Articles | Volume 19, issue 5
https://doi.org/10.5194/amt-19-1951-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-19-1951-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
18 Mar 2026
Research article |
| 18 Mar 2026
| 18 Mar 2026
Observing long-lived longwave contrail forcing
Aaron Sonabend-W
Google Research, Mountain View, CA, USA
Scott Geraedts
Google Research, Mountain View, CA, USA
Nita Goyal
Google Research, Mountain View, CA, USA
Joe Yue-Hei Ng
Google Research, Mountain View, CA, USA
Christopher Van Arsdale
Google Research, Mountain View, CA, USA
Google Research, Mountain View, CA, USA
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J. Env. Com. Air Transp. Sys. Discuss., https://doi.org/10.5194/jecats-2026-4, https://doi.org/10.5194/jecats-2026-4, 2026
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This randomized control trial provides evidence for the efficacy of airline-led contrail avoidance to reduce aviation's climate impact. By integrating ML forecasts into standard flight planning, dispatchers routed flights to avoid warming contrails. Satellite validation showed an 11.6 % overall reduction in contrails, increasing to 62 % for flights strictly following the optimized paths, all with no significant fuel penalty.
Tharun Sankar, Thomas Dean, Tristan Abbott, Jill Blickstein, Alejandra Martín Frías, Mark Galyen, Rebecca Grenham, Paul Hodgson, Kevin McCloskey, Alan Pechman, Tyler Robarge, Dinesh Sanekommu, Aaron Sarna, Aaron Sonabend-W, Marc Stettler, Raimund Zopp, and Scott Geraedts
J. Env. Com. Air Transp. Sys. Discuss., https://doi.org/10.5194/jecats-2026-4, https://doi.org/10.5194/jecats-2026-4, 2026
Preprint under review for JECATS
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This randomized control trial provides evidence for the efficacy of airline-led contrail avoidance to reduce aviation's climate impact. By integrating ML forecasts into standard flight planning, dispatchers routed flights to avoid warming contrails. Satellite validation showed an 11.6 % overall reduction in contrails, increasing to 62 % for flights strictly following the optimized paths, all with no significant fuel penalty.
Scott Geraedts, Aaron Sarna, Susanne Rohs, Roger Teoh, and Kevin McCloskey
J. Env. Com. Air Transp. Sys. Discuss., https://doi.org/10.5194/jecats-2026-6, https://doi.org/10.5194/jecats-2026-6, 2026
Preprint under review for JECATS
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It can be difficult to know if a given aircraft made a contrail. Existing methods that can be applied to any aircraft either use numerical weather data or satellite observations. In this work we create a new method by combining weather data and observations together. By comparison to in-situ measurements we show that the new method is superior to previous methods.
Aaron Sarna, Vincent Meijer, Rémi Chevallier, Allie Duncan, Kyle McConnaughay, Scott Geraedts, and Kevin McCloskey
Atmos. Meas. Tech., 18, 3495–3532, https://doi.org/10.5194/amt-18-3495-2025, https://doi.org/10.5194/amt-18-3495-2025, 2025
Short summary
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Contrails, the clouds formed by aircraft, are have a substantial climate impact. Determining which flight formed each contrail is a critical step to decreasing this impact. We introduce a dataset of synthetic contrail observations with known flight attributions that can be used to develop and assess geostationary-satellite-based contrail-to-flight attribution systems. We additionally introduce a new attribution algorithm and show that it outperforms previous methods.
Cited articles
Abrevaya, J., Hsu, Y.-C., and Lieli, R. P.: Estimating Conditional Average Treatment Effects, J. Bus. Econ. Stat., 33, 485–505, https://doi.org/10.1080/07350015.2014.975555, 2015. a
Agarwal, A., Meijer, V. R., Eastham, S. D., Speth, R. L., and Barrett, S. R.: Reanalysis-driven simulations may overestimate persistent contrail formation by 100 %–250 %, Environ. Res. Lett., 17, 014045, https://doi.org/10.1088/1748-9326/ac38d9, 2022. a
Akhtar Martínez, C., Eastham, S. D., and Jarrett, J. P.: Zero-dimensional contrail models could underpredict lifetime optical depth, Atmos. Chem. Phys., 25, 12875–12891, https://doi.org/10.5194/acp-25-12875-2025, 2025. a
Bickel, M., Ponater, M., Bock, L., Burkhardt, U., and Reineke, S.: Estimating the effective radiative forcing of contrail cirrus, J. Climate, 33, 1991–2005, https://doi.org/10.1175/JCLI-D-19-0467.1, 2020. a
Bickel, M., Ponater, M., Burkhardt, U., Righi, M., Hendricks, J., and Jöckel, P.: Contrail Cirrus Climate Impact: From Radiative Forcing to Surface Temperature Change, J. Climate, 38, 1895–1912, https://doi.org/10.1175/JCLI-D-24-0245.1, 2025. a
Bock, L. and Burkhardt, U.: Reassessing properties and radiative forcing of contrail cirrus using a climate model, J. Geophys. Res.-Atmos., 121, 9717–9736, https://doi.org/10.1002/2016JD025112, 2016. a
Brasseur, G. P., Gupta, M., Anderson, B. E., Balasubramanian, S., Barrett, S., Duda, D., Fleming, G., Forster, P. M., Fuglestvedt, J., Gettelman, A., Halthore, R. N., Jacob, S. D., Jacobson, M. Z., Khodayari, A., Liou, K.-N., Lund, M., T., Miake-Lye, R., C., Minnis, P., Olsen, S., Penner, J. E., Prinn, R., Schumann, U., Selkirk, H. B., Sokolov, A., Unger, N., Wolfe, P., Wong, H.-W., Wuebbles, D. W., Yi, B., Yang, P., and Zhou, C.: Impact of aviation on climate: FAA's aviation climate change research initiative (ACCRI) phase II, B. Am. Meteorol. Soc., 97, 561–583, https://doi.org/10.1175/BAMS-D-13-00089.1, 2016. a
Burkhardt, U. and Kärcher, B.: Global radiative forcing from contrail cirrus, Nat. Clim. Change, 1, 54–58, https://doi.org/10.1038/nclimate1068, 2011. a
Chen, C.-C. and Gettelman, A.: Simulated radiative forcing from contrails and contrail cirrus, Atmos. Chem. Phys., 13, 12525–12536, https://doi.org/10.5194/acp-13-12525-2013, 2013. a
Chen, Y., Haywood, J., Wang, Y., Malavelle, F., Jordan, G., Partridge, D., Fieldsend, J., De Leeuw, J., Schmidt, A., Cho, N., Oreopoulos, L., Platnick, S., Grosvenor, D., Field, P., and Lohmann, U.: Machine learning reveals climate forcing from aerosols is dominated by increased cloud cover, Nat. Geosci., 15, 609–614, https://doi.org/10.1038/s41561-022-00991-6, 2022. a
Chevallier, R., Shapiro, M., Engberg, Z., Soler, M., and Delahaye, D.: Linear Contrails Detection, Tracking and Matching with Aircraft Using Geostationary Satellite and Air Traffic Data, Aerospace, 10, https://doi.org/10.3390/aerospace10070578, 2023. a
Deines, J. M., Wang, S., and Lobell, D. B.: Satellites reveal a small positive yield effect from conservation tillage across the US Corn Belt, Environ. Res. Lett., 14, 124038, https://doi.org/10.1088/1748-9326/ab503b, 2019. a
Demarchi, G., Subervie, J., Catry, T., and Tritsch, I.: Using publicly available remote sensing products to evaluate REDD+ projects in Brazil, Global Environ. Chang., 80, 102653, https://doi.org/10.1016/j.gloenvcha.2023.102653, 2023. a
Doelling, D. R., Sun, M., Nguyen, L. T., Nordeen, M. L., Haney, C. O., Keyes, D. F., and Mlynczak, P. E.: Advances in geostationary-derived longwave fluxes for the CERES synoptic (SYN1deg) product, J. Atmos. Ocean. Tech., 33, 503–521, https://doi.org/10.1175/JTECH-D-15-0147.1, 2016. a
Dong, B., Gregory, J. M., and Sutton, R. T.: Understanding land–sea warming contrast in response to increasing greenhouse gases. Part I: Transient adjustment, J. Climate, 22, 3079–3097, https://doi.org/10.1175/2009JCLI2652.1, 2009. a
Duda, D. P., Minnis, P., Nguyen, L., and Palikonda, R.: A Case Study of the Development of Contrail Clusters over the Great Lakes, J. Atmos. Sci., 61, 1132–1146, https://doi.org/10.1175/1520-0469(2004)061<1132:ACSOTD>2.0.CO;2, 2004. a
Efron, B.: Better Bootstrap Confidence Intervals, J. Am. Stat. Assoc., 82, 171–185, https://doi.org/10.1080/01621459.1987.10478410, 1987. a
Fons, E., Runge, J., Neubauer, D., and Lohmann, U.: Stratocumulus adjustments to aerosol perturbations disentangled with a causal approach, npj Climate and Atmospheric Science, 6, 130, https://doi.org/10.1038/s41612-023-00452-w, 2023. a, b
Freudenthaler, V., Homburg, F., and Jäger, H.: Contrail observations by ground-based scanning lidar: Cross-sectional growth, Geophys. Res. Lett., 22, 3501–3504, https://doi.org/10.1029/95GL03549, 1995. a
Geraedts, S., Brand, E., Dean, T. R., Eastham, S., Elkin, C., Engberg, Z., Hager, U., Langmore, I., McCloskey, K., Ng, J. Y.-H., Platt, J. C., Sankar, T., Sarna, A., Shapiro, M., and Goyal, N.: A scalable system to measure contrail formation on a per-flight basis, Environmental Research Communications, 6, 015008, https://doi.org/10.1088/2515-7620/ad11ab, 2024. a, b
GOES-R Algorithm Working Group and GOES-R Series Program: NOAA GOES-R Series Advanced Baseline Imager (ABI) Level 2 Cloud Cover Layers (CCL), NOAA National Centers for Environmental Information [data set], https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.ncdc:C01573 (last access: 9 March 2026), 2023. a, b
Guan, H., Cober, S. G., and Isaac, G. A.: Verification of Supercooled Cloud Water Forecasts with In Situ Aircraft Measurements, Weather Forecast., 16, 145–155, https://doi.org/10.1175/1520-0434(2001)016<0145:VOSCWF>2.0.CO;2, 2001. a
Heidinger, A. K., Pavolonis, M. J., Calvert, C., Hoffman, J., Nebuda, S., Straka III, W., Walther, A., and Wanzong, S.: ABI cloud products from the GOES-R series, in: The GOES-R Series, Elsevier, 43–62, https://doi.org/10.1016/B978-0-12-814327-8.00004-4, 2020. a
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 hourly data on pressure levels from 1940 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.bd0915c6, 2018. a, b
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.:: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020. a, b
Holland, P. W.: Statistics and causal inference, J. Am. Stat. Assoc., 81, 945–960, https://doi.org/10.1080/01621459.1986.10478354, 1986. a
Honomichl, S. B., Detwiler, A. G., and Smith, P. L.: Observed Hazards to Aircraft in Deep Summertime Convective Clouds from 4–7 km, J. Aircraft, 50, 926–935, https://doi.org/10.2514/1.C032057, 2013. a
Imbens, G. W. and Rubin, D. B.: Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction, Cambridge University Press, Cambridge, UK, https://doi.org/10.1017/CBO9781139025751, 2015. a
Jia, H., Hasekamp, O., and Quaas, J.: Revisiting aerosol–cloud interactions from weekly cycles, Geophys. Res. Lett., 51, e2024GL108266, https://doi.org/10.1029/2024GL108266, 2024. a
Jiménez, P. A.: Assessment of the GOES-16 clear sky mask product over the contiguous USA using CALIPSO retrievals, Remote Sensing, 12, 1630, https://doi.org/10.3390/rs12101630, 2020. a, b, c, d
Kärcher, B., Burkhardt, U., Unterstrasser, S., and Minnis, P.: Factors controlling contrail cirrus optical depth, Atmos. Chem. Phys., 9, 6229–6254, https://doi.org/10.5194/acp-9-6229-2009, 2009. a, b
Kox, S., Bugliaro, L., and Ostler, A.: Retrieval of cirrus cloud optical thickness and top altitude from geostationary remote sensing, Atmos. Meas. Tech., 7, 3233–3246, https://doi.org/10.5194/amt-7-3233-2014, 2014. a
Künsch, H. R.: The jackknife and the bootstrap for general stationary observations, Ann. Stat., 17, 1217–1241, https://doi.org/10.1214/aos/1176347265, 1989. a
Lahiri, S. N.: Comparison of Block Bootstrap Methods, Springer New York, New York, NY, 115–144, https://doi.org/10.1007/978-1-4757-3803-2_5, ISBN 978-1-4757-3803-2, 2003a. a
Lahiri, S. N.: Empirical Choice of the Block Size, Springer New York, New York, NY, 175–197, https://doi.org/10.1007/978-1-4757-3803-2_7, ISBN 978-1-4757-3803-2, 2003b. a
Lee, D. S., Fahey, D., Skowron, A., Allen, M., Burkhardt, U., Chen, Q., Doherty, S., Freeman, S., Forster, P., Fuglestvedt, J., Gettelman, A., De León, R. R., Lim, L. L., Lund, M. T., Millar, R. J., Owen, B., Penner, J. E., Pitari, G., Prather, M. J., Sausen, R., and Wilcox, L. J.: The contribution of global aviation to anthropogenic climate forcing for 2000 to 2018, Atmos. Environ., 244, 117834, https://doi.org/10.1016/j.atmosenv.2020.117834, 2021. a, b
Li, D., Saito, M., and Yang, P.: Time-Dependent Systematic Biases in Inferring Ice Cloud Properties from Geostationary Satellite Observations, Remote Sensing, 15, https://doi.org/10.3390/rs15030855, 2023. a
McCloskey, K., Chen, S., Meijer, V. R., Ng, J. Y.-H., Davis, G., Elkin, C., Van Arsdale, C., and Geraedts, S.: COIN (Collocated Irradiance Network) Estimates of Broadband Upwelling Irradiance from GOES-16 ABI, Google Cloud Storage [data set], https://console.cloud.google.com/storage/browser/upwelling_irradiance/ceres_goes/ (last access: 9 March 2026), 2023a. a, b
McCloskey, K., Chen, S., Meijer, V. R., Ng, J. Y.-H., Davis, G., Elkin, C., Van Arsdale, C., and Geraedts, S.: Estimates of broadband upwelling irradiance from GOES-16 ABI, Remote Sens. Environ., 285, 113376, https://doi.org/10.1016/j.rse.2022.113376, 2023b. a, b, c, d
Meijer, V. R.: Satellite-based Analysis and Forecast Evaluation of Aviation Contrails, PhD thesis, Massachusetts Institute of Technology, https://hdl.handle.net/1721.1/155350 (last access: 9 March 2026), 2024. a
Meijer, V. R., Kulik, L., Eastham, S. D., Allroggen, F., Speth, R. L., Karaman, S., and Barrett, S. R.: Contrail coverage over the United States before and during the COVID-19 pandemic, Environ. Res. Lett., 17, 034039, https://doi.org/10.1088/1748-9326/ac26f0, 2022. a
Moodie, E. E. and Schulz, J.: A Simple Diagnostic for the Positivity Assumption for Continuous Exposures, Stat. Med., 44, e70194, https://doi.org/10.1002/sim.70194, 2025. a
Pavolonis, M.: Enterprise Algorithm Theoretical Basis Document For Cloud Type and Cloud Phase, NOAA National Centers for Environmental Information, https://doi.org/10.7289/V5NP22QW, 2020. a, b
Pearl, J.: Causal inference in statistics: An overview, Statistics Surveys, 3, 96–146, https://doi.org/10.1214/09-SS057, 2009. a
Platt, J. C., Shapiro, M. L., Engberg, Z., McCloskey, K., Geraedts, S., Sankar, T., Stettler, M. E., Teoh, R., Schumann, U., Rohs, S., Brand, E., and Van Arsdale, C.: The effect of uncertainty in humidity and model parameters on the prediction of contrail energy forcing, Environmental Research Communications, 6, 095015, https://doi.org/10.1088/2515-7620/ad6ee5, 2024. a, b
Rubin, D. B.: Estimating causal effects of treatments in randomized and nonrandomized studies, J. Educ. Psychol., 66, 688–701, https://doi.org/10.1037/h0037350, 1974. a
Sarna, A., Meijer, V., Chevallier, R., Duncan, A., McConnaughay, K., Geraedts, S., and McCloskey, K.: Benchmarking and improving algorithms for attributing satellite-observed contrails to flights, Atmos. Meas. Tech., 18, 3495–3532, https://doi.org/10.5194/amt-18-3495-2025, 2025. a, b
Schumann, U.: On conditions for contrail formation from aircraft exhausts, Meteorol. Z., 5, 4–23, https://doi.org/10.1127/metz/5/1996/4, 1996. a
Schumann, U.: A contrail cirrus prediction model, Geosci. Model Dev., 5, 543–580, https://doi.org/10.5194/gmd-5-543-2012, 2012. a, b
Schumann, U. and Graf, K.: Aviation-induced cirrus and radiation changes at diurnal timescales, J. Geophys. Res.-Atmos., 118, 2404–2421, https://doi.org/10.1002/jgrd.50184, 2013. a, b, c
Schumann, U., Penner, J. E., Chen, Y., Zhou, C., and Graf, K.: Dehydration effects from contrails in a coupled contrail–climate model, Atmos. Chem. Phys., 15, 11179–11199, https://doi.org/10.5194/acp-15-11179-2015, 2015. a, b
Schumann, U., Baumann, R., Baumgardner, D., Bedka, S. T., Duda, D. P., Schumann, U., Baumann, R., Baumgardner, D., Bedka, S. T., Duda, D. P., Freudenthaler, V., Gayet, J.-F., Heymsfield, A. J., Minnis, P., Quante, M., Raschke, E., Schlager, H., Vázquez-Navarro, M., Voigt, C., and Wang, Z.: Properties of individual contrails: a compilation of observations and some comparisons, Atmos. Chem. Phys., 17, 403–438, https://doi.org/10.5194/acp-17-403-2017, 2017. a
Schumann, U., Bugliaro, L., Dörnbrack, A., Baumann, R., and Voigt, C.: Aviation contrail cirrus and radiative forcing over Europe during 6 months of COVID-19, Geophys. Res. Lett., 48, e2021GL092771, https://doi.org/10.1029/2021GL092771, 2021. a
Serra-Burriel, F., Delicado, P., Prata, A. T., and Cucchietti, F. M.: Estimating heterogeneous wildfire effects using synthetic controls and satellite remote sensing, Remote Sens. Environ., 265, 112649, https://doi.org/10.1016/j.rse.2021.112649, 2021. a
Shapiro, M., Engberg, Z., Teoh, R., Stettler, M., Dean, T., Schemann, U., and Voigt, C.: pycontrails: Python library for modeling aviation climate impacts, Version v0.52.2, Zenodo [code], https://doi.org/10.5281/zenodo.13151570, 2024. a, b
Sonabend-W, A., Geraedts, S., Goyal, N., Ng, J. Y.-H., Van Arsdale, C., and McCloskey, K.: Causal model regression code for observing long-lived longwave contrail forcing, Version 1.0.0, Zenodo [code], https://doi.org/10.5281/zenodo.18929469, 2026a. a
Sonabend-W, A., Geraedts, S., Goyal, N., Ng, J. Y.-H., Van Arsdale, C., and McCloskey, K.: Collated dataset of sampled pixels for observing long-lived longwave contrail forcing, Version 1.0.0, Zenodo [data set], https://doi.org/10.5281/zenodo.18929653, 2026b. a
Teoh, R., Schumann, U., Gryspeerdt, E., Shapiro, M., Molloy, J., Koudis, G., Voigt, C., and Stettler, M. E. J.: Aviation contrail climate effects in the North Atlantic from 2016 to 2021, Atmos. Chem. Phys., 22, 10919–10935, https://doi.org/10.5194/acp-22-10919-2022, 2022. a
Teoh, R., Engberg, Z., Schumann, U., Voigt, C., Shapiro, M., Rohs, S., and Stettler, M. E. J.: Global aviation contrail climate effects from 2019 to 2021, Atmos. Chem. Phys., 24, 6071–6093, https://doi.org/10.5194/acp-24-6071-2024, 2024. a, b
Tesche, M., Achtert, P., Glantz, P., and Noone, K.: Aviation effects on already-existing cirrus clouds, Nat. Commun., 7, 12016, https://doi.org/10.1038/ncomms12016, 2016. a
Vázquez-Navarro, M., Mannstein, H., and Kox, S.: Contrail life cycle and properties from 1 year of MSG/SEVIRI rapid-scan images, Atmos. Chem. Phys., 15, 8739–8749, https://doi.org/10.5194/acp-15-8739-2015, 2015. a, b, c
Wang, X., Wolf, K., Boucher, O., and Bellouin, N.: Radiative effect of two contrail cirrus outbreaks over Western Europe estimated using geostationary satellite observations and radiative transfer calculations, Geophys. Res. Lett., 51, e2024GL108452, https://doi.org/10.1029/2024GL108452, 2024a. a
Wang, Z., Bugliaro, L., Gierens, K., Hegglin, M. I., Rohs, S., Petzold, A., Kaufmann, S., and Voigt, C.: Machine learning for improvement of upper-tropospheric relative humidity in ERA5 weather model data, Atmos. Chem. Phys., 25, 2845–2861, https://doi.org/10.5194/acp-25-2845-2025, 2024b. a
Wielicki, B. A., Barkstrom, B. R., Harrison, E. F., Lee III, R. B., Smith, G. L., and Cooper, J. E.: Clouds and the Earth's Radiant Energy System (CERES): An earth observing system experiment, B. Am. Meteorol. Soc., 77, 853–868, https://doi.org/10.1175/1520-0477(1996)077<0853:CATERE>2.0.CO;2, 1996. a
Wilhelm, L., Gierens, K., and Rohs, S.: Weather variability induced uncertainty of contrail radiative forcing, Aerospace, 8, 332, https://doi.org/10.3390/aerospace8110332, 2021. a, b
Wimberly, M. C., Cochrane, M. A., Baer, A. D., and Pabst, K.: Assessing fuel treatment effectiveness using satellite imagery and spatial statistics, Ecol. Appl., 19, 1377–1384, https://doi.org/10.1890/08-1685.1, 2009. a
Short summary
Airplane condensation trails trap heat, but their full warming effect is hard to measure because they blend into natural clouds. Using satellite observations, weather data, flight paths, and a causal inference framework, we isolated this effect without simulations or contrail masks. We found contrails trap 46.9 gigajoules of heat per kilometer flown over the Americas. This provides a crucially missing observation-based estimate of a major portion of aviation’s environmental impact.
Airplane condensation trails trap heat, but their full warming effect is hard to measure because...
