the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 18 Aug 2025
An EOF-Based Emulator of Means and Covariances of Monthly Climate Fields
Abstract. Fast emulators of comprehensive climate models are often used to explore the impact of anthropogenic emissions on future climate. A new approach to emulators is introduced that predicts means and covariances of monthly averaged climate variables. The emulator is trained with output from a state-of-the-art climate model and serves as a good first-order representation for the evolution of spatially resolved climate variables and their variability. For illustrative purposes, the emulator is applied to predict changes in the mean and variability of monthly values of both temperature and relative humidity as a function of global mean temperature changes. However, the approach can be applied to any other variable of interest.
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Status: open (until 13 Oct 2025)
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RC1: 'Comment on egusphere-2025-3768', Anonymous Referee #1, 19 Aug 2025
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This manuscript proposes a change of basis method to project spatially explicit means and covariances of monthly climate variables as a function of global average temperature change. There’s nothing wrong I can see in the method and it is an approach to emulation I have not seen before. But it almost reads as not having been developed in close collaboration with a user group and I think suffers some serious shortcomings because of that. Ultimately, I am left asking ‘who is this actually for, specifically? Who is going to pick this up, generate values, and use them? How specifically will they be used?’ I'm confident the authors have something in mind for this, but I think the manuscript would benefit for a more explicit treatment of this question.Â
Major issues
- What is the use case for spatially resolved means and covariances of monthly variables? I’m not trying to be glib, I’m coming at this from the perspective of an impact modeler where I need time series of daily or monthly values of variables. I can’t use the statistics you highlight reconstructing in section 4.2 and Section 5 + appendices. Am I missing something? Do you generate the time series and just demonstrate on statistics (Fig 6-8. E1) because those are more critical for validation? If so, I think having at least an example plot in Appendix E showing an actual time series generated with this method is key, You may also want to consider extending Appendix E and moving it explicitly into the main body of the manuscript.
- If I have to plug into another emulator like DiffESM to get daily values, why do I need this? A skim of the DiffESM paper shows they only need monthly averages and not covariances and other, simpler methods can give monthly averages. The STITCHES approach can generate a decent sized ensemble of time series of multiple variables jointly just from global temperature.Â
- The method seems to apply well to any individual gridded variables (demonstrated in the manuscript with surface temperature and surface relative humidity) but, unless I read it wrong, this doesn’t extend to coherent joint emulation of multiple variables, right? There are certainly some impact models that only need temperature or relative humidity or precipitation, but many need all of those variables coherently together. I’m thinking of hydrology models especially. Can this method handle that? If not, what could be downsides of using independently generated time series of temperature, relative humidity, and precipitation together?
- The authors are clear an ESM needs to have provided a sufficiently large collection of runs to train from, but how large is large?
- You touch on the implications of this in your final paragraph but I think this needs to be expanded. Like most emulation techniques, this approach targets a single ESM. But many studies using outputs from emulation, say to study projections of a novel scenario, are also concerned with multi-model uncertainty, i.e. they would want multiple emulators each trained on a different ESM. Does the collection of ESMs that provided ‘enough’ training have uncertainty properties at all similar uncertainty characteristics as the full collection of models? I don’t have any way of even roughly guessing because I don’t know what the extent of ESMs providing enough data to be individually emulated is.
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Missing relevant citations for exclusively emulators of the class trained to extend ESMs to arbitrary future scenarios:
- Bassetti et al was actually published nearly a year ago https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2023MS004194
- Tebaldi, C., Snyder, A., and Dorheim, K.: STITCHES: creating new scenarios of climate model output by stitching together pieces of existing simulations, Earth Syst. Dynam., 13, 1557–1609, https://doi.org/10.5194/esd-13-1557-2022, 2022.
- Quilcaille et al https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2022GL099012
and Quilcaille et al https://esd.copernicus.org/articles/14/1333/2023/
Citation: https://doi.org/10.5194/egusphere-2025-3768-RC1 - What is the use case for spatially resolved means and covariances of monthly variables? I’m not trying to be glib, I’m coming at this from the perspective of an impact modeler where I need time series of daily or monthly values of variables. I can’t use the statistics you highlight reconstructing in section 4.2 and Section 5 + appendices. Am I missing something? Do you generate the time series and just demonstrate on statistics (Fig 6-8. E1) because those are more critical for validation? If so, I think having at least an example plot in Appendix E showing an actual time series generated with this method is key, You may also want to consider extending Appendix E and moving it explicitly into the main body of the manuscript.
Data sets
GaussianEarth Gosha Geogdzhayev and Andre N. Souza https://github.com/sandreza/GaussianEarth
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