1 / 16

RH distribution and variability crucial to water vapour and cloud feedback

Evaluating water vapour in HadAM3 using 20 years of satellite data Richard Allan, Mark Ringer Met Office, Hadley Centre for Climate Prediction and Research THANKS TO Tony Slingo (ESSC) and John Edwards. RH distribution and variability crucial to water vapour and cloud feedback

thaddeus-hays
Télécharger la présentation

RH distribution and variability crucial to water vapour and cloud feedback

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Evaluating water vapour in HadAM3 using 20 years of satellite dataRichard Allan, Mark RingerMet Office, Hadley Centre for Climate Prediction and ResearchTHANKS TO Tony Slingo (ESSC) and John Edwards • RH distribution and variability crucial to water vapour and cloud feedback • Importance of water vapour feedback • strong positive feedback • robust physical basis • links to cloud feedback • HadAM3 Simulations of UTH radiances • Evaluation of HadAM3 using satellite data

  2. Sensitivity of OLR to RH (using ERA-15) (Allan et al. 1999, QJ, 125, 2103) dOLR/dRH (Wm-2%-1) RH (%) dIv

  3. Robust nature of the water vapour feedback Insensitive to resolution (Ingram 2002, J Clim,15, 917-921) Feedback inferred after Pinatubo consistent with observations and climate change experiments From Soden et al 2002, Science, 296, 727.

  4. Is water vapour feedback really consistent between models? - dOLRc/dTs ~ 2 Wm-2K-1; dOLR/dTs uncertain Cess et al. 1990, JGR, 95, 16601. ? Consistent water vapour feedback, inconsistent cloud feedback -Same dOLRc/dTs in GFDL /HadAM3 models (~2 Wm-2K-1), differing height dependent T and q response... Allan et al. 2002, JGR, 107(D17), 4329, doi:10.1029/2001JD001131. Also, evidence that models cannot simulate recent changes in: - temperature lapse rate (Brown et al, 2000, GRL, 27, 997; Gaffen et al 2000, Science, 287, 1242) - cloud radiative effects (Wielicki et al, 2002, Science, 295, 841)

  5. Large changes in OLR from 7 independent satellite instruments (Wielicki et al, 2002) HadAM3/HadCM3 cannot simulate recent changes in cloudy portion of tropical radiation budget even when current climate forcings are applied (Allan & Slingo 2002, GRL, 29(7), doi: 10.1029/2001GL014620)

  6. Experiment &Observations - Ensemble of AMIP-type HadAM3 runs - Standard res, 19 levels, 1978-1999 - HadISST SST/sea ice forcing - Radiance code active each rad-time-step (see Ringer et al. (2002) QJ, accepted for details) - Additional forcings run - Multiple satellite measurements provide: - 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]

  7. Climatological mean over 60oS-60oN oceans The mean HadAM3 value is shown and then the HadAM3 minus observation climatological bias is calculated as the mean and the RMS difference of all grid-points within the region considered that contain valid observational values. BT12 (1979-98); OLRc (1985-89); CWV (1979-84;1988-98)

  8. OBSERVATIONS HadAM3 w500 T6.7 OLRc CWV

  9. OBSERVATIONS HadAM3 w500 T6.7 OLRc CWV

  10. DJF (HadAM3-OBS) JJA w500 T6.7 OLRc CWV

  11. Interannual monthly anomalies over the tropical oceans -Remove effects of changes in dynamic regime on the local variability by averaging over tropical oceans. -Maximise reliability of satellite data

  12. Interannual monthly anomalies over the tropical oceans (+additional forcings) - Additional forcings (volcanic, solar, ozone, GHG) - clear-sky OLR highly sensitive to volcanic aerosol and decadal trends in well mixed greenhouse gases

  13. Sensitivity to clear-sky sampling: Jan 1998 T_6.7 bias (K) OLRc bias (Wm-2) Climatological differences: Type II “Type I”

  14. Clear-sky sampling: interannual variability Light blue: Type I (weighted by clear-sky fraction) Dark Blue: Type II (unweighted mean)

  15. Summary • Simulations of satellite brightness temperatures sensitive to RH • Consistent decadal variability suggests small DRH realistic • Clear-sky sampling important for infrared channel climatologies but not interannual variability • Overactive circulation in HadAM3 • Note of caution: • can multiple satellite intercalibration artificially remove decadal trends? • Changes in atmos. T also influences T6.7 decadal fluctuations

More Related