022
Short summary
Short summary
Bridges are critical infrastructure components of transport networks. A large number of these critical assets cross or are adjacent to waterways and are therefore exposed to the potentially devastating impact of floods. This paper discusses a series of issues and areas where improvements in research and practice are required in the context of risk assessment and management of bridges exposed to flood hazard, with the ultimate goal of guiding future efforts in improving bridge flood resilience.
Chun-Hsu Su, Nathan Eizenberg, Dörte Jakob, Paul Fox-Hughes, Peter Steinle, Christopher J. White, and Charmaine Franklin
Geosci. Model Dev., 14, 4357–4378, https://doi.org/10.5194/gmd-14-4357-2021, https://doi.org/10.5194/gmd-14-4357-2021, 2021
Short summary
Short summary
The Bureau of Meteorology Atmospheric Regional Reanalysis for Australia (BARRA) has produced a very high-resolution reconstruction of Australian historical weather from 1990 to 2018. This paper demonstrates the added weather and climate information to supplement coarse- or moderate-resolution regional and global reanalyses. The new climate data can allow greater understanding of past weather, including extreme events, at very local kilometre scales.
Suwash Chandra Acharya, Rory Nathan, Quan J. Wang, Chun-Hsu Su, and Nathan Eizenberg
Hydrol. Earth Syst. Sci., 24, 2951–2962, https://doi.org/10.5194/hess-24-2951-2020, https://doi.org/10.5194/hess-24-2951-2020, 2020
Short summary
Short summary
BARRA is a high-resolution reanalysis dataset over the Oceania region. This study evaluates the performance of sub-daily BARRA precipitation at point and spatial scales over Australia. We find that the dataset reproduces some of the sub-daily characteristics of precipitation well, although it exhibits some spatial displacement errors, and it performs better in temperate than in tropical regions. The product is well suited to complement other estimates derived from remote sensing and rain gauges.
Mercy N. Ndalila, Grant J. Williamson, Paul Fox-Hughes, Jason Sharples, and David M. J. S. Bowman
Nat. Hazards Earth Syst. Sci., 20, 1497–1511, https://doi.org/10.5194/nhess-20-1497-2020, https://doi.org/10.5194/nhess-20-1497-2020, 2020
Short summary
Short summary
We analyse the evolution of a pyrocumulonimbus (pyroCb), or fire-induced thunderstorm, during the Forcett–Dunalley fire on 4 January 2013 and relate it to the prevailing fire weather and fire severity patterns. We show that the pyroCb reached an altitude of 15 km, was associated with elevated fire weather, and formed over a severely burned area. Additionally, we show that eastern Tasmania is prone to elevated fire weather which has implications for fire weather forecasting and fire management.
Mike Bush, Tom Allen, Caroline Bain, Ian Boutle, John Edwards, Anke Finnenkoetter, Charmaine Franklin, Kirsty Hanley, Humphrey Lean, Adrian Lock, James Manners, Marion Mittermaier, Cyril Morcrette, Rachel North, Jon Petch, Chris Short, Simon Vosper, David Walters, Stuart Webster, Mark Weeks, Jonathan Wilkinson, Nigel Wood, and Mohamed Zerroukat
Geosci. Model Dev., 13, 1999–2029, https://doi.org/10.5194/gmd-13-1999-2020, https://doi.org/10.5194/gmd-13-1999-2020, 2020
Short summary
Short summary
In this paper we define the first Regional Atmosphere and Land (RAL) science configuration for kilometre-scale modelling using the Unified Model (UM) as the basis for the atmosphere and the Joint UK Land Environment Simulator (JULES) for the land. RAL1 defines the science configuration of the dynamics and physics schemes of the atmosphere and land. This configuration will provide a model baseline for any future weather or climate model developments to be described against.
Suwash Chandra Acharya, Rory Nathan, Quan J. Wang, Chun-Hsu Su, and Nathan Eizenberg
Hydrol. Earth Syst. Sci., 23, 3387–3403, https://doi.org/10.5194/hess-23-3387-2019, https://doi.org/10.5194/hess-23-3387-2019, 2019
Short summary
Short summary
BARRA is a novel regional reanalysis for Australia. Our research demonstrates that it is able to characterize a rich spatial variation in daily precipitation behaviour. In addition, its ability to represent large rainfalls is valuable for the analysis of extremes. It is a useful complement to existing precipitation datasets for Australia, especially in sparsely gauged regions.
Simon F. B. Tett, Kuniko Yamazaki, Michael J. Mineter, Coralia Cartis, and Nathan Eizenberg
Geosci. Model Dev., 10, 3567–3589, https://doi.org/10.5194/gmd-10-3567-2017, https://doi.org/10.5194/gmd-10-3567-2017, 2017
Short summary
Short summary
The paper shows it is possible to automatically calibrate the parameters in the atmospheric component of two climate models. The resulting atmosphere–ocean models are often, but not always, stable and realistic. The computational cost to do this is feasible. The implications are that it is possible to generate multiple configurations of a single model with different parameter values but which all look similar to the standard model and that the techniques could be used to calibrate other models.
C. J. White, S. W. Franks, and D. McEvoy
Proc. IAHS, 370, 229–234, https://doi.org/10.5194/piahs-370-229-2015, https://doi.org/10.5194/piahs-370-229-2015, 2015
M. Newby, S. W. Franks, and C. J. White
Proc. IAHS, 370, 3–7, https://doi.org/10.5194/piahs-370-3-2015, https://doi.org/10.5194/piahs-370-3-2015, 2015
S. W. Franks, C. J. White, and M. Gensen
Proc. IAHS, 369, 31–36, https://doi.org/10.5194/piahs-369-31-2015, https://doi.org/10.5194/piahs-369-31-2015, 2015
Related subject area
Climate and Earth system modeling
Process-based modeling framework for sustainable irrigation management at the regional scale: integrating rice production, water use, and greenhouse gas emissions
Implementing deep soil and dynamic root uptake in Noah-MP (v4.5): impact on Amazon dry-season transpiration
Reducing time and computing costs in EC-Earth: an automatic load-balancing approach for coupled Earth system models
FLAME 1.0: a novel approach for modelling burned area in the Brazilian biomes using the maximum entropy concept
SURFER v3.0: a fast model with ice sheet tipping points and carbon cycle feedbacks for short- and long-term climate scenarios
NMH-CS 3.0: a C# programming language and Windows-system-based ecohydrological model derived from Noah-MP
A method for quantifying uncertainty in spatially interpolated meteorological data with application to daily maximum air temperature
Baseline Climate Variables for Earth System Modelling
PaleoSTeHM v1.0: a modern, scalable spatiotemporal hierarchical modeling framework for paleo-environmental data
The Tropical Basin Interaction Model Intercomparison Project (TBIMIP)
ZEMBA v1.0: an energy and moisture balance climate model to investigate Quaternary climate
Development and evaluation of a new 4DEnVar-based weakly coupled ocean data assimilation system in E3SMv2
TemDeep: a self-supervised framework for temporal downscaling of atmospheric fields at arbitrary time resolutions
The ensemble consistency test: from CESM to MPAS and beyond
Presentation, calibration and testing of the DCESS II Earth system model of intermediate complexity (version 1.0)
Synthesizing global carbon–nitrogen coupling effects – the MAGICC coupled carbon–nitrogen cycle model v1.0
Historical trends and controlling factors of isoprene emissions in CMIP6 Earth system models
Investigating carbon and nitrogen conservation in reported CMIP6 Earth system model data
From weather data to river runoff: using spatiotemporal convolutional networks for discharge forecasting
A Fortran–Python interface for integrating machine learning parameterization into earth system models
ROCKE-3D 2.0: An updated general circulation model for simulating the climates of rocky planets
A rapid-application emissions-to-impacts tool for scenario assessment: Probabilistic Regional Impacts from Model patterns and Emissions (PRIME)
The DOE E3SM version 2.1: overview and assessment of the impacts of parameterized ocean submesoscales
WRF-ELM v1.0: a regional climate model to study land–atmosphere interactions over heterogeneous land use regions
Modeling commercial-scale CO2 storage in the gas hydrate stability zone with PFLOTRAN v6.0
DiuSST: a conceptual model of diurnal warm layers for idealized atmospheric simulations with interactive sea surface temperature
High-Resolution Model Intercomparison Project phase 2 (HighResMIP2) towards CMIP7
T&C-CROP: representing mechanistic crop growth with a terrestrial biosphere model (T&C, v1.5) – model formulation and validation
An updated non-intrusive, multi-scale, and flexible coupling interface in WRF 4.6.0
Monitoring and benchmarking Earth system model simulations with ESMValTool v2.12.0
The Earth Science Box Modeling Toolkit (ESBMTK 0.14.0.11): a Python library for research and teaching
CropSuite v1.0 – a comprehensive open-source crop suitability model considering climate variability for climate impact assessment
ICON ComIn – the ICON Community Interface (ComIn version 0.1.0, with ICON version 2024.01-01)
Using feature importance as an exploratory data analysis tool on Earth system models
A new metrics framework for quantifying and intercomparing atmospheric rivers in observations, reanalyses, and climate models
The real challenges for climate and weather modelling on its way to sustained exascale performance: a case study using ICON (v2.6.6)
COSP-RTTOV-1.0: Flexible radiation diagnostics to enable new science applications in model evaluation, climate change detection, and satellite mission design
Impact of spatial resolution on CMIP6-driven Mediterranean climate simulations: a focus on precipitation distribution over Italy
Improving the representation of major Indian crops in the Community Land Model version 5.0 (CLM5) using site-scale crop data
Evaluation of CORDEX ERA5-forced NARCliM2.0 regional climate models over Australia using the Weather Research and Forecasting (WRF) model version 4.1.2
Design, evaluation, and future projections of the NARCliM2.0 CORDEX-CMIP6 Australasia regional climate ensemble
The Detection and Attribution Model Intercomparison Project (DAMIP v2.0) contribution to CMIP7
Statistical summaries for streamed data from climate simulations: One-pass algorithms (v0.6.2)
Amending the algorithm of aerosol–radiation interactions in WRF-Chem (v4.4)
The very-high-resolution configuration of the EC-Earth global model for HighResMIP
GOSI9: UK Global Ocean and Sea Ice configurations
FACA v1 – Fully Automated Co-Alignment of UAV Point Clouds
Decomposition of skill scores for conditional verification: impact of Atlantic Multidecadal Oscillation phases on the predictability of decadal temperature forecasts
Virtual Integration of Satellite and In-situ Observation Networks (VISION) v1.0: In-Situ Observations Simulator (ISO_simulator)
Climate model downscaling in central Asia: a dynamical and a neural network approach
Yan Bo, Hao Liang, Tao Li, and Feng Zhou
Geosci. Model Dev., 18, 3799–3817, https://doi.org/10.5194/gmd-18-3799-2025, https://doi.org/10.5194/gmd-18-3799-2025, 2025
Short summary
Short summary
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 vital 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 multi-objective optimization of rice irrigation schemes at a large scale.
Carolina A. Bieri, Francina Dominguez, Gonzalo Miguez-Macho, and Ying Fan
Geosci. Model Dev., 18, 3755–3779, https://doi.org/10.5194/gmd-18-3755-2025, https://doi.org/10.5194/gmd-18-3755-2025, 2025
Short summary
Short summary
Access to deep moisture below the Earth's surface is important for vegetation in areas of the Amazon where there is little precipitation for part of the year. Most existing numerical models of the Earth system do not adequately capture where and when deep root water uptake occurs. We address this by adding deep soil layers and a root water uptake feature to an existing model. Out modifications lead to increased dry-month transpiration and improved simulation of the annual transpiration cycle.
Sergi Palomas, Mario C. Acosta, Gladys Utrera, and Etienne Tourigny
Geosci. Model Dev., 18, 3661–3679, https://doi.org/10.5194/gmd-18-3661-2025, https://doi.org/10.5194/gmd-18-3661-2025, 2025
Short summary
Short summary
We present an automatic tool that optimizes resource distribution in coupled climate models, enhancing speed and reducing computational costs without requiring expert knowledge. Users can set energy/time criteria or limit resource usage. Tested on various European Community Earth System Model (EC-Earth) configurations and high-performance computing (HPC) platforms, it achieved up to 34 % faster simulations with fewer resources.
Maria Lucia Ferreira Barbosa, Douglas I. Kelley, Chantelle A. Burton, Igor J. M. Ferreira, Renata Moura da Veiga, Anna Bradley, Paulo Guilherme Molin, and Liana O. Anderson
Geosci. Model Dev., 18, 3533–3557, https://doi.org/10.5194/gmd-18-3533-2025, https://doi.org/10.5194/gmd-18-3533-2025, 2025
Short summary
Short summary
As fire seasons in Brazil become increasingly severe, confidently understanding the factors driving fires is more critical than ever. To address this challenge, we developed FLAME (Fire Landscape Analysis using Maximum Entropy), a new model designed to predict fires and to analyse the spatial influence of both environmental and human factors while accounting for uncertainties. By adapting the model to different regions, we can enhance fire management strategies, making FLAME a powerful tool for protecting landscapes in Brazil and beyond.
Victor Couplet, Marina Martínez Montero, and Michel Crucifix
Geosci. Model Dev., 18, 3081–3129, https://doi.org/10.5194/gmd-18-3081-2025, https://doi.org/10.5194/gmd-18-3081-2025, 2025
Short summary
Short summary
We present SURFER v3.0, a simple climate model designed to estimate the impact of CO2 and CH4 emissions on global temperatures, sea levels, and ocean pH. We added new carbon cycle processes and calibrated the model to observations and results from more complex models, enabling use over timescales ranging from decades to millions of years. SURFER v3.0 is fast, transparent, and easy to use, making it an ideal tool for policy assessments and suitable for educational purposes.
Yong-He Liu and Zong-Liang Yang
Geosci. Model Dev., 18, 3157–3174, https://doi.org/10.5194/gmd-18-3157-2025, https://doi.org/10.5194/gmd-18-3157-2025, 2025
Short summary
Short summary
NMH-CS 3.0 is a C#-based ecohydrological model reconstructed from the WRF-Hydro/Noah-MP model by translating the Fortran code of WRF-Hydro 3.0 and integrating a parallel river routing module. It enables efficient execution on multi-core personal computers. Simulations in the Yellow River basin demonstrate its consistency with WRF-Hydro outputs, providing a reliable alternative to the original Noah-MP model.
Conor T. Doherty, Weile Wang, Hirofumi Hashimoto, and Ian G. Brosnan
Geosci. Model Dev., 18, 3003–3016, https://doi.org/10.5194/gmd-18-3003-2025, https://doi.org/10.5194/gmd-18-3003-2025, 2025
Short summary
Short summary
We present, analyze, and validate a methodology for quantifying uncertainty in gridded meteorological data products produced by spatial interpolation. In a validation case study using daily maximum near-surface air temperature (Tmax), the method works well and produces predictive distributions with closely matching theoretical versus actual coverage levels. Application of the method reveals that the magnitude of uncertainty in interpolated Tmax varies significantly in both space and time.
Martin Juckes, Karl E. Taylor, Fabrizio Antonio, David Brayshaw, Carlo Buontempo, Jian Cao, Paul J. Durack, Michio Kawamiya, Hyungjun Kim, Tomas Lovato, Chloe Mackallah, Matthew Mizielinski, Alessandra Nuzzo, Martina Stockhause, Daniele Visioni, Jeremy Walton, Briony Turner, Eleanor O'Rourke, and Beth Dingley
Geosci. Model Dev., 18, 2639–2663, https://doi.org/10.5194/gmd-18-2639-2025, https://doi.org/10.5194/gmd-18-2639-2025, 2025
Short summary
Short summary
The Baseline Climate Variables for Earth System Modelling (ESM-BCVs) are defined as a list of 135 variables which have high utility for the evaluation and exploitation of climate simulations. The list reflects the most frequently used variables from Earth system models based on an assessment of data publication and download records from the largest archive of global climate projects.
Yucheng Lin, Robert E. Kopp, Alexander Reedy, Matteo Turilli, Shantenu Jha, and Erica L. Ashe
Geosci. Model Dev., 18, 2609–2637, https://doi.org/10.5194/gmd-18-2609-2025, https://doi.org/10.5194/gmd-18-2609-2025, 2025
Short summary
Short summary
PaleoSTeHM v1.0 is a state-of-the-art framework designed to reconstruct past environmental conditions using geological data. Built on modern machine learning techniques, it efficiently handles the sparse and noisy nature of paleo-records, allowing scientists to make accurate and scalable inferences about past environmental change. By using flexible statistical models, PaleoSTeHM separates different sources of uncertainty, improving the precision of historical climate reconstructions.
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
Short summary
Short summary
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.
Daniel F. J. Gunning, Kerim H. Nisancioglu, Emilie Capron, and Roderik S. W. van de Wal
Geosci. Model Dev., 18, 2479–2508, https://doi.org/10.5194/gmd-18-2479-2025, https://doi.org/10.5194/gmd-18-2479-2025, 2025
Short summary
Short summary
This work documents the first results from ZEMBA: an energy balance model of the climate system. The model is a computationally efficient tool designed to study the response of climate to changes in the Earth's orbit. We demonstrate that ZEMBA reproduces many features of the Earth's climate for both the pre-industrial period and the Earth's most recent cold extreme – the Last Glacial Maximum. We intend to develop ZEMBA further and investigate the glacial cycles of the last 2.5 million years.
Pengfei Shi, L. Ruby Leung, and Bin Wang
Geosci. Model Dev., 18, 2443–2460, https://doi.org/10.5194/gmd-18-2443-2025, https://doi.org/10.5194/gmd-18-2443-2025, 2025
Short summary
Short summary
Improving climate predictions has significant socio-economic impacts. In this study, we develop and apply a new weakly coupled ocean data assimilation (WCODA) system to a coupled climate model. The WCODA system improves simulations of ocean temperature and salinity across many global regions. This system is meant to advance our understanding of the ocean's role in climate predictability.
Liwen Wang, Qian Li, Qi Lv, Xuan Peng, and Wei You
Geosci. Model Dev., 18, 2427–2442, https://doi.org/10.5194/gmd-18-2427-2025, https://doi.org/10.5194/gmd-18-2427-2025, 2025
Short summary
Short summary
Our research presents a novel deep learning approach called "TemDeep" for downscaling atmospheric variables at arbitrary time resolutions based on temporal coherence. Results show that our method can accurately recover evolution details superior to other methods, reaching 53.7 % in the restoration rate. Our findings are important for advancing weather forecasting models and enabling more precise and reliable predictions to support disaster preparedness, agriculture, and sustainable development.
Teo Price-Broncucia, Allison Baker, Dorit Hammerling, Michael Duda, and Rebecca Morrison
Geosci. Model Dev., 18, 2349–2372, https://doi.org/10.5194/gmd-18-2349-2025, https://doi.org/10.5194/gmd-18-2349-2025, 2025
Short summary
Short summary
The ensemble consistency test (ECT) and its ultrafast variant (UF-ECT) have become powerful tools in the development community for the identification of unwanted changes in the Community Earth System Model (CESM). We develop a generalized setup framework to enable easy adoption of the ECT approach for other model developers and communities. This framework specifies test parameters to accurately characterize model variability and balance test sensitivity and computational cost.
Esteban Fernández Villanueva and Gary Shaffer
Geosci. Model Dev., 18, 2161–2192, https://doi.org/10.5194/gmd-18-2161-2025, https://doi.org/10.5194/gmd-18-2161-2025, 2025
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Kostas Tsigaridis, Andrew S. Ackerman, Igor Aleinov, Mark A. Chandler, Thomas L. Clune, Christopher M. Colose, Anthony D. Del Genio, Maxwell Kelley, Nancy Y. Kiang, Anthony Leboissetier, Jan P. Perlwitz, Reto A. Ruedy, Gary L. Russell, Linda E. Sohl, Michael J. Way, and Eric T. Wolf
EGUsphere, https://doi.org/10.5194/egusphere-2025-925, https://doi.org/10.5194/egusphere-2025-925, 2025
Short summary
Short summary
We present the second generation of ROCKE-3D, a generalized 3-dimensional model for use in Solar System and exoplanetary simulations of rocky planet climates. We quantify how the different component choices affect model results, and discuss strengths and limitations of using each component, together with how one can select which component to use. ROCKE-3D is publicly available and tutorial sessions are available for the community, greatly facilitating its use by any interested group.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Jonah K. Shaw, Dustin J. Swales, Sergio DeSouza-Machado, David D. Turner, Jennifer E. Kay, and David P. Schneider
EGUsphere, https://doi.org/10.5194/egusphere-2025-169, https://doi.org/10.5194/egusphere-2025-169, 2025
Short summary
Short summary
Satellites have observed earth's emission of infrared radiation since the 1970s. Because infrared wavelengths interact with the atmosphere in distinct ways, these observations contain information about the earth and atmosphere. We present a tool that runs alongside global climate models and produces output that can be directly compared with satellite measurements of infrared radiation. We then use this tool for climate model evaluation, climate change detection, and satellite mission design.
Maria Vittoria Struglia, Alessandro Anav, Marta Antonelli, Sandro Calmanti, Franco Catalano, Alessandro Dell'Aquila, Emanuela Pichelli, and Giovanna Pisacane
EGUsphere, https://doi.org/10.5194/egusphere-2025-387, https://doi.org/10.5194/egusphere-2025-387, 2025
Short summary
Short summary
We present the results of downscaling global climate projections for the Mediterranean and Italian regions aiming to produce high-resolution climate information for the assessment of climate change signals, focusing on extreme events. A general warming is foreseen by the end of century with a mean precipitation reduction accompanied, over Italian Peninsula, by a strong increase in the intensity of extreme precipitation events, particularly relevant for the high emissions scenario during autumn
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
Short summary
Short summary
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, 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
Short summary
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.
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.
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
Short summary
Short summary
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.
Katherine Grayson, Stephan Thober, Aleksander Lacima-Nadolnik, Ehsan Sharifi, Llorenç Lledó, and Francisco Doblas-Reyes
EGUsphere, https://doi.org/10.5194/egusphere-2025-28, https://doi.org/10.5194/egusphere-2025-28, 2025
Short summary
Short summary
To provide the most accurate climate adaptation information, climate models are being run with finer grid resolution, resulting in larger data output. This paper presents intelligent data reduction algorithms that act on streamed data, a novel way of processing climate data as soon as it is produced. Using these algorithms to calculate statistics, we show that the accuracy provided is well within acceptable bounds while still providing memory savings that bypass unfeasible storage requirements.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Nick Schüßler, Jewgenij Torizin, Claudia Gunkel, Karsten Schütze, Lars Tiepolt, Dirk Kuhn, Michael Fuchs, and Steffen Prüfer
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-209, https://doi.org/10.5194/gmd-2024-209, 2025
Revised manuscript accepted for GMD
Short summary
Short summary
FACA – Fully Automated Co-Alignment – is a tool designed to generate co-aligned point clouds. We aim to accelerate the application of the co-alignment method and achieve fast results with evolving temporal data and minimal site-specific preparation. FACA offers multiple ways to interact with the workflow, enabling new users to quickly generate initial results through the custom interface, as well as integration into larger automated workflows via the command line. Test datasets are provided.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Cited articles
Acharya, S. C., Nathan, R., Wang, Q. J., Su, C.-H., and Eizenberg, N.: An
evaluation of daily precipitation from atmospheric reanalyses over Australia,
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2018-607, in
review, 2019.
Arakawa, A. and Lamb, V. R.: Computational design of the basic dynamical
processes of the UCLA general circulation model, Methods of Comp. Phys.:
Adv. Res. Appl., 17, 173–265, https://doi.org/10.1016/B978-0-12-460817-7.50009-4,
1977.
Barros, A. P., Chiao, S., Lang, T. J., Burbank, D., and Putkonen, J.: From
weather to climate – Seasonal and interannual variability of storms and
implications for erosion process in the Himalaya, Geol. Soc.
Am. Spat. Paper 398, Penrose Conference Series, 17–38, 2006.
Becker, A., Finger, P., Meyer-Christoffer, A., Rudolf, B., Schamm, K.,
Schneider, U., and Ziese, M.: A description of the global land-surface
precipitation data products of the Global Precipitation Climatology Centre
with sample applications including centennial (trend) analysis from
1901–present, Earth Syst. Sci. Data, 5, 71–99,
https://doi.org/10.5194/essd-5-71-2013, 2013.
Behrangi, A., Stephens, G., Adler, R. F., Huffman, G. J., Lambrigtsen, B., and
Lebsock, M.: An update on the oceanic precipitation rate and its zonal
distribution in light of advanced observations from space, J. Climate, 27,
3957–3965, https://doi.org/10.1175/JCLI-D-13-00679.1, 2014.
Berg, W., L'Ecuyer, T., and Haynes, J. M.: The distribution of rainfall over
oceans from spaceborne radars, J. Appl. Meteor. Climatol., 49, 535–543,
https://doi.org/10.1175/2009JAMC2330.1, 2010.
Berg, P., Feldmann, H., and Panitz, H.-J.: Bias correction of high
resolution regional climate model data, J. Hydrol., 448–449, 80–92, https://doi.org/10.1016/j.jhydrol.2012.04.026, 2012.
Best, M. J., Pryor, M., Clark, D. B., Rooney, G. G., Essery, R. L. H.,
Ménard, C. B., Edwards, J. M., Hendry, M. A., Porson, A., Gedney, N.,
Mercado, L. M., Sitch, S., Blyth, E., Boucher, O., Cox, P. M., Grimmond, C.
S. B., and Harding, R. J.: The Joint UK Land Environment Simulator (JULES),
model description – Part 1: Energy and water fluxes, Geosci. Model Dev., 4,
677–699, https://doi.org/10.5194/gmd-4-677-2011, 2011.
Bollmeyer, C., Keller, J. D., Ohlwein, C., Wahl, S., Crewell, S.,
Friederichs, P., Hense, A., Keune, J., Kneifel, S., Pscheidt, I., Redl,
S., and Steinke, S.: Towards a high-resolution regional reanalysis for the
European CORDEX domain, Q. J. Roy. Meteorol. Soc., 141, 1–15, https://doi.org/10.1002/qj.2486, 2015.
Borsche, M., Kaiser-Weiss, A. K., Unden, P., and Kaspar, F.: Methodologies
to characterize uncertainties in regional reanalyses, Adv. Sci. Res., 12,
207–218, https://doi.org/10.5194/asr-12-207-2015, 2015.
Borsche, M., Kaiser-Weiss, A. K., and Kaspar, F.: Wind speed variability
between 10 and 116 m height from the regional reanalysis COSMO-REA6 compared
to wind mast measurements over Northern Germany and the Netherlands, Adv.
Sci. Res., 13, 151–161, https://doi.org/10.5194/asr-13-151-2016, 2016.
Bromwich, D., Wilson, A., Bai, L., Liu, Z., Barlage, M., Shih, C., Maldonado, S.,
Hines, K., Wang, S.-H., Woollen, J., Kuo, B., Lin, H., Wee, T., Serreze, M., and
Walsh, J.: The Arctic System Reanalysis Version 2, B. Am. Meteorol. Soc., 99,
805–828, https://doi.org/10.1175/BAMS-D-16-0215.1, 2018.
Brown, A., Milton, S., Cullen, M., Golding, B., Mitchell, J., and Shelly, A.:
Unified modeling and prediction of weather and climate: A 25-Year journey,
B. Am. Meteorol. Soc., 93, 1865–1877, https://doi.org/10.1175/BAMS-D-12-00018.1,
2012.
Bureau of Meteorology: Operational implementation of the ACCESS numerical
weather prediction systems, NMOC Op. Bull. No. 83, available at:
http://www.bom.gov.au/australia/charts/bulletins/apob83.pdf
(last access: 17 May 2019), 2010.
Bureau of Meteorology: APS1 upgrade of the ACCESS-R numerical weather
prediction system, NMOC Op. Bull. No. 98, available at:
http://www.bom.gov.au/australia/charts/bulletins/apob98.pdf
(last access: 17 May 2019), 2013.
Bureau of Meteorology: APS2 upgrade to the ACCESS-G numerical weather
prediction system, BNOC Op. Bull. No. 105, available at:
http://www.bom.gov.au/australia/charts/bulletins/APOB105.pdf
(last access: 17 May 2019), 2016.
Bureau of Meteorology: Atmospheric high-resolution regional reanalysis for
Australia, available at:
http://www.bom.gov.au/research/projects/reanalysis,
last access: 23 May 2019.
Bush, M., Allen, T., Bain, C., Boutle, I., Edwards, J., Finnenkoetter, A.,
Franklin, F., Hanley, K., Lean, H., Lock, A., Manners, J., Mittermaier, M.,
Morcrette, C., North, R., Petch, J., Short, C., Vosper, S., Walters, D.,
Webster, S., Weeks, M., Wilkinson, J., Wood, N., and Zerroukat, M.: The
Met Office Unified Model/JULES Regional Atmosphere and Land configurations
(RAL) – 1st release, submitted, 2019
Carvalho, D., Rocha, A., Gomez-Gesteira, M., and Santos, C. S.: WRF wind
simulation and wind energy production estimates forced by different
reanalyses: comparison with observed data for Portugal, Appl. Energy, 117,
116–126, https://doi.org/10.1016/j.apenergy.2013.12.001, 2014.
Chan, S. C., Kendon, E. J., Fowler, H. J., Blenkinsop, S., Roberts, N. M., and
Ferro, C. A.: The value of high-resolution Met Office regional climate models in
the simulation of multihourly precipitation extremes, J. Climate, 27,
6155–6174, https://doi.org/10.1175/JCLI-D-13-00723.1, 2014.
Charney, J. G. and Phillips, N. A.: Numerical integration of the
quasi-geostrophic equations for barotropic and simple baroclinic flows, J.
Meteorol., 10, 71–99, https://doi.org/10.1175/1520-0469(1953)010<0071:NIOTQG>2.0.CO;2, 1953.
Chen, Y., Ebert, E. E., Walsh, K.
J. E., and Davidson, N. E.: Evaluation of TRMM 3B42 precipitation
estimates of tropical cyclone rainfall using PACRAIN data, J. Geophys. Res.-Atmos.,
118, 2184–2196, https://doi.org/10.1002/jgrd.50250, 2013.
Clark, P., Roberts, N., Lean, H., Ballard, S. P., and Charlton-Perez, C.:
Review: Convection-permitting models: a step-change in rainfall forecasting,
Meteor. App., 23, 165–181, https://doi.org/10.1002/met.1538, 2016.
CliFlo: NIWA's National Climate Database on the Web,
available at: http://cliflo.niwa.co.nz last access: 17 February 2017.
Davies, T., Cullen, M. J. P., Malcolm, A. J., Mawson, M. H., Staniforth, A.,
White, A. A., and Wood, N.: A new dynamical core for the Met Office's global
and regional modelling of the atmosphere, Q. J. Roy. Meteorol. Soc., 131,
1759–1782, https://doi.org/10.1256/qj.04.101, 2005.
Dee, D. P. and Uppala, S.: Variational bias correction of satellite radiance
data in the ERA-Interim reanalysis, Q. J. Roy. Meteorol. Soc., 135, 1830–1841,
https://doi.org/10.1002/qj.493, 2009.
Dee, D. P., Källén, E., Simmons, A. J., and
Haimberger, L.: Comments on “Reanalyses suitable for characterizing
long-term trends.”, B. Am. Meteorol. Soc., 92, 65–70,
https://doi.org/10.1175/2010BAMS3070.1, 2011.
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., Holm, E. V., Isaksen, L., Kallberg, P., Kohler, M.,
Matricardi, M., McNally, A. P., Monge-Sanz, B. M., Morcrette, J. J., Park,
B. K., Peubey, C., de Rosnay, P., Tavolato, C., Thepaut, J. N., and Vitart, F.:
The Era-Interim reanalysis: Configuration and performance of the data
assimilation system, Q. J. Roy. Meteorol. Soc., 137, 553–597, https://doi.org/10.1002/qj.828, 2011.
Dee, D. P., Balmaseda, M., Balsamo, G., Engelen, R., Simmons, A. J., and
Thepaut, J.-N.: Towards a consistent reanalysis of the climate system, B.
Am. Meteorol. Soc., 95, 1235–1248, https://doi.org/10.1175/BAMS-D-13-00043.1, 2014.
Dharssi, I. and Vinodkumar, J.: A prototype high resolution soil
moisture analysis system for Australia, Bureau of Meteorology Report No.
026, available at: http://www.bom.gov.au/research/publications/researchreports/BRR-026.pdf
(last access: 17 May 2019), 2017.
Dharssi, I., Steinle, P., and Candy, B.: Towards a Kalman filter based land
surface data assimilation scheme for ACCESS, Bureau of Meteorology CAWCR
Technical Report No. 54, available at: http://www.cawcr.gov.au/technical-reports/CTR_054.pdf
(last access: 17 May 2019), 2012.
Dharssi, I., Steinle, P., and Fernon, J.: Improved numerical weather
predictions by using optimised urban model parameter values and satellite
derived tree heights, in: MODSIM2015, 21st International Congress on Modelling and Simulation,
edited by: Weber, T., McPhee, M. J., and Anderssen, R. S., Modelling and Simulation
Society of Australia and New Zealand, December
2015, 1161–1167, ISBN: 978-0-9872143-5-5, available at:
https://www.mssanz.org.au/modsim2015/M4/dharssi.pdf
(last access: 17 May 2019), 2015.
Dickinson, R. E., Errico, R. M., Giorgi, F., and Bates, G. T.: A regional
climate model for the western United States, Clim. Change,
15, 383–422, https://doi.org/10.1007/BF00240465, 1989.
Donlon, C. J., Martin, M., Stark, J. D., Roberts-Jones, J., Fiedler, E., and
Wimmer, W.: The Operational Sea Surface Temperature and Sea Ice analysis (OSTIA)
system, Remote Sens. Environ., 116, 140–158, https://doi.org/10.1016/j.rse.2010.10.017,
2012.
Ebert, E. E., Janowiak, J. E., and Kidd, C.: Comparison of near-real-time
precipitation estimates from satellite observations and numerical models,
B. Am. Meteorol. Soc., 88, 47–64, https://doi.org/10.1175/BAMS-88-1-47, 2007.
Ebita, A., Kobayashi, S., Ota, Y., Moriya, M., Kumabe, R., Onogi, K.,
Harada, Y., Yasui, S., Miyaoka, K., Takahashi, K., Kamahori, H., Kobayashi,
C., Endo, H., Soma, M., Oikawa, Y., and Ishimizu, T.: The Japanese 55-year
reanalysis JRA-55: An interim report, SOLA, 7, 149–152, https://doi.org/10.2151/sola.2011-038, 2011.
Edwards, J. M. and Slingo, A.: Studies with a flexible new radiation code.
I: Choosing a configuration for a large-scale model, Q. J. Roy.
Meteorol. Soc., 122, 689–719, https://doi.org/10.1002/qj.49712253107, 1996.
Evans, J. P. and McCabe, M. F.: Effect of model resolution on a regional
climate model simulation over southeast Australia, Clim. Res., 56, 131–145,
https://doi.org/10.3354/cr01151, 2013.
Fall, S., Niyogi, D., Gluhovsky, A., Pielke Sr., R. A., Kalnay, E., and
Rochon, G.: Impacts of land use land cover on temperature trends over the
continental United States: assessment using the North American Regional
Reanalysis, Int. J. Climatol., 30, 1980–1993, https://doi.org/10.1002/joc.1996, 2010.
Fowler, H. J., Blenkinshop, S., and Tebaldi, C.: Linking climate change
modelling to impacts studies: recent advances in downscaling techniques for
hydrological modeling, Int. J. Climatol., 27, 1547–1578, https://doi.org/10.1002/joc.1556, 2007.
Frank, C. W., Wahl, S., Keller, J. D., Pospichal, B., Hense, A., and
Crewell, S.: Bias correction of a novel European reanalysis data set for
solar energy applications, Solar Ener., 164, 12–24, https://doi.org/10.1016/j.solener.2018.02.012, 2018.
Franklin, C. N., Sun, Z., Bi, D., Dix, M., Yan, H., and Bodas-Salcedo, A.:
Evaluation of clouds in ACCESS using the satellite simulator package COSP:
Global, seasonal, and regional cloud properties, J. Geophys. Res.-Atmos.,
118, 732–748, https://doi.org/10.1029/2012JD018469, 2013.
Gauthier, P. and Thépaut, J.-N.: Impact of the digital filter as a weak
constraint in the preoperational 4DVar assimilation system of
Météo-France, Mon. Weather Rev., 129, 2089–2102,
https://doi.org/10.1175/1520-0493(2001)129<2089:IOTDFA>2.0.CO;2, 2001.
Gelaro, R., McCarty, W., Suárez, M. J., Todling, R., Molod, A., Takacs, L.,
Randles, C. A., Darmenov, A., Bosilovich, M. G., Reichle, R., Wargan, K., Coy, L.,
Cullather, R., Draper, C., Akella, S., Buchard, V., Conaty, A., da Silva, A. M.,
Gu, W., Kim, G., Koster, R., Lucchesi, R., Merkova, D., Nielsen, J. E., Partyka, G.,
Pawson, S., Putman, W., Rienecker, M., Schubert, S. D., Sienkiewicz, M., and
Zhao, B.: The Modern-Era Retrospective Analysis for Research and Applications,
Version 2 (MERRA-2), J. Climate, 30, 5419–5454, https://doi.org/10.1175/JCLI-D-16-0758.1, 2017.
Glahn, H. R. and Lowry, D. A.: The use of model output statistics (MOS) in
objective weather forecasting, J. Appl. Meteor., 11, 1203–1211,
https://doi.org/10.1175/1520-0450(1972)011<1203:TUOMOS>2.0.CO;2, 1972.
Gregory, D. and Rowntree, P. R.: A mass flux convection scheme with
representation of cloud ensemble characteristics and stability-dependent
closure, Mon. Weather Rev., 118, 1483–1506, https://doi.org/10.1175/1520-0493(1990)118<1483:AMFCSW>2.0.CO;2, 1990.
Gustafson Jr., W. I., Ma, P.-L., and Singh, B.: Precipitation characteristics
of CAM5 physics at mesoscale resolution during MC3E and the impact of
convective timescale choice, J. Adv. Model. Earth Syst., 6, 1271–1287,
https://doi.org/10.1002/2014MS000334, 2014.
Harris, B. A. and Kelly, G.: A satellite radiance-bias correction scheme for
data assimilation, Q. J. Roy. Meteorol. Soc., 127, 1453–1468,
https://doi.org/10.1002/qj.49712757418, 2001.
Hartmann, D. L., Klein Tank, A. M. G., Rusticucci, M., Alexander, L. V.,
Brönnimann, S., Charabi, Y., Dentener, F. J., Dlugokencky, E. J.,
Easterling, D. R., Kaplan, A., Soden, B. J., Thorne, P. W., Wild, M., and
Zhai, P. M.: Observations: Atmosphere and Surface, in: Climate Change 2013:
The Physical Science Basis. Contribution of Working Group I to the Fifth
Assessment Report of the Intergovernmental Panel on Climate Change.
Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA,
2013.
Hersbach, H. and Dee, D.: ERA5 reanalysis is in production, ECMWF
Newsletter No. 147, 7, available at:
https://www.ecmwf.int/sites/default/files/elibrary/2016/16299-newsletter-no147-spring-2016.pdf
(last access: 17 May 2019), 2016.
Holt, E. and Wang, J.: Trends in wind speed at wind turbine height of 80 m
over the contiguous United States using the North American Regional
Reanalysis (NARR), J. Appl. Meteor. Climatol., 51, 2188–2202,
https://doi.org/10.1175/JAMC-D-11-0205.1, 2012.
Howard, T. and Clark, P.: Correction and downscaling of NWP wind speed
forecasts, Meteorol. Apps., 14, 105–116, https://doi.org/10.1002/met.12, 2007.
Huffman, G. J., Adler, R. F., Bolvin, D. T., Gu, G., Nelkin, E. J., Bowman,
K. P., Hong, Y., Stocker, E. F., and Wolff, D. B.: The TRMM multisatellite
precipitation analysis (TMPA): Quasi-global, multiyear, combined-sensor
precipitation estimates at fine scales, J. Hydrometeor., 8, 38–54,
https://doi.org/10.1175/JHM560.1, 2006.
Ingleby, N. B.: The statistical structure of forecast errors and its
representation in The Met. Office Global 3-D variational data assimilation
scheme, Q. J. Roy. Meteorol. Soc., 127, 209-231, https://doi.org/10.1002/qj.49712757112,
2001.
Jakob, D., Su, C.-H., Eizenberg, N., Kociuba, G., Steinle, P., Fox-Hughes,
P., and Bettio, L.: An atmospheric high-resolution regional reanalysis for
Australia, B. Aus. Meteorol. Oceanog. Soc., 30, 16–23, 2017.
Jermey, P. M. and Renshaw, R. J.: Precipitation representation over a
two-year period in regional reanalysis, Q. J. Roy. Meteorol. Soc., 142,
1300–1310, https://doi.org/10.1002/qj.2733, 2016.
Jones, D. A., Wang, W., and Fawcett, R.: High-quality spatial climate
data-sets for Australia, Aust. Meteorol. Oceanogr. J., 58, 233–248, 2009.
Kallberg, P.: Forecast drift in ERA-Interim. ERA report series 10, available
at: https://www.ecmwf.int/sites/default/files/elibrary/2011/10381-forecast-drift-era-interim.pdf
(last access: 17 May 2019), 2011.
Kalnay, E., Kanamitsu, M., Kistler, R., Collins, W., Deaven, D., Gandin, L.,
Iredell, M., Saha, S., White, G., Woollen, J., Zhu, Y., Chelliah, M., Ebisuzaki, W.,
Higgins, W., Janowiak, J., Mo, K. C., Ropelewski, C., Wang, J., Leetmaa, A.,
Reynolds, R., Jenne, R., and Joseph, D.: The NCEP/NCAR 40-Year Reanalysis Project,
B. Am. Meteorol. Soc., 77, 437–472, https://doi.org/10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2, 1996.
Le Marshall, J., Xiao, Y., Norman, R., Zhang, K., Rea, A., Cucurull, L.,
Seecamp, R., Steinle, P., Puri, K., and Le, T.: The beneficial impact of
radio occultation observations on Australian region forecasts, Aust.
Meteorol. Oceanogr. J., 60, 121–125, 2010.
Le Marshall, J., Seecamp, R., Xiao, Y., Gregory, P., Jung, J., Stienle, P.,
Skinner, T., Tingwell, C., and Le, T.: The Operational Generation of Continuous
Winds in the Australian Region and Their Assimilation with 4DVar, Weather
Forecast., 28, 504–514, https://doi.org/10.1175/WAF-D-12-00018.1, 2013.
Lean, H. W., Clark, P. A., Dixon, M., Roberts, N. M., Fitch, A., Forbes, R.,
and Halliwell, C.: Characteristics of high-resolution versions of the Met
Office Unified Model for forecasting convection over the United Kingdom,
Mon. Weather Rev., 136, 3408–3424, https://doi.org/10.1175/2008MWR2332.1, 2008.
Lock, A. P., Brown, A. R., Bush, M. R., Martin, G. M., and Smith, R. N. B.:
A new boundary layer mixing scheme. Part I: Scheme description and
single-column model tests, Mon. Weather Rev., 128, 3187–3199,
https://doi.org/10.1175/1520-0493(2000)128<3187:ANBLMS>2.0.CO;2,
2000.
Lorenc, A. C.: Modelling of error covariances by 4D-Var data assimilation,
Q. J. Roy. Meteorol. Soc., 129, 3167–3182, https://doi.org/10.1256/qj.02.131, 2003.
Lorenc, A. C. and Hammon, O.: Objective quality control of observations
using Bayesian methods. Theory, and a practical implementation, Q. J. Roy.
Meteorol. Soc., 114, 515–543, https://doi.org/10.1002/qj.49711448012, 1988.
Lorenc, A. C. and Payne, T. J.: 4D-Var and the butterfly Effect:
Statistical four-dimensional data assimilation for a wide range of scales,
Q. J. Roy. Meteorol. Soc., 133, 607–614, https://doi.org/10.1002/qj.36, 2007.
Loveland, T. R., Reed, B. C., Brown, J. F., Ohlen, D. O., Zhu, Z., Yang, L., and
Merchant, J. W.: Development of a global land cover characteristics database
and IGBP DISCover from 1 km AVHRR data, Int. J. Remote Sens., 21, 1303–1330,
https://doi.org/10.1080/014311600210191, 2000.
Ma, Y., Zhou, X., Bi, D., Sun, Z., and Hirst, A. C.: Improved air-sea flux
algorithms in an ocean-atmosphere coupled model for simulation of global
ocean SST and its tropical pacific variability, Clim. Dynam., 44, 1473–1485,
https://doi.org/10.1007/s00382-014-2281-7, 2015.
Mahmood, S., Davie, J., Jermey, P., Renshaw, R., George, J. P., Rajagopal,
E. N., and Rani, S. I.: Indian monsoon data assimilation and analysis
regional reanalysis: Configuration and performance, Atmos.
Sci. Lett., 19, e808, https://doi.org/10.1002/asl.808, 2018.
Malloy, J. W., Krahenbuhl, D. S., Bush, C. E., Balling, R. C., Santoro, M. M.,
White, J. R., Elder, R. C., Pace, M. B., and Cerveny, R. S.: A surface wind extremes
(“wind lulls” and “wind blows”) climatology for central North America
and adjoining oceans (1979–2012), J. Appl. Meteor. Climatol., 54, 643–657,
https://doi.org/10.1175/JAMC-D-14-0009.1, 2015.
Martynov, A., Laprise, R., Sushama, L., Winger, K., Separovic, L., and
Dugas, B.: Reanalysis-driven climate simulation over CORDEX North America
domain using the Canadian Regional Climate Model, version 5: model
performance evaluation, Clim. Dynam., 41, 2973–3005, https://doi.org/10.1007/s00382-013-1778-9, 2013.
Masunaga, R., Nakamura, H., Miyasaka, T., Nishii, K., and Tanimoto, Y.:
Separation of climatological imprints of the Kuroshio Extension and Oyashio
fronts on the wintertime atmospheric boundary layer: Their sensitivity to
SST resolution prescribed for atmospheric reanalysis, J. Climate, 28,
1764–1787, https://doi.org/10.1175/JCLI-D-14-00314.1, 2015.
Masunaga, R., Nakamura, H., Kamahori, H., Onogi, K., and Okajima, S.:
JRA-55CHS: An atmospheric reanalysis produced with high-resolution SST,
SOLA, 14, 6–13, https://doi.org/10.2151/sola.2018-002, 2018.
Matthews, A. J., Pickup, G., Peatman, S. C., Clews, P., and Martin, J.: The
effect of the Madden-Julian Oscillation on station rainfall and riverlevel
in the Fly River System, Papua New Guinea, J. Geophys. Res.-Atmos., 118,
10926–10935, https://doi.org/10.1002/jgrd.50865, 2013.
Mesinger, F., DiMego, G., Kalnay, E., Mitchell, K., Shafran, P. C., Ebisuzaki, W.,
Jović, D., Woollen, J., Rogers, E., Berbery, E. H., Ek, M. B., Fan, Y.,
Grumbine, R., Higgins, W., Li, H., Lin, Y., Manikin, G., Parrish, D., and Shi, W.:
North American Regional Reanalysis, B. Am. Meteorol. Soc., 87, 343–360,
https://doi.org/10.1175/BAMS-87-3-343, 2006.
Moore, R. J.: The PDM rainfall-runoff model, Hydrol. Earth Syst. Sci., 11,
483–499, https://doi.org/10.5194/hess-11-483-2007, 2007.
Puri, K., Dietachmayer, G., Steinle, P., Dix, M., Rikus, L., Logan, L.,
Naughton, M., Tingwell, C., Xiao, Y., Barras, V., Bermous, I., Bowen, R.,
Deschamps, L., Franklin, C., Fraser, J., Glowacki, T., Harris, B., Lee, J., Le, T.,
Roff, G., Sulaiman, A., Sims, H., Sun, X., Sun, Z., Zhu, H., Chattopadhyay, M. and
Engel, C.: Implementation of the initial ACCESS numerical weather prediction
system, Aust. Meteorol. Oceanogr. J., 63, 265–284, 2013.
Radic, V. and Clarke, G. K. C.: Evaluation of IPCC models' performance in
simulating late-twentieth-century climatologies and weather Patterns over
North America, J. Climate, 24, 5257–5274, https://doi.org/10.1175/JCLI-D-11-00011.1,
2011.
Ramella Pralungo, L. and Haimberger, L.: A “Global Radiosonde and
tracked-balloon Archive on Sixteen Pressure levels” (GRASP) going back to
1905 – Part 2: homogeneity adjustments for pilot balloon and radiosonde wind
data, Earth Syst. Sci. Data, 6, 297–316,
https://doi.org/10.5194/essd-6-297-2014, 2014.
Ramella Pralungo, L., Haimberger, L., Stickler, A., and Brönnimann, S.:
A global radiosonde and tracked balloon archive on 16 pressure levels
(GRASP) back to 1905 – Part 1: Merging and interpolation to 00:00 and
12:00 GMT, Earth Syst. Sci. Data, 6, 185–200, https://doi.org/10.5194/essd-6-185-2014, 2014.
Randall, D. A., Wood, R. A., Bony, S., Colman, R., Fichefet, T.,
Fyfe, J., Kattsov, V.,
Pitman, A., Shukla, J., Srinivasan, J., Stouffer, R. J., Sumi, A.,
and Taylor, K. E.: Climate models and their evaluation. In: climate
change 2007: The physical science basis, in: Contribution of working group I to
the fourth assessment report of the intergovernmental panel on climate
change, edited by: Solomon, S., Qin, D., Manning, M., Chen, Z., Marquis, M.,
Averyt, K. B., Tignor, M., and Miller, H. L., Cambridge University
Press, Cambridge and New York, NY, 2007.
Rawlins, F., Ballard, S. P., Bovis, K. J., Clayton, A. M., Li, D.,
Inverarity, G. W., Lorenc, A. C., and Payne, T. J.: The Met Office global
4-dimensional data assimilation system, Q. J. Roy. Meteorol. Soc., 133, 347–362,
https://doi.org/10.1002/qj.32, 2007.
Renshaw, R., Jermey, P., Barker, D., Maycock, A., and Oxley, S.: EURO4M
regional reanalysis system. Forecasting Research Technical Report No. 583,
available at: https://www.metoffice.gov.uk/binaries/content/assets/mohippo/pdf/o/4/frtr583.pdf
(last access: 13 February 2018), 2013.
Ridal, M., Olsson, E., Unden, P., Zimmermann, K., and Ohlsson, A.: HARMONIE
reanalysis report of results and dataset, UERRA Project Deliverable D2.7,
available at: http://www.uerra.eu/ (last access: 13 February 2018), 2017.
Roberts-Jones, J., Fiedler, E. K., and Martin, M. J.: Daily, global,
high-resolution SST and sea ice reanalysis for 1985–2007 Using the OSTIA
system, J. Climate, 25, 6215–6232, https://doi.org/10.1175/JCLI-D-11-00648.1, 2012.
Rose, S. and Apt, J.: Quantifying sources of uncertainty in reanalysis
derived wind speed, Renew. Energy, 94, 157–165, https://doi.org/10.1016/j.renene.2016.03.028, 2016.
Ruiz-Barradas, A. and Nigam, S.: IPCC's twentieth-century climate
simulations: Varied representations of North American hydroclimate
variability, J. Climate, 19, 4041–4058, https://doi.org/10.1175/JCLI3809.1, 2006.
Sapiano, M. R. P. and Arkin, P. A.: An intercomparison and validation of
high-resolution satellite precipitation estimates with 3-hourly gauge data,
J. Hydrometeorol., 10, 149–166, https://doi.org/10.1175/2008JHM1052.1, 2009.
Scinocca, J. F. and McFarlane, N. A.: The variability of modeled tropical
precipitation, J. Atmos. Sci., 61, 1993–2015, 2004.
Sheridan, P., Smith, S., Brown, A., and Vosper, S.: A simple height-based
correction for temperature downscaling in complex terrain, Meteor. App., 17,
329–339, https://doi.org/10.1002/met.177, 2010.
Simard, M., Pinto, N., Fisher, J. B., and Baccini, A.: Mapping forest canopy
height globally with spaceborne lidar, J. Geophys. Res.-Biogeosci., 116,
G04021, https://doi.org/10.1029/2011JG001708, 2011.
Smith, I., Moise, A., Inape, K., Murphy, B., Colman, R., Power, S., and Chung, C.:
ENSO-related rainfall changes over the New Guinea region, J. Geophys. Res.-Atmos.,
118, 10665–10675, https://doi.org/10.1002/jgrd.50818, 2013.
Thorne, P. W. and Vose, R. S.: Reanalyses suitable for characterizing
long-term trends, B. Am. Meteorol. Soc., 91, 353–361,
https://doi.org/10.1175/2009BAMS2858.1, 2010.
UK Met Office: Met Office Science Repository Service,
available at: https://code.metoffice.gov.uk/trac/home,
last access: 23 May 2019.
Walters, D., Boutle, I., Brooks, M., Melvin, T., Stratton, R., Vosper, S.,
Wells, H., Williams, K., Wood, N., Allen, T., Bushell, A., Copsey, D.,
Earnshaw, P., Edwards, J., Gross, M., Hardiman, S., Harris, C., Heming, J.,
Klingaman, N., Levine, R., Manners, J., Martin, G., Milton, S., Mittermaier,
M., Morcrette, C., Riddick, T., Roberts, M., Sanchez, C., Selwood, P.,
Stirling, A., Smith, C., Suri, D., Tennant, W., Vidale, P. L., Wilkinson, J.,
Willett, M., Woolnough, S., and Xavier, P.: The Met Office Unified Model
Global Atmosphere 6.0/6.1 and JULES Global Land 6.0/6.1 configurations,
Geosci. Model Dev., 10, 1487–1520, https://doi.org/10.5194/gmd-10-1487-2017,
2017a.
Walters, D., Baran, A., Boutle, I., Brooks, M., Earnshaw, P., Edwards, J.,
Furtado, K., Hill, P., Lock, A., Manners, J., Morcrette, C., Mulcahy, J.,
Sanchez, C., Smith, C., Stratton, R., Tennant, W., Tomassini, L., Van
Weverberg, K., Vosper, S., Willett, M., Browse, J., Bushell, A., Dalvi, M.,
Essery, R., Gedney, N., Hardiman, S., Johnson, B., Johnson, C., Jones, A.,
Mann, G., Milton, S., Rumbold, H., Sellar, A., Ujiie, M., Whitall, M.,
Williams, K., and Zerroukat, M.: The Met Office Unified Model Global
Atmosphere 7.0/7.1 and JULES Global Land 7.0 configurations, Geosci. Model
Dev. Discuss., https://doi.org/10.5194/gmd-2017-291, in review, 2017b.
Wang, Z., Siems, S. T., Belusic, D., Manton, M. J., and Huang, Y.: A
climatology of the precipitation over the Southern Ocean as observed at
Macquarie Island, J. Appl. Meteorol. Climatol., 54, 2321–2337, https://doi.org/10.1175/JAMC-D-14-0211.1, 2015.
Williamson, D. L.: The effect of time steps and time-scales on
parametrization suites, Q. J. Roy. Meteorol. Soc., 139, 548–560,
https://doi.org/10.1002/qj.1992, 2013.
Wilson, D. R. and Ballard, S. P.: A microphysically based precipitation
scheme for the UK Meteorological Office Unified Model, Q. J. Roy. Meteorol.
Soc., 125, 1607–1636, https://doi.org/10.1002/qj.49712555707, 1999.
Wood, N., Staniforth, A., White, A., Allen, T., Diamantakis, M., Gross, M.,
Melvin, T., Smith, C., Vosper, S., Zerroukat, M., and Thuburn, J.: An
inherently mass-conserving semi-implicit semi-Lagrangian discretization of
the deep-atmosphere global non-hydrostatic equations, Q. J. Roy. Meteorol.
Soc., 140, 1505–1520, https://doi.org/10.1002/qj.2235, 2014.
Zhao, M., Zhang, H.-Q., and Dharssi, I.: Impact of land-surface
initialization on ACCESS-S1 and comparison with POAMA, Bureau of Meteorology
Research Report No. 023, available at:
http://www.bom.gov.au/research/publications/researchreports/BRR-023.pdf
(last access: 17 May 2019), 2017.
Zhu, H. and Dietachmayer, G.: Improving ACCESS-C convection settings,
Bureau Research Report No. 008, available at:
http://www.bom.gov.au/research/publications/researchreports/BRR-008.pdf
(last access: 17 May 2019), 2015.
Ziese, M., Rauthe-Schöch, A., Becker, A., Finger, P., Meyer-Christoffer,
A., and Schneider, U.: GPCC full data daily version.2018 at 1.0∘:
Daily land-surface precipitation from rain-gauges built on GTS-based and
historic data, https://doi.org/10.5676/DWD_GPCC/FD_D_V2018_100, 2018.
Zick, S. E. and Matyas, C. J.: Tropical cyclones in the North American
Regional Reanalysis: An assessment of spatial biases in location, intensity,
and structure, J. Geophys. Res.-Atmos., 120, 1651–1669, https://doi.org/10.1002/2014JD022417, 2015.
Short summary
The Bureau of Meteorology Atmospheric Regional Reanalysis for Australia (BARRA) is the first regional reanalysis for Australia, NZ, and SE Asia. It offers realistic depictions of near-surface meteorology at a scale required for emergency services, defence, and other major sectors such as energy and agriculture. It uses a consistent method of analysing the atmosphere, with a higher-resolution model over 1990 to 2018, and can provide greater understanding of past weather, including extreme events.
The Bureau of Meteorology Atmospheric Regional Reanalysis for Australia (BARRA) is the first...
