Articles | Volume 9, issue 1
https://doi.org/10.5194/essd-9-281-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/essd-9-281-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Data review article
| 
15 May 2017
Data review article |
| 15 May 2017
An open-access CMIP5 pattern library for temperature and precipitation: description and methodology
Pacific Northwest National Laboratory, Joint Global Change Research Institute, 5825 University Research Court, Suite 3500, College Park, MD 20740, USA
Corinne Hartin
Pacific Northwest National Laboratory, Joint Global Change Research Institute, 5825 University Research Court, Suite 3500, College Park, MD 20740, USA
Ben Bond-Lamberty
Pacific Northwest National Laboratory, Joint Global Change Research Institute, 5825 University Research Court, Suite 3500, College Park, MD 20740, USA
Ben Kravitz
Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, 902 Battelle Boulevard, Richland, WA 99352, USA
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- Economic impacts and risks of climate change under failure and success of the Paris Agreement F. Estrada & W. Botzen https://doi.org/10.1111/nyas.14652
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- Building Interpretable Climate Emulators for Economics A. Eftekhari et al. https://doi.org/10.1093/ej/ueaf131
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- A computationally efficient method to model similar and alternate stratospheric aerosol injection experiments using prescribed aerosols in a lower-complexity version of the same model: a case study using CESM(CAM) and CESM(WACCM) J. de Jong et al. https://doi.org/10.5194/gmd-18-8679-2025
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26 citations as recorded by crossref.
- Urban and non-urban contributions to the social cost of carbon F. Estrada et al. https://doi.org/10.1038/s41467-025-59466-y
- AIRCC-Clim: A user-friendly tool for generating regional probabilistic climate change scenarios and risk measures F. Estrada et al. https://doi.org/10.1016/j.envsoft.2022.105528
- Pattern scaling the parameters of a Markov‐chain gamma‐distribution daily precipitation generator S. Kemsley et al. https://doi.org/10.1002/joc.8320
- Can today’s and tomorrow’s world uniformly gain from carbon taxation? L. Kotlikoff et al. https://doi.org/10.1016/j.euroecorev.2024.104819
- Robustness of high-resolution regional climate projections for Greenland: a method for uncertainty distillation M. Olesen et al. https://doi.org/10.3354/cr01536
- Emulating Earth system model temperatures with MESMER: from global mean temperature trajectories to grid-point-level realizations on land L. Beusch et al. https://doi.org/10.5194/esd-11-139-2020
- Emulators of Climate Model Output C. Tebaldi et al. https://doi.org/10.1146/annurev-environ-012125-085838
- Time of emergence of economic impacts of climate change P. Ignjacevic et al. https://doi.org/10.1088/1748-9326/ac0d7a
- From emission scenarios to spatially resolved projections with a chain of computationally efficient emulators: coupling of MAGICC (v7.5.1) and MESMER (v0.8.3) L. Beusch et al. https://doi.org/10.5194/gmd-15-2085-2022
- Understanding pattern scaling errors across a range of emissions pathways C. Wells et al. https://doi.org/10.5194/esd-14-817-2023
- CLIMRISK-RIVER: Accounting for local river flood risk in estimating the economic cost of climate change P. Ignjacevic et al. https://doi.org/10.1016/j.envsoft.2020.104784
- Climate Change Through the Lens of Macroeconomic Modeling J. Fernández-Villaverde et al. https://doi.org/10.1146/annurev-economics-091124-045357
- Carbon majors and the scientific case for climate liability C. Callahan & J. Mankin https://doi.org/10.1038/s41586-025-08751-3
- Non-radiative effects dominate the local surface temperature response to land-cover change—Insights from a semi-empirical model L. Hänchen et al. https://doi.org/10.1016/j.jenvman.2025.124741
- Showcasing MESMER‐X: Spatially Resolved Emulation of Annual Maximum Temperatures of Earth System Models Y. Quilcaille et al. https://doi.org/10.1029/2022GL099012
- Economic impacts and risks of climate change under failure and success of the Paris Agreement F. Estrada & W. Botzen https://doi.org/10.1111/nyas.14652
- Performance of Pattern-Scaled Climate Projections under High-End Warming. Part I: Surface Air Temperature over Land T. Osborn et al. https://doi.org/10.1175/JCLI-D-17-0780.1
- Predicted changes in the potential distribution of seerfish (Scomberomorus sierra) under multiple climate change scenarios in the Colombian Pacific Ocean S. Herrera Montiel et al. https://doi.org/10.1016/j.ecoinf.2019.100985
- Anthropogenic influence in observed regional warming trends and the implied social time of emergence F. Estrada et al. https://doi.org/10.1038/s43247-021-00102-0
- Building Interpretable Climate Emulators for Economics A. Eftekhari et al. https://doi.org/10.1093/ej/ueaf131
- GeoMIP-Pattern – a pattern scaling dataset for efficient generation of custom geoengineering scenarios R. Muñoz-Sánchez et al. https://doi.org/10.1038/s41597-025-05496-6
- Technical note: Deep learning for creating surrogate models of precipitation in Earth system models T. Weber et al. https://doi.org/10.5194/acp-20-2303-2020
- National attribution of historical climate damages C. Callahan & J. Mankin https://doi.org/10.1007/s10584-022-03387-y
- 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
- A computationally efficient method to model similar and alternate stratospheric aerosol injection experiments using prescribed aerosols in a lower-complexity version of the same model: a case study using CESM(CAM) and CESM(WACCM) J. de Jong et al. https://doi.org/10.5194/gmd-18-8679-2025
- Heat stress on agricultural workers exacerbates crop impacts of climate change C. de Lima et al. https://doi.org/10.1088/1748-9326/abeb9f
Saved (final revised paper)
Latest update: 11 Sep 2026
Short summary
Pattern scaling climate model output is a computationally efficient way to produce a large amount of data for purposes of uncertainty quantification. Using a multi-model ensemble we explore pattern scaling methodologies across two future forcing scenarios. We find that the simple least squares approach to pattern scaling produces a close approximation of actual model output, and we use this as a justification for the creation of an open-access pattern library at multiple time increments.
Pattern scaling climate model output is a computationally efficient way to produce a large...
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