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

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Presentation on theme: "IRS2004, Busan, August 2004 Using Satellite Observations and Reanalyses to Evaluate Climate and Weather Models Richard Allan Environmental Systems Science."— Presentation transcript:

1 IRS2004, Busan, August 2004 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

2 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 feedbacks may only be possible on longer time-scales

3 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

4 IRS2004, Busan, August 2004 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

5 IRS2004, Busan, August 2004 OLR (Wm -2 ) (colours) Omega, hPa/day (contours) April 1998 Model Obs Model - Obs

6 IRS2004, Busan, August 2004 Ggg O m e g a hPa day SST (K) Ringer &Allan (2004) Tellus A

7 IRS2004, Busan, August 2004 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

8 IRS2004, Busan, August ) 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

9 IRS2004, Busan, August 2004 Using ERA-40 Daily data to illustrate clear-sky sampling bias of CERES data

10 IRS2004, Busan, August 2004 Model-obs differences & Clear-sky Sampling T 6.7 OLRc Type II HadAM3-OBS Type-I

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

12 IRS2004, Busan, August 2004 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

13 IRS2004, Busan, August 2004 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

14 IRS2004, Busan, August ) 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

15 IRS2004, Busan, August 2004 Evaluation of HadAM3 Climate Model AMIP-type experiments Explicitly simulate 6.7 mm radiance in HadAM3 Modified satellite-like clear-sky diagnostics

16 IRS2004, Busan, August 2004 Interannual variability of Column Water vapour (Allan et al. 2003, QJRMS, p.3371) See also Soden (2000) J.Clim 13 SST CWV

17 IRS2004, Busan, August 2004 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?

18 IRS2004, Busan, August 2004 Can we use reanalyses? 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. Allan et al. 2004, JGR, accepted

19 IRS2004, Busan, August 2004 Clear-sky OLR Interannual monthly anomalies: tropical oceans HadAM3 vs ERBS, ScaRaB and CERES g a =1-(OLRc/ Ts 4 ) (Allan et al. 2003, QJRMS, p.3371)

20 IRS2004, Busan, August 2004 dOLRc/dTs~2 Wm -2 K -1 doesnt indicate consistent water vapour feedback? Allan et al. 2002, JGR, 107(D17), HadAM3 GFDL dTa(p)/dTsdq(p)/dTs HadAM3 GFDL

21 IRS2004, Busan, August 2004 Sensitivity of OLRc to UTH

22 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 ?)

23 IRS2004, Busan, August 2004 (+additional forcings) (Allan et al. 2003, QJRMS, p.3371)

24 IRS2004, Busan, August 2004 Small changes in RH but apparently larger changes in tropical cloudiness? (Wielicki et al, 2002)

25 IRS2004, Busan, August 2004 Following: Wielicki et al. (2002); Allan & Slingo (2002) +Altitude and orbit corrections (40S-40N) Clear LW LW SW

26 IRS2004, Busan, August 2004 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

27 IRS2004, Busan, August ) Comparisons between Geostationary Earth Radiation Budget (GERB) data and Met Office NWP model (SINERGEE) Similar spatiotemporal sampling: –model time step ~ GERB time ~ minutes –Spatial resolution ~ 60 km Near real time comparisons

28 IRS2004, Busan, August 2004 SINERGEE: comparison of Met Office NWP Model with GERB data Example comparison: 31 st March 2004, 12h00 OLR Albedo GERBModel

29 IRS2004, Busan, August 2004 Combining GERB and BSRN radiation data

30 IRS2004, Busan, August 2004 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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