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
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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.
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
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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.
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
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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.
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
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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
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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
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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
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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
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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
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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.
Anna Martin, Veronika Gayler, Benedikt Steil, Klaus Klingmüller, Patrick Jöckel, Holger Tost, Jos Lelieveld, and Andrea Pozzer
Geosci. Model Dev., 17, 5705–5732, https://doi.org/10.5194/gmd-17-5705-2024, https://doi.org/10.5194/gmd-17-5705-2024, 2024
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The study evaluates the land surface and vegetation model JSBACHv4 as a replacement for the simplified submodel SURFACE in EMAC. JSBACH mitigates earlier problems of soil dryness, which are critical for vegetation modelling. When analysed using different datasets, the coupled model shows strong correlations of key variables, such as land surface temperature, surface albedo and radiation flux. The versatility of the model increases significantly, while the overall performance does not degrade.
Hugo Banderier, Christian Zeman, David Leutwyler, Stefan Rüdisühli, and Christoph Schär
Geosci. Model Dev., 17, 5573–5586, https://doi.org/10.5194/gmd-17-5573-2024, https://doi.org/10.5194/gmd-17-5573-2024, 2024
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We investigate the effects of reduced-precision arithmetic in a state-of-the-art regional climate model by studying the results of 10-year-long simulations. After this time, the results of the reduced precision and the standard implementation are hardly different. This should encourage the use of reduced precision in climate models to exploit the speedup and memory savings it brings. The methodology used in this work can help researchers verify reduced-precision implementations of their model.
David Fuchs, Steven C. Sherwood, Abhnil Prasad, Kirill Trapeznikov, and Jim Gimlett
Geosci. Model Dev., 17, 5459–5475, https://doi.org/10.5194/gmd-17-5459-2024, https://doi.org/10.5194/gmd-17-5459-2024, 2024
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Machine learning (ML) of unresolved processes offers many new possibilities for improving weather and climate models, but integrating ML into the models has been an engineering challenge, and there are performance issues. We present a new software plugin for this integration, TorchClim, that is scalable and flexible and thereby allows a new level of experimentation with the ML approach. We also provide guidance on ML training and demonstrate a skillful hybrid ML atmosphere model.
Minjin Lee, Charles A. Stock, John P. Dunne, and Elena Shevliakova
Geosci. Model Dev., 17, 5191–5224, https://doi.org/10.5194/gmd-17-5191-2024, https://doi.org/10.5194/gmd-17-5191-2024, 2024
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Modeling global freshwater solid and nutrient loads, in both magnitude and form, is imperative for understanding emerging eutrophication problems. Such efforts, however, have been challenged by the difficulty of balancing details of freshwater biogeochemical processes with limited knowledge, input, and validation datasets. Here we develop a global freshwater model that resolves intertwined algae, solid, and nutrient dynamics and provide performance assessment against measurement-based estimates.
Hunter York Brown, Benjamin Wagman, Diana Bull, Kara Peterson, Benjamin Hillman, Xiaohong Liu, Ziming Ke, and Lin Lin
Geosci. Model Dev., 17, 5087–5121, https://doi.org/10.5194/gmd-17-5087-2024, https://doi.org/10.5194/gmd-17-5087-2024, 2024
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Explosive volcanic eruptions lead to long-lived, microscopic particles in the upper atmosphere which act to cool the Earth's surface by reflecting the Sun's light back to space. We include and test this process in a global climate model, E3SM. E3SM is tested against satellite and balloon observations of the 1991 eruption of Mt. Pinatubo, showing that with these particles in the model we reasonably recreate Pinatubo and its global effects. We also explore how particle size leads to these effects.
Deifilia Aurora To, Julian Quinting, Gholam Ali Hoshyaripour, Markus Götz, Achim Streit, and Charlotte Debus
EGUsphere, https://doi.org/10.5194/egusphere-2024-1714, https://doi.org/10.5194/egusphere-2024-1714, 2024
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Pangu-Weather is a breakthrough machine learning model in medium-range weather forecasting that considers three-dimensional 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 accessibility of training and working with the model.
Carl Svenhag, Moa K. Sporre, Tinja Olenius, Daniel Yazgi, Sara M. Blichner, Lars P. Nieradzik, and Pontus Roldin
Geosci. Model Dev., 17, 4923–4942, https://doi.org/10.5194/gmd-17-4923-2024, https://doi.org/10.5194/gmd-17-4923-2024, 2024
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Our research shows the importance of modeling new particle formation (NPF) and growth of particles in the atmosphere on a global scale, as they influence the outcomes of clouds and our climate. With the global model EC-Earth3 we show that using a new method for NPF modeling, which includes new detailed processes with NH3 and H2SO4, significantly impacts the number of particles in the air and clouds and changes the radiation balance of the same magnitude as anthropogenic greenhouse emissions.
Mengjie Han, Qing Zhao, Xili Wang, Ying-Ping Wang, Philippe Ciais, Haicheng Zhang, Daniel S. Goll, Lei Zhu, Zhe Zhao, Zhixuan Guo, Chen Wang, Wei Zhuang, Fengchang Wu, and Wei Li
Geosci. Model Dev., 17, 4871–4890, https://doi.org/10.5194/gmd-17-4871-2024, https://doi.org/10.5194/gmd-17-4871-2024, 2024
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The impact of biochar (BC) on soil organic carbon (SOC) dynamics is not represented in most land carbon models used for assessing land-based climate change mitigation. Our study develops a BC model that incorporates our current understanding of BC effects on SOC based on a soil carbon model (MIMICS). The BC model can reproduce the SOC changes after adding BC, providing a useful tool to couple dynamic land models to evaluate the effectiveness of BC application for CO2 removal from the atmosphere.
Kalyn Dorheim, Skylar Gering, Robert Gieseke, Corinne Hartin, Leeya Pressburger, Alexey N. Shiklomanov, Steven J. Smith, Claudia Tebaldi, Dawn L. Woodard, and Ben Bond-Lamberty
Geosci. Model Dev., 17, 4855–4869, https://doi.org/10.5194/gmd-17-4855-2024, https://doi.org/10.5194/gmd-17-4855-2024, 2024
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Hector is an easy-to-use, global climate–carbon cycle model. With its quick run time, Hector can provide climate information from a run in a fraction of a second. Hector models on a global and annual basis. Here, we present an updated version of the model, Hector V3. In this paper, we document Hector’s new features. Hector V3 is capable of reproducing historical observations, and its future temperature projections are consistent with those of more complex models.
Fangxuan Ren, Jintai Lin, Chenghao Xu, Jamiu A. Adeniran, Jingxu Wang, Randall V. Martin, Aaron van Donkelaar, Melanie S. Hammer, Larry W. Horowitz, Steven T. Turnock, Naga Oshima, Jie Zhang, Susanne Bauer, Kostas Tsigaridis, Øyvind Seland, Pierre Nabat, David Neubauer, Gary Strand, Twan van Noije, Philippe Le Sager, and Toshihiko Takemura
Geosci. Model Dev., 17, 4821–4836, https://doi.org/10.5194/gmd-17-4821-2024, https://doi.org/10.5194/gmd-17-4821-2024, 2024
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We evaluate the performance of 14 CMIP6 ESMs in simulating total PM2.5 and its 5 components over China during 2000–2014. PM2.5 and its components are underestimated in almost all models, except that black carbon (BC) and sulfate are overestimated in two models, respectively. The underestimation is the largest for organic carbon (OC) and the smallest for BC. Models reproduce the observed spatial pattern for OC, sulfate, nitrate and ammonium well, yet the agreement is poorer for BC.
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. Discuss., https://doi.org/10.5194/gmd-2024-98, https://doi.org/10.5194/gmd-2024-98, 2024
Revised manuscript accepted for GMD
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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 range of data is likely encountered outside the calibration period. The end result is highly dependent on the method used, and we show that one needs to exclude data in the calibration range to activate the extrapolation functionality also in that time period, else there will be discontinuities in the timeseries.
Yi Xi, Chunjing Qiu, Yuan Zhang, Dan Zhu, Shushi Peng, Gustaf Hugelius, Jinfeng Chang, Elodie Salmon, and Philippe Ciais
Geosci. Model Dev., 17, 4727–4754, https://doi.org/10.5194/gmd-17-4727-2024, https://doi.org/10.5194/gmd-17-4727-2024, 2024
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The ORCHIDEE-MICT model can simulate the carbon cycle and hydrology at a sub-grid scale but energy budgets only at a grid scale. This paper assessed the implementation of a multi-tiling energy budget approach in ORCHIDEE-MICT and found warmer surface and soil temperatures, higher soil moisture, and more soil organic carbon across the Northern Hemisphere compared with the original version.
Maria Rosa 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. Discuss., https://doi.org/10.5194/gmd-2024-73, https://doi.org/10.5194/gmd-2024-73, 2024
Revised manuscript accepted for GMD
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Observational data and modelling capabilities are expanding in recent years, but there are still barriers preventing these two data sources to be used in synergy. Proper comparison requires generating, storing and handling a large amount of data. This manuscript 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.
Georgia Lazoglou, Theo Economou, Christina Anagnostopoulou, George Zittis, Anna Tzyrkalli, Pantelis Georgiades, and Jos Lelieveld
Geosci. Model Dev., 17, 4689–4703, https://doi.org/10.5194/gmd-17-4689-2024, https://doi.org/10.5194/gmd-17-4689-2024, 2024
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This study focuses on the important issue of the drizzle bias effect in regional climate models, described by an over-prediction of the number of rainy days while underestimating associated precipitation amounts. For this purpose, two distinct methodologies are applied and rigorously evaluated. These results are encouraging for using the multivariate machine learning method random forest to increase the accuracy of climate models concerning the projection of the number of wet days.
Xu Yue, Hao Zhou, Chenguang Tian, Yimian Ma, Yihan Hu, Cheng Gong, Hui Zheng, and Hong Liao
Geosci. Model Dev., 17, 4621–4642, https://doi.org/10.5194/gmd-17-4621-2024, https://doi.org/10.5194/gmd-17-4621-2024, 2024
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We develop the interactive Model for Air Pollution and Land Ecosystems (iMAPLE). The model considers the full coupling between carbon and water cycles, dynamic fire emissions, wetland methane emissions, biogenic volatile organic compound emissions, and trait-based ozone vegetation damage. Evaluations show that iMAPLE is a useful tool for the study of the interactions among climate, chemistry, and ecosystems.
Malte Meinshausen, Carl-Friedrich Schleussner, Kathleen Beyer, Greg Bodeker, Olivier Boucher, Josep G. Canadell, John S. Daniel, Aïda Diongue-Niang, Fatima Driouech, Erich Fischer, Piers Forster, Michael Grose, Gerrit Hansen, Zeke Hausfather, Tatiana Ilyina, Jarmo S. Kikstra, Joyce Kimutai, Andrew D. King, June-Yi Lee, Chris Lennard, Tabea Lissner, Alexander Nauels, Glen P. Peters, Anna Pirani, Gian-Kasper Plattner, Hans Pörtner, Joeri Rogelj, Maisa Rojas, Joyashree Roy, Bjørn H. Samset, Benjamin M. Sanderson, Roland Séférian, Sonia Seneviratne, Christopher J. Smith, Sophie Szopa, Adelle Thomas, Diana Urge-Vorsatz, Guus J. M. Velders, Tokuta Yokohata, Tilo Ziehn, and Zebedee Nicholls
Geosci. Model Dev., 17, 4533–4559, https://doi.org/10.5194/gmd-17-4533-2024, https://doi.org/10.5194/gmd-17-4533-2024, 2024
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The scientific community is considering new scenarios to succeed RCPs and SSPs for the next generation of Earth system model runs to project future climate change. To contribute to that effort, we reflect on relevant policy and scientific research questions and suggest categories for representative emission pathways. These categories are tailored to the Paris Agreement long-term temperature goal, high-risk outcomes in the absence of further climate policy and worlds “that could have been”.
Seung H. Baek, Paul A. Ullrich, Bo Dong, and Jiwoo Lee
EGUsphere, https://doi.org/10.5194/egusphere-2024-1456, https://doi.org/10.5194/egusphere-2024-1456, 2024
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We evaluate downscaled products by examining locally relevant covariances during convective and frontal precipitation events. Common statistical downscaling techniques preserve expected covariances during convective precipitation. However, they dampen future intensification of frontal precipitation captured in global climate models and dynamical downscaling. This suggests statistical downscaling may not fully resolve non-stationary hydrologic processes as compared to dynamical downscaling.
Emmanuel Nyenah, Petra Döll, Daniel S. Katz, and Robert Reinecke
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-97, https://doi.org/10.5194/gmd-2024-97, 2024
Revised manuscript accepted for GMD
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Research software is crucial for scientific progress but is often developed by scientists with limited training, time, and funding, leading to software that is hard to understand, (re)use, modify, and maintain. Our study across 10 research sectors highlights strengths in version control, open-source licensing, and documentation while emphasizing the need for containerization and code quality. Recommendations include workshops, code quality metrics, funding, and adherence to FAIR standards.
Yilin Fang, Hoang Viet Tran, and L. Ruby Leung
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-70, https://doi.org/10.5194/gmd-2024-70, 2024
Revised manuscript accepted for GMD
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Hurricanes may worsen the 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 LMRB during Hurricane Ida in 2021, but less pronounced than the observations, indicating areas for model improvement to better understand and manage nutrient runoff loss during hurricanes in the region.
Giovanni G. Seijo-Ellis, Donata Giglio, Gustavo M. Marques, and Frank O. Bryan
EGUsphere, https://doi.org/10.5194/egusphere-2024-1378, https://doi.org/10.5194/egusphere-2024-1378, 2024
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A CESM/MOM6 regional configuration of the Caribbean Sea was developed as a response to the rising need of high-resolution models for climate impact studies. The configuration is validated for the period of 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 across the multiple passages in the Caribbean Sea agree with observations.
Ross Mower, Ethan D. Gutmann, Glen E. Liston, Jessica Lundquist, and Soren Rasmussen
Geosci. Model Dev., 17, 4135–4154, https://doi.org/10.5194/gmd-17-4135-2024, https://doi.org/10.5194/gmd-17-4135-2024, 2024
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Higher-resolution model simulations are better at capturing winter snowpack changes across space and time. However, increasing resolution also increases the computational requirements. This work provides an overview of changes made to a distributed snow-evolution modeling system (SnowModel) to allow it to leverage high-performance computing resources. Continental simulations that were previously estimated to take 120 d can now be performed in 5 h.
Jiaxu Guo, Juepeng Zheng, Yidan Xu, Haohuan Fu, Wei Xue, Lanning Wang, Lin Gan, Ping Gao, Wubing Wan, Xianwei Wu, Zhitao Zhang, Liang Hu, Gaochao Xu, and Xilong Che
Geosci. Model Dev., 17, 3975–3992, https://doi.org/10.5194/gmd-17-3975-2024, https://doi.org/10.5194/gmd-17-3975-2024, 2024
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To enhance the efficiency of experiments using SCAM, we train a learning-based surrogate model to facilitate large-scale sensitivity analysis and tuning of combinations of multiple parameters. Employing a hybrid method, we investigate the joint sensitivity of multi-parameter combinations across typical cases, identifying the most sensitive three-parameter combination out of 11. Subsequently, we conduct a tuning process aimed at reducing output errors in these cases.
Yung-Yao Lan, Huang-Hsiung Hsu, and Wan-Ling Tseng
Geosci. Model Dev., 17, 3897–3918, https://doi.org/10.5194/gmd-17-3897-2024, https://doi.org/10.5194/gmd-17-3897-2024, 2024
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This study uses the CAM5–SIT coupled model to investigate the effects of SST feedback frequency on the MJO simulations with intervals at 30 min, 1, 3, 6, 12, 18, 24, and 30 d. The simulations become increasingly unrealistic as the frequency of the SST feedback decreases. Our results suggest that more spontaneous air--sea interaction (e.g., ocean response within 3 d in this study) with high vertical resolution in the ocean model is key to the realistic simulation of the MJO.
Jiwoo Lee, Peter J. Gleckler, Min-Seop Ahn, Ana Ordonez, Paul A. Ullrich, Kenneth R. Sperber, Karl E. Taylor, Yann Y. Planton, Eric Guilyardi, Paul Durack, Celine Bonfils, Mark D. Zelinka, Li-Wei Chao, Bo Dong, Charles Doutriaux, Chengzhu Zhang, Tom Vo, Jason Boutte, Michael F. Wehner, Angeline G. Pendergrass, Daehyun Kim, Zeyu Xue, Andrew T. Wittenberg, and John Krasting
Geosci. Model Dev., 17, 3919–3948, https://doi.org/10.5194/gmd-17-3919-2024, https://doi.org/10.5194/gmd-17-3919-2024, 2024
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We introduce an open-source software, the PCMDI Metrics Package (PMP), developed for a comprehensive comparison of Earth system models (ESMs) with real-world observations. Using diverse metrics evaluating climatology, variability, and extremes simulated in thousands of simulations from the Coupled Model Intercomparison Project (CMIP), PMP aids in benchmarking model improvements across generations. PMP also enables efficient tracking of performance evolutions during ESM developments.
Haoyue Zuo, Yonggang Liu, Gaojun Li, Zhifang Xu, Liang Zhao, Zhengtang Guo, and Yongyun Hu
Geosci. Model Dev., 17, 3949–3974, https://doi.org/10.5194/gmd-17-3949-2024, https://doi.org/10.5194/gmd-17-3949-2024, 2024
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Compared to the silicate weathering fluxes measured at large river basins, the current models tend to systematically overestimate the fluxes over the tropical region, which leads to an overestimation of the global total weathering flux. The most possible cause of such bias is found to be the overestimation of tropical surface erosion, which indicates that the tropical vegetation likely slows down physical erosion significantly. We propose a way of taking this effect into account in models.
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. Discuss., https://doi.org/10.5194/gmd-2024-85, https://doi.org/10.5194/gmd-2024-85, 2024
Revised manuscript accepted for GMD
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This study provides the first comprehensive assessment of historical fire simulations from 19 CMIP6 ESMs. Most models reproduce global total, spatial pattern, seasonality, and regional historical changes well, but fail to simulate the recent decline in global burned area and underestimate the fire sensitivity to wet-dry conditions. They addressed three critical issues in CMIP5. We present targeted guidance for fire scheme development and methodologies to generate reliable fire projections.
Quentin Pikeroen, Didier Paillard, and Karine Watrin
Geosci. Model Dev., 17, 3801–3814, https://doi.org/10.5194/gmd-17-3801-2024, https://doi.org/10.5194/gmd-17-3801-2024, 2024
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All accurate climate models use equations with poorly defined parameters, where knobs for the parameters are turned to fit the observations. This process is called tuning. In this article, we use another paradigm. We use a thermodynamic hypothesis, the maximum entropy production, to compute temperatures, energy fluxes, and precipitation, where tuning is impossible. For now, the 1D vertical model is used for a tropical atmosphere. The correct order of magnitude of precipitation is computed.
Giovanni Di Virgilio, Jason Evans, Fei Ji, Eugene Tam, Jatin Kala, Julia Andrys, Christopher Thomas, Dipayan Choudhury, Carlos Rocha, Stephen White, Yue Li, Moutassem El Rafei, Rishav Goyal, Matthew Riley, and Jyothi Lingala
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-87, https://doi.org/10.5194/gmd-2024-87, 2024
Revised manuscript accepted for GMD
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We introduce new climate models that simulate Australia’s future climate at regional scales, including at an unprecedented resolution of 4 km for 1950–2100. We describe the model design process used to create these new climate models. We show how the new models perform relative to previous-generation models, and compare their climate projections. This work is of national and international relevance as it can help guide climate model design and the use and interpretation of climate projections.
Sarah Schöngart, Lukas Gudmundsson, Mathias Hauser, Peter Pfleiderer, Quentin Lejeune, Shruti Nath, Sonia Isabelle Seneviratne, and Carl-Friedrich Schleußner
EGUsphere, https://doi.org/10.5194/egusphere-2024-278, https://doi.org/10.5194/egusphere-2024-278, 2024
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Precipitation and temperature are two of the most impact-relevant climatic variables. Their joint distribution largely determines the division into climate regimes. Yet, projecting 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 to generate monthly means of local precipitation and temperature at low computational costs.
Jishi Zhang, Peter Bogenschutz, Qi Tang, Philip Cameron-smith, and Chengzhu Zhang
Geosci. Model Dev., 17, 3687–3731, https://doi.org/10.5194/gmd-17-3687-2024, https://doi.org/10.5194/gmd-17-3687-2024, 2024
Short summary
Short summary
We developed a regionally refined climate model that allows resolved convection and performed a 20-year projection to the end of the century. The model has a resolution of 3.25 km in California, which allows us to predict climate with unprecedented accuracy, and a resolution of 100 km for the rest of the globe to achieve efficient, self-consistent simulations. The model produces superior results in reproducing climate patterns over California that typical modern climate models cannot resolve.
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...
