Articles | Volume 17, issue 9
https://doi.org/10.5194/gmd-17-3919-2024
© Author(s) 2024. 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-17-3919-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model evaluation paper
| 
15 May 2024
Model evaluation paper |
| 15 May 2024
| 15 May 2024
Systematic and objective evaluation of Earth system models: PCMDI Metrics Package (PMP) version 3
Lawrence Livermore National Laboratory, Livermore, CA, USA
Peter J. Gleckler
Lawrence Livermore National Laboratory, Livermore, CA, USA
Min-Seop Ahn
NASA Goddard Space Flight Center, Greenbelt, MD, USA
ESSIC, University of Maryland, College Park, MD, USA
Ana Ordonez
Lawrence Livermore National Laboratory, Livermore, CA, USA
Paul A. Ullrich
Lawrence Livermore National Laboratory, Livermore, CA, USA
Department of Land, Air and Water Resources, University of California, Davis, Davis, CA, USA
Kenneth R. Sperber
Lawrence Livermore National Laboratory, Livermore, CA, USA
retired
Karl E. Taylor
Lawrence Livermore National Laboratory, Livermore, CA, USA
Yann Y. Planton
NOAA Pacific Marine Environmental Laboratory, Seattle, WA, USA
School of Earth Atmosphere and Environment, Monash University, Clayton, VIC, Australia
Eric Guilyardi
LOCEAN-IPSL, CNRS-IRD-MNHN-Sorbonne Université, Paris, France
National Centre for Atmospheric Science – Climate, University of Reading, Reading, UK
Paul Durack
Lawrence Livermore National Laboratory, Livermore, CA, USA
Celine Bonfils
Lawrence Livermore National Laboratory, Livermore, CA, USA
Mark D. Zelinka
Lawrence Livermore National Laboratory, Livermore, CA, USA
Li-Wei Chao
Lawrence Livermore National Laboratory, Livermore, CA, USA
Bo Dong
Lawrence Livermore National Laboratory, Livermore, CA, USA
Charles Doutriaux
Lawrence Livermore National Laboratory, Livermore, CA, USA
Chengzhu Zhang
Lawrence Livermore National Laboratory, Livermore, CA, USA
Tom Vo
Lawrence Livermore National Laboratory, Livermore, CA, USA
Jason Boutte
Lawrence Livermore National Laboratory, Livermore, CA, USA
Michael F. Wehner
Lawrence Berkeley National Laboratory, Berkeley, CA, USA
Angeline G. Pendergrass
Department of Earth and Atmospheric Science, Cornell University, Ithaca, NY, USA
National Center for Atmospheric Research, Boulder, CO, USA
Daehyun Kim
School of Earth and Environmental Sciences, Seoul National University, Seoul, South Korea
Pacific Northwest National Laboratory, Richland, WA, USA
Andrew T. Wittenberg
NOAA Geophysical Fluid Dynamics Laboratory, Princeton, NJ, USA
John Krasting
NOAA Geophysical Fluid Dynamics Laboratory, Princeton, NJ, USA
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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
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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
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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
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Iris Elisabeth de Vries, Sebastian Sippel, Angeline Greene Pendergrass, and Reto Knutti
Earth Syst. Dynam., 14, 81–100, https://doi.org/10.5194/esd-14-81-2023, https://doi.org/10.5194/esd-14-81-2023, 2023
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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
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Sjoukje Y. Philip, Sarah F. Kew, Geert Jan van Oldenborgh, Faron S. Anslow, Sonia I. Seneviratne, Robert Vautard, Dim Coumou, Kristie L. Ebi, Julie Arrighi, Roop Singh, Maarten van Aalst, Carolina Pereira Marghidan, Michael Wehner, Wenchang Yang, Sihan Li, Dominik L. Schumacher, Mathias Hauser, Rémy Bonnet, Linh N. Luu, Flavio Lehner, Nathan Gillett, Jordis S. Tradowsky, Gabriel A. Vecchi, Chris Rodell, Roland B. Stull, Rosie Howard, and Friederike E. L. Otto
Earth Syst. Dynam., 13, 1689–1713, https://doi.org/10.5194/esd-13-1689-2022, https://doi.org/10.5194/esd-13-1689-2022, 2022
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In June 2021, the Pacific Northwest of the US and Canada saw record temperatures far exceeding those previously observed. This attribution study found such a severe heat wave would have been virtually impossible without human-induced climate change. Assuming no nonlinear interactions, such events have become at least 150 times more common, are about 2 °C hotter and will become even more common as warming continues. Therefore, adaptation and mitigation are urgently needed to prepare society.
Yona Silvy, Clément Rousset, Eric Guilyardi, Jean-Baptiste Sallée, Juliette Mignot, Christian Ethé, and Gurvan Madec
Geosci. Model Dev., 15, 7683–7713, https://doi.org/10.5194/gmd-15-7683-2022, https://doi.org/10.5194/gmd-15-7683-2022, 2022
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A modeling framework is introduced to understand and decompose the mechanisms causing the ocean temperature, salinity and circulation to change since the pre-industrial period and into 21st century scenarios of global warming. This framework aims to look at the response to changes in the winds and in heat and freshwater exchanges at the ocean interface in global climate models, throughout the 1850–2100 period, to unravel their individual effects on the changing physical structure of the ocean.
Amy H. Peace, Ben B. B. Booth, Leighton A. Regayre, Ken S. Carslaw, David M. H. Sexton, Céline J. W. Bonfils, and John W. Rostron
Earth Syst. Dynam., 13, 1215–1232, https://doi.org/10.5194/esd-13-1215-2022, https://doi.org/10.5194/esd-13-1215-2022, 2022
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Anthropogenic aerosol emissions have been linked to driving climate responses such as shifts in the location of tropical rainfall. However, the interaction of aerosols with climate remains one of the most uncertain aspects of climate modelling and limits our ability to predict future climate change. We use an ensemble of climate model simulations to investigate what impact the large uncertainty in how aerosols interact with climate has on predicting future tropical rainfall shifts.
Pradeebane Vaittinada Ayar, Laurent Bopp, Jim R. Christian, Tatiana Ilyina, John P. Krasting, Roland Séférian, Hiroyuki Tsujino, Michio Watanabe, Andrew Yool, and Jerry Tjiputra
Earth Syst. Dynam., 13, 1097–1118, https://doi.org/10.5194/esd-13-1097-2022, https://doi.org/10.5194/esd-13-1097-2022, 2022
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The El Niño–Southern Oscillation is the main driver for the natural variability of global atmospheric CO2. It modulates the CO2 fluxes in the tropical Pacific with anomalous CO2 influx during El Niño and outflux during La Niña. This relationship is projected to reverse by half of Earth system models studied here under the business-as-usual scenario. This study shows models that simulate a positive bias in surface carbonate concentrations simulate a shift in the ENSO–CO2 flux relationship.
Po-Lun Ma, Bryce E. Harrop, Vincent E. Larson, Richard B. Neale, Andrew Gettelman, Hugh Morrison, Hailong Wang, Kai Zhang, Stephen A. Klein, Mark D. Zelinka, Yuying Zhang, Yun Qian, Jin-Ho Yoon, Christopher R. Jones, Meng Huang, Sheng-Lun Tai, Balwinder Singh, Peter A. Bogenschutz, Xue Zheng, Wuyin Lin, Johannes Quaas, Hélène Chepfer, Michael A. Brunke, Xubin Zeng, Johannes Mülmenstädt, Samson Hagos, Zhibo Zhang, Hua Song, Xiaohong Liu, Michael S. Pritchard, Hui Wan, Jingyu Wang, Qi Tang, Peter M. Caldwell, Jiwen Fan, Larry K. Berg, Jerome D. Fast, Mark A. Taylor, Jean-Christophe Golaz, Shaocheng Xie, Philip J. Rasch, and L. Ruby Leung
Geosci. Model Dev., 15, 2881–2916, https://doi.org/10.5194/gmd-15-2881-2022, https://doi.org/10.5194/gmd-15-2881-2022, 2022
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An alternative set of parameters for E3SM Atmospheric Model version 1 has been developed based on a tuning strategy that focuses on clouds. When clouds in every regime are improved, other aspects of the model are also improved, even though they are not the direct targets for calibration. The recalibrated model shows a lower sensitivity to anthropogenic aerosols and surface warming, suggesting potential improvements to the simulated climate in the past and future.
Claudia Tebaldi, Kalyn Dorheim, Michael Wehner, and Ruby Leung
Earth Syst. Dynam., 12, 1427–1501, https://doi.org/10.5194/esd-12-1427-2021, https://doi.org/10.5194/esd-12-1427-2021, 2021
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We address the question of how large an initial condition ensemble of climate model simulations should be if we are concerned with accurately projecting future changes in temperature and precipitation extremes. We find that for most cases (and both models considered), an ensemble of 20–25 members is sufficient for many extreme metrics, spatial scales and time horizons. This may leave computational resources to tackle other uncertainties in climate model simulations with our ensembles.
Trevor J. McDougall, Paul M. Barker, Ryan M. Holmes, Rich Pawlowicz, Stephen M. Griffies, and Paul J. Durack
Geosci. Model Dev., 14, 6445–6466, https://doi.org/10.5194/gmd-14-6445-2021, https://doi.org/10.5194/gmd-14-6445-2021, 2021
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We show that the way that the air–sea heat flux is treated in ocean models means that the model's temperature variable should be interpreted as being Conservative Temperature, irrespective of whether the equation of state used in an ocean model is EOS-80 or TEOS-10.
Benjamin M. Sanderson, Angeline G. Pendergrass, Charles D. Koven, Florent Brient, Ben B. B. Booth, Rosie A. Fisher, and Reto Knutti
Earth Syst. Dynam., 12, 899–918, https://doi.org/10.5194/esd-12-899-2021, https://doi.org/10.5194/esd-12-899-2021, 2021
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Emergent constraints promise a pathway to the reduction in climate projection uncertainties by exploiting ensemble relationships between observable quantities and unknown climate response parameters. This study considers the robustness of these relationships in light of biases and common simplifications that may be present in the original ensemble of climate simulations. We propose a classification scheme for constraints and a number of practical case studies.
Christina Heinze-Deml, Sebastian Sippel, Angeline G. Pendergrass, Flavio Lehner, and Nicolai Meinshausen
Geosci. Model Dev., 14, 4977–4999, https://doi.org/10.5194/gmd-14-4977-2021, https://doi.org/10.5194/gmd-14-4977-2021, 2021
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Quantifying dynamical and thermodynamical components of regional precipitation change is a key challenge in climate science. We introduce a novel statistical model (Latent Linear Adjustment Autoencoder) that combines the flexibility of deep neural networks with the robustness advantages of linear regression. The method enables estimation of the contribution of a coarse-scale atmospheric circulation proxy to daily precipitation at high resolution and in a spatially coherent manner.
Prabhat, Karthik Kashinath, Mayur Mudigonda, Sol Kim, Lukas Kapp-Schwoerer, Andre Graubner, Ege Karaismailoglu, Leo von Kleist, Thorsten Kurth, Annette Greiner, Ankur Mahesh, Kevin Yang, Colby Lewis, Jiayi Chen, Andrew Lou, Sathyavat Chandran, Ben Toms, Will Chapman, Katherine Dagon, Christine A. Shields, Travis O'Brien, Michael Wehner, and William Collins
Geosci. Model Dev., 14, 107–124, https://doi.org/10.5194/gmd-14-107-2021, https://doi.org/10.5194/gmd-14-107-2021, 2021
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Detecting extreme weather events is a crucial step in understanding how they change due to climate change. Deep learning (DL) is remarkable at pattern recognition; however, it works best only when labeled datasets are available. We create
ClimateNet– an expert-labeled curated dataset – to train a DL model for detecting weather events and predicting changes in extreme precipitation. This work paves the way for DL-based automated, high-fidelity, and highly precise analytics of climate data.
Hsi-Yen Ma, Chen Zhou, Yunyan Zhang, Stephen A. Klein, Mark D. Zelinka, Xue Zheng, Shaocheng Xie, Wei-Ting Chen, and Chien-Ming Wu
Geosci. Model Dev., 14, 73–90, https://doi.org/10.5194/gmd-14-73-2021, https://doi.org/10.5194/gmd-14-73-2021, 2021
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We propose an experimental design of a suite of multi-year, short-term hindcasts and compare them with corresponding observations or measurements for periods based on different weather and climate phenomena. This atypical way of evaluating model performance is particularly useful and beneficial, as these hindcasts can give scientists a robust picture of modeled precipitation, and cloud and radiation processes from their diurnal variation to year-to-year variability.
Lukas Brunner, Angeline G. Pendergrass, Flavio Lehner, Anna L. Merrifield, Ruth Lorenz, and Reto Knutti
Earth Syst. Dynam., 11, 995–1012, https://doi.org/10.5194/esd-11-995-2020, https://doi.org/10.5194/esd-11-995-2020, 2020
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In this study, we weight climate models by their performance with respect to simulating aspects of historical climate and their degree of interdependence. Our method is found to increase projection skill and to correct for structurally similar models. The weighted end-of-century mean warming (2081–2100 relative to 1995–2014) is 3.7 °C with a likely (66 %) range of 3.1 to 4.6 °C for the strong climate change scenario SSP5-8.5; this is a reduction of 0.4 °C compared with the unweighted mean.
Landon A. Rieger, Jason N. S. Cole, John C. Fyfe, Stephen Po-Chedley, Philip J. Cameron-Smith, Paul J. Durack, Nathan P. Gillett, and Qi Tang
Geosci. Model Dev., 13, 4831–4843, https://doi.org/10.5194/gmd-13-4831-2020, https://doi.org/10.5194/gmd-13-4831-2020, 2020
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Recently, the stratospheric aerosol forcing dataset used as an input to the Coupled Model Intercomparison Project phase 6 was updated. This work explores the impact of those changes on the modelled historical climates in the CanESM5 and EAMv1 models. Temperature differences in the stratosphere shortly after the Pinatubo eruption are found to be significant, but surface temperatures and precipitation do not show a significant change.
Mark D. Risser and Michael F. Wehner
Adv. Stat. Clim. Meteorol. Oceanogr., 6, 115–139, https://doi.org/10.5194/ascmo-6-115-2020, https://doi.org/10.5194/ascmo-6-115-2020, 2020
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Evaluation of modern high-resolution global climate models often does not account for the geographic location of the underlying weather station data. In this paper, we quantify the impact of geographic sampling on the relative performance of climate model representations of precipitation extremes over the United States. We find that properly accounting for the geographic sampling of weather stations can significantly change the assessment of model performance.
Vivek K. Arora, Anna Katavouta, Richard G. Williams, Chris D. Jones, Victor Brovkin, Pierre Friedlingstein, Jörg Schwinger, Laurent Bopp, Olivier Boucher, Patricia Cadule, Matthew A. Chamberlain, James R. Christian, Christine Delire, Rosie A. Fisher, Tomohiro Hajima, Tatiana Ilyina, Emilie Joetzjer, Michio Kawamiya, Charles D. Koven, John P. Krasting, Rachel M. Law, David M. Lawrence, Andrew Lenton, Keith Lindsay, Julia Pongratz, Thomas Raddatz, Roland Séférian, Kaoru Tachiiri, Jerry F. Tjiputra, Andy Wiltshire, Tongwen Wu, and Tilo Ziehn
Biogeosciences, 17, 4173–4222, https://doi.org/10.5194/bg-17-4173-2020, https://doi.org/10.5194/bg-17-4173-2020, 2020
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Since the preindustrial period, land and ocean have taken up about half of the carbon emitted into the atmosphere by humans. Comparison of different earth system models with the carbon cycle allows us to assess how carbon uptake by land and ocean differs among models. This yields an estimate of uncertainty in our understanding of how land and ocean respond to increasing atmospheric CO2. This paper summarizes results from two such model intercomparison projects that use an idealized scenario.
Duane Waliser, Peter J. Gleckler, Robert Ferraro, Karl E. Taylor, Sasha Ames, James Biard, Michael G. Bosilovich, Otis Brown, Helene Chepfer, Luca Cinquini, Paul J. Durack, Veronika Eyring, Pierre-Philippe Mathieu, Tsengdar Lee, Simon Pinnock, Gerald L. Potter, Michel Rixen, Roger Saunders, Jörg Schulz, Jean-Noël Thépaut, and Matthias Tuma
Geosci. Model Dev., 13, 2945–2958, https://doi.org/10.5194/gmd-13-2945-2020, https://doi.org/10.5194/gmd-13-2945-2020, 2020
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This paper provides an update to an international research activity whose objective is to facilitate access to satellite and other types of regional and global datasets for evaluating global models used to produce 21st century climate projections.
Charlotte Pascoe, Bryan N. Lawrence, Eric Guilyardi, Martin Juckes, and Karl E. Taylor
Geosci. Model Dev., 13, 2149–2167, https://doi.org/10.5194/gmd-13-2149-2020, https://doi.org/10.5194/gmd-13-2149-2020, 2020
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We present a methodology for documenting numerical experiments in the context of an information sharing ecosystem which allows the weather, climate, and earth system modelling community to accurately document and share information about their modelling workflow. We describe how through iteration with a range of stakeholders, we rationalized multiple sources of information and improved the clarity of experimental definitions for the Coupled Model Intercomparison Project Phase 6 (CMIP6).
Martin Juckes, Karl E. Taylor, Paul J. Durack, Bryan Lawrence, Matthew S. Mizielinski, Alison Pamment, Jean-Yves Peterschmitt, Michel Rixen, and Stéphane Sénési
Geosci. Model Dev., 13, 201–224, https://doi.org/10.5194/gmd-13-201-2020, https://doi.org/10.5194/gmd-13-201-2020, 2020
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The data request of the Coupled Model Intercomparison Project Phase 6 (CMIP6) defines all the quantities
from CMIP6 simulations that should be archived. The building blocks and structure of the CMIP6 Data Request, which has been constructed to meet these challenges, are described in this paper.
Grzegorz Muszynski, Karthik Kashinath, Vitaliy Kurlin, Michael Wehner, and Prabhat
Geosci. Model Dev., 12, 613–628, https://doi.org/10.5194/gmd-12-613-2019, https://doi.org/10.5194/gmd-12-613-2019, 2019
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We present the automated method for recognizing atmospheric rivers in climate data, i.e., climate model output and reanalysis product. The method is based on topological data analysis and machine learning, both of which are powerful tools that the climate science community often does not use. An advantage of the proposed method is that it is free of selection of subjective threshold conditions on a physical variable. This method is also suitable for rapidly analyzing large amounts of data.
Daniel T. McCoy, Paul R. Field, Gregory S. Elsaesser, Alejandro Bodas-Salcedo, Brian H. Kahn, Mark D. Zelinka, Chihiro Kodama, Thorsten Mauritsen, Benoit Vanniere, Malcolm Roberts, Pier L. Vidale, David Saint-Martin, Aurore Voldoire, Rein Haarsma, Adrian Hill, Ben Shipway, and Jonathan Wilkinson
Atmos. Chem. Phys., 19, 1147–1172, https://doi.org/10.5194/acp-19-1147-2019, https://doi.org/10.5194/acp-19-1147-2019, 2019
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The largest single source of uncertainty in the climate sensitivity predicted by global climate models is how much low-altitude clouds change as the climate warms. Models predict that the amount of liquid within and the brightness of low-altitude clouds increase in the extratropics with warming. We show that increased fluxes of moisture into extratropical storms in the midlatitudes explain the majority of the observed trend and the modeled increase in liquid water within these storms.
Venkatramani Balaji, Karl E. Taylor, Martin Juckes, Bryan N. Lawrence, Paul J. Durack, Michael Lautenschlager, Chris Blanton, Luca Cinquini, Sébastien Denvil, Mark Elkington, Francesca Guglielmo, Eric Guilyardi, David Hassell, Slava Kharin, Stefan Kindermann, Sergey Nikonov, Aparna Radhakrishnan, Martina Stockhause, Tobias Weigel, and Dean Williams
Geosci. Model Dev., 11, 3659–3680, https://doi.org/10.5194/gmd-11-3659-2018, https://doi.org/10.5194/gmd-11-3659-2018, 2018
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We present recommendations for the global data infrastructure needed to support CMIP scientific design and its future growth and evolution. We follow a dataset-centric design less prone to systemic failure. Scientific publication in the digital age is evolving to make data a primary scientific output, alongside articles. We design toward that future scientific data ecosystem, informed by the need for reproducibility, data provenance, future data technologies, and measures of costs and benefits.
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.
Monika J. Barcikowska, Scott J. Weaver, Frauke Feser, Simone Russo, Frederik Schenk, Dáithí A. Stone, Michael F. Wehner, and Matthias Zahn
Earth Syst. Dynam., 9, 679–699, https://doi.org/10.5194/esd-9-679-2018, https://doi.org/10.5194/esd-9-679-2018, 2018
Michael Wehner, Dáithí Stone, Dann Mitchell, Hideo Shiogama, Erich Fischer, Lise S. Graff, Viatcheslav V. Kharin, Ludwig Lierhammer, Benjamin Sanderson, and Harinarayan Krishnan
Earth Syst. Dynam., 9, 299–311, https://doi.org/10.5194/esd-9-299-2018, https://doi.org/10.5194/esd-9-299-2018, 2018
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The United Nations Framework Convention on Climate Change challenged the scientific community to describe the impacts of stabilizing the global temperature at its 21st Conference of Parties. A specific target of 1.5 °C above preindustrial levels had not been seriously considered by the climate modeling community prior to the Paris Agreement. This paper analyzes heat waves in simulations designed for this target. We find there are reductions in extreme temperature compared to a 2 °C target.
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.
Angeline G. Pendergrass, Andrew Conley, and Francis M. Vitt
Earth Syst. Sci. Data, 10, 317–324, https://doi.org/10.5194/essd-10-317-2018, https://doi.org/10.5194/essd-10-317-2018, 2018
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We document and validate radiative kernels for the surface and top-of-atmosphere calculated with NCAR's CESM1 climate model. A radiative kernel is the change in radiation in response to a small change in a property of the atmosphere or surface, essentially a partial derivative. They are used to quantify temperature, water vapor, surface albedo, and cloud feedbacks. We made these kernels because few are available for the surface. We also validate the kernels against the expected model responses.
David Hassell, Jonathan Gregory, Jon Blower, Bryan N. Lawrence, and Karl E. Taylor
Geosci. Model Dev., 10, 4619–4646, https://doi.org/10.5194/gmd-10-4619-2017, https://doi.org/10.5194/gmd-10-4619-2017, 2017
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We present a formal data model for version 1.6 of the CF (Climate and Forecast) metadata conventions that provide a description of the physical meaning of geoscientific data and their spatial and temporal properties. We describe the CF conventions and how they lead to our CF data model, and compare it other data models for storing data and metadata. We present cf-python version 2.1: a software implementation of the CF data model capable of manipulating any CF-compliant dataset.
Yoko Tsushima, Florent Brient, Stephen A. Klein, Dimitra Konsta, Christine C. Nam, Xin Qu, Keith D. Williams, Steven C. Sherwood, Kentaroh Suzuki, and Mark D. Zelinka
Geosci. Model Dev., 10, 4285–4305, https://doi.org/10.5194/gmd-10-4285-2017, https://doi.org/10.5194/gmd-10-4285-2017, 2017
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Cloud feedback is the largest uncertainty associated with estimates of climate sensitivity. Diagnostics have been developed to evaluate cloud processes in climate models. For this understanding to be reflected in better estimates of cloud feedbacks, it is vital to continue to develop such tools and to exploit them fully during the model development process. Code repositories have been created to store and document the programs which will allow climate modellers to compute these diagnostics.
Benjamin M. Sanderson, Yangyang Xu, Claudia Tebaldi, Michael Wehner, Brian O'Neill, Alexandra Jahn, Angeline G. Pendergrass, Flavio Lehner, Warren G. Strand, Lei Lin, Reto Knutti, and Jean Francois Lamarque
Earth Syst. Dynam., 8, 827–847, https://doi.org/10.5194/esd-8-827-2017, https://doi.org/10.5194/esd-8-827-2017, 2017
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We present the results of a set of climate simulations designed to simulate futures in which the Earth's temperature is stabilized at the levels referred to in the 2015 Paris Agreement. We consider the necessary future emissions reductions and the aspects of extreme weather which differ significantly between the 2 and 1.5 °C climate in the simulations.
Benjamin M. Sanderson, Michael Wehner, and Reto Knutti
Geosci. Model Dev., 10, 2379–2395, https://doi.org/10.5194/gmd-10-2379-2017, https://doi.org/10.5194/gmd-10-2379-2017, 2017
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How should climate model simulations be combined to produce an overall assessment that reflects both their performance and their interdependencies? This paper presents a strategy for weighting climate model output such that models that are replicated or models that perform poorly in a chosen set of metrics are appropriately weighted. We perform sensitivity tests to show how the method results depend on variables and parameter values.
Daniel Mitchell, Krishna AchutaRao, Myles Allen, Ingo Bethke, Urs Beyerle, Andrew Ciavarella, Piers M. Forster, Jan Fuglestvedt, Nathan Gillett, Karsten Haustein, William Ingram, Trond Iversen, Viatcheslav Kharin, Nicholas Klingaman, Neil Massey, Erich Fischer, Carl-Friedrich Schleussner, John Scinocca, Øyvind Seland, Hideo Shiogama, Emily Shuckburgh, Sarah Sparrow, Dáithí Stone, Peter Uhe, David Wallom, Michael Wehner, and Rashyd Zaaboul
Geosci. Model Dev., 10, 571–583, https://doi.org/10.5194/gmd-10-571-2017, https://doi.org/10.5194/gmd-10-571-2017, 2017
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This paper provides an experimental design to assess impacts of a world that is 1.5 °C warmer than at pre-industrial levels. The design is a new way to approach impacts from the climate community, and aims to answer questions related to the recent Paris Agreement. In particular the paper provides a method for studying extreme events under relatively high mitigation scenarios.
Veronika Eyring, Peter J. Gleckler, Christoph Heinze, Ronald J. Stouffer, Karl E. Taylor, V. Balaji, Eric Guilyardi, Sylvie Joussaume, Stephan Kindermann, Bryan N. Lawrence, Gerald A. Meehl, Mattia Righi, and Dean N. Williams
Earth Syst. Dynam., 7, 813–830, https://doi.org/10.5194/esd-7-813-2016, https://doi.org/10.5194/esd-7-813-2016, 2016
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We argue that the CMIP community has reached a critical juncture at which many baseline aspects of model evaluation need to be performed much more efficiently to enable a systematic and rapid performance assessment of the large number of models participating in CMIP, and we announce our intention to implement such a system for CMIP6. At the same time, continuous scientific research is required to develop innovative metrics and diagnostics that help narrowing the spread in climate projections.
George J. Boer, Douglas M. Smith, Christophe Cassou, Francisco Doblas-Reyes, Gokhan Danabasoglu, Ben Kirtman, Yochanan Kushnir, Masahide Kimoto, Gerald A. Meehl, Rym Msadek, Wolfgang A. Mueller, Karl E. Taylor, Francis Zwiers, Michel Rixen, Yohan Ruprich-Robert, and Rosie Eade
Geosci. Model Dev., 9, 3751–3777, https://doi.org/10.5194/gmd-9-3751-2016, https://doi.org/10.5194/gmd-9-3751-2016, 2016
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The Decadal Climate Prediction Project (DCPP) investigates our ability to skilfully predict climate variations from a year to a decade ahead by means of a series of retrospective forecasts. Quasi-real-time forecasts are also produced for potential users. In addition, the DCPP investigates how perturbations such as volcanoes affect forecasts and, more broadly, what new information can be learned about the mechanisms governing climate variations by means of case studies of past climate behaviour.
Stephen M. Griffies, Gokhan Danabasoglu, Paul J. Durack, Alistair J. Adcroft, V. Balaji, Claus W. Böning, Eric P. Chassignet, Enrique Curchitser, Julie Deshayes, Helge Drange, Baylor Fox-Kemper, Peter J. Gleckler, Jonathan M. Gregory, Helmuth Haak, Robert W. Hallberg, Patrick Heimbach, Helene T. Hewitt, David M. Holland, Tatiana Ilyina, Johann H. Jungclaus, Yoshiki Komuro, John P. Krasting, William G. Large, Simon J. Marsland, Simona Masina, Trevor J. McDougall, A. J. George Nurser, James C. Orr, Anna Pirani, Fangli Qiao, Ronald J. Stouffer, Karl E. Taylor, Anne Marie Treguier, Hiroyuki Tsujino, Petteri Uotila, Maria Valdivieso, Qiang Wang, Michael Winton, and Stephen G. Yeager
Geosci. Model Dev., 9, 3231–3296, https://doi.org/10.5194/gmd-9-3231-2016, https://doi.org/10.5194/gmd-9-3231-2016, 2016
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The Ocean Model Intercomparison Project (OMIP) aims to provide a framework for evaluating, understanding, and improving the ocean and sea-ice components of global climate and earth system models contributing to the Coupled Model Intercomparison Project Phase 6 (CMIP6). This document defines OMIP and details a protocol both for simulating global ocean/sea-ice models and for analysing their output.
Veronika Eyring, Sandrine Bony, Gerald A. Meehl, Catherine A. Senior, Bjorn Stevens, Ronald J. Stouffer, and Karl E. Taylor
Geosci. Model Dev., 9, 1937–1958, https://doi.org/10.5194/gmd-9-1937-2016, https://doi.org/10.5194/gmd-9-1937-2016, 2016
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The objective of CMIP is to better understand past, present, and future climate change in a multi-model context. CMIP's increasing importance and scope is a tremendous success story, but the need to address an ever-expanding range of scientific questions arising from more and more research communities has made it necessary to revise the organization of CMIP. In response to these challenges, we have adopted a more federated structure for the sixth phase of CMIP (i.e. CMIP6) and subsequent phases.
Veronika Eyring, Mattia Righi, Axel Lauer, Martin Evaldsson, Sabrina Wenzel, Colin Jones, Alessandro Anav, Oliver Andrews, Irene Cionni, Edouard L. Davin, Clara Deser, Carsten Ehbrecht, Pierre Friedlingstein, Peter Gleckler, Klaus-Dirk Gottschaldt, Stefan Hagemann, Martin Juckes, Stephan Kindermann, John Krasting, Dominik Kunert, Richard Levine, Alexander Loew, Jarmo Mäkelä, Gill Martin, Erik Mason, Adam S. Phillips, Simon Read, Catherine Rio, Romain Roehrig, Daniel Senftleben, Andreas Sterl, Lambertus H. van Ulft, Jeremy Walton, Shiyu Wang, and Keith D. Williams
Geosci. Model Dev., 9, 1747–1802, https://doi.org/10.5194/gmd-9-1747-2016, https://doi.org/10.5194/gmd-9-1747-2016, 2016
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A community diagnostics and performance metrics tool for the evaluation of Earth system models (ESMs) in CMIP has been developed that allows for routine comparison of single or multiple models, either against predecessor versions or against observations.
S. Jeon, Prabhat, S. Byna, J. Gu, W. D. Collins, and M. F. Wehner
Adv. Stat. Clim. Meteorol. Oceanogr., 1, 45–57, https://doi.org/10.5194/ascmo-1-45-2015, https://doi.org/10.5194/ascmo-1-45-2015, 2015
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This paper investigates the influence of atmospheric rivers on spatial coherence of extreme precipitation under a changing climate. We use our TECA software developed for detecting atmospheric river events and apply statistical techniques based on extreme value theory to characterize the spatial dependence structure between precipitation extremes within the events. The results show that extreme rainfall caused by atmospheric river events is less spatially correlated under the warming scenario.
M.-P. Moine, S. Valcke, B. N. Lawrence, C. Pascoe, R. W. Ford, A. Alias, V. Balaji, P. Bentley, G. Devine, S. A. Callaghan, and E. Guilyardi
Geosci. Model Dev., 7, 479–493, https://doi.org/10.5194/gmd-7-479-2014, https://doi.org/10.5194/gmd-7-479-2014, 2014
G. A. Schmidt, J. D. Annan, P. J. Bartlein, B. I. Cook, E. Guilyardi, J. C. Hargreaves, S. P. Harrison, M. Kageyama, A. N. LeGrande, B. Konecky, S. Lovejoy, M. E. Mann, V. Masson-Delmotte, C. Risi, D. Thompson, A. Timmermann, L.-B. Tremblay, and P. Yiou
Clim. Past, 10, 221–250, https://doi.org/10.5194/cp-10-221-2014, https://doi.org/10.5194/cp-10-221-2014, 2014
M. S. Johnston, S. Eliasson, P. Eriksson, R. M. Forbes, K. Wyser, and M. D. Zelinka
Atmos. Chem. Phys., 13, 12043–12058, https://doi.org/10.5194/acp-13-12043-2013, https://doi.org/10.5194/acp-13-12043-2013, 2013
S. McGregor, A. Timmermann, M. H. England, O. Elison Timm, and A. T. Wittenberg
Clim. Past, 9, 2269–2284, https://doi.org/10.5194/cp-9-2269-2013, https://doi.org/10.5194/cp-9-2269-2013, 2013
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A protocol for model intercomparison of impacts of marine cloud brightening climate intervention
An extensible perturbed parameter ensemble for the Community Atmosphere Model version 6
Coupling the regional climate model ICON-CLM v2.6.6 to the Earth system model GCOAST-AHOI v2.0 using OASIS3-MCT v4.0
A fully coupled solid-particle microphysics scheme for stratospheric aerosol injections within the aerosol–chemistry–climate model SOCOL-AERv2
The Tropical Basin Interaction Model Intercomparison Project (TBIMIP)
An improved representation of aerosol in the ECMWF IFS-COMPO 49R1 through the integration of EQSAM4Climv12 – a first attempt at simulating aerosol acidity
At-scale Model Output Statistics in mountain environments (AtsMOS v1.0)
Impact of ocean vertical-mixing parameterization on Arctic sea ice and upper-ocean properties using the NEMO-SI3 model
Bridging the gap: a new module for human water use in the Community Earth System Model version 2.2.1
From Weather Data to River Runoff: Leveraging Spatiotemporal Convolutional Networks for Comprehensive Discharge Forecasting
Historical Trends and Controlling Factors of Isoprene Emissions in CMIP6 Earth System Models
Modeling Commercial-Scale CO2 Storage in the Gas Hydrate Stability Zone with PFLOTRAN v6.0
A new lightning scheme in the Canadian Atmospheric Model (CanAM5.1): implementation, evaluation, and projections of lightning and fire in future climates
Methane dynamics in the Baltic Sea: investigating concentration, flux, and isotopic composition patterns using the coupled physical–biogeochemical model BALTSEM-CH4 v1.0
Using feature importance as exploratory data analysis tool on earth system models
CropSuite – A comprehensive open-source crop suitability model considering climate variability for climate impact assessment
ICON ComIn – The ICON Community Interface (ComIn version 0.1.0, with ICON version 2024.01-01)
Split-explicit external mode solver in the finite volume sea ice–ocean model FESOM2
Applying double cropping and interactive irrigation in the North China Plain using WRF4.5
Coupled Carbon-Nitrogen Cycle in MAGICC v1.0.0: Model Description and Calibration
The sea ice component of GC5: coupling SI3 to HadGEM3 using conductive fluxes
CICE on a C-grid: new momentum, stress, and transport schemes for CICEv6.5
HyPhAICC v1.0: a hybrid physics–AI approach for probability fields advection shown through an application to cloud cover nowcasting
CICERO Simple Climate Model (CICERO-SCM v1.1.1) – an improved simple climate model with a parameter calibration tool
A non-intrusive, multi-scale, and flexible coupling interface in WRF
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).
Panagiotis Adamidis, Erik Pfister, Hendryk Bockelmann, Dominik Zobel, Jens-Olaf Beismann, and Marek Jacob
Geosci. Model Dev., 18, 905–919, https://doi.org/10.5194/gmd-18-905-2025, https://doi.org/10.5194/gmd-18-905-2025, 2025
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In this paper, we investigated performance indicators of the climate model ICON (ICOsahedral Nonhydrostatic) on different compute architectures to answer the question of how to generate high-resolution climate simulations. Evidently, it is not enough to use more computing units of the conventionally used architectures; higher memory throughput is the most promising approach. More potential can be gained from single-node optimization rather than simply increasing the number of compute nodes.
Kangari Narender Reddy, Somnath Baidya Roy, Sam S. Rabin, Danica L. Lombardozzi, Gudimetla Venkateswara Varma, Ruchira Biswas, and Devavat Chiru Naik
Geosci. Model Dev., 18, 763–785, https://doi.org/10.5194/gmd-18-763-2025, https://doi.org/10.5194/gmd-18-763-2025, 2025
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The study aimed to improve the representation of wheat and rice in a land model for the Indian region. The modified model performed significantly better than the default model in simulating crop phenology, yield, and carbon, water, and energy fluxes compared to observations. The study highlights the need for global land models to use region-specific crop parameters for accurately simulating vegetation processes and land surface processes.
Giovanni Di Virgilio, Fei Ji, Eugene Tam, Jason P. Evans, Jatin Kala, Julia Andrys, Christopher Thomas, Dipayan Choudhury, Carlos Rocha, Yue Li, and Matthew L. Riley
Geosci. Model Dev., 18, 703–724, https://doi.org/10.5194/gmd-18-703-2025, https://doi.org/10.5194/gmd-18-703-2025, 2025
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We evaluate the skill in simulating the Australian climate of some of the latest generation of regional climate models. We show when and where the models simulate this climate with high skill versus model limitations. We show how new models perform relative to the previous-generation models, assessing how model design features may underlie key performance improvements. This work is of national and international relevance as it can help guide the use and interpretation of climate projections.
Giovanni Di Virgilio, Jason P. Evans, Fei Ji, Eugene Tam, Jatin Kala, Julia Andrys, Christopher Thomas, Dipayan Choudhury, Carlos Rocha, Stephen White, Yue Li, Moutassem El Rafei, Rishav Goyal, Matthew L. Riley, and Jyothi Lingala
Geosci. Model Dev., 18, 671–702, https://doi.org/10.5194/gmd-18-671-2025, https://doi.org/10.5194/gmd-18-671-2025, 2025
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We introduce new climate models that simulate Australia’s future climate at regional scales, including at an unprecedented resolution of 4 km for 1950–2100. We describe the model design process used to create these new climate models. We show how the new models perform relative to previous-generation models and compare their climate projections. This work is of national and international relevance as it can help guide climate model design and the use and interpretation of climate projections.
Jiawang Feng, Chun Zhao, Qiuyan Du, Zining Yang, and Chen Jin
Geosci. Model Dev., 18, 585–603, https://doi.org/10.5194/gmd-18-585-2025, https://doi.org/10.5194/gmd-18-585-2025, 2025
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In this study, we improved the calculation of how aerosols in the air interact with radiation in WRF-Chem. The original model used a simplified method, but we developed a more accurate approach. We found that this method significantly changes the properties of the estimated aerosols and their effects on radiation, especially for dust aerosols. It also impacts the simulated weather conditions. Our work highlights the importance of correctly representing aerosol–radiation interactions in models.
Eduardo Moreno-Chamarro, Thomas Arsouze, Mario Acosta, Pierre-Antoine Bretonnière, Miguel Castrillo, Eric Ferrer, Amanda Frigola, Daria Kuznetsova, Eneko Martin-Martinez, Pablo Ortega, and Sergi Palomas
Geosci. Model Dev., 18, 461–482, https://doi.org/10.5194/gmd-18-461-2025, https://doi.org/10.5194/gmd-18-461-2025, 2025
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We present the high-resolution model version of the EC-Earth global climate model to contribute to HighResMIP. The combined model resolution is about 10–15 km in both the ocean and atmosphere, which makes it one of the finest ever used to complete historical and scenario simulations. This model is compared with two lower-resolution versions, with a 100 km and a 25 km grid. The three models are compared with observations to study the improvements thanks to the increased resolution.
Catherine Guiavarc'h, David Storkey, Adam T. Blaker, Ed Blockley, Alex Megann, Helene Hewitt, Michael J. Bell, Daley Calvert, Dan Copsey, Bablu Sinha, Sophia Moreton, Pierre Mathiot, and Bo An
Geosci. Model Dev., 18, 377–403, https://doi.org/10.5194/gmd-18-377-2025, https://doi.org/10.5194/gmd-18-377-2025, 2025
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The Global Ocean and Sea Ice configuration version 9 (GOSI9) is the new UK hierarchy of model configurations based on the Nucleus for European Modelling of the Ocean (NEMO) and available at three resolutions. It will be used for various applications, e.g. weather forecasting and climate prediction. It improves upon the previous version by reducing global temperature and salinity biases and enhancing the representation of Arctic sea ice and the Antarctic Circumpolar Current.
Andy Richling, Jens Grieger, and Henning W. Rust
Geosci. Model Dev., 18, 361–375, https://doi.org/10.5194/gmd-18-361-2025, https://doi.org/10.5194/gmd-18-361-2025, 2025
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The performance of weather and climate prediction systems is variable in time and space. It is of interest how this performance varies in different situations. We provide a decomposition of a skill score (a measure of forecast performance) as a tool for detailed assessment of performance variability to support model development or forecast improvement. The framework is exemplified with decadal forecasts to assess the impact of different ocean states in the North Atlantic on temperature forecast.
Maria R. Russo, Sadie L. Bartholomew, David Hassell, Alex M. Mason, Erica Neininger, A. James Perman, David A. J. Sproson, Duncan Watson-Parris, and Nathan Luke Abraham
Geosci. Model Dev., 18, 181–191, https://doi.org/10.5194/gmd-18-181-2025, https://doi.org/10.5194/gmd-18-181-2025, 2025
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Observational data and modelling capabilities have expanded in recent years, but there are still barriers preventing these two data sources from being used in synergy. Proper comparison requires generating, storing, and handling a large amount of data. This work describes the first step in the development of a new set of software tools, the VISION toolkit, which can enable the easy and efficient integration of observational and model data required for model evaluation.
Bijan Fallah, Masoud Rostami, Emmanuele Russo, Paula Harder, Christoph Menz, Peter Hoffmann, Iulii Didovets, and Fred F. Hattermann
Geosci. Model Dev., 18, 161–180, https://doi.org/10.5194/gmd-18-161-2025, https://doi.org/10.5194/gmd-18-161-2025, 2025
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We tried to contribute to a local climate change impact study in central Asia, a region that is water-scarce and vulnerable to global climate change. We use regional models and machine learning to produce reliable local data from global climate models. We find that regional models show more realistic and detailed changes in heavy precipitation than global climate models. Our work can help assess the future risks of extreme events and plan adaptation strategies in central Asia.
Thomas Rackow, Xabier Pedruzo-Bagazgoitia, Tobias Becker, Sebastian Milinski, Irina Sandu, Razvan Aguridan, Peter Bechtold, Sebastian Beyer, Jean Bidlot, Souhail Boussetta, Willem Deconinck, Michail Diamantakis, Peter Dueben, Emanuel Dutra, Richard Forbes, Rohit Ghosh, Helge F. Goessling, Ioan Hadade, Jan Hegewald, Thomas Jung, Sarah Keeley, Lukas Kluft, Nikolay Koldunov, Aleksei Koldunov, Tobias Kölling, Josh Kousal, Christian Kühnlein, Pedro Maciel, Kristian Mogensen, Tiago Quintino, Inna Polichtchouk, Balthasar Reuter, Domokos Sármány, Patrick Scholz, Dmitry Sidorenko, Jan Streffing, Birgit Sützl, Daisuke Takasuka, Steffen Tietsche, Mirco Valentini, Benoît Vannière, Nils Wedi, Lorenzo Zampieri, and Florian Ziemen
Geosci. Model Dev., 18, 33–69, https://doi.org/10.5194/gmd-18-33-2025, https://doi.org/10.5194/gmd-18-33-2025, 2025
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Detailed global climate model simulations have been created based on a numerical weather prediction model, offering more accurate spatial detail down to the scale of individual cities ("kilometre-scale") and a better understanding of climate phenomena such as atmospheric storms, whirls in the ocean, and cracks in sea ice. The new model aims to provide globally consistent information on local climate change with greater precision, benefiting environmental planning and local impact modelling.
Yilin Fang, Hoang Viet Tran, and L. Ruby Leung
Geosci. Model Dev., 18, 19–32, https://doi.org/10.5194/gmd-18-19-2025, https://doi.org/10.5194/gmd-18-19-2025, 2025
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Hurricanes may worsen water quality in the lower Mississippi River basin (LMRB) by increasing nutrient runoff. We found that runoff parameterizations greatly affect nitrate–nitrogen runoff simulated using an Earth system land model. Our simulations predicted increased nitrogen runoff in the LMRB during Hurricane Ida in 2021, albeit less pronounced than the observations, indicating areas for model improvement to better understand and manage nutrient runoff loss during hurricanes in the region.
Giovanni Seijo-Ellis, Donata Giglio, Gustavo Marques, and Frank Bryan
Geosci. Model Dev., 17, 8989–9021, https://doi.org/10.5194/gmd-17-8989-2024, https://doi.org/10.5194/gmd-17-8989-2024, 2024
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A CESM–MOM6 regional configuration of the Caribbean Sea was developed in response to the rising need for high-resolution models for climate impact studies. The configuration is validated for the period 2000–2020 and improves significant errors in a low-resolution model. Oceanic properties are well represented. Patterns of freshwater associated with the Amazon River are well captured, and the mean flows of ocean waters across multiple passages in the Caribbean Sea agree with observations.
Deifilia To, Julian Quinting, Gholam Ali Hoshyaripour, Markus Götz, Achim Streit, and Charlotte Debus
Geosci. Model Dev., 17, 8873–8884, https://doi.org/10.5194/gmd-17-8873-2024, https://doi.org/10.5194/gmd-17-8873-2024, 2024
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Pangu-Weather is a breakthrough machine learning model in medium-range weather forecasting that considers 3D atmospheric information. We show that using a simpler 2D framework improves robustness, speeds up training, and reduces computational needs by 20 %–30 %. We introduce a training procedure that varies the importance of atmospheric variables over time to speed up training convergence. Decreasing computational demand increases the accessibility of training and working with the model.
Fang Li, Xiang Song, Sandy P. Harrison, Jennifer R. Marlon, Zhongda Lin, L. Ruby Leung, Jörg Schwinger, Virginie Marécal, Shiyu Wang, Daniel S. Ward, Xiao Dong, Hanna Lee, Lars Nieradzik, Sam S. Rabin, and Roland Séférian
Geosci. Model Dev., 17, 8751–8771, https://doi.org/10.5194/gmd-17-8751-2024, https://doi.org/10.5194/gmd-17-8751-2024, 2024
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This study provides the first comprehensive assessment of historical fire simulations from 19 Earth system models in phase 6 of the Coupled Model Intercomparison Project (CMIP6). Most models reproduce global totals, spatial patterns, seasonality, and regional historical changes well but fail to simulate the recent decline in global burned area and underestimate the fire response to climate variability. CMIP6 simulations address three critical issues of phase-5 models.
Seung H. Baek, Paul A. Ullrich, Bo Dong, and Jiwoo Lee
Geosci. Model Dev., 17, 8665–8681, https://doi.org/10.5194/gmd-17-8665-2024, https://doi.org/10.5194/gmd-17-8665-2024, 2024
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We evaluate downscaled products by examining locally relevant co-variances during precipitation events. Common statistical downscaling techniques preserve expected co-variances during convective precipitation (a stationary phenomenon). However, they dampen future intensification of frontal precipitation (a non-stationary phenomenon) captured in global climate models and dynamical downscaling. Our study quantifies a ramification of the stationarity assumption underlying statistical downscaling.
Emmanuel Nyenah, Petra Döll, Daniel S. Katz, and Robert Reinecke
Geosci. Model Dev., 17, 8593–8611, https://doi.org/10.5194/gmd-17-8593-2024, https://doi.org/10.5194/gmd-17-8593-2024, 2024
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Research software is vital for scientific progress but is often developed by scientists with limited skills, time, and funding, leading to challenges in usability and maintenance. Our study across 10 sectors shows strengths in version control, open-source licensing, and documentation while emphasizing the need for containerization and code quality. We recommend workshops; code quality metrics; funding; and following the findable, accessible, interoperable, and reusable (FAIR) standards.
Chris Smith, Donald P. Cummins, Hege-Beate Fredriksen, Zebedee Nicholls, Malte Meinshausen, Myles Allen, Stuart Jenkins, Nicholas Leach, Camilla Mathison, and Antti-Ilari Partanen
Geosci. Model Dev., 17, 8569–8592, https://doi.org/10.5194/gmd-17-8569-2024, https://doi.org/10.5194/gmd-17-8569-2024, 2024
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Climate projections are only useful if the underlying models that produce them are well calibrated and can reproduce observed climate change. We formalise a software package that calibrates the open-source FaIR simple climate model to full-complexity Earth system models. Observations, including historical warming, and assessments of key climate variables such as that of climate sensitivity are used to constrain the model output.
Jingwei Xie, Xi Wang, Hailong Liu, Pengfei Lin, Jiangfeng Yu, Zipeng Yu, Junlin Wei, and Xiang Han
Geosci. Model Dev., 17, 8469–8493, https://doi.org/10.5194/gmd-17-8469-2024, https://doi.org/10.5194/gmd-17-8469-2024, 2024
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We propose the concept of mesoscale ocean direct numerical simulation (MODNS), which should resolve the first baroclinic deformation radius and ensure the numerical dissipative effects do not directly contaminate the mesoscale motions. It can be a benchmark for testing mesoscale ocean large eddy simulation (MOLES) methods in ocean models. We build an idealized Southern Ocean model using MITgcm to generate a type of MODNS. We also illustrate the diversity of multiscale eddy interactions.
Emily Black, John Ellis, and Ross I. Maidment
Geosci. Model Dev., 17, 8353–8372, https://doi.org/10.5194/gmd-17-8353-2024, https://doi.org/10.5194/gmd-17-8353-2024, 2024
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We present General TAMSAT-ALERT, a computationally lightweight and versatile tool for generating ensemble forecasts from time series data. General TAMSAT-ALERT is capable of combining multiple streams of monitoring and meteorological forecasting data into probabilistic hazard assessments. In this way, it complements existing systems and enhances their utility for actionable hazard assessment.
Sarah Schöngart, Lukas Gudmundsson, Mathias Hauser, Peter Pfleiderer, Quentin Lejeune, Shruti Nath, Sonia Isabelle Seneviratne, and Carl-Friedrich Schleussner
Geosci. Model Dev., 17, 8283–8320, https://doi.org/10.5194/gmd-17-8283-2024, https://doi.org/10.5194/gmd-17-8283-2024, 2024
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Precipitation and temperature are two of the most impact-relevant climatic variables. Yet, projecting future precipitation and temperature data under different emission scenarios relies on complex models that are computationally expensive. In this study, we propose a method that allows us to generate monthly means of local precipitation and temperature at low computational costs. Our modelling framework is particularly useful for all downstream applications of climate model data.
Gang Tang, Zebedee Nicholls, Chris Jones, Thomas Gasser, Alexander Norton, Tilo Ziehn, Alejandro Romero-Prieto, and Malte Meinshausen
EGUsphere, https://doi.org/10.5194/egusphere-2024-3522, https://doi.org/10.5194/egusphere-2024-3522, 2024
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We analyzed carbon and nitrogen mass conservation in data from CMIP6 Earth System Models. Our findings reveal significant discrepancies between flux and pool size data, particularly in nitrogen, where cumulative imbalances can reach hundreds of gigatons. These imbalances appear primarily due to missing or inconsistently reported fluxes – especially for land use and fire emissions. To enhance data quality, we recommend that future climate data protocols address this issue at the reporting stage.
Benjamin M. Sanderson, Ben B. B. Booth, John Dunne, Veronika Eyring, Rosie A. Fisher, Pierre Friedlingstein, Matthew J. Gidden, Tomohiro Hajima, Chris D. Jones, Colin G. Jones, Andrew King, Charles D. Koven, David M. Lawrence, Jason Lowe, Nadine Mengis, Glen P. Peters, Joeri Rogelj, Chris Smith, Abigail C. Snyder, Isla R. Simpson, Abigail L. S. Swann, Claudia Tebaldi, Tatiana Ilyina, Carl-Friedrich Schleussner, Roland Séférian, Bjørn H. Samset, Detlef van Vuuren, and Sönke Zaehle
Geosci. Model Dev., 17, 8141–8172, https://doi.org/10.5194/gmd-17-8141-2024, https://doi.org/10.5194/gmd-17-8141-2024, 2024
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We discuss how, in order to provide more relevant guidance for climate policy, coordinated climate experiments should adopt a greater focus on simulations where Earth system models are provided with carbon emissions from fossil fuels together with land use change instructions, rather than past approaches that have largely focused on experiments with prescribed atmospheric carbon dioxide concentrations. We discuss how these goals might be achieved in coordinated climate modeling experiments.
Peter Berg, Thomas Bosshard, Denica Bozhinova, Lars Bärring, Joakim Löw, Carolina Nilsson, Gustav Strandberg, Johan Södling, Johan Thuresson, Renate Wilcke, and Wei Yang
Geosci. Model Dev., 17, 8173–8179, https://doi.org/10.5194/gmd-17-8173-2024, https://doi.org/10.5194/gmd-17-8173-2024, 2024
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When bias adjusting climate model data using quantile mapping, one needs to prescribe what to do at the tails of the distribution, where a larger data range is likely encountered outside of the calibration period. The end result is highly dependent on the method used. We show that, to avoid discontinuities in the time series, one needs to exclude data in the calibration range to also activate the extrapolation functionality in that time period.
Philip J. Rasch, Haruki Hirasawa, Mingxuan Wu, Sarah J. Doherty, Robert Wood, Hailong Wang, Andy Jones, James Haywood, and Hansi Singh
Geosci. Model Dev., 17, 7963–7994, https://doi.org/10.5194/gmd-17-7963-2024, https://doi.org/10.5194/gmd-17-7963-2024, 2024
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We introduce a protocol to compare computer climate simulations to better understand a proposed strategy intended to counter warming and climate impacts from greenhouse gas increases. This slightly changes clouds in six ocean regions to reflect more sunlight and cool the Earth. Example changes in clouds and climate are shown for three climate models. Cloud changes differ between the models, but precipitation and surface temperature changes are similar when their cooling effects are made similar.
Trude Eidhammer, Andrew Gettelman, Katherine Thayer-Calder, Duncan Watson-Parris, Gregory Elsaesser, Hugh Morrison, Marcus van Lier-Walqui, Ci Song, and Daniel McCoy
Geosci. Model Dev., 17, 7835–7853, https://doi.org/10.5194/gmd-17-7835-2024, https://doi.org/10.5194/gmd-17-7835-2024, 2024
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We describe a dataset where 45 parameters related to cloud processes in the Community Earth System Model version 2 (CESM2) Community Atmosphere Model version 6 (CAM6) are perturbed. Three sets of perturbed parameter ensembles (263 members) were created: current climate, preindustrial aerosol loading and future climate with sea surface temperature increased by 4 K.
Ha Thi Minh Ho-Hagemann, Vera Maurer, Stefan Poll, and Irina Fast
Geosci. Model Dev., 17, 7815–7834, https://doi.org/10.5194/gmd-17-7815-2024, https://doi.org/10.5194/gmd-17-7815-2024, 2024
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The regional Earth system model GCOAST-AHOI v2.0 that includes the regional climate model ICON-CLM coupled to the ocean model NEMO and the hydrological discharge model HD via the OASIS3-MCT coupler can be a useful tool for conducting long-term regional climate simulations over the EURO-CORDEX domain. The new OASIS3-MCT coupling interface implemented in ICON-CLM makes it more flexible for coupling to an external ocean model and an external hydrological discharge model.
Sandro Vattioni, Rahel Weber, Aryeh Feinberg, Andrea Stenke, John A. Dykema, Beiping Luo, Georgios A. Kelesidis, Christian A. Bruun, Timofei Sukhodolov, Frank N. Keutsch, Thomas Peter, and Gabriel Chiodo
Geosci. Model Dev., 17, 7767–7793, https://doi.org/10.5194/gmd-17-7767-2024, https://doi.org/10.5194/gmd-17-7767-2024, 2024
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We quantified impacts and efficiency of stratospheric solar climate intervention via solid particle injection. Microphysical interactions of solid particles with the sulfur cycle were interactively coupled to the heterogeneous chemistry scheme and the radiative transfer code of an aerosol–chemistry–climate model. Compared to injection of SO2 we only find a stronger cooling efficiency for solid particles when normalizing to the aerosol load but not when normalizing to the injection rate.
Ingo Richter, Ping Chang, Gokhan Danabasoglu, Dietmar Dommenget, Guillaume Gastineau, Aixue Hu, Takahito Kataoka, Noel Keenlyside, Fred Kucharski, Yuko Okumura, Wonsun Park, Malte Stuecker, Andrea Taschetto, Chunzai Wang, Stephen Yeager, and Sang-Wook Yeh
EGUsphere, https://doi.org/10.5194/egusphere-2024-3110, https://doi.org/10.5194/egusphere-2024-3110, 2024
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The tropical ocean basins influence each other through multiple pathways and mechanisms, here referred to as tropical basin interaction (TBI). Many researchers have examined TBI using comprehensive climate models, but have obtained conflicting results. This may be partly due to differences in experiment protocols, and partly due to systematic model errors. TBIMIP aims to address this problem by designing a set of TBI experiments that will be performed by multiple models.
Samuel Rémy, Swen Metzger, Vincent Huijnen, Jason E. Williams, and Johannes Flemming
Geosci. Model Dev., 17, 7539–7567, https://doi.org/10.5194/gmd-17-7539-2024, https://doi.org/10.5194/gmd-17-7539-2024, 2024
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In this paper we describe the development of the future operational cycle 49R1 of the IFS-COMPO system, used for operational forecasts of atmospheric composition in the CAMS project, and focus on the implementation of the thermodynamical model EQSAM4Clim version 12. The implementation of EQSAM4Clim significantly improves the simulated secondary inorganic aerosol surface concentration. The new aerosol and precipitation acidity diagnostics showed good agreement against observational datasets.
Maximillian Van Wyk de Vries, Tom Matthews, L. Baker Perry, Nirakar Thapa, and Rob Wilby
Geosci. Model Dev., 17, 7629–7643, https://doi.org/10.5194/gmd-17-7629-2024, https://doi.org/10.5194/gmd-17-7629-2024, 2024
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This paper introduces the AtsMOS workflow, a new tool for improving weather forecasts in mountainous areas. By combining advanced statistical techniques with local weather data, AtsMOS can provide more accurate predictions of weather conditions. Using data from Mount Everest as an example, AtsMOS has shown promise in better forecasting hazardous weather conditions, making it a valuable tool for communities in mountainous regions and beyond.
Sofia Allende, Anne Marie Treguier, Camille Lique, Clément de Boyer Montégut, François Massonnet, Thierry Fichefet, and Antoine Barthélemy
Geosci. Model Dev., 17, 7445–7466, https://doi.org/10.5194/gmd-17-7445-2024, https://doi.org/10.5194/gmd-17-7445-2024, 2024
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We study the parameters of the turbulent-kinetic-energy mixed-layer-penetration scheme in the NEMO model with regard to sea-ice-covered regions of the Arctic Ocean. This evaluation reveals the impact of these parameters on mixed-layer depth, sea surface temperature and salinity, and ocean stratification. Our findings demonstrate significant impacts on sea ice thickness and sea ice concentration, emphasizing the need for accurately representing ocean mixing to understand Arctic climate dynamics.
Sabin I. Taranu, David M. Lawrence, Yoshihide Wada, Ting Tang, Erik Kluzek, Sam Rabin, Yi Yao, Steven J. De Hertog, Inne Vanderkelen, and Wim Thiery
Geosci. Model Dev., 17, 7365–7399, https://doi.org/10.5194/gmd-17-7365-2024, https://doi.org/10.5194/gmd-17-7365-2024, 2024
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In this study, we improved a climate model by adding the representation of water use sectors such as domestic, industry, and agriculture. This new feature helps us understand how water is used and supplied in various areas. We tested our model from 1971 to 2010 and found that it accurately identifies areas with water scarcity. By modelling the competition between sectors when water availability is limited, the model helps estimate the intensity and extent of individual sectors' water shortages.
Florian Börgel, Sven Karsten, Karoline Rummel, and Ulf Gräwe
EGUsphere, https://doi.org/10.5194/egusphere-2024-2685, https://doi.org/10.5194/egusphere-2024-2685, 2024
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Forecasting river runoff, crucial for managing water resources and understanding climate impacts, can be challenging. This study introduces a new method using Convolutional Long Short-Term Memory (ConvLSTM) networks, a machine learning model that processes spatial and temporal data. Focusing on the Baltic Sea region, our model uses weather data as input to predict daily river runoff for 97 rivers.
Thi Nhu Ngoc Do, Kengo Sudo, Akihiko Ito, Louisa Emmons, Vaishali Naik, Kostas Tsigaridis, Øyvind Seland, Gerd A. Folberth, and Douglas I. Kelley
EGUsphere, https://doi.org/10.5194/egusphere-2024-2313, https://doi.org/10.5194/egusphere-2024-2313, 2024
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Understanding historical isoprene emission changes is important for predicting future climate, but trends and their controlling factors remain uncertain. This study shows that long-term isoprene trends vary among Earth System Models mainly due to partially incorporating CO2 effects and land cover changes rather than climate. Future models that refine these factors’ effects on isoprene emissions, along with long-term observations, are essential for better understanding plant-climate interactions.
Michael Nole, Jonah Bartrand, Fawz Naim, and Glenn Hammond
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-162, https://doi.org/10.5194/gmd-2024-162, 2024
Revised manuscript accepted for GMD
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Safe carbon dioxide (CO2) storage is likely to be critical for mitigating some of the most dangerous effects of climate change. We present a simulation framework for modeling CO2 storage beneath the seafloor where CO2 can form a solid. This can aid in permanent CO2 storage for long periods of time. Our models show what a commercial-scale CO2 injection would look like in a marine environment. We discuss what would need to be considered when designing a sub-sea CO2 injection.
Cynthia Whaley, Montana Etten-Bohm, Courtney Schumacher, Ayodeji Akingunola, Vivek Arora, Jason Cole, Michael Lazare, David Plummer, Knut von Salzen, and Barbara Winter
Geosci. Model Dev., 17, 7141–7155, https://doi.org/10.5194/gmd-17-7141-2024, https://doi.org/10.5194/gmd-17-7141-2024, 2024
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This paper describes how lightning was added as a process in the Canadian Earth System Model in order to interactively respond to climate changes. As lightning is an important cause of global wildfires, this new model development allows for more realistic projections of how wildfires may change in the future, responding to a changing climate.
Erik Gustafsson, Bo G. Gustafsson, Martijn Hermans, Christoph Humborg, and Christian Stranne
Geosci. Model Dev., 17, 7157–7179, https://doi.org/10.5194/gmd-17-7157-2024, https://doi.org/10.5194/gmd-17-7157-2024, 2024
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Methane (CH4) cycling in the Baltic Proper is studied through model simulations, enabling a first estimate of key CH4 fluxes. A preliminary budget identifies benthic CH4 release as the dominant source and two main sinks: CH4 oxidation in the water (92 % of sinks) and outgassing to the atmosphere (8 % of sinks). This study addresses CH4 emissions from coastal seas and is a first step toward understanding the relative importance of open-water outgassing compared with local coastal hotspots.
Daniel Ries, Katherine Goode, Kellie McClernon, and Benjamin Hillman
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-133, https://doi.org/10.5194/gmd-2024-133, 2024
Revised manuscript accepted for GMD
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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.
Florian Zabel, Matthias Knüttel, and Benjamin Poschlod
EGUsphere, https://doi.org/10.5194/egusphere-2024-2526, https://doi.org/10.5194/egusphere-2024-2526, 2024
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CropSuite is a fuzzy-logic based high resolution open-source crop suitability model considering the impact of climate variability. We apply CropSuite for 48 important staple and cash crops at 1 km spatial resolution for Africa. We find that climate variability significantly impacts on suitable areas, but also affects optimal sowing dates, and multiple cropping potentials. The results provide information that can be used for climate impact assessments, adaptation and land-use planning.
Kerstin Hartung, Bastian Kern, Nils-Arne Dreier, Jörn Geisbüsch, Mahnoosh Haghighatnasab, Patrick Jöckel, Astrid Kerkweg, Wilton Jaciel Loch, Florian Prill, and Daniel Rieger
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-135, https://doi.org/10.5194/gmd-2024-135, 2024
Revised manuscript accepted for GMD
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The Icosahedral Nonhydrostatic (ICON) Model Community Interface (ComIn) library supports connecting third-party modules to the ICON model. Third-party modules can range from simple diagnostic Python scripts to full chemistry models. ComIn offers a low barrier for code extensions to ICON, provides multi-language support (Fortran, C/C++ and Python) and reduces the migration effort in response to new ICON releases. This paper presents the ComIn design principles and a range of use cases.
Tridib Banerjee, Patrick Scholz, Sergey Danilov, Knut Klingbeil, and Dmitry Sidorenko
Geosci. Model Dev., 17, 7051–7065, https://doi.org/10.5194/gmd-17-7051-2024, https://doi.org/10.5194/gmd-17-7051-2024, 2024
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In this paper we propose a new alternative to one of the functionalities of the sea ice model FESOM2. The alternative we propose allows the model to capture and simulate fast changes in quantities like sea surface elevation more accurately. We also demonstrate that the new alternative is faster and more adept at taking advantages of highly parallelized computing infrastructure. We therefore show that this new alternative is a great addition to the sea ice model FESOM2.
Yuwen Fan, Zhao Yang, Min-Hui Lo, Jina Hur, and Eun-Soon Im
Geosci. Model Dev., 17, 6929–6947, https://doi.org/10.5194/gmd-17-6929-2024, https://doi.org/10.5194/gmd-17-6929-2024, 2024
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Irrigated agriculture in the North China Plain (NCP) has a significant impact on the local climate. To better understand this impact, we developed a specialized model specifically for the NCP region. This model allows us to simulate the double-cropping vegetation and the dynamic irrigation practices that are commonly employed in the NCP. This model shows improved performance in capturing the general crop growth, such as crop stages, biomass, crop yield, and vegetation greenness.
Gang Tang, Zebedee Nicholls, Alexander Norton, Sönke Zaehle, and Malte Meinshausen
EGUsphere, https://doi.org/10.5194/egusphere-2024-1941, https://doi.org/10.5194/egusphere-2024-1941, 2024
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We studied the coupled carbon-nitrogen cycle effect in Earth System Models by developing a carbon-nitrogen coupling in a reduced complexity model, MAGICC. Our model successfully emulated the global carbon-nitrogen cycle dynamics seen in CMIP6 complex models. Results indicate consistent nitrogen limitations on plant growth (net primary production) from 1850 to 2100. Our findings suggest that nitrogen deficiency could reduce future land carbon sequestration.
Ed Blockley, Emma Fiedler, Jeff Ridley, Luke Roberts, Alex West, Dan Copsey, Daniel Feltham, Tim Graham, David Livings, Clement Rousset, David Schroeder, and Martin Vancoppenolle
Geosci. Model Dev., 17, 6799–6817, https://doi.org/10.5194/gmd-17-6799-2024, https://doi.org/10.5194/gmd-17-6799-2024, 2024
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This paper documents the sea ice model component of the latest Met Office coupled model configuration, which will be used as the physical basis for UK contributions to CMIP7. Documentation of science options used in the configuration are given along with a brief model evaluation. This is the first UK configuration to use NEMO’s new SI3 sea ice model. We provide details on how SI3 was adapted to work with Met Office coupling methodology and documentation of coupling processes in the model.
Jean-François Lemieux, William H. Lipscomb, Anthony Craig, David A. Bailey, Elizabeth C. Hunke, Philippe Blain, Till A. S. Rasmussen, Mats Bentsen, Frédéric Dupont, David Hebert, and Richard Allard
Geosci. Model Dev., 17, 6703–6724, https://doi.org/10.5194/gmd-17-6703-2024, https://doi.org/10.5194/gmd-17-6703-2024, 2024
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We present the latest version of the CICE model. It solves equations that describe the dynamics and the growth and melt of sea ice. To do so, the domain is divided into grid cells and variables are positioned at specific locations in the cells. A new implementation (C-grid) is presented, with the velocity located on cell edges. Compared to the previous B-grid, the C-grid allows for a natural coupling with some oceanic and atmospheric models. It also allows for ice transport in narrow channels.
Rachid El Montassir, Olivier Pannekoucke, and Corentin Lapeyre
Geosci. Model Dev., 17, 6657–6681, https://doi.org/10.5194/gmd-17-6657-2024, https://doi.org/10.5194/gmd-17-6657-2024, 2024
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This study introduces a novel approach that combines physics and artificial intelligence (AI) for improved cloud cover forecasting. This approach outperforms traditional deep learning (DL) methods in producing realistic and physically consistent results while requiring less training data. This architecture provides a promising solution to overcome the limitations of classical AI methods and contributes to open up new possibilities for combining physical knowledge with deep learning models.
Marit Sandstad, Borgar Aamaas, Ane Nordlie Johansen, Marianne Tronstad Lund, Glen Philip Peters, Bjørn Hallvard Samset, Benjamin Mark Sanderson, and Ragnhild Bieltvedt Skeie
Geosci. Model Dev., 17, 6589–6625, https://doi.org/10.5194/gmd-17-6589-2024, https://doi.org/10.5194/gmd-17-6589-2024, 2024
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The CICERO-SCM has existed as a Fortran model since 1999 that calculates the radiative forcing and concentrations from emissions and is an upwelling diffusion energy balance model of the ocean that calculates temperature change. In this paper, we describe an updated version ported to Python and publicly available at https://github.com/ciceroOslo/ciceroscm (https://doi.org/10.5281/zenodo.10548720). This version contains functionality for parallel runs and automatic calibration.
Sébastien Masson, Swen Jullien, Eric Maisonnave, David Gill, Guillaume Samson, Mathieu Le Corre, and Lionel Renault
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-140, https://doi.org/10.5194/gmd-2024-140, 2024
Revised manuscript accepted for GMD
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This article details a new feature we implemented in the most popular regional atmospheric model (WRF). This feature allows data to be exchanged between WRF and any other model (e.g. an ocean model) using the coupling library Ocean-Atmosphere-Sea-Ice-Soil – Model Coupling Toolkit (OASIS3-MCT). This coupling interface is designed to be non-intrusive, flexible and modular. It also offers the possibility of taking into account the nested zooms used in WRF or in the models with which it is coupled.
Cited articles
Adler, R. F., Sapiano, M. R., Huffman, G. J., Wang, J. J., Gu, G., Bolvin, D., Chiu, L., Schneider, U., Becker, A., Nelkin, E., Xie, P., Ferraro, R., and Shin, D.-B.: The Global Precipitation Climatology Project (GPCP) monthly analysis (new version 2.3) and a review of 2017 global precipitation. Atmosphere, 9, 138, https://doi.org/10.3390/atmos9040138, 2018.
Ahn, M.-S., Kim, D. H., Sperber, K. R., Kang, I.-S., Maloney, E. D., Waliser, D. E., and Hendon, H. H.: MJO simulation in CMIP5 climate models: MJO skill metrics and process-oriented diagnosis, Clim. Dynam., 49, 4023–4045, https://doi.org/10.1007/s00382-017-3558-4, 2017.
Ahn, M.-S., Gleckler, P. J., Lee, J., Pendergrass, A. G., and Jakob, C.: Benchmarking Simulated Precipitation Variability Amplitude across Time Scales, J. Climate, 35, 3173–3196, https://doi.org/10.1175/jcli-d-21-0542.1, 2022.
Ahn, M.-S., Ullrich, P. A., Gleckler, P. J., Lee, J., Ordonez, A. C., and Pendergrass, A. G.: Evaluating precipitation distributions at regional scales: a benchmarking framework and application to CMIP5 and 6 models, Geosci. Model Dev., 16, 3927–3951, https://doi.org/10.5194/gmd-16-3927-2023, 2023.
Anaconda pcmdi_metrics: https://anaconda.org/conda-forge/pcmdi_metrics, last access: 8 May 2024.
Arcodia, M., Barnes, E. A., Mayer, K., Lee, J., Ordonez, A., and Ahn, M.-S.: Assessing decadal variability of subseasonal forecasts of opportunity using explainable AI, Environ. Res., 2, 045002, https://doi.org/10.1088/2752-5295/aced60, 2023.
Ashfaq, M., Rastogi, D., Kitson, J., Abid, M. A., and Kao, S.-C.: Evaluation of CMIP6 GCMs over the CONUS for downscaling studies, J. Geophys. Res.-Atmos., 127, e2022JD036659, https://doi.org/10.1029/2022JD036659, 2022.
Bayr, T., Wengel, C., Latif, M., Dommenget, D., Lübbecke, J., and Park, W.: Error compensation of ENSO atmospheric feedbacks in climate models and its influence on simulated ENSO dynamics, Clim. Dynam., 53, 155–172, https://doi.org/10.1007/s00382-018-4575-7, 2019.
Biard, J. C. and Kunkel, K. E.: Automated detection of weather fronts using a deep learning neural network, Adv. Stat. Clim. Meteorol. Oceanogr., 5, 147–160, https://doi.org/10.5194/ascmo-5-147-2019, 2019.
Bellenger, H., Guilyardi, E., Leloup, J., Lengaigne, M., and Vialard, J.: ENSO representation in climate models: from CMIP3 to CMIP5, Clim. Dynam., 42, 1999–2018, https://doi.org/10.1007/s00382-013-1783-z, 2014.
Boucher, O., Servonnat, J., Albright, A. L., Aumont, O., Balkanski, Y., Bastrikov, V., Bekki, S., Bonnet, R., Bony, S., Bopp, L., Braconnot, P., Brockmann, P., Cadule, P., Caubel, A., Cheruy, F., Codron, F., Cozic, A., Cugnet, D., D'Andrea, F., Davini, P., De Lavergne, C., Denvil, S., Deshayes, J., Devilliers, M., Ducharne, A., Dufresne, J. L., Dupont, E., Ethé, C., Fairhead, L., Falletti, L., Flavoni, S., Foujols, M. A., Gardoll, S., Gastineau, G., Ghattas, J., Grandpeix, J. Y., Guenet, B., Lionel, E. G., Guilyardi, E., Guimberteau, M., Hauglustaine, D., Hourdin, F., Idelkadi, A., Joussaume, S., Kageyama, M., Khodri, M., Krinner, G., Lebas, N., Levavasseur, G., Lévy, C., Li, L., Lott, F., Lurton, T., Luyssaert, S., Madec, G., Madeleine, J.-B., Maignan, F., Marchand, M., Marti, O., Mellul, L., Meurdesoif, Y., Mignot, J., Musat, I., Ottlé, C., Peylin, P., Planton, Y., Polcher, J., Rio, C., Rochetin, N., Rousset, C., Sepulchre, P., Sima, A., Swingedouw, D., Thiéblemont, R., Traore, A. K., Vancoppenolle, M., Vial, J., Vialard, J., Viovy, N., and Vuichard, N.: Presentation and evaluation of the IPSL-CM6A-LR Climate Model, J. Adv. Model. Earth Sy., 12, e2019MS002010, https://doi.org/10.1029/2019ms002010, 2020.
Caldwell, P., Mametjanov, A., Tang, Q., Van Roekel, L., Golaz, J.-C., Lin, W., Bader, D. C., Keen, N. D., Feng, Y., Jacob, R., Maltrud, M., Roberts, A., Taylor, M. A., Veneziani, M., Wang, H., Wolfe, J. D., Balaguru, K., Cameron-Smith, P. J., Dong, L., Klein, S. A., Leung, L. R., Li, H., Li, Q., Liu, X., Neale, R., Pinheiro, M. C., Qian, Y., Ullrich, P. A., Xie, S., Yang, Y., Zhang, Y., Zhang, K., and Zhou, T.: The DOE E3SM Coupled Model Version 1: description and results at high resolution, J. Adv. Model. Earth Sy., 11, 4095–4146, https://doi.org/10.1029/2019ms001870, 2019.
Chen, H.-C., Jin, F.-F., Zhao, S., Wittenberg, A. T., and Xie, S.: ENSO dynamics in the E3SM-1-0, CESM2, and GFDL-CM4 climate models, J. Climate, 34, 9365–9384, https://doi.org/10.1175/JCLI-D-21-0355.1, 2021.
Collier, N., Hoffman, F. M., Lawrence, D. M., Keppel-Aleks, G., Koven, C. D., Riley, W. J., Mu, M., and Randerson, J. T.: The International Land Model Benchmarking (ILAMB) System: Design, theory, and implementation, J. Adv. Model. Earth Sy., 10, 2731–2754, https://doi.org/10.1029/2018ms001354, 2018.
Covey, C., AchutaRao, K., Cubasch, U., Jones, P., Lambert, S. J., Mann, M., Phillips, T. J., and Taylor, K. E.: An overview of results from the Coupled Model Intercomparison Project, Global. Planet. Change, 37, 103–133, https://doi.org/10.1016/s0921-8181(02)00193-5, 2003.
Covey, C., Gleckler, P. J., Doutriaux, C., Williams, D. N., Dai, A., Fasullo, J. T., Trenberth, K. E., and Berg, A.: Metrics for the diurnal cycle of precipitation: toward routine benchmarks for climate models, J. Climate, 29, 4461–4471, https://doi.org/10.1175/jcli-d-15-0664.1, 2016.
Crockford, D.: The application/json media type for javascript object notation (json) (No. rfc4627), https://www.rfc-editor.org/rfc/pdfrfc/rfc4627.txt.pdf (last access: 4 April 2024), 2006.
Crockford, D. and Morningstar, C.: The JSON Data Interchange Syntax, ECMA-404, ECMA International, https://doi.org/10.13140/RG.2.2.28181.14560, 2017.
Dalelane, C., Winderlich, K., and Walter, A.: Evaluation of global teleconnections in CMIP6 climate projections using complex networks, Earth Syst. Dynam., 14, 17–37, https://doi.org/10.5194/esd-14-17-2023, 2023.
Dawson, A.: eofs: A Library for EOF Analysis of Meteorological, Oceanographic, and Climate Data, J. Open Res. Software, 4, e14, https://doi.org/10.5334/jors.122, 2016.
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P., Bechtold, P., Beljaars, A. C. M., van de Berg, L., Bidlot, J., Bormann, N., Delsol, C., Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S. B., Hersbach, H., Hólm, E. V., Isaksen, L., Kållberg, P., Köhler, M., Matricardi, M., McNally, A. P., Monge-Sanz, B. M., Morcrette, J.-J., Park, B.-K., Peubey, C., de Rosnay, P., Tavolato, C., Thépaut, J.-N., and Vitart, F.: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system, Q. J. Roy. Meteor. Soc., 137, 553–597, https://doi.org/10.1002/qj.828, 2011.
Deser, C. and Phillips, A. S.: Defining the internal component of Atlantic multidecadal variability in a changing climate, Geophys. Res. Lett., 48, e2021GL095023, https://doi.org/10.1029/2021gl095023, 2021.
Doutriaux, C., Nadeau, D., Wittenburg, S., Lipsa, D., Muryanto, L., Chaudhary, A., and Williams, D. N.: CDAT/cdat: CDAT 8.1, Zenodo [code], https://doi.org/10.5281/zenodo.2586088, 2019.
Durack, P. J., Taylor, K. E., Eyring, V., Ames, S., Hoang, T., Nadeau, D., Doutriaux, C., Stockhause, M., and Gleckler, P. J.: Toward standardized data sets for climate model experimentation, Eos T. Am. Geophys. Un., 99, https://doi.org/10.1029/2018eo101751, 2018.
Eaton, B., Gregory, J., Drach, B., Taylor, K., Hankin, S., Blower, J., Caron, J., Signell, R., Bentley, P., Rappa, G., Höck, H., Pamment, A., Juckes, M., Raspaud, M., Horne, R., Whiteaker, T., Blodgett, D., Zender, C., Lee, D., Hassell, D., Snow, A. D., Kölling, T., Allured, D., Jelenak, A., Soerensen, A. M., Gaultier, L., and Herlédan, S.: NetCDF Climate and Forecast (CF) Meta-data Conventions V1.10, http://cfconventions.org/Data/cf-conventions/cf-conventions-1.10/cf-conventions.html (last access: 4 April 2024), 2022.
ESGF LLNL Metagrid: https://esgf-node.llnl.gov/, last access: 8 May 2024.
Eyring, V., Righi, M., Lauer, A., Evaldsson, M., Wenzel, S., Jones, C., Anav, A., Andrews, O., Cionni, I., Davin, E. L., Deser, C., Ehbrecht, C., Friedlingstein, P., Gleckler, P., Gottschaldt, K.-D., Hagemann, S., Juckes, M., Kindermann, S., Krasting, J., Kunert, D., Levine, R., Loew, A., Mäkelä, J., Martin, G., Mason, E., Phillips, A. S., Read, S., Rio, C., Roehrig, R., Senftleben, D., Sterl, A., van Ulft, L. H., Walton, J., Wang, S., and Williams, K. D.: ESMValTool (v1.0) – a community diagnostic and performance metrics tool for routine evaluation of Earth system models in CMIP, Geosci. Model Dev., 9, 1747–1802, https://doi.org/10.5194/gmd-9-1747-2016, 2016a.
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, 2016b.
Eyring, V., Cox, P. M., Flato, G. M., Gleckler, P. J., Abramowitz, G., Caldwell, P., Collins, W. D., Gier, B. K., Hall, A., Hoffman, F. M., Hurtt, G. C., Jahn, A., Jones, C. D., Klein, S. A., Krasting, J. P., Kwiatkowski, L., Lorenz, R., Maloney, E. D., Meehl, G. A., Pendergrass, A. G., Pincus, R., Ruane, A. C., Russell, J. L., Sanderson, B. M., Santer, B. D., Sherwood, S. C., Simpson, I. R., Stouffer, R. J., and Williamson, M. S.: Taking climate model evaluation to the next level, Nat. Clim. Change, 9, 102–110, https://doi.org/10.1038/s41558-018-0355-y, 2019.
Eyring, V., Bock, L., Lauer, A., Righi, M., Schlund, M., Andela, B., Arnone, E., Bellprat, O., Brötz, B., Caron, L.-P., Carvalhais, N., Cionni, I., Cortesi, N., Crezee, B., Davin, E. L., Davini, P., Debeire, K., de Mora, L., Deser, C., Docquier, D., Earnshaw, P., Ehbrecht, C., Gier, B. K., Gonzalez-Reviriego, N., Goodman, P., Hagemann, S., Hardiman, S., Hassler, B., Hunter, A., Kadow, C., Kindermann, S., Koirala, S., Koldunov, N., Lejeune, Q., Lembo, V., Lovato, T., Lucarini, V., Massonnet, F., Müller, B., Pandde, A., Pérez-Zanón, N., Phillips, A., Predoi, V., Russell, J., Sellar, A., Serva, F., Stacke, T., Swaminathan, R., Torralba, V., Vegas-Regidor, J., von Hardenberg, J., Weigel, K., and Zimmermann, K.: Earth System Model Evaluation Tool (ESMValTool) v2.0 – an extended set of large-scale diagnostics for quasi-operational and comprehensive evaluation of Earth system models in CMIP, Geosci. Model Dev., 13, 3383–3438, https://doi.org/10.5194/gmd-13-3383-2020, 2020.
Eyring, V., Gillett, N. P., Achuta Rao, K. M., Barimalala, R., Barreiro Parrillo, M., Bellouin, N., Cassou, C., Durack, P. J., Kosaka, Y., McGregor, S. and Min, S., Morgenstern, O., and Sun, Y.: Human Influence on the Climate System, in: Climate Change 2021: The Physical Science Basis, Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, 105, 423–552, https://doi.org/10.1017/9781009157896.005, 2021.
Fasullo, J. T.: Evaluating simulated climate patterns from the CMIP archives using satellite and reanalysis datasets using the Climate Model Assessment Tool (CMATv1), Geosci. Model Dev., 13, 3627–3642, https://doi.org/10.5194/gmd-13-3627-2020, 2020.
Fasullo, J. T., Phillips, A. S., and Deser, C.: Evaluation of leading modes of climate variability in the CMIP archives, J. Climate, 33, 5527–5545, https://doi.org/10.1175/jcli-d-19-1024.1, 2020.
Ferraro, R., Waliser, D. E., Gleckler, P. J., Taylor, K. E., and Eyring, V.: Evolving OBS4MIPS to support Phase 6 of the Coupled Model Intercomparison Project (CMIP6), B. Am. Meteorol. Soc., 96, ES131–ES133, https://doi.org/10.1175/bams-d-14-00216.1, 2015.
Flato, G., Marotzke, J., Abiodun, B., Braconnot, P., Chou, S. C., Collins, W., Cox, P., Driouech, F., Emori, S., Eyring, V., and Forest, C.: Evaluation of climate models, in: Climate change 2013: the physical science basis, Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, 741–866, 2014.
Fu, W., Moore, J. K., Primeau, F., Collier, N., Ogunro, O. O., Hoffman, F. M., and Randerson, J. T.: Evaluation of ocean biogeochemistry and carbon cycling in CMIP earth system models with the international ocean model benchmarking (IOMB) software System. J. Geophys. Res.-Oceans, 127, e2022JC018965, https://doi.org/10.1029/2022JC018965, 2022.
Gates, W. L.: AN AMS continuing series: Global CHANGE–AMIP: The Atmospheric Model Intercomparison Project, B. Am. Meteorol. Soc., 73, 1962–1970, 1992.
Gates, W. L., Henderson-Sellers, A., Boer, G. J., Folland, C. K., Kitoh, A., McAvaney, B. J., Semazzi, F., Smith, N., Weaver, A. J., and Zeng, Q. C.: Climate models – evaluation, Climate Change, 1, 229–284, 1995.
Gates, W. L., Boyle, J. S., Covey, C., Dease, C. G., Doutriaux, C. M., Drach, R. S., Fiorino, M., Gleckler, P. J., Hnilo, J. J., Marlais, S. M., and Phillips, T. J.: An overview of the results of the Atmospheric Model Intercomparison Project (AMIP I), B. Am. Meteorol. Soc., 80, 29–56, 1999.
Gleckler, P. J., Taylor, K. E., and Doutriaux, C.: Performance metrics for climate models, J. Geophys. Res., 113, D06104, https://doi.org/10.1029/2007jd008972, 2008.
Gleckler, P. J., Ferraro, R., and Waliser, D. E.: Improving use of satellite data in evaluating climate models, Eos T. Am. Geophys. Un., 92, 172, https://doi.org/10.1029/2011eo200005, 2011.
Gleckler, P. J., Doutriaux, C., Durack, P. J., Taylor, K. E., Zhang, Y., Williams, D. N., Mason, E., and Servonnat, J.: A more powerful reality test for climate models, Eos T. Am. Geophys. Un., 97, https://doi.org/10.1029/2016eo051663, 2016.
Golaz, J.-C., Caldwell, P., Van Roekel, L., Petersen, M. R., Tang, Q., Wolfe, J. D., Abeshu, G. W., Anantharaj, V., Asay-Davis, X., Bader, D. C., Baldwin, S., Bisht, G., Bogenschutz, P., Branstetter, M. L., Brunke, M. A., Brus, S., Burrows, S. M., Cameron-Smith, P. J., Donahue, A. S., Deakin, M., Easter, R. C., Evans, K. J., Feng, Y., Flanner, M., Foucar, J. G., Fyke, J., Griffin, B. M., Hannay, C., Harrop, B. E., Hoffman, M. J., Hunke, E., Jacob, R., Jacobsen, D. W., Jeffery, N., Jones, P. W., Keen, N. D., Klein, S. A., Larson, V. E., Leung, L. R., Li, H. Y., Lin, W., Lipscomb, W. H., Lun, P., Mahajan, S., Maltrud, M., Mametjanov, A., McClean, J. L., McCoy, R., Neale, R., Price, S., Qian, Y., Rasch, P. J., Eyre, J. E. J. R., Riley, W. J., Ringler, T. D., Roberts, A., Roesler, E. L., Salinger, A. G., Shaheen, Z., Shi, X., Singh, B., Tang, J., Taylor, M. A., Thornton, P. E., Turner, A. K., Veneziani, M., Wan, H., Wang, H., Wang, S., Williams, D. N., Wolfram, P. J., Worley, P. H., Xie, S., Yang, Y., Yoon, J., Zelinka, M. D., Zender, C. S., Zeng, X., Zhang, C., Zhang, K., Zhang, Y., Zheng, X., Zhou, T., and Zhu, Q.: The DOE E3SM Coupled Model Version 1: Overview and evaluation at standard resolution, J. Adv. Model. Earth Sy., 11, 2089–2129, https://doi.org/10.1029/2018ms001603, 2019.
Goldenson, N., Leung, L. R., Mearns, L. O., Pierce, D. W., Reed, K. A., Simpson, I. R., Ullrich, P., Krantz, W., Hall, A., Jones, A., and Rahimi, S.: Use-Inspired, Process-Oriented GCM Selection: Prioritizing Models for Regional Dynamical Downscaling, B. Am. Meteorol. Soc., 104, E1619–E1629, https://doi.org/10.1175/BAMS-D-23-0100.1, 2023.
Guilyardi, E., Wittenberg, A., Fedorov, A., Collins, M., Wang, C., Capotondi, A., Van Oldenborgh, G. J., and Stockdale, T.: Understanding El Niño in ocean–atmosphere general circulation models: Progress and challenges, B. Am. Meteorol. Soc., 90, 325–340, https://doi.org/10.1175/2008BAMS2387.1, 2009.
Guilyardi, E., Capotondi, A., Lengaigne, M., Thual, S., and Wittenberg, A. T.: ENSO modelling: history, progress and challenges, in: El Niño in a changing climate, edited by: McPhaden, M. J., Santoso, A., Cai, W., AGU monograph, ISBN 9781119548164, https://doi.org/10.1002/9781119548164.ch9, 2020.
Gutowski Jr., W. J., Giorgi, F., Timbal, B., Frigon, A., Jacob, D., Kang, H.-S., Raghavan, K., Lee, B., Lennard, C., Nikulin, G., O'Rourke, E., Rixen, M., Solman, S., Stephenson, T., and Tangang, F.: WCRP COordinated Regional Downscaling EXperiment (CORDEX): a diagnostic MIP for CMIP6, Geosci. Model Dev., 9, 4087–4095, https://doi.org/10.5194/gmd-9-4087-2016, 2016.
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.
Hannah, W. M., Bradley, A. M., Guba, O., Tang, Q., Golaz, J.-C., and Wolfe, J. D.: Separating physics and dynamics grids for improved computational efficiency in spectral element Earth system models, J. Adv. Model. Earth Sy., 13, e2020MS002419, https://doi.org/10.1029/2020ms002419, 2021.
Hassan, K. A., Rönnberg, N., Forsell, C., Cooper, M., and Johansson, J.: A study on 2D and 3D parallel coordinates for pattern identification in temporal multivariate data, in: 2019 23rd International Conference Information Visualisation (IV), 145–150, https://doi.org/10.1109/IV.2019.00033, 2019.
Hassell, D., Gregory, J., Blower, J., Lawrence, B. N., and Taylor, K. E.: A data model of the Climate and Forecast metadata conventions (CF-1.6) with a software implementation (cf-python v2.1), Geosci. Model Dev., 10, 4619–4646, https://doi.org/10.5194/gmd-10-4619-2017, 2017.
Hausfather, Z., Marvel, K., Schmidt, G. A., Nielsen-Gammon, J. W., and Zelinka, M.: Climate simulations: Recognize the “hot model” problem, Nature, 605, 26–29, https://doi.org/10.1038/d41586-022-01192-2, 2022.
Held, I. M., Guo, H., Adcroft, A., Dunne, J. P., Horowitz, L. W., Krasting, J., Shevliakova, E., Winton, M., Zhao, M., Bushuk, M., Wittenberg, A. T., and coauthors: Structure and performance of GFDL's CM4.0 climate model, J. Adv. Model. Earth Sy., 11, 3691–3727, https://doi.org/10.1029/2019MS001829, 2019.
Hendon, H. H., Zhang, C., and Glick, J. D.: Interannual Variation of the Madden–Julian Oscillation during Austral Summer, J. Climate, 12, 2538–2550, 1999.
Herger, N., Abramowitz, G., Knutti, R., Angélil, O., Lehmann, K., and Sanderson, B. M.: Selecting a climate model subset to optimise key ensemble properties, Earth Syst. Dynam., 9, 135–151, https://doi.org/10.5194/esd-9-135-2018, 2018.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., and coauthors: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020.
Hintze, J. L. and Nelson, R. D.: Violin plots: A box plot-density trace synergism, Am. Stat., 52, 181–184, https://doi.org/10.1080/00031305.1998.10480559, 1998.
Hoyer, S. and Hamman, J.: xarray: N-D labeled Arrays and Datasets in Python, J. Open Res. Software, 5, 10, https://doi.org/10.5334/jors.148, 2017.
Huffman, G. J., Adler, R. F., Morrissey, M. M., Bolvin, D. T., Curtis, S., Joyce, R., McGavock, B., and Susskind, J.: Global precipitation at one-degree daily resolution from multisatellite observations, J. Hydrometeorol., 2, 36–50, 2001.
Huffman, G. J., Bolvin, D. T., Braithwaite, D., Hsu, K., Joyce, R., Kidd, C., Nelkin, E. J., Sorooshian, S., Tan, J., and Xie, P.: NASA global precipitation measurement (GPM) integrated multi-satellite retrievals for GPM (IMERG), Algorithm theoretical basis document (ATBD) version, 4, p. 30, 2015.
Inselberg, A.: Multidimensional detective, in: Proceedings of IEEE Symposium on Information Visualization, 100–107, https://doi.org/10.1109/INFVIS.1997.636793, 1997.
Inselberg, A.: Parallel Coordinates: Visualization, Exploration and Classification of High-Dimensional Data, in: Handbook of Data Visualization, edited by: Chen, C., Härdle, W., and Unwin, A., Springer, Berlin, Heidelberg, Germany, 643–680, https://doi.org/10.1007/978-3-540-33037-0_25, 2008.
Inselberg, A.: Parallel Coordinates, in: Encyclopedia of Database Systems, Springer, edited by: Liu, L., and Özsu, M. T., Springer, New York, NY, U.S.A., https://doi.org/10.1007/978-1-4899-7993-3_262-2, 2016.
Jakob, C., Gettelman, A., and Pitman, A.: The need to operationalize climate modelling, Nat. Clim. Change, 13, 1158–1160, https://doi.org/10.1038/s41558-023-01849-4, 2023.
Johansson, J. and Forsell, C.: Evaluation of parallel coordinates: Overview, categorization and guidelines for future research, IEEE T. Vis. Comput. G. R., 22, 579–588, https://doi.org/10.1109/TVCG.2015.2466992, 2016.
Joyce, R. J., Janowiak, J. E., Arkin, P. A., and Xie, P.: CMORPH: A method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution, J. Hydrometeorol., 5, 487–503, 2004.
Kang, D., Kim, D. H., Ahn, M.-S., Neale, R., Lee, J., and Gleckler, P. J.: The role of the mean state on MJO simulation in CESM2 ensemble simulation, Geophys. Res. Lett., 47, e2020GL089824, https://doi.org/10.1029/2020gl089824, 2020.
Kim, D., Sperber, K. R., Stern, W., Waliser, D. E., Kang, I. S., Maloney, E. D., Wang, W., Weickmann, K. M., Benedict, J. J., Khairoutdinov, M., Lee, M.-I., Neale, R., Suarez, M. J., Thayer-Calder, K., and Zhang, G.: Application of MJO simulation diagnostics to climate models, J. Climate, 22, 6413–6436, https://doi.org/10.1175/2009jcli3063.1, 2009.
Kim, H., Caron, J. M., Richter, J. H. and Simpson, I. R.: The lack of QBO-MJO connection in CMIP6 models, Geophys. Res. Lett., 47, e2020GL087295, https://doi.org/10.1029/2020GL087295, 2020.
Klein, S. A., Zhang, Y., Zelinka, M. D., Pincus, R., Boyle, J., and Gleckler, P. J.: Are climate model simulations of clouds improving? An evaluation using the ISCCP simulator, J. Geophys. Res.-Atmos., 118, 1329–1342, https://doi.org/10.1002/jgrd.50141, 2013.
Klingaman, N. P., Martin, G. M., and Moise, A.: ASoP (v1.0): a set of methods for analyzing scales of precipitation in general circulation models, Geosci. Model Dev., 10, 57–83, https://doi.org/10.5194/gmd-10-57-2017, 2017.
Knutti, R.: The end of model democracy? Climatic Change, 102, 395–404, https://doi.org/10.1007/s10584-010-9800-2, 2010.
Knutti, R., Abramowitz, G., Collins, M., Eyring, V., Gleckler, P. J., Hewitson, B., and Mearns, L.: Good Practice Guidance Paper on Assessing and Combining Multi Model Climate Projections, in: Meeting Report of the Intergovernmental Panel on Climate Change Expert Meeting on Assessing and Combining Multi Model Climate Projections, edited by: Stocker, T. F., Qin, D., Plattner, G.-K., Tignor, M., and Midgley, P. M., IPCC Working Group I Technical Support Unit, University of Bern, Bern, Switzerland, 2010.
Knutti, R., Sedláček, J., Sanderson, B. M., Lorenz, R., Fischer, E. M., and Eyring, V.: A climate model projection weighting scheme accounting for performance and interdependence, Geophys. Res. Lett., 44, 1909–1918, https://doi.org/10.1002/2016gl072012, 2017.
Labe, Z. M. and Barnes, E. A.: Comparison of Climate Model Large Ensembles With Observations in the Arctic Using Simple Neural Networks, Earth Space Sci., 9, e2022EA002348, https://doi.org/10.1029/2022EA002348, 2022.
Lambert, S. J. and Boer, G. J.: CMIP1 evaluation and intercomparison of coupled climate models, Clim. Dynam., 17, 83–106, https://doi.org/10.1007/PL00013736, 2001.
Lee, H., Goodman, A., McGibbney, L., Waliser, D. E., Kim, J., Loikith, P. C., Gibson, P. B., and Massoud, E. C.: Regional Climate Model Evaluation System powered by Apache Open Climate Workbench v1.3.0: an enabling tool for facilitating regional climate studies, Geosci. Model Dev., 11, 4435–4449, https://doi.org/10.5194/gmd-11-4435-2018, 2018.
Lee, J., Gleckler, P., Sperber, K., Doutriaux, C., and Williams, D.: High-dimensional Data Visualization for Climate Model Intercomparison: Application of the Circular Plot, in: Proceedings of the 8th International Workshop on Climate Informatics: CI 2018, NCAR Technical Note NCAR/TN-550+PROC, 12–14, https://doi.org/10.5065/D6BZ64XQ, 2018.
Lee, J., Sperber, K. R., Gleckler, P. J., Bonfils, C., and Taylor, K. E.: Quantifying the agreement between observed and simulated extratropical modes of interannual variability, Clim. Dynam., 52, 4057–4089, https://doi.org/10.1007/s00382-018-4355-4, 2019a.
Lee, J., Xue, Y., De Sales, F., Diallo, I., Marx, L., Ek, M., Sperber, K. R., and Gleckler, P. J.: Evaluation of multi-decadal UCLA-CFSv2 simulation and impact of interactive atmospheric-ocean feedback on global and regional variability, Clim. Dynam., 52, 3683–3707, https://doi.org/10.1007/s00382-018-4351-8, 2019b.
Lee, J., Planton, Y., Gleckler, P. J., Sperber, K. R., Guilyardi, E., Wittenberg, A. T., McPhaden, M. J., and Pallotta, G.: Robust evaluation of ENSO in climate models: How many ensemble members are needed?, Geophys. Res. Lett., 48, e2021GL095041, https://doi.org/10.1029/2021gl095041, 2021a.
Lee, J., Sperber, K. R., Gleckler, P. J., Taylor, K. E., and Bonfils, C.: Benchmarking performance changes in the simulation of extratropical modes of variability across CMIP generations, J. Climate, 34, 6945–6969, https://doi.org/10.1175/jcli-d-20-0832.1, 2021b.
Lee, J., Ahn, M.-S., Ordonez, A., Gleckler, P., and Ullrich, P.: PCMDI/pcmdi_metrics_results_archive, Zenodo [data set], https://doi.org/10.5281/zenodo.10181201, 2023a.
Lee, J., Gleckler, P., Ordonez, A., Ahn, M.-S., Ullrich, P., Tom, V., Jason, B., Charles, D., Durack, P., Shaheen, Z., Muryanto, L., Painter, J., and Krasting, J.: PCMDI/pcmdi_metrics: PMP Version 3.1.1, Zenodo [code], https://doi.org/10.5281/zenodo.592790, 2023b.
Leung, L. R., Boos, W. R., Catto, J. L., DeMott, C. A., Martin, G. M., Neelin, J. D., O'Brien, T. A., Xie, S., Feng, Z., Klingaman, N. P. Kuo, Y.-H., Lee, R. W., Martinez-Villalobos, C., Vishnu S., Priestley, M. D. K., Tao, C., and Zhou, Y.: Exploratory precipitation metrics: Spatiotemporal characteristics, process-oriented, and phenomena-based, J. Climate, 35, 3659–3686, https://doi.org/10.1175/JCLI-D-21-0590.1, 2022.
Lin, J.-P., Kiladis, G. N., Mapes, B. E., Weickmann, K. M., Sperber, K. R., Lin, W., Wheeler, M. C., Schubert, S. D., Del Genio, A. D., Donner, L. J., Emori, S., Guérémy, J.-F., Hourdin, F., Rasch, P. J., Roeckner, E., and Scinocca, J.: Tropical intraseasonal variability in 14 IPCC AR4 climate Models. Part I: Convective Signals, J. Climate, 19, 2665–2690, https://doi.org/10.1175/jcli3735.1, 2006.
Lin, Y., Huang, X., Liang, Y., Qin, Y., Xu, S., Huang, W., Xu, F., Liu, L., Wang, Y., Peng, Y., and Wang, L.: Community integrated earth system model (CIESM): Description and evaluation, J. Adv. Model. Earth Sy., 12, e2019MS002036, https://doi.org/10.1029/2019ms002036, 2020.
Loeb, N. G., Doelling, D. R., Wang, H., Su, W., Nguyen, C., Corbett, J. G., Liang, L., Mitrescu, C., Rose, F. G., and Seiji, K.: Clouds and the Earth's Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) top-of-atmosphere (TOA) Edition-4.0 data product, Int. J. Climatol., 31, 895–918, https://doi.org/10.1175/JCLI-D-17-0208.1, 2018.
Longmate, J. M., Risser, M. D., and Feldman, D. R.: Prioritizing the selection of CMIP6 model ensemble members for downscaling projections of CONUS temperature and precipitation, Clim. Dynam., 61, 5171–5197, https://doi.org/10.1007/s00382-023-06846-z, 2023.
Lu, L., Wang, W. and Tan, Z.: Double-arc parallel coordinates and its axes re-ordering methods, Mobile Networks and Applications, 25, 1376–1391, https://doi.org/10.1007/s11036-019-01455-9, 2020.
Madden, R. A. and Julian, P.: Detection of a 40–50 day oscillation in the zonal wind in the Tropical Pacific, J. Atmos. Sci., 28, 702–708, https://doi.org/10.1175/1520-0469(1971)028, 1971.
Madden, R. A. and Julian, P.: Description of Global-Scale Circulation Cells in the Tropics with a 40–50 Day Period, J. Atmos. Sci., 29, 1109–1123, https://doi.org/10.1175/1520-0469(1972)029, 1972.
Madden, R. A. and Julian, P.: Observations of the 40–50-Day Tropical Oscillation – A Review, Mon. Weather Rev., 122, 814–837, https://doi.org/10.1175/1520-0493(1994)122, 1994.
Maloney, E. D., Gettelman, A., Ming, Y., Neelin, J. D., Barrie, D., Mariotti, A., Chen, C., Coleman, D., Kuo, Y. H., Singh, B., Annamalai, H., Berg, A., Booth, J. F., Camargo, S. J., Dai, A., Gonzalez, A., Hafner, J., Jiang, X., Jing, X., Kim, D. H., Kumar, A., Moon, Y., Naud, C. M., Sobel, A. H., Suzuki, K., Wang, F., Wang, J., Wing, A. A., Xu, X., and Zhao, M.: Process-Oriented evaluation of climate and weather forecasting models, B. Am. Meteorol. Soc., 100, 1665–1686, https://doi.org/10.1175/bams-d-18-0042.1, 2019.
Martin, G. M., Klingaman, N. P., and Moise, A. F.: Connecting spatial and temporal scales of tropical precipitation in observations and the MetUM-GA6, Geosci. Model Dev., 10, 105–126, https://doi.org/10.5194/gmd-10-105-2017, 2017.
McAvaney, B. J., Covey, C., Joussaume, S., Kattsov, V., Kitoh, A., Ogana, W., Pitman, A. J., Weaver, A. J., Wood, R. A., and Zhao, Z. C.: Model evaluation. In Climate Change 2001: The scientific basis, Contribution of WG1 to the Third Assessment Report of the IPCC (TAR) 471-523, Cambridge University Press, ISBN 0521 80767 0, 2001.
McPhaden, M. J., Zebiak, S. E., and Glantz, M. H.: ENSO as an integrating concept in Earth Science, Science, 314, 1740–1745, https://doi.org/10.1126/science.1132588, 2006.
McPhaden, M. J., Santoso, A., and Cai, W. (Eds.): El Niño Southern oscillation in a changing climate, American Geophysical Union, USA, 528 pp., ISBN 9781119548126, https://doi.org/10.1002/9781119548164, 2020.
Meehl, G. A., Boer, G. J., Covey, C., Latif, M., and Stouffer, R. J.: Intercomparison makes for a better climate model, Eos T. Am. Geophys. Un., 78, 445, https://doi.org/10.1029/97eo00276, 1997.
Meehl, G. A., Boer, G. J., Covey, C., Latif, M., and Stouffer, R. J.: The Coupled Model Intercomparison Project (CMIP), B. Am. Meteorol. Soc., 81, 313–318, 2000.
Meehl, G. A., Covey, C., Delworth, T. L., Latif, M., McAvaney, B. J., Mitchell, J. F. B., Stouffer, R. J., and Taylor, K. E.: THE WCRP CMIP3 Multimodel Dataset: A new era in climate change research, B. Am. Meteorol. Soc., 88, 1383–1394, https://doi.org/10.1175/bams-88-9-1383, 2007.
Merrifield, A. L., Brunner, L., Lorenz, R., Humphrey, V., and Knutti, R.: Climate model Selection by Independence, Performance, and Spread (ClimSIPS v1.0.1) for regional applications, Geosci. Model Dev., 16, 4715–4747, https://doi.org/10.5194/gmd-16-4715-2023, 2023.
Neelin, J. D., Krasting, J. P., Radhakrishnan, A., Liptak, J., Jackson, T. J., Ming, Y., Dong, W., Gettelman, A., Coleman, D., Maloney, E. D., Wing, A. A., Kuo, Y. H., Ahmed, F., Ullrich, P. A., Bitz, C. M., Neale, R., Ordonez, A., and Maroon, E.: Process-oriented diagnostics: principles, practice, community development and common standards, B. Am. Meteorol. Soc., 104, E1452–E1468, https://doi.org/10.1175/bams-d-21-0268.1, 2023.
Nowack, P., Runge, J., Eyring, V., and Haigh, J. D.: Causal networks for climate model evaluation and constrained projections, Nat. Commun., 11, 1415, https://doi.org/10.1038/s41467-020-15195-y, 2020.
Orbe, C., Van Roekel, L., Adames, Á. F., Dezfuli, A., Fasullo, J. T., Gleckler, P. J., Lee, J., Li, W., Nazarenko, L., Schmidt, G. A., Sperber, K. R., and Zhao, M.: Representation of modes of variability in six U.S. climate models, J. Climate, 33, 7591–7617, https://doi.org/10.1175/jcli-d-19-0956.1, 2020.
Ordonez, A. C., Klingaman, N. P., and Martin, G.: Analysing scales of precipitation, OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information), https://doi.org/10.11578/dc.20211029.5, 2021.
Papalexiou, S. M., Rajulapati, C. R., Clark, M. P., and Lehner, F.: Robustness of CMIP6 historical global mean temperature simulations: Trends, long-term persistence, autocorrelation, and distributional shape, Earth's Future, 8, e2020EF001667, https://doi.org/10.1029/2020EF001667, 2020.
Pascoe, C., Lawrence, B. N., Guilyardi, E., Juckes, M., and Taylor, K. E.: Documenting numerical experiments in support of the Coupled Model Intercomparison Project Phase 6 (CMIP6), Geosci. Model Dev., 13, 2149–2167, https://doi.org/10.5194/gmd-13-2149-2020, 2020.
PCMDI Simulation Summaries: https://pcmdi.llnl.gov/metrics/, last access: 8 May 2024.
Pendergrass, A. G., Gleckler, P. J., Leung, L. R., and Jakob, C.: Benchmarking simulated precipitation in earth system models, B. Am. Meteorol. Soc., 101, E814–E816, https://doi.org/10.1175/bams-d-19-0318.1, 2020.
Phillips, A. S., Deser, C., and Fasullo, J. T.: Evaluating modes of variability in climate models, Eos T. Am. Geophys. Un., 95, 453–455, https://doi.org/10.1002/2014eo490002, 2014.
Planton, Y., Guilyardi, E., Wittenberg, A. T., Lee, J., Gleckler, P. J., Bayr, T., McGregor, S., McPhaden, M. J., Power, S. B., Roehrig, R., Vialard, J., and Voldoire, A.: Evaluating Climate Models with the CLIVAR 2020 ENSO Metrics Package, B. Am. Meteorol. Soc., 102, E193–E217, https://doi.org/10.1175/bams-d-19-0337.1, 2021.
Planton, Y. Y., Lee, J., Wittenberg, A. T. Gleckler, P. J., Guilyardi, E., McGregor, S., and McPhaden, M. J.: Estimating uncertainty in simulated ENSO statistics, J. Adv. Model. Earth Sy., ESS Open Archive [preprint], https://doi.org10.22541/essoar.170196744.48068128/v1, 2023.
PMP Installation: http://pcmdi.github.io/pcmdi_metrics/install.html, last access: 8 May 2024.
Potter, G. L., Bader, D. C., Riches, M., Bamzai, A. and Joseph, R.: Celebrating two decades of the Program for Climate Model Diagnosis and Intercomparison, B. Am. Meteorol. Soc., 92, 629–631, https://doi.org/10.1175/2011BAMS3018.1, 2011.
Qin, Y., Zelinka, M. D., and Klein, S. A.: On the Correspondence Between Atmosphere-Only and Coupled Simulations for Radiative Feedbacks and Forcing From CO2, J. Geophys. Res.-Atmos., 127, e2021JD035460, https://doi.org/10.1029/2021jd035460, 2022.
Randall, D. A., Wood, R. A., Bony, S., Colman, R., Fichefet, T., Fyfe, J., Kattsov, V., Pitman, A., Shukla, J., Srinivasan, J., and Stouffer, R. J.: Climate models and their evaluation, in: Climate change 2007: The physical science basis. Contribution of Working Group I to the Fourth Assessment Report of the IPCC (FAR), Cambridge University Press, 589–662, ISBN 978-0-521-88009-1, 2007.
Rasch, P. J., Xie, S., Ma, P.-L., Lin, W., Wang, H., Tang, Q., Burrows, S. M., Caldwell, P., Zhang, K., Easter, R. C., Cameron-Smith, P. J., Singh, B., Wan, H., Golaz, J.-C., Harrop, B. E., Roesler, E. L., Bacmeister, J. T., Larson, V. E., Evans, K. J., Qian, Y., Taylor, M. A., Leung, L. R., Zhang, Y., Brent, L., Branstetter, M. L., Hannay, C., Mahajan, S., Mametjanov, A., Neale, R., Richter, J. H., Yoon, J.-H., Zender, C. S., Bader, D. C., Flanner, M., Foucar, J. G., Jacob, R., Keen, N. D., Klein, S. A., Liu, X., Salinger, A. G., Shrivastava, M., and Yang, Y.: An overview of the atmospheric component of the Energy Exascale Earth System model, J. Adv. Model. Earth Sy., 11, 2377–2411, https://doi.org/10.1029/2019ms001629, 2019.
Reed, K. A., Goldenson, N., Grotjahn, R., Gutowski, W. J., Jagannathan, K., Jones, A. D., Leung, L. R., McGinnis, S. A., Pryor, S. C., Srivastava, A. K., Ullrich, P. A., and Zarzycki, C. M.: Metrics as tools for bridging climate science and applications, WIREs Climate Change, 13, e799, https://doi.org/10.1002/wcc.799, 2022.
Reichler, T. and Kim, J.: How well do coupled models simulate today's climate?, B. Am. Meteorol. Soc., 89, 303–312, https://doi.org/10.1175/bams-89-3-303, 2008.
Righi, M., Andela, B., Eyring, V., Lauer, A., Predoi, V., Schlund, M., Vegas-Regidor, J., Bock, L., Brötz, B., de Mora, L., Diblen, F., Dreyer, L., Drost, N., Earnshaw, P., Hassler, B., Koldunov, N., Little, B., Loosveldt Tomas, S., and Zimmermann, K.: Earth System Model Evaluation Tool (ESMValTool) v2.0 – technical overview, Geosci. Model Dev., 13, 1179–1199, https://doi.org/10.5194/gmd-13-1179-2020, 2020.
Sanderson, B. M. and Wehner, M. F.: Weighting strategy for the Fourth National Climate Assessment, in: Climate Science Special Report: Fourth National Climate Assessment, Volume I, edited by: Wuebbles, D. J., Fahey, D. W., Hibbard, K. A., Dokken, D. J., Stewart, B. C., and Maycock, T. K., U.S. Global Change Research Program, Washington, DC, USA, 436–442, https://doi.org/10.7930/J06T0JS3, 2017.
Sanderson, B. M., Wehner, M., and Knutti, R.: Skill and independence weighting for multi-model assessments, Geosci. Model Dev., 10, 2379–2395, https://doi.org/10.5194/gmd-10-2379-2017, 2017.
Sherwood, S. C., Webb, M. J., Annan, J. D., Armour, K. C., Forster, P. M., Hargreaves, J. C., Hegerl, G. C., Klein, S. A., Marvel, K., Rohling, E. J., Watanabe, M., Andrews, T., Braconnot, P., Bretherton, C. S., Foster, G. L., Hausfather, Z., Von Der Heydt, A. S., Knutti, R., Mauritsen, T., Norris, J. R., Proistosescu, C., Rugenstein, M., Schmidt, G. A., Tokarska, K., and Zelinka, M. D.: An assessment of Earth's climate sensitivity using multiple lines of evidence, Rev. Geophys., 58, e2019RG000678, https://doi.org/10.1029/2019rg000678, 2020.
Singh, R., and AchutaRao, K.: Sensitivity of future climate change and uncertainty over India to performance-based model weighting, Climatic Change, 160, 385–406, https://doi.org/10.1007/s10584-019-02643-y, 2020.
Sillmann, J., Kharin, V. V., Zhang, X., Zwiers, F. W., and Bronaugh, D.: Climate extremes indices in the CMIP5 multimodel ensemble: Part 1. Model evaluation in the present climate, J. Geophys. Res.-Atmos., 118, 1716–1733, https://doi.org/10.1002/jgrd.50203, 2013.
Sperber, K. R.: Madden-Julian variability in NCAR CAM2.0 and CCSM2.0, Clim. Dynam., 23, 259–278, https://doi.org/10.1007/s00382-004-0447-4, 2004.
Sperber, K. R. and Annamalai, H.: The use of fractional accumulated precipitation for the evaluation of the annual cycle of monsoons, Clim. Dynam., 43, 3219–3244, https://doi.org/10.1007/s00382-014-2099-3, 2014.
Sperber, K. R., Annamalai, H., Kang, I.-S., Kitoh, A., Moise, A., Turner, A., Wang, B., and Zhou, T.: The Asian summer monsoon: an intercomparison of CMIP5 vs. CMIP3 simulation of the late 20th century, Clim. Dynam., 41, 2711–2744, https://doi.org/10.1007/s00382-012-1607-6, 2013.
Sperber, K. R., Gualdi, S., Legutke, S., and Gayler, V.: The Madden–Julian oscillation in ECHAM4 coupled and uncoupled general circulation models, Clim. Dynam., 25, 117–140, https://doi.org/10.1007/s00382-005-0026-3, 2005.
Srivastava, A., Grotjahn, R., and Ullrich, P. A.: Evaluation of historical CMIP6 model simulations of extreme precipitation over contiguous US regions, Weather Climate Extremes, 29, 100268, https://doi.org/10.1016/j.wace.2020.100268, 2020.
Steed, C. A., Shipman, G., Thornton, P., Ricciuto, D., Erickson, D. and Branstetter, M.: Practical application of parallel coordinates for climate model analysis, Procedia Comput. Sci., 9, 877-886, https://doi.org/10.1016/j.procs.2012.04.094, 2012.
Stevens, B., Satoh, M., Auger, L., Biercamp, J., Bretherton, C. S., Chen, X., Düben, P., Judt, F., Khairoutdinov, M., Klocke, D., Kodama, C., Kornblueh, L., Lin, S.-J., Neumann, P., Putman, W. M., Röber, N., Shibuya, R., Vanniere, B., Vidale, P. L., Wedi, N., and Zhou, L.: DYAMOND: the DYnamics of the Atmospheric general circulation Modeled On Non-hydrostatic Domains, Prog. Earth Planet. Sci., 6, 61, https://doi.org/10.1186/s40645-019-0304-z, 2019.
Stoner, A. M. K., Hayhoe, K., and Wuebbles, D. J.: Assessing general circulation model simulations of atmospheric teleconnection patterns, J. Climate, 22, 4348–4372, https://doi.org/10.1175/2009jcli2577.1, 2009.
Sung, H. M., Kim, J., Shim, S., Seo, J., Kwon, S.-H., Sun, M.-A., Moon, H.-J., Lee, J., Lim, Y. C., Boo, K.-O., Kim, Y., Lee, J., Lee, J., Kim, J.-S., Marzin, C., and Byun, Y.-H.: Climate change projection in the Twenty-First Century simulated by NIMS-KMA CMIP6 model based on new GHGs concentration pathways, Asia-Pac. J. Atmos. Sci., 57, 851–862, https://doi.org/10.1007/s13143-021-00225-6, 2021.
Tang, Q., Prather, M. J., Hsu, J., Ruiz, D. J., Cameron-Smith, P. J., Xie, S., and Golaz, J.-C.: Evaluation of the interactive stratospheric ozone (O3v2) module in the E3SM version 1 Earth system model, Geosci. Model Dev., 14, 1219–1236, https://doi.org/10.5194/gmd-14-1219-2021, 2021.
Tang, S., Fast, J. D., Zhang, K., Hardin, J. C., Varble, A. C., Shilling, J. E., Mei, F., Zawadowicz, M. A., and Ma, P.-L.: Earth System Model Aerosol–Cloud Diagnostics (ESMAC Diags) package, version 1: assessing E3SM aerosol predictions using aircraft, ship, and surface measurements, Geosci. Model Dev., 15, 4055–4076, https://doi.org/10.5194/gmd-15-4055-2022, 2022.
Tang, S., Varble, A. C., Fast, J. D., Zhang, K., Wu, P., Dong, X., Mei, F., Pekour, M., Hardin, J. C., and Ma, P.-L.: Earth System Model Aerosol–Cloud Diagnostics (ESMAC Diags) package, version 2: assessing aerosols, clouds, and aerosol–cloud interactions via field campaign and long-term observations, Geosci. Model Dev., 16, 6355–6376, https://doi.org/10.5194/gmd-16-6355-2023, 2023.
Taylor, K. E.: Summarizing multiple aspects of model performance in a single diagram, J. Geophys. Res., 106, 7183–7192, https://doi.org/10.1029/2000jd900719, 2001.
Taylor, K. E.: Truly conserving with conservative remapping methods, Geosci. Model Dev., 17, 415–430, https://doi.org/10.5194/gmd-17-415-2024, 2024.
Taylor, K. E., Stouffer, R. J., and Meehl, G. A.: An overview of CMIP5 and the experiment design, B. Am. Meteorol. Soc., 93, 485–498, https://doi.org/10.1175/bams-d-11-00094.1, 2012.
Teixeira, J., Waliser, D. E., Ferraro, R., Gleckler, P. J., Lee, T., and Potter, G. L.: Satellite observations for CMIP5: The Genesis of OBS4MIPs, B. Am. Meteorol. Soc., 95, 1329–1334, https://doi.org/10.1175/bams-d-12-00204.1, 2014.
Tian, B. and Dong, X.: The Double-ITCZ Bias in CMIP3, CMIP5, and CMIP6 Models Based on Annual Mean Precipitation, Geophys. Res. Lett., 47, e2020GL087232, https://doi.org/10.1029/2020GL087232, 2020.
Ullrich, P. A. and Zarzycki, C. M.: TempestExtremes: a framework for scale-insensitive pointwise feature tracking on unstructured grids, Geosci. Model Dev., 10, 1069–1090, https://doi.org/10.5194/gmd-10-1069-2017, 2017.
Ullrich, P. A., Zarzycki, C. M., McClenny, E. E., Pinheiro, M. C., Stansfield, A. M., and Reed, K. A.: TempestExtremes v2.1: a community framework for feature detection, tracking, and analysis in large datasets, Geosci. Model Dev., 14, 5023–5048, https://doi.org/10.5194/gmd-14-5023-2021, 2021.
U.S. Department of Energy (DOE): Benchmarking Simulated Precipitation in Earth System Models Workshop Report, DOE/SC-0203, U.S. Department of Energy Office of Science, Biological and Environmental Research (BER) Program. Germantown, Maryland, USA, 2020.
Vo, T., Po-Chedley, P., Boutte, J., Zhang, C., Lee, J., Gleckler, P., Durack, P., Taylor, K., and Golaz, J.-C.: Xarray Climate Data Analysis Tools (xCDAT): A Python Package for Simple and Robust Analysis of Climate Data, The 103rd AMS Annual Meeting, Abstract, 8–12 January, 2023, in Denver, Colorado, 11.3, 412648, 2023.
Waliser, D., Gleckler, P. J., Ferraro, R., Taylor, K. E., Ames, S., Biard, J., Bosilovich, M. G., Brown, O., Chepfer, H., Cinquini, L., Durack, P. J., Eyring, V., Mathieu, P.-P., Lee, T., Pinnock, S., Potter, G. L., Rixen, M., Saunders, R., Schulz, J., Thépaut, J.-N., and Tuma, M.: Observations for Model Intercomparison Project (Obs4MIPs): status for CMIP6, Geosci. Model Dev., 13, 2945–2958, https://doi.org/10.5194/gmd-13-2945-2020, 2020.
Waliser, D. E., Sperber, K. R., Hendon, H. H., Kim, D., Maloney, E. D., Wheeler, M. C., Weickmann, K. M., Zhang, C., Donner, L. J., Gottschalck, J., Higgins, W., Kang, I. S., Legler, D. M., Moncrieff, M. W., Schubert, S. D., Stern, W., Vitart, F., Wang, B., Wang, W., and Woolnough, S. J.: MJO Simulation Diagnostics, J. Climate, 22, 3006–3030, https://doi.org/10.1175/2008jcli2731.1, 2009.
Wang, J., Liu, X., Shen, H. W., and Lin, G.: Multi-resolution climate ensemble parameter analysis with nested parallel coordinates plots, IEEE T. Vis. Comput. G. R., 23, 81–90, https://doi.org/10.1109/TVCG.2016.2598830, 2017.
Wehner, M., Gleckler, P. J., and Lee, J.: Characterization of long period return values of extreme daily temperature and precipitation in the CMIP6 models: Part 1, model evaluation, Weather Climate Extremes, 30, 100283, https://doi.org/10.1016/j.wace.2020.100283, 2020.
Wehner, M., Lee, J., Risser, M. D., Ullrich, P. A., Gleckler, P. J., and Collins, W. D.: Evaluation of extreme sub-daily precipitation in high-resolution global climate model simulations, Philos. T. R. Soc. A., 379, 20190545, https://doi.org/10.1098/rsta.2019.0545, 2021.
Williams, D. N.: Visualization and analysis tools for ultrascale climate data, Eos T. Am. Geophys. Un., 95, 377–378, https://doi.org/10.1002/2014eo420002, 2014.
Williams, D. N., Doutriaux, C., Drach, R., and McCoy, R.: The Flexible Climate Data Analysis Tools (CDAT) for Multi-model Climate Simulation Data, IEEE International Conference on Data Mining Workshops, 254–261, https://doi.org/10.1109/icdmw.2009.64, 2009.
Williams, D. N., Balaji, V., Cinquini, L., Denvil, S., Duffy, D. Q., Evans, B., Ferraro, R., Hansen, R., Lautenschlager, M., and Trenham, C.: A global repository for Planet-Sized experiments and observations, B. Am. Meteorol. Soc., 97, 803–816, https://doi.org/10.1175/bams-d-15-00132.1, 2016.
Wong, P. C., Shen, H. W., Leung, R., Hagos, S., Lee, T. Y., Tong, X. and Lu, K.: Visual analytics of large-scale climate model data, in: 2014 IEEE 4th Symposium on Large Data Analysis and Visualization (LDAV), 85–92, https://doi.org/10.1109/LDAV.2014.7013208, 2014.
Xie, P., Joyce, R., Wu, S., Yoo, S. H., Yarosh, Y., Sun, F. and Lin, R.: Reprocessed, bias-corrected CMORPH global high-resolution precipitation estimates from 1998, J. Hydrometeorol., 18, 1617–1641, 2017.
Xue, Z. and Ullrich, P. A.: A Comprehensive Intermediate-Term Drought Evaluation System and Evaluation of Climate Data Products over the Conterminous United States, J. Hydrometeorol., 22, 2311–2337, https://doi.org/10.1175/jhm-d-20-0314.1, 2021.
Young, A. H., Knapp, K. R., Inamdar, A., Hankins, W., and Rossow, W. B.: The International Satellite Cloud Climatology Project H-Series climate data record product, Earth Syst. Sci. Data, 10, 583–593, https://doi.org/10.5194/essd-10-583-2018, 2018.
Zelinka, M. D., Myers, T. A., McCoy, D. T., Po-Chedley, S., Caldwell, P. M., Ceppi, P., Klein, S. A., and Taylor, K. E.: Causes of higher climate sensitivity in CMIP6 models, Geophys. Res. Lett., 47, e2019GL085782, https://doi.org/10.1029/2019GL085782, 2020.
Zelinka, M. D., Klein, S. A., Qin, Y., and Myers, T. A.: Evaluating climate models' cloud feedbacks against expert judgment, J. Geophys. Res.-Atmos., 127, e2021JD035198, https://doi.org/10.1029/2021jd035198, 2022.
Zhang, C. and Hendon, H. H.: Propagating and standing components of the intraseasonal oscillation in tropical convection, J. Atmos. Sci., 54, 741–752, https://doi.org/10.1175/1520-0469(1997)054, 1997.
Zhang, C., Xie, S., Klein, S. A., Ma, H. Y., Tang, S., Van Weverberg, K., Morcrette, C. J., and Petch, J.: CAUSES: Diagnosis of the summertime warm bias in CMIP5 climate models at the ARM Southern Great Plains site. J. Geophys. Res.-Atmos., 123, 2968–2992, https://doi.org/10.1002/2017JD027200, 2018.
Zhang, C., Xie, S., Tao, C., Tang, S., Emmenegger, T., Neelin, J. D., Schiro, K. A., Lin, W., and Shaheen, Z.: The ARM data-oriented metrics and diagnostics package for climate models: A new tool for evaluating climate models with field data, B. Am. Meteorol. Soc., 101, E1619-E1627, https://doi.org/10.1175/BAMS-D-19-0282.1, 2020.
Zhang, C., Golaz, J.-C., Forsyth, R., Vo, T., Xie, S., Shaheen, Z., Potter, G. L., Asay-Davis, X. S., Zender, C. S., Lin, W., Chen, C.-C., Terai, C. R., Mahajan, S., Zhou, T., Balaguru, K., Tang, Q., Tao, C., Zhang, Y., Emmenegger, T., Burrows, S., and Ullrich, P. A.: The E3SM Diagnostics Package (E3SM Diags v2.7): a Python-based diagnostics package for Earth system model evaluation, Geosci. Model Dev., 15, 9031–9056, https://doi.org/10.5194/gmd-15-9031-2022, 2022.
Zhao, B., Lin, P., Hu, A., Liu, H., Ding, M., Yu, Z., and Yu, Y.: Uncertainty in Atlantic Multidecadal Oscillation derived from different observed datasets and their possible causes, Front. Mar. Sci., 9, 1007646, https://doi.org/10.3389/fmars.2022.1007646, 2022.
Zhao, M., Golaz, J.-C., Held, I. M., Guo, H., Balaji, V., Benson, R., Chen, J. H., Chen, X., Donner, L. J., Dunne, J., Dunne, K. A., Durachta, J., Fan, S.-M., Freidenreich, S. M., Garner, S. T., Ginoux, P., Harris, L., Horowitz, L. W., Krasting, J. P., Langenhorst, A. R., Zhi, L., Lin, P., Lin, S. J., Malyshev, S., Mason, E., Milly, P. C. D., Ming, Y., Naik, V., Paulot, F., Paynter, D., Phillipps, P. J., Radhakrishnan, A., Ramaswamy, V., Robinson, T., Schwarzkopf, D., Seman, C. J., Shevliakova, E., Shen, Z., Shin, H. H., Silvers, L. G., Wilson, J. R., Winton, M., Wittenberg, A. T., Wyman, B., and Xiang, B.: The GFDL Global Atmosphere and Land Model AM4.0/LM4.0: 1. Simulation characteristics with prescribed SSTS, J. Adv. Model. Earth Sy., 10, 691–734, https://doi.org/10.1002/2017ms001208, 2018.
Short summary
We introduce an open-source software, the PCMDI Metrics Package (PMP), developed for a comprehensive comparison of Earth system models (ESMs) with real-world observations. Using diverse metrics evaluating climatology, variability, and extremes simulated in thousands of simulations from the Coupled Model Intercomparison Project (CMIP), PMP aids in benchmarking model improvements across generations. PMP also enables efficient tracking of performance evolutions during ESM developments.
We introduce an open-source software, the PCMDI Metrics Package (PMP), developed for a...
