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Evidence for climate change in the satellite cloud record

Nature volume 536, pages 72–75 (2016)Cite this article

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Abstract

Clouds substantially affect Earth’s energy budget by reflecting solar radiation back to space and by restricting emission of thermal radiation to space1. They are perhaps the largest uncertainty in our understanding of climate change, owing to disagreement among climate models and observational datasets over what cloud changes have occurred during recent decades and will occur in response to global warming2,3. This is because observational systems originally designed for monitoring weather have lacked sufficient stability to detect cloud changes reliably over decades unless they have been corrected to remove artefacts4,5. Here we show that several independent, empirically corrected satellite records exhibit large-scale patterns of cloud change between the 1980s and the 2000s that are similar to those produced by model simulations of climate with recent historical external radiative forcing. Observed and simulated cloud change patterns are consistent with poleward retreat of mid-latitude storm tracks, expansion of subtropical dry zones, and increasing height of the highest cloud tops at all latitudes. The primary drivers of these cloud changes appear to be increasing greenhouse gas concentrations and a recovery from volcanic radiative cooling. These results indicate that the cloud changes most consistently predicted by global climate models are currently occurring in nature.

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Figure 1: Change in observed and simulated cloud amount and albedo between the 1980s and 2000s.
Figure 2: Zonal mean change in observed and simulated cloud amount and albedo between the 1980s and the 2000s.
Figure 3: Zonal mean change in observed and simulated cloud amount during the period 1983–2009 in seven pressure intervals.

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References

  1. Ramanathan, V. et al. Cloud-radiative forcing and climate: results from the Earth Radiation Budget Experiment. Science 243, 57–63 (1989)

    Article  ADS  CAS  Google Scholar 

  2. Boucher, O. et al. In Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (eds Stocker, T. F. et al. ) Ch. 7, 571–657 (Cambridge Univ. Press, 2013)

  3. 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)

    Article  ADS  Google Scholar 

  4. 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)

    Article  ADS  Google Scholar 

  5. Norris, J. R. & Evan, A. T. Empirical removal of artifacts from the ISCCP and PATMOS-x satellite cloud records. J. Atmos. Ocean. Technol. 32, 691–702 (2015)

    Article  ADS  Google Scholar 

  6. Rossow, W. B. & Schiffer, R. A. Advances in understanding clouds from ISCCP. Bull. Am. Meteorol. Soc. 80, 2261–2287 (1999)

    Article  ADS  Google Scholar 

  7. Heidinger, A. K., Foster, M. J., Walther, A. & Zhao, Z. The Pathfinder Atmospheres Extended (PATMOS-x) AVHRR climate data set. Bull. Am. Meteorol. Soc. 95, 909–922 (2014)

    Article  ADS  Google Scholar 

  8. Marchand, R. Trends in ISCCP, MISR, and MODIS cloud-top-height and optical-depth histograms. J. Geophys. Res. 118, 1941–1949 (2013)

    Article  Google Scholar 

  9. Bender, F. A.-M., Ramanathan, V. & Tselioudis, G. Changes in extratropical storm track cloudiness 1983–2008: observational support for a poleward shift. Clim. Dyn. 38, 2037–2053 (2012)

    Article  Google Scholar 

  10. Marvel, K. et al. External influences on modeled and observed cloud trends. J. Clim. 28, 4820–4840 (2015)

    Article  ADS  Google Scholar 

  11. Eastman, R. & Warren, S. G. A 39-yr survey of cloud changes from land stations worldwide 1971–2009: long-term trends, relation to aerosols, and expansion of the Tropical belt. J. Clim. 26, 1286–1303 (2013)

    Article  ADS  Google Scholar 

  12. Zelinka, M. D., Klein, S. A. & Hartmann, D. L. Computing and partitioning cloud feedbacks using cloud property histograms. Part II: Attribution changes in cloud amount, altitude, and optical depth. J. Clim. 25, 37363754 (2012)

    Google Scholar 

  13. Barkstrom, B. E. et al. Earth Radiation Budget Experiment (ERBE) archival and April 1985 results. Bull. Am. Meteorol. Soc. 70, 1254–1262 (1989)

    Article  ADS  Google Scholar 

  14. Wielicki, B. A. et al. Clouds and the Earth’s Radiant Energy System (CERES): an Earth observing experiment. Bull. Am. Meteorol. Soc. 77, 853–868 (1996)

    Article  ADS  Google Scholar 

  15. Loeb, N. G. et al. Toward optimal closure of the Earth’s TOA radiation budget. J. Clim. 22, 748–766 (2009)

    Article  ADS  Google Scholar 

  16. O’Dell, C. W., Wentz, F. J. & Bennartz, R. Cloud liquid water path from satellite-based passive microwave observations: a new climatology over the global oceans. J. Clim. 21, 1721–1739 (2008)

    Article  ADS  Google Scholar 

  17. Taylor, K. E., Stouffer, R. J. & Meehl, G. A. An overview of CMIP5 and the experimental design. Bull. Am. Meteorol. Soc. 93, 485–498 (2012)

    Article  ADS  Google Scholar 

  18. Wetherald, R. T. & Manabe, S. Cloud feedback processes in a general circulation model. J. Atmos. Sci. 45, 1397–1416 (1988)

    Article  ADS  Google Scholar 

  19. Seidel, D. J., Fu, Q., Randel, W. J. & Reichler, T. J. Widening of the tropical belt in a changing climate. Nat. Geosci. 1, 21–24 (2008)

    Article  ADS  CAS  Google Scholar 

  20. Chepfer, H., Noel, V., Winker, D. & Chiriaco, M. Where and when will we observe cloud changes due to climate warming? Geophys. Res. Lett. 41, 8387–8395 (2014)

    Article  ADS  Google Scholar 

  21. Grise, K. M., Polvani, L. M., Tselioudis, G., Wu, Y. & Zelinka, M. D. The ozone hole indirect effect: cloud-radiative anomalies accompanying the poleward shift of the eddy-driven jet in the Southern Hemisphere. Geophys. Res. Lett. 40, 3688–3692 (2013)

    Article  ADS  CAS  Google Scholar 

  22. Vallis, G. K., Zurita-Gotor, P., Cairns, C. & Kidston, J. Response of the large-scale structure of the atmosphere to global warming. Q. J. R. Meteorol. Soc. 141, 1479–1501 (2015)

    Article  ADS  Google Scholar 

  23. Dutton, E. G. & Christy, J. R. Solar radiative forcing at selected locations and evidence for global lower tropospheric cooling following the eruptions of El Chichón and Pinatubo. Geophys. Res. Lett. 19, 2313–2316 (1992)

    Article  ADS  Google Scholar 

  24. Labitzke, K. & McCormick, M. P. Stratospheric temperature increases due to Pinatubo aerosols. Geophys. Res. Lett. 19, 207–210 (1992)

    Article  ADS  Google Scholar 

  25. Hartmann, D. L. & Larson, K. An important constraint on tropical cloud-climate feedback. Geophys. Res. Lett. 29, http://dx.doi.org/10.1029/2002GL015835 (2002)

  26. Zelinka, M. D. & Hartmann, D. L. Why is longwave cloud feedback positive? J. Geophys. Res. 115, D16117 (2010)

    Article  ADS  Google Scholar 

  27. Held, I. M. & Hou, A. Y. Nonlinear axially symmetric circulations in a nearly inviscid atmosphere. J. Atmos. Sci. 37, 515–533 (1980)

    Article  ADS  MathSciNet  Google Scholar 

  28. Lu, J., Vecchi, G. A. & Reichler, Y. Expansion of the Hadley cell under global warming. Geophys. Res. Lett. 34, L06805 (2007)

    ADS  Google Scholar 

  29. Polvani, L. M., Waugh, D. W., Correa, G. J. P. & Son, S. W. Stratospheric ozone depletion: the main driver of Twentieth-Century atmospheric circulation changes in the Southern Hemisphere. J. Clim. 24, 795–812 (2011)

    Article  ADS  Google Scholar 

  30. Mace, G. G. & Wrenn, F. J. Evaluation of the hydrometeor layers in the East and West Pacific within ISCCP cloud-top pressure-optical depth bins using merged CloudSat and CALIPSO data. J. Clim. 26, 9429–9444 (2013)

    Article  ADS  Google Scholar 

  31. Sherwood, S. C., Chae, J.-H., Minnis, P. & McGill, M. M., Underestimation of deep convective cloud tops by thermal imagery. Geophys. Res. Lett. 31, L11102 (2004)

    Article  ADS  Google Scholar 

  32. Hilburn, K. A. & Wentz, F. J. Intercalibrated passive microwave rain products from the unified microwave ocean retrieval algorithm (UMORA). J. Appl. Meteorol. Climatol. 47, 778–794 (2008)

    Article  ADS  Google Scholar 

  33. Bodas-Salcedo, A. et al. 2011: COSP: Satellite simulation software for model assessment. Bull. Am. Meteorol. Soc. 92, 1023–1043 (2011)

    Article  ADS  Google Scholar 

  34. Bretherton, C. S., Widmann, M., Dymnikov, V. P., Wallace, J. M. & Bladé, I. The effective number of spatial degrees of freedom of a time-varying field. J. Clim. 12, 1990–2009 (1999)

    Article  ADS  Google Scholar 

  35. Zhang, Y., Wallace, J. M. & Battisti, D. S. ENSO-like interdecadal variability: 1900–93. J. Clim. 10, 1004–1020 (1997)

    Article  ADS  Google Scholar 

Download references

Acknowledgements

National Oceanic and Atmospheric Administration (NOAA) awards NA10OAR4310140 and NA10OAR4310141 supported work by J.R.N. and A.T.E. The efforts of M.D.Z. and S.A.K. were supported by the US Department of Energy (DOE), Office of Science, Office of Biological and Environmental Research through its Regional and Global Climate Modeling Program and were performed under the auspices of the DOE by Lawrence Livermore National Laboratory under contract DE-AC52-07NA27344. Part of the work by M.D.Z. was supported by the National Aeronautics and Space Administration (NASA) New Investigator Program (NNH14AX83I). The MAC-LWP climatology is supported by the NASA MEaSUREs Program (NNH12ZDA001N). We acknowledge the World Climate Research Programme’s (WCRP’s) Working Group on Coupled Modelling, which is responsible for the Coupled Model Intercomparison Project (CMIP), and we thank the climate modelling groups for producing and making available their model output. For CMIP the US DOE’s Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals.

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Authors and Affiliations

  1. Scripps Institution of Oceanography, University of California at San Diego, La Jolla, California, USA

    Joel R. Norris & Amato T. Evan

  2. Department of Earth Sciences, University of California at Riverside, Riverside, California, USA

    Robert J. Allen

  3. Program for Climate Model Diagnosis and Intercomparison, Lawrence Livermore National Laboratory, Livermore, California, USA

    Mark D. Zelinka & Stephen A. Klein

  4. Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, Colorado, USA

    Christopher W. O’Dell

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Contributions

J.R.N. designed the study, provided ERBS, CERES, and ISCCP data, did the main analysis, and wrote the paper; R.J.A. provided standard model cloud output for CMIP5 simulations and analysed CMIP5 meteorological output; A.T.E. provided corrected PATMOS-x data; M.D.Z. provided CMIP5 COSP cloud output; C.W.O. provided MAC-LWP liquid water path data; and S.A.K. provided background information and ideas. All authors discussed the results and commented on the manuscript.

Corresponding author

Correspondence to Joel R. Norris.

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Competing interests

The authors declare no competing financial interests.

Additional information

Corrected ISCCP and PATMOS-x cloud amount data are available from the Research Data Archive at NCAR at http://dx.doi.org/10.5065/D62J68XR.

Extended data figures and tables

Extended Data Figure 1 Change in observed liquid water path during the period 1988–2014.

a, Linear trend in MAC-LWP liquid water path during January 1988 to December 2014. b, Zonal mean climatology (red) and trend (black) in MAC-LWP liquid water path during January 1988–December 2014 given as g m-2 per 25-year period. Circles indicate trend statistical significance (P < 0.05 two-sided). All trends are relative to the 60° S–60° N mean trend given as g m-2 per 25 year period.

Extended Data Figure 2 Change in observed cloud amount and albedo between January 1985 to December 1989 and January 2003 to December 2009.

a, Change in ISCCP total cloud amount given as a percentage amount per 25-year period. b, Change in PATMOS-x total cloud amount given as a percentage amount per 25-year period. c, Change in ERBS/CERES albedo given as a percentage albedo per 25-year period. Black dots indicate agreement among all three satellite records on sign of change in a, b and c. All changes are relative to the 60° S–60° N mean change.

Extended Data Figure 3 Correlation between forced simulated, unforced simulated, and observed cloud trend patterns.

Distribution of correlation between patterns from multiple unforced CMIP5 preindustrial simulations and the ensemble mean of CMIP5 simulations with all radiative forcings (ALL, black), only greenhouse radiative forcings (GHG, red), and only natural radiative forcings (NAT, blue) for 27-year trends. a, Grid box total cloud amount. b, Zonal mean total cloud amount. c, Zonal mean cloud amount in the 50–180 hPa and 180–320 hPa intervals. Vertical lines indicate correlation between the observed pattern and ensemble mean ALL, GHG, or NAT patterns. Fractional area under each distribution to the right of the vertical line is the corresponding P value in Table 1.

Extended Data Figure 4 Absolute change in simulated cloud amount during the period 1983–2009.

a, Linear trend in ensemble mean total cloud amount given as a percentage amount per 25-year period 1983–2009 from CMIP5 historical simulations with all radiative forcings (ALL). Data given as a percentage amount per 25-year period. b, Zonal mean climatology (red) and trend (black) in ensemble mean total cloud amount 1983–2009 from ALL simulations. c, Zonal mean trend in ensemble mean cloud amount 1983–2009 in seven pressure intervals from ALL simulations. Data given as a percentage amount per 25-year period. Black dots and circles indicate trend statistical significance (P < 0.05 two-sided), and bars indicate interquartile range of individual simulations. Unlike Figs 1c, 2d and 3d, trends are not relative to the 60° S–60° N mean trend.

Extended Data Figure 5 Change in simulated cloud amount between the 1980s and 2000s for different types of forcing.

Linear trend in ensemble mean total cloud amount given as a percentage amount per 25-year period. from CMIP5 simulations and locations where the majority of observational (Obs) datasets and the majority of simulations show increasing (blue) or decreasing (orange) cloud amount or albedo. a, b, Only greenhouse gas (GHG) forcing 1979–2005. c, d, Only anthropogenic aerosol (AA) forcing 1979–2005. e, f, Only ozone (OZ) forcing 1983–2009. g, h, Only natural (NAT) forcing 1983–2009. Black dots indicate trend statistical significance (P < 0.05 two-sided). All trends are relative to the 60° S–60° N mean trend.

Extended Data Figure 6 Zonal mean change in simulated cloud amount between the 1980s and 2000s for different types of forcing.

Climatology is red and linear trend is black for ensemble mean total cloud amount from CMIP5 simulations. Data given as a percentage amount per 25-year period. a, Only greenhouse gas (GHG) forcing 1979–2005. b, Only anthropogenic aerosol (AA) forcing 1979–2005. c, Only ozone (OZ) forcing 1983–2009. d, Only natural (NAT) forcing 1983–2009. Circles indicate trend statistical significance (P < 0.05 two-sided) and bars indicate interquartile range of individual simulations. All trends are relative to the 60° S–60° N mean trend.

Extended Data Figure 7 Zonal mean change in simulated cloud amount between the 1980s and 2000s in seven pressure intervals for different types of forcing.

Linear trend for ensemble mean cloud amount from CMIP5 simulations. a, Only greenhouse gas (GHG) forcing 1979–2005. Data given as a percentage amount per 25-year period. b, Only anthropogenic aerosol (AA) forcing 1979–2005. c, Only natural (NAT) forcing 1983–2009. Black dots indicate trend statistical significance (P < 0.05 two-sided). All trends are relative to the 60° S–60° N mean trend for that pressure interval.

Extended Data Figure 8 Change in simulated cloud amount during the period 1983–2009 from COSP.

a, Linear trend in ensemble mean total cloud amount 1983–2009 from CMIP5 historical simulations with all radiative forcings (ALL). Data given as a percentage amount per 25-year period. b, Zonal mean climatology (red) and trend (black) in ensemble mean total cloud amount 1983–2009 from ALL simulations. c, Zonal mean trend in ensemble mean cloud amount 1983–2009 in seven pressure intervals from ALL simulations. Black dots and circles indicate trend statistical significance (P < 0.05 two-sided), and bars indicate interquartile range of individual simulations. All trends relative to the 60° S–60° N mean trend.

Extended Data Table 1 CMIP5 models and number of simulations used for each forcing experiment
Full size table
Extended Data Table 2 CMIP5 models and number of years in each preindustrial control simulation
Full size table

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Norris, J., Allen, R., Evan, A. et al. Evidence for climate change in the satellite cloud record. Nature 536, 72–75 (2016). https://doi.org/10.1038/nature18273

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