Articles | Volume 11, issue 1
https://doi.org/10.5194/esd-11-139-2020
© 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/esd-11-139-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
17 Feb 2020
Research article |
| 17 Feb 2020
| 17 Feb 2020
Emulating Earth system model temperatures with MESMER: from global mean temperature trajectories to grid-point-level realizations on land
Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland
Lukas Gudmundsson
Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland
Sonia I. Seneviratne
Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland
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37 citations as recorded by crossref.
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35 citations as recorded by crossref.
- Understanding pattern scaling errors across a range of emissions pathways C. Wells et al. 10.5194/esd-14-817-2023
- Showcasing MESMER‐X: Spatially Resolved Emulation of Annual Maximum Temperatures of Earth System Models Y. Quilcaille et al. 10.1029/2022GL099012
- MESMER-M: an Earth system model emulator for spatially resolved monthly temperature S. Nath et al. 10.5194/esd-13-851-2022
- Extending MESMER-X: a spatially resolved Earth system model emulator for fire weather and soil moisture Y. Quilcaille et al. 10.5194/esd-14-1333-2023
- Assessing the potential for simplification in global climate model cloud microphysics U. Proske et al. 10.5194/acp-22-4737-2022
- ClimateBench v1.0: A Benchmark for Data‐Driven Climate Projections D. Watson‐Parris et al. 10.1029/2021MS002954
- STITCHES: creating new scenarios of climate model output by stitching together pieces of existing simulations C. Tebaldi et al. 10.5194/esd-13-1557-2022
- Sequential learning of climate change via a physical-parameter-based state-space model and Bayesian inference Y. Lai & M. Pozzi 10.1007/s10584-024-03739-w
- The climate in climate economics D. Folini et al. 10.2139/ssrn.3885021
- Modeling the non-CO2 contribution to climate change C. Smith & T. Gasser 10.1016/j.oneear.2022.11.007
- TIMBER v0.1: a conceptual framework for emulating temperature responses to tree cover change S. Nath et al. 10.5194/gmd-16-4283-2023
- Assessing the representational accuracy of data-driven models: The case of the effect of urban green infrastructure on temperature M. Zumwald et al. 10.1016/j.envsoft.2021.105048
- Producing realistic climate data with generative adversarial networks C. Besombes et al. 10.5194/npg-28-347-2021
- Responsibility of major emitters for country-level warming and extreme hot years L. Beusch et al. 10.1038/s43247-021-00320-6
- Large-scale emulation of spatio-temporal variation in temperature under climate change X. Yuan et al. 10.1088/1748-9326/abd213
- Toward Efficient Calibration of Higher‐Resolution Earth System Models C. Fletcher et al. 10.1029/2021MS002836
- The IPCC Sixth Assessment Report WGIII climate assessment of mitigation pathways: from emissions to global temperatures J. Kikstra et al. 10.5194/gmd-15-9075-2022
- Sensitivity of Air Pollution Exposure and Disease Burden to Emission Changes in China Using Machine Learning Emulation L. Conibear et al. 10.1029/2021GH000570
- Machine learning for weather and climate are worlds apart D. Watson-Parris 10.1098/rsta.2020.0098
- National attribution of historical climate damages C. Callahan & J. Mankin 10.1007/s10584-022-03387-y
- Isolating the Evolving Contributions of Anthropogenic Aerosols and Greenhouse Gases: A New CESM1 Large Ensemble Community Resource C. Deser et al. 10.1175/JCLI-D-20-0123.1
- Partitioning climate projection uncertainty with multiple large ensembles and CMIP5/6 F. Lehner et al. 10.5194/esd-11-491-2020
- Heat stress in Africa under high intensity climate change B. Parkes et al. 10.1007/s00484-022-02295-1
- The Value of Initial Condition Large Ensembles to Robust Adaptation Decision‐Making J. Mankin et al. 10.1029/2020EF001610
- The Climate in Climate Economics D. Folini et al. 10.1093/restud/rdae011
- Crossbreeding CMIP6 Earth System Models With an Emulator for Regionally Optimized Land Temperature Projections L. Beusch et al. 10.1029/2019GL086812
- STITCHES: a Python package to amalgamate existing Earth system model output into new scenario realizations A. Snyder et al. 10.21105/joss.05525
- A perspective on the next generation of Earth system model scenarios: towards representative emission pathways (REPs) M. Meinshausen et al. 10.5194/gmd-17-4533-2024
- Pangeo-Enabled ESM Pattern Scaling (PEEPS): A customizable dataset of emulated Earth System Model output B. Kravitz et al. 10.1371/journal.pclm.0000159
- Latent Linear Adjustment Autoencoder v1.0: a novel method for estimating and emulating dynamic precipitation at high resolution C. Heinze-Deml et al. 10.5194/gmd-14-4977-2021
- A range of outcomes: the combined effects of internal variability and anthropogenic forcing on regional climate trends over Europe C. Deser & A. Phillips 10.5194/npg-30-63-2023
- The inherent uncertainty of precipitation variability, trends, and extremes due to internal variability, with implications for Western US water resources K. McKinnon & C. Deser 10.1175/JCLI-D-21-0251.1
- 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
- Interactions between atmospheric composition and climate change – progress in understanding and future opportunities from AerChemMIP, PDRMIP, and RFMIP S. Fiedler et al. 10.5194/gmd-17-2387-2024
- Representing natural climate variability in an event attribution context: Indo-Pakistani heatwave of 2022 S. Nath et al. 10.1016/j.wace.2024.100671
2 citations as recorded by crossref.
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Short summary
Earth system models (ESMs) are invaluable to study the climate system but expensive to run. Here, we present a statistical tool which emulates ESMs at a negligible computational cost by creating stochastic realizations of yearly land temperature field time series. Thereby, 40 ESMs are considered, and for each ESM, a single simulation is required to train the tool. The resulting ESM-specific realizations closely resemble ESM simulations not employed during training at point to regional scales.
Earth system models (ESMs) are invaluable to study the climate system but expensive to run....
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