Luo, H., Quaas, J. & Han, Y. Examining cloud vertical structure and radiative effects from satellite retrievals and evaluation of CMIP6 scenarios. Atmos. Chem. Phys. 23, 8169–8186 (2023).
Ramanathan, V. et al. Cloud-radiative forcing and climate: results from the Earth Radiation Budget Experiment. Science 243, 57–63 (1989).
Dufresne, J.-L. & Bony, S. An assessment of the primary sources of spread of global warming estimates from coupled atmosphere–ocean models. J. Clim. 21, 5135–5144 (2008).
Vial, J., Dufresne, J.-L. & Bony, S. On the interpretation of inter-model spread in CMIP5 climate sensitivity estimates. Clim. Dyn. 41, 3339–3362 (2013).
Zelinka, M. D. et al. Causes of higher climate sensitivity in CMIP6 models. Geophys. Res. Lett. 47, e2019GL085782 (2020).
Forster, P. T. et al. in Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (eds Masson-Delmotte, V. P. et al.) Ch. 7 (Cambridge Univ. Press, 2021).
Sherwood, S. C. et al. An assessment of Earth’s climate sensitivity using multiple lines of evidence. Rev. Geophys. 58, e2019RG000678 (2020).
Ceppi, P., Brient, F., Zelinka, M. D. & Hartmann, D. L. Cloud feedback mechanisms and their representation in global climate models. Wiley Interdiscip. Rev. Clim. Change 8, e465 (2017).
Mace, G. G. & Berry, E. Using active remote sensing to evaluate cloud-climate feedbacks: a review and a look to the future. Curr. Clim. Change Rep. 3, 185–192 (2017).
Winker, D., Chepfer, H., Noel, V. & Cai, X. Observational constraints on cloud feedbacks: the role of active satellite sensors. Surv. Geophys. 38, 1483–1508 (2017).
Liu, L., Huang, Y., Gyakum, J. R., Turner, D. D. & Gero, P. J. Trends in downwelling longwave radiance over the Southern Great Plains. J. Geophys. Res. Atmos. 127, e2021JD035949 (2022).
Hasselmann, K. Multi-pattern fingerprint method for detection and attribution of climate change. Clim. Dyn. 13, 601–611 (1997).
Leroy, S., Anderson, J., Dykema, J. & Goody, R. Testing climate models using thermal infrared spectra. J. Clim. 21, 1863–1875 (2008).
Huang, Y., Leroy, S., Gero, P. J., Dykema, J. & Anderson, J. Separation of longwave climate feedbacks from spectral observations. J. Geophys. Res. Atmos. 115, D07104 (2010).
Huang, Y., Leroy, S. S. & Anderson, J. G. Determining longwave forcing and feedback using infrared spectra and GNSS radio occultation. J. Clim. 23, 6027–6035 (2010).
Huang, Y. & Ramaswamy, V. Evolution and trend of the outgoing longwave radiation spectrum. J. Clim. 22, 4637–4651 (2009).
Feldman, D. R., Collins, W. D. & Paige, J. L. Pan-spectral observing system simulation experiments of shortwave reflectance and long-wave radiance for climate model evaluation. Geosci. Model Dev. 8, 1943–1954 (2015).
Brindley, H. & Bantges, R. The spectral signature of recent climate change. Curr. Clim. Change Rep. 2, 112–126 (2016).
Mülmenstädt, J. et al. An underestimated negative cloud feedback from cloud lifetime changes. Nat. Clim. Change 11, 508–513 (2021).
Dong, Y. et al. Intermodel spread in the pattern effect and its contribution to climate sensitivity in CMIP5 and CMIP6 models. J. Clim. 33, 7755–7775 (2020).
Gordon, N. D., Jonko, A. K., Forster, P. M. & Shell, K. M. An observationally based constraint on the water‐vapor feedback. J. Geophys. Res. Atmos. 118, 12,435–412,443 (2013).
Cesana, G. V. & Del Genio, A. D. Observational constraint on cloud feedbacks suggests moderate climate sensitivity. Nat. Clim. Change 11, 213–218 (2021).
Myers, T. A. et al. Observational constraints on low cloud feedback reduce uncertainty of climate sensitivity. Nat. Clim. Change 11, 501–507 (2021).
Kramer, R. J. et al. Observational evidence of increasing global radiative forcing. Geophys. Res. Lett. 48, e2020GL091585 (2021).
Stubenrauch, C. J. et al. Assessment of global cloud datasets from satellites: project and database initiated by the GEWEX radiation panel. Bull. Am. Meteorol. Soc. 94, 1031–1049 (2013).
Soden, B. J. et al. Quantifying climate feedbacks using radiative kernels. J. Clim. 21, 3504–3520 (2008).
Zelinka, M. D., Klein, S. A. & Hartmann, D. L. Computing and partitioning cloud feedbacks using cloud property histograms. Part I: cloud radiative kernels. J. Clim. 25, 3715–3735 (2012).
Norris, J. R. et al. Evidence for climate change in the satellite cloud record. Nature 536, 72–75 (2016).
Evan, A. T., Heidinger, A. K. & Vimont, D. J. Arguments against a physical long‐term trend in global ISCCP cloud amounts. Geophys. Res. Lett. 34, L04701 (2007).
Platnick, S. et al. The NASA MODIS-VIIRS continuity cloud optical properties products. Remote Sens. 13, 2 (2020).
Zhao, C. et al. Toward understanding of differences in current cloud retrievals of ARM ground‐based measurements. J. Geophys. Res. Atmos. 117, D10206 (2012).
Zhao, C., Xie, S., Chen, X., Jensen, M. P. & Dunn, M. Quantifying uncertainties of cloud microphysical property retrievals with a perturbation method. J. Geophys. Res. Atmos. 119, 5375–5385 (2014).
Lai, R. et al. Comparison of cloud properties from Himawari-8 and FengYun-4A geostationary satellite radiometers with MODIS cloud retrievals. Remote Sens. 11, 1703 (2019).
Feldman, D. R. et al. Observational determination of surface radiative forcing by CO2 from 2000 to 2010. Nature 519, 339–343 (2015).
Feng, J., Menzel, R. & Paynter, D. A flexible approach to parameterize the optical properties of clouds and precipitation. Preprint at ESS Open Archive https://doi.org/10.22541/essoar.171804933.35767571/v1 (2024).
Shakespeare, C. J. & Roderick, M. L. Diagnosing instantaneous forcing and feedbacks of downwelling longwave radiation at the surface: a simple methodology and its application to CMIP5 models. J. Clim. 35, 3785–3801 (2022).
Clark, J., Clothiaux, E., Feldstein, S. & Lee, S. Drivers of global clear sky surface downwelling longwave irradiance trends from 1984 to 2017. Geophys. Res. Lett. 48, e2021GL093961 (2021).
Goosse, H. et al. Quantifying climate feedbacks in polar regions. Nat. Commun. 9, 1919 (2018).
Huang, Y. On the longwave climate feedbacks. J. Clim. 26, 7603–7610 (2013).
Hersbach, H. et al. The ERA5 global reanalysis. Q. J. R. Meteorol. Soc. 146, 1999–2049 (2020).
Huang, H. & Huang, Y. Radiative sensitivity quantified by a new set of radiation flux kernels based on the ECMWF Reanalysis v5 (ERA5). Earth Syst. Sci. Data 15, 3001–3021 (2023).
Kato, S. et al. Surface irradiances of edition 4.0 clouds and the earth’s radiant energy system (CERES) energy balanced and filled (EBAF) data product. J. Clim. 31, 4501–4527 (2018).
Loeb, N. G. et al. Clouds and the earth’s radiant energy system (CERES) energy balanced and filled (EBAF) top-of-atmosphere (TOA) edition-4.0 data product. J. Clim. 31, 895–918 (2018).
Raghuraman, S. P., Paynter, D., Menzel, R. & Ramaswamy, V. Forcing, cloud feedbacks, cloud masking, and internal variability in the cloud radiative effect satellite record. J. Clim. 36, 4151–4167 (2023).
Loeb, N. G. et al. Satellite and ocean data reveal marked increase in Earth’s heating rate. Geophys. Res. Lett. 48, e2021GL093047 (2021).
Chao, L.-W. & Dessler, A. E. An assessment of climate feedbacks in observations and climate models using different energy balance frameworks. J. Clim. 34, 9763–9773 (2021).
Ceppi, P. & Nowack, P. Observational evidence that cloud feedback amplifies global warming. Proc. Natl Acad. Sci. USA 118, e2026290118 (2021).
Knuteson, R. et al. Atmospheric emitted radiance interferometer. Part II: instrument performance. J. Atmos. Ocean. Technol. 21, 1777–1789 (2004).
Jacobson, A. R. et al. CarbonTracker CT2019B (NOAA Global Monitoring Laboratory, 2020).
Dlugokencky, E. J., Mund, J. W., Crotwell, A. M., Crotwell, M. J. & Thoning, K. W. Atmospheric carbon dioxide dry air mole fractions from the NOAA GML Carbon Cycle Cooperative Global Air Sampling Network 1968-2020, Version: 2021-07-30. National Oceanic and Atmospheric Administration (NOAA), Global Monitoring Laboratory (GML) https://doi.org/10.15138/wkgj-f215 (2021).
Peters, W. et al. An atmospheric perspective on North American carbon dioxide exchange: CarbonTracker. Proc. Natl Acad. Sci. USA 104, 18925–18930 (2007).
Dlugokencky, E. J., Crotwell, A. M., Mund, J. W., Crotwell, M. J. & Thoning, K. W. Atmospheric methane dry air mole fractions from the NOAA GML Carbon Cycle Cooperative Global Air Sampling Network, 1983-2020, Version: 2021-07-30. National Oceanic and Atmospheric Administration (NOAA), Global Monitoring Laboratory (GML) https://doi.org/10.15138/wkgj-f215 (2021).
Dutton, G., Elkins II, J., Hall, B. & NOAA ESRL. Earth System Research Laboratory Halocarbons and Other Atmospheric Trace Gases Chromatograph for Atmospheric Trace Species (CATS) Measurements, Version 1 (nitrous oxide (N2O), chlorofluorocarbons (CFC-11 and CFC-11) and carbon tetrachloride (CCl4)). NOAA National Centers for Environmental Information https://doi.org/10.7289/V5X0659V (2017).
Atmospheric Radiation Measurement (ARM) user facility. 2001, updated hourly. Improved MICROBASE product with Uncertainties (MICROBASEKAPLUS). 2011-01-18 to 2020-06-30, Southern Great Plains (SGP) Central Facility, Lamont, OK (C1). Compiled by M. Wang, S. Giangrande, K. Johnson and M. Jensen. ARM Data Center https://doi.org/10.5439/1768890.
Atmospheric Radiation Measurement (ARM) user facility. 1996, updated hourly. Continuous Baseline Microphysical Retrieval (MICROBASEPI2). 1996-11-08 to 2010-12-30, Southern Great Plains (SGP) Central Facility, Lamont, OK (C1). Compiled by S. Giangrande and K. Johnson. ARM Data Center https://doi.org/10.5439/1034923.
Li, J. Gaussian quadrature and its application to infrared radiation. J. Atmos. Sci. 57, 753–765 (2000).
Pawlowicz, R. M_Map: a mapping package for MATLAB, version 1.4m (2020); www.eoas.ubc.ca/~rich/map.html.
Liu, L., Huang, Y. & Gyakum, J. Data and codes for “Clouds attenuate the increase of downwelling longwave radiation over land in climate warming”. Zenodo https://doi.org/10.5281/zenodo.13058643 (2024).