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Evaluating water vapour in HadAM3 using 20 years of satellite data

Richard 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

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dIv

dOLR/dRH

(Wm-2%-1)

RH (%)

Sensitivity of OLR to RH (using ERA-15) (Allan et al. 1999, QJ, 125, 2103)

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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.

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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)

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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)

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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]

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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)

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OBSERVATIONS HadAM3

500

T6.7

OLRc

CWV

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OBSERVATIONS HadAM3

500

T6.7

OLRc

CWV

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DJF (HadAM3-OBS) JJA

500

T6.7

OLRc

CWV

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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

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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

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T_6.7 bias (K) OLRc bias (Wm-2)

Sensitivity to clear-sky sampling: Jan 1998

Type II “Type I”Climatological differences:

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Clear-sky sampling: interannual variability

Light blue: Type I (weighted by clear-sky fraction)

Dark Blue: Type II (unweighted mean)

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Summary Simulations of satellite brightness

temperatures sensitive to RH Consistent decadal variability

suggests small RH 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