Articles | Volume 20, issue 4
https://doi.org/10.5194/acp-20-2303-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-20-2303-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Technical note
| 
26 Feb 2020
Technical note |
| 26 Feb 2020
| 26 Feb 2020
Technical note: Deep learning for creating surrogate models of precipitation in Earth system models
Theodore Weber
Computer Science Department, Western Washington University,
Bellingham, WA, USA
Austin Corotan
Computer Science Department, Western Washington University,
Bellingham, WA, USA
Brian Hutchinson
CORRESPONDING AUTHOR
Computer Science Department, Western Washington University,
Bellingham, WA, USA
Computing and Analytics Division, Pacific Northwest
National Laboratory, Seattle, WA, USA
Ben Kravitz
Department of Earth and Atmospheric Sciences, Indiana University,
Bloomington, IN, USA
Atmospheric Sciences and Global Change Division, Pacific Northwest
National Laboratory, Richland, WA, USA
Robert Link
Joint Global Change Research
Institute, Pacific Northwest National Laboratory, College Park, MD, USA
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18 citations as recorded by crossref.
- Machine learning for weather and climate are worlds apart D. Watson-Parris https://doi.org/10.1098/rsta.2020.0098
- Leveraging physics-informed neural networks for efficient modelling of coastal ecosystems dynamics: A case study of Sundarbans mangrove forest M. Fanous et al. https://doi.org/10.1016/j.ecoinf.2025.103302
- Emulating grid-based forest carbon dynamics using machine learning: an LPJ-GUESS v4.1.1 application C. Natel et al. https://doi.org/10.5194/gmd-18-4317-2025
- Emulating radiative transfer with artificial neural networks S. Sethuram et al. https://doi.org/10.1093/mnras/stad2524
- Identifying drivers and dynamics of phytoplankton in the Black Sea: application of a neural emulator P. Smith et al. https://doi.org/10.3389/fmars.2026.1760162
- Developing Guidelines for working with Multi-Model Ensembles in CMIP A. Katzenberger et al. https://doi.org/10.5194/esd-17-495-2026
- Deep-learning-based surrogate model for solute transport in rough-walled fractures H. Xue et al. https://doi.org/10.1016/j.compgeo.2025.107813
- Using Machine Learning for Climate Modelling: Application of Neural Networks to a Slow-Fast Chaotic Dynamical System as a Case Study S. Soldatenko & Y. Angudovich https://doi.org/10.3390/cli12110189
- AI-Supported Reality: Revisiting Models and Techniques of Systems Analysis in Water Resources and Agriculture Management B. Srđević & Z. Srđević https://doi.org/10.3390/w18080914
- From rain to data: A review of the creation of monthly and daily station‐based gridded precipitation datasets R. Serrano‐Notivoli & E. Tejedor https://doi.org/10.1002/wat2.1555
- Transferring climate change physical knowledge F. Immorlano et al. https://doi.org/10.1073/pnas.2413503122
- Partial differential equations discovery with EPDE framework: Application for real and synthetic data M. Maslyaev et al. https://doi.org/10.1016/j.jocs.2021.101345
- Grouped convolution dual-attention network for time series forecasting of water temperature in offshore aquaculture net pen X. Sun et al. https://doi.org/10.1016/j.eswa.2025.127438
- A comprehensive review of deep learning applications in hydrology and water resources M. Sit et al. https://doi.org/10.2166/wst.2020.369
- Statistical mechanics in climate emulation: Challenges and perspectives I. Sudakow et al. https://doi.org/10.1017/eds.2022.15
- Pangeo-Enabled ESM Pattern Scaling (PEEPS): A customizable dataset of emulated Earth System Model output B. Kravitz et al. https://doi.org/10.1371/journal.pclm.0000159
- Performance assessment for climate intervention (PACI): preliminary application to a stratospheric aerosol injection scenario L. Wheeler et al. https://doi.org/10.3389/fenvs.2023.1205515
- A comprehensive review of digital twin — part 1: modeling and twinning enabling technologies A. Thelen et al. https://doi.org/10.1007/s00158-022-03425-4
18 citations as recorded by crossref.
- Machine learning for weather and climate are worlds apart D. Watson-Parris https://doi.org/10.1098/rsta.2020.0098
- Leveraging physics-informed neural networks for efficient modelling of coastal ecosystems dynamics: A case study of Sundarbans mangrove forest M. Fanous et al. https://doi.org/10.1016/j.ecoinf.2025.103302
- Emulating grid-based forest carbon dynamics using machine learning: an LPJ-GUESS v4.1.1 application C. Natel et al. https://doi.org/10.5194/gmd-18-4317-2025
- Emulating radiative transfer with artificial neural networks S. Sethuram et al. https://doi.org/10.1093/mnras/stad2524
- Identifying drivers and dynamics of phytoplankton in the Black Sea: application of a neural emulator P. Smith et al. https://doi.org/10.3389/fmars.2026.1760162
- Developing Guidelines for working with Multi-Model Ensembles in CMIP A. Katzenberger et al. https://doi.org/10.5194/esd-17-495-2026
- Deep-learning-based surrogate model for solute transport in rough-walled fractures H. Xue et al. https://doi.org/10.1016/j.compgeo.2025.107813
- Using Machine Learning for Climate Modelling: Application of Neural Networks to a Slow-Fast Chaotic Dynamical System as a Case Study S. Soldatenko & Y. Angudovich https://doi.org/10.3390/cli12110189
- AI-Supported Reality: Revisiting Models and Techniques of Systems Analysis in Water Resources and Agriculture Management B. Srđević & Z. Srđević https://doi.org/10.3390/w18080914
- From rain to data: A review of the creation of monthly and daily station‐based gridded precipitation datasets R. Serrano‐Notivoli & E. Tejedor https://doi.org/10.1002/wat2.1555
- Transferring climate change physical knowledge F. Immorlano et al. https://doi.org/10.1073/pnas.2413503122
- Partial differential equations discovery with EPDE framework: Application for real and synthetic data M. Maslyaev et al. https://doi.org/10.1016/j.jocs.2021.101345
- Grouped convolution dual-attention network for time series forecasting of water temperature in offshore aquaculture net pen X. Sun et al. https://doi.org/10.1016/j.eswa.2025.127438
- A comprehensive review of deep learning applications in hydrology and water resources M. Sit et al. https://doi.org/10.2166/wst.2020.369
- Statistical mechanics in climate emulation: Challenges and perspectives I. Sudakow et al. https://doi.org/10.1017/eds.2022.15
- Pangeo-Enabled ESM Pattern Scaling (PEEPS): A customizable dataset of emulated Earth System Model output B. Kravitz et al. https://doi.org/10.1371/journal.pclm.0000159
- Performance assessment for climate intervention (PACI): preliminary application to a stratospheric aerosol injection scenario L. Wheeler et al. https://doi.org/10.3389/fenvs.2023.1205515
- A comprehensive review of digital twin — part 1: modeling and twinning enabling technologies A. Thelen et al. https://doi.org/10.1007/s00158-022-03425-4
Saved (final revised paper)
Latest update: 13 Jun 2026
Short summary
Climate model emulators can save computer time but are less accurate than full climate models. We use neural networks to build emulators of precipitation, trained on existing climate model runs. By doing so, we can capture nonlinearities and how the past state of a model (to some degree) shapes the future state. Our emulator outperforms a persistence forecast of precipitation.
Climate model emulators can save computer time but are less accurate than full climate models....
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