Quantifying sub-grid cloud structure and representing it GCMs

1. Quantifying sub-grid cloud structure and representing it GCMs Robin Hogan Anthony Illingworth, Sarah Kew, Jean-Jacques Morcrette, Itumeleng Kgololo, Joe Daron, Anna Townsend

2. Overview • Cloud overlap from radar • Maximum-random overlap underestimates cloud radiative effect • Inhomogeneity scaling factors from MODIS • Homogeneous clouds overestimate cloud radiative effect • Dependence on gridbox size, cloud type, spectral region etc. • Vertical structure of inhomogeneity from radar • Overlap of inhomogeneities in ice clouds • Experiments with a 3D stochastic cirrus model • Trade-off between overlap and inhomogeneity errors • Representing the heating-rate profile • Priorities for radiation schemes

3. Cloud overlap assumption in models • Cloud fraction and mean ice water content alone not sufficient to constrain the radiation budget • Assumptions generate very different cloud covers • Most models now use “maximum-random” overlap, but there has been very little validation of this assumption

4. Cloud overlap from radar: example • Radar can observe the actual overlap of clouds • We next quantify the overlap from 3 months of data

5. “Exponential-random” overlap • Overlap of vertically continuous clouds becomes random with increasing thickness as an inverse exponential • Vertically isolated clouds are randomly overlapped • Higher total cloud cover than maximum-random overlap Hogan and Illingworth (QJ 2000), Mace and Benson-Troth (2002)

6. Exponential-random: global impact New overlap scheme is easy to implement and has a significant effect on the radiation budget in the tropics Difference in OLR between “maximum-random” overlap and “exponential-random” overlap ~5 Wm-2 globally ECMWF model, Jean-Jacques Morcrette

7. Over black surface Cloud structure in the shortwave and longwave Clear air Cloud Inhomogeneous cloud Non-uniform clouds have lower emissivity & albedo for same mean optical depth due to curvature in the relationships Can we simply scale the optical depth/water content?

8. Results from MODIS • Reduction factor depends strongly on: • Cloud type & variability • Gridbox size • Solar zenith angle • Shortwave/longwave • Mean optical depth itself • ECMWF use 0.7 • All clouds, SW and LW • Value derived from around a month of Sc data: equivalent to a huge gridbox! • Not appropriate for model with 40-km resolution MODIS Sc/Cu 1-km resolution, 100-km boxes Itumeleng Kgololo

9. Shortwave albedo Stratocumulus cases Ice-cloud cases Cumulus cases True Plane-parallel model Modified model Longwave emissivity Stratocumulus cases Ice-cloud cases Cumulus cases Emissivity True Plane-parallel model Modified model Joe Daron

10. Solar zenith angle Asymmetry factor Anna Townsend

11. Vertical structure of inhomogeneity Decorrelation length ~700m Low shear High shear We estimate IWC from radar reflectivity IWC PDFs are approximately lognormal: Characterize width by the fractional variance Lower emissivity and albedo Higher emissivity and albedo

12. Results from 18 months of radar data Fractional variance of IWC Vertical decorrelation length • Variance and decorrelation increase with gridbox size • Shear makes overlap of inhomogeneities more random, thereby reducing the vertical decorrelation length • Shear increases mixing, reducing variance of ice water content • Best-fit relationship: log10fIWC = 0.3log10d - 0.04s - 0.93 Increasing shear Hogan and Illingworth (JAS 2003)

13. 3D stochastic cirrus model Radar data Slice through simulation • “Generalizes” 2D observations to 3D • A tool for studying the effect of cloud structure on radiative transfer Hogan & Kew (QJ 2005)

14. Thin cirrus example • Independent column calculation: • SW radiative effect at TOA: 40 W m-2 • LW radiative effect at TOA: -21 W m-2 • GCM with exact overlap • SW change: +50 W m-2 (+125%) • LW change: -31 W m-2 (+148%) • Large inhomogeneity error • GCM, maximum-random overlap • SW change: +9 W m-2 (+23%) • LW change: -9 W m-2 (+43%) • Substantial compensation of errors

15. Thin case: heating rate • GCM scheme with max-rand overlap outperforms GCM with true overlap due to compensation of errors • Maximum-random overlap -> underestimate cloud radiative effect • Horizontal homogeneity -> overestimate cloud radiative effect Shortwave Longwave

16. Thick ice cloud example • Independent column: • SW radiative effect: 290 W m-2 • LW radiative effect: -105 W m-2 • GCM with exact overlap • SW change: +14 W m-2 (+5%) • LW change: -10 W m-2 (+10%) • Near-saturation in both SW and LW • GCM, maximum-random overlap • SW change: +12 W m-2 (+4%) • LW change: -9 W m-2 (+9%) • Overlap virtually irrelevant

17. Thick case: heating rate • Large error in GCM heating rate profile • Inhomogeneity important to allow radiation to penetrate to (or escape from) the correct depth, even though TOA error is small • Cloud fraction near 1 at all heights: overlap irrelevant • More important to represent inhomogeneity than overlap Shortwave Longwave

18. Summary • Cloud overlap: GCMs underestimate radiative effect • Exponential-random overlap easy to add • Important mainly in partially cloudy skies: 40 W m-2 OLR bias in deep tropics but only around 5 W m-2 elsewhere • Inhomogeneity: GCMs overestimate radiative effect • Affects all clouds, can double the TOA radiative effect • Scaling factor too crude: depends on gridbox size, cloud type, solar zenith angle, spectral region; and heating rate still wrong! • Need more sophisticated method: McICA, triple-region etc. • What about other errors? • In climate mode, radiation schemes typically run every 3 hours: introduces random error and possibly bias via errors in diurnal cycle. How does this error compare with inhomogeneity? • Is spectral resolution over-specified, given large biases in other areas? Why not relax the spectral resolution and use the computational time to treat the clouds better?