Robin Hogan

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
Cloud overlap approach

• Consider combined cloud cover of pairs of levels
• Group into vertically continuous and
  non-continuous pairs
• Plot combined cloud cover versus level separation
• Compare true cover values from various overlap
  assumptions
• Define overlap parameter 0 random and 1
  maximum overlap

6
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)
7
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
8
Cloud structure in the shortwave and longwave
Over black surface

• 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?

9
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
10
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
11
Solar zenith angle
Asymmetry factor
Anna Townsend
12
Vertical structure of inhomogeneity
Low shear High shear

• Decorrelation length 700m

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
13
Results from 18 months of radar data
Fractional variance of IWC
Vertical decorrelation length
Increasing shear

• 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 log10 fIWC 0.3log10d -
  0.04s - 0.93

Hogan and Illingworth (JAS 2003)
14
Distance from cloud boundaries

• Can refine this further consider shear lt10
  ms-1/km
• Variance greatest at cloud boundaries, at its
  least around a third of the distance up from
  cloud base
• Thicker clouds tend to have lower fractional
  variance
• Can represent this reasonably well analytically

15
3D stochastic cirrus model

• Generalizes 2D observations to 3D
• A tool for studying the effect of cloud structure
  on radiative transfer

Hogan Kew (QJ 2005)
16
with increased shear

• Both gridbox-mean albedo and emissivity increase
  for the same mean optical depth/IWP

Shear 20 m s-1km-1
17
Radiative effect control experiment
Upwelling shortwave (?60º)
Upwelling longwave (Wm-2)
27 Dec 1999
18
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

19
Thin case heating rate
Shortwave Longwave

• GCM scheme with max-rand overlap outperforms GCM
  with true overlap due to compensation of errors
• Maximum-random overlap -gt underestimate cloud
  radiative effect
• Horizontal homogeneity -gt overestimate cloud
  radiative effect

20
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

21
Thick case heating rate
Shortwave Longwave

• 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

22
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?