Articles | Volume 15, issue 17
https://doi.org/10.5194/gmd-15-6759-2022
© Author(s) 2022. 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-15-6759-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Methods for assessment of models
| 
06 Sep 2022
Methods for assessment of models |
| 06 Sep 2022
| 06 Sep 2022
Intercomparison of four algorithms for detecting tropical cyclones using ERA5
Stella Bourdin
CORRESPONDING AUTHOR
Laboratoire des Sciences du Climat et de l'Environnement, LSCE/IPSL, CEA-CNRS-UVSQ-Université Paris-Saclay, Gif-sur-Yvette, France
Sébastien Fromang
Laboratoire des Sciences du Climat et de l'Environnement, LSCE/IPSL, CEA-CNRS-UVSQ-Université Paris-Saclay, Gif-sur-Yvette, France
William Dulac
Centre National de Recherches Météorologiques, Université de Toulouse, Météo France, CNRS, Toulouse, France
Julien Cattiaux
Centre National de Recherches Météorologiques, Université de Toulouse, Météo France, CNRS, Toulouse, France
Fabrice Chauvin
Centre National de Recherches Météorologiques, Université de Toulouse, Météo France, CNRS, Toulouse, France
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Post-tropical cyclones (PTCs) can bring severe weather to Europe. By tracking and identifying PTCs in five global climate models, we investigate how the frequency and intensity of PTCs may change across Europe by 2100. We find no robust change in the frequency or intensity of Europe-impacting PTCs in the future. This study indicates that large uncertainties surround future Europe-impacting PTCs and provides a framework for evaluating PTCs in future generations of climate models.
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Weather Clim. Dynam., 3, 1359–1379, https://doi.org/10.5194/wcd-3-1359-2022, https://doi.org/10.5194/wcd-3-1359-2022, 2022
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Post-tropical cyclones (PTCs) can bring severe weather to Europe. By tracking and identifying PTCs in five global climate models, we investigate how the frequency and intensity of PTCs may change across Europe by 2100. We find no robust change in the frequency or intensity of Europe-impacting PTCs in the future. This study indicates that large uncertainties surround future Europe-impacting PTCs and provides a framework for evaluating PTCs in future generations of climate models.
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Weather Clim. Dynam., 3, 1311–1340, https://doi.org/10.5194/wcd-3-1311-2022, https://doi.org/10.5194/wcd-3-1311-2022, 2022
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We analyze the atmospheric circulation leading to impactful extreme events for the calendar year 2021 such as the Storm Filomena, Westphalia floods, Hurricane Ida and Medicane Apollo. For some of the events, we find that climate change has contributed to their occurrence or enhanced their intensity; for other events, we find that they are unprecedented. Our approach underscores the importance of considering changes in the atmospheric circulation when performing attribution studies.
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In this study, we improved the calculation of how aerosols in the air interact with radiation in WRF-Chem. The original model used a simplified method, but we developed a more accurate approach. We found that this method significantly changes the properties of the estimated aerosols and their effects on radiation, especially for dust aerosols. It also impacts the simulated weather conditions. Our work highlights the importance of correctly representing aerosol–radiation interactions in models.
Eduardo Moreno-Chamarro, Thomas Arsouze, Mario Acosta, Pierre-Antoine Bretonnière, Miguel Castrillo, Eric Ferrer, Amanda Frigola, Daria Kuznetsova, Eneko Martin-Martinez, Pablo Ortega, and Sergi Palomas
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We present the high-resolution model version of the EC-Earth global climate model to contribute to HighResMIP. The combined model resolution is about 10–15 km in both the ocean and atmosphere, which makes it one of the finest ever used to complete historical and scenario simulations. This model is compared with two lower-resolution versions, with a 100 km and a 25 km grid. The three models are compared with observations to study the improvements thanks to the increased resolution.
Catherine Guiavarc'h, David Storkey, Adam T. Blaker, Ed Blockley, Alex Megann, Helene Hewitt, Michael J. Bell, Daley Calvert, Dan Copsey, Bablu Sinha, Sophia Moreton, Pierre Mathiot, and Bo An
Geosci. Model Dev., 18, 377–403, https://doi.org/10.5194/gmd-18-377-2025, https://doi.org/10.5194/gmd-18-377-2025, 2025
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The Global Ocean and Sea Ice configuration version 9 (GOSI9) is the new UK hierarchy of model configurations based on the Nucleus for European Modelling of the Ocean (NEMO) and available at three resolutions. It will be used for various applications, e.g. weather forecasting and climate prediction. It improves upon the previous version by reducing global temperature and salinity biases and enhancing the representation of Arctic sea ice and the Antarctic Circumpolar Current.
Andy Richling, Jens Grieger, and Henning W. Rust
Geosci. Model Dev., 18, 361–375, https://doi.org/10.5194/gmd-18-361-2025, https://doi.org/10.5194/gmd-18-361-2025, 2025
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The performance of weather and climate prediction systems is variable in time and space. It is of interest how this performance varies in different situations. We provide a decomposition of a skill score (a measure of forecast performance) as a tool for detailed assessment of performance variability to support model development or forecast improvement. The framework is exemplified with decadal forecasts to assess the impact of different ocean states in the North Atlantic on temperature forecast.
Maria R. Russo, Sadie L. Bartholomew, David Hassell, Alex M. Mason, Erica Neininger, A. James Perman, David A. J. Sproson, Duncan Watson-Parris, and Nathan Luke Abraham
Geosci. Model Dev., 18, 181–191, https://doi.org/10.5194/gmd-18-181-2025, https://doi.org/10.5194/gmd-18-181-2025, 2025
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Observational data and modelling capabilities have expanded in recent years, but there are still barriers preventing these two data sources from being used in synergy. Proper comparison requires generating, storing, and handling a large amount of data. This work describes the first step in the development of a new set of software tools, the VISION toolkit, which can enable the easy and efficient integration of observational and model data required for model evaluation.
Bijan Fallah, Masoud Rostami, Emmanuele Russo, Paula Harder, Christoph Menz, Peter Hoffmann, Iulii Didovets, and Fred F. Hattermann
Geosci. Model Dev., 18, 161–180, https://doi.org/10.5194/gmd-18-161-2025, https://doi.org/10.5194/gmd-18-161-2025, 2025
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We tried to contribute to a local climate change impact study in central Asia, a region that is water-scarce and vulnerable to global climate change. We use regional models and machine learning to produce reliable local data from global climate models. We find that regional models show more realistic and detailed changes in heavy precipitation than global climate models. Our work can help assess the future risks of extreme events and plan adaptation strategies in central Asia.
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Geosci. Model Dev., 18, 33–69, https://doi.org/10.5194/gmd-18-33-2025, https://doi.org/10.5194/gmd-18-33-2025, 2025
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Detailed global climate model simulations have been created based on a numerical weather prediction model, offering more accurate spatial detail down to the scale of individual cities ("kilometre-scale") and a better understanding of climate phenomena such as atmospheric storms, whirls in the ocean, and cracks in sea ice. The new model aims to provide globally consistent information on local climate change with greater precision, benefiting environmental planning and local impact modelling.
Yilin Fang, Hoang Viet Tran, and L. Ruby Leung
Geosci. Model Dev., 18, 19–32, https://doi.org/10.5194/gmd-18-19-2025, https://doi.org/10.5194/gmd-18-19-2025, 2025
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Hurricanes may worsen water quality in the lower Mississippi River basin (LMRB) by increasing nutrient runoff. We found that runoff parameterizations greatly affect nitrate–nitrogen runoff simulated using an Earth system land model. Our simulations predicted increased nitrogen runoff in the LMRB during Hurricane Ida in 2021, albeit less pronounced than the observations, indicating areas for model improvement to better understand and manage nutrient runoff loss during hurricanes in the region.
Giovanni Seijo-Ellis, Donata Giglio, Gustavo Marques, and Frank Bryan
Geosci. Model Dev., 17, 8989–9021, https://doi.org/10.5194/gmd-17-8989-2024, https://doi.org/10.5194/gmd-17-8989-2024, 2024
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A CESM–MOM6 regional configuration of the Caribbean Sea was developed in response to the rising need for high-resolution models for climate impact studies. The configuration is validated for the period 2000–2020 and improves significant errors in a low-resolution model. Oceanic properties are well represented. Patterns of freshwater associated with the Amazon River are well captured, and the mean flows of ocean waters across multiple passages in the Caribbean Sea agree with observations.
Deifilia To, Julian Quinting, Gholam Ali Hoshyaripour, Markus Götz, Achim Streit, and Charlotte Debus
Geosci. Model Dev., 17, 8873–8884, https://doi.org/10.5194/gmd-17-8873-2024, https://doi.org/10.5194/gmd-17-8873-2024, 2024
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Pangu-Weather is a breakthrough machine learning model in medium-range weather forecasting that considers 3D atmospheric information. We show that using a simpler 2D framework improves robustness, speeds up training, and reduces computational needs by 20 %–30 %. We introduce a training procedure that varies the importance of atmospheric variables over time to speed up training convergence. Decreasing computational demand increases the accessibility of training and working with the model.
Fang Li, Xiang Song, Sandy P. Harrison, Jennifer R. Marlon, Zhongda Lin, L. Ruby Leung, Jörg Schwinger, Virginie Marécal, Shiyu Wang, Daniel S. Ward, Xiao Dong, Hanna Lee, Lars Nieradzik, Sam S. Rabin, and Roland Séférian
Geosci. Model Dev., 17, 8751–8771, https://doi.org/10.5194/gmd-17-8751-2024, https://doi.org/10.5194/gmd-17-8751-2024, 2024
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This study provides the first comprehensive assessment of historical fire simulations from 19 Earth system models in phase 6 of the Coupled Model Intercomparison Project (CMIP6). Most models reproduce global totals, spatial patterns, seasonality, and regional historical changes well but fail to simulate the recent decline in global burned area and underestimate the fire response to climate variability. CMIP6 simulations address three critical issues of phase-5 models.
Seung H. Baek, Paul A. Ullrich, Bo Dong, and Jiwoo Lee
Geosci. Model Dev., 17, 8665–8681, https://doi.org/10.5194/gmd-17-8665-2024, https://doi.org/10.5194/gmd-17-8665-2024, 2024
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We evaluate downscaled products by examining locally relevant co-variances during precipitation events. Common statistical downscaling techniques preserve expected co-variances during convective precipitation (a stationary phenomenon). However, they dampen future intensification of frontal precipitation (a non-stationary phenomenon) captured in global climate models and dynamical downscaling. Our study quantifies a ramification of the stationarity assumption underlying statistical downscaling.
Emmanuel Nyenah, Petra Döll, Daniel S. Katz, and Robert Reinecke
Geosci. Model Dev., 17, 8593–8611, https://doi.org/10.5194/gmd-17-8593-2024, https://doi.org/10.5194/gmd-17-8593-2024, 2024
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Research software is vital for scientific progress but is often developed by scientists with limited skills, time, and funding, leading to challenges in usability and maintenance. Our study across 10 sectors shows strengths in version control, open-source licensing, and documentation while emphasizing the need for containerization and code quality. We recommend workshops; code quality metrics; funding; and following the findable, accessible, interoperable, and reusable (FAIR) standards.
Chris Smith, Donald P. Cummins, Hege-Beate Fredriksen, Zebedee Nicholls, Malte Meinshausen, Myles Allen, Stuart Jenkins, Nicholas Leach, Camilla Mathison, and Antti-Ilari Partanen
Geosci. Model Dev., 17, 8569–8592, https://doi.org/10.5194/gmd-17-8569-2024, https://doi.org/10.5194/gmd-17-8569-2024, 2024
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Climate projections are only useful if the underlying models that produce them are well calibrated and can reproduce observed climate change. We formalise a software package that calibrates the open-source FaIR simple climate model to full-complexity Earth system models. Observations, including historical warming, and assessments of key climate variables such as that of climate sensitivity are used to constrain the model output.
Jingwei Xie, Xi Wang, Hailong Liu, Pengfei Lin, Jiangfeng Yu, Zipeng Yu, Junlin Wei, and Xiang Han
Geosci. Model Dev., 17, 8469–8493, https://doi.org/10.5194/gmd-17-8469-2024, https://doi.org/10.5194/gmd-17-8469-2024, 2024
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We propose the concept of mesoscale ocean direct numerical simulation (MODNS), which should resolve the first baroclinic deformation radius and ensure the numerical dissipative effects do not directly contaminate the mesoscale motions. It can be a benchmark for testing mesoscale ocean large eddy simulation (MOLES) methods in ocean models. We build an idealized Southern Ocean model using MITgcm to generate a type of MODNS. We also illustrate the diversity of multiscale eddy interactions.
Emily Black, John Ellis, and Ross I. Maidment
Geosci. Model Dev., 17, 8353–8372, https://doi.org/10.5194/gmd-17-8353-2024, https://doi.org/10.5194/gmd-17-8353-2024, 2024
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We present General TAMSAT-ALERT, a computationally lightweight and versatile tool for generating ensemble forecasts from time series data. General TAMSAT-ALERT is capable of combining multiple streams of monitoring and meteorological forecasting data into probabilistic hazard assessments. In this way, it complements existing systems and enhances their utility for actionable hazard assessment.
Sarah Schöngart, Lukas Gudmundsson, Mathias Hauser, Peter Pfleiderer, Quentin Lejeune, Shruti Nath, Sonia Isabelle Seneviratne, and Carl-Friedrich Schleussner
Geosci. Model Dev., 17, 8283–8320, https://doi.org/10.5194/gmd-17-8283-2024, https://doi.org/10.5194/gmd-17-8283-2024, 2024
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Precipitation and temperature are two of the most impact-relevant climatic variables. Yet, projecting future precipitation and temperature data under different emission scenarios relies on complex models that are computationally expensive. In this study, we propose a method that allows us to generate monthly means of local precipitation and temperature at low computational costs. Our modelling framework is particularly useful for all downstream applications of climate model data.
Gang Tang, Zebedee Nicholls, Chris Jones, Thomas Gasser, Alexander Norton, Tilo Ziehn, Alejandro Romero-Prieto, and Malte Meinshausen
EGUsphere, https://doi.org/10.5194/egusphere-2024-3522, https://doi.org/10.5194/egusphere-2024-3522, 2024
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We analyzed carbon and nitrogen mass conservation in data from CMIP6 Earth System Models. Our findings reveal significant discrepancies between flux and pool size data, particularly in nitrogen, where cumulative imbalances can reach hundreds of gigatons. These imbalances appear primarily due to missing or inconsistently reported fluxes – especially for land use and fire emissions. To enhance data quality, we recommend that future climate data protocols address this issue at the reporting stage.
Benjamin M. Sanderson, Ben B. B. Booth, John Dunne, Veronika Eyring, Rosie A. Fisher, Pierre Friedlingstein, Matthew J. Gidden, Tomohiro Hajima, Chris D. Jones, Colin G. Jones, Andrew King, Charles D. Koven, David M. Lawrence, Jason Lowe, Nadine Mengis, Glen P. Peters, Joeri Rogelj, Chris Smith, Abigail C. Snyder, Isla R. Simpson, Abigail L. S. Swann, Claudia Tebaldi, Tatiana Ilyina, Carl-Friedrich Schleussner, Roland Séférian, Bjørn H. Samset, Detlef van Vuuren, and Sönke Zaehle
Geosci. Model Dev., 17, 8141–8172, https://doi.org/10.5194/gmd-17-8141-2024, https://doi.org/10.5194/gmd-17-8141-2024, 2024
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We discuss how, in order to provide more relevant guidance for climate policy, coordinated climate experiments should adopt a greater focus on simulations where Earth system models are provided with carbon emissions from fossil fuels together with land use change instructions, rather than past approaches that have largely focused on experiments with prescribed atmospheric carbon dioxide concentrations. We discuss how these goals might be achieved in coordinated climate modeling experiments.
Peter Berg, Thomas Bosshard, Denica Bozhinova, Lars Bärring, Joakim Löw, Carolina Nilsson, Gustav Strandberg, Johan Södling, Johan Thuresson, Renate Wilcke, and Wei Yang
Geosci. Model Dev., 17, 8173–8179, https://doi.org/10.5194/gmd-17-8173-2024, https://doi.org/10.5194/gmd-17-8173-2024, 2024
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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 data range is likely encountered outside of the calibration period. The end result is highly dependent on the method used. We show that, to avoid discontinuities in the time series, one needs to exclude data in the calibration range to also activate the extrapolation functionality in that time period.
Philip J. Rasch, Haruki Hirasawa, Mingxuan Wu, Sarah J. Doherty, Robert Wood, Hailong Wang, Andy Jones, James Haywood, and Hansi Singh
Geosci. Model Dev., 17, 7963–7994, https://doi.org/10.5194/gmd-17-7963-2024, https://doi.org/10.5194/gmd-17-7963-2024, 2024
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We introduce a protocol to compare computer climate simulations to better understand a proposed strategy intended to counter warming and climate impacts from greenhouse gas increases. This slightly changes clouds in six ocean regions to reflect more sunlight and cool the Earth. Example changes in clouds and climate are shown for three climate models. Cloud changes differ between the models, but precipitation and surface temperature changes are similar when their cooling effects are made similar.
Trude Eidhammer, Andrew Gettelman, Katherine Thayer-Calder, Duncan Watson-Parris, Gregory Elsaesser, Hugh Morrison, Marcus van Lier-Walqui, Ci Song, and Daniel McCoy
Geosci. Model Dev., 17, 7835–7853, https://doi.org/10.5194/gmd-17-7835-2024, https://doi.org/10.5194/gmd-17-7835-2024, 2024
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We describe a dataset where 45 parameters related to cloud processes in the Community Earth System Model version 2 (CESM2) Community Atmosphere Model version 6 (CAM6) are perturbed. Three sets of perturbed parameter ensembles (263 members) were created: current climate, preindustrial aerosol loading and future climate with sea surface temperature increased by 4 K.
Ha Thi Minh Ho-Hagemann, Vera Maurer, Stefan Poll, and Irina Fast
Geosci. Model Dev., 17, 7815–7834, https://doi.org/10.5194/gmd-17-7815-2024, https://doi.org/10.5194/gmd-17-7815-2024, 2024
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The regional Earth system model GCOAST-AHOI v2.0 that includes the regional climate model ICON-CLM coupled to the ocean model NEMO and the hydrological discharge model HD via the OASIS3-MCT coupler can be a useful tool for conducting long-term regional climate simulations over the EURO-CORDEX domain. The new OASIS3-MCT coupling interface implemented in ICON-CLM makes it more flexible for coupling to an external ocean model and an external hydrological discharge model.
Sandro Vattioni, Rahel Weber, Aryeh Feinberg, Andrea Stenke, John A. Dykema, Beiping Luo, Georgios A. Kelesidis, Christian A. Bruun, Timofei Sukhodolov, Frank N. Keutsch, Thomas Peter, and Gabriel Chiodo
Geosci. Model Dev., 17, 7767–7793, https://doi.org/10.5194/gmd-17-7767-2024, https://doi.org/10.5194/gmd-17-7767-2024, 2024
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We quantified impacts and efficiency of stratospheric solar climate intervention via solid particle injection. Microphysical interactions of solid particles with the sulfur cycle were interactively coupled to the heterogeneous chemistry scheme and the radiative transfer code of an aerosol–chemistry–climate model. Compared to injection of SO2 we only find a stronger cooling efficiency for solid particles when normalizing to the aerosol load but not when normalizing to the injection rate.
Ingo Richter, Ping Chang, Gokhan Danabasoglu, Dietmar Dommenget, Guillaume Gastineau, Aixue Hu, Takahito Kataoka, Noel Keenlyside, Fred Kucharski, Yuko Okumura, Wonsun Park, Malte Stuecker, Andrea Taschetto, Chunzai Wang, Stephen Yeager, and Sang-Wook Yeh
EGUsphere, https://doi.org/10.5194/egusphere-2024-3110, https://doi.org/10.5194/egusphere-2024-3110, 2024
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The tropical ocean basins influence each other through multiple pathways and mechanisms, here referred to as tropical basin interaction (TBI). Many researchers have examined TBI using comprehensive climate models, but have obtained conflicting results. This may be partly due to differences in experiment protocols, and partly due to systematic model errors. TBIMIP aims to address this problem by designing a set of TBI experiments that will be performed by multiple models.
Samuel Rémy, Swen Metzger, Vincent Huijnen, Jason E. Williams, and Johannes Flemming
Geosci. Model Dev., 17, 7539–7567, https://doi.org/10.5194/gmd-17-7539-2024, https://doi.org/10.5194/gmd-17-7539-2024, 2024
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In this paper we describe the development of the future operational cycle 49R1 of the IFS-COMPO system, used for operational forecasts of atmospheric composition in the CAMS project, and focus on the implementation of the thermodynamical model EQSAM4Clim version 12. The implementation of EQSAM4Clim significantly improves the simulated secondary inorganic aerosol surface concentration. The new aerosol and precipitation acidity diagnostics showed good agreement against observational datasets.
Maximillian Van Wyk de Vries, Tom Matthews, L. Baker Perry, Nirakar Thapa, and Rob Wilby
Geosci. Model Dev., 17, 7629–7643, https://doi.org/10.5194/gmd-17-7629-2024, https://doi.org/10.5194/gmd-17-7629-2024, 2024
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This paper introduces the AtsMOS workflow, a new tool for improving weather forecasts in mountainous areas. By combining advanced statistical techniques with local weather data, AtsMOS can provide more accurate predictions of weather conditions. Using data from Mount Everest as an example, AtsMOS has shown promise in better forecasting hazardous weather conditions, making it a valuable tool for communities in mountainous regions and beyond.
Sofia Allende, Anne Marie Treguier, Camille Lique, Clément de Boyer Montégut, François Massonnet, Thierry Fichefet, and Antoine Barthélemy
Geosci. Model Dev., 17, 7445–7466, https://doi.org/10.5194/gmd-17-7445-2024, https://doi.org/10.5194/gmd-17-7445-2024, 2024
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We study the parameters of the turbulent-kinetic-energy mixed-layer-penetration scheme in the NEMO model with regard to sea-ice-covered regions of the Arctic Ocean. This evaluation reveals the impact of these parameters on mixed-layer depth, sea surface temperature and salinity, and ocean stratification. Our findings demonstrate significant impacts on sea ice thickness and sea ice concentration, emphasizing the need for accurately representing ocean mixing to understand Arctic climate dynamics.
Sabin I. Taranu, David M. Lawrence, Yoshihide Wada, Ting Tang, Erik Kluzek, Sam Rabin, Yi Yao, Steven J. De Hertog, Inne Vanderkelen, and Wim Thiery
Geosci. Model Dev., 17, 7365–7399, https://doi.org/10.5194/gmd-17-7365-2024, https://doi.org/10.5194/gmd-17-7365-2024, 2024
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In this study, we improved a climate model by adding the representation of water use sectors such as domestic, industry, and agriculture. This new feature helps us understand how water is used and supplied in various areas. We tested our model from 1971 to 2010 and found that it accurately identifies areas with water scarcity. By modelling the competition between sectors when water availability is limited, the model helps estimate the intensity and extent of individual sectors' water shortages.
Florian Börgel, Sven Karsten, Karoline Rummel, and Ulf Gräwe
EGUsphere, https://doi.org/10.5194/egusphere-2024-2685, https://doi.org/10.5194/egusphere-2024-2685, 2024
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Forecasting river runoff, crucial for managing water resources and understanding climate impacts, can be challenging. This study introduces a new method using Convolutional Long Short-Term Memory (ConvLSTM) networks, a machine learning model that processes spatial and temporal data. Focusing on the Baltic Sea region, our model uses weather data as input to predict daily river runoff for 97 rivers.
Thi Nhu Ngoc Do, Kengo Sudo, Akihiko Ito, Louisa Emmons, Vaishali Naik, Kostas Tsigaridis, Øyvind Seland, Gerd A. Folberth, and Douglas I. Kelley
EGUsphere, https://doi.org/10.5194/egusphere-2024-2313, https://doi.org/10.5194/egusphere-2024-2313, 2024
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Understanding historical isoprene emission changes is important for predicting future climate, but trends and their controlling factors remain uncertain. This study shows that long-term isoprene trends vary among Earth System Models mainly due to partially incorporating CO2 effects and land cover changes rather than climate. Future models that refine these factors’ effects on isoprene emissions, along with long-term observations, are essential for better understanding plant-climate interactions.
Michael Nole, Jonah Bartrand, Fawz Naim, and Glenn Hammond
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-162, https://doi.org/10.5194/gmd-2024-162, 2024
Revised manuscript accepted for GMD
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Safe carbon dioxide (CO2) storage is likely to be critical for mitigating some of the most dangerous effects of climate change. We present a simulation framework for modeling CO2 storage beneath the seafloor where CO2 can form a solid. This can aid in permanent CO2 storage for long periods of time. Our models show what a commercial-scale CO2 injection would look like in a marine environment. We discuss what would need to be considered when designing a sub-sea CO2 injection.
Cynthia Whaley, Montana Etten-Bohm, Courtney Schumacher, Ayodeji Akingunola, Vivek Arora, Jason Cole, Michael Lazare, David Plummer, Knut von Salzen, and Barbara Winter
Geosci. Model Dev., 17, 7141–7155, https://doi.org/10.5194/gmd-17-7141-2024, https://doi.org/10.5194/gmd-17-7141-2024, 2024
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This paper describes how lightning was added as a process in the Canadian Earth System Model in order to interactively respond to climate changes. As lightning is an important cause of global wildfires, this new model development allows for more realistic projections of how wildfires may change in the future, responding to a changing climate.
Erik Gustafsson, Bo G. Gustafsson, Martijn Hermans, Christoph Humborg, and Christian Stranne
Geosci. Model Dev., 17, 7157–7179, https://doi.org/10.5194/gmd-17-7157-2024, https://doi.org/10.5194/gmd-17-7157-2024, 2024
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Methane (CH4) cycling in the Baltic Proper is studied through model simulations, enabling a first estimate of key CH4 fluxes. A preliminary budget identifies benthic CH4 release as the dominant source and two main sinks: CH4 oxidation in the water (92 % of sinks) and outgassing to the atmosphere (8 % of sinks). This study addresses CH4 emissions from coastal seas and is a first step toward understanding the relative importance of open-water outgassing compared with local coastal hotspots.
Tridib Banerjee, Patrick Scholz, Sergey Danilov, Knut Klingbeil, and Dmitry Sidorenko
Geosci. Model Dev., 17, 7051–7065, https://doi.org/10.5194/gmd-17-7051-2024, https://doi.org/10.5194/gmd-17-7051-2024, 2024
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In this paper we propose a new alternative to one of the functionalities of the sea ice model FESOM2. The alternative we propose allows the model to capture and simulate fast changes in quantities like sea surface elevation more accurately. We also demonstrate that the new alternative is faster and more adept at taking advantages of highly parallelized computing infrastructure. We therefore show that this new alternative is a great addition to the sea ice model FESOM2.
Yuwen Fan, Zhao Yang, Min-Hui Lo, Jina Hur, and Eun-Soon Im
Geosci. Model Dev., 17, 6929–6947, https://doi.org/10.5194/gmd-17-6929-2024, https://doi.org/10.5194/gmd-17-6929-2024, 2024
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Irrigated agriculture in the North China Plain (NCP) has a significant impact on the local climate. To better understand this impact, we developed a specialized model specifically for the NCP region. This model allows us to simulate the double-cropping vegetation and the dynamic irrigation practices that are commonly employed in the NCP. This model shows improved performance in capturing the general crop growth, such as crop stages, biomass, crop yield, and vegetation greenness.
Gang Tang, Zebedee Nicholls, Alexander Norton, Sönke Zaehle, and Malte Meinshausen
EGUsphere, https://doi.org/10.5194/egusphere-2024-1941, https://doi.org/10.5194/egusphere-2024-1941, 2024
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We studied the coupled carbon-nitrogen cycle effect in Earth System Models by developing a carbon-nitrogen coupling in a reduced complexity model, MAGICC. Our model successfully emulated the global carbon-nitrogen cycle dynamics seen in CMIP6 complex models. Results indicate consistent nitrogen limitations on plant growth (net primary production) from 1850 to 2100. Our findings suggest that nitrogen deficiency could reduce future land carbon sequestration.
Ed Blockley, Emma Fiedler, Jeff Ridley, Luke Roberts, Alex West, Dan Copsey, Daniel Feltham, Tim Graham, David Livings, Clement Rousset, David Schroeder, and Martin Vancoppenolle
Geosci. Model Dev., 17, 6799–6817, https://doi.org/10.5194/gmd-17-6799-2024, https://doi.org/10.5194/gmd-17-6799-2024, 2024
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This paper documents the sea ice model component of the latest Met Office coupled model configuration, which will be used as the physical basis for UK contributions to CMIP7. Documentation of science options used in the configuration are given along with a brief model evaluation. This is the first UK configuration to use NEMO’s new SI3 sea ice model. We provide details on how SI3 was adapted to work with Met Office coupling methodology and documentation of coupling processes in the model.
Jean-François Lemieux, William H. Lipscomb, Anthony Craig, David A. Bailey, Elizabeth C. Hunke, Philippe Blain, Till A. S. Rasmussen, Mats Bentsen, Frédéric Dupont, David Hebert, and Richard Allard
Geosci. Model Dev., 17, 6703–6724, https://doi.org/10.5194/gmd-17-6703-2024, https://doi.org/10.5194/gmd-17-6703-2024, 2024
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We present the latest version of the CICE model. It solves equations that describe the dynamics and the growth and melt of sea ice. To do so, the domain is divided into grid cells and variables are positioned at specific locations in the cells. A new implementation (C-grid) is presented, with the velocity located on cell edges. Compared to the previous B-grid, the C-grid allows for a natural coupling with some oceanic and atmospheric models. It also allows for ice transport in narrow channels.
Victor Couplet, Marina Martínez Montero, and Michel Crucifix
EGUsphere, https://doi.org/10.5194/egusphere-2024-2279, https://doi.org/10.5194/egusphere-2024-2279, 2024
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We present SURFER v3.0, a simple climate model designed to estimate the impact of CO2 and CH4 emissions on global temperatures, sea levels, and ocean pH. We added new carbon cycle processes and calibrated the model to observations and results from more complex models, enabling use over time scales ranging from decades to millions of years. SURFER v3.0 is fast, transparent, and easy to use, making it an ideal tool for policy assessments and suitable for educational purposes.
Rachid El Montassir, Olivier Pannekoucke, and Corentin Lapeyre
Geosci. Model Dev., 17, 6657–6681, https://doi.org/10.5194/gmd-17-6657-2024, https://doi.org/10.5194/gmd-17-6657-2024, 2024
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This study introduces a novel approach that combines physics and artificial intelligence (AI) for improved cloud cover forecasting. This approach outperforms traditional deep learning (DL) methods in producing realistic and physically consistent results while requiring less training data. This architecture provides a promising solution to overcome the limitations of classical AI methods and contributes to open up new possibilities for combining physical knowledge with deep learning models.
Marit Sandstad, Borgar Aamaas, Ane Nordlie Johansen, Marianne Tronstad Lund, Glen Philip Peters, Bjørn Hallvard Samset, Benjamin Mark Sanderson, and Ragnhild Bieltvedt Skeie
Geosci. Model Dev., 17, 6589–6625, https://doi.org/10.5194/gmd-17-6589-2024, https://doi.org/10.5194/gmd-17-6589-2024, 2024
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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.
Cited articles
Arias, P., Bellouin, N., Coppola, E., Jones, R., Krinner, G., Marotzke, J.,
Naik, V., Palmer, M., Plattner, G.-K., Rogelj, J., Rojas, M., Sillmann, J.,
Storelvmo, T., Thorne, P., Trewin, B., Achuta Rao, K., Adhikary, B., Allan,
R., Armour, K., Bala, G., Barimalala, R., Berger, S., Canadell, J., Cassou,
C., Cherchi, A., Collins, W., Collins, W., Connors, S., Corti, S., Cruz, F.,
Dentener, F., Dereczynski, C., Di Luca, A., Diongue Niang, A., Doblas-Reyes,
F., Dosio, A., Douville, H., Engelbrecht, F., Eyring, V., Fischer, E.,
Forster, P., Fox-Kemper, B., Fuglestvedt, J., Fyfe, J., Gillett, N.,
Goldfarb, L., Gorodetskaya, I., Gutierrez, J., Hamdi, R., Hawkins, E.,
Hewitt, H., Hope, P., Islam, A., Jones, C., Kaufman, D., Kopp, R., Kosaka,
Y., Kossin, J., Krakovska, S., Lee, J.-Y., Li, J., Mauritsen, T., Maycock,
T., Meinshausen, M., Min, S.-K., Monteiro, P., Ngo-Duc, T., Otto, F., Pinto,
I., Pirani, A., Raghavan, K., Ranasinghe, R., Ruane, A., Ruiz, L., Sallée,
J.-B., Samset, B., Sathyendranath, S., Seneviratne, S., Sörensson, A.,
Szopa, S., Takayabu, I., Tréguier, A.-M., van den Hurk, B., Vautard, R., von
Schuckmann, K., Zaehle, S., Zhang, X., and Zickfeld, K.: Technical Summary,
33–144, Cambridge University Press, Cambridge, United Kingdom and New
York, NY, USA, https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_TS.pdf (last access: 22 August 2022), 2021. a
Avila, L. A., Pasch, R. J., and Jiing, J.-G.: Atlantic tropical systems of 1996
and 1997: Years of contrasts, Mon. Weather Rev., 128, 3695–3706, 2000. a
Ayrault, F.: Environnement, structure et évolution des dépressions
météorologiques: réalité climatologique et modèles types,
PhD thesis, Toulouse, 3, 1998. a
Bengtsson, L., Hodges, K. I., Esch, M., Keenlyside, N., Kornblueh, L., Luo,
J.-J., and Yamagata, T.: How may tropical cyclones change in a warmer
climate?, Tellus A, 59, 539–561,
https://doi.org/10.1111/j.1600-0870.2007.00251.x, 2007. a
Bourdin, S.: Bourdin et al. 2022, Intercomparison of Four Tropical Cyclones Detection Algorithms on ERA5 – Code and Data (Version v1), Zenodo [code and data], https://doi.org/10.5281/zenodo.6424432, 2022a. a
Bourdin, S.: DynamicoPy v0.6 (0.6), Zenodo [code and data], https://doi.org/10.5281/zenodo.7015245, 2022b. a
Camargo, S. J.: Global and Regional Aspects of Tropical Cyclone
Activity in the CMIP5 Models, J. Climate, 26, 9880–9902,
https://doi.org/10.1175/JCLI-D-12-00549.1, 2013. a
Camargo, S. J. and Wing, A. A.: Tropical cyclones in climate models, WIREs
Climate Change, 7, 211–237, https://doi.org/10.1002/wcc.373, 2016. a
Camargo, S. J. and Zebiak, S. E.: Improving the Detection and Tracking of
Tropical Cyclones in Atmospheric General Circulation Models,
Weather Forecast., 17, 1152–1162,
https://doi.org/10.1175/1520-0434(2002)017<1152:ITDATO>2.0.CO;2, 2002. a, b
Cattiaux, J., Chauvin, F., Bousquet, O., Malardel, S., and Tsai, C.-L.:
Projected Changes in the Southern Indian Ocean Cyclone Activity
Assessed from High-Resolution Experiments and CMIP5 Models,
J. Climate, 33, 4975–4991, https://doi.org/10.1175/JCLI-D-19-0591.1, 2020. a
Chauvin, F., Royer, J.-F., and Déqué, M.: Response of hurricane-type vortices
to global warming as simulated by ARPEGE-Climat at high resolution,
Clim. Dynam., 27, 377–399, https://doi.org/10.1007/s00382-006-0135-7, 2006. a, b, c, d
Chauvin, F., Pilon, R., Palany, P., and Belmadani, A.: Future changes in
Atlantic hurricanes with the rotated-stretched ARPEGE-Climat at very
high resolution, Clim. Dynam., 54, 947–972,
https://doi.org/10.1007/s00382-019-05040-4, 2020. a
Chavas, D. R., Reed, K. A., and Knaff, J. A.: Physical understanding of the
tropical cyclone wind-pressure relationship, Nat. Commun., 8, 1360,
https://doi.org/10.1038/s41467-017-01546-9, 2017. a
Davis, C. A.: Resolving Tropical Cyclone Intensity in Models,
Geophys. Res. Lett., 45, 2082–2087,
https://doi.org/10.1002/2017GL076966, 2018. a
Duvel, J.-P.: On vortices initiated over West Africa and their impact on North
Atlantic tropical cyclones, Mon. Weather Re., 149, 585–601, 2021. a
Haarsma, R. J., Roberts, M. J., Vidale, P. L., Senior, C. A., Bellucci, A., Bao, Q., Chang, P., Corti, S., Fučkar, N. S., Guemas, V., von Hardenberg, J., Hazeleger, W., Kodama, C., Koenigk, T., Leung, L. R., Lu, J., Luo, J.-J., Mao, J., Mizielinski, M. S., Mizuta, R., Nobre, P., Satoh, M., Scoccimarro, E., Semmler, T., Small, J., and von Storch, J.-S.: High Resolution Model Intercomparison Project (HighResMIP v1.0) for CMIP6, Geosci. Model Dev., 9, 4185–4208, https://doi.org/10.5194/gmd-9-4185-2016, 2016. a
Harper, B., Kepert, J. D., and Ginger, J. D.: Guidelines for converting between
various wind averaging periods in tropical cyclone conditions,
world Meteorological Organization Rep., 54 pp.,
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.730.9719&rep=rep1&type=pdf (last access: 22 August 2022), 2010. a
Hart, R. E.: A Cyclone Phase Space Derived from Thermal Wind and
Thermal Asymmetry, Mon. Weather Rev., 131, 585–616,
https://doi.org/10.1175/1520-0493(2003)131<0585:ACPSDF>2.0.CO;2, 2003. a, b, c
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A.,
Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons,
A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati,
G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D.,
Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer,
A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M.,
Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P.,
Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 global
reanalysis, Q. J. Roy. Meteor. Soc., 146,
1999–2049, https://doi.org/10.1002/qj.3803, 2020. a, b, c
Hodges, K. I.: A General Method for Tracking Analysis and Its
Application to Meteorological Data, Mon. Weather Rev., 122,
2573–2586, https://doi.org/10.1175/1520-0493(1994)122<2573:AGMFTA>2.0.CO;2, 1994. a, b, c, d
Hodges, K. I.: Feature Tracking on the Unit Sphere, Mon. Weather
Rev., 123, 3458–3465,
https://doi.org/10.1175/1520-0493(1995)123<3458:FTOTUS>2.0.CO;2, 1995. a
Hodges, K. I.: Adaptive Constraints for Feature Tracking, Mon. Weather
Rev., 127, 1362–1373,
https://doi.org/10.1175/1520-0493(1999)127<1362:ACFFT>2.0.CO;2, 1999. a
Hopsch, S. B., Thorncroft, C. D., Hodges, K., and Aiyyer, A.: West African
storm tracks and their relationship to Atlantic tropical cyclones, J.
Climate, 20, 2468–2483, 2007. a
Horn, M., Walsh, K., Zhao, M., Camargo, S. J., Scoccimarro, E., Murakami, H.,
Wang, H., Ballinger, A., Kumar, A., Shaevitz, D. A., Jonas, J. A., and
Oouchi, K.: Tracking Scheme Dependence of Simulated Tropical
Cyclone Response to Idealized Climate Simulations, J.
Climate, 27, 9197–9213, https://doi.org/10.1175/JCLI-D-14-00200.1, 2014. a, b, c
Klotzbach, P. J., Bell, M. M., Bowen, S. G., Gibney, E. J., Knapp, K. R., and
Schreck, C. J.: Surface Pressure a More Skillful Predictor of
Normalized Hurricane Damage than Maximum Sustained Wind, B. Am. Meteorol. Soc., 101, E830–E846,
https://doi.org/10.1175/BAMS-D-19-0062.1, 2020. a, b, c, d
Knapp, K. R., Diamond, H. J., Kossin, J. P., Kruk, M. C., and Schreck, C.:
International best track archive for climate stewardship (IBTrACS) project,
version 4, https://doi.org/10.25921/82ty-9e16, subset: since1980, 2018. a
Knutson, T., Camargo, S. J., Chan, J. C. L., Emanuel, K., Ho, C.-H., Kossin,
J., Mohapatra, M., Satoh, M., Sugi, M., Walsh, K., and Wu, L.: Tropical
Cyclones and Climate Change Assessment: Part I: Detection and
Attribution, B. Am. Meteorol. Soc., 100,
1987–2007, https://doi.org/10.1175/BAMS-D-18-0189.1, 2019. a
Knutson, T., Camargo, S. J., Chan, J. C. L., Emanuel, K., Ho, C.-H., Kossin,
J., Mohapatra, M., Satoh, M., Sugi, M., Walsh, K., and Wu, L.: Tropical
Cyclones and Climate Change Assessment: Part II: Projected
Response to Anthropogenic Warming, B. Am.
Meteorol. Soc., 101, E303–E322, https://doi.org/10.1175/BAMS-D-18-0194.1, 2020. a, b
Knutson, T. R., Sirutis, J. J., Zhao, M., Tuleya, R. E., Bender, M., Vecchi,
G. A., Villarini, G., and Chavas, D.: Global Projections of Intense
Tropical Cyclone Activity for the Late Twenty-First Century
from Dynamical Downscaling of CMIP5/RCP4.5 Scenarios, J.
Climate, 28, 7203–7224, https://doi.org/10.1175/JCLI-D-15-0129.1, 2015. a
Landsea, C. W.: A climatology of intense (or major) Atlantic hurricanes,
Mon. Weather Rev., 121, 1703–1713, 1993. a
Manganello, J. V., Hodges, K. I., Kinter, J. L., Cash, B. A., Marx, L., Jung,
T., Achuthavarier, D., Adams, J. M., Altshuler, E. L., Huang, B., Jin, E. K.,
Stan, C., Towers, P., and Wedi, N.: Tropical Cyclone Climatology in a
10-km Global Atmospheric GCM: Toward Weather-Resolving Climate
Modeling, J. Climate, 25, 3867–3893,
https://doi.org/10.1175/JCLI-D-11-00346.1,
2012. a, b
Murakami, H.: Tropical cyclones in reanalysis data sets, Geophysical Research
Letters, 41, 2133–2141, https://doi.org/10.1002/2014GL059519, 2014. a, b, c, d
Murakami, H., Vecchi, G. A., Underwood, S., Delworth, T. L., Wittenberg, A. T.,
Anderson, W. G., Chen, J.-H., Gudgel, R. G., Harris, L. M., Lin, S.-J., and
Zeng, F.: Simulation and Prediction of Category 4 and 5 Hurricanes in
the High-Resolution GFDL HiFLOR Coupled Climate Model, J. Climate, 28, 9058–9079, https://doi.org/10.1175/JCLI-D-15-0216.1, 2015. a
Patricola, C. M., Saravanan, R., and Chang, P.: The response of Atlantic
tropical cyclones to suppression of African easterly waves, Geophys.
Res. Lett., 45, 471–479, 2018. a
Raavi, P. H. and Walsh, K. J. E.: Sensitivity of Tropical Cyclone
Formation to Resolution-Dependent and Independent Tracking
Schemes in High-Resolution Climate Model Simulations, Earth
Space Sci., 7, e2019EA000906, https://doi.org/10.1029/2019EA000906, 2020. a, b, c
Roberts, M. J., Vidale, P. L., Mizielinski, M. S., Demory, M.-E., Schiemann,
R., Strachan, J., Hodges, K., Bell, R., and Camp, J.: Tropical Cyclones in
the UPSCALE Ensemble of High-Resolution Global Climate Models,
J. Climate, 28, 574–596, https://doi.org/10.1175/JCLI-D-14-00131.1, 2015. a
Roberts, M. J., Camp, J., Seddon, J., Vidale, P. L., Hodges, K., Vanniere, B.,
Mecking, J., Haarsma, R., Bellucci, A., Scoccimarro, E., Caron, L.-P.,
Chauvin, F., Terray, L., Valcke, S., Moine, M.-P., Putrasahan, D., Roberts,
C., Senan, R., Zarzycki, C., and Ullrich, P.: Impact of Model Resolution
on Tropical Cyclone Simulation Using the HighResMIP–PRIMAVERA
Multimodel Ensemble, J. Climate, 33, 2557–2583,
https://doi.org/10.1175/JCLI-D-19-0639.1,
2020a. a, b, c, d, e, f, g, h
Roberts, M. J., Camp, J., Seddon, J., Vidale, P. L., Hodges, K., Vannière, B.,
Mecking, J., Haarsma, R., Bellucci, A., Scoccimarro, E., Caron, L.-P.,
Chauvin, F., Terray, L., Valcke, S., Moine, M.-P., Putrasahan, D., Roberts,
C. D., Senan, R., Zarzycki, C., Ullrich, P., Yamada, Y., Mizuta, R., Kodama,
C., Fu, D., Zhang, Q., Danabasoglu, G., Rosenbloom, N., Wang, H., and Wu, L.:
Projected Future Changes in Tropical Cyclones Using the CMIP6
HighResMIP Multimodel Ensemble, Geophys. Res. Lett., 47,
e2020GL088662, https://doi.org/10.1029/2020GL088662,
2020b. a, b
Schenkel, B. A. and Hart, R. E.: An Examination of Tropical Cyclone
Position, Intensity, and Intensity Life Cycle within Atmospheric
Reanalysis Datasets, J. Climate, 25, 3453–3475,
https://doi.org/10.1175/2011JCLI4208.1,
2012. a, b
Simpson, R. H. and Saffir, H.: The hurricane disaster potential scale,
Weatherwise, 27, 169, 1974. a
Strachan, J., Vidale, P. L., Hodges, K., Roberts, M., and Demory, M.-E.:
Investigating Global Tropical Cyclone Activity with a Hierarchy of
AGCMs: The Role of Model Resolution, J. Climate, 26,
133–152, https://doi.org/10.1175/JCLI-D-12-00012.1, 2013. a, b
Thorncroft, C. and Hodges, K.: African Easterly Wave Variability and
Its Relationship to Atlantic Tropical Cyclone Activity, J. Climate, 14, 1166–1179,
https://doi.org/10.1175/1520-0442(2001)014<1166:AEWVAI>2.0.CO;2, 2001. a
Tory, K. J., Chand, S. S., Dare, R. A., and McBride, J. L.: An Assessment of
a Model-, Grid-, and Basin-Independent Tropical Cyclone
Detection Scheme in Selected CMIP3 Global Climate Models,
J. Climate, 26, 5508–5522, https://doi.org/10.1175/JCLI-D-12-00511.1,
2013a. a, b
Tory, K. J., Dare, R. A., Davidson, N. E., McBride, J. L., and Chand, S. S.: The importance of low-deformation vorticity in tropical cyclone formation, Atmos. Chem. Phys., 13, 2115–2132, https://doi.org/10.5194/acp-13-2115-2013, 2013c. a, b, c
Ullrich, P. A. and Zarzycki, C. M.: TempestExtremes: a framework for scale-insensitive pointwise feature tracking on unstructured grids, Geosci. Model Dev., 10, 1069–1090, https://doi.org/10.5194/gmd-10-1069-2017, 2017. a, b, c
Ullrich, P. A., Zarzycki, C. M., McClenny, E. E., Pinheiro, M. C., Stansfield, A. M., and Reed, K. A.: TempestExtremes v2.1: a community framework for feature detection, tracking, and analysis in large datasets, Geosci. Model Dev., 14, 5023–5048, https://doi.org/10.5194/gmd-14-5023-2021, 2021. a, b, c, d, e, f
Vecchi, G. A., Delworth, T. L., Murakami, H., Underwood, S. D., Wittenberg,
A. T., Zeng, F., Zhang, W., Baldwin, J. W., Bhatia, K. T., Cooke, W., He, J.,
Kapnick, S. B., Knutson, T. R., Villarini, G., van der Wiel, K., Anderson,
W., Balaji, V., Chen, J., Dixon, K. W., Gudgel, R., Harris, L. M., Jia, L.,
Johnson, N. C., Lin, S.-J., Liu, M., Ng, C. H. J., Rosati, A., Smith, J. A.,
and Yang, X.: Tropical cyclone sensitivities to CO2 doubling: roles of
atmospheric resolution, synoptic variability and background climate changes,
Clim. Dynam., 53, 5999–6033, https://doi.org/10.1007/s00382-019-04913-y, 2019. a
Walsh, K., Lavender, S., Scoccimarro, E., and Murakami, H.: Resolution
dependence of tropical cyclone formation in CMIP3 and finer resolution
models, Clim. Dynam., 3-4, 585–599, https://doi.org/10.1007/s00382-012-1298-z,
2013. a
Walsh, K. J. E., Fiorino, M., Landsea, C. W., and McInnes, K. L.: Objectively
Determined Resolution-Dependent Threshold Criteria for the
Detection of Tropical Cyclones in Climate Models and Reanalyses,
J. Climate, 20, 2307–2314, https://doi.org/10.1175/JCLI4074.1, 2007. a
Walsh, K. J. E., Camargo, S. J., Vecchi, G. A., Daloz, A. S., Elsner, J.,
Emanuel, K., Horn, M., Lim, Y.-K., Roberts, M., Patricola, C., Scoccimarro,
E., Sobel, A. H., Strazzo, S., Villarini, G., Wehner, M., Zhao, M., Kossin,
J. P., LaRow, T., Oouchi, K., Schubert, S., Wang, H., Bacmeister, J., Chang,
P., Chauvin, F., Jablonowski, C., Kumar, A., Murakami, H., Ose, T., Reed,
K. A., Saravanan, R., Yamada, Y., Zarzycki, C. M., Vidale, P. L., Jonas,
J. A., and Henderson, N.: Hurricanes and Climate: The U.S. CLIVAR
Working Group on Hurricanes, B. Am. Meteorol.
Soc., 96, 997–1017, https://doi.org/10.1175/BAMS-D-13-00242.1, 2015. a
Zhao, M., Held, I. M., Lin, S.-J., and Vecchi, G. A.: Simulations of Global
Hurricane Climatology, Interannual Variability, and Response to
Global Warming Using a 50-km Resolution GCM, J. Climate,
22, 6653–6678, https://doi.org/10.1175/2009JCLI3049.1, 2009. a
Short summary
When studying tropical cyclones in a large dataset, one needs objective and automatic procedures to detect their specific pattern. Applying four different such algorithms to a reconstruction of the climate, we show that the choice of the algorithm is crucial to the climatology obtained. Mainly, the algorithms differ in their sensitivity to weak storms so that they provide different frequencies and durations. We review the different options to consider for the choice of the tracking methodology.
When studying tropical cyclones in a large dataset, one needs objective and automatic procedures...
