the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 12 Aug 2025
Constructing Extreme Heatwave Storylines with Differentiable Climate Models
Abstract. Understanding the plausible upper bounds of extreme weather events is essential for risk assessment in a warming climate. Existing methods, based on large ensembles of physics-based models, are often computationally expensive or lack the fidelity needed to simulate rare, high-impact extremes. Here, we present a novel framework that leverages a differentiable hybrid climate model, NeuralGCM, to optimize initial conditions and generate physically consistent worst-case heatwave trajectories. Applied to the 2021 Pacific Northwest heatwave, our method produces heatwave intensity up to 3.7 °C above the most extreme member of a 75-member ensemble. These trajectories feature intensified atmospheric blocking and amplified Rossby wave patterns—hallmarks of severe heat events. Our results demonstrate that differentiable climate models can efficiently explore the upper tails of event likelihoods, providing a powerful new approach for constructing targeted storylines of extreme weather under climate change.
Status: final response (author comments only)
- RC1: 'Comment on egusphere-2025-3748', Anonymous Referee #1, 14 Sep 2025
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RC2: 'Comment on egusphere-2025-3748', Anonymous Referee #2, 23 Oct 2025
Review of Constructing Extreme Heatwave Storylines with Differentiable Climate Models
Summary
The authors leverage the differentiable nature of NeuralGCM to identify the optimal initial conditions (ICs) for the 2021 Pacific Northwest heatwave, and then demonstrate that the simulations following these ICs indeed produce very extreme heat in the region that exceeds values produced in a standard NeuralGCM ensemble. The paper is a nice demonstration of the potential to use differentiation to produce boosted ensembles.
Major comments
- Table 2 provides information about the perturbations used in the optimized runs (and the range for the ensemble). Is the max across space? This is not clear. What is the spatial structure of the perturbations? Is it constrained in some way, or emerges directly from the differentiation? Can the authors show the perturbations in a figure, and can we learn from their structure?
- Can the authors comment further on the use of N=50 vs N=75? How were these chosen? If we wanted to reduce compute, would we still have success with e.g. N=10? How different would N=75 be from N=something large? I don't necessarily expect the author to do this experiment, but simply comment on their expectations for the sensitivity of the results to this choice.
- The authors compare to ERA5 early on, but then drop the comparison. Are the optimized heatwaves comparable to ERA5, perhaps after accounting for any biases in the mean state during the early summer period? Do the other variables shown in Figure 5 follow similar trajectories to ERA5?
- Figure 5 shows that some plausible drivers of the heatwave are largely within the envelope of the original NeuralGCM ensemble, raising the question of what actually caused the extreme heat. One option is that no single driver is extreme, but they are collectively extreme given correlation between them. It may also be worth looking at shortwave radiation and advection if these are available in NeuralGCM.
Specific comments
- Please include line numbers in future drafts.
- Page 1: There are a number of papers about the dynamics of the 2021 PNW heatwave beyond the Mass et al study that should be cited, e.g. White et al, https://www.nature.com/articles/s41467-023-36289-3; Neal et al, https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2021GL097699; Duan et al, https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2025EF006216 as a starter package
- Page 2: McKinnon and Simpson, https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2022GL100380 is also relevant for the use of large ensembles specific to the 2021 PNW heatwave
- Page 2: The computational cost of training the models is high, although prediction is relatively cheap. At what point does the cheapness of the prediction outweigh the expense of the training?
- Page 3: The potential role of quasi-resonant amplification is not fully established within the literature for the 2021 heatwave.
- Page 4: Have you confirmed that the 1000 hPa is above the surface at all points in the domain? I suspect it is not based on the topography. Why use 1000 hPa rather than temperature 2m above the surface, which is the more typical choice of variable for heat?
- Page 5 / Table 1: Could the authors provide some intuition about the choice of the two sets of parameters for the optimization process?
- Table 2: The difference between the first and second columns is not clear, and the title of the third column could be improved.
- Figure 1a: Given that the heatwave happened in summer, suggest subsetting the data to a relevant summer period (e.g. June-July) and then comparing histograms.
- Figure 2: Please reduce the thickness and/or number of contours in the middle panel, since it is hard to see the shading.
- Page 13: Could the authors say more about the dual-initial-condition requirement of purely data driven models?
- There are typos and citation errors with respect to use of in-text vs parenthetical citations that should be corrected.Citation: https://doi.org/10.5194/egusphere-2025-3748-RC2
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- 1
Dear Editor,
Thank you for inviting me to review this manuscript. Please find below my comments and suggestions to the authors.
General Comment
First of all, I would like to thank the authors for investigating the promising field of finding alternative, less-computationally demanding tools for simulating extreme events. The overarching aim of the study is timely and well chosen.
In this paper, the authors propose a method to simulate storylines of extreme weather events by perturbing the initial conditions of the hybrid climate model NeuralGCM, which is differentiable and therefore amenable to gradient-based optimization. Their framework identifies small but plausible initial perturbations that evolve into more extreme heatwave trajectories. As a proof of concept, the method is applied to the 2021 Pacific Northwest heatwave (PNW2021). The optimized runs produce heatwaves that are up to 3–4 °C hotter than any member of a 75-member stochastic NeuralGCM ensemble, with strengthened blocking and Rossby wave patterns consistent with known physical mechanisms. Importantly, the required perturbations remain within ensemble variability, suggesting plausibility.
The methodology is promising, and I consider the article worthy of publication after revisions. My general impression is that the material is interesting but the presentation sometimes makes it difficult for readers to follow, particularly in Sections 2 and 3, which should undergo some changes. By contrast, I found the Discussion (Section 4) concise, clear, and well situated within the literature. Below, I provide more detailed comments that I hope will help the authors clarify and strengthen the manuscript.
Specific Comments
Section 2.1
Section 2.2
Section 2.3
Section 3.1
Section 3.2
Section 3.4
Conclusion
Final Remark
Overall, I found this to be a promising and well-motivated study, but one that would benefit from greater clarity in the methodology and results sections. The suggestions above aim to improve accessibility and transparency for readers. I believe the manuscript is suitable for publication after these issues are addressed.