the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Observing long-lived longwave contrail forcing
Abstract. Contrail microphysical simulations and climate simulations have indicated that contrail cirrus cause a substantial fraction of aviation’s climate impact. While the approximations and parameter selections in these simulations have been well-validated over the past two decades, the heat trapping of contrails has not been observed using satellite data beyond a few hours. This is because contrails lose their linear shape after a few hours, making them difficult to distinguish from natural cirrus clouds. Here we provide satellite-driven analysis of long-lived heat trapping by contrails over North and South America. We aggregate a dataset of GOES-16 estimated outgoing longwave radiation and advected trace density of flight paths, and apply causal inference to discern the effect of contrails while controlling for radiative and cloud confounders. As a means of validation, we also generate synthetic datasets with known ground truth, and confirm that applying the causal inference method is able to recover the synthetic ground truth. Since this method yields an estimate which has some differences from both “instantaneous radiative forcing” (iRF) and “effective radiative forcing” (ERF) estimates which have been reported in the literature so far, we introduce the new term “observational radiative forcing, 12 hours” (oRF12). Our analysis estimates the longwave oRF12 from contrails over the Americas averaged 46.9 gigajoules per flight kilometer (95 % CI: 35.8 to 58.0 GJ/km) during April 2019 to April 2020.
Competing interests: Authors are employees of Google Inc. Google is a technology company that sells computing and machine learning services as part of its business.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.- Preprint
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Status: open (until 27 Oct 2025)
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RC1: 'Comment on egusphere-2025-3739', Anonymous Referee #2, 07 Oct 2025
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The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-3739/egusphere-2025-3739-RC1-supplement.pdfReplyCitation: https://doi.org/
10.5194/egusphere-2025-3739-RC1 -
RC2: 'Comment on egusphere-2025-3739', Anonymous Referee #1, 10 Oct 2025
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In this paper, a Sonabend-W et al. quantified the longwave radiative forcing of contrails at the top-of-atmosphere (TOA) based on GOES-16 satellite observations and ERA5 reanalysis, and estimated that the longwave radiative forcing of contrails is 46.9 GJ on average over the Americas. The authors apply causal inference to discern the effect of contrails while controlling for radiative and cloud confounders. The authors compared their results with CoCiP data, and illustrates how the longwave warming of contrails varies between day and night.
This method is plausible, but the results are unreasonable, so the authors should check the details to correct any potential mistakes. The paper might be accepted after addressing the following issues:
- According to Fig. 10, the new method yields a longwave forcing of zero at noon, which is not reasonable. Unless there is no contrail at all at noon (which is untrue), the longwave forcing of contrails should not be zero. Furthermore, the surface temperature is higher at noon-time, so theoretically the longwave forcing of a contrail should be significant. Therefore, the CoCiP RF is more reasonable than that calculated by the new method.
Note: The authors added some discussions to address this issue, but it is hard to believe a zero longwave contrail forcing at noon. The unrealistic zero forcing at noon might be induced by issues in the regression process (see the next comment).
- In Eq. (3), a simple linear regression is used to calculate the parameters in the equation. However, as the authors pointed out, the correlation between independent variables Ai and Aj is large, so linear regression is not valid in this case. If the authors keep using simple linear regression, then this equation should be rewritten.
Citation: https://doi.org/10.5194/egusphere-2025-3739-RC2
Model code and software
Example code Aaron Sonabend-W, Scott Geraedts, Nita Goyal, Joe Yue-Hei Ng, Christopher Van Arsdale, and Kevin McCloskey https://github.com/google-research/google-research/tree/master/contrails_longwave/
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