Letter | Published:

Impact of decadal cloud variations on the Earth’s energy budget

Nature Geoscience volume 9, pages 871874 (2016) | Download Citation

Abstract

Feedbacks of clouds on climate change strongly influence the magnitude of global warming1,2,3. Cloud feedbacks, in turn, depend on the spatial patterns of surface warming4,5,6,7,8,9, which vary on decadal timescales. Therefore, the magnitude of the decadal cloud feedback could deviate from the long-term cloud feedback4. Here we present climate model simulations to show that the global mean cloud feedback in response to decadal temperature fluctuations varies dramatically due to time variations in the spatial pattern of sea surface temperature. We find that cloud anomalies associated with these patterns significantly modify the Earth’s energy budget. Specifically, the decadal cloud feedback between the 1980s and 2000s is substantially more negative than the long-term cloud feedback. This is a result of cooling in tropical regions where air descends, relative to warming in tropical ascent regions, which strengthens low-level atmospheric stability. Under these conditions, low-level cloud cover and its reflection of solar radiation increase, despite an increase in global mean surface temperature. These results suggest that sea surface temperature pattern-induced low cloud anomalies could have contributed to the period of reduced warming between 1998 and 2013, and offer a physical explanation of why climate sensitivities estimated from recently observed trends are probably biased low4.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

References

  1. 1.

    & An assessment of the primary sources of spread of global warming estimates from coupled atmosphere–ocean models. J. Clim. 21, 5135–5144 (2008).

  2. 2.

    et al. On the contribution of local feedback mechanisms to the range of climate sensitivity in two GCM ensembles. Clim. Dynam. 27, 17–38 (2006).

  3. 3.

    , , & Quantifying the sources of intermodel spread in equilibrium climate sensitivity. J. Clim. 29, 513–524 (2016).

  4. 4.

    & Variation in climate sensitivity and feedback parameters during the historical period. Geophys. Res. Lett. 43, 3911–3920 (2016).

  5. 5.

    , & Distinct energy budgets for anthropogenic and natural changes during global warming hiatus. Nat. Geosci. 9, 29–33 (2016).

  6. 6.

    , & Time-varying climate sensitivity from regional feedbacks. J. Clim. 26, 4518–4534 (2013).

  7. 7.

    Using an AGCM to diagnose historical effective radiative forcing and mechanisms of recent decadal climate change. J. Clim. 27, 1193–1209 (2014).

  8. 8.

    , & The dependence of radiative forcing and feedback on evolving patterns of surface temperature change in climate models. J. Clim. 28, 1630–1648 (2015).

  9. 9.

    , , & The relationship between interannual and long-term cloud feedbacks. Geophys. Res. Lett. 42, 10463–10469 (2015).

  10. 10.

    , , & Top-of-atmosphere radiative contribution to unforced decadal global temperature variability in climate models. Geophys. Res. Lett. 41, 5175–5183 (2014).

  11. 11.

    , & An overview of CMIP5 and the experiment design. Bull. Am. Meteorol. Soc. 93, 485–498 (2012).

  12. 12.

    et al. Description of the NCAR Community Atmosphere Model (CAM 5.0) NCAR/TN-486+STR (National Centre for Atmospheric Research, 2012)

  13. 13.

    & On the relationship between stratiform low cloud cover and lower-tropospheric stability. J. Clim. 19, 6425–6432 (2006).

  14. 14.

    & Marine boundary layer clouds at the heart of tropical cloud feedback uncertainties in climate models. Geophys. Res. Lett. 32, L20806 (2005).

  15. 15.

    & The vertical distribution of cloud feedback in coupled ocean–atmosphere models. Geophys. Res. Lett. 38, L12704 (2011).

  16. 16.

    , & Spread in model climate sensitivity traced to atmospheric convective mixing. Nature 505, 37–42 (2014).

  17. 17.

    , , & Positive tropical marine low-cloud cover feedback inferred from cloud-controlling factors. Geophys. Res. Lett. 42, 7767–7775 (2015).

  18. 18.

    & Low cloud reduction in a greenhouse-warmed climate: results from Lagrangian LES of a subtropical marine cloudiness transition. J. Adv. Model. Earth Syst. 6, 91–114 (2014).

  19. 19.

    , , & The strength of the tropical inversion and its response to climate change in 18 CMIP5 models. Clim. Dynam. 45, 375–396 (2015).

  20. 20.

    , & The weak temperature gradient approximation and balanced tropical moisture waves. J. Atmos. Sci. 58, 3650–3665 (2001).

  21. 21.

    , & How has subtropical stratocumulus and associated meteorology changed since the 1980s? J. Clim. 28, 8396–8410 (2015).

  22. 22.

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

  23. 23.

    et al. Contribution of natural decadal variability to global warming acceleration and hiatus. Nat. Clim. Change 4, 893–897 (2014).

  24. 24.

    & Pacific trade winds accelerated by aerosol forcing over the past two decades. Nat. Clim. Change 6, 768–772 (2016).

  25. 25.

    et al. An update on Earth’s energy balance in light of the latest global observations. Nat. Geosci. 5, 691–696 (2012).

  26. 26.

    et al. Earth’s energy imbalance since 1960 in observations and CMIP5 models. Geophys. Res. Lett. 42, 1205–1213 (2015).

  27. 27.

    et al. Volcanic contribution to decadal changes in tropospheric temperature. Nat. Geosci. 7, 185–189 (2014).

  28. 28.

    , & Statistically derived contributions of diverse human influences to twentieth-century temperature changes. Nat. Geosci. 6, 1050–1055 (2013).

  29. 29.

    et al. Quantifying climate feedbacks using radiative kernels. J. Clim. 21, 3504–3520 (2008).

  30. 30.

    & Intermodel variances of subtropical stratocumulus environments simulated in CMIP5 models. Geophys. Res. Lett. 41, 7754–7761 (2014).

Download references

Acknowledgements

The authors thank J. Norris for providing the corrected ISCCP and PATMOS-x data, and thank J. Gregory, A. Dessler, A. Hall, H. Su, X. Qu, C. Terai and A. DeAngelis for valuable discussions. This work was supported by the Regional and Global Climate Modeling Program of the Office of Science at the US Department of Energy (DOE) under the project ‘Identifying Robust Cloud Feedbacks in Observations and Models’ and was performed under the auspices of DOE by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344. IM Release #LLNL-JRNL-692260.

Author information

Affiliations

  1. Cloud Processes Research Group, Lawrence Livermore National Laboratory, Livermore, California 94550, USA

    • Chen Zhou
    • , Mark D. Zelinka
    •  & Stephen A. Klein

Authors

  1. Search for Chen Zhou in:

  2. Search for Mark D. Zelinka in:

  3. Search for Stephen A. Klein in:

Contributions

C.Z. performed the analysis. C.Z. and M.D.Z. designed the experiments. S.A.K. proposed the cloud analyses. The paper was discussed and written by all authors.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Chen Zhou.

Supplementary information

PDF files

  1. 1.

    Supplementary Information

    Supplementary Information

About this article

Publication history

Received

Accepted

Published

DOI

https://doi.org/10.1038/ngeo2828

Further reading