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Evaluating water vapour in HadAM3 with 20 years of satellite data

Evaluating water vapour in HadAM3 with 20 years of satellite data. Richard P. Allan Mark A. Ringer Met Office, Hadley Centre for Climate Prediction and Research Thanks to Tony Slingo (ESSC) and John Edwards. Water vapour feedback.

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Evaluating water vapour in HadAM3 with 20 years of satellite data

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  1. Evaluating water vapour in HadAM3 with 20 years of satellite data Richard P. Allan Mark A. Ringer Met Office, Hadley Centre for Climate Prediction and Research Thanks to Tony Slingo (ESSC) and John Edwards

  2. Water vapour feedback • RH distribution and variability crucial to water vapour and cloud feedback (e.g. Soden et al; Wielicki et al. (2002) Science) • Importance of water vapour feedback • strong positive feedback • robust physical basis • links to cloud feedback Simulations of satellite UTH radiances to evaluate HadAM3

  3. Satellite clear-sky sampling Model simulations: ERA-40 and CERES daily data (Jan 98)

  4. Sensitivity to clear-sky sampling Jan 1998 T_6.7 bias (K) OLRc bias (Wm-2)

  5. Experiment • Ensemble of AMIP-type HadAM3 runs • Standard resolution, 19 levels, 1978-1999 • HadISST SST/sea-ice forcing • Radiance code active each rad-time-step • (see Ringer et al. (2003) QJ, in press) • Additional forcings

  6. Observations rms bias • Satellite observations • column water vapour, CWV • SMMR 1979-84, SSM/I 1987-99 • clear-sky OLR • ERBS (1985-89), ScaRaB (1994/5), CERES (1998) • UTH channel brightness temperature, T6.7 • HIRS 1979-1998 ~2.5 kg m-2 ~5 W m-2 ~1.5 K

  7. Mean and difference to observations60oS-60oN oceans

  8. T6.7 (K) (mb/day) OLRc (W m-2) CWV (kg m-2)

  9. Column Water Vapour difference(kg m-2 HadAM3-OBS) DJF JJA

  10. Effects of clear-sky sampling Type II HadAM3-OBS (JJA) Type I T6.7c OLRc

  11. EOF analysis of HadAM3 and HIRS channel 12 T_6.7 radiances

  12. Interannual monthly anomalies over the tropical oceans • Reduce effects of local changes in atmospheric circulation • Maximise reliability of satellite data • Also additional forcings considered

  13. Clear-sky sampling:interannual variability Light blue: Type I (weighted by clear-sky fraction) Dark Blue: Type II (standard diagnostic)

  14. Summary • Simulations of satellite brightness temperatures sensitive to RH • Consistent decadal variability suggests small DRH realistic • Clear-sky sampling is important for infrared channel climatologies but not interannual variability • Overactive circulation in HadAM3 • Limitations of data

  15. Visit theMet Office at booth 145

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