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.
Review article
| 
15 May 2017
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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- Performance of Pattern-Scaled Climate Projections under High-End Warming. Part I: Surface Air Temperature over Land T. Osborn et al. 10.1175/JCLI-D-17-0780.1
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- CLIMRISK-RIVER: Accounting for local river flood risk in estimating the economic cost of climate change P. Ignjacevic et al. 10.1016/j.envsoft.2020.104784
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- Exploring precipitation pattern scaling methodologies and robustness among CMIP5 models B. Kravitz et al. 10.5194/gmd-10-1889-2017
17 citations as recorded by crossref.
- Showcasing MESMER‐X: Spatially Resolved Emulation of Annual Maximum Temperatures of Earth System Models Y. Quilcaille et al. 10.1029/2022GL099012
- Economic impacts and risks of climate change under failure and success of the Paris Agreement F. Estrada & W. Botzen 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. 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. 10.1016/j.ecoinf.2019.100985
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- AIRCC-Clim: A user-friendly tool for generating regional probabilistic climate change scenarios and risk measures F. Estrada et al. 10.1016/j.envsoft.2022.105528
- Pattern scaling the parameters of a Markov‐chain gamma‐distribution daily precipitation generator S. Kemsley et al. 10.1002/joc.8320
- Technical note: Deep learning for creating surrogate models of precipitation in Earth system models T. Weber et al. 10.5194/acp-20-2303-2020
- National attribution of historical climate damages C. Callahan & J. Mankin 10.1007/s10584-022-03387-y
- Robustness of high-resolution regional climate projections for Greenland: a method for uncertainty distillation M. Olesen et al. 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. 10.5194/esd-11-139-2020
- Time of emergence of economic impacts of climate change P. Ignjacevic et al. 10.1088/1748-9326/ac0d7a
- Pangeo-Enabled ESM Pattern Scaling (PEEPS): A customizable dataset of emulated Earth System Model output B. Kravitz et al. 10.1371/journal.pclm.0000159
- 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. 10.5194/gmd-15-2085-2022
- Understanding pattern scaling errors across a range of emissions pathways C. Wells et al. 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. 10.1016/j.envsoft.2020.104784
- Heat stress on agricultural workers exacerbates crop impacts of climate change C. de Lima et al. 10.1088/1748-9326/abeb9f
1 citations as recorded by crossref.
Latest update: 29 Jun 2024
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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