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
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
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
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
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
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. 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
Wooldridge, J. M.: Introductory econometrics a modern approach, South-Western Cengage learning,
https://www.cengage.com/c/introductory-econometrics-a-modern-approach-6e-wooldridge/9781305270107/ (last access: 9 March 2026), 2016.
a,
b,
c,
d
