Transcript Document

Quantifying sub-grid
cloud structure and
representing it GCMs
Robin Hogan
Anthony Illingworth, Sarah Kew, JeanJacques Morcrette, Itumeleng Kgololo,
Joe Daron, Anna Townsend
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
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
Cloud overlap from radar: example
• Radar can
observe the
actual
overlap of
clouds
• We next
quantify
the overlap
from 3
months of
data
“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)
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
“maximumrandom”
overlap and
“exponentialrandom”
overlap
~5 Wm-2
globally
ECMWF model, Jean-Jacques Morcrette
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?
Results from MODIS
1
0.9
0.8
• Reduction factor
depends strongly on:
reduction factor
0.7
0.6
0.5
stratocumulus
cumulus
midlat cirrus
tropical cirrus
0.4
0.3
0.2
0.1
0
0
100
200
300
400
grid box size (km)
MODIS
Sc/Cu
1-km
resolution,
100-km
boxes
–
–
–
–
–
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
Itumeleng Kgololo
Shortwave albedo
1
• Stratocumulus cases
• Ice-cloud cases
• Cumulus cases
0.95
Correction Factor
0.9
0.85
0.8
0.75
0.7
0.65
0.6
• True
• Plane-parallel model
• Modified model
0.55
0.5
0
50
100
0.7
250
300
• Stratocumulus cases
• Ice-cloud cases
• Cumulus cases
Correction Factor
0.6
Emissivity
200
Resolution (km)
Longwave emissivity
• True
• Plane-parallel model
• Modified model
150
0.5
0.4
0.3
0.2
0.1
0
50
100
150
Resolution (km)
200
250
300
Joe Daron
Albedo as a function of Solar Zenith Angle
Solar zenith angle
1
0.8
0
20
40
60
80
Correction Factor
Albedo
1
0.6
0.4
0
20
40
60
80
0.2
0
0
20
40
60
Optical Depth
80
0.9
0.8
0.7
0.6
0.5
0.4
100
0
100
200
300
Model Resolution (km)
Asymmetry factor
Albedo as a function of Asymmetry Factor
1
Polycrystals(0.74)
Water(0.85)
0.8
Columns(0.8)
Plates(0.9)
0.6
Correction Factor
Albedo
1
0.4
Polycrystals(0.74)
Columns(0.8)
Water(0.85)
Plates(0.9)
0.2
0
0
20
40
60
Optical Depth
80
100
0.9
0.8
0.7
0.6
0.5
0.4
0
100
200
Model Resolution (km)
300
Anna Townsend
Vertical structure of inhomogeneity
Low shear
High shear
We estimate IWC from radar reflectivity
IWC PDFs are
approximately
lognormal:
Characterize
width by the
fractional
variance
Decorrelation
length ~700m
Lower emissivity and albedo
Higher emissivity and albedo
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)
3D stochastic
cirrus model
• “Generalizes” 2D
observations to 3D
• A tool for studying
the effect of cloud
structure on
radiative transfer
Radar data
Slice through simulation
Hogan & Kew (QJ 2005)
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
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 -> underestimate cloud radiative effect
– Horizontal homogeneity -> overestimate cloud radiative effect
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
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
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?