Skip to main content

Advertisement

Log in

A process oriented characterization of tropical oceanic clouds for climate model evaluation, based on a statistical analysis of daytime A-train observations

  • Published:
Climate Dynamics Aims and scope Submit manuscript

Abstract

This paper aims at characterizing how different key cloud properties (cloud fraction, cloud vertical distribution, cloud reflectance, a surrogate of the cloud optical depth) vary as a function of the others over the tropical oceans. The correlations between the different cloud properties are built from 2 years of collocated A-train observations (CALIPSO-GOCCP and MODIS) at a scale close to cloud processes; it results in a characterization of the physical processes in tropical clouds, that can be used to better understand cloud behaviors, and constitute a powerful tool to develop and evaluate cloud parameterizations in climate models. First, we examine a case study of shallow cumulus cloud observed simultaneously by the two sensors (CALIPSO, MODIS), and develop a methodology that allows to build global scale statistics by keeping the separation between clear and cloudy areas at the pixel level (250, 330 m). Then we build statistical instantaneous relationships between the cloud cover, the cloud vertical distribution and the cloud reflectance. The vertical cloud distribution indicates that the optically thin clouds (optical thickness <1.5) dominate the boundary layer over the trade wind regions. Optically thick clouds (optical thickness >3.4) are composed of high and mid-level clouds associated with deep convection along the ITCZ and SPCZ and over the warm pool, and by stratocumulus low level clouds located along the East coast of tropical oceans. The cloud properties are analyzed as a function of the large scale circulation regime. Optically thick high clouds are dominant in convective regions (CF > 80 %), while low level clouds with low optical thickness (<3.5) are present in regimes of subsidence but in convective regimes as well, associated principally to low cloud fractions (CF < 50 %). A focus on low-level clouds allows us to quantify how the cloud optical depth increases with cloud top altitude and with cloud fraction.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  • 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

    Article  Google Scholar 

  • Bacmeister JT, Stephens GL (2011) Spatial statistics of likely convective clouds in CloudSat data. J Geophys Res 116:D04104. doi:10.1029/2010JD014444

    Article  Google Scholar 

  • Bodas-Salcedo A, Webb MJ, Brooks ME, Ringer MA, William KD, Milton SF, Wilson DR (2008) Evaluating cloud systems in the Met Office global forecast model using simulated CloudSat radar reflectivities. J Geophys Res 113:D00A13. doi:10.1029/2007JD009620

    Article  Google Scholar 

  • 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 Meteor Soc 92(8):1023–1043. doi:10.1175/2011BAMS2856.1

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Bony S, Dufresne J-L, Le Treut H, Morcette J–J, Senior C (2004) On dynamic and thermodynamic components of cloud changes. Clim Dyn 22:71–86

    Article  Google Scholar 

  • Bony S, Colman R, Kattsov VM, Allan RP, Bretherton CS, Dufresne J-L, Hall A, Hallegatte S, Holland MM, Ingram W, Randall DA, Soden BJ, Tselioudis G, Webb MJ (2006) How well do we understand and evaluate climate change feedback processes? J Clim 19:3445–3482

    Article  Google Scholar 

  • Chepfer H, Goloub P, Riedi J, De Haan J, Hovenier J, Flamant PH (2001) Ice crystal shapes in cirrus clouds derived from POLDER-1/ADEOS-1. J Geophys Res 106(D8):7955–7966

    Article  Google Scholar 

  • Chepfer H, Bony S, Winker D, Chiriaco M, Dufresne J-L, Sèze G (2008) Use of CALIPSO lidar observations to evaluate the cloudiness simulated by a climate model. Geophys Res Lett 35:L15704. doi:10.1029/2008GL034207

    Article  Google Scholar 

  • 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:D16. doi:10.1029/2009JD012251

    Article  Google Scholar 

  • Coakley JA, Friedman MA, Tahnk WR (2005) Retrieval of cloud properties for partly cloudy imager pixels. J Atmos Ocean Technol 22:3–17. doi:http://dx.doi.org/10.1175/JTECH-1681.1

    Google Scholar 

  • Cole J, Barker HW, Loeb NG, von Salzen K (2011) Assessing simulated clouds and radiative fluxes using properties of clouds whose tops are exposed to space. J Clim 24:2715–2727. doi: http://dx.doi.org/10.1175/2011JCLI3652.1

    Google Scholar 

  • De Haan J, Bosma PB, Hovenier JW (1986) The adding method for multiple scattering of polarized light. Astron. Astrophys 183:371–391

    Google Scholar 

  • Dufresne J-L, Bony S (2008) An assessment of the Primary sources of spread of global warming estimates from coupled atmosphere-ocean models. J Clim 21:5135–5144

    Article  Google Scholar 

  • Eitzen ZA, Xu K-M, Wong T (2008) Statistical analyses of satellite cloud object data from CERES. Part V: relationships between physical properties of marine boundary layer clouds. J Clim 21:6668–6688

    Article  Google Scholar 

  • Haynes JM, Marchand RT, Luo Z, Bodas-Salcedo A, Stephens GL (2007) A multipurpose radar simulation package: QuickBeam. Bull Am Meteorol Soc 88:1723–1727. doi:10.1175/BAMS-88-11-1723

    Article  Google Scholar 

  • King MD, Kaufman YJ, Menzel WP, Tanre R (1992) Remote sensing of cloud, aerosol, and water vapor properties from the Moderate Resolution Imaging Spectrometer (MODIS). IEEE Trans Geosci Remote Sens 30:2–27

    Article  Google Scholar 

  • Klein SA, Hartmann DL (1993) The seasonal cycle of low stratiform clouds. J Climate 6:1587–1606

    Article  Google Scholar 

  • Klein SA, Jacob C (1999) Validation and sensitivities of frontal clouds simulated by the ECMWF model. Mon Weather Rev 127:2514–2531. doi:10.1175/1520-0493(1999)127<2514:VASOFC>2.02.CO;2

    Article  Google Scholar 

  • Konsta D, Dufresne J-L, Chepfer H, Idelkadi A, Cesana G (2012) Evaluation of clouds simulated by the LMDZ5 GCM using A-train satellite observations (CALIPSO-PARASOL-CERES). Clim Dyn (submitted)

  • Mace GG, Marchand R, Zhang Q, Stephens G (2007) Global hydrometeor occurrence as observed by CloudSat: initial observations from summer 2006. Geophys Res Lett 34:L09808. doi:10.1029/2006GL029017

    Article  Google Scholar 

  • Marchand R, Haynes J, Mace GG, Ackerman T, Stephens G (2009) A comparison of simulated cloud radar output from the multiscale modelling framework global climate model with CloudSat cloud radar observations. J Geophys Res 114:20

    Article  Google Scholar 

  • 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. doi:10.1029/2009JD013422

    Article  Google Scholar 

  • Medeiros B, Nuijens L, Antoniazzi C, Stevens B (2010) Low-latitude boundary layer clouds as seen by CALIPSO. J Geophys Res 115:D23207. doi:10.1029/2010JD014437

    Article  Google Scholar 

  • Noël V, Ledanois G, Chepfer H, Flamant PH (2001) Computation of single scattering matrix for non-spherical particles randomly or horizontally oriented in space. Appl Opt 40:4365–4375

    Article  Google Scholar 

  • 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) Cilmate models and their evaluation. In: Solomon S, Qin D, 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 aassessment report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge

  • Rossow WB, Schiffer RA (1991) ISCCP cloud data products. Bull Am Meteorol Soc 71:2–20

    Article  Google Scholar 

  • Siebesma AP, Bretherton CS, Brown A, Chlond A, Cuxart J, Duynkerke PG, Jiang H, Khairoutdinov M, Lewellen D, Moeng C-H, Sanchez E, Stevens B, Stevens DE (2003) A large eddy simulation intercomparison study of shallow cumulus convection. J Atmosp Sci 60:1201–1219

    Article  Google Scholar 

  • Simmons A, Uppala S, Dee D, Kobayashi S (2007) ERA-interim: new ECMWF reanalysis products from 1989 onwards. ECMWF Newsl 110:29–35

    Google Scholar 

  • Soden BJ, Held IM (2006) An assessment of climate feedbacks in coupled ocean atmosphere models. J Clim 19(14):3354–3360. doi:10.1175/JCLI3799.1

    Article  Google Scholar 

  • Stephens GL (2005) Cloud feedbacks in the climate system: a critical review. J. Climate 18:237–273. doi:10.1175/JCLI-3243.1

    Article  Google Scholar 

  • Su H, Jiang JH, Vane DG, Stephens GL (2008) Observed vertical structure of tropical oceanic clouds sorted in large-scale regimes. Geophys Res Lett 35:L24704. doi:10.1029/2008GL035888

    Article  Google Scholar 

  • Webb M, Senior C, Bony S, Morcette JJ (2001) Combining ERBE and ISCCP data to assess clouds in the Hadley Center, ECMWF and LMD atmospheric climate models. Clim Dyn 17:905–922

    Article  Google Scholar 

  • Wielicki BA, Welch RM (1986) Cumulus cloud properties derived using LANDSAT satellite data. J Clim Appl Meteor 25:261–276

    Article  Google Scholar 

  • Winker D, Hunt W, McGill M (2007) Initial performance assessment of CALIOP. Geophys Res Lett 34:L19803. doi:10.1029/2007GL030135

    Article  Google Scholar 

  • Wood R, Field PR (2000) Relationships between total water, condensed water, and cloud fraction in stratiform clouds examined using aircraft data. J Atmos Sci 57(12):1888–1905

    Article  Google Scholar 

  • Wood R, Hartmann DL (2006) Spatial variability of liquid water path in marine low cloud: the importance of mesoscale cellular convection. J Clim 19(9):1748–1764

    Article  Google Scholar 

  • Xu K-M, Wong T, Wielicki BA, Parker L, Eitzen ZA (2005) Statistical analyses of satellite cloud object data from CERES. Part I: methodology and preliminary results of the 1998 El Nino/2000 La Nina. J Clim 18:2497–2514

    Article  Google Scholar 

  • Xu K-M, Wong T, Wielicki BA, Parker L, Lin B, Eitzen ZA, Branson M (2007) Statistical analyses of satellite cloud object data from CERES. Part II: tropical convective cloud objects during 1998 El Nino and evidence for supporting the fixed anvil temperature hypothesis. J Clim 20:819–842

    Article  Google Scholar 

  • Xu K-M, Wong T, Wielicki BA, Parker L (2008) Statistical analyses of satellite cloud object data from CERES. Part IV: boundary layer cloud objects during 1998 El Nino. J Clim 21:1500–1521

    Article  Google Scholar 

  • Zhang MH et al (2005) Comparing clouds and their seasonal variations in 10 atmospheric general circulation models with satellite measurements. J Geophys Res 110:D15S02. doi:10.1029/2004JD005021

    Article  Google Scholar 

  • Zhang GJ, Vogelmann AM, Jensen MP, Collins WD, Luke EP (2010) Relating satellite-observed cloud properties from MODIS to meteorological conditions for marine boundary layer Clouds. J Clim 23:1374–1391. doi:10.1175/2009JCLI2897.1

    Article  Google Scholar 

Download references

Acknowledgments

Thanks are due to NASA and CNES for CALIPSO, MODIS observations. ICARE is acknowledged for data access, and for doing the collocation between CALIPSO (330 m) and MODIS (250 m) reflectance along the lidar track. Thanks are due to Climserv for computing facility. We would like to thank the anonymous reviewers for their useful comments that helped to improve the manuscript.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Dimitra Konsta.

Appendices

Appendix 1: Description of sensors, cloud products and variables

Table 1 describes the different sensors and variables used in this study, their spatial resolution, and some of their technical characteristics. The analysis is based on 2 years of observations (January 2007–December 2008).

Table 1 Description of sensors and observations

Appendix 2: Conversion of cloud reflectance in cloud optical depth

The cloud reflectance (CRef) in a fixed viewing direction is used in this study as a surrogate of the cloud optical thickness (Cτ). The relationship between the cloud reflectance and the cloud optical thickness depends on the solar zenith angle and the satellite viewing zenithal and azimuthal angles, and on the phase function of the clouds particles. For the A-train in the tropics, θs varies between 19° and 60°, and 18° < θv-modis < 65°, and 0° < ϕsv < 150°. Changes of cloud reflectances values due to solar zenith angle variations are lower than 0.1 in the tropical regions (30°S–30°N, 18° < θs < 60°) considering a given phase function. Hence latitudinal reflectance’s variations larger than 0.1 in the Tropics can not be attributed to variations of θs, they are caused by changes in the atmosphere composition (clouds). The sensitivity of the reflectance to the cloud particles scattering phase function is of 0.07 in the tropics. Figure 14 shows the cloud reflectance as a function of the cloud optical depth for a cloud composed of spherical particles and a cloud composed of non spherical particles in the viewing direction of MODIS in the tropics. This curves have been computed following Chepfer et al. (2001) in using an adding doubling radiative transfer code (De Haan et al. 1986), the optical properties of spherical particles are obtained with the Mie theory, and the non-spherical ones with ray-tracing computations (Noël et al. 2001) (Fig. 14).

Fig. 14
figure 14

Relationship between clouds optical thickness and reflectance for spherical and non spherical particles at θs = 30° and in the viewing direction of MODIS in the tropical belt 18° < θv < 65° and 0° < ϕsv < 150°

Appendix 3: Link between cloud cover and cloud reflectance for low and high clouds only

To eliminate the existence of clouds at other levels when studying the relation between cloud fraction and cloud reflectance for low and high clouds mainly, we use the criterion of CF-low > 0.9*CF & CF-mid + CF-high < 0.1*CF (CF-high > 0.9*CF & CF-low + CF-mid < 0.1*CF for high clouds only) referring to low clouds only with no clouds above (and high clouds with no clouds beneath respectively). For low clouds only (Fig. 15a) this criterion eliminated the upper right part of Fig. 6a (CF > 0.7) corresponding to low clouds with high reflectances and high cloud fractions, found at the East part of tropical oceans (when looking at their geographical distribution). We identified that the high values of cloud reflectance correspond to low optically thick clouds, these latter are eliminated when using the criterion of ‘low clouds only’ probably due to noise caused by the low and very reflective clouds. The population of high clouds ‘only’ (Fig. 15b) is much less numerous and misses the clouds with CRef > 0.2, the optically thick clouds that may co-exist with low and mid level clouds (Fig. 15).

Fig. 15
figure 15

2D histograms of instantaneous cloud reflectance (CRef MODIS-250 m) and cloud fraction (CF CALIPSO-GOCCP) over the tropical oceans. a For low clouds only (CF-low > 90 % CF and CF-mid + CF-high < 10 % CF) and b for high clouds only (CF-high > 90 % CF and CF-low + CF-mid < 10 % CF). The colorbar represents the number of points at each box (cloud fraction-cloud reflectance) divided by the total number of measurements (of Fig. 5)

Appendix 4: Link between the cloud cover, the cloud reflectance, and the cloud vertical distribution for identified cloud types

Three small regions corresponding to identified cloud types (Marchand et al. 2009; Bodas-Salcedo et al. 2008; Chepfer et al. 2010) are examined in more detail (Fig. 16): Tropical Western Pacific, Californian Stratus and North Pacific. The optically thick atmospheric columns (Cτ > 3.4, Fig. 8c, i) are not uniformly distributed over the regions: optically thick low level clouds are encountered along the Californian coast composed of stratus with cloud cover between 40 and 60 % depending on the season and deep convective high clouds are encountered in the warm pool (35–50 %). Optically thick clouds are also observed over the North Pacific for boundary layer clouds (CF ≈ 25–30 %) and for high frontal clouds mainly during the winter (CF ≈ 22 %) (Fig. 16).

Fig. 16
figure 16

Mean normalised cloud fraction profile (NCF3D CALIPSO-GOCCP) in relation to the pressure level divided by the cloudy portion in each 1° × 1° gridbox for JFM (a, c, e, left column) and JJA (b, d, f, right column), for three regions: tropical western Pacific (5°S–20°N, 70°E-150°E, green line), California stratus region (15°N–35°N, 110°W–140°W, blue line), and North Pacific (30°N–60°N, 160°E–140°W, black line) and for the three different classes of cloud reflectance according to MODIS 250 m (from the top to the bottom: (a, b) 0.03 < CRef < 0.1, (c, d) 0.1 < CRef < 0.2 and (e, f) CRef > 0.2). Only CF-tot > 0.1 are reported

Rights and permissions

Reprints and permissions

About this article

Cite this article

Konsta, D., Chepfer, H. & Dufresne, JL. A process oriented characterization of tropical oceanic clouds for climate model evaluation, based on a statistical analysis of daytime A-train observations. Clim Dyn 39, 2091–2108 (2012). https://doi.org/10.1007/s00382-012-1533-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00382-012-1533-7

Keywords

Navigation