Advertisement

Observational Constraints on Cloud Feedbacks: The Role of Active Satellite Sensors

  • David WinkerEmail author
  • Helene Chepfer
  • Vincent Noel
  • Xia Cai
Chapter
  • 824 Downloads
Part of the Space Sciences Series of ISSI book series (SSSI, volume 65)

Abstract

Cloud profiling from active lidar and radar in the A-train satellite constellation has significantly advanced our understanding of clouds and their role in the climate system. Nevertheless, the response of clouds to a warming climate remains one of the largest uncertainties in predicting climate change and for the development of adaptions to change. Both observation of long-term changes and observational constraints on the processes responsible for those changes are necessary. We review recent progress in our understanding of the cloud feedback problem. Capabilities and advantages of active sensors for observing clouds are discussed, along with the importance of active sensors for deriving constraints on cloud feedbacks as an essential component of a global climate observing system.

Keywords

Cloud feedback Satellite lidar Radar Deep convection Shallow clouds 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Notes

Acknowledgements

The authors acknowledge Sandrine Bony, Steve Klein, Robert Pincus, Bjorn Stevens, and Rob Wood for valuable comments and technical discussions. We acknowledge the assistance of Jason Tackett, who generated Fig. 1. This paper additionally benefited from discussions at the Workshop on ‘‘Shallow clouds and water vapor, circulation and climate sensitivity’’ at the International Space Science Institute (ISSI) and we would like to thank the two reviewers for valuable comments and suggestions, which allowed us to significantly improve this paper. This work has been supported by NASA and CNES.

References

  1. Albrecht BA (1989) Aerosols, cloud microphysics and fractional cloudiness. Science 245:1227–1230Google Scholar
  2. Amiri-Farahani A, Allen RJ, Neubauer D, Lohmann U (2017) Impact of Saharan dust on North Atlantic marine stratocumulus clouds: importance of the semidirect effect. Atmos Chem Phys 17:6305–6322.  https://doi.org/10.5194/acp-17-6305-2017
  3. Armour KC, Bitz CM, Roe GH (2013) Time-varying climate sensitivity from regional feedbacks. J Climate 26:4518–4534.  https://doi.org/10.1175/JCLI-D-12-00544.1
  4. Blossey PN et al (2013) Sensitivity of marine low clouds to an idealized climate change: the CGILS LES intercomparison. JAMES 5:234–258.  https://doi.org/10.1002/jame.20025
  5. Bodas-Salcedo A, Webb MJ, Bony S, Chepfer H, Dufresne J-L, Klein SA, Zhang Y, Marchand R, Haynes JM, Pincus R, John VO (2011) COSP: satellite simulation software for model assessment. Bull Am Meteorol Soc 92:1023–1043.  https://doi.org/10.1175/2011BAMS2856.1
  6. Bodas-Salcedo A, Williams KD, Ringer MA, Beau I, Cole JNS, Dufresne J-L, Koshiro T, Stevens B, Wang Z, Yokohata T (2014) Origins of the solar radiation biases over the Southern Ocean in CFMIP2 models. J Climate 27:41–56.  https://doi.org/10.1175/JCLI-D-13-00169.1
  7. Bodas-Salcedo A, Hill PG, Furtado K, Williams KD, Field PR, Manners JC, Hyder P, Kato S (2016) Large contribution of supercooled liquid clouds to the solar radiation budget of the Southern Ocean. J Climate 29:4213–4228.  https://doi.org/10.1175/JCLI-D-15-0564.1
  8. Bony S, Dufresne J-L (2005) Marine boundary layer clouds at the heart of tropical cloud feedback uncertainties in climate models. Geophys Res Lett 32:L20806.  https://doi.org/10.1029/2005GL023851
  9. Bony S et al (2006) How well do we understand and evaluate climate change feedback processes? J Climate 10:3445–3482Google Scholar
  10. Bony S, Stevens B, Frierson D et al (2015) Clouds, circulation, and climate sensitivity. Nat Geosci 8:261–268.  https://doi.org/10.1038/ngeo2398
  11. Bony S, Stevens B, Coppin D, Becker T, Reed K, Voigt A, Medeiros B (2016) Thermodynamic control of anvil-cloud amount. Proc Nat Acad Sci.  https://doi.org/10.1073/pnas.1601472113
  12. Bretherton CS (2015) Insights into low-latitude cloud feedbacks from high-resolution models. Philos Trans R Soc A 373:20140415.  https://doi.org/10.1098/rsta.2014.0415
  13. Brient F, Schneider T (2016) Constraints on climate sensitivity from space-based measurements of lowcloud reflection. J Climate 29:5821–5834.  https://doi.org/10.1175/JCLI-D-15-00897.1
  14. 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 47:433–449Google Scholar
  15. Cahalan RF, McGill M, Kolasinski J, Varnai T, Yetzer K (2005) THOR: cloud thickness from off-beam lidar returns. J Atmos Ocean Technol 22:605–627Google Scholar
  16. Caldwell PM, Zhang Y, Klein SA (2013) CMIP3 subtropical stratocumulus cloud feedback interpreted through a mixed-layer model. J Climate 26:1607–1623.  https://doi.org/10.1175/JCLI-D-12-00188.1
  17. Carslaw KS, Boucher O, Spracklen DV, Mann GW, Rae JGL, Woodward S, Kulmala M (2010) A review of natural aerosol interactions and feedbacks within the Earth system. Atmos Chem Phys 10:1701–1737Google Scholar
  18. Ceppi P, McCoy DT, Hartmann DL (2016) Observational evidence for a negative shortwave cloud feedback in middle to high latitudes. Geophys Res Lett 43:1331–1339Google Scholar
  19. Cesana G, Waliser DE, Jiang X, Li J-LF (2015) Multi-model evaluation of cloud phase transition using satellite and reanalysis data. J Geophys Res 120:7871–7892.  https://doi.org/10.1002/2014JD022932
  20. Charney J, Arakawa A, Baker D et al (1979) Carbon dioxide and climate: a scientific assessment. National Research Council, Washington, DC, p 22Google Scholar
  21. Chepfer H, Bony S, Winker DM, Chiriaco M, Dufresne J-L, Seze G (2008) Use of CALIPSO lidar observations to evaluate the cloudiness simulated by a climate model. Geophys Res Lett 35:L15704.  https://doi.org/10.1029/2008GL034207
  22. Chepfer H, Bony S, Winker D, Cesana G, Dufresne JL, Minnis P, Stubenrauch CJ, Zeng S (2010) The GCM oriented CALIPSO cloud product (CALIPSO-GOCCP). J Geophys Res 115:D00H16.  https://doi.org/10.1029/2009JD012251
  23. Chepfer H, Noel V, Winker D, Chiriaco M (2014) Where and when will we observe cloud changes due to climate warming? Geophys Res Lett 41:8387–8395.  https://doi.org/10.1002/2014GL061792
  24. Cho H-M, Nasiri SL, Yang P (2009) Application of CALIOP measurements to the evaluation of cloud phase derived from MODIS infrared channels. J Appl Meteorol Climatol 48:2169–2180.  https://doi.org/10.1175/2009JAMC2238.1
  25. Chung E-S, Soden BJ, Clement AC (2012) Diagnosing climate feedbacks in coupled ocean–atmosphere models. Surv Geophys 33:733–744.  https://doi.org/10.1007/s10712-012-9187-x
  26. Cooke R, Wielicki BA, Young DF, Mlynczak MG (2013) Value of information for climate observing systems. Environ Syst Decis 34:98–109Google Scholar
  27. di Michele S, McNally T, Bauer P, Genkova I (2013) Quality assessment of cloud-top height estimates from satellite IR radiances using the CALIPSO lidar. IEEE Trans Geosci Remote Sens 51:2454–2464.  https://doi.org/10.1109/TGRS.2012.2210721
  28. Enfield DB, Mestaz-Nunez AM, Trimble PJ (2001) The Atlantic multidecadal oscillation and its relation to rainfall and river flows in the continental US. Geophys Res Lett 28:2077–2080Google Scholar
  29. Evan AT, Heidinger AK, Vimont DJ (2007) Arguments against a physical long-term trend in global ISCCP cloud amounts. Geophys Res Lett 34:L04701.  https://doi.org/10.1029/2006GL028083
  30. Folland CK, Parker DE, Colman A (1999) Large scale modes of ocean surface temperature since the late nineteenth century. In: Navarra A (ed) Beyond El Nino: decadal and interdecadal climate variability. Springer, New York, pp 73–102Google Scholar
  31. Forbes R, Geer A, Lonitz K, Ahlgrimm M (2016) Reducing systematic errors in cold-air outbreaks. ECMWF Newsl 146:17–22Google Scholar
  32. Forster PM (2016) Inference of climate sensitivity from analysis of Earth’s energy budget. Ann Rev Earth Planet Sci 44:85–106Google Scholar
  33. Garnier A, Pelon J, Vaughan MA, Winker DM, Trepte CR, Dubuisson P (2015) Lidar multiple scattering factors inferred from CALIPSO lidar and IIR retrievals of semi-transparent cirrus cloud optical depths over oceans. Atmos Meas Technol 8:2759–2774.  https://doi.org/10.5194/amt-8-2759-2015
  34. Gettelman A, Lin L, Medeiros B, Olson J (2016) Climate feedback variance and the interaction of aerosol forcing and feedbacks. J Climate.  https://doi.org/10.1175/JCLI-D-16-0151.1
  35. Gordon ND, Klein SA (2014) Low-cloud optical depth feedback in climate models. J Geophys Res 119:6052–6065.  https://doi.org/10.1002/2013JD021052
  36. Gregory JM, Andrews T (2016) Variation in climate sensitivity and feedback parameters during the historical period. Geophys Res Lett 43:3911–3920.  https://doi.org/10.1002/2016GL068406
  37. Hair JW, Hostetler CA, Cook AL, Harper DB, Ferrare RA, Mack TL, Welch W, Izquierdo LR, Hovis FE (2008) Airborne high spectral resolution lidar for profiling aerosol optical properties. Appl Opt 47:6734–6753Google Scholar
  38. Hartmann DL, Larson K (2002) An important constraint on cloud-climate feedback. Geophys Res Lett 20:1951–1954Google Scholar
  39. Haynes JM, Vonder Haar TH, L’Ecuyer T, Henderson D (2013) Radiative heating characteristics of Earth’s cloudy atmosphere from vertically resolved active sensors. Geophys Res Lett 40:624–630.  https://doi.org/10.1002/grl.50145
  40. Holz R, Ackerman S, Nagle F, Frey R, Dutcher S, Kuehn R, Vaughan M, Baum B (2008) Global MODIS cloud detection and height evaluation using CALIOP. J Geophys Res 113:D00A19.  https://doi.org/10.1029/2008JD009837
  41. Hu Y, Winker D, Vaughan M et al (2009) CALIPSO/CALIOP cloud phase discrimination algorithm. J Atmos Ocean Tech 26:2293–2309.  https://doi.org/10.1175/2009JTECHA1280.1
  42. Illingworth AJ, Barker HW, Beljaars A et al (2015) The EarthCARE satellite. Bull Am Meteorol Soc 96:1311–1332.  https://doi.org/10.1175/BAMS-D-12-00227.1
  43. IPCC (2013) Summary for policy makers. In: Stocker TF, Qin D, Plattner G-K, Tignor M, Allen SK, Boschung J, Nauels A, Xia Y, Bex V, Midgley PM (eds) Climate change 2013: the physical science basis. Cambridge University Press, CambridgeGoogle Scholar
  44. Jin H, Nasiri SL (2014) Evaluation of AIRS cloud thermodynamic phase determination with CALIPSO. J Appl Meteorol Climatol 53:1012–1027.  https://doi.org/10.1175/JAMC-D-13-0137.1
  45. Kato S, Rose FG, Mack SS, Miller WF et al (2011) Improvements of top-of-atmosphere and surface irradiance computations with CALIPSO-, CloudSat-, and MODIS-derived cloud and aerosol properties. J Geophys Res 116:D19209.  https://doi.org/10.1029/2011JD016050
  46. Key JR (1993) Estimating the area fraction of geophysical fields from measurements along a transect. IEEE Trans Geosci Remote Sci 31:1099–1102Google Scholar
  47. Klein SA, Hall A (2015) Emergent constraints for cloud feedbacks. Curr Clim Change Rep 1:276–287.  https://doi.org/10.1007/s40641-015-0027-1
  48. Klein SA, Hall A, Norris JR, Pincus R (2017) Low-cloud feedbacks from cloud-controlling factors: a review. Surv Geophys.  https://doi.org/10.1007/s10712-017-9433-3
  49. Knutti R, Hegerl GC (2008) The equilibrium climate sensitivity of the Earth’s temperature to radiation changes. Nat Geosci 1:735–743Google Scholar
  50. L’Ecuyer TS, Jiang JH (2010) Touring the atmosphere aboard the A-Train. Phys Today 63(10):36–41Google Scholar
  51. Lewis N, Curry JA (2015) The implications for climate sensitivity of AR5 forcing and heat uptake estimates. Clim Dyn 45:1009–1023.  https://doi.org/10.1007/s00382-014-2342-y
  52. Li Y, Yang P, North GR, Dessler A (2012) Test of the fixed anvil temperature hypothesis. J Atmos Sci 69:2317–2328.  https://doi.org/10.1175/JAS-D-11-0158.1
  53. Loeb NG, Wielicki BA, Wong T, Parker PA (2009) Impact of data gaps on satellite broadband radiation records. J Geophys Res 114:D11109.  https://doi.org/10.1029/2008JD011183
  54. Mace GG, Wrenn FJ (2013) 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 Climate 26:9429–9444.  https://doi.org/10.1175/JCLI-D-12-00207.1
  55. Mace G, Zhang Q, Vaughan M, Marchand R, Stephens G, Trepte C, Winker D (2009) A description of hydrometeor layer occurrence statistics derived from the first year of merged Cloudsat and CALIPSO data. J Geophys Res 114:D00A26.  https://doi.org/10.1029/2007JD009755
  56. Marchand R, Ackerman T, Smyth M, Rossow WB (2010) A review of cloud top height and optical depth histograms from MISR, ISCCP, and MODIS. J Geophys Res 115:D16206.  https://doi.org/10.1029/2009JD013422
  57. Marvel K, Zelinka M, Klein SA, Bonfils C, Caldwell P, Doutriaux C, Santer BD, Taylor KE (2015) External influences on modeled and observed cloud trends. J Climate 28:4820–4840.  https://doi.org/10.1175/JCLI-D-14-00734.1
  58. Matus AV, L’Ecuyer TS (2017) The role of cloud phase in Earth’s radiation budget. J Geophys Res Atmos 122:2559–2578.  https://doi.org/10.1002/2016JD025951
  59. McCoy DT, Eastman R, Hartmann DL, Wood R (2017) The change in low-cloud cover in a warmed climate inferred from AIRS, MODIS, and ECMWF-interim analyses. J Climate 30:3609–3620.  https://doi.org/10.1175/JCLI-D-15-0734.1
  60. Myers TA, Norris JR (2015) On the relationships between subtropical clouds and meteorology in observations and CMIP3 and CMIP5 models. J Climate 28:2945–2967.  https://doi.org/10.1175/JCLI-D-14-00475.s1
  61. Myers TA, Norris JR (2016) Reducing the uncertainty in subtropical cloud feedback. Geophys Res Lett 43:2144–2148.  https://doi.org/10.1002/2015GL067416
  62. Myhre G, Myhre A, Stordal F (2001) Historical time evolution of total radiative forcing. Atmos Environ 35:2361–2373Google Scholar
  63. Nam CCW, Bony S, Dufresne J-L, Chepfer H (2012) The ‘too few, too bright’ tropical low-cloud problem in CMIP5 models. Geophys Res Lett 39:L21801.  https://doi.org/10.1029/2012GL053421
  64. Neubersch D, Held H, Otto A (2014) Operationalizing climate targets under learning: an application of costrisk analysis. Clim Change 126:305–318.  https://doi.org/10.1007/s10584-014-1223-z
  65. Norris JR, Evan AT (2015) Empirical removal of artifacts from the ISCCP and PATMOS-x satellite cloud records. J Atmos Ocean Technol 32:691–702Google Scholar
  66. Norris JR, Allen RJ, Evan AT, Zelinka MD, O’Dell CW, Klein S (2016) Evidence for climate change in the satellite cloud record. Nature 536:72–75.  https://doi.org/10.1038/nature18273
  67. Nuijens L, Medeiros B, Sandu I, Ahlgrimm M (2015) The behavior of trade-wind cloudiness in observations and models: the major cloud components and their variability. JAMES 7:600–616.  https://doi.org/10.1002/2014MS000390
  68. Ohring G (2004) Satellite instrument calibration for measuring global climate change. In: Ohring G, Wielicki B, Spencer R, Emery B, Datla R (eds) NISTIR 7047Google Scholar
  69. Otto AF, Otto O Boucher et al (2013) Energy budget constraints on climate response. Nat Geosci 6:415–416Google Scholar
  70. Pincus R, Platnick S, Ackerman SA, Hemler RS, Hofmann RJP (2012) Reconciling simulated and observed view of clouds: MODIS, ISCCP, and the limits of instrument simulators. J Climate 25:4699–4720.  https://doi.org/10.1175/JCLI-D-11-00267.1
  71. Powell KA, Hostetler CA, Liu Z, Vaughan MA, Kuehn RE, Hunt WH, Lee K, Trepte CR, Rogers RR, Young SA, Winker DM (2009) CALIPSO lidar calibration algorithms: part I—nighttime 532 nm parallel channel and 532 nm perpendicular channel. J Atmos Ocean Technol 26:2015–2033.  https://doi.org/10.1175/2009-JTECHA1242.1
  72. Qu X, Hall A, Klein SA, DeAngelis AM (2015) Positive tropical marine low-cloud cover feedback inferred from cloud-controlling factors. Geophys Res Lett 42:7767–7775.  https://doi.org/10.1002/2015GL065627
  73. Rossow WB, Zhang Y (2010) Evaluation of a statistical model of cloud vertical structure using combined CloudSat and CALIPSO cloud layer profiles. J Clim 23:6641–6653.  https://doi.org/10.1175/2010JCLI3734.1
  74. Rugenstein MAA, Caldeira K, Knutti R (2016) Dependence of global radiative feedbacks on evolving patterns of surface heat fluxes. Geophys Res Lett 43:9877–9885.  https://doi.org/10.1002/2016GL070907
  75. Seifert A, Heus T, Pincus R, Stevens B (2015) Large-eddy simulation of the transient and near-equilibrium behavior of precipitating shallow convection. JAMES 7:1918–1937.  https://doi.org/10.1002/2015MS000489
  76. She C, Alvarez RJ II, Caldwell LM, Krueger DA (1992) High-spectral-resolution Rayleigh–Mie lidar measurement of aerosol and atmospheric profiles. Opt Lett 17:541–543Google Scholar
  77. Shea YL, Wielicki BA, Sun-Mack S, Minnis P (2017) Quantifying the dependence of satellite cloud retrievals on instrument uncertainty. J Clim.  https://doi.org/10.1175/JCLI-D-16-0429.1
  78. Sherwood SC, Bony S, Dufresne JL (2014) Spread in model climate sensitivity traced to atmospheric convective mixing. Nature 505:37–42.  https://doi.org/10.1038/nature12829
  79. Soden BJ, Held IM (2006) An assessment of climate feedbacks in coupled ocean–atmosphere models. J Climate 19:3354–3360Google Scholar
  80. Soden BJ, Held IM, Colman R, Shell KM, Kiehl JT, Shields CA (2008) Quantifying climate feedbacks using radiative kernels. J Climate 21:3504–3520.  https://doi.org/10.1175/2007JCLI2110.1
  81. Stephens GL, Kummerow CD (2007) The remote sensing of clouds and precipitation from space: a review. J Atmos Sci 64:3742–3765.  https://doi.org/10.1175/2006JAS2375.1
  82. Stephens GL, Vane DG, Boain RJ et al (2002) The CloudSat mission and the A-Train. Bull Am Meteorol Soc 83:1771–1790.  https://doi.org/10.1175/BAMS-83-12-1771
  83. Stephens GL, Wild M, Stackhouse PW, L’Ecuyer T, Kato S, Henderson DS (2012) The global character of the flux of downward longwave radiation. J Clim 25(7):2329–2340.  https://doi.org/10.1175/JCLI-D-11-00262.1
  84. Stephens GL, Winker DM, Pelon J et al (2017) CloudSat and CALIPSO within the A-Train: ten years of actively observing the Earth system. Bull Am Meteorol Soc.  https://doi.org/10.1175/BAMS-D-16-0324.1
  85. Stevens B (2007) On the growth of layers of nonprecipitating cumulus convection. J Atmos Sci 64:2916–2931.  https://doi.org/10.1175/JAS3983.1
  86. Stevens B, Feingold G (2009) Untangling aerosol effects on clouds and precipitation in a buffered system. Nature 461:607–613.  https://doi.org/10.1038/nature08281
  87. Stevens B, Sherwood SC, Bony S, Webb MJ (2016a) Prospects for narrowing bounds on Earth’s equilibrium climate sensitivity. Earth’s Future.  https://doi.org/10.1002/11016EF000376
  88. Stevens B, Farrell D, Hirsch L, Jansen F, Nuijens L, Serikov I, Brügmann B, Forde M, Linne H, Lonitz K, Prospero JM (2016b) The Barbados Cloud Observatory: anchoring investigations of clouds and circulation on the edge of the ITCZ. Bull Am Meteorol Soc 97:787–801.  https://doi.org/10.1175/BAMSD-14-00247.1
  89. Stubenrauch C, Rossow W, Kinne S (2012) Assessment of global cloud data sets from satellites. WCRP Report No. 23Google Scholar
  90. Su H, Jiang JH (2013) Tropical clouds and circulation changes during the 2006–07 and 2009–10 El Niños. J Climate 26:399–413.  https://doi.org/10.1175/JCLI-D-12-00152.1
  91. Su H, Jiang JH, Zhai C, Perun V, Shen JT, Del Genio AD, Nazarenko LS, Donner LJ, Horowitz LW, Seman CJ, Morcrette CJ, Petch J, Ringer MA, Cole J, dos Santos Mesquita M, Iversen T, Kristjansson JE, Gettelman A, Rotstayn LD, Jeffrey SJ, Dufresne J-L, Watanabe M, Kawai H, Koshiro T, Wu T, Volodin EM, L’Ecuyer T, Teixeira J, Stephens GL (2013) Diagnosis of regime-dependent cloud simulation errors in CMIP5 models using ‘A-Train’ satellite observations and reanalysis data. J Geophys Res 118:2762–2780.  https://doi.org/10.1029/2012JD018575
  92. Su H, Jiang JH, Zhai C, Shen TJ, Neelin JD, Stephens GL, Yung YL (2014) Weakening and strengthening structures in the Hadley circulation change under global warming and implications for cloud response and climate sensitivity. J Geophys Res 119:5787–5805.  https://doi.org/10.1002/2014JD021642
  93. Tan I, Storelvmo T, Zelinka MD (2016) Observational constraints on mixed-phase clouds imply higher climate sensitivity. Science 352:224–227.  https://doi.org/10.1126/science.aad5300
  94. Terai CR, Klein SA, Zelinka MD (2016) Constraining the low-cloud optical depth feedback at middle and high latitudes using satellite observations. J Geophys Res 121:9696–9716.  https://doi.org/10.1002/2016JD025233
  95. Tomassini L et al (2013) The respective roles of surface temperature driven feedbacks and tropospheric adjustment to CO2 in CMIP5 transient climate simulations. Clim Dyn.  https://doi.org/10.1007/s00382-013-1682-3
  96. Tselioudis G, Rossow W, Zhang Y, Konsta D (2013) Global weather states and their properties from passive and active satellite cloud retrievals. J Clim 26:7734–7746.  https://doi.org/10.1175/JCLI-D-13-00024.1
  97. Twomey S (1977) The influence of pollution on the shortwave albedo of clouds. J Atmos Sci 34:1149–1152Google Scholar
  98. Várnai T, Marshak A (2009) MODIS observations of enhanced clear sky reflectance near clouds. Geophys Res Lett 36:L06807Google Scholar
  99. Vial J, Dufresne J-L, Bony S (2013) On the interpretation of inter-model spread in CMIP5 climate sensitivity estimates. Clim Dyn 41:3339–3362.  https://doi.org/10.1007/s00382-013-1725-9
  100. Vial J, Bony S, Dufresne J-L, Roehrig R (2016) Coupling between lower-tropospheric convective mixing and low-level clouds: physical mechanisms and dependence on convection scheme. JAMES 8:1892–1911.  https://doi.org/10.1002/2016MS000740
  101. Vial J, Bony S, Stevens B, Vogel R (2017) Mechanisms and model diversity of trade-wind shallow cumulus cloud feedbacks: a review. Surv Geophys.  https://doi.org/10.1007/s10712-017-9418-2
  102. Weatherhead EC, Reinsel GC, Tiao GC et al (1998) Factors affecting the detection of trends: statistical considerations and applications to environmental data. J Geophys Res 103:17149–17161Google Scholar
  103. Webb M, Senior C, Bony S, Morcrette J-J (2001) Combining ERBE and ISCCP data to assess clouds in the Hadley Centre, ECMWF, and LMD atmospheric climate models. Clim Dyn 17:905–922Google Scholar
  104. Webb MJ, Lambert FH, Gregory JM (2013) Origins of differences in climate sensitivity, forcing, and feedback in climate models. Clim Dyn 40:677–707.  https://doi.org/10.1007/s00382-012-1336-x
  105. Wielicki BA, Parker L (1992) On the determination of cloud cover from satellite sensors: the effect of sensor spatial resolution. J Geophys Res 97:12799–12823Google Scholar
  106. Wielicki BA, Young DF, Mlynczak MG et al (2013) Achieving climate change absolute accuracy in orbit. Bull Am Meteorol Soc 94:1520–1539.  https://doi.org/10.1175/BAMS-D-12-00149.1
  107. Winker DM, Hunt WH, McGill MJ (2007) Initial performance assessment of CALIOP. Geophys Res Lett 34:L19803.  https://doi.org/10.1029/2007GL030135
  108. Winker DM, Pelon J, Coakley JA Jr, Ackerman SA, Charlson RJ, Colarco PR, Flamant P, Fu Q, Hoff R, Kittaka C, Kubar TL, LeTreut H, McCormick MP, Megie G, Poole L, Powell K, Trepte C, Vaughan MA, Wielicki BA (2010) The CALIPSO mission: a global 3D view of aerosols and clouds. Bull Am Meteorol Soc 91:1211–1229.  https://doi.org/10.1175/2010BAMS3009.1
  109. Wood R (2007) Cancellation of aerosol indirect effects in marine stratocumulus through cloud thinning. J Atmos Sci 64:2657–2669Google Scholar
  110. Wood R (2012) Stratocumulus clouds. Mon Weather Rev 140:2373–2423.  https://doi.org/10.1175/MWR-D-11-00121.1
  111. Xie S-P, Kosaka Y, Okumura YM (2016) Distinct energy budgets for anthropogenic and natural changes during global warming hiatus. Nat Geosci 9:29–34Google Scholar
  112. Zelinka MD, Hartmann DL (2010) Why is longwave cloud feedback positive? J Geophys Res 115:D16117.  https://doi.org/10.1029/2010JD013817
  113. Zelinka MD, Hartmann DL (2011) The observed sensitivity of high clouds to mean surface temperature anomalies in the tropics. J Geophys Res 116:D23103.  https://doi.org/10.1029/2011JD016459
  114. Zelinka MD, Zhou C, Klein SA (2016) Insights from a refined decomposition of cloud feedbacks. Geophys Res Lett 43:9259–9269.  https://doi.org/10.1002/2016GL069917
  115. Zeng S, Riedi J, Trepte CR, Winker DM, Hu Y-X (2014) Study of global droplet number concentration with A-Train satellites. Atmos Chem Phys 14:7125–7134.  https://doi.org/10.5194/acp-14-7125-2014
  116. 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 42:8729–8737.  https://doi.org/10.1002/2015GL065911
  117. Zhao G, Di Girolamo L (2006) Cloud fraction errors for trade wind cumuli from EOS-Terra instruments. Geophys Res Lett 33:L20802.  https://doi.org/10.1029/2006GL027088
  118. Zhou C, Zelinka MD, Klein SA (2016) Impact of decadal cloud variations on the Earth’s energy budget. Nat Geosci 9:871–875.  https://doi.org/10.1038/NGEO2828

Copyright information

© The Author(s) 2017

Authors and Affiliations

  1. 1.MS/475, NASA Langley Research CenterHamptonUSA
  2. 2.LMD/IPSL, CNRS, UPMCUniversity of Paris 06ParisFrance
  3. 3.Laboratoire d’Aérologie, CNRSToulouseFrance
  4. 4.Science Systems and Applications, Inc (SSAI)HamptonUSA

Personalised recommendations