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. Discuss., https://doi.org/10.5194/gmd-2024-201, https://doi.org/10.5194/gmd-2024-201, 2024
Preprint under review for GMD
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-km 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 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. Discuss., https://doi.org/10.5194/gmd-2024-201, https://doi.org/10.5194/gmd-2024-201, 2024
Preprint under review for GMD
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-km 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 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
Preprint 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
Preprint under review 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
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
Multi-year simulations at kilometre scale with the Integrated Forecasting System coupled to FESOM2.5 and NEMOv3.4
Subsurface hydrological controls on the short-term effects of hurricanes on nitrate–nitrogen runoff loading: a case study of Hurricane Ida using the Energy Exascale Earth System Model (E3SM) Land Model (v2.1)
CARIB12: a regional Community Earth System Model/Modular Ocean Model 6 configuration of the Caribbean Sea
Architectural insights into and training methodology optimization of Pangu-Weather
Evaluation of global fire simulations in CMIP6 Earth system models
Evaluating downscaled products with expected hydroclimatic co-variances
Software sustainability of global impact models
fair-calibrate v1.4.1: calibration, constraining, and validation of the FaIR simple climate model for reliable future climate projections
ISOM 1.0: a fully mesoscale-resolving idealized Southern Ocean model and the diversity of multiscale eddy interactions
A computationally lightweight model for ensemble forecasting of environmental hazards: General TAMSAT-ALERT v1.2.1
Introducing the MESMER-M-TPv0.1.0 module: spatially explicit Earth system model emulation for monthly precipitation and temperature
The need for carbon-emissions-driven climate projections in CMIP7
Robust handling of extremes in quantile mapping – “Murder your darlings”
A protocol for model intercomparison of impacts of marine cloud brightening climate intervention
An extensible perturbed parameter ensemble for the Community Atmosphere Model version 6
Coupling the regional climate model ICON-CLM v2.6.6 to the Earth system model GCOAST-AHOI v2.0 using OASIS3-MCT v4.0
A fully coupled solid-particle microphysics scheme for stratospheric aerosol injections within the aerosol–chemistry–climate model SOCOL-AERv2
An improved representation of aerosol in the ECMWF IFS-COMPO 49R1 through the integration of EQSAM4Climv12 – a first attempt at simulating aerosol acidity
At-scale Model Output Statistics in mountain environments (AtsMOS v1.0)
Impact of ocean vertical-mixing parameterization on Arctic sea ice and upper-ocean properties using the NEMO-SI3 model
Bridging the gap: a new module for human water use in the Community Earth System Model version 2.2.1
Modeling Commercial-Scale CO2 Storage in the Gas Hydrate Stability Zone with PFLOTRAN v6.0
A new lightning scheme in the Canadian Atmospheric Model (CanAM5.1): implementation, evaluation, and projections of lightning and fire in future climates
Methane dynamics in the Baltic Sea: investigating concentration, flux, and isotopic composition patterns using the coupled physical–biogeochemical model BALTSEM-CH4 v1.0
Using feature importance as exploratory data analysis tool on earth system models
CropSuite – 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)
Split-explicit external mode solver in the finite volume sea ice–ocean model FESOM2
Applying double cropping and interactive irrigation in the North China Plain using WRF4.5
The sea ice component of GC5: coupling SI3 to HadGEM3 using conductive fluxes
CICE on a C-grid: new momentum, stress, and transport schemes for CICEv6.5
HyPhAICC v1.0: a hybrid physics–AI approach for probability fields advection shown through an application to cloud cover nowcasting
CICERO Simple Climate Model (CICERO-SCM v1.1.1) – an improved simple climate model with a parameter calibration tool
A non-intrusive, multi-scale, and flexible coupling interface in WRF
T&C-CROP: Representing mechanistic crop growth with a terrestrial biosphere model (T&C, v1.5): Model formulation and validation
Development of a plant carbon–nitrogen interface coupling framework in a coupled biophysical-ecosystem–biogeochemical model (SSiB5/TRIFFID/DayCent-SOM v1.0)
The Earth Science Box Modeling Toolkit (ESBMTK)
High Resolution Model Intercomparison Project phase 2 (HighResMIP2) towards CMIP7
Dynamical Madden–Julian Oscillation forecasts using an ensemble subseasonal-to-seasonal forecast system of the IAP-CAS model
Baseline Climate Variables for Earth System Modelling
The DOE E3SM Version 2.1: Overview and Assessment of the Impacts of Parameterized Ocean Submesoscales
Evaluation of atmospheric rivers in reanalyses and climate models in a new metrics framework
Implementation of a brittle sea ice rheology in an Eulerian, finite-difference, C-grid modeling framework: impact on the simulated deformation of sea ice in the Arctic
HSW-V v1.0: localized injections of interactive volcanic aerosols and their climate impacts in a simple general circulation model
A 3D-Var assimilation scheme for vertical velocity with CMA-MESO v5.0
Updating the radiation infrastructure in MESSy (based on MESSy version 2.55)
An urban module coupled with the Variable Infiltration Capacity model to improve hydrothermal simulations in urban systems
Bayesian hierarchical model for bias-correcting climate models
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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Emily Black, John Ellis, and Ross I. Maidment
Geosci. Model Dev., 17, 8353–8372, https://doi.org/10.5194/gmd-17-8353-2024, https://doi.org/10.5194/gmd-17-8353-2024, 2024
Short summary
Short summary
We present General TAMSAT-ALERT, a computationally lightweight and versatile tool for generating ensemble forecasts from time series data. General TAMSAT-ALERT is capable of combining multiple streams of monitoring and meteorological forecasting data into probabilistic hazard assessments. In this way, it complements existing systems and enhances their utility for actionable hazard assessment.
Sarah Schöngart, Lukas Gudmundsson, Mathias Hauser, Peter Pfleiderer, Quentin Lejeune, Shruti Nath, Sonia Isabelle Seneviratne, and Carl-Friedrich Schleussner
Geosci. Model Dev., 17, 8283–8320, https://doi.org/10.5194/gmd-17-8283-2024, https://doi.org/10.5194/gmd-17-8283-2024, 2024
Short summary
Short summary
Precipitation and temperature are two of the most impact-relevant climatic variables. Yet, projecting future precipitation and temperature data under different emission scenarios relies on complex models that are computationally expensive. In this study, we propose a method that allows us to generate monthly means of local precipitation and temperature at low computational costs. Our modelling framework is particularly useful for all downstream applications of climate model data.
Benjamin M. Sanderson, Ben B. B. Booth, John Dunne, Veronika Eyring, Rosie A. Fisher, Pierre Friedlingstein, Matthew J. Gidden, Tomohiro Hajima, Chris D. Jones, Colin G. Jones, Andrew King, Charles D. Koven, David M. Lawrence, Jason Lowe, Nadine Mengis, Glen P. Peters, Joeri Rogelj, Chris Smith, Abigail C. Snyder, Isla R. Simpson, Abigail L. S. Swann, Claudia Tebaldi, Tatiana Ilyina, Carl-Friedrich Schleussner, Roland Séférian, Bjørn H. Samset, Detlef van Vuuren, and Sönke Zaehle
Geosci. Model Dev., 17, 8141–8172, https://doi.org/10.5194/gmd-17-8141-2024, https://doi.org/10.5194/gmd-17-8141-2024, 2024
Short summary
Short summary
We discuss how, in order to provide more relevant guidance for climate policy, coordinated climate experiments should adopt a greater focus on simulations where Earth system models are provided with carbon emissions from fossil fuels together with land use change instructions, rather than past approaches that have largely focused on experiments with prescribed atmospheric carbon dioxide concentrations. We discuss how these goals might be achieved in coordinated climate modeling experiments.
Peter Berg, Thomas Bosshard, Denica Bozhinova, Lars Bärring, Joakim Löw, Carolina Nilsson, Gustav Strandberg, Johan Södling, Johan Thuresson, Renate Wilcke, and Wei Yang
Geosci. Model Dev., 17, 8173–8179, https://doi.org/10.5194/gmd-17-8173-2024, https://doi.org/10.5194/gmd-17-8173-2024, 2024
Short summary
Short summary
When bias adjusting climate model data using quantile mapping, one needs to prescribe what to do at the tails of the distribution, where a larger data range is likely encountered outside of the calibration period. The end result is highly dependent on the method used. We show that, to avoid discontinuities in the time series, one needs to exclude data in the calibration range to also activate the extrapolation functionality in that time period.
Philip J. Rasch, Haruki Hirasawa, Mingxuan Wu, Sarah J. Doherty, Robert Wood, Hailong Wang, Andy Jones, James Haywood, and Hansi Singh
Geosci. Model Dev., 17, 7963–7994, https://doi.org/10.5194/gmd-17-7963-2024, https://doi.org/10.5194/gmd-17-7963-2024, 2024
Short summary
Short summary
We introduce a protocol to compare computer climate simulations to better understand a proposed strategy intended to counter warming and climate impacts from greenhouse gas increases. This slightly changes clouds in six ocean regions to reflect more sunlight and cool the Earth. Example changes in clouds and climate are shown for three climate models. Cloud changes differ between the models, but precipitation and surface temperature changes are similar when their cooling effects are made similar.
Trude Eidhammer, Andrew Gettelman, Katherine Thayer-Calder, Duncan Watson-Parris, Gregory Elsaesser, Hugh Morrison, Marcus van Lier-Walqui, Ci Song, and Daniel McCoy
Geosci. Model Dev., 17, 7835–7853, https://doi.org/10.5194/gmd-17-7835-2024, https://doi.org/10.5194/gmd-17-7835-2024, 2024
Short summary
Short summary
We describe a dataset where 45 parameters related to cloud processes in the Community Earth System Model version 2 (CESM2) Community Atmosphere Model version 6 (CAM6) are perturbed. Three sets of perturbed parameter ensembles (263 members) were created: current climate, preindustrial aerosol loading and future climate with sea surface temperature increased by 4 K.
Ha Thi Minh Ho-Hagemann, Vera Maurer, Stefan Poll, and Irina Fast
Geosci. Model Dev., 17, 7815–7834, https://doi.org/10.5194/gmd-17-7815-2024, https://doi.org/10.5194/gmd-17-7815-2024, 2024
Short summary
Short summary
The regional Earth system model GCOAST-AHOI v2.0 that includes the regional climate model ICON-CLM coupled to the ocean model NEMO and the hydrological discharge model HD via the OASIS3-MCT coupler can be a useful tool for conducting long-term regional climate simulations over the EURO-CORDEX domain. The new OASIS3-MCT coupling interface implemented in ICON-CLM makes it more flexible for coupling to an external ocean model and an external hydrological discharge model.
Sandro Vattioni, Rahel Weber, Aryeh Feinberg, Andrea Stenke, John A. Dykema, Beiping Luo, Georgios A. Kelesidis, Christian A. Bruun, Timofei Sukhodolov, Frank N. Keutsch, Thomas Peter, and Gabriel Chiodo
Geosci. Model Dev., 17, 7767–7793, https://doi.org/10.5194/gmd-17-7767-2024, https://doi.org/10.5194/gmd-17-7767-2024, 2024
Short summary
Short summary
We quantified impacts and efficiency of stratospheric solar climate intervention via solid particle injection. Microphysical interactions of solid particles with the sulfur cycle were interactively coupled to the heterogeneous chemistry scheme and the radiative transfer code of an aerosol–chemistry–climate model. Compared to injection of SO2 we only find a stronger cooling efficiency for solid particles when normalizing to the aerosol load but not when normalizing to the injection rate.
Samuel Rémy, Swen Metzger, Vincent Huijnen, Jason E. Williams, and Johannes Flemming
Geosci. Model Dev., 17, 7539–7567, https://doi.org/10.5194/gmd-17-7539-2024, https://doi.org/10.5194/gmd-17-7539-2024, 2024
Short summary
Short summary
In this paper we describe the development of the future operational cycle 49R1 of the IFS-COMPO system, used for operational forecasts of atmospheric composition in the CAMS project, and focus on the implementation of the thermodynamical model EQSAM4Clim version 12. The implementation of EQSAM4Clim significantly improves the simulated secondary inorganic aerosol surface concentration. The new aerosol and precipitation acidity diagnostics showed good agreement against observational datasets.
Maximillian Van Wyk de Vries, Tom Matthews, L. Baker Perry, Nirakar Thapa, and Rob Wilby
Geosci. Model Dev., 17, 7629–7643, https://doi.org/10.5194/gmd-17-7629-2024, https://doi.org/10.5194/gmd-17-7629-2024, 2024
Short summary
Short summary
This paper introduces the AtsMOS workflow, a new tool for improving weather forecasts in mountainous areas. By combining advanced statistical techniques with local weather data, AtsMOS can provide more accurate predictions of weather conditions. Using data from Mount Everest as an example, AtsMOS has shown promise in better forecasting hazardous weather conditions, making it a valuable tool for communities in mountainous regions and beyond.
Sofia Allende, Anne Marie Treguier, Camille Lique, Clément de Boyer Montégut, François Massonnet, Thierry Fichefet, and Antoine Barthélemy
Geosci. Model Dev., 17, 7445–7466, https://doi.org/10.5194/gmd-17-7445-2024, https://doi.org/10.5194/gmd-17-7445-2024, 2024
Short summary
Short summary
We study the parameters of the turbulent-kinetic-energy mixed-layer-penetration scheme in the NEMO model with regard to sea-ice-covered regions of the Arctic Ocean. This evaluation reveals the impact of these parameters on mixed-layer depth, sea surface temperature and salinity, and ocean stratification. Our findings demonstrate significant impacts on sea ice thickness and sea ice concentration, emphasizing the need for accurately representing ocean mixing to understand Arctic climate dynamics.
Sabin I. Taranu, David M. Lawrence, Yoshihide Wada, Ting Tang, Erik Kluzek, Sam Rabin, Yi Yao, Steven J. De Hertog, Inne Vanderkelen, and Wim Thiery
Geosci. Model Dev., 17, 7365–7399, https://doi.org/10.5194/gmd-17-7365-2024, https://doi.org/10.5194/gmd-17-7365-2024, 2024
Short summary
Short summary
In this study, we improved a climate model by adding the representation of water use sectors such as domestic, industry, and agriculture. This new feature helps us understand how water is used and supplied in various areas. We tested our model from 1971 to 2010 and found that it accurately identifies areas with water scarcity. By modelling the competition between sectors when water availability is limited, the model helps estimate the intensity and extent of individual sectors' water shortages.
Michael Nole, Jonah Bartrand, Fawz Naim, and Glenn Hammond
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-162, https://doi.org/10.5194/gmd-2024-162, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
Safe carbon dioxide (CO2) storage is likely to be critical for mitigating some of the most dangerous 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 sub-sea CO2 injection.
Cynthia Whaley, Montana Etten-Bohm, Courtney Schumacher, Ayodeji Akingunola, Vivek Arora, Jason Cole, Michael Lazare, David Plummer, Knut von Salzen, and Barbara Winter
Geosci. Model Dev., 17, 7141–7155, https://doi.org/10.5194/gmd-17-7141-2024, https://doi.org/10.5194/gmd-17-7141-2024, 2024
Short summary
Short summary
This paper describes how lightning was added as a process in the Canadian Earth System Model in order to interactively respond to climate changes. As lightning is an important cause of global wildfires, this new model development allows for more realistic projections of how wildfires may change in the future, responding to a changing climate.
Erik Gustafsson, Bo G. Gustafsson, Martijn Hermans, Christoph Humborg, and Christian Stranne
Geosci. Model Dev., 17, 7157–7179, https://doi.org/10.5194/gmd-17-7157-2024, https://doi.org/10.5194/gmd-17-7157-2024, 2024
Short summary
Short summary
Methane (CH4) cycling in the Baltic Proper is studied through model simulations, enabling a first estimate of key CH4 fluxes. A preliminary budget identifies benthic CH4 release as the dominant source and two main sinks: CH4 oxidation in the water (92 % of sinks) and outgassing to the atmosphere (8 % of sinks). This study addresses CH4 emissions from coastal seas and is a first step toward understanding the relative importance of open-water outgassing compared with local coastal hotspots.
Daniel Ries, Katherine Goode, Kellie McClernon, and Benjamin Hillman
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-133, https://doi.org/10.5194/gmd-2024-133, 2024
Revised manuscript accepted for GMD
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.
Florian Zabel, Matthias Knüttel, and Benjamin Poschlod
EGUsphere, https://doi.org/10.5194/egusphere-2024-2526, https://doi.org/10.5194/egusphere-2024-2526, 2024
Short summary
Short summary
CropSuite is a fuzzy-logic based high resolution open-source crop suitability model considering the impact of climate variability. We apply CropSuite for 48 important staple and cash crops at 1 km spatial resolution for Africa. We find that climate variability significantly impacts on suitable areas, but also affects optimal sowing dates, and multiple cropping potentials. The results provide information that can be used 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. Discuss., https://doi.org/10.5194/gmd-2024-135, https://doi.org/10.5194/gmd-2024-135, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
The Icosahedral Nonhydrostatic (ICON) Model 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.
Tridib Banerjee, Patrick Scholz, Sergey Danilov, Knut Klingbeil, and Dmitry Sidorenko
Geosci. Model Dev., 17, 7051–7065, https://doi.org/10.5194/gmd-17-7051-2024, https://doi.org/10.5194/gmd-17-7051-2024, 2024
Short summary
Short summary
In this paper we propose a new alternative to one of the functionalities of the sea ice model FESOM2. The alternative we propose allows the model to capture and simulate fast changes in quantities like sea surface elevation more accurately. We also demonstrate that the new alternative is faster and more adept at taking advantages of highly parallelized computing infrastructure. We therefore show that this new alternative is a great addition to the sea ice model FESOM2.
Yuwen Fan, Zhao Yang, Min-Hui Lo, Jina Hur, and Eun-Soon Im
Geosci. Model Dev., 17, 6929–6947, https://doi.org/10.5194/gmd-17-6929-2024, https://doi.org/10.5194/gmd-17-6929-2024, 2024
Short summary
Short summary
Irrigated agriculture in the North China Plain (NCP) has a significant impact on the local climate. To better understand this impact, we developed a specialized model specifically for the NCP region. This model allows us to simulate the double-cropping vegetation and the dynamic irrigation practices that are commonly employed in the NCP. This model shows improved performance in capturing the general crop growth, such as crop stages, biomass, crop yield, and vegetation greenness.
Ed Blockley, Emma Fiedler, Jeff Ridley, Luke Roberts, Alex West, Dan Copsey, Daniel Feltham, Tim Graham, David Livings, Clement Rousset, David Schroeder, and Martin Vancoppenolle
Geosci. Model Dev., 17, 6799–6817, https://doi.org/10.5194/gmd-17-6799-2024, https://doi.org/10.5194/gmd-17-6799-2024, 2024
Short summary
Short summary
This paper documents the sea ice model component of the latest Met Office coupled model configuration, which will be used as the physical basis for UK contributions to CMIP7. Documentation of science options used in the configuration are given along with a brief model evaluation. This is the first UK configuration to use NEMO’s new SI3 sea ice model. We provide details on how SI3 was adapted to work with Met Office coupling methodology and documentation of coupling processes in the model.
Jean-François Lemieux, William H. Lipscomb, Anthony Craig, David A. Bailey, Elizabeth C. Hunke, Philippe Blain, Till A. S. Rasmussen, Mats Bentsen, Frédéric Dupont, David Hebert, and Richard Allard
Geosci. Model Dev., 17, 6703–6724, https://doi.org/10.5194/gmd-17-6703-2024, https://doi.org/10.5194/gmd-17-6703-2024, 2024
Short summary
Short summary
We present the latest version of the CICE model. It solves equations that describe the dynamics and the growth and melt of sea ice. To do so, the domain is divided into grid cells and variables are positioned at specific locations in the cells. A new implementation (C-grid) is presented, with the velocity located on cell edges. Compared to the previous B-grid, the C-grid allows for a natural coupling with some oceanic and atmospheric models. It also allows for ice transport in narrow channels.
Rachid El Montassir, Olivier Pannekoucke, and Corentin Lapeyre
Geosci. Model Dev., 17, 6657–6681, https://doi.org/10.5194/gmd-17-6657-2024, https://doi.org/10.5194/gmd-17-6657-2024, 2024
Short summary
Short summary
This study introduces a novel approach that combines physics and artificial intelligence (AI) for improved cloud cover forecasting. This approach outperforms traditional deep learning (DL) methods in producing realistic and physically consistent results while requiring less training data. This architecture provides a promising solution to overcome the limitations of classical AI methods and contributes to open up new possibilities for combining physical knowledge with deep learning models.
Marit Sandstad, Borgar Aamaas, Ane Nordlie Johansen, Marianne Tronstad Lund, Glen Philip Peters, Bjørn Hallvard Samset, Benjamin Mark Sanderson, and Ragnhild Bieltvedt Skeie
Geosci. Model Dev., 17, 6589–6625, https://doi.org/10.5194/gmd-17-6589-2024, https://doi.org/10.5194/gmd-17-6589-2024, 2024
Short summary
Short summary
The CICERO-SCM has existed as a Fortran model since 1999 that calculates the radiative forcing and concentrations from emissions and is an upwelling diffusion energy balance model of the ocean that calculates temperature change. In this paper, we describe an updated version ported to Python and publicly available at https://github.com/ciceroOslo/ciceroscm (https://doi.org/10.5281/zenodo.10548720). This version contains functionality for parallel runs and automatic calibration.
Sébastien Masson, Swen Jullien, Eric Maisonnave, David Gill, Guillaume Samson, Mathieu Le Corre, and Lionel Renault
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-140, https://doi.org/10.5194/gmd-2024-140, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
This article details a new feature we implemented in the most popular regional atmospheric model (WRF). This feature allows data to be exchanged 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.
Jordi Buckley Paules, Simone Fatichi, Bonnie Warring, and Athanasios Paschalis
EGUsphere, https://doi.org/10.5194/egusphere-2024-2072, https://doi.org/10.5194/egusphere-2024-2072, 2024
Short summary
Short summary
We outline and validate developments to the pre-existing process-based model T&C to better represent cropland processes. Foreseen applications of T&C-CROP include hydrological and carbon storage implications of land-use transitions involving crop, forest, and pasture conversion, as well as studies on optimal irrigation and fertilization under a changing climate.
Zheng Xiang, Yongkang Xue, Weidong Guo, Melannie D. Hartman, Ye Liu, and William J. Parton
Geosci. Model Dev., 17, 6437–6464, https://doi.org/10.5194/gmd-17-6437-2024, https://doi.org/10.5194/gmd-17-6437-2024, 2024
Short summary
Short summary
A process-based plant carbon (C)–nitrogen (N) interface coupling framework has been developed which mainly focuses on plant resistance and N-limitation effects on photosynthesis, plant respiration, and plant phenology. A dynamic C / N ratio is introduced to represent plant resistance and self-adjustment. The framework has been implemented in a coupled biophysical-ecosystem–biogeochemical model, and testing results show a general improvement in simulating plant properties with this framework.
Ulrich Georg Wortmann, Tina Tsan, Mahrukh Niazi, Ruben Navasardyan, Magnus-Roland Marun, Bernardo S. Chede, Jingwen Zhong, and Morgan Wolfe
EGUsphere, https://doi.org/10.5194/egusphere-2024-1864, https://doi.org/10.5194/egusphere-2024-1864, 2024
Short summary
Short summary
The Earth Science Box Modeling Toolkit (ESBMTK) is a Python library designed to separate model description from numerical implementation. This approach results in well-documented, easily readable, and maintainable model code, allowing students and researchers to concentrate on conceptual challenges rather than mathematical intricacies.
Malcolm John 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
EGUsphere, https://doi.org/10.5194/egusphere-2024-2582, https://doi.org/10.5194/egusphere-2024-2582, 2024
Short summary
Short summary
HighResMIP2 is a model intercomparison project focussing on high resolution global climate models, that is those with grid spacings of 25 km or less in atmosphere and ocean, using simulations of decades to a century or so 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.
Yangke Liu, Qing Bao, Bian He, Xiaofei Wu, Jing Yang, Yimin Liu, Guoxiong Wu, Tao Zhu, Siyuan Zhou, Yao Tang, Ankang Qu, Yalan Fan, Anling Liu, Dandan Chen, Zhaoming Luo, Xing Hu, and Tongwen Wu
Geosci. Model Dev., 17, 6249–6275, https://doi.org/10.5194/gmd-17-6249-2024, https://doi.org/10.5194/gmd-17-6249-2024, 2024
Short summary
Short summary
We give an overview of the Institute of Atmospheric Physics–Chinese Academy of Sciences subseasonal-to-seasonal ensemble forecasting system and Madden–Julian Oscillation forecast evaluation of the system. Compared to other S2S models, the IAP-CAS model has its benefits but also biases, i.e., underdispersive ensemble, overestimated amplitude, and faster propagation speed when forecasting MJO. We provide a reason for these biases and prospects for further improvement of this system in the future.
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
EGUsphere, https://doi.org/10.5194/egusphere-2024-2363, https://doi.org/10.5194/egusphere-2024-2363, 2024
Short summary
Short summary
The Baseline Climate Variables for Earth System Modelling (ESM-BCVs) are defined as a list of 132 variables which have high utility for the evaluation and exploitation of climate simulations. The list reflects the most heavily used variables from Earth System Models, based on an assessment of data publication and download records from the largest archive of global climate projects.
Katherine Smith, Alice M. Barthel, LeAnn M. Conlon, Luke P. Van Roekel, Anthony Bartoletti, Jean-Christophe Golez, Chengzhu Zhang, Carolyn Branecky Begeman, James J. Benedict, Gautum 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. Ordonez
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-149, https://doi.org/10.5194/gmd-2024-149, 2024
Revised manuscript accepted for GMD
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 biases reduction in temperature, salinity, and sea-ice extent in the North Atlantic, a small strengthening of the Atlantic Meridional Overturning Circulation, and improvements in many atmospheric climatological variables.
Bo Dong, Paul Ullrich, Jiwoo Lee, Peter Gleckler, Kristin Chang, and Travis O'Brien
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-142, https://doi.org/10.5194/gmd-2024-142, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
1. A metrics package designed for easy analysis of AR characteristics and statistics is presented. 2. The tool is efficient for diagnosing systematic AR bias in climate models, and useful for evaluating new AR characteristics in model simulations. 3. 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).
Laurent Brodeau, Pierre Rampal, Einar Ólason, and Véronique Dansereau
Geosci. Model Dev., 17, 6051–6082, https://doi.org/10.5194/gmd-17-6051-2024, https://doi.org/10.5194/gmd-17-6051-2024, 2024
Short summary
Short summary
A new brittle sea ice rheology, BBM, has been implemented into the sea ice component of NEMO. We describe how a new spatial discretization framework was introduced to achieve this. A set of idealized and realistic ocean and sea ice simulations of the Arctic have been performed using BBM and the standard viscous–plastic rheology of NEMO. When compared to satellite data, our simulations show that our implementation of BBM leads to a fairly good representation of sea ice deformations.
Joseph P. Hollowed, Christiane Jablonowski, Hunter Y. Brown, Benjamin R. Hillman, Diana L. Bull, and Joseph L. Hart
Geosci. Model Dev., 17, 5913–5938, https://doi.org/10.5194/gmd-17-5913-2024, https://doi.org/10.5194/gmd-17-5913-2024, 2024
Short summary
Short summary
Large volcanic eruptions deposit material in the upper atmosphere, which is capable of altering temperature and wind patterns of Earth's atmosphere for subsequent years. This research describes a new method of simulating these effects in an idealized, efficient atmospheric model. A volcanic eruption of sulfur dioxide is described with a simplified set of physical rules, which eventually cools the planetary surface. This model has been designed as a test bed for climate attribution studies.
Hong Li, Yi Yang, Jian Sun, Yuan Jiang, Ruhui Gan, and Qian Xie
Geosci. Model Dev., 17, 5883–5896, https://doi.org/10.5194/gmd-17-5883-2024, https://doi.org/10.5194/gmd-17-5883-2024, 2024
Short summary
Short summary
Vertical atmospheric motions play a vital role in convective-scale precipitation forecasts by connecting atmospheric dynamics with cloud development. A three-dimensional variational vertical velocity assimilation scheme is developed within the high-resolution CMA-MESO model, utilizing the adiabatic Richardson equation as the observation operator. A 10 d continuous run and an individual case study demonstrate improved forecasts, confirming the scheme's effectiveness.
Matthias Nützel, Laura Stecher, Patrick Jöckel, Franziska Winterstein, Martin Dameris, Michael Ponater, Phoebe Graf, and Markus Kunze
Geosci. Model Dev., 17, 5821–5849, https://doi.org/10.5194/gmd-17-5821-2024, https://doi.org/10.5194/gmd-17-5821-2024, 2024
Short summary
Short summary
We extended the infrastructure of our modelling system to enable the use of an additional radiation scheme. After calibrating the model setups to the old and the new radiation scheme, we find that the simulation with the new scheme shows considerable improvements, e.g. concerning the cold-point temperature and stratospheric water vapour. Furthermore, perturbations of radiative fluxes associated with greenhouse gas changes, e.g. of methane, tend to be improved when the new scheme is employed.
Yibing Wang, Xianhong Xie, Bowen Zhu, Arken Tursun, Fuxiao Jiang, Yao Liu, Dawei Peng, and Buyun Zheng
Geosci. Model Dev., 17, 5803–5819, https://doi.org/10.5194/gmd-17-5803-2024, https://doi.org/10.5194/gmd-17-5803-2024, 2024
Short summary
Short summary
Urban expansion intensifies challenges like urban heat and urban dry islands. To address this, we developed an urban module, VIC-urban, in the Variable Infiltration Capacity (VIC) model. Tested in Beijing, VIC-urban accurately simulated turbulent heat fluxes, runoff, and land surface temperature. We provide a reliable tool for large-scale simulations considering urban environment and a systematic urban modelling framework within VIC, offering crucial insights for urban planners and designers.
Jeremy Carter, Erick A. Chacón-Montalván, and Amber Leeson
Geosci. Model Dev., 17, 5733–5757, https://doi.org/10.5194/gmd-17-5733-2024, https://doi.org/10.5194/gmd-17-5733-2024, 2024
Short summary
Short summary
Climate models are essential tools in the study of climate change and its wide-ranging impacts on life on Earth. However, the output is often afflicted with some bias. In this paper, a novel model is developed to predict and correct bias in the output of climate models. The model captures uncertainty in the correction and explicitly models underlying spatial correlation between points. These features are of key importance for climate change impact assessments and resulting decision-making.
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...
