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Water vapor, clouds, aerosol and radiation in the atmosphere Reinout Boers, KNMI

Water vapor, clouds, aerosol and radiation in the atmosphere Reinout Boers, KNMI. Purpose What is sensitivity of radiation to constituent variability? How well can we attribute TOA flux variations to cloud, aerosol and water vapor? Can we observe trends?. Structure of talk.

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Water vapor, clouds, aerosol and radiation in the atmosphere Reinout Boers, KNMI

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  1. Water vapor, clouds, aerosol and radiation in the atmosphere Reinout Boers, KNMI

  2. Purpose • What is sensitivity of radiation to constituent variability? • How well can we attribute TOA flux variations to cloud, aerosol and water vapor? • Can we observe trends?

  3. Structure of talk First part, some RTE calculation to assess sensitivity of TOA to various constituent Second part, cloudiness and albedo from ISCCP data set

  4. Putting this work into context of AO • Passive retrieval of cloud parameters from Operational Meteorological Satellites (Arnout Feijt) • Passive retrieval of cloud parameters from ozone satellites (Piet Stammes) • Active retrievals from satellites (Dave Donovan) • Active retrievals from Cabauw radar/ lidar (Dave Donovan, Henk Klein Baltink) • Radiation measurements (Wouter Knap) • Attribution work (Rob v Dorland)

  5. Purpose of Retrieval of cloud properties Validation of processes in climate models Linking TOA fluxes to variation in atmospheric constituents

  6. Fluxes at top of atmosphere Balance between SW and LW fluxes Fsw = 1367 Wm-2 A = 0.3 Flw= Teff4 = 239 Wm-2 assume  = 1 > Teff = 255 K

  7. Fluxes at top of atmosphere Observable satellite TOA fluxes are: 1) Upwelling LW flux (Flw) (>4 m) 2) Reflected SW flux (A Fsw) (0 - 4 m) 3) Various SW, LW spectral channels

  8. Central Questions What is the sensitivity of TOA fluxes to constituent variability? Can we measure these constituents with enough precision to quantify their contribution to the TOA flux? Specified accuracy of TOA flux of 1 Wm-2

  9. Calculations 1) Water vapor only (clear sky) 2) Aerosol and water vapor (clear sky) 3) Clouds at various heights, with mean water vapor, with / without aerosol, variable microphysics 4) Clouds at various heights, with correlated water vapor,with / without aerosol,variable microphysics

  10. Radiation calculations 1) SW 24-band, Ozone, Water Vapor,variable microphysics, delta-Eddington approximation 2) Industrial strength NASA GSFC-code (Harshvardhan et al, 199.’s), CO2 hardwired at 330 ppm 3) Cosz = 0.82 (i.e. July 15, 12 Z, de Bilt) summertime T, ozone 425 DU

  11. Radiation calculations 4) Aerosol optical thickness (Piet Stammes Lowtran 7 adaptation) 5) Water cloud optics only 6) Effective radius in clouds at 8 or 10 m

  12. Effect of water vapor variation on TOA LW fluxes

  13. Effect of PBL and middle atmosphere water vapor variability on TOA LW fluxes

  14. Effect of upper atmospheric water vapor on TOA LW fluxes

  15. Effect of stratospheric water vapor on TOA LW fluxes doubling stratospheric water vapor yields a reduction of 1 Wm-2 in LW fluxes ! !

  16. Effect of aerosol on TOA SW fluxes aerosol=0.2 increases TOA SW flux by 14 Wm-2

  17. Sensitivity of TOA fluxes to water vapor, clouds and aerosols • Data sets such as ISCCP yield cloud retrievals under the assumption that there are no aerosols in the atmosphere • For many current data sets, the size of the cloud particle is unknown, but in the near IR, SW absorption is cloud droplet size dependent

  18. Calculate TOA fluxes with varying aerosol, cloud droplet sizes, but assuming that in the visible part of the spectrum, the upward reflected flux is known and fixed Simulations of TOA: I500 = f(aerosol = 0.0 ;cloud = 10; Reff =10m) I500 = f(aerosol = 0.2 ;cloud = 10; Reff =10m) I500 = f(aerosol = 0.0 ;cloud = 10; Reff = 8m) I500 = f(aerosol = 0.2 ;cloud = 10; Reff = 8m)

  19. TOA fluxes as a function of aerosol optical depth and cloud droplet effective radius Reducing Reff by 2 m increases TOA SW flux by 9 W m-2 inclusion of aerosol has almost no effect

  20. TOA fluxes in the presence of clouds as a function of water vapor variability Correlation of water vapor with cloud presence introduces 10 -15 Wm-2 variability inTOA SW flux

  21. TOA LW fluxes as function of cloud top pressure (PC) Over a PC range of 550 hPa variation in TOA LW of 65 Wm-2

  22. Initial conclusions (1) on the importance of constituent variability on TOA fluxes • The height variability of water vapor is extremely important(!) in quantifying clear sky TOA SW and LW fluxes (LW 15 - 30 Wm-2, SW ’less’) • For clear sky (and optically thin clouds) the presence of (non-absorbing) aerosol introduces a 5 - 20 Wm-2 extra TOA SW flux • For absorbing aerosol ……?!

  23. For clouds, TOA SW fluxes are hardly affected by the presence of aerosols • In the presence of clouds, the presence of water vapor is correlated with clouds, and introduces 10 - 15 Wm-2 variability in TOA SW • LW fluxes are very sensitive to cloud top pressure • Reduction in Reff of 2m --10 Wm-2 extra TOA SW

  24. Overall conclusion Present limited capability to measure aerosol , water vapor and cloud particle size limits the precision of computed TOA fluxes to plus/minus 15 Wm-2, which is not good enough for attribution studies (required precision 1 Wm-2) To improve: measure aerosol and water vapor first!

  25. Can we do anything?? (1) Measure water vapor / aerosol / clouds at Cabauw, calculate TOA, Surf fluxes, compare with observations at surface and at satellite (2) Assimilation studies of clouds and aerosols using RACMO /TM3 with detailed radiative transfer, and compare against CERES etc.

  26. Add-on…... • Can we observe trends in narrow band SW data? • Use ISCCP cloud data set • ISCCP period = 1984 - 1998: 15 years • Do not extend conclusions to total SW!

  27. Use of ISCCP data set (1) • Only daytime data (night time is interpolated and imprecise) • Calculate cloudiness and cloud albedo from all 15 ISCCP cloud types (6 water, 9 ice clouds) • Integrate over the full sunny size of the Earth

  28. Use of ISCCP data set (2) • Single scatter albedo = 1 • For water clouds asymmetry parameter = 0.85 • For ice clouds asymmetry parameter = 0.80

  29. Use of ISCCP data set (3) Definitions cloudiness: with i summation over 15 cloud types j summation over all sunlit areas cij cloud cover

  30. Use of ISCCP data set (4) Definition of mean cloud albedo with ij cloud albedo

  31. Use of ISCCP data set (5) Definition of mean scene cloud albedo with ij cloud albedo

  32. Use of ISCCP data set (6) Definition of planetary cloud albedo with j the solar zenith angle in the jth scene 

  33. Trends in global cloudiness (daytime)

  34. Trends in global cloudiness (daytime)

  35. Trends in global mean cloud albedo

  36. Trends in global albedo weighted by cloudiness

  37. Trends in global cloud albedo

  38. Sensitivity of temperature to changes in albedo Filling in the numbers (A=0.01)

  39. Trends in global cloud albedo What about the Netherlands??

  40. Trends in cloud cover over the Netherlands

  41. Trends in cloud albedo over the Netherlands

  42. Conclusions (1984 - 1998) from ISCCP 1) Total cloudiness is on the decrease 2) Average cloud albedo is on the increase 3) Planetary cloud albedo is on the decrease 4) Over the Netherlands cloud albedo and cloudiness decreases 5) Over the Netherlands cloudiness weighted cloud albedo decreases

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