the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 20 May 2026
From Physics to AI: A Multidisciplinary Review of Contrail Prediction Models
Abstract. Aviation-induced condensation trails (contrails) and contrail cirrus represent a dominant yet uncertain component of effective radiative forcing (ERF), potentially exceeding the warming impact of accumulated carbon dioxide. As the aviation sector targets climate-optimal operations by 2030, the demand for scalable, real-time contrail forecasting has driven a fundamental paradigm shift in modeling strategies. This review provides a comprehensive analysis of contrail prediction methodologies spanning eight decades, classifying the evolution into five distinct epochs: (1) Thermodynamic and Analytical Foundations (1940s–1990s), rooted in the Schmidt-Appleman Criterion (SAC) for binary formation thresholds; (2) Microphysical Simulation (1990s–2010s), exemplified by the Contrail Cirrus Prediction (CoCiP) and APCEMM models, which resolve complex particle dynamics and lifecycle evolution; (3) NWP-Integrated Frameworks (2000s–Present), such as ECMWF IFS and WRF-Chem, which embed contrail parameterizations into global weather systems; (4) Satellite-Empirical Models, leveraging AVHRR, MODIS, and CALIOP data to establish climatological baselines and validate physical assumptions; and (5) AI-Driven and Hybrid Frontiers (2020–2026), where deep learning architectures, including U-Net segmentation, Physics-Informed Neural Networks (PINNs), and the Google-DLR hybrid system, are revolutionizing real-time detection and flight attribution. By critically evaluating the trade-offs between physical interpretability and computational scalability, this paper identifies the emerging consensus that future operational systems must adopt hybrid architectures – merging the robust constraints of first-principles physics with the adaptive precision of artificial intelligence – to enable verifiable contrail avoidance and sustainable flight planning.
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Status: final response (author comments only)
- RC1: 'Comment on egusphere-2026-799', Dharmendra Kumar Singh, 12 Jul 2026
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RC2: 'Comment on egusphere-2026-799', Anonymous Referee #2, 19 Jul 2026
This manuscript presents a comprehensive review of contrail prediction models, covering the evolution from physics-based approaches to recent AI-driven methods. The topic is relevant given the increasing interest in aviation climate mitigation and climate-optimal flight operations. The manuscript is generally well organized, and the extensive literature survey, together with the summary tables in the Appendix, makes it a potentially valuable reference for researchers entering this field.
However, I believe the manuscript should have several improvements before publication.
First, the review is largely descriptive rather than critical. Many sections summarize individual studies sequentially, but there is relatively limited comparison across different models. As a review paper, I encourage the authors to place greater emphasis on discussing the strengths, limitations, uncertainties, and operational applicability of different prediction models, instead of primarily describing individual models.
Second, the manuscript could be made more concise. The citation format is unusual and differs from the journal's standard style. Many paragraphs are unnecessarily long. The space saved could be used to strengthen the discussion on topics that are currently underrepresented, such as prediction uncertainty, evaluation metrics, operational implementation for climate-optimal routing, and future research directions.
Third, the discussion of AI is somewhat optimistic. It would provide a more balanced perspective if the authors also discussed current limitations of AI-based approaches, including data availability, model generalization, interpretability, and dependence on high-quality meteorological inputs.
Overall, this is a timely review with broad literature coverage. By reducing repetitive descriptions and strengthening the critical analysis and discussion sections, the manuscript would become a more useful reference for both the atmospheric science and aviation research communities.
Recommendation: Major Revision. Although the scientific content is generally sound, the manuscript would benefit from a more concise presentation and stronger critical synthesis of the existing literature.
Citation: https://doi.org/10.5194/egusphere-2026-799-RC2
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Recommendation: Major Revision
This manuscript reviews the development of contrail prediction models from classical thermodynamic approaches to recent AI-based methods. The topic is timely and relevant, and the manuscript covers a broad range of literature that could be useful to researchers entering this field.
However, in its current form, I do not believe the manuscript is ready for publication in ACP. My main concern is that it reads more like a compilation of published studies than a critical review. Many sections summarize individual papers without providing sufficient comparison, discussion, or critical assessment of the different modeling approaches. A review paper should help readers understand not only what has been done, but also the strengths, limitations, and remaining challenges of each approach.
The section on AI-based methods is interesting, but it should present a more balanced discussion of their current limitations, such as model interpretability, uncertainty, and dependence on training data. Likewise, greater emphasis should be given to model validation and the uncertainties associated with meteorological inputs, especially humidity fields, which remain a major challenge in contrail prediction.
The manuscript would also benefit from careful editing for language and style. There are several long paragraphs, repeated statements, and minor grammatical issues that affect readability.
Overall, the topic is important and the authors have invested considerable effort in compiling the literature. With stronger critical analysis, improved organization, and careful revision of the text, this manuscript could become a valuable review for the community.