Skip to main content
SpringerLink
Log in

Evaluation of clouds and radiative fluxes in the EC-Earth general circulation model

  • Published:
Climate Dynamics Aims and scope Submit manuscript

Abstract

Observations, mostly from the International Satellite Cloud Climatology (ISCCP), are used to assess clouds and radiative fluxes in the EC-Earth general circulation model, when forced by prescribed observed sea surface temperatures. An ISCCP instrument simulator is employed to consistently compare model outputs with satellite observations. The use of a satellite simulator is shown to be imperative for model evaluation. EC-Earth exhibits the largest cloud biases in the tropics. It generally underestimates the total cloud cover but overestimates the optically thick clouds, with the net result that clouds exert an overly strong cooling effect in the model. Every cloud type has its own source of bias. The magnitude of the cooling due to the shortwave cloud radiative effect (\(\mid \hbox {SWCRE}\mid\)) is underestimated for the stratiform low-clouds, because the model simulates too few of them. In contrast, \(\mid \hbox {SWCRE}\mid\) is overestimated for trade wind cumulus clouds, because in the model these are too thick. The clouds in the deep convection regions also lead to overestimate the \(\mid \hbox {SWCRE}\mid\). These clouds are generally too thick and there are too few mid and high thin clouds. These biases are consistent with the positive precipitation bias and the overly strong mass flux for deep convective plumes. Potential sources for the various cloud biases in the model are discussed.

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

Access this article

Log in via an institution

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

Similar content being viewed by others

Notes

  1. http://cmip-pcmdi.llnl.gov/cmip5/docs/Taylor_CMIP5_22Jan11_marked.

  2. In the ISCCP simulator, the cloud albedo is the average over cloudy areas and thus is not a function of the cloud amount.

  3. We have also compared the standard output updraft \(M_{c}\) in mid-troposphere from the model with the observations and the overestimation is even larger (not shown).

  4. Mass of cloud liquid (or ice) water in the grid cell divided by the mass of moist air.

  5. When statistically significant, the correlation coefficient between \(\gamma\)-EFC and \(\overline{\delta {C}}\) is always positive by construction. We adjust it with the sign of the correlation between spatially and seasonally averaged \(C\) and \(\gamma\), to make this relationship visible, as in Lacagnina and Selten (2013b).

References

  • Adler RF, Huffman GJ, Chang A, Ferraro R, Xie P, Janowiak J, Rudolf B, Schneider U, Curtis S, Bolvin D, Gruber A, Susskind J, Arkin P (2003) The version 2 global precipitation climatology project (GPCP) monthly precipitation analysis (1979-Present). J Hydrometeorol 4:1147–1167

    Article  Google Scholar 

  • Baker MB (1997) Cloud microphysics and climate. Science 276:1072–1078

    Article  Google Scholar 

  • Barker HW, Stephens GL, Fu Q (1999) The sensitivity of domain-averaged solar fluxes to assumptions about cloud geometry. Q J R Meteorol Soc 125:2127–2152

    Google Scholar 

  • Bechtold P, Köhler M, Jung T, Leutbecher M, Doblas-Reyes F, Rodwell MJ, Vitart F, Balsamo G (2008) Advances in simulating atmospheric variability with the ECMWF model: from synoptic to decadal time-scales. Q J R Meteorol Soc 134:1337–1351

    Article  Google Scholar 

  • Beljaars ACM, Viterbo P (1999) The role of the boundary layer in a numerical weather prediction model. In: Holtslag AAM, Duynkerke PG (eds) Clear and cloudy boundary layers. North Holland Publishers, Amsterdam

    Google Scholar 

  • Bender FA-M, Rodhe H, Charlson RJ, Ekman AML, Loeb N (2006) 22 views of the global albedo-comparison between 20 GCMs and two satellites. Tellus 58A:320–330

    Article  Google Scholar 

  • Bennartz R (2007) Global assessment of marine boundary layer cloud droplet number concentration from satellite. J Geophys Res 112:D02201. doi:10.1029/2006JD007547

    Google Scholar 

  • Bodas-Salcedo A, Webb MJ, Bony S, Chepfer H, Dufresne JL, 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(8):1023–1043. doi:10.1175/2011BAMS2856.1

    Article  Google Scholar 

  • Bony S, Lau K-M, Sud YC (1997) Sea surface temperature and large-scale circulation influences on tropical greenhouse effect and cloud radiative forcing. J Clim 10:2055–2077

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    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 DJ, Tselioudis G, Webb MJ (2006) How well do we understand and evaluate climate change feedback processes? J Clim 19:3445–3482. doi:10.1175/JCLI3819.1

    Article  Google Scholar 

  • Bretherton CS, de Roode SR, Jakob C, Andreas EL, Intrieri J, Moritz RE, Persson POG (2000) A comparison of the ECMWF forecast model with observations over the annual cycle at SHEBA. J Geophys Res volume: FIRE Arctic Clouds Experiment Special Issue

  • Bretherton CS, Hartmann DL (2009) Large-scale controls on cloudiness. Peer-reviewed book chapter for perturbed clouds in the climate system: their relationship to energy balance, atmospheric dynamics and precipitation. In: Heintzenberg J, Charlson RJ (eds) Strungmann Forum Reports, vol. 2, MIT Press, pp 217–234

  • Bretherton CS, Wyant MC (1997) Moisture transport, lower-tropospheric stability, and decoupling of cloud-topped boundary layers. J Atmos Sci 54:148–167

    Article  Google Scholar 

  • Bretherton CS et al (1999) An intercomparison of radiatively-driven entrainment and turbulence in a smoke cloud, as simulated by different numerical models. Q J R Meteorol Soc 125:391–423

    Article  Google Scholar 

  • Cess RD et al. (1996) Cloud feedback in atmospheric general circulation models: an update. J Geophys Res 101:12791–12794

    Google Scholar 

  • Chang FL, Li ZQ (2005) A new method for detection of cirrus overlapping water clouds and determination of their optical properties. J Atmos Sci 62:3993–4009

    Article  Google Scholar 

  • Charlock TP, Ramanathan V (1985) The albedo field and cloud radiative forcing produced by a general circulation model with internally generated cloud optics. J Atmos Sci 42:1405–1429

    Article  Google Scholar 

  • Chen R, Wood R, Li Z, Ferraro R, Chang F-L (2008) Studying the vertical variation of cloud droplet effective radius using ship and space-borne remote sensing data. J Geophys Res 113:D00A02. doi:10.1029/2007JD009596

  • Clement AC, Burgman R, Norris JR (2009) Observational and model evidence for positive low level cloud feedback. Science 325:460–464. doi:10.1126/science.1171255

    Article  Google Scholar 

  • Derbyshire SH, Beau I, Bechtold P, Grandpeix J-Y, Piriou J-M, Redelsperger J-L, Soares PMM (2004) Sensitivity of moist convection to environmental humidity. Q J R Meteorol Soc 130:3055–3079

    Article  Google Scholar 

  • Dhuria HL, Kyle HL (1990) Cloud types and the tropical earth radiation budget. J Clim 3:1409–1434

    Article  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(19):5135–5144

    Article  Google Scholar 

  • Emanuel KA (1991) A scheme for representing cumulus convection in large-scale models. J Atmos Sci 48:2313–2335

    Article  Google Scholar 

  • Emanuel KA, Neelin JD, Bretherton CS (1994) On large-scale circulations in convecting atmospheres. Q J R Meteorol Soc 120:1111–1143

    Article  Google Scholar 

  • Forbes RM, Tompkins A (2011) An improved representation of cloud and precipitation. ECMWF Newsletter No. 129, 13–18

  • Fouquart Y, Bonnel B (1980) Computations of solar heating of the earth’s atmosphere: a new parameterization. Beitr Phys Atmos 53:35–62

    Google Scholar 

  • Greuell W, van Meijgaard E, Clerbaux N, Meirink JF (2011) Evaluation of model-predicted top-of-atmosphere radiation and cloud parameters over Africa with observations from GERB and SEVIRI. J Clim 24:4015–4036. doi:10.1175/2011JCLI3856.1

    Article  Google Scholar 

  • Guilyardi E, Braconnot P, Jin F-F, Kim ST, Kolasinski M, Li T, Musat I (2009) Atmosphere feedbacks during ENSO in a coupled GCM with a modified atmospheric convection scheme. J Clim 22:5698–5718

    Article  Google Scholar 

  • Harrison EF, Minnis P, Barkstrom BR, Ramanathan V, Cess RD, Gibson GG (1990) Seasonal variation of cloud radiative forcing derived from the Earth Radiation Budget Experiment. J Geophys Res 95:18687–18703

    Google Scholar 

  • Hazeleger W, Wang X, Severijns C, Stefanescu S, Bintanja R, Sterl A, Wyser K, Semmler T, Yang S, van den Hurk B, van Noije T, van der Linden E, van der Wiel K (2012) EC-Earth V2.2: description and validation of a new seamless earth system prediction model. Clim Dyn 39:2611–2629. doi:10.1007/s00382-011-1228-5

    Article  Google Scholar 

  • Hazeleger W, Severijns C, Semmler T, Stefanescu S, Yang S, Wang X, Wyser K, Dutra E, Bintanja R, van den Hurk B, van Noije T, Selten F, Sterl A, et al (2010) EC-Earth: a seamless earth-system prediction approach in action. Bull Am Meteorol Soc 91:1357–1363. doi:10.1175/2010BAMS2877.1

    Google Scholar 

  • Held IM, Soden BJ (2006) Robust responses of the hydrological cycle to global warming. J Clim 19:5686–5699. doi:10.1175/JCLI3990.1

    Article  Google Scholar 

  • Heymsfield AJ (2003) Properties of tropical and midlatitude ice cloud particle ensembles: part I: median mass diameters and terminal velocities. J Atmos Sci 60:2592–2611

    Article  Google Scholar 

  • Houze RA (1993) Cloud dynamics. Academic Press, New York

    Google Scholar 

  • Hu Y, Rodier S, Xu K, Sun W, Huang J, Lin B, Zhai P, Josset D (2010) Occurrence, liquid water content, and fraction of supercooled water clouds from combined CALIOP/IIR/MODIS measurements. J Geophys Res 115:D00H34. doi:10.1029/2009JD012384

  • Jakob C (2001) The representation of cloud cover in atmospheric general circulation models. PhD thesis, Ludwig-Maximilians-Universitaet Muenchen, p 193

  • Jin Y, Rossow WB, Wylie DP (1996) Comparison of the climatologies of high-level clouds from HIRS and ISCCP. J Clim 9:2850–2879

    Article  Google Scholar 

  • Kanamitsu M, Ebisuzaki W, Woollen J, Yang SK, Hnilo JJ, Fiorino M, Potter G (2002) NCEP-DOE AMIP-II reanalysis. Bull Am Meteorol Soc 83:1631–1643

    Article  Google Scholar 

  • Kay JE et al (2012) Exposing global cloud biases in the community atmosphere model (CAM) using satellite observations and their corresponding instrument simulators. J Clim 25:5190–5207

    Google Scholar 

  • Kessler E (1969) On the distribution and continuity of water substance in atmospheric circulation. Meteorological Monographs, vol. 10. Am Meteorol Soc, Boston, MA

  • Klein SA, Hartmann DL (1993) The seasonal cycle of low stratiform clouds. J Clim 6:1588–1606

    Article  Google Scholar 

  • Klein SA, Jakob C (1999) Validation and sensitivities of frontal clouds simulated by the ECMWF model. Mon Weather Rev 127:2514–2531

    Article  Google Scholar 

  • Klein SA, Zhang Y, Zelinka MD, Pincus R, Boyle J, Gleckler PJ (2013) Are climate model simulations of clouds improving? An evaluation using the ISCCP simulator. J Geophys Res Atmos 118:1329–1342. doi:10.1002/jgrd.50141

    Article  Google Scholar 

  • Klocke D, Pincus R, Quaas J (2011) On constraining estimates of climate sensitivity with present-day observations through model weighting. J Clim 24:6092–6099

    Article  Google Scholar 

  • Koenigk T, Brodeau L, Graversen RG, Karlsson J, Svensson G, Tjernstrom M, Willen U, Wyser K (2013) Arctic climate change in 21st century CMIP5 simulations with EC-Earth. Clim Dyn 40(11):2719–2743. doi:10.1007/s00382-012-1505-y

    Article  Google Scholar 

  • Köhler M (2005) Improved prediction of boundary layer clouds. ECMWF Newsletter, (104), ECMWF, Shinfield Park, Reading RG2 9AX, UK

  • Korolev A, Field PR (2008) The effect of dynamics on mixed-phase clouds: theoretical considerations. J Atmos Sci 65:66–86

    Article  Google Scholar 

  • Kubar TL, Hartmann DL, Wood R (2007) Radiative and convective driving of tropical high clouds. J Clim 20:5510–5526

    Article  Google Scholar 

  • Kuo HL, Raymond WH (1980) A quasi-one-dimensional cumulus cloud model and parametrization of cumulus heating and mixing effects. Mon Weather Rev 108:991–1009

    Article  Google Scholar 

  • Lacagnina C, Selten F (2013a) Changes in the cloud properties in response to El Niño: a bivariate approach. Clim Dyn 40(11–12):2973–2991

    Article  Google Scholar 

  • Lacagnina C, Selten F (2013b) A novel diagnostic technique to investigate cloud-controlling factors. J Geophys Res Atmos 118:5979–5991. doi:10.1002/jgrd.50511

    Article  Google Scholar 

  • Lauer A, Hamilton K (2013) Simulating clouds with global climate models: a comparison of CMIP5 results with CMIP3 and satellite data. J Clim 26:3823–3845

    Article  Google Scholar 

  • Lin WY, Zhang MH, Loeb NG (2009) Seasonal variation of the physical properties of marine boundary layer clouds off the California coast. J Clim 22:2624–2638

    Article  Google Scholar 

  • Loeb NG, Wielicki BA, Doelling DR, Smith GL, Keyes DF, Kato S, Manalo-Smith N, Wong T (2009) Toward optimal closure of the earths top-of-atmosphere radiation budget. J Clim 22:748–766. doi:10.1175/2008JCLI2637.1

    Article  Google Scholar 

  • Ma C-C, Mechoso CR, Robertson AW, Arakawa A (1996) Peruvian stratus clouds and the tropical Pacific circulation: a coupled ocean atmosphere GCM study. J Clim 9:1635–1645

    Article  Google Scholar 

  • Madec G (2008) NEMO ocean engine. Note du Pole de modelisation, Institut Pierre-Simon Laplace (IPSL), France, No. 27 ISSN No. 1288–1619

  • Marchand R, Ackerman T (2010) An analysis of cloud cover in multiscale modeling framework global climate model simulations using 4 and 1 km horizontal grids. J Geophys Res 115:D16207. doi:10.1029/2009JD013423

    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 

  • Marchand R, Ackerman T (2011) A cloud-resolving model with an adaptive vertical grid for boundary layer clouds. J Atmos Sci 68:1058–1074. doi:10.1175/2010JAS3638.1

    Article  Google Scholar 

  • Martin GM, Johnson DW, Spice A (1994) The measurement and parameterization of effective radius of droplets in warm stratocumulus clouds. J Atmos Sci 51:1823–1842

    Article  Google Scholar 

  • Matheson MA, Coakley JA, Tahnk WR (2005) Aerosol and cloud property relationships for summertime stratiform clouds in the northeastern Atlantic from advanced very high resolution radiometer observations. J Geophys Res 110:D24204

    Article  Google Scholar 

  • Morcrette JJ, Clough SA, Mlawer EJ, Iacono MJ (1998) Impact of a validated radiative transfer scheme, RRTM, on the ECMWF model climate and 10-day forecasts. ECMWF Technical Memo No. 252, p 47

  • Morcrette J-J, Fouquart Y (1986) The overlapping of cloud layers in shortwave radiation parameterizations. J Atmos Sci 43:321–328

    Article  Google Scholar 

  • Murphy J, Sexton DH, Barnett DN, Jones GS, Webb MJ, Collins M, Stainforth DA (2004) Quantification of modelling uncertainties in a large ensemble of climate change simulations. Nature 430:768772

    Article  Google Scholar 

  • Nam CCW, Quaas J (2012) Evaluation of clouds and precipitation in the ECHAM5 general circulation model using CALIPSO and CloudSat satellite data. J Clim 25:4975–4992. doi:10.1175/JCLI-D-11-00347.1

    Article  Google Scholar 

  • Nieuwstadt FTM (1984) The turbulent structure of the stable, nocturnal boundary layer. J Atmos Sci 41:2202–2216

    Article  Google Scholar 

  • Nitta T (1975) Observational determination of cloud mass flux distributions. J Atmos Sci 32:73–91

    Article  Google Scholar 

  • Nordeng T-E (1994) Extended versions of the convection parametrization scheme at ECMWF and their impact upon the mean climate and transient activity of the model in the tropics. ECMWF Tech. Memo. No 206

  • Ou SC, Liou K-N (1995) Ice microphysics and climatic temperature feedback. Atmos Res 35:127–138

    Article  Google Scholar 

  • Pincus R, Baker MB (1994) Effect of precipitation on the albedo susceptibility of clouds in the marine boundary layer. Nature 372:250252

    Article  Google Scholar 

  • Pincus R, Platnick S, Ackerman SA, Hemler RS, Hofmann RJP (2012) Reconciling simulated and observed views of clouds: MODIS, ISCCP, and the limits of instrument simulators. J Clim 25:4699–4720. doi:10.1175/JCLI-D-11-00267.1

    Article  Google Scholar 

  • Platnick S (2000) Vertical photon transport in cloud remote sensing problems. J Geophys Res 105:22919–22935. doi:10.1029/2000JD900333

    Google Scholar 

  • Platnick S, King MD, Ackerman SA, Menzel WP, Baum BA, Riedi JC, Frey RA (2003) The MODIS cloud products: algorithms and examples from Terra. IEEE Trans Geosci Remote Sens 41:459–473

    Article  Google Scholar 

  • Rauber RM, Tokay A (1991) An explanation for the existence of supercooled water at the top of cold clouds. J Atmos Sci 48:1005–1023

    Article  Google Scholar 

  • Rossow WB, Schiffer RA (1999) Advances in understanding clouds from ISCCP. Bull Am Meteorol Soc 80:2261–2288

    Article  Google Scholar 

  • Ryan BF et al (2000) Simulations of a cold front by cloud-resolving, limited-area, and large-scale models, and a model evaluation using in situ and satellite observations. Mon Weather Rev 128:3218–3235

    Article  Google Scholar 

  • Sandu I, Stevens B, Pincus R (2010) On the transitions in marine boundary layer cloudiness. Atmos Chem Phys 10:2377–2391

    Google Scholar 

  • Sekiguchi M et al (2003) A study of the direct and indirect effects of aerosols using global satellite data sets of aerosol and cloud parameters. J Geophys Res 108, doi:10.1029/2002JD003359

  • Shonk JKP, Hogan RJ, Manner J (2012) Impact of improved representation of horizontal and vertical cloud structure in a climate model. Clim Dyn 38:2365–2376. doi:10.1007/s00382-011-1174-2

    Article  Google Scholar 

  • Siebesma AP, et al (2004) Cloud representation in general circulation models over the northern Pacific Ocean: AEUROCS intercomparison study. Q J R Meteorol Soc 130:3245–3267

    Google Scholar 

  • Siebesma AP, Pedro MM (2007) A combined eddy-diffusivity mass-flux approach for the convective boundary layer. J Atmos Sci 64:1230–1248. doi:10.1175/JAS3888.1

    Article  Google Scholar 

  • Simmons A, Uppala C, Dee D, Kobayashi S (2007) ERA-Interim: new ECMWF reanalysis products from 1989 onwards. ECMWF Newsletter No. 110, pp 25–35

  • Simpson J (1971) On cumulus entrainment and one-dimensional models. J Atmos Sci 28:449–455

    Article  Google Scholar 

  • Simpson J, Kummerow C, Tao WK, Adler RF (1996) On the tropical rainfall measuring mission (TRMM). Meteorol Atmos Phys 60:19–36

    Article  Google Scholar 

  • Slingo A (1990) Sensitivity of the Earths radiation budget to changes is low clouds. Nature 343:49–51. doi:10.1038/343049a0

    Article  Google Scholar 

  • Smith TM, Reynolds RW (2004) Improved extended reconstruction of SST (1854–1997). J Clim 17:2466–2477

    Article  Google Scholar 

  • Soden BJ, Held IM (2006) An assessment of climate feedbacks in coupled atmosphere-ocean models. J Clim 19:3354–3360

    Article  Google Scholar 

  • Solomon S, et al (2007) Climate change 2007: the physical science basis: contribution of working group I to the fourth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge. ISBN 0521705967

  • Spencer RW, Braswell WD (1997) How dry is the tropical free troposphere? Implications for global warming theory. Bull Am Meteorol Soc 78:1097–1106

    Article  Google Scholar 

  • Stackhouse PW Jr, Gupta SK, Cox SJ, Zhang T, Mikovitz JC, Hinkelman LM (2011) 24.5-year SRB data set released. GEWEX News 21:10–12

    Google Scholar 

  • Stephens GL (1978) Radiation profiles in extended water clouds. II: parameterization schemes. J Atmos Sci 35:2123–2132. doi:10.1175/1520-0469(1978)0352123:RPIEWC2.0.CO;2

    Article  Google Scholar 

  • Stephens GL, Platt CMR (1987) Aircraft observations of the radiative and microphysical properties of stratocumulus and cumulus cloud fields. J Clim Appl Meteorol 26:1243–1269

    Article  Google Scholar 

  • Stevens B et al (2003) On entrainment in nocturnal marine stratocumulus. Q J R Meteorol Soc 129(595):3469–3492

    Article  Google Scholar 

  • Stevens B, Brenguier J-L (2009) Cloud-contolling factors: low clouds. In: Heintzenberg J, Charlson R (eds) Clouds in the perturbed climate system: their relationship to energy balance, atmospheric dynamics, and precipitation. pp 173–196

  • Stocker TF, et al (2001) Physical climate processes and feedbacks. In: Houghton JT, et al (eds) Climate change 2001: the scientific basis, contributions of working group I to the third assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge

  • Stubenrauch CJ, Rossow WB, Kinne S, Ackerman S, Cesana G, Chepfer H, Di Girolamo L, Getzewich B, Guignard A, Heidinger A, Maddux BC, Menzel WP, Minnis P, Pearl C, Platnick S, Poulsen C, Riedi J, Sun-Mack S, Walther A, Winker D, Zeng S, Zhao G (2012) Assessment of global cloud datasets from satellites: project and database initiated by the GEWEX radiation panel. Bull Am Meteorol Soc. doi:10.1175/BAMS-D-12-00117

  • Sundqvist H (1978) A parameterization scheme for non-convective condensation including prediction of cloud water content. Q J R Meteorol Soc 104:677–690

    Article  Google Scholar 

  • Tiedtke M (1989) A comprehensive mass flux scheme for cumulus parameterization in large-scale models. Mon Weather Rev 117:1779–1800

    Article  Google Scholar 

  • Tiedtke M (1993) Representation of clouds in large-scale models. Mon Weather Rev 121:3040–3061

    Article  Google Scholar 

  • Valcke S (2006) OASIS3 user guide (prism 2–5). CERFACS technical report TR/CMGC/06/73, PRISM Report No 3, Toulouse, France. p 60

  • Vecchi GA, Soden BJ (2007) Global warming and the weakening of the tropical circulation. J Clim 20:4316–4340

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Viterbo P, Beljaars ACM, Mahouf J-F, Teixeira J (1999) The representation of soil moisture freezing and its impact on the stable boundary layer. Q J R Meteorol Soc 125:2401–2426

    Article  Google Scholar 

  • Wallace JM (1992) Effect of deep convection on the regulation of tropical sea surface temperature. Nature 357:230–231

    Article  Google Scholar 

  • Walther A, Heidinger A (2012) Implementation of the daytime cloud optical and microphysical properties algorithm (DCOMP) in PATMOS-x. J Appl Meteorol Clim 51:1371–1390

    Article  Google Scholar 

  • Wang L, Wang Y, Lauer A, Xie S-P (2011) Simulation of seasonal variation of marine boundary layer clouds over the Eastern Pacific with a regional climate model. J Clim 24:3190–3210. doi:10.1175/2010JCLI3935.1

    Article  Google Scholar 

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

    Google Scholar 

  • Webb M, Senior C, Bony S, Morcrette J-J (2001) Combining ERBE and ISCCP data to assess clouds in the Hadley Centre, ECWMF and LMD atmospheric climate models. Clim Dyn 17:905–922

    Article  Google Scholar 

  • Wetherald RT, Manabe S (1988) Cloud feedback process in a general circulation model. J Atmos Sci 45:1397–1415

    Article  Google Scholar 

  • Williams KD, Ringer MA, Senior CA (2003) Evaluating the cloud response to climate change and current climate variability. Clim Dyn 20:705–721

    Google Scholar 

  • Williams KD, Ringer MA, Senior CA, Webb MJ, McAvaney BJ, Andronova N, Bony S, Dufresne J-L, Emori S, Gudgel R, Knutson T, Li B, Lo K, Musat I, Wegner J, Slingo A, Mitchell JFB (2006) Evaluation of a component of the cloud response to climate change in an intercomparison of climate models. Clim Dyn 26:145–165. doi:10.1007/s00382-005-0067-7

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Wyant MC et al (2010) The PreVOCA experiment: modeling the lower troposphere in the southeast Pacific. Atmos Chem Phys 10:4757–4774

    Google Scholar 

  • Yu L, Jin X, Weller RA (2008) Multidecade global flux datasets from the objectively analyzed air-sea fluxes (OAFlux) project: latent and sensible heat fluxes, ocean evaporation, and related surface meteorological variables. Woods Hole Oceanographic Institution, OAFlux Project Technical Report. OA-2008-01, p 64. Woods Hole. Massachusetts

  • 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 

  • Zhang Z, Platnick S (2011) An assessment of differences between cloud effective particle radius retrievals for marine water clouds from three MODIS spectral bands. J Geophys Res 116:D20215. doi:10.1029/2011JD016216

    Article  Google Scholar 

Download references

Acknowledgments

The research leading to these results has received funding from the European Union’s Seventh Framework Program (FP7/2007-2013) under Grant agreement no. 244067. The authors thank Pier Siebesma and Jelle van den Berk for the useful suggestions on the presentation. We are also grateful to the anonymous reviewers for their constructive comments that have helped the improvement of this paper. SRB data were obtained from the NASA Langley Research Center Atmospheric Science Data Center. COSP was obtained from the CFMIP website. ISCCP, MODIS and PATMOS-x data were obtained from the ClimServ Data Center of IPSL/CNRS.

Author information

Authors and Affiliations

  1. KNMI, Royal Netherlands Meteorological Institute, De Bilt, The Netherlands

    Carlo Lacagnina & Frank Selten

Authors
  1. Carlo Lacagnina
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Frank Selten
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Carlo Lacagnina.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Lacagnina, C., Selten, F. Evaluation of clouds and radiative fluxes in the EC-Earth general circulation model. Clim Dyn 43, 2777–2796 (2014). https://doi.org/10.1007/s00382-014-2093-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00382-014-2093-9

Keywords

Search

Navigation