Articles | Volume 14, issue 8
https://doi.org/10.5194/gmd-14-5023-2021
© Author(s) 2021. 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-14-5023-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Methods for assessment of models
| 
13 Aug 2021
Methods for assessment of models |
| 13 Aug 2021
| 13 Aug 2021
TempestExtremes v2.1: a community framework for feature detection, tracking, and analysis in large datasets
Department of Land, Air and Water Resources, University of California, Davis, CA, USA
Colin M. Zarzycki
Department of Meteorology and Atmospheric Science, Pennsylvania State University, University Park, PA, USA
Elizabeth E. McClenny
Department of Land, Air and Water Resources, University of California, Davis, CA, USA
Marielle C. Pinheiro
Department of Land, Air and Water Resources, University of California, Davis, CA, USA
Alyssa M. Stansfield
School of Marine and Atmospheric Sciences, State University of New York at Stony Brook, Stony Brook, NY, USA
Kevin A. Reed
School of Marine and Atmospheric Sciences, State University of New York at Stony Brook, Stony Brook, NY, USA
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Climate models involve several different components, such as the atmosphere, ocean, and land models. Information needs to be exchanged, or remapped, between these models, and devising algorithms for performing this exchange is important for ensuring the accuracy of climate simulations. In this paper, we examine the efficacy of several traditional and novel approaches to remapping on the sphere and demonstrate where our approaches offer improvement.
Chengzhu Zhang, Jean-Christophe Golaz, Ryan Forsyth, Tom Vo, Shaocheng Xie, Zeshawn Shaheen, Gerald L. Potter, Xylar S. Asay-Davis, Charles S. Zender, Wuyin Lin, Chih-Chieh Chen, Chris R. Terai, Salil Mahajan, Tian Zhou, Karthik Balaguru, Qi Tang, Cheng Tao, Yuying Zhang, Todd Emmenegger, Susannah Burrows, and Paul A. Ullrich
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Geosci. Model Dev., 13, 2743–2762, https://doi.org/10.5194/gmd-13-2743-2020, https://doi.org/10.5194/gmd-13-2743-2020, 2020
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Paul A. Ullrich, Christiane Jablonowski, James Kent, Peter H. Lauritzen, Ramachandran Nair, Kevin A. Reed, Colin M. Zarzycki, David M. Hall, Don Dazlich, Ross Heikes, Celal Konor, David Randall, Thomas Dubos, Yann Meurdesoif, Xi Chen, Lucas Harris, Christian Kühnlein, Vivian Lee, Abdessamad Qaddouri, Claude Girard, Marco Giorgetta, Daniel Reinert, Joseph Klemp, Sang-Hun Park, William Skamarock, Hiroaki Miura, Tomoki Ohno, Ryuji Yoshida, Robert Walko, Alex Reinecke, and Kevin Viner
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Paul A. Ullrich and Colin M. Zarzycki
Geosci. Model Dev., 10, 1069–1090, https://doi.org/10.5194/gmd-10-1069-2017, https://doi.org/10.5194/gmd-10-1069-2017, 2017
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Jorge E. Guerra and Paul A. Ullrich
Geosci. Model Dev., 9, 2007–2029, https://doi.org/10.5194/gmd-9-2007-2016, https://doi.org/10.5194/gmd-9-2007-2016, 2016
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P. A. Ullrich
Geosci. Model Dev., 7, 3017–3035, https://doi.org/10.5194/gmd-7-3017-2014, https://doi.org/10.5194/gmd-7-3017-2014, 2014
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This paper compares continuous and discontinuous discretizations of the shallow-water equations on the sphere using the flux reconstruction formulation. The discontinuous framework comes at a cost, including a reduced time step size and higher computational expense, but has a number of desirable properties which may make it desirable for future use in atmospheric models.
O. Guba, M. A. Taylor, P. A. Ullrich, J. R. Overfelt, and M. N. Levy
Geosci. Model Dev., 7, 2803–2816, https://doi.org/10.5194/gmd-7-2803-2014, https://doi.org/10.5194/gmd-7-2803-2014, 2014
P. H. Lauritzen, P. A. Ullrich, C. Jablonowski, P. A. Bosler, D. Calhoun, A. J. Conley, T. Enomoto, L. Dong, S. Dubey, O. Guba, A. B. Hansen, E. Kaas, J. Kent, J.-F. Lamarque, M. J. Prather, D. Reinert, V. V. Shashkin, W. C. Skamarock, B. Sørensen, M. A. Taylor, and M. A. Tolstykh
Geosci. Model Dev., 7, 105–145, https://doi.org/10.5194/gmd-7-105-2014, https://doi.org/10.5194/gmd-7-105-2014, 2014
Vincent Larson, Zhun Guo, Benjamin Stephens, Colin Zarzycki, Gerhard Dikta, Yun Qian, and Shaocheng Xie
EGUsphere, https://doi.org/10.5194/egusphere-2025-1593, https://doi.org/10.5194/egusphere-2025-1593, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
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Global models of the atmosphere contain errors that lead to inaccurate simulations. A software tool ("QuadTune") is presented that attempts to mitigate some of the inaccuracies. It also displays diagnostic plots that provide hints about where the errors might lie in the model.
Malcolm J. Roberts, Kevin A. Reed, Qing Bao, Joseph J. Barsugli, Suzana J. Camargo, Louis-Philippe Caron, Ping Chang, Cheng-Ta Chen, Hannah M. Christensen, Gokhan Danabasoglu, Ivy Frenger, Neven S. Fučkar, Shabeh ul Hasson, Helene T. Hewitt, Huanping Huang, Daehyun Kim, Chihiro Kodama, Michael Lai, Lai-Yung Ruby Leung, Ryo Mizuta, Paulo Nobre, Pablo Ortega, Dominique Paquin, Christopher D. Roberts, Enrico Scoccimarro, Jon Seddon, Anne Marie Treguier, Chia-Ying Tu, Paul A. Ullrich, Pier Luigi Vidale, Michael F. Wehner, Colin M. Zarzycki, Bosong Zhang, Wei Zhang, and Ming Zhao
Geosci. Model Dev., 18, 1307–1332, https://doi.org/10.5194/gmd-18-1307-2025, https://doi.org/10.5194/gmd-18-1307-2025, 2025
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HighResMIP2 is a model intercomparison project focusing on high-resolution global climate models, that is, those with grid spacings of 25 km or less in the atmosphere and ocean, using simulations of decades to a century in length. We are proposing an update of our simulation protocol to make the models more applicable to key questions for climate variability and hazard in present-day and future projections and to build links with other communities to provide more robust climate information.
Colin M. Zarzycki, Benjamin D. Ascher, Alan M. Rhoades, and Rachel R. McCrary
Nat. Hazards Earth Syst. Sci., 24, 3315–3335, https://doi.org/10.5194/nhess-24-3315-2024, https://doi.org/10.5194/nhess-24-3315-2024, 2024
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We developed an automated workflow to detect rain-on-snow events, which cause flooding in the northeastern United States, in climate data. Analyzing the Susquehanna River basin, this technique identified known events affecting river flow. Comparing four gridded datasets revealed variations in event frequency and severity, driven by different snowmelt and runoff estimates. This highlights the need for accurate climate data in flood management and risk prediction for these compound extremes.
Allison A. Wing, Levi G. Silvers, and Kevin A. Reed
Geosci. Model Dev., 17, 6195–6225, https://doi.org/10.5194/gmd-17-6195-2024, https://doi.org/10.5194/gmd-17-6195-2024, 2024
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This paper presents the experimental design for a model intercomparison project to study tropical clouds and climate. It is a follow-up from a prior project that used a simplified framework for tropical climate. The new project adds one new component – a specified pattern of sea surface temperatures as the lower boundary condition. We provide example results from one cloud-resolving model and one global climate model and test the sensitivity to the experimental parameters.
Justin L. Willson, Kevin A. Reed, Christiane Jablonowski, James Kent, Peter H. Lauritzen, Ramachandran Nair, Mark A. Taylor, Paul A. Ullrich, Colin M. Zarzycki, David M. Hall, Don Dazlich, Ross Heikes, Celal Konor, David Randall, Thomas Dubos, Yann Meurdesoif, Xi Chen, Lucas Harris, Christian Kühnlein, Vivian Lee, Abdessamad Qaddouri, Claude Girard, Marco Giorgetta, Daniel Reinert, Hiroaki Miura, Tomoki Ohno, and Ryuji Yoshida
Geosci. Model Dev., 17, 2493–2507, https://doi.org/10.5194/gmd-17-2493-2024, https://doi.org/10.5194/gmd-17-2493-2024, 2024
Short summary
Short summary
Accurate simulation of tropical cyclones (TCs) is essential to understanding their behavior in a changing climate. One way this is accomplished is through model intercomparison projects, where results from multiple climate models are analyzed to provide benchmark solutions for the wider climate modeling community. This study describes and analyzes the previously developed TC test case for nine climate models in an intercomparison project, providing solutions that aid in model development.
Skyler Graap and Colin M. Zarzycki
Geosci. Model Dev., 17, 1627–1650, https://doi.org/10.5194/gmd-17-1627-2024, https://doi.org/10.5194/gmd-17-1627-2024, 2024
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A key target for improving climate models is how low, bright clouds are predicted over tropical oceans, since they have important consequences for the Earth's energy budget. A climate model has been updated to improve the physical realism of the treatment of how momentum is moved up and down in the atmosphere. By comparing this updated model to real-world observations from balloon launches, it can be shown to more accurately depict atmospheric structure in trade-wind areas close to the Equator.
Lele Shu, Paul Ullrich, Xianhong Meng, Christopher Duffy, Hao Chen, and Zhaoguo Li
Geosci. Model Dev., 17, 497–527, https://doi.org/10.5194/gmd-17-497-2024, https://doi.org/10.5194/gmd-17-497-2024, 2024
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Our team developed rSHUD v2.0, a toolkit that simplifies the use of the SHUD, a model simulating water movement in the environment. We demonstrated its effectiveness in two watersheds, one in the USA and one in China. The toolkit also facilitated the creation of the Global Hydrological Data Cloud, a platform for automatic data processing and model deployment, marking a significant advancement in hydrological research.
Min-Seop Ahn, Paul A. Ullrich, Peter J. Gleckler, Jiwoo Lee, Ana C. Ordonez, and Angeline G. Pendergrass
Geosci. Model Dev., 16, 3927–3951, https://doi.org/10.5194/gmd-16-3927-2023, https://doi.org/10.5194/gmd-16-3927-2023, 2023
Short summary
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We introduce a framework for regional-scale evaluation of simulated precipitation distributions with 62 climate reference regions and 10 metrics and apply it to evaluate CMIP5 and CMIP6 models against multiple satellite-based precipitation products. The common model biases identified in this study are mainly associated with the overestimated light precipitation and underestimated heavy precipitation. These biases persist from earlier-generation models and have been slightly improved in CMIP6.
Qi Tang, Jean-Christophe Golaz, Luke P. Van Roekel, Mark A. Taylor, Wuyin Lin, Benjamin R. Hillman, Paul A. Ullrich, Andrew M. Bradley, Oksana Guba, Jonathan D. Wolfe, Tian Zhou, Kai Zhang, Xue Zheng, Yunyan Zhang, Meng Zhang, Mingxuan Wu, Hailong Wang, Cheng Tao, Balwinder Singh, Alan M. Rhoades, Yi Qin, Hong-Yi Li, Yan Feng, Yuying Zhang, Chengzhu Zhang, Charles S. Zender, Shaocheng Xie, Erika L. Roesler, Andrew F. Roberts, Azamat Mametjanov, Mathew E. Maltrud, Noel D. Keen, Robert L. Jacob, Christiane Jablonowski, Owen K. Hughes, Ryan M. Forsyth, Alan V. Di Vittorio, Peter M. Caldwell, Gautam Bisht, Renata B. McCoy, L. Ruby Leung, and David C. Bader
Geosci. Model Dev., 16, 3953–3995, https://doi.org/10.5194/gmd-16-3953-2023, https://doi.org/10.5194/gmd-16-3953-2023, 2023
Short summary
Short summary
High-resolution simulations are superior to low-resolution ones in capturing regional climate changes and climate extremes. However, uniformly reducing the grid size of a global Earth system model is too computationally expensive. We provide an overview of the fully coupled regionally refined model (RRM) of E3SMv2 and document a first-of-its-kind set of climate production simulations using RRM at an economic cost. The key to this success is our innovative hybrid time step method.
Abhishekh Kumar Srivastava, Paul Aaron Ullrich, Deeksha Rastogi, Pouya Vahmani, Andrew Jones, and Richard Grotjahn
Geosci. Model Dev., 16, 3699–3722, https://doi.org/10.5194/gmd-16-3699-2023, https://doi.org/10.5194/gmd-16-3699-2023, 2023
Short summary
Short summary
Stakeholders need high-resolution regional climate data for applications such as assessing water availability and mountain snowpack. This study examines 3 h and 24 h historical precipitation over the contiguous United States in the 12 km WRF version 4.2.1-based dynamical downscaling of the ERA5 reanalysis. WRF improves precipitation characteristics such as the annual cycle and distribution of the precipitation maxima, but it also displays regionally and seasonally varying precipitation biases.
Koichi Sakaguchi, L. Ruby Leung, Colin M. Zarzycki, Jihyeon Jang, Seth McGinnis, Bryce E. Harrop, William C. Skamarock, Andrew Gettelman, Chun Zhao, William J. Gutowski, Stephen Leak, and Linda Mearns
Geosci. Model Dev., 16, 3029–3081, https://doi.org/10.5194/gmd-16-3029-2023, https://doi.org/10.5194/gmd-16-3029-2023, 2023
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We document details of the regional climate downscaling dataset produced by a global variable-resolution model. The experiment is unique in that it follows a standard protocol designed for coordinated experiments of regional models. We found negligible influence of post-processing on statistical analysis, importance of simulation quality outside of the target region, and computational challenges that our model code faced due to rapidly changing super computer systems.
Zeyu Xue, Paul Ullrich, and Lai-Yung Ruby Leung
Hydrol. Earth Syst. Sci., 27, 1909–1927, https://doi.org/10.5194/hess-27-1909-2023, https://doi.org/10.5194/hess-27-1909-2023, 2023
Short summary
Short summary
We examine the sensitivity and robustness of conclusions drawn from the PGW method over the NEUS by conducting multiple PGW experiments and varying the perturbation spatial scales and choice of perturbed meteorological variables to provide a guideline for this increasingly popular regional modeling method. Overall, we recommend PGW experiments be performed with perturbations to temperature or the combination of temperature and wind at the gridpoint scale, depending on the research question.
David H. Marsico and Paul A. Ullrich
Geosci. Model Dev., 16, 1537–1551, https://doi.org/10.5194/gmd-16-1537-2023, https://doi.org/10.5194/gmd-16-1537-2023, 2023
Short summary
Short summary
Climate models involve several different components, such as the atmosphere, ocean, and land models. Information needs to be exchanged, or remapped, between these models, and devising algorithms for performing this exchange is important for ensuring the accuracy of climate simulations. In this paper, we examine the efficacy of several traditional and novel approaches to remapping on the sphere and demonstrate where our approaches offer improvement.
Chengzhu Zhang, Jean-Christophe Golaz, Ryan Forsyth, Tom Vo, Shaocheng Xie, Zeshawn Shaheen, Gerald L. Potter, Xylar S. Asay-Davis, Charles S. Zender, Wuyin Lin, Chih-Chieh Chen, Chris R. Terai, Salil Mahajan, Tian Zhou, Karthik Balaguru, Qi Tang, Cheng Tao, Yuying Zhang, Todd Emmenegger, Susannah Burrows, and Paul A. Ullrich
Geosci. Model Dev., 15, 9031–9056, https://doi.org/10.5194/gmd-15-9031-2022, https://doi.org/10.5194/gmd-15-9031-2022, 2022
Short summary
Short summary
Earth system model (ESM) developers run automated analysis tools on data from candidate models to inform model development. This paper introduces a new Python package, E3SM Diags, that has been developed to support ESM development and use routinely in the development of DOE's Energy Exascale Earth System Model. This tool covers a set of essential diagnostics to evaluate the mean physical climate from simulations, as well as several process-oriented and phenomenon-based evaluation diagnostics.
Vijay S. Mahadevan, Jorge E. Guerra, Xiangmin Jiao, Paul Kuberry, Yipeng Li, Paul Ullrich, David Marsico, Robert Jacob, Pavel Bochev, and Philip Jones
Geosci. Model Dev., 15, 6601–6635, https://doi.org/10.5194/gmd-15-6601-2022, https://doi.org/10.5194/gmd-15-6601-2022, 2022
Short summary
Short summary
Coupled Earth system models require transfer of field data between multiple components with varying spatial resolutions to determine the correct climate behavior. We present the Metrics for Intercomparison of Remapping Algorithms (MIRA) protocol to evaluate the accuracy, conservation properties, monotonicity, and local feature preservation of four different remapper algorithms for various unstructured mesh problems of interest. Future extensions to more practical use cases are also discussed.
Lele Shu, Paul A. Ullrich, and Christopher J. Duffy
Geosci. Model Dev., 13, 2743–2762, https://doi.org/10.5194/gmd-13-2743-2020, https://doi.org/10.5194/gmd-13-2743-2020, 2020
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Hydrologic modeling is an essential strategy for understanding and predicting natural flows. The paper introduces the design of Simulator for Hydrologic Unstructured Domains (SHUD), from the conceptual and mathematical description of hydrologic processes in a watershed to the model's computational structures. To demonstrate and validate the model performance, we employ three hydrologic experiments: the V-Catchment experiment, Vauclin's experiment, and a model study of the Cache Creek Watershed.
Leonardus van Kampenhout, Alan M. Rhoades, Adam R. Herrington, Colin M. Zarzycki, Jan T. M. Lenaerts, William J. Sacks, and Michiel R. van den Broeke
The Cryosphere, 13, 1547–1564, https://doi.org/10.5194/tc-13-1547-2019, https://doi.org/10.5194/tc-13-1547-2019, 2019
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A new tool is evaluated in which the climate and surface mass balance (SMB) of the Greenland ice sheet are resolved at 55 and 28 km resolution, while the rest of the globe is modelled at ~110 km. The local refinement of resolution leads to improved accumulation (SMB > 0) compared to observations; however ablation (SMB < 0) is deteriorated in some regions. This is attributed to changes in cloud cover and a reduced effectiveness of a model-specific vertical downscaling technique.
Colin M. Zarzycki, Christiane Jablonowski, James Kent, Peter H. Lauritzen, Ramachandran Nair, Kevin A. Reed, Paul A. Ullrich, David M. Hall, Mark A. Taylor, Don Dazlich, Ross Heikes, Celal Konor, David Randall, Xi Chen, Lucas Harris, Marco Giorgetta, Daniel Reinert, Christian Kühnlein, Robert Walko, Vivian Lee, Abdessamad Qaddouri, Monique Tanguay, Hiroaki Miura, Tomoki Ohno, Ryuji Yoshida, Sang-Hun Park, Joseph B. Klemp, and William C. Skamarock
Geosci. Model Dev., 12, 879–892, https://doi.org/10.5194/gmd-12-879-2019, https://doi.org/10.5194/gmd-12-879-2019, 2019
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We summarize the results of the Dynamical Core Model Intercomparison Project's idealized supercell test case. Supercells are storm-scale weather phenomena that are a key target for next-generation, non-hydrostatic weather prediction models. We show that the dynamical cores of most global numerical models converge between approximately 1 and 0.5 km grid spacing for this test, although differences in final solution exist, particularly due to differing grid discretizations and numerical diffusion.
Christine A. Shields, Jonathan J. Rutz, Lai-Yung Leung, F. Martin Ralph, Michael Wehner, Brian Kawzenuk, Juan M. Lora, Elizabeth McClenny, Tashiana Osborne, Ashley E. Payne, Paul Ullrich, Alexander Gershunov, Naomi Goldenson, Bin Guan, Yun Qian, Alexandre M. Ramos, Chandan Sarangi, Scott Sellars, Irina Gorodetskaya, Karthik Kashinath, Vitaliy Kurlin, Kelly Mahoney, Grzegorz Muszynski, Roger Pierce, Aneesh C. Subramanian, Ricardo Tome, Duane Waliser, Daniel Walton, Gary Wick, Anna Wilson, David Lavers, Prabhat, Allison Collow, Harinarayan Krishnan, Gudrun Magnusdottir, and Phu Nguyen
Geosci. Model Dev., 11, 2455–2474, https://doi.org/10.5194/gmd-11-2455-2018, https://doi.org/10.5194/gmd-11-2455-2018, 2018
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ARTMIP (Atmospheric River Tracking Method Intercomparison Project) is a community effort with the explicit goal of understanding the uncertainties, and the implications of those uncertainties, in atmospheric river science solely due to detection algorithm. ARTMIP strives to quantify these differences and provide guidance on appropriate algorithmic choices for the science question posed. Project goals, experimental design, and preliminary results are provided.
David J. Gardner, Jorge E. Guerra, François P. Hamon, Daniel R. Reynolds, Paul A. Ullrich, and Carol S. Woodward
Geosci. Model Dev., 11, 1497–1515, https://doi.org/10.5194/gmd-11-1497-2018, https://doi.org/10.5194/gmd-11-1497-2018, 2018
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As the computational power of supercomputing systems increases, and models for simulating the fluid flow of the Earth's atmosphere operate at higher resolutions, new approaches for advancing these models in time will be necessary. In order to produce the best possible result in the least amount of time, we evaluate a number of splittings, methods, and solvers on two test cases. Based on these results, we identify the most accurate and efficient approaches for consideration in production models.
Allison A. Wing, Kevin A. Reed, Masaki Satoh, Bjorn Stevens, Sandrine Bony, and Tomoki Ohno
Geosci. Model Dev., 11, 793–813, https://doi.org/10.5194/gmd-11-793-2018, https://doi.org/10.5194/gmd-11-793-2018, 2018
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RCEMIP, an intercomparison of multiple types of numerical models, is proposed. In RCEMIP, the climate system is modeled in an idealized manner with no spatial dependence of boundary conditions (i.e., sea surface temperature) or forcing (i.e., incoming sunlight). This set of simulations will be used to investigate how the amount of cloudiness changes with warming, how the clustering of clouds changes with warming, and how the state of the atmosphere in this idealized setup varies between models.
Michael F. Wehner, Kevin A. Reed, Burlen Loring, Dáithí Stone, and Harinarayan Krishnan
Earth Syst. Dynam., 9, 187–195, https://doi.org/10.5194/esd-9-187-2018, https://doi.org/10.5194/esd-9-187-2018, 2018
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The United Nations Framework Convention on Climate Change invited the scientific community to explore the impacts of a world in which anthropogenic global warming is stabilized at only 1.5 °C above preindustrial average temperatures. We present a projection of future tropical cyclone statistics for both 1.5 and 2.0 °C stabilized warming scenarios using a high-resolution global climate model. We find more frequent and intense tropical cyclones, but a reduction in weaker storms.
Paul A. Ullrich, Christiane Jablonowski, James Kent, Peter H. Lauritzen, Ramachandran Nair, Kevin A. Reed, Colin M. Zarzycki, David M. Hall, Don Dazlich, Ross Heikes, Celal Konor, David Randall, Thomas Dubos, Yann Meurdesoif, Xi Chen, Lucas Harris, Christian Kühnlein, Vivian Lee, Abdessamad Qaddouri, Claude Girard, Marco Giorgetta, Daniel Reinert, Joseph Klemp, Sang-Hun Park, William Skamarock, Hiroaki Miura, Tomoki Ohno, Ryuji Yoshida, Robert Walko, Alex Reinecke, and Kevin Viner
Geosci. Model Dev., 10, 4477–4509, https://doi.org/10.5194/gmd-10-4477-2017, https://doi.org/10.5194/gmd-10-4477-2017, 2017
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Atmospheric dynamical cores are a fundamental component of global atmospheric modeling systems and are responsible for capturing the dynamical behavior of the Earth's atmosphere. To better understand modern dynamical cores, this paper aims to provide a comprehensive review of 11 dynamical cores, drawn from modeling centers and groups that participated in the 2016 Dynamical Core Model Intercomparison Project (DCMIP) workshop and summer school.
Paul A. Ullrich and Colin M. Zarzycki
Geosci. Model Dev., 10, 1069–1090, https://doi.org/10.5194/gmd-10-1069-2017, https://doi.org/10.5194/gmd-10-1069-2017, 2017
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Automated pointwise feature tracking is used for objective identification and tracking of meteorological features, such as extratropical cyclones, tropical cyclones and tropical easterly waves, and has emerged as an important and desirable data-processing capability in climate science. In the interest of exploring tracking functionality, this paper introduces a framework for the development of robust tracking algorithms that is useful for intercomparison and optimization of tracking schemes.
Jorge E. Guerra and Paul A. Ullrich
Geosci. Model Dev., 9, 2007–2029, https://doi.org/10.5194/gmd-9-2007-2016, https://doi.org/10.5194/gmd-9-2007-2016, 2016
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This work introduces a collection of advances in the field of numerical simulation of the atmosphere using mixed finite element methods. We emphasize vertical motions in the atmosphere and apply state-of-the-art mathematics and programming paradigms to solve the differential equations that govern air flow cast in a coordinate-free formulation. The simulations show accurate flow features over a wide range of spatial scales including several important phenomena.
Colin M. Zarzycki, Kevin A. Reed, Julio T. Bacmeister, Anthony P. Craig, Susan C. Bates, and Nan A. Rosenbloom
Geosci. Model Dev., 9, 779–788, https://doi.org/10.5194/gmd-9-779-2016, https://doi.org/10.5194/gmd-9-779-2016, 2016
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This paper highlights the sensitivity of simulated tropical cyclone climatology to the choice of ocean coupling grid in high-resolution climate simulations. When computations of atmosphere–ocean interactions are carried out on the coarser grid in the system, key quantities such as surface wind drag and heat fluxes are incorrectly calculated. In the case of a coarser ocean grid, significantly stronger cyclone winds result, due to misaligned frictional vectors in the atmospheric dynamical core.
P. A. Ullrich
Geosci. Model Dev., 7, 3017–3035, https://doi.org/10.5194/gmd-7-3017-2014, https://doi.org/10.5194/gmd-7-3017-2014, 2014
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This paper compares continuous and discontinuous discretizations of the shallow-water equations on the sphere using the flux reconstruction formulation. The discontinuous framework comes at a cost, including a reduced time step size and higher computational expense, but has a number of desirable properties which may make it desirable for future use in atmospheric models.
O. Guba, M. A. Taylor, P. A. Ullrich, J. R. Overfelt, and M. N. Levy
Geosci. Model Dev., 7, 2803–2816, https://doi.org/10.5194/gmd-7-2803-2014, https://doi.org/10.5194/gmd-7-2803-2014, 2014
P. H. Lauritzen, P. A. Ullrich, C. Jablonowski, P. A. Bosler, D. Calhoun, A. J. Conley, T. Enomoto, L. Dong, S. Dubey, O. Guba, A. B. Hansen, E. Kaas, J. Kent, J.-F. Lamarque, M. J. Prather, D. Reinert, V. V. Shashkin, W. C. Skamarock, B. Sørensen, M. A. Taylor, and M. A. Tolstykh
Geosci. Model Dev., 7, 105–145, https://doi.org/10.5194/gmd-7-105-2014, https://doi.org/10.5194/gmd-7-105-2014, 2014
Related subject area
Climate and Earth system modeling
Correction of sea surface biases in the NEMO ocean general circulation model using neural networks
Representing lateral groundwater flow from land to river in Earth system models
FINAM is not a model (v1.0): a new Python-based model coupling framework
The Detection and Attribution Model Intercomparison Project (DAMIP v2.0) contribution to CMIP7
Enhancing winter climate simulations of the Great Lakes: insights from a new coupled lake–ice–atmosphere (CLIAv1) system on the importance of integrating 3D hydrodynamics with a regional climate model
Modelling emission and transport of key components of primary marine organic aerosol using the global aerosol–climate model ECHAM6.3–HAM2.3
Assessing the climate impact of an improved volcanic sulfate aerosol representation in E3SM
Advanced climate model evaluation with ESMValTool v2.11.0 using parallel, out-of-core, and distributed computing
ICON-HAM-lite 1.0: simulating the Earth system with interactive aerosols at kilometer scales
Process-based modeling framework for sustainable irrigation management at the regional scale: integrating rice production, water use, and greenhouse gas emissions
Implementing deep soil and dynamic root uptake in Noah-MP (v4.5): impact on Amazon dry-season transpiration
Reducing time and computing costs in EC-Earth: an automatic load-balancing approach for coupled Earth system models
FLAME 1.0: a novel approach for modelling burned area in the Brazilian biomes using the maximum entropy concept
SURFER v3.0: a fast model with ice sheet tipping points and carbon cycle feedbacks for short- and long-term climate scenarios
NMH-CS 3.0: a C# programming language and Windows-system-based ecohydrological model derived from Noah-MP
A method for quantifying uncertainty in spatially interpolated meteorological data with application to daily maximum air temperature
Baseline Climate Variables for Earth System Modelling
PaleoSTeHM v1.0: a modern, scalable spatiotemporal hierarchical modeling framework for paleo-environmental data
The Tropical Basin Interaction Model Intercomparison Project (TBIMIP)
ZEMBA v1.0: an energy and moisture balance climate model to investigate Quaternary climate
Development and evaluation of a new 4DEnVar-based weakly coupled ocean data assimilation system in E3SMv2
TemDeep: a self-supervised framework for temporal downscaling of atmospheric fields at arbitrary time resolutions
The ensemble consistency test: from CESM to MPAS and beyond
Linear Meta-Model optimization for regional climate models (LiMMo version 1.0)
An example of how data quality hinders progress: translating the latest findings on the regulation of leaf senescence timing in trees into the DP3 model (v1.0)
Presentation, calibration and testing of the DCESS II Earth system model of intermediate complexity (version 1.0)
Synthesizing global carbon–nitrogen coupling effects – the MAGICC coupled carbon–nitrogen cycle model v1.0
Historical trends and controlling factors of isoprene emissions in CMIP6 Earth system models
Investigating carbon and nitrogen conservation in reported CMIP6 Earth system model data
OpenBench: a land models evaluation system
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A Fortran–Python interface for integrating machine learning parameterization into earth system models
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A rapid-application emissions-to-impacts tool for scenario assessment: Probabilistic Regional Impacts from Model patterns and Emissions (PRIME)
The sensitivity of EC-Earth3 decadal predictions to the choice of volcanic forcing dataset: Insights for the next major eruption
A first calibration of the JULES-crop version 7.4 for rice using the novel O3-FACE experiment in China
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WRF-ELM v1.0: a regional climate model to study land–atmosphere interactions over heterogeneous land use regions
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DiuSST: a conceptual model of diurnal warm layers for idealized atmospheric simulations with interactive sea surface temperature
High-Resolution Model Intercomparison Project phase 2 (HighResMIP2) towards CMIP7
T&C-CROP: representing mechanistic crop growth with a terrestrial biosphere model (T&C, v1.5) – model formulation and validation
An updated non-intrusive, multi-scale, and flexible coupling interface in WRF 4.6.0
Monitoring and benchmarking Earth system model simulations with ESMValTool v2.12.0
The Earth Science Box Modeling Toolkit (ESBMTK 0.14.0.11): a Python library for research and teaching
CropSuite v1.0 – a comprehensive open-source crop suitability model considering climate variability for climate impact assessment
ICON ComIn – the ICON Community Interface (ComIn version 0.1.0, with ICON version 2024.01-01)
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A new metrics framework for quantifying and intercomparing atmospheric rivers in observations, reanalyses, and climate models
nextGEMS: entering the era of kilometer-scale Earth system modeling
Andrea Storto, Sergey Frolov, Laura Slivinski, and Chunxue Yang
Geosci. Model Dev., 18, 4789–4804, https://doi.org/10.5194/gmd-18-4789-2025, https://doi.org/10.5194/gmd-18-4789-2025, 2025
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Inaccuracies in air–sea heat fluxes severely degrade the accuracy of ocean numerical simulations. Here, we use artificial neural networks to correct air–sea heat fluxes as a function of oceanic and atmospheric state predictors. The correction successfully improves surface and subsurface ocean temperatures beyond the training period and in prediction experiments.
Chang Liao, L. Ruby Leung, Yilin Fang, Teklu Tesfa, and Robinson Negron-Juarez
Geosci. Model Dev., 18, 4601–4624, https://doi.org/10.5194/gmd-18-4601-2025, https://doi.org/10.5194/gmd-18-4601-2025, 2025
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Understanding horizontal groundwater flow is important for understanding how water moves through the ground. Current climate models often simplify this process because they do not have information about the land surface that is detailed enough. Our study developed a new model that divides the land surface into hillslopes to better represent how groundwater flows. This model can help improve predictions of water availability and how it affects ecosystems.
Sebastian Müller, Martin Lange, Thomas Fischer, Sara König, Matthias Kelbling, Jeisson Javier Leal Rojas, and Stephan Thober
Geosci. Model Dev., 18, 4483–4498, https://doi.org/10.5194/gmd-18-4483-2025, https://doi.org/10.5194/gmd-18-4483-2025, 2025
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This study presents FINAM (
FINAM is not a model), a new coupling framework written in Python to dynamically connect independently developed models. Python, as the ultimate glue language, enables the use of codes from nearly any programming language like Fortran, C++, Rust, and others. FINAM is designed to simplify the integration of various models with minimal effort, as demonstrated through various examples ranging from simple to complex systems.
Nathan P. Gillett, Isla R. Simpson, Gabi Hegerl, Reto Knutti, Dann Mitchell, Aurélien Ribes, Hideo Shiogama, Dáithí Stone, Claudia Tebaldi, Piotr Wolski, Wenxia Zhang, and Vivek K. Arora
Geosci. Model Dev., 18, 4399–4416, https://doi.org/10.5194/gmd-18-4399-2025, https://doi.org/10.5194/gmd-18-4399-2025, 2025
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Climate model simulations of the response to human and natural influences together, natural climate influences alone and greenhouse gases alone are key to quantifying human influence on the climate. The last set of such coordinated simulations underpinned key findings in the last Intergovernmental Panel on Climate Change (IPCC) report. Here we propose a new set of such simulations to be used in the next generation of attribution studies and to underpin the next IPCC report.
Pengfei Xue, Chenfu Huang, Yafang Zhong, Michael Notaro, Miraj B. Kayastha, Xing Zhou, Chuyan Zhao, Christa Peters-Lidard, Carlos Cruz, and Eric Kemp
Geosci. Model Dev., 18, 4293–4316, https://doi.org/10.5194/gmd-18-4293-2025, https://doi.org/10.5194/gmd-18-4293-2025, 2025
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This study introduces a new 3D lake–ice–atmosphere coupled model that significantly improves winter climate simulations for the Great Lakes compared to traditional 1D lake model coupling. The key contribution is the identification of critical hydrodynamic processes – ice transport, heat advection, and shear-driven turbulence production – that influence lake thermal structure and ice cover and explain the superior performance of 3D lake models to their 1D counterparts.
Anisbel Leon-Marcos, Moritz Zeising, Manuela van Pinxteren, Sebastian Zeppenfeld, Astrid Bracher, Elena Barbaro, Anja Engel, Matteo Feltracco, Ina Tegen, and Bernd Heinold
Geosci. Model Dev., 18, 4183–4213, https://doi.org/10.5194/gmd-18-4183-2025, https://doi.org/10.5194/gmd-18-4183-2025, 2025
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This study represents the primary marine organic aerosol (PMOA) emissions, focusing on their sea–atmosphere transfer. Using the FESOM2.1–REcoM3 model, concentrations of key organic biomolecules were estimated and integrated into the ECHAM6.3–HAM2.3 aerosol–climate model. Results highlight the influence of marine biological activity and surface winds on PMOA emissions, with reasonably good agreement with observations improving aerosol representation in the southern oceans.
Ziming Ke, Qi Tang, Jean-Christophe Golaz, Xiaohong Liu, and Hailong Wang
Geosci. Model Dev., 18, 4137–4153, https://doi.org/10.5194/gmd-18-4137-2025, https://doi.org/10.5194/gmd-18-4137-2025, 2025
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This study assesses volcanic aerosol representation in E3SM (Energy Exascale Earth System Model), showing that an emission-based approach moderately improves temperature variability and cloud responses compared to a prescribed forcing approach, yet significant bias persists.
Manuel Schlund, Bouwe Andela, Jörg Benke, Ruth Comer, Birgit Hassler, Emma Hogan, Peter Kalverla, Axel Lauer, Bill Little, Saskia Loosveldt Tomas, Francesco Nattino, Patrick Peglar, Valeriu Predoi, Stef Smeets, Stephen Worsley, Martin Yeo, and Klaus Zimmermann
Geosci. Model Dev., 18, 4009–4021, https://doi.org/10.5194/gmd-18-4009-2025, https://doi.org/10.5194/gmd-18-4009-2025, 2025
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The Earth System Model Evaluation Tool (ESMValTool) is a community diagnostics and performance metrics tool for the evaluation of Earth system models. Here, we describe recent significant improvements of ESMValTool’s computational efficiency including parallel, out-of-core, and distributed computing. Evaluations with the enhanced version of ESMValTool are faster, use less computational resources, and can handle input data larger than the available memory.
Philipp Weiss, Ross Herbert, and Philip Stier
Geosci. Model Dev., 18, 3877–3894, https://doi.org/10.5194/gmd-18-3877-2025, https://doi.org/10.5194/gmd-18-3877-2025, 2025
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Aerosols strongly influence Earth's climate as they interact with radiation and clouds. New Earth system models run at resolutions of a few kilometers. To simulate the Earth system with interactive aerosols, we developed a new aerosol module. It represents aerosols as an ensemble of lognormal modes with given sizes and compositions. We present a year-long simulation with four modes at a resolution of 5 km. It captures key processes like the formation of dust storms in the Sahara.
Yan Bo, Hao Liang, Tao Li, and Feng Zhou
Geosci. Model Dev., 18, 3799–3817, https://doi.org/10.5194/gmd-18-3799-2025, https://doi.org/10.5194/gmd-18-3799-2025, 2025
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This study proposed an advancing framework for modeling regional rice production, water use, and greenhouse gas emissions. The framework integrated a process-based soil-crop model with vital physiological effects, a novel model upscaling method, and the NSGA-II multi-objective optimization algorithm at a parallel computing platform. The framework provides a valuable tool for multi-objective optimization of rice irrigation schemes at a large scale.
Carolina A. Bieri, Francina Dominguez, Gonzalo Miguez-Macho, and Ying Fan
Geosci. Model Dev., 18, 3755–3779, https://doi.org/10.5194/gmd-18-3755-2025, https://doi.org/10.5194/gmd-18-3755-2025, 2025
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Access to deep moisture below the Earth's surface is important for vegetation in areas of the Amazon where there is little precipitation for part of the year. Most existing numerical models of the Earth system do not adequately capture where and when deep root water uptake occurs. We address this by adding deep soil layers and a root water uptake feature to an existing model. Out modifications lead to increased dry-month transpiration and improved simulation of the annual transpiration cycle.
Sergi Palomas, Mario C. Acosta, Gladys Utrera, and Etienne Tourigny
Geosci. Model Dev., 18, 3661–3679, https://doi.org/10.5194/gmd-18-3661-2025, https://doi.org/10.5194/gmd-18-3661-2025, 2025
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We present an automatic tool that optimizes resource distribution in coupled climate models, enhancing speed and reducing computational costs without requiring expert knowledge. Users can set energy/time criteria or limit resource usage. Tested on various European Community Earth System Model (EC-Earth) configurations and high-performance computing (HPC) platforms, it achieved up to 34 % faster simulations with fewer resources.
Maria Lucia Ferreira Barbosa, Douglas I. Kelley, Chantelle A. Burton, Igor J. M. Ferreira, Renata Moura da Veiga, Anna Bradley, Paulo Guilherme Molin, and Liana O. Anderson
Geosci. Model Dev., 18, 3533–3557, https://doi.org/10.5194/gmd-18-3533-2025, https://doi.org/10.5194/gmd-18-3533-2025, 2025
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As fire seasons in Brazil become increasingly severe, confidently understanding the factors driving fires is more critical than ever. To address this challenge, we developed FLAME (Fire Landscape Analysis using Maximum Entropy), a new model designed to predict fires and to analyse the spatial influence of both environmental and human factors while accounting for uncertainties. By adapting the model to different regions, we can enhance fire management strategies, making FLAME a powerful tool for protecting landscapes in Brazil and beyond.
Victor Couplet, Marina Martínez Montero, and Michel Crucifix
Geosci. Model Dev., 18, 3081–3129, https://doi.org/10.5194/gmd-18-3081-2025, https://doi.org/10.5194/gmd-18-3081-2025, 2025
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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 timescales ranging from decades to millions of years. SURFER v3.0 is fast, transparent, and easy to use, making it an ideal tool for policy assessments and suitable for educational purposes.
Yong-He Liu and Zong-Liang Yang
Geosci. Model Dev., 18, 3157–3174, https://doi.org/10.5194/gmd-18-3157-2025, https://doi.org/10.5194/gmd-18-3157-2025, 2025
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NMH-CS 3.0 is a C#-based ecohydrological model reconstructed from the WRF-Hydro/Noah-MP model by translating the Fortran code of WRF-Hydro 3.0 and integrating a parallel river routing module. It enables efficient execution on multi-core personal computers. Simulations in the Yellow River basin demonstrate its consistency with WRF-Hydro outputs, providing a reliable alternative to the original Noah-MP model.
Conor T. Doherty, Weile Wang, Hirofumi Hashimoto, and Ian G. Brosnan
Geosci. Model Dev., 18, 3003–3016, https://doi.org/10.5194/gmd-18-3003-2025, https://doi.org/10.5194/gmd-18-3003-2025, 2025
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We present, analyze, and validate a methodology for quantifying uncertainty in gridded meteorological data products produced by spatial interpolation. In a validation case study using daily maximum near-surface air temperature (Tmax), the method works well and produces predictive distributions with closely matching theoretical versus actual coverage levels. Application of the method reveals that the magnitude of uncertainty in interpolated Tmax varies significantly in both space and time.
Martin Juckes, Karl E. Taylor, Fabrizio Antonio, David Brayshaw, Carlo Buontempo, Jian Cao, Paul J. Durack, Michio Kawamiya, Hyungjun Kim, Tomas Lovato, Chloe Mackallah, Matthew Mizielinski, Alessandra Nuzzo, Martina Stockhause, Daniele Visioni, Jeremy Walton, Briony Turner, Eleanor O'Rourke, and Beth Dingley
Geosci. Model Dev., 18, 2639–2663, https://doi.org/10.5194/gmd-18-2639-2025, https://doi.org/10.5194/gmd-18-2639-2025, 2025
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The Baseline Climate Variables for Earth System Modelling (ESM-BCVs) are defined as a list of 135 variables which have high utility for the evaluation and exploitation of climate simulations. The list reflects the most frequently used variables from Earth system models based on an assessment of data publication and download records from the largest archive of global climate projects.
Yucheng Lin, Robert E. Kopp, Alexander Reedy, Matteo Turilli, Shantenu Jha, and Erica L. Ashe
Geosci. Model Dev., 18, 2609–2637, https://doi.org/10.5194/gmd-18-2609-2025, https://doi.org/10.5194/gmd-18-2609-2025, 2025
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PaleoSTeHM v1.0 is a state-of-the-art framework designed to reconstruct past environmental conditions using geological data. Built on modern machine learning techniques, it efficiently handles the sparse and noisy nature of paleo-records, allowing scientists to make accurate and scalable inferences about past environmental change. By using flexible statistical models, PaleoSTeHM separates different sources of uncertainty, improving the precision of historical climate reconstructions.
Ingo Richter, Ping Chang, Ping-Gin Chiu, Gokhan Danabasoglu, Takeshi Doi, Dietmar Dommenget, Guillaume Gastineau, Zoe E. Gillett, Aixue Hu, Takahito Kataoka, Noel S. Keenlyside, Fred Kucharski, Yuko M. Okumura, Wonsun Park, Malte F. Stuecker, Andréa S. Taschetto, Chunzai Wang, Stephen G. Yeager, and Sang-Wook Yeh
Geosci. Model Dev., 18, 2587–2608, https://doi.org/10.5194/gmd-18-2587-2025, https://doi.org/10.5194/gmd-18-2587-2025, 2025
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Tropical ocean basins influence each other through multiple pathways and mechanisms, referred to here as tropical basin interaction (TBI). Many researchers have examined TBI using comprehensive climate models but have obtained conflicting results. This may be partly due to differences in experiment protocols and partly due to systematic model errors. The Tropical Basin Interaction Model Intercomparison Project (TBIMIP) aims to address this problem by designing a set of TBI experiments that will be performed by multiple models.
Daniel F. J. Gunning, Kerim H. Nisancioglu, Emilie Capron, and Roderik S. W. van de Wal
Geosci. Model Dev., 18, 2479–2508, https://doi.org/10.5194/gmd-18-2479-2025, https://doi.org/10.5194/gmd-18-2479-2025, 2025
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This work documents the first results from ZEMBA: an energy balance model of the climate system. The model is a computationally efficient tool designed to study the response of climate to changes in the Earth's orbit. We demonstrate that ZEMBA reproduces many features of the Earth's climate for both the pre-industrial period and the Earth's most recent cold extreme – the Last Glacial Maximum. We intend to develop ZEMBA further and investigate the glacial cycles of the last 2.5 million years.
Pengfei Shi, L. Ruby Leung, and Bin Wang
Geosci. Model Dev., 18, 2443–2460, https://doi.org/10.5194/gmd-18-2443-2025, https://doi.org/10.5194/gmd-18-2443-2025, 2025
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Improving climate predictions has significant socio-economic impacts. In this study, we develop and apply a new weakly coupled ocean data assimilation (WCODA) system to a coupled climate model. The WCODA system improves simulations of ocean temperature and salinity across many global regions. This system is meant to advance our understanding of the ocean's role in climate predictability.
Liwen Wang, Qian Li, Qi Lv, Xuan Peng, and Wei You
Geosci. Model Dev., 18, 2427–2442, https://doi.org/10.5194/gmd-18-2427-2025, https://doi.org/10.5194/gmd-18-2427-2025, 2025
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Our research presents a novel deep learning approach called "TemDeep" for downscaling atmospheric variables at arbitrary time resolutions based on temporal coherence. Results show that our method can accurately recover evolution details superior to other methods, reaching 53.7 % in the restoration rate. Our findings are important for advancing weather forecasting models and enabling more precise and reliable predictions to support disaster preparedness, agriculture, and sustainable development.
Teo Price-Broncucia, Allison Baker, Dorit Hammerling, Michael Duda, and Rebecca Morrison
Geosci. Model Dev., 18, 2349–2372, https://doi.org/10.5194/gmd-18-2349-2025, https://doi.org/10.5194/gmd-18-2349-2025, 2025
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The ensemble consistency test (ECT) and its ultrafast variant (UF-ECT) have become powerful tools in the development community for the identification of unwanted changes in the Community Earth System Model (CESM). We develop a generalized setup framework to enable easy adoption of the ECT approach for other model developers and communities. This framework specifies test parameters to accurately characterize model variability and balance test sensitivity and computational cost.
Sergei Petrov, Andreas Will, and Beate Geyer
EGUsphere, https://doi.org/10.5194/egusphere-2025-710, https://doi.org/10.5194/egusphere-2025-710, 2025
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This study introduces a new method that helps improve the accuracy of climate models by automatically selecting the best parameters to match real-world observations. Instead of manually adjusting many parameters, the method uses a mathematical tool to find the most appropriate settings for the model. It can be especially helpful for researchers who introduce new physical parameters into climate models to assess their impact on model results and optimize them along with the old ones.
Michael Meier, Christof Bigler, and Isabelle Chuine
EGUsphere, https://doi.org/10.5194/egusphere-2025-460, https://doi.org/10.5194/egusphere-2025-460, 2025
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Formulated according to the leaf development process, the DP3 model of leaf coloring considerably contrasts previous models and allows to set up new hypotheses, e.g. on aging versus stress caused color changes. The DP3 model was as accurate as previous models and a comparison to the constant simulation of the mean date of leaf coloring indicated that noisy leaf coloring data forced the models to resort to this mean, which hinders model evaluation.
Esteban Fernández Villanueva and Gary Shaffer
Geosci. Model Dev., 18, 2161–2192, https://doi.org/10.5194/gmd-18-2161-2025, https://doi.org/10.5194/gmd-18-2161-2025, 2025
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We describe, calibrate and test the Danish Center for Earth System Science (DCESS) II model, a new, broad, adaptable and fast Earth system model. DCESS II is designed for global simulations over timescales of years to millions of years using limited computer resources like a personal computer. With its flexibility and comprehensive treatment of the global carbon cycle, DCESS II is a useful, computationally friendly tool for simulations of past climates as well as for future Earth system projections.
Gang Tang, Zebedee Nicholls, Alexander Norton, Sönke Zaehle, and Malte Meinshausen
Geosci. Model Dev., 18, 2193–2230, https://doi.org/10.5194/gmd-18-2193-2025, https://doi.org/10.5194/gmd-18-2193-2025, 2025
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We studied carbon–nitrogen coupling in Earth system models by developing a global carbon–nitrogen cycle model (CNit v1.0) within the widely used emulator MAGICC. CNit effectively reproduced the global carbon–nitrogen cycle dynamics observed in complex models. Our results show persistent nitrogen limitations on plant growth (net primary production) from 1850 to 2100, suggesting that nitrogen deficiency may constrain future land carbon sequestration.
Ngoc Thi Nhu Do, Kengo Sudo, Akihiko Ito, Louisa K. Emmons, Vaishali Naik, Kostas Tsigaridis, Øyvind Seland, Gerd A. Folberth, and Douglas I. Kelley
Geosci. Model Dev., 18, 2079–2109, https://doi.org/10.5194/gmd-18-2079-2025, https://doi.org/10.5194/gmd-18-2079-2025, 2025
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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 to climate. Future models that refine these factors’ effects on isoprene emissions, along with long-term observations, are essential for better understanding plant–climate interactions.
Gang Tang, Zebedee Nicholls, Chris Jones, Thomas Gasser, Alexander Norton, Tilo Ziehn, Alejandro Romero-Prieto, and Malte Meinshausen
Geosci. Model Dev., 18, 2111–2136, https://doi.org/10.5194/gmd-18-2111-2025, https://doi.org/10.5194/gmd-18-2111-2025, 2025
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We analyzed carbon and nitrogen mass conservation in data from various Earth system models. Our findings reveal significant discrepancies between flux and pool size data, where cumulative imbalances can reach hundreds of gigatons of carbon or nitrogen. These imbalances appear primarily due to missing or inconsistently reported fluxes – especially for land-use and fire emissions. To enhance data quality, we recommend that future climate data protocols address this issue at the reporting stage.
Zhongwang Wei, Qingchen Xu, Fan Bai, Xionghui Xu, Zixin Wei, Wenzong Dong, Hongbin Liang, Nan Wei, Xingjie Lu, Lu Li, Shupeng Zhang, Hua Yuan, Laibo Liu, and Yongjiu Dai
EGUsphere, https://doi.org/10.5194/egusphere-2025-1380, https://doi.org/10.5194/egusphere-2025-1380, 2025
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Land surface models are used for simulating earth's surface interacts with the atmosphere. As models grow more complex and detailed, researchers need better tools to evaluate their performance. OpenBench, a new software system that makes evaluation process more comprehensive and efficient. It stands out by incorporating various factors and working with data at any scale which enabling scientists to incorporate new types of models and measurements as our understanding of Earth’s systems evolves.
Florian Börgel, Sven Karsten, Karoline Rummel, and Ulf Gräwe
Geosci. Model Dev., 18, 2005–2019, https://doi.org/10.5194/gmd-18-2005-2025, https://doi.org/10.5194/gmd-18-2005-2025, 2025
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Forecasting river runoff, which is crucial for managing water resources and understanding climate impacts, can be challenging. This study introduces a new method using convolutional long short-term memory (ConvLSTM) networks, a machine learning model that processes spatial and temporal data. Focusing on the Baltic Sea region, our model uses weather data as input to predict daily river runoff for 97 rivers.
Tao Zhang, Cyril Morcrette, Meng Zhang, Wuyin Lin, Shaocheng Xie, Ye Liu, Kwinten Van Weverberg, and Joana Rodrigues
Geosci. Model Dev., 18, 1917–1928, https://doi.org/10.5194/gmd-18-1917-2025, https://doi.org/10.5194/gmd-18-1917-2025, 2025
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Earth system models (ESMs) struggle with the uncertainties associated with parameterizing subgrid physics. Machine learning (ML) algorithms offer a solution by learning the important relationships and features from high-resolution models. To incorporate ML parameterizations into ESMs, we develop a Fortran–Python interface that allows for calling Python functions within Fortran-based ESMs. Through two case studies, this interface demonstrates its feasibility, modularity, and effectiveness.
Kostas Tsigaridis, Andrew S. Ackerman, Igor Aleinov, Mark A. Chandler, Thomas L. Clune, Christopher M. Colose, Anthony D. Del Genio, Maxwell Kelley, Nancy Y. Kiang, Anthony Leboissetier, Jan P. Perlwitz, Reto A. Ruedy, Gary L. Russell, Linda E. Sohl, Michael J. Way, and Eric T. Wolf
EGUsphere, https://doi.org/10.5194/egusphere-2025-925, https://doi.org/10.5194/egusphere-2025-925, 2025
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We present the second generation of ROCKE-3D, a generalized 3-dimensional model for use in Solar System and exoplanetary simulations of rocky planet climates. We quantify how the different component choices affect model results, and discuss strengths and limitations of using each component, together with how one can select which component to use. ROCKE-3D is publicly available and tutorial sessions are available for the community, greatly facilitating its use by any interested group.
Camilla Mathison, Eleanor J. Burke, Gregory Munday, Chris D. Jones, Chris J. Smith, Norman J. Steinert, Andy J. Wiltshire, Chris Huntingford, Eszter Kovacs, Laila K. Gohar, Rebecca M. Varney, and Douglas McNeall
Geosci. Model Dev., 18, 1785–1808, https://doi.org/10.5194/gmd-18-1785-2025, https://doi.org/10.5194/gmd-18-1785-2025, 2025
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We present PRIME (Probabilistic Regional Impacts from Model patterns and Emissions), which is designed to take new emissions scenarios and rapidly provide regional impact information. PRIME allows large ensembles to be run on multi-centennial timescales, including the analysis of many important variables for impact assessments. Our evaluation shows that PRIME reproduces the climate response for known scenarios, providing confidence in using PRIME for novel scenarios.
Roberto Bilbao, Thomas J. Aubry, Matthew Toohey, and Pablo Ortega
EGUsphere, https://doi.org/10.5194/egusphere-2025-609, https://doi.org/10.5194/egusphere-2025-609, 2025
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Large volcanic eruptions are unpredictable and can have significant climatic impacts. If one occurs, operational decadal forecasts will become invalid and must be rerun including the volcanic forcing. By analyzing the climate response in EC-Earth3 retrospective predictions, we show that idealised forcings produced with two simple models could be used in operational decadal forecasts to account for the radiative impacts of the next major volcanic eruption.
Beiyao Xu, Steven Dobbie, Huiyi Yang, Lianxin Yang, Yu Jiang, Andrew Challinor, Karina Williams, Yunxia Wang, and Tijian Wang
EGUsphere, https://doi.org/10.5194/egusphere-2024-4077, https://doi.org/10.5194/egusphere-2024-4077, 2025
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Ozone (O3) pollution harms rice production and threatens food security. To understand these impacts, we calibrated a crop model using unique data from experiments where rice was grown in open fields under controlled O3 exposure (free air). This is the first time such data has been used to improve a model’s ability to predict how rice responds to O3 pollution. Our work provides a more accurate tool to study O3’s effects and guide strategies to protect agriculture.
Katherine M. Smith, Alice M. Barthel, LeAnn M. Conlon, Luke P. Van Roekel, Anthony Bartoletti, Jean-Christophe Golaz, Chengzhu Zhang, Carolyn Branecky Begeman, James J. Benedict, Gautam Bisht, Yan Feng, Walter Hannah, Bryce E. Harrop, Nicole Jeffery, Wuyin Lin, Po-Lun Ma, Mathew E. Maltrud, Mark R. Petersen, Balwinder Singh, Qi Tang, Teklu Tesfa, Jonathan D. Wolfe, Shaocheng Xie, Xue Zheng, Karthik Balaguru, Oluwayemi Garuba, Peter Gleckler, Aixue Hu, Jiwoo Lee, Ben Moore-Maley, and Ana C. Ordoñez
Geosci. Model Dev., 18, 1613–1633, https://doi.org/10.5194/gmd-18-1613-2025, https://doi.org/10.5194/gmd-18-1613-2025, 2025
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Version 2.1 of the U.S. Department of Energy's Energy Exascale Earth System Model (E3SM) adds the Fox-Kemper et al. (2011) mixed-layer eddy parameterization, which restratifies the ocean surface layer through an overturning streamfunction. Results include surface layer bias reduction in temperature, salinity, and sea ice extent in the North Atlantic; a small strengthening of the Atlantic meridional overturning circulation; and improvements to many atmospheric climatological variables.
Huilin Huang, Yun Qian, Gautam Bisht, Jiali Wang, Tirthankar Chakraborty, Dalei Hao, Jianfeng Li, Travis Thurber, Balwinder Singh, Zhao Yang, Ye Liu, Pengfei Xue, William J. Sacks, Ethan Coon, and Robert Hetland
Geosci. Model Dev., 18, 1427–1443, https://doi.org/10.5194/gmd-18-1427-2025, https://doi.org/10.5194/gmd-18-1427-2025, 2025
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We integrate the E3SM Land Model (ELM) with the WRF model through the Lightweight Infrastructure for Land Atmosphere Coupling (LILAC) Earth System Modeling Framework (ESMF). This framework includes a top-level driver, LILAC, for variable communication between WRF and ELM and ESMF caps for ELM initialization, execution, and finalization. The LILAC–ESMF framework maintains the integrity of the ELM's source code structure and facilitates the transfer of future ELM model developments to WRF-ELM.
Michael Nole, Jonah Bartrand, Fawz Naim, and Glenn Hammond
Geosci. Model Dev., 18, 1413–1425, https://doi.org/10.5194/gmd-18-1413-2025, https://doi.org/10.5194/gmd-18-1413-2025, 2025
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Safe carbon dioxide (CO2) storage is likely to be critical for mitigating some of the most severe effects of climate change. We present a simulation framework for modeling CO2 storage beneath the seafloor, where CO2 can form a solid. This can aid in permanent CO2 storage for long periods of time. Our models show what a commercial-scale CO2 injection would look like in a marine environment. We discuss what would need to be considered when designing a subsea CO2 injection.
Reyk Börner, Jan O. Haerter, and Romain Fiévet
Geosci. Model Dev., 18, 1333–1356, https://doi.org/10.5194/gmd-18-1333-2025, https://doi.org/10.5194/gmd-18-1333-2025, 2025
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The daily cycle of sea surface temperature (SST) impacts clouds above the ocean and could influence the clustering of thunderstorms linked to extreme rainfall and hurricanes. However, daily SST variability is often poorly represented in modeling studies of how clouds cluster. We present a simple, wind-responsive model of upper-ocean temperature for use in atmospheric simulations. Evaluating the model against observations, we show that it performs significantly better than common slab models.
Malcolm J. Roberts, Kevin A. Reed, Qing Bao, Joseph J. Barsugli, Suzana J. Camargo, Louis-Philippe Caron, Ping Chang, Cheng-Ta Chen, Hannah M. Christensen, Gokhan Danabasoglu, Ivy Frenger, Neven S. Fučkar, Shabeh ul Hasson, Helene T. Hewitt, Huanping Huang, Daehyun Kim, Chihiro Kodama, Michael Lai, Lai-Yung Ruby Leung, Ryo Mizuta, Paulo Nobre, Pablo Ortega, Dominique Paquin, Christopher D. Roberts, Enrico Scoccimarro, Jon Seddon, Anne Marie Treguier, Chia-Ying Tu, Paul A. Ullrich, Pier Luigi Vidale, Michael F. Wehner, Colin M. Zarzycki, Bosong Zhang, Wei Zhang, and Ming Zhao
Geosci. Model Dev., 18, 1307–1332, https://doi.org/10.5194/gmd-18-1307-2025, https://doi.org/10.5194/gmd-18-1307-2025, 2025
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HighResMIP2 is a model intercomparison project focusing on high-resolution global climate models, that is, those with grid spacings of 25 km or less in the atmosphere and ocean, using simulations of decades to a century in length. We are proposing an update of our simulation protocol to make the models more applicable to key questions for climate variability and hazard in present-day and future projections and to build links with other communities to provide more robust climate information.
Jordi Buckley Paules, Simone Fatichi, Bonnie Warring, and Athanasios Paschalis
Geosci. Model Dev., 18, 1287–1305, https://doi.org/10.5194/gmd-18-1287-2025, https://doi.org/10.5194/gmd-18-1287-2025, 2025
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We present and validate enhancements to the process-based T&C model aimed at improving its representation of crop growth and management practices. The updated model, T&C-CROP, enables applications such as analysing the hydrological and carbon storage impacts of land use transitions (e.g. conversions between crops, forests, and pastures) and optimizing irrigation and fertilization strategies in response to climate change.
Sébastien Masson, Swen Jullien, Eric Maisonnave, David Gill, Guillaume Samson, Mathieu Le Corre, and Lionel Renault
Geosci. Model Dev., 18, 1241–1263, https://doi.org/10.5194/gmd-18-1241-2025, https://doi.org/10.5194/gmd-18-1241-2025, 2025
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This article details a new feature we implemented in the popular regional atmospheric model WRF. This feature allows for data exchange between WRF and any other model (e.g. an ocean model) using the coupling library Ocean–Atmosphere–Sea–Ice–Soil Model Coupling Toolkit (OASIS3-MCT). This coupling interface is designed to be non-intrusive, flexible and modular. It also offers the possibility of taking into account the nested zooms used in WRF or in the models with which it is coupled.
Axel Lauer, Lisa Bock, Birgit Hassler, Patrick Jöckel, Lukas Ruhe, and Manuel Schlund
Geosci. Model Dev., 18, 1169–1188, https://doi.org/10.5194/gmd-18-1169-2025, https://doi.org/10.5194/gmd-18-1169-2025, 2025
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Earth system models are important tools to improve our understanding of current climate and to project climate change. Thus, it is crucial to understand possible shortcomings in the models. New features of the ESMValTool software package allow one to compare and visualize a model's performance with respect to reproducing observations in the context of other climate models in an easy and user-friendly way. We aim to help model developers assess and monitor climate simulations more efficiently.
Ulrich G. Wortmann, Tina Tsan, Mahrukh Niazi, Irene A. Ma, Ruben Navasardyan, Magnus-Roland Marun, Bernardo S. Chede, Jingwen Zhong, and Morgan Wolfe
Geosci. Model Dev., 18, 1155–1167, https://doi.org/10.5194/gmd-18-1155-2025, https://doi.org/10.5194/gmd-18-1155-2025, 2025
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The Earth Science Box Modeling Toolkit (ESBMTK) is a user-friendly Python library that simplifies the creation of models to study earth system processes, such as the carbon cycle and ocean chemistry. It enhances learning by emphasizing concepts over programming and is accessible to students and researchers alike. By automating complex calculations and promoting code clarity, ESBMTK accelerates model development while improving reproducibility and the usability of scientific research.
Florian Zabel, Matthias Knüttel, and Benjamin Poschlod
Geosci. Model Dev., 18, 1067–1087, https://doi.org/10.5194/gmd-18-1067-2025, https://doi.org/10.5194/gmd-18-1067-2025, 2025
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CropSuite is a new open-source crop suitability model. It provides a GUI and a wide range of options, including a spatial downscaling of climate data. We apply CropSuite to 48 staple and opportunity crops at a 1 km spatial resolution in Africa. We find that climate variability significantly impacts suitable areas but also affects optimal sowing dates and multiple cropping potential. The results provide valuable information for climate impact assessments, adaptation, and land-use planning.
Kerstin Hartung, Bastian Kern, Nils-Arne Dreier, Jörn Geisbüsch, Mahnoosh Haghighatnasab, Patrick Jöckel, Astrid Kerkweg, Wilton Jaciel Loch, Florian Prill, and Daniel Rieger
Geosci. Model Dev., 18, 1001–1015, https://doi.org/10.5194/gmd-18-1001-2025, https://doi.org/10.5194/gmd-18-1001-2025, 2025
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The ICOsahedral Non-hydrostatic (ICON) model system Community Interface (ComIn) library supports connecting third-party modules to the ICON model. Third-party modules can range from simple diagnostic Python scripts to full chemistry models. ComIn offers a low barrier for code extensions to ICON, provides multi-language support (Fortran, C/C++, and Python), and reduces the migration effort in response to new ICON releases. This paper presents the ComIn design principles and a range of use cases.
Daniel Ries, Katherine Goode, Kellie McClernon, and Benjamin Hillman
Geosci. Model Dev., 18, 1041–1065, https://doi.org/10.5194/gmd-18-1041-2025, https://doi.org/10.5194/gmd-18-1041-2025, 2025
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Machine learning has advanced research in the climate science domain, but its models are difficult to understand. In order to understand the impacts and consequences of climate interventions such as stratospheric aerosol injection, complex models are often necessary. We use a case study to illustrate how we can understand the inner workings of a complex model. We present this technique as an exploratory tool that can be used to quickly discover and assess relationships in complex climate data.
Bo Dong, Paul Ullrich, Jiwoo Lee, Peter Gleckler, Kristin Chang, and Travis A. O'Brien
Geosci. Model Dev., 18, 961–976, https://doi.org/10.5194/gmd-18-961-2025, https://doi.org/10.5194/gmd-18-961-2025, 2025
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A metrics package designed for easy analysis of atmospheric river (AR) characteristics and statistics is presented. The tool is efficient for diagnosing systematic AR bias in climate models and useful for evaluating new AR characteristics in model simulations. In climate models, landfalling AR precipitation shows dry biases globally, and AR tracks are farther poleward (equatorward) in the North and South Atlantic (South Pacific and Indian Ocean).
Hans Segura, Xabier Pedruzo-Bagazgoitia, Philipp Weiss, Sebastian K. Müller, Thomas Rackow, Junhong Lee, Edgar Dolores-Tesillos, Imme Benedict, Matthias Aengenheyster, Razvan Aguridan, Gabriele Arduini, Alexander J. Baker, Jiawei Bao, Swantje Bastin, Eulàlia Baulenas, Tobias Becker, Sebastian Beyer, Hendryk Bockelmann, Nils Brüggemann, Lukas Brunner, Suvarchal K. Cheedela, Sushant Das, Jasper Denissen, Ian Dragaud, Piotr Dziekan, Madeleine Ekblom, Jan Frederik Engels, Monika Esch, Richard Forbes, Claudia Frauen, Lilli Freischem, Diego García-Maroto, Philipp Geier, Paul Gierz, Álvaro González-Cervera, Katherine Grayson, Matthew Griffith, Oliver Gutjahr, Helmuth Haak, Ioan Hadade, Kerstin Haslehner, Shabeh ul Hasson, Jan Hegewald, Lukas Kluft, Aleksei Koldunov, Nikolay Koldunov, Tobias Kölling, Shunya Koseki, Sergey Kosukhin, Josh Kousal, Peter Kuma, Arjun U. Kumar, Rumeng Li, Nicolas Maury, Maximilian Meindl, Sebastian Milinski, Kristian Mogensen, Bimochan Niraula, Jakub Nowak, Divya Sri Praturi, Ulrike Proske, Dian Putrasahan, René Redler, David Santuy, Domokos Sármány, Reiner Schnur, Patrick Scholz, Dmitry Sidorenko, Dorian Spät, Birgit Sützl, Daisuke Takasuka, Adrian Tompkins, Alejandro Uribe, Mirco Valentini, Menno Veerman, Aiko Voigt, Sarah Warnau, Fabian Wachsmann, Marta Wacławczyk, Nils Wedi, Karl-Hermann Wieners, Jonathan Wille, Marius Winkler, Yuting Wu, Florian Ziemen, Janos Zimmermann, Frida A.-M. Bender, Dragana Bojovic, Sandrine Bony, Simona Bordoni, Patrice Brehmer, Marcus Dengler, Emanuel Dutra, Saliou Faye, Erich Fischer, Chiel van Heerwaarden, Cathy Hohenegger, Heikki Järvinen, Markus Jochum, Thomas Jung, Johann H. Jungclaus, Noel S. Keenlyside, Daniel Klocke, Heike Konow, Martina Klose, Szymon Malinowski, Olivia Martius, Thorsten Mauritsen, Juan Pedro Mellado, Theresa Mieslinger, Elsa Mohino, Hanna Pawłowska, Karsten Peters-von Gehlen, Abdoulaye Sarré, Pajam Sobhani, Philip Stier, Lauri Tuppi, Pier Luigi Vidale, Irina Sandu, and Bjorn Stevens
EGUsphere, https://doi.org/10.5194/egusphere-2025-509, https://doi.org/10.5194/egusphere-2025-509, 2025
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The nextGEMS project developed two Earth system models that resolve processes of the order of 10 km, giving more fidelity to the representation of local phenomena, globally. In its fourth cycle, nextGEMS performed simulations with coupled ocean, land, and atmosphere over the 2020–2049 period under the SSP3-7.0 scenario. Here, we provide an overview of nextGEMS, insights into the model development, and the realism of multi-decadal, kilometer-scale simulations.
Cited articles
Atkinson, G. D. and Holliday, C. R.: Tropical cyclone minimum sea level
pressure/maximum sustained wind relationship for the Western North
Pacific, Mon. Weather Rev., 105, 421–427,
https://doi.org/10.1175/1520-0493(1977)105<0421:TCMSLP>2.0.CO;2, 1977. a
Balaguru, K., Leung, L. R., Van Roekel, L. P., Golaz, J.-C., Ullrich, P. A.,
Caldwell, P. M., Hagos, S. M., Harrop, B. E., and Mametjanov, A.:
Characterizing Tropical Cyclones in the Energy Exascale Earth System
Model Version 1, J. Adv. Model. Earth Sy., 12,
e2019MS002024, https://doi.org/10.1029/2019MS002024, 2020. a
Bell, G. D., Halpert, M. S., Schnell, R. C., Higgins, R. W.,
Lawrimore, J., Kousky, V. E., Tinker, R., Thiaw, W., Chelliah, M.,
and Artusa, A.: Climate Assessment for 1999, B. Am.
Meteorol. Soc., 81, S1–S50,
https://doi.org/10.1175/1520-0477(2000)81[s1:CAF]2.0.CO;2, 2000. a
Benestad, R. and Chen, D.: The use of a calculus-based cyclone identification
method for generating storm statistics, Tellus A, 58, 473–486, 2006. a
Browning, K. A.: Conceptual Models of Precipitation Systems, Weather
Forecast., 1, 23–41, https://doi.org/10.1175/1520-0434(1986)001<0023:CMOPS>2.0.CO;2,
1986. a
Camargo, S. J., Giulivi, C. F., Sobel, A. H., Wing, A. A., Kim, D., Moon, Y.,
Strong, J. D., Del Genio, A. D., Kelley, M., Murakami, H., Reed, K.,
Scoccimarro, E., Vecchi, G., Wehner, M., Zarzycki, C., and Zhao, M.:
Characteristics of model tropical cyclone climatology and the large-scale
environment, J. Climate, 33, 4463–4487, 2020. a
Catalano, A. J., Broccoli, A. J., Kapnick, S. B., and Janoski, T. P.:
High-Impact Extratropical Cyclones along the Northeast Coast of the United
States in a Long Coupled Climate Model Simulation, J. Climate, 32,
2131–2143, https://doi.org/10.1175/JCLI-D-18-0376.1, 2019. a
Chavas, D. R. and Reed, K. A.: Dynamical aquaplanet experiments with uniform
thermal forcing: System dynamics and implications for tropical cyclone
genesis and size, J. Atmos. Sci., 76, 2257–2274, 2019. a
Chavas, D. R., Reed, K. A., and Knaff, J. A.: Physical understanding of the
tropical cyclone wind-pressure relationship, Nat. Commun., 8, 1–11,
https://doi.org/10.1038/s41467-017-01546-9, 2017. a, b
Chipilski, H. G., Wang, X., and Parsons, D. B.: Object-based algorithm for the identification and tracking of convective outflow boundaries in numerical
models, Mon. Weather Rev., 146, 4179–4200, 2018. a
Clark, A. J., Bullock, R. G., Jensen, T. L., Xue, M., and Kong, F.: Application of object-based time-domain diagnostics for tracking precipitation systems in convection-allowing models, Weather Forecast., 29, 517–542, 2014. a
Colle, B. A., Booth, J. F., and Chang, E. K. M.: A Review of Historical and
Future Changes of Extratropical Cyclones and Associated Impacts Along the
US East Coast, Curr. Clim. Change Rep., 1, 125–143,
https://doi.org/10.1007/s40641-015-0013-7, 2015. a
Dacre, H.: A review of extratropical cyclones: Observations and conceptual
models over the past 100 years, Weather, 75, 4–7, https://doi.org/10.1002/wea.3653,
2020. a
Davini, P. and D’Andrea, F.: Northern Hemisphere atmospheric blocking
representation in global climate models: Twenty years of improvements?,
J. Climate, 29, 8823–8840, 2016. a
Dean, J. and Ghemawat, S.: MapReduce: simplified data processing on large
clusters, Commun. ACM, 51, 107–113, 2008. a
Delanoy, R. L. and Troxel, S. W.: Machine Intelligent Gust Front Detection,
Lincoln Lab. J., 6, 187–212, 1993. a
Di Luca, A., Evans, J. P., Pepler, A., Alexander, L., and Argüeso, D.:
Resolution sensitivity of cyclone climatology over eastern Australia using
six reanalysis products, J. Climate, 28, 9530–9549, 2015. a
Dole, R. M. and Gordon, N. D.: Persistent anomalies of the extratropical
Northern Hemisphere wintertime circulation: Geographical distribution and
regional persistence characteristics, Mon. Weather Rev., 111,
1567–1586, 1983. a
Emanuel, K.: Increasing destructiveness of tropical cyclones over the past 30
years, Nature, 436, 686–688, 2005. a
European Centre for Medium-Range Weather Forecasts: ERA5 Reanalysis (0.25
Degree Latitude-Longitude Grid), in: Research Data Archive at the National
Center for Atmospheric Research, Computational and Information Systems
Laboratory, National Center for Atmospheric Research (NCAR),
https://doi.org/10.5065/BH6N-5N20, 2019. a, b
Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., and Taylor, K. E.: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization, Geosci. Model Dev., 9, 1937–1958, https://doi.org/10.5194/gmd-9-1937-2016, 2016. a
Field, P. R. and Wood, R.: Precipitation and cloud structure in midlatitude
cyclones, J. Climate, 20, 233–254, https://doi.org/10.1175/JCLI3998.1, 2007. a
Gelaro, R., McCarty, W., Suárez, M. J., Todling, R., Molod, A., Takacs, L., Randles, C. A., Darmenov, A., Bosilovich, M. G., Reichle, R., Wargan, K.,
Coy, L., Cullather, R., Draper, C., Akella, S., Buchard, V., Conaty, A.,
da Silva, A. M., Gu, W., Kim, G.-K., Koster, R., Lucchesi, R., Merkova, D.,
Nielsen, J. E., Partyka, G., Pawson, S., Putman, W., Rienecker, M., Schubert,
S. D., Sienkiewicz, M., and Zhao, B.: The Modern-Era Retrospective Analysis
for Research and Applications, version 2 (MERRA-2), J. Climate, 30,
5419–5454, 2017. a
Grotjahn, R., Black, R., Leung, R., Wehner, M. F., Barlow, M., Bosilovich, M., Gershunov, A., Gutowski, W. J., Gyakum, J. R., Katz, R. W., Lee, Y.-Y., Lim, Y.-K., and Prabhat: North American extreme temperature events and related large scale meteorological patterns: A review of statistical methods,
dynamics, modeling, and trends, Clim. Dynam., 46, 1151–1184, 2016. 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
Hart, R. E.: A cyclone phase space derived from thermal wind and thermal
asymmetry, Mon. Weather Rev., 131, 585–616, 2003. a
Hassani, H., Huang, X., and Silva, E.: Big Data and climate change, Big Data
and Cognitive Computing, 3, 12, https://doi.org/10.3390/bdcc3010012, 2019. a
Heikenfeld, M., Marinescu, P. J., Christensen, M., Watson-Parris, D., Senf, F., van den Heever, S. C., and Stier, P.: tobac 1.2: towards a flexible framework for tracking and analysis of clouds in diverse datasets, Geosci. Model Dev., 12, 4551–4570, https://doi.org/10.5194/gmd-12-4551-2019, 2019. a, b
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., Chiara, G. D., 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.: The ERA5 global
reanalysis, Q. J. Roy. Meteor. Soc., 146,
1999–2049, 2020. a
Hodges, K., Cobb, A., and Vidale, P. L.: How well are tropical cyclones
represented in reanalysis datasets?, J. Climate, 30, 5243–5264,
https://doi.org/10.1175/JCLI-D-16-0557.1, 2017. a
Holland, G.: A revised hurricane pressure-wind model, Mon. Weather Rev.,
136, 3432–3445, https://doi.org/10.1175/2008MWR2395.1, 2008. a
Hope, P., Keay, K., Pook, M., Catto, J., Simmonds, I., Mills, G., McIntosh, P.,
Risbey, J., and Berry, G.: A comparison of automated methods of front
recognition for climate studies: A case study in southwest Western Australia,
Mon. Weather Rev., 142, 343–363, 2014. a
Huang, H., Patricola, C. M., Bercos-Hickey, E., Zhou, Y., Rhoades, A., Risser, M. D., and Collins, W. D.: Sources of Subseasonal-To-Seasonal Predictability
of Atmospheric Rivers and Precipitation in the Western United States, J. Geophys. Res.-Atmos., 126, e2020JD034053, https://doi.org/10.1029/2020JD034053, 2021. a
Huffman, G. J., Bolvin, D. T., Nelkin, E. J., Wolff, D. B., Adler, R. F., Gu,
G., Hong, Y., Bowman, K. P., and Stocker, E. F.: The TRMM Multisatellite
Precipitation Analysis (TMPA): Quasi-global, multiyear,
combined-sensor precipitation estimates at fine scales, J.
Hydrometeor., 8, 38–55, https://doi.org/10.1175/JHM560.1, 2007. a
Hurley, J. V. and Boos, W. R.: A global climatology of monsoon low-pressure
systems, Q. J. Roy. Meteor. Soc., 141,
1049–1064, 2015. a
Kay, J. E., Deser, C., Phillips, A., Mai, A., Hannay, C., Strand, G.,
Arblaster, J. M., Bates, S., Danabasoglu, G., Edwards, J., Holland, M.,
Kushner, P., Lamarque, J.-F., Lawrence, D., Lindsay, K., Middleton, A.,
Munoz, E., Neale, R., Oleson, K., Polvani, L., and Vertenstein, M.: The
Community Earth System Model (CESM) large ensemble project: A community
resource for studying climate change in the presence of internal climate
variability, B. Am. Meteorol. Soc., 96,
1333–1349, 2015. a, b
Khouakhi, A., Villarini, G., and Vecchi, G. A.: Contribution of tropical
cyclones to rainfall at the global scale, J. Climate, 30, 359–372,
https://doi.org/10.1175/JCLI-D-16-0298.1, 2017. a
Kiehl, J. T., Zarzycki, C. M., Shields, C. A., and Rothstein, M. V.: Simulated
changes to tropical cyclones across the Paleocene-Eocene Thermal Maximum
(PETM) boundary, Palaeogeogr. Palaeoclimatol., 572,
110421, https://doi.org/10.1016/j.palaeo.2021.110421, 2021. a
Knaff, J. A. and Zehr, R. M.: Reexamination of tropical cyclone wind–pressure relationships, Weather Forecast., 22, 71–88, https://doi.org/10.1175/WAF965.1, 2007. a
Knapp, K. R., Kruk, M. C., Levinson, D. H., Diamond, H. J., and Neumann, C. J.: The International Best Track Archive for Climate Stewardship (IBTrACS) unifying tropical cyclone data, B. Am. Meteorol.
Soc., 91, 363–376, 2010. a
Li, F., Chavas, D. R., Reed, K. A., and Dawson II, D. T.: Climatology of
severe local storm environments and synoptic-scale features over North
America in ERA5 reanalysis and CAM6 simulation, J. Climate, 33,
8339–8365, https://doi.org/10.1175/JCLI-D-19-0986.1, 2020. a
Marchok, T. P.: How the NCEP tropical cyclone tracker works, in: Preprints,
25th Conf. on Hurricanes and Tropical Meteorology, San Diego, CA, Am.
Meteor. Soc., vol. 1, 2002. a
McClenny, E. E., Ullrich, P. A., and Grotjahn, R.: Sensitivity of atmospheric
river vapor transport and precipitation to uniform sea-surface temperature
increases, J. Geophys. Res.-Atmos., 125, e2020JD033421,
https://doi.org/10.1029/2020JD033421, 2020. a, b, c, d
Michaelis, A. C. and Lackmann, G. M.: Climatological changes in the
extratropical transition of tropical cyclones in high-resolution global
simulations, J. Climate, 32, 8733–8753, 2019. a
Michaelis, A. C. and Lackmann, G. M.: Storm-scale dynamical changes of
extratropical transition events in present-day and future high-resolution
global simulations, J. Climate, 34, 5037–5062, 2021. a
Moon, Y., Kim, D., Camargo, S. J., Wing, A. A., Sobel, A. H., Murakami, H.,
Reed, K. A., Scoccimarro, E., Vecchi, G. A., Wehner, M. F., Zarzycki, C. M.,
and Zhao, M.: Azimuthally averaged wind and thermodynamic structures of
tropical cyclones in global climate models and their sensitivity to
horizontal resolution, J. Climate, 33, 1575–1595, 2020. a
Mundhenk, B. D., Barnes, E. A., and Maloney, E. D.: All-season climatology and variability of atmospheric river frequencies over the North Pacific, J. Climate, 29, 4885–4903, 2016. a
Murakami, H.: Tropical cyclones in reanalysis data sets, Geophys. Res.
Lett., 41, 2133–2141, https://doi.org/10.1002/2014GL059519, 2014. a
Murata, A., Sasaki, H., Kawase, H., and Nosaka, M.: The development of a
resolution-independent tropical cyclone detection scheme for high-resolution
climate model simulations, J. Meteorol. Soci. Jpn.
Ser. II, 97, 519–531, 2019. a
Murray, R. J. and Simmonds, I.: A numerical scheme for tracking cyclone centres from digital data, Aust. Meteorol. Mag., 39, 155–166, 1991. a
Neu, U., Akperov, M. G., Bellenbaum, N., Benestad, R., Blender, R., Caballero, R., Cocozza, A., Dacre, H. F., Feng, Y., Fraedrich, K., Grieger, J., Gulev, S., Hanley, J., Hewson, T., Inatsu, M., Keay, K., Kew, S. F., Kindem, I., Leckebusch, G. C., Liberato, M. L. R., Lionello, P., Mokhov, I., Pinto, J. G., Raible, C. C., Reale, M., Rudeva, I., Schuster, M., Simmonds, I., Sinclair, M.,, Sprenger, M., Tilinina, N. D., Trigo, I. F., Ulbrich, S., Ulbrich, U., Wang, X. L., and Wernli, H.: IMILAST: A
community effort to intercompare extratropical cyclone detection and tracking
algorithms, B. Am. Meteorol. Soc., 94, 529–547,
2013. a, b, c
Parfitt, R., Czaja, A., and Seo, H.: A simple diagnostic for the detection of
atmospheric fronts, Geophys. Res. Lett., 44, 4351–4358, 2017. a
Patricola, C. M., O’Brien, J. P., Risser, M. D., Rhoades, A. M., O’Brien,
T. A., Ullrich, P. A., Stone, D. A., and Collins, W. D.: Maximizing ENSO as a
source of western US hydroclimate predictability, Clim. Dynam., 54,
351–372, 2020. a
Payne, A. E., Demory, M.-E., Leung, L. R., Ramos, A. M., Shields, C. A., Rutz, J. J., Siler, N., Villarini, G., Hall, A., and Ralph, F. M.: Responses and
impacts of atmospheric rivers to climate change, Nat. Rev. Earth
Environ., 1, 143–157, 2020. a
Pendergrass, A. G., Reed, K. A., and Medeiros, B.: The link between extreme
precipitation and convective organization in a warming climate: Global
radiative-convective equilibrium simulations, Geophys. Res. Lett.,
43, 11445–11452, https://doi.org/10.1002/2016GL071285, 2016. a
Powell, M. D. and Reinhold, T. A.: Tropical cyclone destructive potential by
integrated kinetic energy, B. Am. Meteorol. Soc.,
88, 513–526, 2007. a
Prat, O. P. and Nelson, B. R.: Mapping the world's tropical cyclone rainfall
contribution over land using the TRMM Multi-satellite Precipitation
Analysis, Water Resour. Res., 49, 7236–7254,
https://doi.org/10.1002/wrcr.20527, 2013. a
Prein, A. F., Liu, C., Ikeda, K., Trier, S. B., Rasmussen, R. M., Holland,
G. J., and Clark, M. P.: Increased rainfall volume from future convective
storms in the US, Nat. Clim. Change, 7, 880–884, 2017. a
Reed, K., Wehner, M. F., Stansfield, A. M., and Zarzycki, C. M.: Anthropogenic influence on hurricane Dorian’s extreme rainfall, B. Am.
Meteorol. Soc., 102, S9–S15, 2021. a
Reed, K. A., Stansfield, A., Wehner, M., and Zarzycki, C.: Forecasted
attribution of the human influence on Hurricane Florence, Sci. Adv.,
6, eaaw9253, https://doi.org/10.1126/sciadv.aaw9253, 2020. a
Rhoades, A., Jones, A., Srivastava, A., Huang, H., O'Brien, T., Patricola, C., Ullrich, P., Wehner, M., and Zhou, Y.: The shifting scales of Western US
landfalling atmospheric rivers under climate change, Geophys. Res.
Lett., 47, e2020GL089096, https://doi.org/10.1029/2020GL089096, 2020a. a, b
Rhoades, A. M., Jones, A. D., O'Brien, T. A., O'Brien, J. P., Ullrich, P. A.,
and Zarzycki, C. M.: Influences of North Pacific Ocean domain extent on the
Western US winter hydroclimatology in variable-resolution CESM, J. Geophys. Res.-Atmos., 125, e2019JD031977, https://doi.org/10.1029/2019JD031977, 2020b. a, b
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., 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,
2020a. a, b
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., Ullrich, P., Yamada, Y., Mizuta, R., Kodama, C.,
Fu, D., Zhang, Q., Danabasoglu, G., Rosenbloom, N., Wang, H., and Wu, L.:
Impact of model resolution on tropical cyclone simulation using the
HighResMIP–PRIMAVERA multimodel ensemble, J. Climate, 33,
2557–2583, 2020b. a
Rutz, J. J., Shields, C. A., Lora, J. M., Payne, A. E., Guan, B., Ullrich, P., O’Brien, T., Leung, L. R., Ralph, F. M., Wehner, M., Brands, S., Collow,
A., Goldenson, N., Gorodetskaya, I., Griffith, H., Kashinath, K., Kawzenuk,
B., Krishnan, H., Kurlin, V., Lavers, D., Magnusdottir, G., Mahoney, K.,
McClenny, E., Muszynski, G., Nguyen, P. D., Prabhat, Qian, Y., Ramos, A. M.,
Sarangi, C., Sellars, S., Shulgina, T., Tome, R., Waliser, D., Walton, D.,
Wick, G., Wilson, A. M., and Viale, M.: The Atmospheric River Tracking
Method Intercomparison Project (ARTMIP): Quantifying uncertainties in
atmospheric river climatology, J. Geophys. Res.-Atmos.,
124, 13777–13802, 2019. a, b, c
Schemm, S., Rudeva, I., and Simmonds, I.: Extratropical fronts in the lower
troposphere–global perspectives obtained from two automated methods,
Q. J. Roy. Meteor. Soc., 141, 1686–1698, 2015. a
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
Schenkel, B. A., Lin, N., Chavas, D., Oppenheimer, M., and Brammer, A.:
Evaluating outer tropical cyclone size in reanalysis datasets using QuikSCAT
data, J. Climate, 30, 8745–8762, 2017. a
Scherrer, S. C., Croci-Maspoli, M., Schwierz, C., and Appenzeller, C.:
Two-dimensional indices of atmospheric blocking and their statistical
relationship with winter climate patterns in the Euro-Atlantic region,
Int. J. Climatol., 26, 233–249, 2006. a
Schnase, J. L., Lee, T. J., Mattmann, C. A., Lynnes, C. S., Cinquini, L.,
Ramirez, P. M., Hart, A. F., Williams, D. N., Waliser, D., Rinsland, P.,
Webster, W. P., Duffy, D. Q., McInerney, M. A., Tamkin, G. S., Potter, G. L.,
and Carriere, L.: Big data challenges in climate science: Improving the
next-generation cyberinfrastructure, IEEE Geosci. Remote Sens. Mag., 4, 10–22, 2016. a
Schultz, D. M., Bosart, L. F., Colle, B. A., Davies, H. C., Dearden, C.,
Keyser, D., Martius, O., Roebber, P. J., Steenburgh, W. J., Volkert, H., and
Winters, A. C.: Extratropical cyclones: A century of research on
meteorology's centerpiece, Meteorol. Monogr., 59, 16.1–16.56,
https://doi.org/10.1175/AMSMONOGRAPHS-D-18-0015.1, 2019. a
Shields, C. A., Rutz, J. J., Leung, L.-Y., Ralph, F. M., Wehner, M., Kawzenuk, B., Lora, J. M., McClenny, E., Osborne, T., Payne, A. E., Ullrich, P., Gershunov, A., Goldenson, N., Guan, B., Qian, Y., Ramos, A. M., Sarangi, C., Sellars, S., Gorodetskaya, I., Kashinath, K., Kurlin, V., Mahoney, K., Muszynski, G., Pierce, R., Subramanian, A. C., Tome, R., Waliser, D., Walton, D., Wick, G., Wilson, A., Lavers, D., Prabhat, Collow, A., Krishnan, H., Magnusdottir, G., and Nguyen, P.: Atmospheric River Tracking Method Intercomparison Project (ARTMIP): project goals and experimental design, Geosci. Model Dev., 11, 2455–2474, https://doi.org/10.5194/gmd-11-2455-2018, 2018. a, b, c, d
Small, R. J., Msadek, R., Kwon, Y.-O., Booth, J. F., and Zarzycki, C.:
Atmosphere surface storm track response to resolved ocean mesoscale in two
sets of global climate model experiments, Clim. Dynam., 52, 2067–2089,
2019. a
Steptoe, H., Savage, N. H., Sadri, S., Salmon, K., Maalick, Z., and Webster,
S.: Tropical cyclone simulations over Bangladesh at convection permitting 4.4 km & 1.5 km resolution, Sci. Data, 8, 1–12, 2021. a
Ullrich, P. A.: TempestExtremes User Guide,
available at: https://climate.ucdavis.edu/tempestextremes.php (last access: 15 July 2021), 2020. a
Ullrich, P. A.: TempestExtremes GitHub Repository, GitHub, https://github.com/ClimateGlobalChange/tempestextremes, last access: 15 July 2021. a
Ullrich, P. A., Jablonowski, C., Kent, J., Lauritzen, P. H., Nair, R., Reed, K. A., Zarzycki, C. M., Hall, D. M., Dazlich, D., Heikes, R., Konor, C., Randall, D., Dubos, T., Meurdesoif, Y., Chen, X., Harris, L., Kühnlein, C., Lee, V., Qaddouri, A., Girard, C., Giorgetta, M., Reinert, D., Klemp, J., Park, S.-H., Skamarock, W., Miura, H., Ohno, T., Yoshida, R., Walko, R., Reinecke, A., and Viner, K.: DCMIP2016: a review of non-hydrostatic dynamical core design and intercomparison of participating models, Geosci. Model Dev., 10, 4477–4509, https://doi.org/10.5194/gmd-10-4477-2017, 2017. a
Ullrich, P. A., Pinheiro, M. C., Stachowicz, K., and Zarzycki, C. M.: ClimateGlobalChange/tempestextremes: Version 2.1, Zenodo, https://doi.org/10.5281/zenodo.4385656, 2021. a
Vishnu, S., Boos, W., Ullrich, P., and O'Brien, T.: Assessing historical
variability of South Asian monsoon lows and depressions with an optimized
tracking algorithm, J. Geophys. Res.-Atmos., 125, e2020JD032977. https://doi.org/10.1029/2020JD032977, 2020. a, b
Vitart, F., Anderson, J., and Stern, W.: Simulation of interannual variability of tropical storm frequency in an ensemble of GCM integrations, J. Climate, 10, 745–760, 1997. 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
Wing, A. A., Camargo, S. J., Sobel, A. H., Kim, D., Moon, Y., Murakami, H.,
Reed, K. A., Vecchi, G. A., Wehner, M. F., Zarzycki, C., and Zhao, M.: Moist static
energy budget analysis of tropical cyclone intensification in high-resolution
climate models, J. Climate, 32, 6071–6095, 2019. a
You, Y. and Ting, M.: Observed Trends in the South Asian Monsoon Low-Pressure
Systems and Rainfall Extremes Since the Late 1970s, Geophys. Res.
Lett., 48, e2021GL092378, https://doi.org/10.1029/2021GL092378, 2021. a
Zarzycki, C. M.: Tropical cyclone intensity errors associated with lack of
two-way ocean coupling in high-resolution global simulations, J. Climate, 29, 8589–8610, 2016. a
Zarzycki, C. M., Thatcher, D. R., and Jablonowski, C.: Objective tropical
cyclone extratropical transition detection in high-resolution reanalysis and
climate model data, J. Adv. Model. Earth Sy., 9,
130–148, 2017. a
Zarzycki, C. M., Ullrich, P. A., and Reed, K. A.: Metrics for evaluating
tropical cyclones in climate data, J. Appl. Meteorol. Climatol., 60, 643–660, 2021. a
Zender, C. S.: Analysis of self-describing gridded geoscience data with
netCDF Operators (NCO), Environ. Model. Softw., 23,
1338–1342, https://doi.org/10.1016/j.envsoft.2008.03.004, 2008. a
Zhang, W., Hari, V., and Villarini, G.: Potential impacts of anthropogenic
forcing on the frequency of tropical depressions in the North Indian Ocean in
2018, J. Mar. Sci. Eng., 7, 436, https://doi.org/10.3390/jmse7120436, 2019.
a
Zhang, W., Villarini, G., Scoccimarro, E., and Napolitano, F.: Examining the
precipitation associated with Medicanes in the high-resolution ERA-5
reanalysis data, Int. J. Climatol., 41, E126–E132, 2021. a
Zhang, Z. and Colle, B. A.: Changes in Extratropical Cyclone Precipitation and
Associated Processes during the Twenty-First Century over Eastern North
America and the Western Atlantic Using a Cyclone-Relative Approach,
J. Climate, 30, 8633–8656, https://doi.org/10.1175/JCLI-D-16-0906.1, 2017. 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,
2009. a
Zhou, Y., O'Brien, T. A., Ullrich, P. A., Collins, W. D., Patricola, C. M., and Rhoades, A. M.: Uncertainties in Atmospheric River Lifecycles by Detection Algorithms: Climatology and Variability, J. Geophys. Res.-Atmos., 126, e2020JD033711, https://doi.org/10.1029/2020JD033711, 2021. a
Short summary
TempestExtremes (TE) is a multifaceted framework for feature detection, tracking, and scientific analysis of regional or global Earth system datasets. Version 2.1 of TE now provides extensive support for nodal and areal features. This paper describes the algorithms that have been added to the TE framework since version 1.0 and gives several examples of how these can be combined to produce composite algorithms for evaluating and understanding atmospheric features.
TempestExtremes (TE) is a multifaceted framework for feature detection, tracking, and scientific...
