IRS2004, Busan, August 2004 Using Satellite Observations and Reanalyses to Evaluate Climate and Weather Models Richard Allan Environmental Systems Science.

IRS2004, Busan, August 2004

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


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


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


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


OLR (Wm-2) (colours) Omega, hPa/day (contours)

April 1998

Model

Obs

Model - Obs


Ggg

OmegahPaday

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


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


Using ERA-40 Daily data to illustrate clear-sky sampling bias of CERES data


Model-obs differences & Clear-sky Sampling

T6.7

OLRc

Type II HadAM3-OBS Type-I

IRS2004, Busan, August 2004OLRc (Wm-2)


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


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


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


Evaluation of HadAM3 Climate Model

AMIP-type 1979-1998 experiments

Explicitly simulate 6.7 m radiance in HadAM3

Modified “satellite-like” clear-sky diagnostics


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


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?


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


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


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


Sensitivity of OLRc to UTH


Interannual monthly anomalies of 6.7 micron radiance: HadAM3 vs HIRS (tropical

oceans)


Small changes in T_6.7 (or RH) in model and obs (dUTH/dTs ~ 0 ?)


(+additional forcings)



Small changes in RH but apparently larger changes in tropical cloudiness? (Wielicki et al, 2002)


Following: Wielicki et al. (2002); Allan & Slingo (2002)

+Altitude and orbit corrections (40S-40N)

Clear LW

LW

SW


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


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


SINERGEE: comparison

of Met Office NWP Model with GERB data

Example comparison:

31st March 2004, 12h00

OLR

Albedo

GERB Model


Combining GERB and BSRN radiation data


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 Using Satellite Observations and Reanalyses to Evaluate Climate and Weather Models Richard Allan Environmental Systems Science.

Documents

science slide

uth slide

accepted slide

grl slide

surface radiation slide

model obs model obs

era40 clear sky olr

clearsky situations