Articles | Volume 18, issue 3
https://doi.org/10.5194/gmd-18-787-2025
© Author(s) 2025. 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-18-787-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Review and perspective paper
| Highlight paper
| 
11 Feb 2025
Review and perspective paper | Highlight paper |
| 11 Feb 2025
| 11 Feb 2025
Moving beyond post hoc explainable artificial intelligence: a perspective paper on lessons learned from dynamical climate modeling
Ryan J. O'Loughlin
CORRESPONDING AUTHOR
Philosophy Department, Queens College, City University of New York, New York, NY 11367, USA
Department of Philosophy, Baruch College, City University of New York, New York, NY 10010, USA
Richard Neale
National Center for Atmospheric Research, Boulder, CO 80305, USA
Travis A. O'Brien
Department of Earth and Atmospheric Sciences, Indiana University, Bloomington, IN 47405, USA
Lawrence Berkeley Lab Climate and Ecosystem Sciences Division, Berkeley, CA 94720, USA
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Ankur Mahesh, William Collins, Boris Bonev, Noah Brenowitz, Yair Cohen, Joshua Elms, Peter Harrington, Karthik Kashinath, Thorsten Kurth, Joshua North, Travis O'Brien, Michael Pritchard, David Pruitt, Mark Risser, Shashank Subramanian, and Jared Willard
EGUsphere, https://doi.org/10.48550/arXiv.2408.03100, https://doi.org/10.48550/arXiv.2408.03100, 2024
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Simulating extreme weather events in a warming world is a challenging task for current weather and climate models. These models' computational cost poses a challenge in studying low-probability extreme weather. We use machine learning to construct a new probabilistic system. We explain in-depth how we constructed this system. We present a thorough pipeline to validate our method. Our method requires fewer computational resources than existing weather and climate models.
Ankur Mahesh, William Collins, Boris Bonev, Noah Brenowitz, Yair Cohen, Peter Harrington, Karthik Kashinath, Thorsten Kurth, Joshua North, Travis A. O'Brien, Michael Pritchard, David Pruitt, Mark Risser, Shashank Subramanian, and Jared Willard
EGUsphere, https://doi.org/10.48550/arXiv.2408.01581, https://doi.org/10.48550/arXiv.2408.01581, 2024
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We use machine learning to create a massive database of simulated weather extremes. This database provides a large sample size, which is essential to characterize the statistics of extreme weather events and study their physical mechanisms. Also, such large simulations can be beneficial to accurately forecast the probability of low-likelihood extreme weather.
Bo Dong, Paul Ullrich, Jiwoo Lee, Peter Gleckler, Kristin Chang, and Travis O'Brien
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-142, https://doi.org/10.5194/gmd-2024-142, 2024
Revised manuscript accepted for GMD
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1. A metrics package designed for easy analysis of AR characteristics and statistics is presented. 2. The tool is efficient for diagnosing systematic AR bias in climate models, and useful for evaluating new AR characteristics in model simulations. 3. 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).
Ankur Mahesh, Travis A. O'Brien, Burlen Loring, Abdelrahman Elbashandy, William Boos, and William D. Collins
Geosci. Model Dev., 17, 3533–3557, https://doi.org/10.5194/gmd-17-3533-2024, https://doi.org/10.5194/gmd-17-3533-2024, 2024
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Atmospheric rivers (ARs) are extreme weather events that can alleviate drought or cause billions of US dollars in flood damage. We train convolutional neural networks (CNNs) to detect ARs with an estimate of the uncertainty. We present a framework to generalize these CNNs to a variety of datasets of past, present, and future climate. Using a simplified simulation of the Earth's atmosphere, we validate the CNNs. We explore the role of ARs in maintaining energy balance in the Earth system.
Arjun Babu Nellikkattil, Danielle Lemmon, Travis Allen O'Brien, June-Yi Lee, and Jung-Eun Chu
Geosci. Model Dev., 17, 301–320, https://doi.org/10.5194/gmd-17-301-2024, https://doi.org/10.5194/gmd-17-301-2024, 2024
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This study introduces a new computational framework called Scalable Feature Extraction and Tracking (SCAFET), designed to extract and track features in climate data. SCAFET stands out by using innovative shape-based metrics to identify features without relying on preconceived assumptions about the climate model or mean state. This approach allows more accurate comparisons between different models and scenarios.
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.
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.
Travis A. O'Brien, Mark D. Risser, Burlen Loring, Abdelrahman A. Elbashandy, Harinarayan Krishnan, Jeffrey Johnson, Christina M. Patricola, John P. O'Brien, Ankur Mahesh, Prabhat, Sarahí Arriaga Ramirez, Alan M. Rhoades, Alexander Charn, Héctor Inda Díaz, and William D. Collins
Geosci. Model Dev., 13, 6131–6148, https://doi.org/10.5194/gmd-13-6131-2020, https://doi.org/10.5194/gmd-13-6131-2020, 2020
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Researchers utilize various algorithms to identify extreme weather features in climate data, and we seek to answer this question: given a
plausibleweather event detector, how does uncertainty in the detector impact scientific results? We generate a suite of statistical models that emulate expert identification of weather features. We find that the connection between El Niño and atmospheric rivers – a specific extreme weather type – depends systematically on the design of the detector.
Qi Tang, Stephen A. Klein, Shaocheng Xie, Wuyin Lin, Jean-Christophe Golaz, Erika L. Roesler, Mark A. Taylor, Philip J. Rasch, David C. Bader, Larry K. Berg, Peter Caldwell, Scott E. Giangrande, Richard B. Neale, Yun Qian, Laura D. Riihimaki, Charles S. Zender, Yuying Zhang, and Xue Zheng
Geosci. Model Dev., 12, 2679–2706, https://doi.org/10.5194/gmd-12-2679-2019, https://doi.org/10.5194/gmd-12-2679-2019, 2019
Peter A. Bogenschutz, Andrew Gettelman, Cecile Hannay, Vincent E. Larson, Richard B. Neale, Cheryl Craig, and Chih-Chieh Chen
Geosci. Model Dev., 11, 235–255, https://doi.org/10.5194/gmd-11-235-2018, https://doi.org/10.5194/gmd-11-235-2018, 2018
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This paper compares results of developmental versions of a widely used climate model. The simulations only differ in the choice of how to model the sub-grid-scale physics in the atmospheric model. This work is novel because it is the first time that a particular physics option has been tested in a fully coupled climate model. Here, we demonstrate that this physics option has the ability to produce credible coupled climate simulations, with improved metrics in certain fields.
Gavin A. Schmidt, David Bader, Leo J. Donner, Gregory S. Elsaesser, Jean-Christophe Golaz, Cecile Hannay, Andrea Molod, Richard B. Neale, and Suranjana Saha
Geosci. Model Dev., 10, 3207–3223, https://doi.org/10.5194/gmd-10-3207-2017, https://doi.org/10.5194/gmd-10-3207-2017, 2017
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The development of coupled ocean atmosphere climate models is a complex process that inevitably includes multiple calibration steps (sometimes called
tuning). Tuning uses degrees of freedom allowed by uncertainties in model approximations to modify parameters to make the simulation better align with some selected observed target(s). We describe how these tuning targets, parameters, and philosophy vary across six US modeling centers in order to increase the transparency of the practice.
Leo J. Donner, Travis A. O'Brien, Daniel Rieger, Bernhard Vogel, and William F. Cooke
Atmos. Chem. Phys., 16, 12983–12992, https://doi.org/10.5194/acp-16-12983-2016, https://doi.org/10.5194/acp-16-12983-2016, 2016
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Uncertainties in both climate forcing and sensitivity limit the extent to which climate projections can meet society's needs for actionable climate science. Advances in observing and modeling atmospheric vertical velocities provide a potential breakthrough in understanding climate forcing and sensitivity, with concurrent reductions in uncertainty.
A. H. Baker, D. M. Hammerling, M. N. Levy, H. Xu, J. M. Dennis, B. E. Eaton, J. Edwards, C. Hannay, S. A. Mickelson, R. B. Neale, D. Nychka, J. Shollenberger, J. Tribbia, M. Vertenstein, and D. Williamson
Geosci. Model Dev., 8, 2829–2840, https://doi.org/10.5194/gmd-8-2829-2015, https://doi.org/10.5194/gmd-8-2829-2015, 2015
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Climate simulation codes are especially complex, and their ongoing state of development requires frequent software quality assurance to both
preserve code quality and instil model confidence. To formalize and simplify this previously subjective and expensive process, we
have developed a new tool for evaluating climate consistency.
The tool has proven its utility in detecting errors in software and hardware
environments and providing rapid feedback to model developers.
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EGUsphere, https://doi.org/10.48550/arXiv.2406.13627, https://doi.org/10.48550/arXiv.2406.13627, 2024
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Spatial proxies, such as coordinates and distances, are often used as predictors in random forest models for predictive mapping. In a simulation and two case studies, we investigated the conditions under which their use is appropriate. We found that spatial proxies are not always beneficial and should not be used as a default approach without careful consideration. We also provide insights into the reasons behind their suitability, how to detect them, and potential alternatives.
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Geosci. Model Dev., 17, 5939–5959, https://doi.org/10.5194/gmd-17-5939-2024, https://doi.org/10.5194/gmd-17-5939-2024, 2024
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With ERA5 hourly data, we show spatiotemporal characteristics of pressure and zenith wet delay (ZWD) and propose an empirical global pressure and ZWD grid model with a broader operating space which can provide accurate pressure, ZWD, zenith hydrostatic delay, and zenith tropospheric delay estimates for any selected time and location over globe. IGPZWD will be of great significance for the tropospheric augmentation in real-time GNSS positioning and atmospheric water vapor remote sensing.
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Geosci. Model Dev., 17, 5897–5912, https://doi.org/10.5194/gmd-17-5897-2024, https://doi.org/10.5194/gmd-17-5897-2024, 2024
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Estimation of map accuracy based on cross-validation (CV) in spatial modelling is pervasive but controversial. Here, we build upon our previous work and propose a novel, prediction-oriented k-fold CV strategy for map accuracy estimation in which the distribution of geographical distances between prediction and training points is taken into account when constructing the CV folds. Our method produces more reliable estimates than other CV methods and can be used for large datasets.
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Geosci. Model Dev., 17, 4077–4094, https://doi.org/10.5194/gmd-17-4077-2024, https://doi.org/10.5194/gmd-17-4077-2024, 2024
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Lagrangian particle dispersion models are key for studying atmospheric transport but can be computationally intensive. To speed up simulations, the MPTRAC model was ported to graphics processing units (GPUs). Performance optimization of data structures and memory alignment resulted in runtime improvements of up to 75 % on NVIDIA A100 GPUs for ERA5-based simulations with 100 million particles. These optimizations make the MPTRAC model well suited for future high-performance computing systems.
Mohamad Hakam Shams Eddin and Juergen Gall
Geosci. Model Dev., 17, 2987–3023, https://doi.org/10.5194/gmd-17-2987-2024, https://doi.org/10.5194/gmd-17-2987-2024, 2024
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Geosci. Model Dev., 17, 2325–2345, https://doi.org/10.5194/gmd-17-2325-2024, https://doi.org/10.5194/gmd-17-2325-2024, 2024
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We present a major release of the Tomofast-x open-source gravity and magnetic inversion code that is enhancing its performance and applicability for both industrial and academic studies. We focus on real-world mineral exploration scenarios, while offering flexibility for applications at regional scale or for crustal studies. The optimisation work described in this paper is fundamental to allowing more complete descriptions of the controls on magnetisation, including remanence.
Jonathan Hobbs, Matthias Katzfuss, Hai Nguyen, Vineet Yadav, and Junjie Liu
Geosci. Model Dev., 17, 1133–1151, https://doi.org/10.5194/gmd-17-1133-2024, https://doi.org/10.5194/gmd-17-1133-2024, 2024
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The cycling of carbon among the land, oceans, and atmosphere is a closely monitored process in the global climate system. These exchanges between the atmosphere and the surface can be quantified using a combination of atmospheric carbon dioxide observations and computer models. This study presents a statistical method for investigating the similarities and differences in the estimated surface–atmosphere carbon exchange when different computer model assumptions are invoked.
Jiateng Guo, Zhibin Liu, Xulei Wang, Lixin Wu, Shanjun Liu, and Yunqiang Li
Geosci. Model Dev., 17, 847–864, https://doi.org/10.5194/gmd-17-847-2024, https://doi.org/10.5194/gmd-17-847-2024, 2024
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This study proposes a 3D and temporally dynamic (4D) geological modeling method. Several simulation and actual cases show that the 4D spatial and temporal evolution of regional geological formations can be modeled easily using this method with smooth boundaries. The 4D modeling system can dynamically present the regional geological evolution process under the timeline, which will be helpful to the research and teaching on the formation of typical and complex geological features.
Catherine O. de Burgh-Day and Tennessee Leeuwenburg
Geosci. Model Dev., 16, 6433–6477, https://doi.org/10.5194/gmd-16-6433-2023, https://doi.org/10.5194/gmd-16-6433-2023, 2023
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Machine learning (ML) is an increasingly popular tool in the field of weather and climate modelling. While ML has been used in this space for a long time, it is only recently that ML approaches have become competitive with more traditional methods. In this review, we have summarized the use of ML in weather and climate modelling over time; provided an overview of key ML concepts, methodologies, and terms; and suggested promising avenues for further research.
Danica L. Lombardozzi, William R. Wieder, Negin Sobhani, Gordon B. Bonan, David Durden, Dawn Lenz, Michael SanClements, Samantha Weintraub-Leff, Edward Ayres, Christopher R. Florian, Kyla Dahlin, Sanjiv Kumar, Abigail L. S. Swann, Claire M. Zarakas, Charles Vardeman, and Valerio Pascucci
Geosci. Model Dev., 16, 5979–6000, https://doi.org/10.5194/gmd-16-5979-2023, https://doi.org/10.5194/gmd-16-5979-2023, 2023
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We present a novel cyberinfrastructure system that uses National Ecological Observatory Network measurements to run Community Terrestrial System Model point simulations in a containerized system. The simple interface and tutorials expand access to data and models used in Earth system research by removing technical barriers and facilitating research, educational opportunities, and community engagement. The NCAR–NEON system enables convergence of climate and ecological sciences.
Qianqian Han, Yijian Zeng, Lijie Zhang, Calimanut-Ionut Cira, Egor Prikaziuk, Ting Duan, Chao Wang, Brigitta Szabó, Salvatore Manfreda, Ruodan Zhuang, and Bob Su
Geosci. Model Dev., 16, 5825–5845, https://doi.org/10.5194/gmd-16-5825-2023, https://doi.org/10.5194/gmd-16-5825-2023, 2023
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Using machine learning, we estimated global surface soil moisture (SSM) to aid in understanding water, energy, and carbon exchange. Ensemble models outperformed individual algorithms in predicting SSM under different climates. The best-performing ensemble included K-neighbours Regressor, Random Forest Regressor, and Extreme Gradient Boosting. This is important for hydrological and climatological applications such as water cycle monitoring, irrigation management, and crop yield prediction.
Xiaoyi Shao, Siyuan Ma, and Chong Xu
Geosci. Model Dev., 16, 5113–5129, https://doi.org/10.5194/gmd-16-5113-2023, https://doi.org/10.5194/gmd-16-5113-2023, 2023
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Scientific understandings of the distribution of coseismic landslides, followed by emergency and medium- and long-term risk assessment, can reduce landslide risk. The aim of this study is to propose an improved three-stage spatial prediction strategy and develop corresponding hazard assessment software called Mat.LShazard V1.0, which provides a new application tool for coseismic landslide disaster prevention and mitigation in different stages.
Junda Zhan, Sensen Wu, Jin Qi, Jindi Zeng, Mengjiao Qin, Yuanyuan Wang, and Zhenhong Du
Geosci. Model Dev., 16, 2777–2794, https://doi.org/10.5194/gmd-16-2777-2023, https://doi.org/10.5194/gmd-16-2777-2023, 2023
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We develop a generalized spatial autoregressive neural network model used for three-dimensional spatial interpolation. Taking the different changing trend of geographic elements along various directions into consideration, the model defines spatial distance in a generalized way and integrates it into the process of spatial interpolation with the theories of spatial autoregression and neural network. Compared with traditional methods, the model achieves better performance and is more adaptable.
Dominikus Heinzeller, Ligia Bernardet, Grant Firl, Man Zhang, Xia Sun, and Michael Ek
Geosci. Model Dev., 16, 2235–2259, https://doi.org/10.5194/gmd-16-2235-2023, https://doi.org/10.5194/gmd-16-2235-2023, 2023
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The Common Community Physics Package is a collection of physical atmospheric parameterizations for use in Earth system models and a framework that couples the physics to a host model’s dynamical core. A primary goal for this effort is to facilitate research and development of physical parameterizations and physics–dynamics coupling methods while offering capabilities for numerical weather prediction operations, for example in the upcoming implementation of the Global Forecast System (GFS) v17.
Tobias Tesch, Stefan Kollet, and Jochen Garcke
Geosci. Model Dev., 16, 2149–2166, https://doi.org/10.5194/gmd-16-2149-2023, https://doi.org/10.5194/gmd-16-2149-2023, 2023
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A recent statistical approach for studying relations in the Earth system is to train deep learning (DL) models to predict Earth system variables given one or several others and use interpretable DL to analyze the relations learned by the models. Here, we propose to combine the approach with a theorem from causality research to ensure that the deep learning model learns causal rather than spurious relations. As an example, we apply the method to study soil-moisture–precipitation coupling.
Yao Hu, Chirantan Ghosh, and Siamak Malakpour-Estalaki
Geosci. Model Dev., 16, 1925–1936, https://doi.org/10.5194/gmd-16-1925-2023, https://doi.org/10.5194/gmd-16-1925-2023, 2023
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Data-driven models (DDMs) gain popularity in earth and environmental systems, thanks in large part to advancements in data collection techniques and artificial intelligence (AI). The performance of these models is determined by the underlying machine learning (ML) algorithms. In this study, we develop a framework to improve the model performance by optimizing ML algorithms and demonstrate the effectiveness of the framework using a DDM to predict edge-of-field runoff in the Maumee domain, USA.
Ruidong Li, Ting Sun, Fuqiang Tian, and Guang-Heng Ni
Geosci. Model Dev., 16, 751–778, https://doi.org/10.5194/gmd-16-751-2023, https://doi.org/10.5194/gmd-16-751-2023, 2023
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We developed SHAFTS (Simultaneous building Height And FootprinT extraction from Sentinel imagery), a multi-task deep-learning-based Python package, to estimate average building height and footprint from Sentinel imagery. Evaluation in 46 cities worldwide shows that SHAFTS achieves significant improvement over existing machine-learning-based methods.
Feng Yin, Philip E. Lewis, and Jose L. Gómez-Dans
Geosci. Model Dev., 15, 7933–7976, https://doi.org/10.5194/gmd-15-7933-2022, https://doi.org/10.5194/gmd-15-7933-2022, 2022
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The proposed SIAC atmospheric correction method provides consistent surface reflectance estimations from medium spatial-resolution satellites (Sentinel 2 and Landsat 8) with per-pixel uncertainty information. The outputs from SIAC have been validated against a wide range of ground measurements, and it shows that SIAC can provide accurate estimations of both surface reflectance and atmospheric parameters, with meaningful uncertainty information.
Martina Stockhause and Michael Lautenschlager
Geosci. Model Dev., 15, 6047–6058, https://doi.org/10.5194/gmd-15-6047-2022, https://doi.org/10.5194/gmd-15-6047-2022, 2022
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The Data Distribution Centre (DDC) of the Intergovernmental Panel on Climate Change (IPCC) celebrates its 25th anniversary in 2022. DDC Partner DKRZ has supported the IPCC Assessments and preserved the quality-assured, citable climate model data underpinning the Assessment Reports over these years over the long term. With the introduction of the IPCC FAIR Guidelines into the current AR6, the value of DDC services has been recognized. However, DDC sustainability remains unresolved.
Daiane Iglesia Dolci, Felipe A. G. Silva, Pedro S. Peixoto, and Ernani V. Volpe
Geosci. Model Dev., 15, 5857–5881, https://doi.org/10.5194/gmd-15-5857-2022, https://doi.org/10.5194/gmd-15-5857-2022, 2022
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We investigate and compare the theoretical and computational characteristics of several absorbing boundary conditions (ABCs) for the full-waveform inversion (FWI) problem. The different ABCs are implemented in an optimized computational framework called Devito. The computational efficiency and memory requirements of the ABC methods are evaluated in the forward and adjoint wave propagators, from simple to realistic velocity models.
Mauro Rossi, Txomin Bornaetxea, and Paola Reichenbach
Geosci. Model Dev., 15, 5651–5666, https://doi.org/10.5194/gmd-15-5651-2022, https://doi.org/10.5194/gmd-15-5651-2022, 2022
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LAND-SUITE is a software package designed to support landslide susceptibility zonation. The software integrates, extends, and completes LAND-SE (Rossi et al., 2010; Rossi and Reichenbach, 2016). The software is implemented in R, a free software environment for statistical computing and graphics, and gives expert users the possibility to perform easier, more flexible, and more informed statistically based landslide susceptibility applications and zonations.
Ashesh Chattopadhyay, Mustafa Mustafa, Pedram Hassanzadeh, Eviatar Bach, and Karthik Kashinath
Geosci. Model Dev., 15, 2221–2237, https://doi.org/10.5194/gmd-15-2221-2022, https://doi.org/10.5194/gmd-15-2221-2022, 2022
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There is growing interest in data-driven weather forecasting, i.e., to predict the weather by using a deep neural network that learns from the evolution of past atmospheric patterns. Here, we propose three components to add to the current data-driven weather forecast models to improve their performance. These components involve a feature that incorporates physics into the neural network, a method to add data assimilation, and an algorithm to use several different time intervals in the forecast.
Paul F. Baumeister and Lars Hoffmann
Geosci. Model Dev., 15, 1855–1874, https://doi.org/10.5194/gmd-15-1855-2022, https://doi.org/10.5194/gmd-15-1855-2022, 2022
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The efficiency of the numerical simulation of radiative transport is shown on modern server-class graphics cards (GPUs). The low-cost prefactor on GPUs compared to general-purpose processors (CPUs) enables future large retrieval campaigns for multi-channel data from infrared sounders aboard low-orbit satellites. The validated research software JURASSIC is available in the public domain.
Gregory E. Tucker, Eric W. H. Hutton, Mark D. Piper, Benjamin Campforts, Tian Gan, Katherine R. Barnhart, Albert J. Kettner, Irina Overeem, Scott D. Peckham, Lynn McCready, and Jaia Syvitski
Geosci. Model Dev., 15, 1413–1439, https://doi.org/10.5194/gmd-15-1413-2022, https://doi.org/10.5194/gmd-15-1413-2022, 2022
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Scientists use computer simulation models to understand how Earth surface processes work, including floods, landslides, soil erosion, river channel migration, ocean sedimentation, and coastal change. Research benefits when the software for simulation modeling is open, shared, and coordinated. The Community Surface Dynamics Modeling System (CSDMS) is a US-based facility that supports research by providing community support, computing tools and guidelines, and educational resources.
Danilo César de Mello, Gustavo Vieira Veloso, Marcos Guedes de Lana, Fellipe Alcantara de Oliveira Mello, Raul Roberto Poppiel, Diego Ribeiro Oquendo Cabrero, Luis Augusto Di Loreto Di Raimo, Carlos Ernesto Gonçalves Reynaud Schaefer, Elpídio Inácio Fernandes Filho, Emilson Pereira Leite, and José Alexandre Melo Demattê
Geosci. Model Dev., 15, 1219–1246, https://doi.org/10.5194/gmd-15-1219-2022, https://doi.org/10.5194/gmd-15-1219-2022, 2022
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We used soil parent material, terrain attributes, and geophysical data from the soil surface to test and compare different and unprecedented geophysical sensor combination, as well as different machine learning algorithms to model and predict several soil attributes. Also, we analyzed the importance of pedoenvironmental variables. The soil attributes were modeled throughout different machine learning algorithms and related to different geophysical sensor combinations.
Duncan Watson-Parris, Andrew Williams, Lucia Deaconu, and Philip Stier
Geosci. Model Dev., 14, 7659–7672, https://doi.org/10.5194/gmd-14-7659-2021, https://doi.org/10.5194/gmd-14-7659-2021, 2021
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The Earth System Emulator (ESEm) provides a fast and flexible framework for emulating a wide variety of Earth science datasets and tools for constraining (or tuning) models of any complexity. Three distinct use cases are presented that demonstrate the utility of ESEm and provide some insight into the use of machine learning for emulation in these different settings. The open-source Python package is freely available so that it might become a valuable tool for the community.
Chongyang Wang, Li Wang, Danni Wang, Dan Li, Chenghu Zhou, Hao Jiang, Qiong Zheng, Shuisen Chen, Kai Jia, Yangxiaoyue Liu, Ji Yang, Xia Zhou, and Yong Li
Geosci. Model Dev., 14, 6833–6846, https://doi.org/10.5194/gmd-14-6833-2021, https://doi.org/10.5194/gmd-14-6833-2021, 2021
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The turbidity maximum zone (TMZ) is a special phenomenon in estuaries worldwide. However, the extraction methods and criteria used to describe the TMZ vary significantly both spatially and temporally. This study proposes an new index, the turbidity maximum zone index, based on the corresponding relationship of total suspended solid concentration and Chl a concentration, which could better extract TMZs in different estuaries and on different dates.
Ranee Joshi, Kavitha Madaiah, Mark Jessell, Mark Lindsay, and Guillaume Pirot
Geosci. Model Dev., 14, 6711–6740, https://doi.org/10.5194/gmd-14-6711-2021, https://doi.org/10.5194/gmd-14-6711-2021, 2021
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We have developed a software that allows the user to extract and standardize drill hole information from legacy datasets and/or different drilling campaigns. It also provides functionality to upscale the lithological information. These functionalities were possible by developing thesauri to identify and group geological terminologies together.
David Meyer, Thomas Nagler, and Robin J. Hogan
Geosci. Model Dev., 14, 5205–5215, https://doi.org/10.5194/gmd-14-5205-2021, https://doi.org/10.5194/gmd-14-5205-2021, 2021
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A major limitation in training machine-learning emulators is often caused by the lack of data. This paper presents a cheap way to increase the size of training datasets using statistical techniques and thereby improve the performance of machine-learning emulators.
Mark Jessell, Vitaliy Ogarko, Yohan de Rose, Mark Lindsay, Ranee Joshi, Agnieszka Piechocka, Lachlan Grose, Miguel de la Varga, Laurent Ailleres, and Guillaume Pirot
Geosci. Model Dev., 14, 5063–5092, https://doi.org/10.5194/gmd-14-5063-2021, https://doi.org/10.5194/gmd-14-5063-2021, 2021
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We have developed software that allows the user to extract sufficient information from unmodified digital maps and associated datasets that we are able to use to automatically build 3D geological models. By automating the process we are able to remove human bias from the procedure, which makes the workflow reproducible.
Martí Bosch, Maxence Locatelli, Perrine Hamel, Roy P. Remme, Jérôme Chenal, and Stéphane Joost
Geosci. Model Dev., 14, 3521–3537, https://doi.org/10.5194/gmd-14-3521-2021, https://doi.org/10.5194/gmd-14-3521-2021, 2021
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The article presents a novel approach to simulate urban heat mitigation from land use/land cover data based on three biophysical mechanisms: tree shade, evapotranspiration and albedo. An automated procedure is proposed to calibrate the model parameters to best fit temperature observations from monitoring stations. A case study in Lausanne, Switzerland, shows that the approach outperforms regressions based on satellite data and provides valuable insights into design heat mitigation policies.
Quang-Van Doan, Hiroyuki Kusaka, Takuto Sato, and Fei Chen
Geosci. Model Dev., 14, 2097–2111, https://doi.org/10.5194/gmd-14-2097-2021, https://doi.org/10.5194/gmd-14-2097-2021, 2021
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This study proposes a novel structural self-organizing map (S-SOM) algorithm. The superiority of S-SOM is that it can better recognize the difference (or similarity) among spatial (or temporal) data used for training and thus improve the clustering quality compared to traditional SOM algorithms.
Batunacun, Ralf Wieland, Tobia Lakes, and Claas Nendel
Geosci. Model Dev., 14, 1493–1510, https://doi.org/10.5194/gmd-14-1493-2021, https://doi.org/10.5194/gmd-14-1493-2021, 2021
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Extreme gradient boosting (XGBoost) can provide alternative insights that conventional land-use models are unable to generate. Shapley additive explanations (SHAP) can interpret the results of the purely data-driven approach. XGBoost achieved similar and robust simulation results. SHAP values were useful for analysing the complex relationship between the different drivers of grassland degradation.
Juan A. Añel, Michael García-Rodríguez, and Javier Rodeiro
Geosci. Model Dev., 14, 923–934, https://doi.org/10.5194/gmd-14-923-2021, https://doi.org/10.5194/gmd-14-923-2021, 2021
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This work shows that it continues to be hard, if not impossible, to obtain some of the most used climate models worldwide. We reach this conclusion through a systematic study and encourage all development teams and research centres to make public the models they use to produce scientific results.
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.
Xiang Que, Xiaogang Ma, Chao Ma, and Qiyu Chen
Geosci. Model Dev., 13, 6149–6164, https://doi.org/10.5194/gmd-13-6149-2020, https://doi.org/10.5194/gmd-13-6149-2020, 2020
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This paper presents a spatiotemporal weighted regression (STWR) model for exploring nonstationary spatiotemporal processes in nature and socioeconomics. A value change rate is introduced in the temporal kernel, which presents significant model fitting and accuracy in both simulated and real-world data. STWR fully incorporates observed data in the past and outperforms geographic temporal weighted regression (GTWR) and geographic weighted regression (GWR) models in several experiments.
Sheri Mickelson, Alice Bertini, Gary Strand, Kevin Paul, Eric Nienhouse, John Dennis, and Mariana Vertenstein
Geosci. Model Dev., 13, 5567–5581, https://doi.org/10.5194/gmd-13-5567-2020, https://doi.org/10.5194/gmd-13-5567-2020, 2020
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Every generation of MIP exercises introduces new layers of complexity and an exponential growth in the amount of data requested. CMIP6 required us to develop a new tool chain and forced us to change our methodologies. The new methods discussed in this paper provided us with an 18 times faster speedup over our existing methods. This allowed us to meet our deadlines and we were able to publish more than half a million data sets on the Earth System Grid Federation (ESGF) for the CMIP6 project.
Benjamin Campforts, Charles M. Shobe, Philippe Steer, Matthias Vanmaercke, Dimitri Lague, and Jean Braun
Geosci. Model Dev., 13, 3863–3886, https://doi.org/10.5194/gmd-13-3863-2020, https://doi.org/10.5194/gmd-13-3863-2020, 2020
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Landslides shape the Earth’s surface and are a dominant source of terrestrial sediment. Rivers, then, act as conveyor belts evacuating landslide-produced sediment. Understanding the interaction among rivers and landslides is important to predict the Earth’s surface response to past and future environmental changes and for mitigating natural hazards. We develop HyLands, a new numerical model that provides a toolbox to explore how landslides and rivers interact over several timescales.
Jorge Vicent, Jochem Verrelst, Neus Sabater, Luis Alonso, Juan Pablo Rivera-Caicedo, Luca Martino, Jordi Muñoz-Marí, and José Moreno
Geosci. Model Dev., 13, 1945–1957, https://doi.org/10.5194/gmd-13-1945-2020, https://doi.org/10.5194/gmd-13-1945-2020, 2020
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The modeling of light propagation through the atmosphere is key to process satellite images and to understand atmospheric processes. However, existing atmospheric models can be complex to use in practical applications. Here we aim at providing a new software tool to facilitate using advanced models and to generate large databases of simulated data. As a test case, we use this tool to analyze differences between several atmospheric models, showing the capabilities of this open-source tool.
Jiali Wang, Prasanna Balaprakash, and Rao Kotamarthi
Geosci. Model Dev., 12, 4261–4274, https://doi.org/10.5194/gmd-12-4261-2019, https://doi.org/10.5194/gmd-12-4261-2019, 2019
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Parameterizations are frequently used in models representing physical phenomena and are often the computationally expensive portions of the code. Using model output from simulations performed using a weather model, we train deep neural networks to provide an accurate alternative to a physics-based parameterization. We demonstrate that a domain-aware deep neural network can successfully simulate the entire diurnal cycle of the boundary layer physics and the results are transferable.
Gianandrea Mannarini and Lorenzo Carelli
Geosci. Model Dev., 12, 3449–3480, https://doi.org/10.5194/gmd-12-3449-2019, https://doi.org/10.5194/gmd-12-3449-2019, 2019
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The VISIR ship-routing model is updated in order to deal with ocean currents.
The optimal tracks we computed through VISIR in the Atlantic ocean show great seasonal and regional variability, following a variable influence of surface gravity waves and currents. We assess how these tracks contribute to voyage energy-efficiency gains through a standard indicator (EEOI) of the International Maritime Organization. Also, the new model features are validated against an exact analytical benchmark.
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.
Christina Papagiannopoulou, Diego G. Miralles, Matthias Demuzere, Niko E. C. Verhoest, and Willem Waegeman
Geosci. Model Dev., 11, 4139–4153, https://doi.org/10.5194/gmd-11-4139-2018, https://doi.org/10.5194/gmd-11-4139-2018, 2018
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Common global land cover and climate classifications are based on vegetation–climatic characteristics derived from observational data, ignoring the interaction between the local climate and biome. Here, we model the interplay between vegetation and local climate by discovering spatial relationships among different locations. The resulting global
hydro-climatic biomescorrespond to regions of coherent climate–vegetation interactions that agree well with traditional global land cover maps.
Wendy Sharples, Ilya Zhukov, Markus Geimer, Klaus Goergen, Sebastian Luehrs, Thomas Breuer, Bibi Naz, Ketan Kulkarni, Slavko Brdar, and Stefan Kollet
Geosci. Model Dev., 11, 2875–2895, https://doi.org/10.5194/gmd-11-2875-2018, https://doi.org/10.5194/gmd-11-2875-2018, 2018
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Next-generation geoscientific models are based on complex model implementations and workflows. Next-generation HPC systems require new programming paradigms and code optimization. In order to meet the challenge of running complex simulations on new massively parallel HPC systems, we developed a run control framework that facilitates code portability, code profiling, and provenance tracking to reduce both the duration and the cost of code migration and development, while ensuring reproducibility.
Daojun Zhang, Na Ren, and Xianhui Hou
Geosci. Model Dev., 11, 2525–2539, https://doi.org/10.5194/gmd-11-2525-2018, https://doi.org/10.5194/gmd-11-2525-2018, 2018
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Geographically weighted regression is a widely used method to deal with spatial heterogeneity, which is common in geostatistics. However, most existing software does not support logistic regression and cannot deal with missing data, which exist extensively in mineral prospectivity mapping. This work generalized logistic regression to spatial statistics based on a spatially weighted technique. The new model also supports an anisotropic local window, which is another innovative point.
Cited articles
Balmaceda-Huarte, R., Baño-Medina, J., Olmo, M. E., and Bettolli, M. L.: On the use of convolutional neural networks for downscaling daily temperatures over southern South America in a climate change scenario, Clim. Dynam., 62, 383–397, https://doi.org/10.1007/s00382-023-06912-6, 2023.
Barnes, E. A., Barnes, R. J., Martin, Z. K., and Rader, J. K.: This Looks Like That There: Interpretable Neural Networks for Image Tasks When Location Matters, Artif. Intell. Earth Syst., 1, e220001, https://doi.org/10.1175/AIES-D-22-0001.1, 2022.
Baron, S.: Explainable AI and Causal Understanding: Counterfactual Approaches Considered, Minds Mach., 33, 347–377, https://doi.org/10.1007/s11023-023-09637-x, 2023.
Bau, D., Zhu, J.-Y., Strobelt, H., Zhou, B., Tenenbaum, J. B., Freeman, W. T., and Torralba, A.: GAN Dissection: Visualizing and Understanding Generative Adversarial Networks, arXiv [preprint], https://doi.org/10.48550/arXiv.1811.10597, 8 December 2018.
Baumberger, C., Knutti, R., and Hadorn, G. H.: Building confidence in climate model projections: an analysis of inferences from fit, WIREs Clim. Change, 8, e454, https://doi.org/10.1002/wcc.454, 2017.
Beroche, H.: Generative Adversarial Networks for Climate Change Scenarios, URBAN AI, https://urbanai.fr/generative-adversarial-networks-for-climate-change-scenarios/ (last access: 16 December 2024), 2021.
Besombes, C., Pannekoucke, O., Lapeyre, C., Sanderson, B., and Thual, O.: Producing realistic climate data with generative adversarial networks, Nonlin. Processes Geophys., 28, 347–370, https://doi.org/10.5194/npg-28-347-2021, 2021.
Beucler, T., Rasp, S., Pritchard, M., and Gentine, P.: Achieving Conservation of Energy in Neural Network Emulators for Climate Modeling, arXiv [preprint], https://doi.org/10.48550/arXiv.1906.06622, 15 June 2019.
Beucler, T., Pritchard, M., Rasp, S., Ott, J., Baldi, P., and Gentine, P.: Enforcing Analytic Constraints in Neural Networks Emulating Physical Systems, Phys. Rev. Lett., 126, 098302, https://doi.org/10.1103/PhysRevLett.126.098302, 2021.
Bommer, P., Kretschmer, M., Hedström, A., Bareeva, D., and Höhne, M. M.-C.: Finding the right XAI method – A Guide for the Evaluation and Ranking of Explainable AI Methods in Climate Science, arXiv [preprint], https://doi.org/10.48550/arXiv.2303.00652, 1 March 2023.
Bonev, B., Kurth, T., Hundt, C., Pathak, J., Baust, M., Kashinath, K., and Anandkumar, A.: Spherical Fourier Neural Operators: Learning Stable Dynamics on the Sphere, arXiv [preprint], https://doi.org/10.48550/arXiv.2306.03838, 6 June 2023.
Brenowitz, N. D., Beucler, T., Pritchard, M., and Bretherton, C. S.: Interpreting and Stabilizing Machine-Learning Parametrizations of Convection, J. Atmospheric Sci., 77, 4357–4375, https://doi.org/10.1175/JAS-D-20-0082.1, 2020.
Bukovsky, M. S., McCrary, R. R., Seth, A., and Mearns, L. O.: A Mechanistically Credible, Poleward Shift in Warm-Season Precipitation Projected for the U.S. Southern Great Plains?, J. Climate, 30, 8275–8298, https://doi.org/10.1175/JCLI-D-16-0316.1, 2017.
Carrier, M. and Lenhard, J.: Climate Models: How to Assess Their Reliability, Int. Stud. Philos. Sci., 32, 81–100, https://doi.org/10.1080/02698595.2019.1644722, 2019.
Cess, R. D., Potter, G. L., Blanchet, J. P., Boer, G. J., Ghan, S. J., Kiehl, J. T., Treut, H. L., Li, Z.-X., Liang, X.-Z., Mitchell, J. F. B., Morcrette, J.-J., Randall, D. A., Riches, M. R., Roeckner, E., Schlese, U., Slingo, A., Taylor, K. E., Washington, W. M., Wetherald, R. T., and Yagai, I.: Interpretation of Cloud-Climate Feedback as Produced by 14 Atmospheric General Circulation Models, Science, 245, 513–516, https://doi.org/10.1126/science.245.4917.513, 1989.
Chakraborty, D., Başağaoğlu, H., Gutierrez, L., and Mirchi, A.: Explainable AI reveals new hydroclimatic insights for ecosystem-centric groundwater management, Environ. Res. Lett., 16, 114024, https://doi.org/10.1088/1748-9326/ac2fde, 2021.
Cilli, R., Elia, M., D'Este, M., Giannico, V., Amoroso, N., Lombardi, A., Pantaleo, E., Monaco, A., Sanesi, G., Tangaro, S., Bellotti, R., and Lafortezza, R.: Explainable artificial intelligence (XAI) detects wildfire occurrence in the Mediterranean countries of Southern Europe, Sci. Rep., 12, 16349, https://doi.org/10.1038/s41598-022-20347-9, 2022.
Clare, M. C. A., Sonnewald, M., Lguensat, R., Deshayes, J., and Balaji, V.: Explainable Artificial Intelligence for Bayesian Neural Networks: Toward Trustworthy Predictions of Ocean Dynamics, J. Adv. Model. Earth Sy., 14, e2022MS003162, https://doi.org/10.1029/2022MS003162, 2022.
Collins, W. D., Hackney, J. K., and Edwards, D. P.: An updated parameterization for infrared emission and absorption by water vapor in the National Center for Atmospheric Research Community Atmosphere Model, J. Geophys. Res.-Atmos., 107, ACL 17-1–ACL 17-20, https://doi.org/10.1029/2001JD001365, 2002.
Cuomo, S., Di Cola, V. S., Giampaolo, F., Rozza, G., Raissi, M., and Piccialli, F.: Scientific Machine Learning Through Physics–Informed Neural Networks: Where we are and What's Next, J. Sci. Comput., 92, 88, https://doi.org/10.1007/s10915-022-01939-z, 2022.
de Burgh-Day, C. O. and Leeuwenburg, T.: Machine learning for numerical weather and climate modelling: a review, Geosci. Model Dev., 16, 6433–6477, https://doi.org/10.5194/gmd-16-6433-2023, 2023.
De Regt, H. W.: Understanding Scientific Understanding, Oxford University Press, 321 pp., ISBN: 9780190652913, 2017.
De Regt, H. W. and Dieks, D.: A Contextual Approach to Scientific Understanding, Synthese, 144, 137–170, https://doi.org/10.1007/s11229-005-5000-4, 2005.
Diffenbaugh, N. S. and Barnes, E. A.: Data-Driven Predictions of the Time Remaining until Critical Global Warming Thresholds Are Reached, P. Natl. Acad. Sci. USA, 120, e2207183120, https://doi.org/10.1073/pnas.2207183120, 2023.
Easterbrook, S. M.: Computing the Climate: How We Know What We Know About Climate Change, Cambridge University Press, Cambridge, https://doi.org/10.1017/9781316459768, 2023.
Eidhammer, T., Gettelman, A., Thayer-Calder, K., Watson-Parris, D., Elsaesser, G., Morrison, H., van Lier-Walqui, M., Song, C., and McCoy, D.: An extensible perturbed parameter ensemble for the Community Atmosphere Model version 6, Geosci. Model Dev., 17, 7835–7853, https://doi.org/10.5194/gmd-17-7835-2024, 2024.
Eyring, V., Cox, P. M., Flato, G. M., Gleckler, P. J., Abramowitz, G., Caldwell, P., Collins, W. D., Gier, B. K., Hall, A. D., Hoffman, F. M., Hurtt, G. C., Jahn, A., Jones, C. D., Klein, S. A., Krasting, J. P., Kwiatkowski, L., Lorenz, R., Maloney, E., 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.
Felsche, E. and Ludwig, R.: Applying machine learning for drought prediction in a perfect model framework using data from a large ensemble of climate simulations, Nat. Hazards Earth Syst. Sci., 21, 3679–3691, https://doi.org/10.5194/nhess-21-3679-2021, 2021.
Forster, P., Storelvmo, T., Armour, K., Collins, W., Dufresne, J.-L., Frame, D., Lunt, D. J., Mauritsen, T., Palmer, M. D., Watanabe, M., Wild, M., and Zhang, H.: The Earth’s Energy Budget, Climate Feedbacks, and Climate Sensitivity, 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, edited by: Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S. L., Péan, C., Berger, S., Caud, N., Chen, Y., Goldfarb, L., Gomis, M. I., Huang, M., Leitzell, K., Lonnoy, E., Matthews, J. B. R., Maycock, T. K., Waterfield, T., Yelekçi, O., Yu, R., and Zhou, B., Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 923–1054, https://doi.org/10.1017/9781009157896.009, 2021.
Frigg, R., Thompson, E., and Werndl, C.: Philosophy of Climate Science Part II: Modelling Climate Change, Philos. Compass, 10, 965–977, https://doi.org/10.1111/phc3.12297, 2015.
Gates, W. L.: AMIP: The Atmospheric Model Intercomparison Project, B. Am. Meteorol. Soc., 73, 1962–1970, https://doi.org/10.1175/1520-0477(1992)073<1962:ATAMIP>2.0.CO;2, 1992.
Gettelman, A., Hannay, C., Bacmeister, J. T., Neale, R. B., Pendergrass, A. G., Danabasoglu, G., Lamarque, J.-F., Fasullo, J. T., Bailey, D. A., Lawrence, D. M., and Mills, M. J.: High Climate Sensitivity in the Community Earth System Model Version 2 (CESM2), Geophys. Res. Lett., 46, 8329–8337, https://doi.org/10.1029/2019GL083978, 2019.
Gleckler, P. J., Randall, D. A., Boer, G., Colman, R., Dix, M., Galin, V., Helfand, M., Kiehl, J., Kitoh, A., Lau, W., Liang, X.-Y., Lykossov, V., McAvaney, B., Miyakoda, K., Planton, S., and Stern, W.: Cloud-radiative effects on implied oceanic energy transports as simulated by Atmospheric General Circulation Models, Geophys. Res. Lett., 22, 791–794, https://doi.org/10.1029/95GL00113, 1995.
Gleckler, P. J., Taylor, K. E., and Doutriaux, C.: Performance metrics for climate models, J. Geophys. Res.-Atmos., 113, D06104, https://doi.org/10.1029/2007JD008972, 2008.
González-Abad, J., Baño-Medina, J., and Gutiérrez, J. M.: Using Explainability to Inform Statistical Downscaling Based on Deep Learning Beyond Standard Validation Approaches, arXiv [preprint], https://doi.org/10.48550/arXiv.2302.01771, 3 February 2023.
Gordon, E. M., Barnes, E. A., and Hurrell, J. W.: Oceanic Harbingers of Pacific Decadal Oscillation Predictability in CESM2 Detected by Neural Networks, Geophys. Res. Lett., 48, e2021GL095392, https://doi.org/10.1029/2021GL095392, 2021.
Grenier, H. and Bretherton, C. S.: A Moist PBL Parameterization for Large-Scale Models and Its Application to Subtropical Cloud-Topped Marine Boundary Layers, Mon. Weather Rev., 129, 357–377, https://doi.org/10.1175/1520-0493(2001)129<0357:AMPPFL>2.0.CO;2, 2001.
Grundner, A., Beucler, T., Gentine, P., Iglesias-Suarez, F., Giorgetta, M. A., and Eyring, V.: Deep Learning Based Cloud Cover Parameterization for ICON, J. Adv. Model. Earth Sy., 14, e2021MS002959, https://doi.org/10.1029/2021MS002959, 2022.
Hall, A. and Qu, X.: Using the current seasonal cycle to constrain snow albedo feedback in future climate change, Geophys. Res. Lett., 33, L03502, https://doi.org/10.1029/2005GL025127, 2006.
Ham, Y.-G., Kim, J.-H., and Luo, J.-J.: Deep learning for multi-year ENSO forecasts, Nature, 573, 568–572, https://doi.org/10.1038/s41586-019-1559-7, 2019.
Hausfather, Z., Drake, H. F., Abbott, T., and Schmidt, G. A.: Evaluating the Performance of Past Climate Model Projections, Geophys. Res. Lett., 47, e2019GL085378, https://doi.org/10.1029/2019GL085378, 2020.
He, R., Zhang, L., and Chew, A. W. Z.: Data-driven multi-step prediction and analysis of monthly rainfall using explainable deep learning, Expert Syst. Appl., 235, 121160, https://doi.org/10.1016/j.eswa.2023.121160, 2024.
Hedström, A., Weber, L., Krakowczyk, D., Bareeva, D., Motzkus, F., Samek, W., Lapuschkin, S., and Höhne, M. M.-C.: Quantus: An explainable ai toolkit for responsible evaluation of neural network explanations and beyond, J. Mach. Learn. Res., 24, 1–11, http://jmlr.org/papers/v24/22-0142.html (last access: 16 December 2024), 2023.
Hegerl, G. C., Zwiers, F. W., Braconnot, P., Gillett, N. P., Luo, Y., Orsini, J. A. M., Nicholls, N., Penner, J. E., Stott, P. A., Allen, M., Ammann, C., Andronova, N., Betts, R. A., Clement, A., Collins, W. D., Crooks, S., Delworth, T. L., Forest, C., Forster, P., Goosse, H., Gregory, J. M., Harvey, D., Jones, G. S., Joos, F., Kenyon, J., Kettleborough, J., Kharin, V., Knutti, R., Lambert, F. H., Lavine, M., Lee, T. C. K., Levinson, D., Masson-Delmotte, V., Nozawa, T., Otto-Bliesner, B., Pierce, D., Power, S., Rind, D., Rotstayn, L., Santer, B. D., Senior, C., Sexton, D., Stark, S., Stone, D. A., Tett, S., Thorne, P., van Dorland, R., Wong, T., Xu, L., Zhang, X., Zorita, E., Karoly, D. J., Ogallo, L., and Planton, S.: Understanding and Attributing Climate Change, in: Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Solomon, S., Qin, D., Manning, M., Chen, Z., Marquis, M., Avery, K. B., Tignor, M., and Miller, H. L., Cambridge University Press, 84, https://www.ipcc.ch/report/ar4/wg1/ (last access: 16 December 2024), 2007.
Held, I. M.: The Gap between Simulation and Understanding in Climate Modeling, B. Am. Meteorol. Soc., 86, 1609–1614, https://doi.org/10.1175/BAMS-86-11-1609, 2005.
Hourdin, F., Grandpeix, J.-Y., Rio, C., Bony, S., Jam, A., Cheruy, F., Rochetin, N., Fairhead, L., Idelkadi, A., Musat, I., Dufresne, J.-L., Lahellec, A., Lefebvre, M.-P., and Roehrig, R.: LMDZ5B: the atmospheric component of the IPSL climate model with revisited parameterizations for clouds and convection, Clim. Dynam., 40, 2193–2222, https://doi.org/10.1007/s00382-012-1343-y, 2013.
Humphreys, P.: The philosophical novelty of computer simulation methods, Synthese, 169, 615–626, https://doi.org/10.1007/s11229-008-9435-2, 2009.
Jebeile, J., Lam, V., and Räz, T.: Understanding climate change with statistical downscaling and machine learning, Synthese, 199, 1877–1897, https://doi.org/10.1007/s11229-020-02865-z, 2021.
Jeevanjee, N., Hassanzadeh, P., Hill, S., and Sheshadri, A.: A perspective on climate model hierarchies, J. Adv. Model. Earth Sy., 9, 1760–1771, https://doi.org/10.1002/2017MS001038, 2017.
Kashinath, K., Mustafa, M., Albert, A., Wu, J.-L., Jiang, C., Esmaeilzadeh, S., Azizzadenesheli, K., Wang, R., Chattopadhyay, A., Singh, A., Manepalli, A., Chirila, D., Yu, R., Walters, R., White, B., Xiao, H., Tchelepi, H. A., Marcus, P., Anandkumar, A., Hassanzadeh, P., and Prabhat, null: Physics-informed machine learning: case studies for weather and climate modelling, Philos. T. R. Soc. A, 379, 20200093, https://doi.org/10.1098/rsta.2020.0093, 2021.
Katzav, J. and Parker, W. S.: The future of climate modeling, Climatic Change, 132, 475–487, https://doi.org/10.1007/s10584-015-1435-x, 2015.
Knüsel, B. and Baumberger, C.: Understanding climate phenomena with data-driven models, Stud. Hist. Philos. Sci. Part A, 84, 46–56, https://doi.org/10.1016/j.shpsa.2020.08.003, 2020.
Knutti, R.: Why are climate models reproducing the observed global surface warming so well?, Geophys. Res. Lett., 35, L18704, https://doi.org/10.1029/2008GL034932, 2008.
Kravitz, B., Robock, A., Forster, P. M., Haywood, J. M., Lawrence, M. G., and Schmidt, H.: An overview of the Geoengineering Model Intercomparison Project (GeoMIP), J. Geophys. Res.-Atmos., 118, 13103–13107, https://doi.org/10.1002/2013JD020569, 2013.
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, 2022a.
Labe, Z. M. and Barnes, E. A.: Predicting Slowdowns in Decadal Climate Warming Trends With Explainable Neural Networks, Geophys. Res. Lett., 49, e2022GL098173, https://doi.org/10.1029/2022GL098173, 2022b.
Krishna, S., Han, T., Gu, A., Pombra, J., Jabbari, S., Wu, S., and Lakkaraju, H.: The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective, arXiv [preprint], https://doi.org/10.48550/arXiv.2202.01602, 8 February 2022.
Labe, Z. M. and Barnes, E. A.: Detecting Climate Signals Using Explainable AI With Single-Forcing Large Ensembles, J. Adv. Model. Earth Sy., 13, e2021MS002464, https://doi.org/10.1029/2021MS002464, 2021.
Lenhard, J.: Surprised by a Nanowire: Simulation, Control, and Understanding, Philos. Sci., 73, 605–616, https://doi.org/10.1086/518330, 2006.
Lenhard, J. and Winsberg, E.: Holism, entrenchment, and the future of climate model pluralism, Stud. Hist. Philos. Sci. Part B Stud. Hist. Philos. Mod. Phys., 41, 253–262, https://doi.org/10.1016/j.shpsb.2010.07.001, 2010.
Li, D.: Machines Learn Better with Better Data Ontology: Lessons from Philosophy of Induction and Machine Learning Practice, Minds Mach., 33, 429–450, https://doi.org/10.1007/s11023-023-09639-9, 2023.
Li, W., Migliavacca, M., Forkel, M., Denissen, J. M. C., Reichstein, M., Yang, H., Duveiller, G., Weber, U., and Orth, R.: Widespread increasing vegetation sensitivity to soil moisture, Nat. Commun., 13, 3959, https://doi.org/10.1038/s41467-022-31667-9, 2022.
Li, Z., Kovachki, N., Azizzadenesheli, K., Liu, B., Bhattacharya, K., Stuart, A., and Anandkumar, A.: Fourier Neural Operator for Parametric Partial Differential Equations, arXiv [preprint], https://doi.org/10.48550/arXiv.2010.08895, 16 May 2021.
Lin, Q.-J., Mayta, V. C., and Adames Corraliza, Á. F.: Assessment of the Madden-Julian Oscillation in CMIP6 Models Based on Moisture Mode Theory, Geophys. Res. Lett., 51, e2023GL106693, https://doi.org/10.1029/2023GL106693, 2024.
Lipton, Z. C.: The mythos of model interpretability (2016), arXiv [preprint], https://doi.org/10.48550/arXiv.1606.03490, 2016.
Liu, Y., Duffy, K., Dy, J. G., and Ganguly, A. R.: Explainable deep learning for insights in El Niño and river flows, Nat. Commun., 14, 339, https://doi.org/10.1038/s41467-023-35968-5, 2023.
Lloyd, E. A.: Confirmation and Robustness of Climate Models, Philos. Sci., 77, 971–984, https://doi.org/10.1086/657427, 2010.
Lloyd, E. A.: Model robustness as a confirmatory virtue: The case of climate science, Stud. Hist. Philos. Sci. Part A, 49, 58–68, https://doi.org/10.1016/j.shpsa.2014.12.002, 2015.
Mahesh, A., O'Brien, T. A., Loring, B., Elbashandy, A., Boos, W., and Collins, W. D.: Identifying atmospheric rivers and their poleward latent heat transport with generalizable neural networks: ARCNNv1, Geosci. Model Dev., 17, 3533–3557, https://doi.org/10.5194/gmd-17-3533-2024, 2024.
Maloney, E. D., Gettelman, A., Ming, Y., Neelin, J. D., Barrie, D., Mariotti, A., Chen, C.-C., Coleman, D. R. B., 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., 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.
Mamalakis, A., Ebert-Uphoff, I., and Barnes, E. A.: Explainable Artificial Intelligence in Meteorology and Climate Science: Model Fine-Tuning, Calibrating Trust and Learning New Science, in: xxAI – Beyond Explainable AI: International Workshop, Held in Conjunction with ICML 2020, July 18, 2020, Vienna, Austria, Revised and Extended Papers, edited by: Holzinger, A., Goebel, R., Fong, R., Moon, T., Müller, K.-R., and Samek, W., Springer International Publishing, Cham, 315–339, https://doi.org/10.1007/978-3-031-04083-2_16, 2022a.
Mamalakis, A., Ebert-Uphoff, I., and Barnes, E. A.: Neural network attribution methods for problems in geoscience: A novel synthetic benchmark dataset, Environ. Data Sci., 1, e8, https://doi.org/10.1017/eds.2022.7, 2022b.
Mayernik, M. S.: Credibility via Coupling: Institutions and Infrastructures in Climate Model Intercomparisons:, Engag. Sci. Technol. Soc., 7, 10–32, https://doi.org/10.17351/ests2021.769, 2021.
McGinnis, S., Korytina, D., Bukovsky, M., McCrary, R., and Mearns, L.: Credibility Evaluation of a Convolutional Neural Net for Downscaling GCM Output over the Southern Great Plains, 2021, GC42A-03, https://ui.adsabs.harvard.edu/abs/2021AGUFMGC42A..03M%2F/abstract (last access: 3 February 2025), 2021.
Molina, M. J., O'Brien, T. A., Anderson, G., Ashfaq, M., Bennett, K. E., Collins, W. D., Dagon, K., Restrepo, J. M., and Ullrich, P. A.: A Review of Recent and Emerging Machine Learning Applications for Climate Variability and Weather Phenomena, Artif. Intell. Earth Syst., 2, 220086, https://doi.org/10.1175/AIES-D-22-0086.1, 2023.
Neelin, J. D., Krasting, J. P., Radhakrishnan, A., Liptak, J., Jackson, T., Ming, Y., Dong, W., Gettelman, A., Coleman, D. R., Maloney, E. D., Wing, A. A., Kuo, Y.-H., Ahmed, F., Ullrich, P., Bitz, C. M., Neale, R. B., Ordonez, A., and Maroon, E. A.: 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.
Notz, D., Haumann, F. A., Haak, H., Jungclaus, J. H., and Marotzke, J.: Arctic sea-ice evolution as modeled by Max Planck Institute for Meteorology's Earth system model, J. Adv. Model. Earth Sy., 5, 173–194, https://doi.org/10.1002/jame.20016, 2013.
NSF AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography (AI2ES), https://www.ai2es.org/, last access: 13 August 2024.
O'Brien, T. A., Li, F., Collins, W. D., Rauscher, S. A., Ringler, T. D., Taylor, M., Hagos, S. M., and Leung, L. R.: Observed Scaling in Clouds and Precipitation and Scale Incognizance in Regional to Global Atmospheric Models, J. Climate, 26, 9313–9333, https://doi.org/10.1175/JCLI-D-13-00005.1, 2013.
O'Loughlin, R.: Robustness reasoning in climate model comparisons, Stud. Hist. Philos. Sci. Part A, 85, 34–43, https://doi.org/10.1016/j.shpsa.2020.12.005, 2021.
O'Loughlin, R.: Diagnosing errors in climate model intercomparisons, Eur. J. Philos. Sci., 13, 20, https://doi.org/10.1007/s13194-023-00522-z, 2023.
Oreopoulos, L., Mlawer, E., Delamere, J., Shippert, T., Cole, J., Fomin, B., Iacono, M., Jin, Z., Li, J., Manners, J., Räisänen, P., Rose, F., Zhang, Y., Wilson, M. J., and Rossow, W. B.: The Continual Intercomparison of Radiation Codes: Results from Phase I, J. Geophys. Res.-Atmos., 117, D06118, https://doi.org/10.1029/2011JD016821, 2012.
Park, M., Tran, D. Q., Bak, J., and Park, S.: Advanced wildfire detection using generative adversarial network-based augmented datasets and weakly supervised object localization, Int. J. Appl. Earth Obs., 114, 103052, https://doi.org/10.1016/j.jag.2022.103052, 2022.
Pathak, J., Subramanian, S., Harrington, P., Raja, S., Chattopadhyay, A., Mardani, M., Kurth, T., Hall, D., Li, Z., Azizzadenesheli, K., Hassanzadeh, P., Kashinath, K., and Anandkumar, A.: FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators, arXiv [preprint], https://doi.org/10.48550/arXiv.2202.11214, 22 February 2022.
Pincus, R., Forster, P. M., and Stevens, B.: The Radiative Forcing Model Intercomparison Project (RFMIP): experimental protocol for CMIP6, Geosci. Model Dev., 9, 3447–3460, https://doi.org/10.5194/gmd-9-3447-2016, 2016.
Pitari, G., Aquila, V., Kravitz, B., Robock, A., Watanabe, S., Cionni, I., Luca, N. D., Genova, G. D., Mancini, E., and Tilmes, S.: Stratospheric ozone response to sulfate geoengineering: Results from the Geoengineering Model Intercomparison Project (GeoMIP), J. Geophys. Res.-Atmos., 119, 2629–2653, https://doi.org/10.1002/2013JD020566, 2014.
Rader, J. K., Barnes, E. A., Ebert-Uphoff, I., and Anderson, C.: Detection of Forced Change Within Combined Climate Fields Using Explainable Neural Networks, J. Adv. Model. Earth Sy., 14, e2021MS002941, https://doi.org/10.1029/2021MS002941, 2022.
Rasp, S., Pritchard, M. S., and Gentine, P.: Deep learning to represent subgrid processes in climate models, P. Natl. Acad. Sci. USA, 115, 9684–9689, https://doi.org/10.1073/pnas.1810286115, 2018.
Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J., Carvalhais, N., and Prabhat: Deep learning and process understanding for data-driven Earth system science, Nature, 566, 195–204, https://doi.org/10.1038/s41586-019-0912-1, 2019.
Rudin, C.: Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead, Nat. Mach. Intell., 1, 206–215, https://doi.org/10.1038/s42256-019-0048-x, 2019.
Schmidt, G. A. and Sherwood, S.: A practical philosophy of complex climate modelling, Eur. J. Philos. Sci., 5, 149–169, https://doi.org/10.1007/s13194-014-0102-9, 2015.
Schneider, S. H.: On the Carbon Dioxide–Climate Confusion, J. Atmos. Sci., 32, 2060–2066, https://doi.org/10.1175/1520-0469(1975)032<2060:OTCDC>2.0.CO;2, 1975.
Schneider, S. H.: Verification of Parameterizations in Climate Modeling, in: Report of the Study Conference on Climate Models: Performance, Intercomparison and Sensitivity Studies, edited by: Gates, W. L., World Meteorological Organization, Global Atmospheric Research Program, GARP Publications Series no. 22, Vol. 2, 728–751, 1979.
Stocker, T. F.: Working Group 1: 7.2.2.3 Boundary-layer mixing and cloudiness from the IPCC's Third Assessment Report (TAR), https://archive.ipcc.ch/ipccreports/tar/wg1/273.htm (last access: 26 October 2023), 2001.
Stocker, T. F., et al.: Physical climate processes and feedbacks, IPCC, Climate change 2001: The scientific basis. Contribution of working group I to the third assessment report of the intergovernmental panel on climate change, Cambridge University Press, 417–470, 2001.
Subel, A., Guan, Y., Chattopadhyay, A., and Hassanzadeh, P.: Explaining the physics of transfer learning in data-driven turbulence modeling, PNAS Nexus, 2, pgad015, https://doi.org/10.1093/pnasnexus/pgad015, 2023.
Toms, B. A., Barnes, E. A., and Hurrell, J. W.: Assessing Decadal Predictability in an Earth-System Model Using Explainable Neural Networks, Geophys. Res. Lett., 48, e2021GL093842, https://doi.org/10.1029/2021GL093842, 2021.
Touzé-Peiffer, L., Barberousse, A., and Treut, H. L.: The Coupled Model Intercomparison Project: History, uses, and structural effects on climate research, WIREs Clim. Change, 11, e648, https://doi.org/10.1002/wcc.648, 2020.
van den Hurk, B., Kim, H., Krinner, G., Seneviratne, S. I., Derksen, C., Oki, T., Douville, H., Colin, J., Ducharne, A., Cheruy, F., Viovy, N., Puma, M. J., Wada, Y., Li, W., Jia, B., Alessandri, A., Lawrence, D. M., Weedon, G. P., Ellis, R., Hagemann, S., Mao, J., Flanner, M. G., Zampieri, M., Materia, S., Law, R. M., and Sheffield, J.: LS3MIP (v1.0) contribution to CMIP6: the Land Surface, Snow and Soil moisture Model Intercomparison Project – aims, setup and expected outcome, Geosci. Model Dev., 9, 2809–2832, https://doi.org/10.5194/gmd-9-2809-2016, 2016.
Wang, S., Sankaran, S., and Perdikaris, P.: Respecting causality is all you need for training physics-informed neural networks, arXiv [preprint], https://doi.org/10.48550/arXiv.2203.07404, 14 March 2022.
Webb, M. J., Andrews, T., Bodas-Salcedo, A., Bony, S., Bretherton, C. S., Chadwick, R., Chepfer, H., Douville, H., Good, P., Kay, J. E., Klein, S. A., Marchand, R., Medeiros, B., Siebesma, A. P., Skinner, C. B., Stevens, B., Tselioudis, G., Tsushima, Y., and Watanabe, M.: The Cloud Feedback Model Intercomparison Project (CFMIP) contribution to CMIP6, Geosci. Model Dev., 10, 359–384, https://doi.org/10.5194/gmd-10-359-2017, 2017.
Xue, P., Wagh, A., Ma, G., Wang, Y., Yang, Y., Liu, T., and Huang, C.: Integrating Deep Learning and Hydrodynamic Modeling to Improve the Great Lakes Forecast, Remote Sens., 14, 2640, https://doi.org/10.3390/rs14112640, 2022.
Yuan, H., Yu, H., Gui, S., and Ji, S.: Explainability in Graph Neural Networks: A Taxonomic Survey, IEEE Trans. Pattern Anal. Mach. Intell., 45, 5782–5799, https://doi.org/10.1109/TPAMI.2022.3204236, 2023.
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.
Zhu, J., Otto-Bliesner, B. L., Brady, E. C., Poulsen, C. J., Tierney, J. E., Lofverstrom, M., and DiNezio, P.: Assessment of Equilibrium Climate Sensitivity of the Community Earth System Model Version 2 Through Simulation of the Last Glacial Maximum, Geophys. Res. Lett., 48, e2020GL091220, https://doi.org/10.1029/2020GL091220, 2021.
Executive editor
This perspective paper examines in detail the concept of explicability in a climate model, whether conventional physics-based dynamical models, or those incorporating components based on machine learning. Everyone with an interest in climate models or their outputs would benefit from understanding the processes by which we can understand the importance and accuracy of these models and the methods by which it is possible to make sense of those outputs. This paper is a major contribution to that understanding. It is also very well written and should be widely read in the field.
This perspective paper examines in detail the concept of explicability in a climate model,...
Short summary
We draw from traditional climate modeling practices to make recommendations for machine-learning (ML)-driven climate science. Our intended audience is climate modelers who are relatively new to ML. We show how component-level understanding – obtained when scientists can link model behavior to parts within the overall model – should guide the development and evaluation of ML models. Better understanding yields a stronger basis for trust in the models. We highlight several examples to demonstrate.
We draw from traditional climate modeling practices to make recommendations for machine-learning...
