A global record of single-layered ice cloud properties and associated radiative heating rate profiles from an A-Train perspective

  • Erica K. Dolinar
  • Xiquan DongEmail author
  • Baike Xi
  • Jonathan H. Jiang
  • Norman G. Loeb
  • James R. Campbell
  • Hui Su


A record of global single-layered ice cloud properties has been generated using the CloudSat and CALIPSO Ice Cloud Property Product (2C-ICE) during the period 2007–2010. These ice cloud properties are used as inputs for the NASA Langley modified Fu–Liou radiative transfer model to calculate cloud radiative heating rate profiles and are compared with the NASA CERES observed top-of-atmosphere fluxes. The radiative heating rate profiles calculated in the CloudSat/CALIPSO 2B-FLXHR-LIDAR and CCCM_CC products are also examined to assess consistency and uncertainty of their properties using independent methods. Based on the methods and definitions used herein, single-layered ice clouds have a global occurrence frequency of ~ 18%, with most of them occurring in the tropics above 12 km. Zonal mean cloud radiative heating rate profiles from the three datasets are similar in their patterns of SW warming and LW cooling with small differences in magnitude; nevertheless, all three datasets show that the strongest net heating (> + 1.0 K day−1) occurs in the tropics (latitude < 30°) near the cloud-base while cooling occurs at higher latitudes (> ~ 50°). Differences in radiative heating rates are also assessed based on composites of the 2C-ICE ice water path (IWP) and total column water vapor (TCWV) mixing ratio to facilitate model evaluation and guide ice cloud parameterization improvement. Positive net cloud radiative heating rates are maximized in the upper troposphere for large IWPs and large TCWV, with an uncertainty of 10–25% in the magnitude and vertical structure of this heating.


Single-layered ice cloud properties Radiative heating rate profiles Satellite remote sensing 



This research was primarily supported by the NASA CERES project under Grant NNX17AC52G at the University of Arizona. Ms Erica Dolinar was supported by NASA Earth and Space Science Fellowship Program (NESSF) for her PhD degree. She was also supported by the NASA ROSES-MAP program during her internship at the Jet Propulsion Laboratory and the American Society for Engineering Edcation (ASEE) during her post-doc. Jonathan Jiang and Hui Su acknowledge the support by the NASA ROSES CCST and MAP programs, and by Jet Propulsion Laboratory, California Institute of Technology, under contract with NASA. Norman Loeb is supported by NASA’s Radiation Budget Science Project. Author JRC acknowledges the support of the Naval Research Laboratory Base Program (BE033-03-45-T008-17). We would also like to thank Dr Greg Elsaesser for his correspondence regarding the importance of ice clouds properties in the NASA GISS GCM and Dr. Min Deng for her help to explain the 2C-ICE product.


  1. Ackerman SA, Liou KN, Valero FPJ, Pfister L (1988) Heating rates in tropical anvils. J Atmos Sci 45:1606–1623CrossRefGoogle Scholar
  2. Ackerman SA, Holz RE, Frey R, Eloranta EW, Maddux BC, McGill M (2008) Cloud detection with MODIS. Part II: validation. J Atmos Ocean Technol 25:1073–1086. CrossRefGoogle Scholar
  3. Austin RT, Stephens GL (2001) Retrieval of stratus cloud microphysical parameters using millimeter-wave radar and visible optical depth in preparation for CloudSat. J Geophys Res 106(D22):28233–28242CrossRefGoogle Scholar
  4. Austin RT, Heymsfield AJ, Stephens GL (2009) Retrieval of ice cloud microphysical parameters using the CloudSat millimeter-wave radar and temperature. J Geophys Res 114(A23):D00. Google Scholar
  5. Barker HW, Stephens GL, Fu Q (1999) The sensitivity of domain-averaged solar fluxes to assumptions about cloud geometry. Q J R Meteorol Soc 125:2127–2152CrossRefGoogle Scholar
  6. Berry E, Mace GG (2014) Cloud properties and radiative effects of the Asian summer monsoon derived from A-Train data. J Geophys Res Atmos. Google Scholar
  7. Campbell JR, Sassen K, Welton EJ (2008) Elevated cloud and aerosol layer retrievals from micropulse lidar signal profiles. J Atmos Ocean Technol 25:685–700. CrossRefGoogle Scholar
  8. Campbell JR, Vaughan MA, Oo M, Holz RE, Lewis JR, Welton EJ (2015) Distinguishing cirrus cloud presence in autonomous lidar measurements. Atmos Meas Tech 8:435–449. CrossRefGoogle Scholar
  9. Campbell JR, Lolli S, Lewis JR, Gu Y, Welton EJ (2016) Daytime cirrus cloud top-of-the-atmosphere radiative forcing properties at a midlatitude site and their global consequences. J Appl Meteorol Climatol 55:1667–1679. CrossRefGoogle Scholar
  10. Cesana G, Waliser DE, L’Ecuyer T, Jiang X, Li J-LF (2017) Evaluation of radiative heating rate profiles in eight GCMs using A-train satellite observations. PAIP Conf Proc 1810:070001. Google Scholar
  11. Chiriaco M et al (2007) Comparison of CALIPSO-like, LaRC, and MODIS retrievals of ice-cloud properties over SIRTA in France and Florida during CRYSTAL-FACE. J Appl Meteorol Clim 46:249–272. CrossRefGoogle Scholar
  12. Crueger T, Stevens B (2015) The effect of atmospheric radiative heating by clouds on the Madden–Julian Oscillation. J Adv Model Earth Syst 7:854–864. CrossRefGoogle Scholar
  13. Cziczo DJ, Froyd KD, Hoose C, Jensen EJ, Diao M, Zondlo MA, Smith JB, Twohy CH, Murphy DM (2013) Clarifying the dominant sources and mechanisms of cirrus cloud formation. Science 340:1320–1324. CrossRefGoogle Scholar
  14. Del Genio AD, Wu J, Chen Y (2012) Characteristics of mesoscale organization in WRF simulations of convection during TWP-ICE. J Clim 25:5666–5688. CrossRefGoogle Scholar
  15. Deng M, Mace GG, Wang Z, Okamoto H (2010) Tropical composition, cloud and climate coupling experiment validation for cirrus cloud profiling retrieval using CloudSat radar and CALIPSO lidar. J Geophys Res 115(J15):D00. Google Scholar
  16. Deng M, Mace GG, Wang Z, Berry E (2015) CloudSat 2C-ICE product update with a new Ze parameterization in lidar-only region. J Geophys Res Atmos 120(208):198–212. Google Scholar
  17. Elsaesser GS, Del Genio AD, Jiang J, van Lier-Walqui M (2017) An improved convective ice parameterization for the NASA GISS global climate model and impacts on cloud ice simulation. J Clim 30(1):317–336. CrossRefGoogle Scholar
  18. Froidevaux L et al. (2008) Validation of aura microwave limb sounder stratospheric ozone measurements. J Geophys Res 113:D15S20. Google Scholar
  19. Fu Q, Liou KN (1993) Parameterization or the radiative properties of cirrus clouds. J Atmos Sci 50:2008–2025CrossRefGoogle Scholar
  20. Fu Q, Liou KN, Cribb M, Charlock T, Grossman A (1997) On multiple scattering in thermal infrared radiative transfer. J Atmos Sci 54:2799–2812,;2.CrossRefGoogle Scholar
  21. Harrop BE, Hartmann DL (2016) The role of cloud radiative heating in determining the location of the ITCZ in aquaplanet simulations. J Clim 29:2741–2763. CrossRefGoogle Scholar
  22. Hartmann DL, Ockert B-ME, Michelsen ML (1992) The effect of cloud type on earth’s energy balance: global analysis. J Clim 5:1281–1304CrossRefGoogle Scholar
  23. Haynes JM, L’Ecuyer TS, Stephens GL, Miller SD, Mitrescu C, Wood NB, Tanelli S (2009) Rainfall retrieval over the ocean with spaceborne W-band radar. J Geophys Res 114:D00A22.
  24. Henderson DS, L’Ecuyer T, Stephens G, Partain P, Sekiguchi M (2013) A multisensor perspective on the radiative impacts of clouds and aerosols. J Appl Meteorol Clim 52:853–871. CrossRefGoogle Scholar
  25. Hogan RJ, Illingworth AJ (2000) Deriving cloud overlap statistics from radar. Q J R Meteorol Soc 126:2903–2909CrossRefGoogle Scholar
  26. Holz RE et al (2016) Resolving ice cloud optical thickness biases between CALIOP and MODIS using infrared retrievals. Atmos Chem Phys 16:5075–5090. CrossRefGoogle Scholar
  27. Jiang JH et al (2010) Five-year (2004–2009) observations of upper tropospheric water vapor and cloud ice from MLS and comparisons with GEOS-5 analyses. J Geophys Res 115:D15103. CrossRefGoogle Scholar
  28. Jiang JH et al (2012) Evaluation of cloud and water vapor simulations in CMIP5 climate models using NASA “A-Train” satellite observations. J Geophys Res 117:D14105. Google Scholar
  29. Jiang JH et al. (2015) Evaluating the diurnal cycle of upper-tropospheric ice clouds in climate models using SMILES observations. J Atmos Sci. Google Scholar
  30. Jiang JH et al. (2017) A simulation of ice cloud particle size, humidity and temperature measurements from the TWICE CubeSat. Earth Sp SciGoogle Scholar
  31. Kahn BH, Takahashi H, Stephens GL, Yue Q, Delanoë J, Manipon G, Manning EM, Heymsfield AJ (2018) Ice cloud microphysical trends observed by the atmospheric infrared sounder. Atmos Chem Phys 18:10715–10739. CrossRefGoogle Scholar
  32. Kato S, Ackerman TP, Mather JH, Clothiaux EE (1999) The k-distribution method and correlated-k approximation for a shortwave radiative transfer model. J Quant Spectrosc Radiat Transf 62:109–121. CrossRefGoogle Scholar
  33. Kato S, Rose FG, Charlock TP (2005) Computation of domain‐averaged irradiance using satellite derived cloud properties. J Atmos Oceanic Technol 22:146–164. CrossRefGoogle Scholar
  34. Kato S, Sun-Mack S, Miller WF, Rose FG, Chen Y, Minnis P, Wielicki BA (2010) Relationships among cloud occurrence frequency, overlap, and effective thickness derived from CALIPSO and CloudSat merged cloud vertical profiles. J Geophys Res 115:D00H28.
  35. Kato S 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. CrossRefGoogle Scholar
  36. Kato S, Miller WF, Sun-Mack S, Rose FG, Chen Y, Mlynczak PE (2014) Variable descriptions of the A-Train integrated CALIPSO, CloudSat, CERES, and MODIS merged product (CCCM or C3M), NEWS A-Train variable descriptionsGoogle Scholar
  37. Kato SNG, Loeb DA, Rutan, Rose FG (2015) Clouds and the earth’s radiant energy system (CERES) data products for climate research. J Meteorol Soc Jpn 93:597–612. CrossRefGoogle Scholar
  38. Kox S, Bugliaro L, Ostler A (2014) Retrieval of cirrus cloud optical thickness and top altitude from geostationary remote sensing. Atmos Meas Tech 7:3233–3246. CrossRefGoogle Scholar
  39. L’Ecuyer TS, Jiang JH (2010) Touring the atmosphere aboard the A-Train. Phys Today 63(7):36–41. CrossRefGoogle Scholar
  40. L’Ecuyer TS, Wood NB, Haladay T, Stephens GL, Stackhouse PW Jr (2008) Impact of clouds on atmospheric heating based on the R04 CloudSat fluxes and heating rates data set. J Geophys Res 113:D00A15. Google Scholar
  41. L’Ecuyer TS et al (2015) The observed state of the energy budget in the early twenty-first century. J Clim 28:8319–8346. CrossRefGoogle Scholar
  42. Larson K, Hartmann DL (2003) Interactions among cloud, water vapor, radiation, and large-scale circulation in the tropical climate. Part I: sensitivity to uniform sea surface temperature changes. J Clim 16:1425–1440CrossRefGoogle Scholar
  43. Li J, Yi Y, Minnis P, Huang J, Yan H, Ma Y, Wang W, Ayers JK (2011) Radiative effect differences between multi-layered and single-layer clouds derived from CERES, CALIPSO, and CloudSat data. J Quant Spect Rad Trans 112:361–375CrossRefGoogle Scholar
  44. Li J-LF et al (2012) An observationally based evaluation of cloud ice water in CMIP3 and CMIP5 GCMsand contemporary reanalyses using contemporary satellite data. J Geophys Res 117:D16105. Google Scholar
  45. Liang XZ, Wang WC (1997) Cloud overlap effects on general circulation model climate simulations. J Geophys Res 102:11039–11047CrossRefGoogle Scholar
  46. Loeb NG, Kato S, Loukachine K, Manalo-Smith N (2005) Angular distribution models for top-of-atmosphere radiative flux estimation from the clouds and the earth’s radiant energy system instrument on the terra satellite. Part I: methodology. J Atmos Ocean Technol 22:338–351. CrossRefGoogle Scholar
  47. Loeb NG, Wielicki BA, Rose FG, Doelling DR (2007) Variability in global top-of-atmosphere shortwave radiation between 2000 and 2005. Geophys Res Lett 34:L03704. CrossRefGoogle Scholar
  48. Loeb NG, Yang P, Rose FG, Hong G, Sun-Mack S, Minnis P, Kato S, Ham S, Smith WL, Hioki S, Tang G (2018) Impact of ice cloud microphysics on satellite cloud retrievals and broadband flux radiative transfer model calculations. J Clim 31:1851–1864. CrossRefGoogle Scholar
  49. Long CN, Shi Y (2008) An automated quality assessment and control algorithm for surface radiation measurements. J Open Atmos Sci 2:23–37CrossRefGoogle Scholar
  50. Mace GG, 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. CrossRefGoogle Scholar
  51. Marquis JW, Bogdanoff AS, Campbell JR, Cummings JA, Westphal DL, Smith NJ, Zhang J (2017) Estimating infrared radiometric satellite sea surface temperature retrieval cold biases in the tropics due to unscreened optically thin cirrus clouds. J Atmos Ocean Technol 34:355–373. CrossRefGoogle Scholar
  52. Mather JH, McFarlane SA, Miller MA, Johnson KL (2007) Cloud properties and associated radiative heating rates in the tropical western Pacific. J Geophys Res Atmos. Google Scholar
  53. Minnis P, Yost CR, Sun-Mack S, Chen Y (2008) Estimating the top altitude of optically thick ice clouds from thermal infrared satellite observations using CALIPSO data. Geophys Res Lett 35:L12801. CrossRefGoogle Scholar
  54. Nazaryan H, McCormick MP, Menzel WP (2008) Global characterization of cirrus clouds using CALIPSO data. J Geophys Res 113:D16211. CrossRefGoogle Scholar
  55. Ramanathan V, Cess RD, Harrison EF, Minnis P, Barkstrom BR, Ahmad E, Hartmann D (1989) Cloud-radiative forcing and climate: results from the earth radiation budget experiment. Science 243:57–63CrossRefGoogle Scholar
  56. Randall DA, Wood RA, Bony S, Colman R, Fichefet T, Fyfe J, Kattsov V, Pitman A, Shukla J, Srinivasan J, Stouffer RJ, Sumi A, Taylor KE (2007) Climate models and their evaluation. In: Qin SD, Manning M, Chen Z, Marquis M, Averyt KB, Tignor M, Miller HL (eds) Climate change 2007: the physical science basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [Solomon]. Cambridge University Press, CambridgeGoogle Scholar
  57. Read WG et al (2007) Aura microwave limb sounder upper tropospheric and lower stratospheric H2O and relative humidity with respect to ice validation. J Geophys Res 112(S35):D24. Google Scholar
  58. Sassen K, Cho BS (1992) Subvisual-thin cirrus lidar dataset for satellite verification and climatological research. J Appl Meteorol 31:1275–1285CrossRefGoogle Scholar
  59. Sassen K, Wang Z, Liu D (2008) Global distribution of cirrus clouds from CloudSat/Cloud-Aerosol lidar and infrared pathfinder satellite observations (CALIPSO) measurements. J Geophys Res 113(A12):D00. Google Scholar
  60. Schwartz MJ et al. (2008) Validation of the aura microwave limb sounder temperature and geopotential height measurements. J Geophys Res 113:D15S11. CrossRefGoogle Scholar
  61. Stephens GL, Webster PJ (1981) Clouds and climate: sensitivity of simple systems. J Atmos Sci 38:235–247.,0235:CACSOS.2.0.CO;2 CrossRefGoogle Scholar
  62. Stephens GL et al. (2002) The CloudSat mission and the A-Train: a new dimension of space-based observations of clouds and precipitation. Bull Am Meteorol Soc. Google Scholar
  63. Stubenrauch CJ et al (2013) Assessment of global cloud datasets from satellites. Bull Am Meteorol Soc 94:1031–1049. CrossRefGoogle Scholar
  64. Su H, Jiang JH, Stephens GL, Vane DG, Livesey NJ (2009) Radiative effects of upper tropospheric clouds observed by Aura MLS and CloudSat. Geophys Res Lett 36:L09815. CrossRefGoogle Scholar
  65. Virts KS, Wallace JM (2010) Annual, interannual, and intraseasonal variability of tropical tropopause transition layer cirrus. J Atmos Sci 67:3097–3112. CrossRefGoogle Scholar
  66. Wielicki BA, Barkstrom BR, Harrison EF, Lee RB III, Smith GL, Cooper JE (1996) Clouds and the earth’s radiant energy system (CERES): an earth observing system. Bull Am Meteorol Soc 72:853–868CrossRefGoogle Scholar
  67. Wild M, Ohmura A, Gilgen H, Rosenfeld D (2004) On the consistency of trends in radiation and temperature records and implications for the global hydrological cycle. Geophys Res Lett 31:L11201. CrossRefGoogle Scholar
  68. Winker DM, Vaughan MA, Omar A, Hu Y, Powell KA, Liu Z, Hunt WH, Young SA (2009) Overview of the CALIPSO mission and CALIOP data processing algorithms. J Atmos Ocean Technol 26:2310–2323CrossRefGoogle Scholar
  69. Yi B, Rapp AD, Yang P, Baum BA, King MD (2017a) A comparison of Aqua MODIS ice and liquid water cloud physical and optical properties between collection 6 and collection 5.1: pixel-to-pixel comparisons, J Geophys Res Atmos. Google Scholar
  70. Yi B, Rapp AD, Yang P, Baum BA, King MD (2017b) A comparison of Aqua MODIS ice and liquid water cloud physical and optical properties between collection 6 and collection 5.1: cloud radiative effects. J Geophys Res Atmos. Google Scholar
  71. Zhang H, Peng J, Jing X, Li JN (2013) The features of cloud overlapping in Eastern Asia and their effect on cloud radiative forcing. Sci China Earth Sci 56:737–747CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Erica K. Dolinar
    • 1
    • 6
  • Xiquan Dong
    • 2
    Email author
  • Baike Xi
    • 2
  • Jonathan H. Jiang
    • 3
  • Norman G. Loeb
    • 4
  • James R. Campbell
    • 5
  • Hui Su
    • 3
  1. 1.Department of Atmospheric SciencesUniversity of North DakotaGrand ForksUSA
  2. 2.Department of Hydrology and Atmospheric SciencesUniversity of ArizonaTucsonUSA
  3. 3.Jet Propulsion LaboratoryPasadenaUSA
  4. 4.NASA Langley Research CenterHamptonUSA
  5. 5.Naval Research LaboratoryMontereyUSA
  6. 6.American Society for Engineering EducationWashingonUSA

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