Variability in modeled cloud feedback tied to differences in the climatological spatial pattern of clouds

Abstract

Despite the increasing sophistication of climate models, the amount of surface warming expected from a doubling of atmospheric CO\(_2\) (equilibrium climate sensitivity) remains stubbornly uncertain, in part because of differences in how models simulate the change in global albedo due to clouds (the shortwave cloud feedback). Here, model differences in the shortwave cloud feedback are found to be closely related to the spatial pattern of the cloud contribution to albedo (\(\alpha\)) in simulations of the current climate: high-feedback models exhibit lower (higher) \(\alpha\) in regions of warm (cool) sea-surface temperatures, and therefore predict a larger reduction in global-mean \(\alpha\) as temperatures rise and warm regions expand. The spatial pattern of \(\alpha\) is found to be strongly predictive (\(r=0.84\)) of a model’s global cloud feedback, with satellite observations indicating a most-likely value of \(0.58\pm 0.31\) Wm\(^{-2}\) K\(^{-1}\) (90% confidence). This estimate is higher than the model-average cloud feedback of 0.43 Wm\(^{-2}\) K\(^{-1}\), with half the range of uncertainty. The observational constraint on climate sensitivity is weaker but still significant, suggesting a likely value of 3.68 ± 1.30 K (90% confidence), which also favors the upper range of model estimates. These results suggest that uncertainty in model estimates of the global cloud feedback may be substantially reduced by ensuring a realistic distribution of clouds between regions of warm and cool SSTs in simulations of the current climate.

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Notes

  1. 1.

    The negative shortwave contributions to GCF variability in the eastern Indian/western Pacific Oceans are also noteworthy (Fig. 5b). We do not attempt to explain them here, but simply note that they are consistent with an increase in the correlation between \(\alpha\) and the GCF observed in the same region (Figs. 4c, d).

  2. 2.

    Although we focus here on the cloud-water mixing ratio, similar changes are observed in the correlations between the GCF and cloud fraction at each level (not shown).

References

  1. Bony S, Dufresne JL (2005) Marine boundary layer clouds at the heart of tropical cloud feedback uncertainties in climate models. Geophys Res Lett 32(20):L20806. doi:10.1029/2005GL023851

    Article  Google Scholar 

  2. Brient F, Schneider T (2016) Constraints on climate sensitivity from space-based measurements of low-cloud reflection. J Clim 29(16):5821–5835. doi:10.1175/JCLI-D-15-0897.1

    Article  Google Scholar 

  3. Brient F, Schneider T, Tan Z, Bony S, Qu X, Hall A (2015) Shallowness of tropical low clouds as a predictor of climate models’ response to warming. Clim Dyn:1–17. doi:10.1007/s00382-015-2846-0

  4. Caldwell PM, Zelinka MD, Taylor KE, Marvel K (2016) Quantifying the sources of inter-model spread in equilibrium climate sensitivity. J Clim 29:513–524. doi:10.1175/JCLI-D-15-0352.1

    Article  Google Scholar 

  5. Christian JE, Siler N, Koutnik M, Roe G, Christian JE, Siler N, Koutnik M, Roe G (2016) Identifying dynamically induced variability in glacier mass-balance records. J Clim 29(24):8915–8929. doi:10.1175/JCLI-D-16-0128.1

    Article  Google Scholar 

  6. Dessler AE (2010) A determination of the cloud feedback from climate variations over the past decade. Science 330(6010):1523–1527. doi:10.1126/science.1192546

    Article  Google Scholar 

  7. Forster PM, Andrews T, Good P, Gregory JM, Jackson LS, Zelinka M (2013) Evaluating adjusted forcing and model spread for historical and future scenarios in the CMIP5 generation of climate models. J Geophys Res Atmosp 118(3):1139–1150. doi:10.1002/jgrd.50174

    Article  Google Scholar 

  8. Gent PR, Danabasoglu G, Donner LJ, Holland MM, Hunke EC, Jayne SR, Lawrence DM, Neale RB, Rasch PJ, Vertenstein M, Worley PH, Yang ZL, Zhang M (2011) The community climate system model version 4. J Clim 24(19):4973–4991. doi:10.1175/2011JCLI4083.1

    Article  Google Scholar 

  9. Hartmann DL, Ockert-Bell ME, Michelsen ML, Hartmann DL, Ockert-Bell ME, Michelsen ML (1992) The effect of cloud type on earth’s energy balance: global analysis. J Clim 5(11):1281–1304. doi:10.1175/1520-0442(1992) 005<1281:TEOCTO>2.0.CO;2

    Article  Google Scholar 

  10. Hourdin F, Mauritsen T, Gettelman A, Golaz JC, Balaji V, Duan Q, Folini D, Ji D, Klocke D, Qian Y, Rauser F, Rio C, Tomassini L, Watanabe M, Williamson D, Hourdin F, Mauritsen T, Gettelman A, Golaz JC, Balaji V, Duan Q, Folini D, Ji D, Klocke D, Qian Y, Rauser F, Rio C, Tomassini L, Watanabe M, Williamson D (2016) The art and science of climate model tuning. Bull Am Meteorol Soc:BAMS-D-15-00,135.1. doi:10.1175/BAMS-D-15-00135.1

  11. Kay JE, Wall C, Yettella V, Medeiros B, Hannay C, Caldwell P, Bitz C, Kay JE, Wall C, Yettella V, Medeiros B, Hannay C, Caldwell P, Bitz C (2016) Global climate impacts of fixing the Southern Ocean shortwave radiation bias in the Community Earth System Model (CESM). J Clim 29(12):4617–4636. doi:10.1175/JCLI-D-15-0358.1

    Article  Google Scholar 

  12. Mauritsen T, Stevens B, Roeckner E, Crueger T, Esch M, Giorgetta M, Haak H, Jungclaus J, Klocke D, Matei D, Mikolajewicz U, Notz D, Pincus R, Schmidt H, Tomassini L (2012) Tuning the climate of a global model. J Adv Model Earth Syst 4(3):M00A01. doi:10.1029/2012MS000154

    Article  Google Scholar 

  13. McCoy DT, Hartmann DL, Zelinka MD, Ceppi P, Grosvenor DP (2015) Mixed-phase cloud physics and Southern Ocean cloud feedback in climate models. J Geophys Res Atmos 120(18):9539–9554. doi:10.1002/2015JD023603

    Article  Google Scholar 

  14. Myers TA, Norris JR (2016) Reducing the uncertainty in subtropical cloud feedback. Geophys Res Lett 43(5):2144–2148. doi:10.1002/2015GL067416

    Article  Google Scholar 

  15. Qu X, Hall A, Klein SA, Caldwell PM (2014) The strength of the tropical inversion and its response to climate change in 18 CMIP5 models. Clim Dyn 45(1–2):375–396. doi:10.1007/s00382-014-2441-9

    Google Scholar 

  16. Qu X, Hall A, Klein SA, DeAngelis AM (2015) Positive tropical marine low-cloud cover feedback inferred from cloud-controlling factors. Geophys Res Lett 42(18):7767–7775. doi:10.1002/2015GL065627

    Article  Google Scholar 

  17. Sanderson BM, Piani C, Ingram WJ, Stone DA, Allen MR (2008) Towards constraining climate sensitivity by linear analysis of feedback patterns in thousands of perturbed-physics GCM simulations. Clim Dyn 30(2–3):175–190. doi:10.1007/s00382-007-0280-7

    Article  Google Scholar 

  18. Sherwood SC, Bony S, Dufresne JL (2014) Spread in model climate sensitivity traced to atmospheric convective mixing. Nature 505(7481):37–42

    Article  Google Scholar 

  19. Smoliak BV, Wallace JM, Stoelinga MT, Mitchell TP (2010) Application of partial least squares regression to the diagnosis of year-to-year variations in Pacific Northwest snowpack and Atlantic hurricanes. Geophys Res Lett 37(3):L03,801. doi:10.1029/2009GL041478

    Article  Google Scholar 

  20. Soden BJ, Vecchi GA (2011) The vertical distribution of cloud feedback in coupled ocean-atmosphere models. Geophys Res Lett 38(12):L12,704. doi:10.1029/2011GL047632

    Article  Google Scholar 

  21. Stocker TF, Dahe Q, Plattner GK (eds) (2013) The physical science basis. IPCC, Cambridge University Press, Cambridge

    Google Scholar 

  22. Taylor KE, Stouffer RJ, Meehl GA (2011) An overview of CMIP5 and the experiment design. Bull Am Meteorol Soc 93(4):485–498. doi:10.1175/BAMS-D-11-00094.1

    Article  Google Scholar 

  23. Tomassini L, Voigt A, Stevens B (2015) On the connection between tropical circulation, convective mixing, and climate sensitivity. Q J R Meteorol Soc 141(689):1404–1416. doi:10.1002/qj.2450

    Article  Google Scholar 

  24. Vial J, Dufresne JL, Bony S (2013) On the interpretation of inter-model spread in CMIP5 climate sensitivity estimates. Clim Dyn 41(11–12):3339–3362. doi:10.1007/s00382-013-1725-9

    Article  Google Scholar 

  25. Volodin EM (2008) Relation between temperature sensitivity to doubled carbon dioxide and the distribution of clouds in current climate models. Izvestiya Atmos Ocean Phys 44(3):288–299. doi:10.1134/S0001433808030043

    Article  Google Scholar 

  26. Wallace JM, Fu Q, Smoliak BV, Lin P, Johanson CM (2012) Simulated versus observed patterns of warming over the extratropical Northern Hemisphere continents during the cold season. Proc Natl Acad Sci USA 109(36):14,337–14,342. doi:10.1073/pnas.1204875109

    Article  Google Scholar 

  27. Watanabe M, Suzuki T, O’Ishi R, Komuro Y, Watanabe S, Emori S, Takemura T, Chikira M, Ogura T, Sekiguchi M, Takata K, Yamazaki D, Yokohata T, Nozawa T, Hasumi H, Tatebe H, Kimoto M (2010) Improved climate simulation by MIROC5: Mean states, variability, and climate sensitivity. J Clim 23(23):6312–6335. doi:10.1175/2010JCLI3679.1

    Article  Google Scholar 

  28. Webb M, Lambert F, Gregory J (2013) Origins of differences in climate sensitivity, forcing and feedback in climate models. Clim Dyn 40(3–4):677–707. doi:10.1007/s00382-012-1336-x

    Article  Google Scholar 

  29. Zelinka MD, Klein SA, Taylor KE, Andrews T, Webb MJ, Gregory JM, Forster PM (2013) Contributions of different cloud types to feedbacks and rapid adjustments in CMIP5. J Clim 26(14):5007–5027. doi:10.1175/JCLI-D-12-00555.1

    Article  Google Scholar 

  30. Zhai C, Jiang JH, Su H (2015) Long-term cloud change imprinted in seasonal cloud variation: more evidence of high climate sensitivity. Geophys Res Lett 42(20):8729–8737. doi:10.1002/2015GL065911

    Article  Google Scholar 

  31. Zhao M (2014) An investigation of the connections among convection, clouds, and climate sensitivity in a global climate model. J Clim 27(5):1845–1862. doi:10.1175/JCLI-D-13-00145.1

    Article  Google Scholar 

  32. Zhao M, Golaz JC, Held IM, Ramaswamy V, Lin SJ, Ming Y, Ginoux P, Wyman B, Donner LJ, Paynter D, Guo H (2016) Uncertainty in model climate sensitivity traced to representations of cumulus precipitation microphysics. J Clim 29:543–560. doi:10.1175/JCLI-D-15-0191.1

    Article  Google Scholar 

  33. Zhou C, Zelinka MD, Dessler AE, Yang P (2013) An analysis of the short-term cloud feedback using MODIS data. J Clim 26(13):4803–4815. doi:10.1175/JCLI-D-12-00547.1

    Article  Google Scholar 

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Acknowledgements

We thank Tyler Thorsen for providing the data from the Aqua and Terra satellites independently, and Kyle Armour and three anonymous reviewers for their thoughtful and most helpful criticism of previous drafts. SPC’s contribution was funded by the UW IGERT Program on Ocean Change award #NSF 1068839. CSB’s contribution was funded by NOAA MAPP Grant NA13OAR4310104.

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Correspondence to Nicholas Siler.

Appendix: Estimating the GCF and ECS

Appendix: Estimating the GCF and ECS

Regressions in Fig. 9 were performed using orthogonal (i.e., Deming) regression after normalizing each variable to unit variance. Unlike ordinary least-squares regression, orthogonal regression minimizes the sum of the squared perpendicular distances from the (normalized) data points to the regression line. This is appropriate when using the partial least-squares method, for which uncertainty exists in both the predictor (A) and the predictand (GCF or ECS). Error estimates were calculated using standard algorithms and the t-statistic for 90% confidence (two-tailed).

The range of uncertainty in our estimates of the GCF and ECS was calculated as follows. First, the difference between the diurnally-corrected top-of-atmosphere fluxes measured by the two CERES satellites (Aqua and Terra) was used as an approximation of the error in \(\alpha\). This difference pattern was multiplied by a random number selected from a standard normal distribution and added to \(\alpha\), which was then projected onto the correlation map in Fig. 1a to estimate the observed albedo index, A. This process was repeated 5000 times using a different random number each time. The standard deviation of A was then scaled by the t-statistic for 90% confidence to approximate the uncertainty.

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Siler, N., Po-Chedley, S. & Bretherton, C.S. Variability in modeled cloud feedback tied to differences in the climatological spatial pattern of clouds. Clim Dyn 50, 1209–1220 (2018). https://doi.org/10.1007/s00382-017-3673-2

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Keywords

  • Cloud feedback
  • Equilibrium climate sensitivity
  • Emergent constraint