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).
Google Scholar
Ramanathan, V. et al. Cloud-radiative forcing and climate: results from the Earth Radiation Budget Experiment. Science 243, 57–63 (1989).
Google Scholar
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).
Google Scholar
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).
Google Scholar
Zelinka, M. D. et al. Causes of higher climate sensitivity in CMIP6 models. Geophys. Res. Lett. 47, e2019GL085782 (2020).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
Hasselmann, K. Multi-pattern fingerprint method for detection and attribution of climate change. Clim. Dyn. 13, 601–611 (1997).
Google Scholar
Leroy, S., Anderson, J., Dykema, J. & Goody, R. Testing climate models using thermal infrared spectra. J. Clim. 21, 1863–1875 (2008).
Google Scholar
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).
Google Scholar
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).
Google Scholar
Huang, Y. & Ramaswamy, V. Evolution and trend of the outgoing longwave radiation spectrum. J. Clim. 22, 4637–4651 (2009).
Google Scholar
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).
Google Scholar
Brindley, H. & Bantges, R. The spectral signature of recent climate change. Curr. Clim. Change Rep. 2, 112–126 (2016).
Google Scholar
Mülmenstädt, J. et al. An underestimated negative cloud feedback from cloud lifetime changes. Nat. Clim. Change 11, 508–513 (2021).
Google Scholar
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).
Google Scholar
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).
Google Scholar
Cesana, G. V. & Del Genio, A. D. Observational constraint on cloud feedbacks suggests moderate climate sensitivity. Nat. Clim. Change 11, 213–218 (2021).
Google Scholar
Myers, T. A. et al. Observational constraints on low cloud feedback reduce uncertainty of climate sensitivity. Nat. Clim. Change 11, 501–507 (2021).
Google Scholar
Kramer, R. J. et al. Observational evidence of increasing global radiative forcing. Geophys. Res. Lett. 48, e2020GL091585 (2021).
Google Scholar
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).
Google Scholar
Soden, B. J. et al. Quantifying climate feedbacks using radiative kernels. J. Clim. 21, 3504–3520 (2008).
Google Scholar
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).
Google Scholar
Norris, J. R. et al. Evidence for climate change in the satellite cloud record. Nature 536, 72–75 (2016).
Google Scholar
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).
Google Scholar
Platnick, S. et al. The NASA MODIS-VIIRS continuity cloud optical properties products. Remote Sens. 13, 2 (2020).
Google Scholar
Zhao, C. et al. Toward understanding of differences in current cloud retrievals of ARM ground‐based measurements. J. Geophys. Res. Atmos. 117, D10206 (2012).
Google Scholar
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).
Google Scholar
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).
Google Scholar
Feldman, D. R. et al. Observational determination of surface radiative forcing by CO2 from 2000 to 2010. Nature 519, 339–343 (2015).
Google Scholar
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).
Google Scholar
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).
Google Scholar
Goosse, H. et al. Quantifying climate feedbacks in polar regions. Nat. Commun. 9, 1919 (2018).
Google Scholar
Huang, Y. On the longwave climate feedbacks. J. Clim. 26, 7603–7610 (2013).
Google Scholar
Hersbach, H. et al. The ERA5 global reanalysis. Q. J. R. Meteorol. Soc. 146, 1999–2049 (2020).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
Google Scholar
Loeb, N. G. et al. Satellite and ocean data reveal marked increase in Earth’s heating rate. Geophys. Res. Lett. 48, e2021GL093047 (2021).
Google Scholar
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).
Google Scholar
Ceppi, P. & Nowack, P. Observational evidence that cloud feedback amplifies global warming. Proc. Natl Acad. Sci. USA 118, e2026290118 (2021).
Google Scholar
Knuteson, R. et al. Atmospheric emitted radiance interferometer. Part II: instrument performance. J. Atmos. Ocean. Technol. 21, 1777–1789 (2004).
Google Scholar
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).
Google Scholar
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).
Google Scholar
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).
