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Using Satellite Observations and Reanalyses to Evaluate Climate and Weather Models
Richard AllanEnvironmental Systems Science Centre, University of Reading
Thanks to: Tony Slingo and Mark Ringer
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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
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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
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Important for the radiative/convective balance of model
Valuable diagnostic of model clouds, water vapour, etc
1) Evaluating model simulations of top of
atmosphere radiation budget
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OLR (Wm-2) (colours) Omega, hPa/day (contours)
April 1998
Model
Obs
Model - Obs
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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
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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
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Using ERA-40 Daily data to illustrate clear-sky sampling bias of CERES data
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Model-obs differences & Clear-sky Sampling
T6.7
OLRc
Type II HadAM3-OBS Type-I
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Using ERA40 clear-sky OLR to
evaluate dynamical regimes ERA40-CERES similar
ERA40 < CERES
ERA40 minus CERES clear-sky OLR
(January-August 1998)
Allan & Ringer 2003, GRL
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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
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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
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Evaluation of HadAM3 Climate Model
AMIP-type 1979-1998 experiments
Explicitly simulate 6.7 m radiance in HadAM3
Modified “satellite-like” clear-sky diagnostics
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Interannual variability of Column Water vapour (Allan et al. 2003, QJRMS, p.3371)
1980 1985 1990 1995 See also Soden (2000) J.Clim 13
SST
CWV
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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?
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Can we use reanalyses?
Reanalyses are currently unsuitable for detection of subtle trends associated with water vapour feedbacksBUT… Climatology from ERA40 is good.
…Variability from 24 hr forecast from ERA40 is much better than above.
Allan et al. 2004, JGR, accepted
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Clear-sky OLR
Interannual monthly anomalies: tropical oceansHadAM3 vs ERBS, ScaRaB and CERES
ga=1-(OLRc/Ts4)
(Allan et al. 2003, QJRMS, p.3371)
1980 1985 1990 1995
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dOLRc/dTs~2 Wm-2 K-1 doesn’t indicate consistent water vapour
feedback?
Allan et al. 2002, JGR, 107(D17), 4329.
HadAM3
GFDL
dTa(p)/dTs dq(p)/dTs
HadAM3
GFDL
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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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Small changes in RH but apparently larger changes in tropical cloudiness? (Wielicki et al, 2002)
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Following: Wielicki et al. (2002); Allan & Slingo (2002)
+Altitude and orbit corrections (40S-40N)
Clear LW
LW
SW
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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
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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.ht
ml
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SINERGEE: comparison
of Met Office NWP Model with GERB data
Example comparison:
31st March 2004, 12h00
OLR
Albedo
GERB Model
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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
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