Transcript Using Satellite Observations and Reanalyses to Evaluate Climate and Weather Models

Using Satellite Observations and Reanalyses to
Evaluate Climate and Weather Models
Richard Allan
Environmental Systems Science Centre, University of Reading
Thanks to: Tony Slingo and Mark Ringer
IRS2004, Busan, August 2004
INTRODUCTION
– Evaluation of Weather and Climate Prediction Models
(some examples)
– Climate prediction uncertainty dependent on
feedback processes
» What time/space-scales are important for climate change
» Feedbacks generally operating on shorter time-scales
» …but diagnosis of feedback’s may only be possible on longer time-scales
IRS2004, Busan, August 2004
OVERVIEW OF TALK
– 1) Evaluating simulated radiation budget
»
dynamical regimes, climate model, reanalysis
– 2) Clear-sky radiation and sampling
– 3) Interannual Variability
» Water vapour, cloud radiative effect, reanalyses?
– 4) Geostationary Earth Radiation Budget
»
GERB, Met Office NWP model, surface radiation
IRS2004, Busan, August 2004
1) Evaluating model simulations of
top of atmosphere radiation budget

Important for the radiative/convective
balance of model

Valuable diagnostic of model clouds,
water vapour, etc
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OLR (Wm-2)
(colours)
Omega, hPa/day
(contours)
April 1998
Model
Obs
Model - Obs
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Ggg
O
m
e
g
a
hPa
day
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SST (K)
Ringer &Allan (2004) Tellus A

Climate models must simulate adequately the
properties of cloud within each dynamic
regime and how they respond to warming

See also, e.g.:
– Bony et al. (2003) Clim. Dyn
– Williams et al. (2003) Clim. Dyn.
– Tselioudis and Jakob (2002) JGR
– Chen et al. (2002) Science
IRS2004, Busan, August 2004
2) Clear-sky radiation

Longwave cooling important for determining
subtropical subsidence

Clear-sky OLR important diagnostic for water
vapour and temperature

Difficulties in observing clear-sky radiation

Monthly mean clear-sky radiation over
convective regions:
– Satellite will sample highly anomalous situations
IRS2004, Busan, August 2004
Using ERA-40 Daily data to illustrate
clear-sky sampling bias of CERES data
IRS2004, Busan, August 2004
Model-obs differences & Clear-sky Sampling
Type II
DT6.7
DOLRc
IRS2004, Busan, August 2004
HadAM3-OBS
Type-I
DOLRc
IRS2004,
-2) Busan, August 2004
(Wm
Using ERA40 clear-sky OLR to
evaluate dynamical regimes
ERA40-CERES similar
ERA40 < CERES
ERA40 minus CERES
clear-sky OLR
(January-August 1998)
IRS2004, Busan, August 2004
Allan & Ringer 2003, GRL

Need to account for clear-sky sampling
differences between satellite and models
– Reanalyses offer one alternative

Especially important where clear-sky
situations are rare
– e.g. monthly mean clear-sky OLR differences of
about 15 Wm-2 for tropical convective regimes
IRS2004, Busan, August 2004
3) Interannual variability in
water vapour and clouds

How do clouds and water vapour respond to
global warming?

Interannual variability one example of range of
tests of climate models
– e.g. paleo, century, decadal, ENSO, seasonal, diurnal, etc

Water vapour variation
– Boundary layer, free tropospheric RH, reanalyses?

Decadal changes in cloud radiative effect
IRS2004, Busan, August 2004
Evaluation of HadAM3 Climate Model

AMIP-type 1979-1998 experiments

Explicitly simulate 6.7 mm radiance in HadAM3

Modified “satellite-like” clear-sky diagnostics
IRS2004, Busan, August 2004
Interannual variability of Column
Water vapour (Allan et al. 2003, QJRMS, p.3371)
SST
CWV
1980
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1985
1990
1995
See also Soden (2000) J.Clim 13
CWV Sensitivity to SST

dCWV/dTs = 3.5 kgm-2 K-1 for HadAM3 and
Satellite Microwave Observations (SMMR,
SSM/I) over tropical oceans

Corresponds to ~9%K-1 in agreement with
Wentz & Schabel (2000) who analysed
observed trends

But what about moisture away from the
marine Boundary Layer?
IRS2004, Busan, August 2004
Can we use reanalyses?
Allan et al. 2004, JGR, accepted
Reanalyses are currently unsuitable for detection of
subtle trends associated with water vapour feedbacks
BUT… Climatology from ERA40 is good.
…Variability from 24 hr forecast from ERA40 is much better than above.
IRS2004, Busan, August 2004
Clear-sky OLR
Interannual monthly anomalies: tropical oceans
HadAM3 vs ERBS, ScaRaB and CERES
ga=1-(OLRc/sTs4)
1980
1985
1990
1995
(Allan et al. 2003, QJRMS, p.3371)
IRS2004, Busan, August 2004
dOLRc/dTs~2 Wm-2 K-1 doesn’t indicate
consistent water vapour feedback?
HadAM3
GFDL
HadAM3
GFDL
dTa(p)/dTs
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dq(p)/dTs
Allan et al. 2002, JGR,
107(D17), 4329.
Sensitivity of OLRc to UTH
IRS2004, Busan, August 2004
Interannual monthly anomalies of 6.7 micron
radiance: HadAM3 vs HIRS (tropical oceans)
(Allan et al. 2003, QJRMS, p.3371)
Small changes in T_6.7 (or RH) in
model and obs (dUTH/dTs ~ 0 ?)
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(+additional forcings)
(Allan et al. 2003, QJRMS, p.3371)
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Small changes in RH but apparently larger changes
in tropical cloudiness? (Wielicki et al, 2002)
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+Altitude and orbit corrections (40S-40N)
Clear LW
LW
SW
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Following:
Wielicki et al. (2002);
Allan & Slingo (2002)

Water vapour changes in models and satellite
data consistent with constant RH

Variability in cloud radiative effect in models
appears underestimated compared to ERB
data even after recent corrections

Reanalysis are at present unsuitable for looking at
subtle changes and trends in water vapour and cloud
IRS2004, Busan, August 2004
4) Comparisons between
Geostationary Earth Radiation
Budget (GERB) data and Met Office
NWP model (SINERGEE)

Similar spatiotemporal sampling:
– model time step ~ GERB time ~ 15-20 minutes
– Spatial resolution ~ 60 km

Near real time comparisons

http://www.nerc-essc.ac.uk/~rpa/GERB/gerb.html
IRS2004, Busan, August 2004
OLR
GERB
Model
SINERGEE:
comparison
of Met
Office NWP
Model with
GERB data
Example
comparison:
31st March
2004, 12h00
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Albedo
Combining GERB and BSRN radiation data
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CONCLUSIONS

Radiation budget as function of dynamical
regimes: evaluate cloud radiative effect in models

Need to account for different clear-sky sampling
between models and data

Interannual variability
– Decadal variations of RH small in models and data
– Variations in cloud radiative effect appear to be
underestimated by models

Comparisons of GERB with NWP model: shorter
timescales closer to details of parametrizations
IRS2004, Busan, August 2004