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INTRODUCTION

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. INTRODUCTION. Evaluation of Weather and Climate Prediction Models (some examples)

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INTRODUCTION

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  1. Using Satellite Observations and Reanalyses to Evaluate Climate and Weather ModelsRichard AllanEnvironmental Systems Science Centre, University of ReadingThanks to: Tony Slingo and Mark Ringer

  2. 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

  3. 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. 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

  5. OLR (Wm-2) (colours) Omega, hPa/day (contours) April 1998 Model Obs Model - Obs

  6. Ggg Omega hPaday SST (K) Ringer &Allan (2004) Tellus A

  7. 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. 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

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

  10. Model-obs differences & Clear-sky Sampling Type II HadAM3-OBS Type-I DT6.7 DOLRc

  11. DOLRc (Wm-2)

  12. 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. 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-2for tropical convective regimes

  14. 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

  15. Evaluation of HadAM3 Climate Model • AMIP-type 1979-1998 experiments • Explicitly simulate 6.7 mm radiance in HadAM3 • Modified “satellite-like” clear-sky diagnostics

  16. Interannual variability of Column Water vapour (Allan et al. 2003, QJRMS, p.3371) SST CWV 1980 1985 1990 1995 See also Soden (2000) J.Clim 13

  17. 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. 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.

  19. Clear-sky OLRInterannual monthly anomalies: tropical oceansHadAM3 vs ERBS, ScaRaB and CERES ga=1-(OLRc/sTs4) 1980 1985 1990 1995 (Allan et al. 2003, QJRMS, p.3371)

  20. dOLRc/dTs~2 Wm-2 K-1 doesn’t indicate consistent water vapour feedback? HadAM3 GFDL HadAM3 GFDL dTa(p)/dTs dq(p)/dTs Allan et al. 2002, JGR, 107(D17), 4329.

  21. Sensitivity of OLRc to UTH

  22. 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. (+additional forcings) (Allan et al. 2003, QJRMS, p.3371)

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

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

  26. 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. 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

  28. OLR SINERGEE: comparisonof Met Office NWP Model with GERB data GERB Model Example comparison: 31st March 2004, 12h00 Albedo

  29. Combining GERB and BSRN radiation data

  30. 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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