Articles | Volume 8, issue 2
https://doi.org/10.5194/gmd-8-363-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/gmd-8-363-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Development and technical paper
| 
19 Feb 2015
Development and technical paper |
| 19 Feb 2015
MetUM-GOML1: a near-globally coupled atmosphere–ocean-mixed-layer model
National Centre for Atmospheric Science-Climate and Department of Meteorology, University of Reading, P.O. Box 243, Reading, Berkshire, RG6 6BB, UK
N. P. Klingaman
National Centre for Atmospheric Science-Climate and Department of Meteorology, University of Reading, P.O. Box 243, Reading, Berkshire, RG6 6BB, UK
S. J. Woolnough
National Centre for Atmospheric Science-Climate and Department of Meteorology, University of Reading, P.O. Box 243, Reading, Berkshire, RG6 6BB, UK
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Juan Manuel Castillo, Huw W. Lewis, Akhilesh Mishra, Ashis Mitra, Jeff Polton, Ashley Brereton, Andrew Saulter, Alex Arnold, Segolene Berthou, Douglas Clark, Julia Crook, Ananda Das, John Edwards, Xiangbo Feng, Ankur Gupta, Sudheer Joseph, Nicholas Klingaman, Imranali Momin, Christine Pequignet, Claudio Sanchez, Jennifer Saxby, and Maria Valdivieso da Costa
Geosci. Model Dev., 15, 4193–4223, https://doi.org/10.5194/gmd-15-4193-2022, https://doi.org/10.5194/gmd-15-4193-2022, 2022
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A new environmental modelling system has been developed to represent the effect of feedbacks between atmosphere, land, and ocean in the Indian region. Different approaches to simulating tropical cyclones Titli and Fani are demonstrated. It is shown that results are sensitive to the way in which the ocean response to cyclone evolution is captured in the system. Notably, we show how a more rigorous formulation for the near-surface energy budget can be included when air–sea coupling is included.
Ambrogio Volonté, Andrew G. Turner, Reinhard Schiemann, Pier Luigi Vidale, and Nicholas P. Klingaman
Weather Clim. Dynam., 3, 575–599, https://doi.org/10.5194/wcd-3-575-2022, https://doi.org/10.5194/wcd-3-575-2022, 2022
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In this study we analyse the complex seasonal evolution of the East Asian summer monsoon. Using reanalysis data, we show the importance of the interaction between tropical and extratropical air masses converging at the monsoon front, particularly during its northward progression. The upper-level flow pattern (e.g. the westerly jet) controls the balance between the airstreams and thus the associated rainfall. This framework provides a basis for studies of extreme events and climate variability.
Paul-Arthur Monerie, Amulya Chevuturi, Peter Cook, Nicholas P. Klingaman, and Christopher E. Holloway
Geosci. Model Dev., 13, 4749–4771, https://doi.org/10.5194/gmd-13-4749-2020, https://doi.org/10.5194/gmd-13-4749-2020, 2020
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In this study, we assess how increasing the horizontal resolution of HadGEM3-GC31 can allow simulating better tropical and subtropical South American precipitation. We compare simulations of HadGEM3-GC3.1, performed at three different horizontal resolutions. We show that increasing resolution allows decreasing precipitation biases over the Andes and northeast Brazil and improves the simulation of daily precipitation distribution.
Claudia Christine Stephan, Nicholas P. Klingaman, Pier Luigi Vidale, Andrew G. Turner, Marie-Estelle Demory, and Liang Guo
Geosci. Model Dev., 11, 3215–3233, https://doi.org/10.5194/gmd-11-3215-2018, https://doi.org/10.5194/gmd-11-3215-2018, 2018
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Summer precipitation over China in the MetUM reaches twice its observed values. Increasing the horizontal resolution of the model and adding air–sea coupling have little effect on these biases. Nevertheless, MetUM correctly simulates spatial patterns of temporally coherent precipitation and the associated large-scale processes. This suggests that the model may provide useful predictions of summer intraseasonal variability despite the substantial biases in overall intraseasonal variance.
Claudia Christine Stephan, Nicholas P. Klingaman, Pier Luigi Vidale, Andrew G. Turner, Marie-Estelle Demory, and Liang Guo
Geosci. Model Dev., 11, 1823–1847, https://doi.org/10.5194/gmd-11-1823-2018, https://doi.org/10.5194/gmd-11-1823-2018, 2018
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Climate simulations are evaluated for their ability to reproduce year-to-year variability of precipitation over China. Mean precipitation and variability are too high in all simulations but improve with finer resolution and coupling. Simulations reproduce the observed spatial patterns of rainfall variability. However, not all of these patterns are associated with observed mechanisms. For example, simulations do not reproduce summer rainfall along the Yangtze valley in response to El Niño.
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Geosci. Model Dev., 17, 3839–3866, https://doi.org/10.5194/gmd-17-3839-2024, https://doi.org/10.5194/gmd-17-3839-2024, 2024
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Emma Howard, Steven Woolnough, Nicholas Klingaman, Daniel Shipley, Claudio Sanchez, Simon C. Peatman, Cathryn E. Birch, and Adrian J. Matthews
Geosci. Model Dev., 17, 3815–3837, https://doi.org/10.5194/gmd-17-3815-2024, https://doi.org/10.5194/gmd-17-3815-2024, 2024
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Andrés Yarce Botero, Michiel van Weele, Arjo Segers, Pier Siebesma, and Henk Eskes
Geosci. Model Dev., 17, 3765–3781, https://doi.org/10.5194/gmd-17-3765-2024, https://doi.org/10.5194/gmd-17-3765-2024, 2024
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HARMONIE WINS50 reanalysis data with 0.025° × 0.025° resolution from 2019 to 2021 were coupled with the LOTOS-EUROS Chemical Transport Model. HARMONIE and ECMWF meteorology configurations against Cabauw observations (52.0° N, 4.9° W) were evaluated as simulated NO2 concentrations with ground-level sensors. Differences in crucial meteorological input parameters (boundary layer height, vertical diffusion coefficient) between the hydrostatic and non-hydrostatic models were analysed.
Ulrich Voggenberger, Leopold Haimberger, Federico Ambrogi, and Paul Poli
Geosci. Model Dev., 17, 3783–3799, https://doi.org/10.5194/gmd-17-3783-2024, https://doi.org/10.5194/gmd-17-3783-2024, 2024
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This paper presents a method for calculating balloon drift from historical radiosonde ascent data. The drift can reach distances of several hundred kilometres and is often neglected. Verification shows the beneficial impact of the more accurate balloon position on model assimilation. The method is not limited to radiosondes but would also work for dropsondes, ozonesondes, or any other in situ sonde carried by the wind in the pre-GNSS era, provided the necessary information is available.
Philippe Thunis, Jeroen Kuenen, Enrico Pisoni, Bertrand Bessagnet, Manjola Banja, Lech Gawuc, Karol Szymankiewicz, Diego Guizardi, Monica Crippa, Susana Lopez-Aparicio, Marc Guevara, Alexander De Meij, Sabine Schindlbacher, and Alain Clappier
Geosci. Model Dev., 17, 3631–3643, https://doi.org/10.5194/gmd-17-3631-2024, https://doi.org/10.5194/gmd-17-3631-2024, 2024
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An ensemble emission inventory is created with the aim of monitoring the status and progress made with the development of EU-wide inventories. This emission ensemble serves as a common benchmark for the screening and allows for the comparison of more than two inventories at a time. Because the emission “truth” is unknown, the approach does not tell which inventory is the closest to reality, but it identifies inconsistencies that require special attention.
Laurent Menut, Bertrand Bessagnet, Arineh Cholakian, Guillaume Siour, Sylvain Mailler, and Romain Pennel
Geosci. Model Dev., 17, 3645–3665, https://doi.org/10.5194/gmd-17-3645-2024, https://doi.org/10.5194/gmd-17-3645-2024, 2024
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This study is about the modelling of the atmospheric composition in Europe during the summer of 2022, when massive wildfires were observed. It is a sensitivity study dedicated to the relative impacts of two modelling processes that are able to modify the meteorology used for the calculation of the atmospheric chemistry and transport of pollutants.
Shuai Wang, Mengyuan Zhang, Yueqi Gao, Peng Wang, Qingyan Fu, and Hongliang Zhang
Geosci. Model Dev., 17, 3617–3629, https://doi.org/10.5194/gmd-17-3617-2024, https://doi.org/10.5194/gmd-17-3617-2024, 2024
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Numerical models are widely used in air pollution modeling but suffer from significant biases. The machine learning model designed in this study shows high efficiency in identifying such biases. Meteorology (relative humidity and cloud cover), chemical composition (secondary organic components and dust aerosols), and emission sources (residential activities) are diagnosed as the main drivers of bias in modeling PM2.5, a typical air pollutant. The results will help to improve numerical models.
Shoma Yamanouchi, Shayamilla Mahagammulla Gamage, Sara Torbatian, Jad Zalzal, Laura Minet, Audrey Smargiassi, Ying Liu, Ling Liu, Forood Azargoshasbi, Jinwoong Kim, Youngseob Kim, Daniel Yazgi, and Marianne Hatzopoulou
Geosci. Model Dev., 17, 3579–3597, https://doi.org/10.5194/gmd-17-3579-2024, https://doi.org/10.5194/gmd-17-3579-2024, 2024
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Air pollution is a major health hazard, and chemical transport models (CTMs) are valuable tools that aid in our understanding of the risks of air pollution at both local and regional scales. In this study, the Polair3D CTM of the Polyphemus air quality modeling platform was set up over Quebec, Canada, to assess the model’s capability in predicting key air pollutant species over the region, at seasonal temporal scales and at regional spatial scales.
Rohith Thundathil, Florian Zus, Galina Dick, and Jens Wickert
Geosci. Model Dev., 17, 3599–3616, https://doi.org/10.5194/gmd-17-3599-2024, https://doi.org/10.5194/gmd-17-3599-2024, 2024
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Global Navigation Satellite Systems (GNSS) provides moisture observations through its densely distributed ground station network. In this research, we assimilate a new type of observation called tropospheric gradient observations, which has never been incorporated into a weather model. We develop a forward operator for gradient-based observations and conduct an assimilation impact study. The study shows significant improvements in the model's humidity fields.
Ankur Mahesh, Travis A. O'Brien, Burlen Loring, Abdelrahman Elbashandy, William Boos, and William D. Collins
Geosci. Model Dev., 17, 3533–3557, https://doi.org/10.5194/gmd-17-3533-2024, https://doi.org/10.5194/gmd-17-3533-2024, 2024
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Atmospheric rivers (ARs) are extreme weather events that can alleviate drought or cause billions of US dollars in flood damage. We train convolutional neural networks (CNNs) to detect ARs with an estimate of the uncertainty. We present a framework to generalize these CNNs to a variety of datasets of past, present, and future climate. Using a simplified simulation of the Earth's atmosphere, we validate the CNNs. We explore the role of ARs in maintaining energy balance in the Earth system.
Alexandra Rivera, Kostas Tsigaridis, Gregory Faluvegi, and Drew Shindell
Geosci. Model Dev., 17, 3487–3505, https://doi.org/10.5194/gmd-17-3487-2024, https://doi.org/10.5194/gmd-17-3487-2024, 2024
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This paper describes and evaluates an improvement to the representation of acetone in the GISS ModelE2.1 Earth system model. We simulate acetone's concentration and transport across the atmosphere as well as its dependence on chemistry, the ocean, and various global emissions. Comparisons of our model’s estimates to past modeling studies and field measurements have shown encouraging results. Ultimately, this paper contributes to a broader understanding of acetone's role in the atmosphere.
Alok K. Samantaray, Priscilla A. Mooney, and Carla A. Vivacqua
Geosci. Model Dev., 17, 3321–3339, https://doi.org/10.5194/gmd-17-3321-2024, https://doi.org/10.5194/gmd-17-3321-2024, 2024
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Any interpretation of climate model data requires a comprehensive evaluation of the model performance. Numerous error metrics exist for this purpose, and each focuses on a specific aspect of the relationship between reference and model data. Thus, a comprehensive evaluation demands the use of multiple error metrics. However, this can lead to confusion. We propose a clustering technique to reduce the number of error metrics needed and a composite error metric to simplify the interpretation.
Richard Maier, Fabian Jakub, Claudia Emde, Mihail Manev, Aiko Voigt, and Bernhard Mayer
Geosci. Model Dev., 17, 3357–3383, https://doi.org/10.5194/gmd-17-3357-2024, https://doi.org/10.5194/gmd-17-3357-2024, 2024
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Based on the TenStream solver, we present a new method to accelerate 3D radiative transfer towards the speed of currently used 1D solvers. Using a shallow-cumulus-cloud time series, we evaluate the performance of this new solver in terms of both speed and accuracy. Compared to a 3D benchmark simulation, we show that our new solver is able to determine much more accurate irradiances and heating rates than a 1D δ-Eddington solver, even when operated with a similar computational demand.
Julia Maillard, Jean-Christophe Raut, and François Ravetta
Geosci. Model Dev., 17, 3303–3320, https://doi.org/10.5194/gmd-17-3303-2024, https://doi.org/10.5194/gmd-17-3303-2024, 2024
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Atmospheric models struggle to reproduce the strong temperature inversions in the vicinity of the surface over forested areas in the Arctic winter. In this paper, we develop modified simplified versions of surface layer schemes widely used by the community. Our modifications are used to correct the fact that original schemes place strong limits on the turbulent collapse, leading to a lower surface temperature gradient at low wind speeds. Modified versions show a better performance.
Jana Fischereit, Henrik Vedel, Xiaoli Guo Larsén, Natalie E. Theeuwes, Gregor Giebel, and Eigil Kaas
Geosci. Model Dev., 17, 2855–2875, https://doi.org/10.5194/gmd-17-2855-2024, https://doi.org/10.5194/gmd-17-2855-2024, 2024
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Wind farms impact local wind and turbulence. To incorporate these effects in weather forecasting, the explicit wake parameterization (EWP) is added to the forecasting model HARMONIE–AROME. We evaluate EWP using flight data above and downstream of wind farms, comparing it with an alternative wind farm parameterization and another weather model. Results affirm the correct implementation of EWP, emphasizing the necessity of accounting for wind farm effects in accurate weather forecasting.
Clément Bouvier, Daan van den Broek, Madeleine Ekblom, and Victoria A. Sinclair
Geosci. Model Dev., 17, 2961–2986, https://doi.org/10.5194/gmd-17-2961-2024, https://doi.org/10.5194/gmd-17-2961-2024, 2024
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An analytical initial background state has been developed for moist baroclinic wave simulation on an aquaplanet and implemented into OpenIFS. Seven parameters can be controlled, which are used to generate the background states and the development of baroclinic waves. The meteorological and numerical stability has been assessed. Resulting baroclinic waves have proven to be realistic and sensitive to the jet's width.
Jelena Radović, Michal Belda, Jaroslav Resler, Kryštof Eben, Martin Bureš, Jan Geletič, Pavel Krč, Hynek Řezníček, and Vladimír Fuka
Geosci. Model Dev., 17, 2901–2927, https://doi.org/10.5194/gmd-17-2901-2024, https://doi.org/10.5194/gmd-17-2901-2024, 2024
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Boundary conditions are of crucial importance for numerical model (e.g., PALM) validation studies and have a large influence on the model results, especially when studying the atmosphere of real, complex, and densely built urban environments. Our experiments with different driving conditions for the large-eddy simulation model PALM show its strong dependency on boundary conditions, which is important for the proper separation of errors coming from the boundary conditions and the model itself.
Sonya L. Fiddes, Marc D. Mallet, Alain Protat, Matthew T. Woodhouse, Simon P. Alexander, and Kalli Furtado
Geosci. Model Dev., 17, 2641–2662, https://doi.org/10.5194/gmd-17-2641-2024, https://doi.org/10.5194/gmd-17-2641-2024, 2024
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In this study we present an evaluation that considers complex, non-linear systems in a holistic manner. This study uses XGBoost, a machine learning algorithm, to predict the simulated Southern Ocean shortwave radiation bias in the ACCESS model using cloud property biases as predictors. We then used a novel feature importance analysis to quantify the role that each cloud bias plays in predicting the radiative bias, laying the foundation for advanced Earth system model evaluation and development.
Gaurav Govardhan, Sachin D. Ghude, Rajesh Kumar, Sumit Sharma, Preeti Gunwani, Chinmay Jena, Prafull Yadav, Shubhangi Ingle, Sreyashi Debnath, Pooja Pawar, Prodip Acharja, Rajmal Jat, Gayatry Kalita, Rupal Ambulkar, Santosh Kulkarni, Akshara Kaginalkar, Vijay K. Soni, Ravi S. Nanjundiah, and Madhavan Rajeevan
Geosci. Model Dev., 17, 2617–2640, https://doi.org/10.5194/gmd-17-2617-2024, https://doi.org/10.5194/gmd-17-2617-2024, 2024
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A newly developed air quality forecasting framework, Decision Support System (DSS), for air quality management in Delhi, India, provides source attribution with numerous emission reduction scenarios besides forecasts. DSS shows that during post-monsoon and winter seasons, Delhi and its neighboring districts contribute to 30 %–40 % each to pollution in Delhi. On average, a 40 % reduction in the emissions in Delhi and the surrounding districts would result in a 24 % reduction in Delhi's pollution.
Simon Rosanka, Holger Tost, Rolf Sander, Patrick Jöckel, Astrid Kerkweg, and Domenico Taraborrelli
Geosci. Model Dev., 17, 2597–2615, https://doi.org/10.5194/gmd-17-2597-2024, https://doi.org/10.5194/gmd-17-2597-2024, 2024
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The capabilities of the Modular Earth Submodel System (MESSy) are extended to account for non-equilibrium aqueous-phase chemistry in the representation of deliquescent aerosols. When applying the new development in a global simulation, we find that MESSy's bias in modelling routinely observed reduced inorganic aerosol mass concentrations, especially in the United States. Furthermore, the representation of fine-aerosol pH is particularly improved in the marine boundary layer.
Junyu Li, Yuxin Wang, Lilong Liu, Yibin Yao, Liangke Huang, and Feijuan Li
Geosci. Model Dev., 17, 2569–2581, https://doi.org/10.5194/gmd-17-2569-2024, https://doi.org/10.5194/gmd-17-2569-2024, 2024
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In this study, we have developed a model (RF-PWV) to characterize precipitable water vapor (PWV) variation with altitude in the study area. RF-PWV can significantly reduce errors in vertical correction, enhance PWV fusion product accuracy, and provide insights into PWV vertical distribution, thereby contributing to climate research.
Rolf Sander
Geosci. Model Dev., 17, 2419–2425, https://doi.org/10.5194/gmd-17-2419-2024, https://doi.org/10.5194/gmd-17-2419-2024, 2024
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The open-source software MEXPLORER 1.0.0 is presented here. The program can be used to analyze, reduce, and visualize complex chemical reaction mechanisms. The mathematics behind the tool is based on graph theory: chemical species are represented as vertices, and reactions as edges. MEXPLORER is a community model published under the GNU General Public License.
Leonardo Olivetti and Gabriele Messori
Geosci. Model Dev., 17, 2347–2358, https://doi.org/10.5194/gmd-17-2347-2024, https://doi.org/10.5194/gmd-17-2347-2024, 2024
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In the last decades, weather forecasting up to 15 d into the future has been dominated by physics-based numerical models. Recently, deep learning models have challenged this paradigm. However, the latter models may struggle when forecasting weather extremes. In this article, we argue for deep learning models specifically designed to handle extreme events, and we propose a foundational framework to develop such models.
Stefan Rahimi, Lei Huang, Jesse Norris, Alex Hall, Naomi Goldenson, Will Krantz, Benjamin Bass, Chad Thackeray, Henry Lin, Di Chen, Eli Dennis, Ethan Collins, Zachary J. Lebo, Emily Slinskey, Sara Graves, Surabhi Biyani, Bowen Wang, Stephen Cropper, and the UCLA Center for Climate Science Team
Geosci. Model Dev., 17, 2265–2286, https://doi.org/10.5194/gmd-17-2265-2024, https://doi.org/10.5194/gmd-17-2265-2024, 2024
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Here, we project future climate across the western United States through the end of the 21st century using a regional climate model, embedded within 16 latest-generation global climate models, to provide the community with a high-resolution physically based ensemble of climate data for use at local scales. Strengths and weaknesses of the data are frankly discussed as we overview the downscaled dataset.
Romain Pilon and Daniela I. V. Domeisen
Geosci. Model Dev., 17, 2247–2264, https://doi.org/10.5194/gmd-17-2247-2024, https://doi.org/10.5194/gmd-17-2247-2024, 2024
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This paper introduces a new method for detecting atmospheric cloud bands to identify long convective cloud bands that extend from the tropics to the midlatitudes. The algorithm allows for easy use and enables researchers to study the life cycle and climatology of cloud bands and associated rainfall. This method provides insights into the large-scale processes involved in cloud band formation and their connections between different regions, as well as differences across ocean basins.
Salvatore Larosa, Domenico Cimini, Donatello Gallucci, Saverio Teodosio Nilo, and Filomena Romano
Geosci. Model Dev., 17, 2053–2076, https://doi.org/10.5194/gmd-17-2053-2024, https://doi.org/10.5194/gmd-17-2053-2024, 2024
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PyRTlib is an attractive educational tool because it provides a flexible and user-friendly way to broadly simulate how electromagnetic radiation travels through the atmosphere as it interacts with atmospheric constituents (such as gases, aerosols, and hydrometeors). PyRTlib is a so-called radiative transfer model; these are commonly used to simulate and understand remote sensing observations from ground-based, airborne, or satellite instruments.
Joffrey Dumont Le Brazidec, Pierre Vanderbecken, Alban Farchi, Grégoire Broquet, Gerrit Kuhlmann, and Marc Bocquet
Geosci. Model Dev., 17, 1995–2014, https://doi.org/10.5194/gmd-17-1995-2024, https://doi.org/10.5194/gmd-17-1995-2024, 2024
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Our research presents an innovative approach to estimating power plant CO2 emissions from satellite images of the corresponding plumes such as those from the forthcoming CO2M satellite constellation. The exploitation of these images is challenging due to noise and meteorological uncertainties. To overcome these obstacles, we use a deep learning neural network trained on simulated CO2 images. Our method outperforms alternatives, providing a positive perspective for the analysis of CO2M images.
Kyoung-Min Kim, Si-Wan Kim, Seunghwan Seo, Donald R. Blake, Seogju Cho, James H. Crawford, Louisa K. Emmons, Alan Fried, Jay R. Herman, Jinkyu Hong, Jinsang Jung, Gabriele G. Pfister, Andrew J. Weinheimer, Jung-Hun Woo, and Qiang Zhang
Geosci. Model Dev., 17, 1931–1955, https://doi.org/10.5194/gmd-17-1931-2024, https://doi.org/10.5194/gmd-17-1931-2024, 2024
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Three emission inventories were evaluated for East Asia using data acquired during a field campaign in 2016. The inventories successfully reproduced the daily variations of ozone and nitrogen dioxide. However, the spatial distributions of model ozone did not fully agree with the observations. Additionally, all simulations underestimated carbon monoxide and volatile organic compound (VOC) levels. Increasing VOC emissions over South Korea resulted in improved ozone simulations.
Sanam Noreen Vardag and Robert Maiwald
Geosci. Model Dev., 17, 1885–1902, https://doi.org/10.5194/gmd-17-1885-2024, https://doi.org/10.5194/gmd-17-1885-2024, 2024
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We use the atmospheric transport model GRAMM/GRAL in a Bayesian inversion to estimate urban CO2 emissions on a neighbourhood scale. We analyse the effect of varying number, precision and location of CO2 sensors for CO2 flux estimation. We further test the inclusion of co-emitted species and correlation in the inversion. The study showcases the general usefulness of GRAMM/GRAL in measurement network design.
Abhishek Savita, Joakim Kjellsson, Robin Pilch Kedzierski, Mojib Latif, Tabea Rahm, Sebastian Wahl, and Wonsun Park
Geosci. Model Dev., 17, 1813–1829, https://doi.org/10.5194/gmd-17-1813-2024, https://doi.org/10.5194/gmd-17-1813-2024, 2024
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The OpenIFS model is used to examine the impact of horizontal resolutions (HR) and model time steps. We find that the surface wind biases over the oceans, in particular the Southern Ocean, are sensitive to the model time step and HR, with the HR having the smallest biases. When using a coarse-resolution model with a shorter time step, a similar improvement is also found. Climate biases can be reduced in the OpenIFS model at a cheaper cost by reducing the time step rather than increasing the HR.
Ferdinand Briegel, Jonas Wehrle, Dirk Schindler, and Andreas Christen
Geosci. Model Dev., 17, 1667–1688, https://doi.org/10.5194/gmd-17-1667-2024, https://doi.org/10.5194/gmd-17-1667-2024, 2024
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We present a new approach to model heat stress in cities using artificial intelligence (AI). We show that the AI model is fast in terms of prediction but accurate when evaluated with measurements. The fast-predictive AI model enables several new potential applications, including heat stress prediction and warning; downscaling of potential future climates; evaluation of adaptation effectiveness; and, more fundamentally, development of guidelines to support urban planning and policymaking.
Hauke Schmidt, Sebastian Rast, Jiawei Bao, Amrit Cassim, Shih-Wei Fang, Diego Jimenez-de la Cuesta, Paul Keil, Lukas Kluft, Clarissa Kroll, Theresa Lang, Ulrike Niemeier, Andrea Schneidereit, Andrew I. L. Williams, and Bjorn Stevens
Geosci. Model Dev., 17, 1563–1584, https://doi.org/10.5194/gmd-17-1563-2024, https://doi.org/10.5194/gmd-17-1563-2024, 2024
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A recent development in numerical simulations of the global atmosphere is the increase in horizontal resolution to grid spacings of a few kilometers. However, the vertical grid spacing of these models has not been reduced at the same rate as the horizontal grid spacing. Here, we assess the effects of much finer vertical grid spacings, in particular the impacts on cloud quantities and the atmospheric energy balance.
Tao Zheng, Sha Feng, Jeffrey Steward, Xiaoxu Tian, David Baker, and Martin Baxter
Geosci. Model Dev., 17, 1543–1562, https://doi.org/10.5194/gmd-17-1543-2024, https://doi.org/10.5194/gmd-17-1543-2024, 2024
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The tangent linear and adjoint models have been successfully implemented in the MPAS-CO2 system, which has undergone rigorous accuracy testing. This development lays the groundwork for a global carbon flux data assimilation system, which offers the flexibility of high-resolution focus on specific areas, while maintaining a coarser resolution elsewhere. This approach significantly reduces computational costs and is thus perfectly suited for future CO2 geostationery and imager satellites.
Kelvin H. Bates, Mathew J. Evans, Barron H. Henderson, and Daniel J. Jacob
Geosci. Model Dev., 17, 1511–1524, https://doi.org/10.5194/gmd-17-1511-2024, https://doi.org/10.5194/gmd-17-1511-2024, 2024
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Accurate representation of rates and products of chemical reactions in atmospheric models is crucial for simulating concentrations of pollutants and climate forcers. We update the widely used GEOS-Chem atmospheric chemistry model with reaction parameters from recent compilations of experimental data and demonstrate the implications for key atmospheric chemical species. The updates decrease tropospheric CO mixing ratios and increase stratospheric nitrogen oxide mixing ratios, among other changes.
François Roberge, Alejandro Di Luca, René Laprise, Philippe Lucas-Picher, and Julie Thériault
Geosci. Model Dev., 17, 1497–1510, https://doi.org/10.5194/gmd-17-1497-2024, https://doi.org/10.5194/gmd-17-1497-2024, 2024
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Our study addresses a challenge in dynamical downscaling using regional climate models, focusing on the lack of small-scale features near the boundaries. We introduce a method to identify this “spatial spin-up” in precipitation simulations. Results show spin-up distances up to 300 km, varying by season and driving variable. Double nesting with comprehensive variables (e.g. microphysical variables) offers advantages. Findings will help optimize simulations for better climate projections.
Eloisa Raluy-López, Juan Pedro Montávez, and Pedro Jiménez-Guerrero
Geosci. Model Dev., 17, 1469–1495, https://doi.org/10.5194/gmd-17-1469-2024, https://doi.org/10.5194/gmd-17-1469-2024, 2024
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Atmospheric rivers (ARs) represent a significant source of water but are also related to extreme precipitation events. Here, we present a new regional-scale AR identification algorithm and apply it to three simulations that include aerosol interactions at different levels. The results show that aerosols modify the intensity and trajectory of ARs and redistribute the AR-related precipitation. Thus, the correct inclusion of aerosol effects is important in the simulation of AR behavior.
Cited articles
Alexander, M. A., Scott, J. D., and Deser, C.: Processes that influence sea surface temperature and ocean mixed layer depth variability in a coupled model, J. Geophys. Res, 105, 16823–16842, 2000.
Arribas, A., Glover, M., Maidens, A., Peterson, K., Gordon, M., MacLachlan, C. D., Fereday, R. G., Camp, J., Scaife, A. A., Xavier, P., Coleman, A., and Cusack, S.: The GloSea4 ensemble prediction system for seasonal forecasting, Mon. Weather Rev., 139, 1891–1910, 2011.
Benedict, J. J. and Randall, D. A.: Impacts of Idealized Air-Sea coupling on Madden-Julian Oscillation sctructure in the Superparameterized CAM, J. Atmos. Sci., 68, 1990–2008, 2011.
Bernie, A. J., Guilyardi, E., Madec, G., Slingo, J. M., Woolnough, S. J., and Cole, J.: Impact of resolving the diurnal cycle in an ocean-atmosphere GCM. Part 2: A diurnally coupled CGCM, Clim. Dynam., 31, 909–925, 2008.
Bhatt, U. S., Alexander, M. A., Battisti, D. S., Houghton, D. D., and Keller, L. M.: Atmosphere-Ocean Interaction in the North Atlantic: Near-Surface Climate Variability, J. Climate, 11, 1615–1632, 1998.
Blackmon, M. L.: A Climatological Spectral Study of the 500 mb Geopotential Height of the Northern Hemisphere, J. Atmos. Sci., 33, 1607–1623, 1979.
Cassou, C.: Intraseasonal interaction between the Madden-Julian Oscillation and the North Atlantic Oscillation, Nature, 455, 523–527, 2008.
Cassou, C., Terray, L., and Phillips, A. S.: Tropical Atlantic Influence on European Heat Waves, J. Climate, 18, 2805–2811, 2005.
Cassou, C., Deser, C., and Alexander, M. A.: Investigating the Impact of Reemerging Sea Surface Temperature Anomlaies on the Winter Atmospheric Circulation over the North Atlantic, J. Climate, 20, 3510–3526, 2007.
Crueger, T., Stevens, B., and Brokopf, R.: The Madden-Julian Oscillation in ECHAM6 and the introduction of an objective MJO Index, J. Climate, 26, 3241–3257, 2013.
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., Peuby, 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, 2011.
DeMott, C. A., Stan, C., Randall, D. A., and Branson, M. D.: Intraseasonal Variability in Coupled GCMs: The Roles of Ocean Feedbacks and Model Physics, J. Climate, 27, 4970–4995, 2014.
Doblas-Reyes, F., Casado, M. J., and Pastor, M. A.: Sensitivity of the Northern Hemisphere blocking frequency to the detection index, J. Geophys. Res., 107, ACL6.1–ACL.6.22, https://doi.org/10.1029/2000JD000290, 2002.
Feudale, L. and Shukla, J.: Influence of sea surface temperature on the European heat wave of 2003 summer. Part I: an observational study, Clim. Dynam., 36, 1691–1703, 2011.
Fu, X., Wang, B., Li, T., and McCreary, J. P.: Coupling between Northward-Propagating, Intraseasonal Oscillations and Sea Surface Temperature in the Indian Ocean, J. Atmos. Sci., 60, 1733–1753, 2003.
Giannini, A., Saravanan, R., and Chang, P.: Oceanic Forcing of Sahel Rainfall on Interannual to Interdecadal Time Scales, Science, 302, 1027–1030, 2003.
Guemas, V., Salas-Mèlia, D., Kageyama, M., Giordani, H., and Voldoire, A.: Impact of the ocean diurnal cycle on the North Atlantic mean sea surface temperatures in a regionally coupled model, Dynam. Atmos. Oceans., 60, 28–45, 2013.
Ham, Y.-G. and Kug, J.-S.: Impact of diurnal atmosphere-ocean coupling on tropical climate simulations using a coupled GCM, Clim. Dynam., 34, 905–917, 2010.
Hendon, H. H. and Liebmann, B.: The Intraseasonal (30–50 day) Oscillation of the Australian Summer Monsoon, J. Atmos. Sci., 47, 2909–2923, 1990.
Hendon, H. H., Lim, E.-P., and Luo, G.: The Role of Air-Sea Interaction for Prediction of Australian Summer Monsoon Rainfall, J. Climate, 25, 1278–1290, 2012.
Hoskins, B. J. and Hodges, K. I.: New Perspectives on the Northern Hemisphere Winter Storm Tracks, J. Atmos. Sci., 59, 1041–1061, 2002.
Inness, P. M., Slingo, J. M., Guilyardi, E., and Cole, J.: Simulation of the Madden-Julian Oscillation in a Coupled General Circulation Model: Part II: The Role of the Basic State, J. Climate, 16, 365–382, 2003.
Jullien, S., Marchesiello, P., Menkes, C. E., Lefévre, J., Jourdain, N. C., Samson, G., and Lengaigne, M.: Ocean feedback to tropical cyclones: climatology and processes, Clim. Dynam., 43, 2831–2854, 2014.
Kim, D., Sperber, K., Stern, W., Waliser, D., Kang, I.-S., Maloney, E. D., Wang, W., Weickmann, K. J., Benedict, M. K., Lee, M.-I., Neale, R., Suarez, M., Thayer-Calder, K., and Zhang, G.: Application of MJO Simulation Diagnostics to Climate Models, J. Climate, 22, 6413–6436, 2009.
Klingaman, N. P. and Woolnough, S. J.: The Role of air–sea coupling in the simulation of the Madden-Julian oscillation in the Hadley Centre model, Q. J. Roy. Meteorol. Soc., 140, 2272–2286, https://doi.org/10.1002/qj.2295, 2014.
Klingaman, N. P., Woolnough, S. J., Weller, H., and Slingo, J. M.: The Impact of Finer-Resolution Air-Sea Coupling on the Intraseasonal Oscillation of the Indian Monsoon, J. Climate, 24, 2451–2468, 2011.
Kummerow, C., Barnes, W., Kozu, T., Shiue, J., and Simpson, J.: The Tropical Rainfall Measuring Mission (TRMM) sensor package, J. Atmos. Ocean. Tech., 15, 809–817, 1998.
Kwon, Y.-O., Deser, C., and Cassou, C.: Coupled atmosphere-mixed layer ocean response to ocean heat flux convergence along the Kuroshio Current Extension., Clim. Dynam., 36, 2295–2312, 2011.
Large, W., McWilliams, J., and Doney, S.: Oceanic vertical mising: A review and a model with a nonlocal boundary layer parameterization, Rev. Geophys., 32, 363–403, 1994.
Lavender, S. L. and Matthews, A. J.: Response of the West African Monsoon to the Madden-Julian Oscillation, J. Climate, 22, 4097–4116, 2009.
Lawrence, D. M. and Webster, P. J.: The Boreal Summer Intraseasonal Oscillation: Relationship between Northward and Eastward Movement of Convection, J. Atmos. Sci., 59, 1593–1606, 2002.
Madden, R. A. and Julian, P. R.: Detection of a 40–50 Day Oscillation in the Zonal Wind in the Tropical Pacific, J. Atmos. Sci., 28, 702–708, 1971.
Madden, R. A. and Julian, P. R.: Description of Global-Scale Circulation Cells in the Tropics with a 40-50 Day Period, J. Atmos. Sci., 29, 1109–1123, 1972.
Maloney, E. D. and Sobel, A. H.: Surface Fluxes and Ocean Coupling in the Tropical Intraseasonal Oscillation, J. Climate., 17, 4368–4386, 2004.
Martin, G. M., Dearden, C., Greeves, C., Hinton, T., Inness, P., James, P., Pope, V., Ringer, M., Slingo, J. M., Stratton, R., and Yang, G.-Y.: Evaluation of the atmospheric performance of HadGAM/GEM1, Tech. Rep. 54, Hadley Centre Tech. Note, 2004.
Matthews, A. J.: Intraseasonal Variability over Tropical Africa during Northern Summer, J. Climate, 17, 2427–2440, 2004.
Nakamura, M. and Yamane, S.: Dominant Anomaly Patterns in the Near-Surface Baroclinicity and Accompanying Anomalies in the Atmosphere and Oceans. Part I: North Atlantic Basin, J. Climate, 22, 880–904, 2009.
Neelin, J. D., Battisti, D. S., Hirst, A. C., Jin, F.-F., Wakata, Y., Yamagata, T., and Zebiak, S. E.: ENSO theory, J. Geophys. Res, 103, 14261–14290, 1998.
Pezza, A. B., van Rensch, P., and Cai, W.: Severe heat waves in Southern Australia: synoptic climatology and large scale connections, Clim. Dynam., 38, 209–224, 2012.
Prodhomme, C., Terray, P., Masson, S., Boschat, G., and Izumo, T.: Oceanic factors controlling the Indian summer monsoon onset in a coupled model, Clim. Dynam., 2014.
Rajendran, K. and Kitoh, A.: Modulation of Tropical Intraseasonal Oscillations by Ocean-Atmosphere Coupling, J. Climate, 19, 366–391, 2006.
Rajendran, K., Kitoh, A., and Arakawa, O.: Monsoon low-frequency inrtaseasonal oscillation and ocean-atmosphere coupling over the Indian Ocean, Geophys. Res. Lett., 31, L02210, https://doi.org/10.1029/2003GL019031, 2004.
Ray, P., Zhang, C., Moncrieff, M. W., Dudhia, J., Caron, J. M., Leung, L. R., and Bruyere, C.: Role of the atmospheric mean state on the initiation of the Madden-Julian Oscillation in a tropical channel model, Clim. Dynam., 36, 161–184, 2011.
Ringer, M. A., Martin, G. M., Greeves, C. Z., Hinton, T. J., James, P. M., Pope, V. D., Scaife, A. A., Stratton, R. A., Inness, P. M., Slingo, J. M., and Yand, G.-Y.: The Phyiscal Properties of the Atmosphere in the New Hadley Centre Global Environmental Model (HadGEM1). Part II: Aspects of Variability and Regional Climate, J. Climate, 19, 1302–1326, 2006.
Sandery, P. A., Brassington, G. B., Craig, A., and Pugh, T.: Impacts of ocean-atmosphere coupling on tropical cyclone intensity change and ocean prediction in the Australian region., Mon. Weather Rev., 138, 2074–2091, 2010.
Sausen, R., Barthel, K., and Hasselmann, K.: Coupled ocean-atmosphere models with flux corrections, Clim. Dynam., 2, 145–163, 1988.
Scaife, A. A., Woolings, T., Knight, J. R., Martin, G., and Hinton, T.: Atmospheric blocking and mean biases in climate models, J. Climate, 23, 6143–6152, 2010.
Scherrer, S. C., Croci-Maspoli, M., Schwierz, C., and Appenzeller, C.: Two-Dimensional indicies of Atmospheric Blocking and their statistical relationship with wither climate pattens in the Euro-Atlantic region, Int. J. Climatol., 26, 233–249, 2006.
Seo, K.-H., Schemm, J.-K. E., Wang, W., and Kumar, A.: The Boreal Summer Intraseasonal Oscillation Simulated in the NCEP Climate Forecast System: The Effect of Sea Surface Temperature, Mon. Weather Rev., 135, 1807–1827, 2007.
Shinoda, T., Jensen, T. G., Flatau, M., Chen, S., Han, W., and Wang, C.: Large-Scale Oceanic Variability Associaed with the Madden-Julian Oscillation during the CINDY/DYNAMO Field Campaign from Satellite Observations, Remote Sens., 5, 2072–2092, 2013.
Smith, D. M. and Murphey, J. M.: An objective ocean temperature and salinity analysis using covariances from global climate models, J. Geophys. Res., 112, C02022, https://doi.org/10.1029/2005JC003172, 2007.
Smith, W. H. F. and Sandwell, D. T.: Global Sea Floor Topography from Satellite Altimetry and Ship Depth Soundings, Science, 277, 1956–1962, 1997.
Sperber, K. R., Annamalai, H., Kang, L.-S., Kitoh, A., Moise, A., Turner, A., Wang, B., and Zhou, T.: The Asian summer monsoon: an intercomparison of CMIP5 vs. CMIP3 simulations of the late 20th century, Clim. Dynam., 41, 2711–2744, 2013.
Straub, K. H. and Kiladis, G. N.: Extratropical forcing of convectively coupled Kelvin waves during austral winter, J. Atmos. Sci., 60, 526–543, 2003.
Sutton, R. T. and Hodson, D. L. R.: Influences of the Ocean on North Atlantic Climate Variability 1871–1999, J. Climate., 16, 3296–3313, 2003.
Sutton, R. T. and Hodson, D. L. R.: Atlantic Ocean Forcing of North American and European Summer Climate, Science, 309, 115–118, 2005.
Taylor, K. E., Stouffer, R. J., and Meehl, G. A.: An Overview of CMIP5 and the Experiment Design, B. Am. Meteorol. Soc., 93, 485–498, 2012.
Tibaldi, S. and Molteni, F.: On the operational predictability of blocking, Tellus, 42A, 343–365, 1990.
Tseng, W.-L., Tsuang, B.-J., Keenlyside, N. S., Hsu, H.-H., and Tu, C.-Y.: Resolving the upper-ocean warm layer improves the simulation of the Madden-Julian oscillation, Clim. Dynam., 112, 1–17, https://doi.org/10.1007/s00382-014-2315-1, 2014.
Valcke, S., Caubel, A., Declat, D., and Terray, L.: OASIS3 Ocean Atmosphere Sea Ice Soil user's guide, Tech. Rep. TR/CMGC/03/69, CERFACS, Toulouse, France, 2003.
Vannière, B., Guilyardi, E., Madec, G., Doblas-Reyes, F., and Woolnough, S. J.: Using seasonal hindcasts to understand the origin of the equatorial cold toungue bias in CGCMs and its impact on ENSO, Clim. Dynam., 40, 963–981, 2012.
Vecchi, G. A. and Harrison, D. E.: Monsoon breaks and subseasonal sea surface temperature variability in the Bay of Bengal, J. Climate, 15, 1485–1493, 2002.
Vitart, F.: Impact of the Madden Julian Oscillation on tropical storms and risk of landfall in the ECMWF forecast system, Geophys. Res. Lett., 36, L15802, https://doi.org/10.1029/2009GL039089, 2009.
Vitart, F. and Molteni, F.: Dynamical Extended-Range Prediction of Early Monsoon Rainfall over India, Mon. Weather Rev., 137, 1480–1492, 2009.
Vitart, F. and Molteni, F.: Simulation of the MJO and its teleconnections in the ECMWF forecast system, Q. J. Roy. Meteorol. Soc., 136, 842–855, 2010.
Walters, D. N., Best, M. J., Bushell, A. C., Copsey, D., Edwards, J. M., Falloon, P. D., Harris, C. M., Lock, A. P., Manners, J. C., Morcrette, C. J., Roberts, M. J., Stratton, R. A., Webster, S., Wilkinson, J. M., Willett, M. R., Boutle, I. A., Earnshaw, P. D., Hill, P. G., MacLachlan, C., Martin, G. M., Moufouma-Okia, W., Palmer, M. D., Petch, J. C., Rooney, G. G., Scaife, A. A., and Williams, K. D.: The Met Office Unified Model Global Atmosphere 3.0/3.1 and JULES Global Land 3.0/3.1 configurations, Geosci. Model Dev., 4, 919–941, https://doi.org/10.5194/gmd-4-919-2011, 2011.
Wang, W., Chen, M., and Kumar, A.: Impacts of Ocean Surface on the Northward Propagation of the Boreal Summer Intraseasonal Oscillation in the NCEP Climate Forecast System, J. Climate, 22, 6561–6576, 2009.
Watterson, I. G.: The sensitivity of subannual and intraseasonal tropical variability to model ocean mixed layer depth, J. Geophys. Res., 107, ACL12.1–ACL12.15, 2002.
Wheeler, M. C. and Hendon, H. H.: An All-Seasonal Real-Time Multivariate MJO Index: Development of an Index for Monitoring and Prediction, Mon. Weather Rev., 132, 1917–1932, 2004.
Wheeler, M. C. and Kiladis, G. N.: Convectively Coupled Equatorial Waves: Analysis of Clouds and Temperature in the Wavenumber-Frequency Diagram, J. Atmos. Sci., 56, 374–399, 1999.
Woolnough, S. J., Vitart, F., and Balmaseda, M. A.: The role of the ocean in the Madden-Julian Oscillation: Implications for MJO prediction, Q. J. Roy. Meteorol. Soc., 133, 117–128, 2007.
Wu, G., Guan, Y., and Liu, Y.: Air-sea interaction and formation of the Asian summer monsoon onset vortex over the Bay of Bengal, Clim. Dynam., 38, 261–279, 2012.
Zhang, C.: Madden-Julian Oscillation, Rev. Geophys., 43, RG2003, https://doi.org/10.1029/2004RG000158, 2005.
Short summary
Atmosphere-ocean interactions are best isolated in models rather than observations, but state-of-the-art models are expensive and often simulate these interactions poorly. We present a less expensive modelling framework that resolves air-sea interactions well, and permits a more rigorous identification of these interactions' effects than previously possible. In our model, air-sea interactions improve tropical rainfall variations but have limited effects on midlatitude jet streams.
Atmosphere-ocean interactions are best isolated in models rather than observations, but...
