Trends in Water Cycle meeting, Paris, November 2004

                                                                              

Cloud and water vapour variability: models, reanalyses

and observations

Richard P. Allan and Tony SlingoEnvironmental Systems Science Centre, University of Reading

Trends in Water Cycle meeting, Paris, November 2004

INTRODUCTION– Hydrological cycle and climate

feedbacks» What determines the trends and variability of water cycle?

» Unless we understand reasons for variation there is little chance for initiating improvement in climate model processes and predictions

– Analysis of decadal changes in cloud, water vapour and the radiation budget

» satellite data

» experiments with HadAM3 model

» can we use reanalyses (e.g. ERA40)?

Trends in Water Cycle meeting, Paris, November 2004

Decadal variability of Column Water Vapour (see Allan et al. 2003, QJRMS, p.3371)

1980 1985 1990 1995

SST

CWV

Trends in Water Cycle meeting, Paris, November 2004

Robust, positive water vapour feedback at low-altitudes over low-latitude oceans

– dCWV/dTs ~ 3.5 kgm-2 K-1 ~ 10%/K

– e.g. Wentz and Shabel (2000) Nature 403 p.414, Soden (2000) J.Clim 13, p.538,

Allan et al. (2003) QJRMS, 129, p.3371, ….

What about free tropospheric humidity?– Unsaturated, not governed by simple

thermodynamic processes?

Can we use reanalyses?

Trends in Water Cycle meeting, Paris, November 2004

Allan et al. 2004, JGR, vol. 109

Variability in low latitude column integrated water vapour (1979-2002)

Reanalyses (ERA40 and NCEP), HadAM3 model and microwave observations (SMMR; SSM/I)

Trends in Water Cycle meeting, Paris, November 2004

Allan et al. 2004, JGR, vol. 109 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.

– Use of dynamical parameters with observations of hydrological cycle of considerable utility

– See also…Bengtsson et al. (2004) JGR 109; Ringer and Allan (2004) Tellus A, 56, p.308.

Can we use clear-sky OLR to infer information on free tropospheric humidity?

– Models and obs agree: dOLRc/dTs ~ 2 Wm-2 K-1

– Interannual variability OK (Soden 2000, Allan et al. 2003)

Trends in Water Cycle meeting, Paris, November 2004

dOLRc/dTs~2 Wm-2 K-1 doesn’t indicate consistent water vapour

feedback?

Allan et al. 2002, JGR, 107(D17), 4329.

HadAM3

GFDL

dTa(p)/dTs dq(p)/dTs

HadAM3

GFDL

Trends in Water Cycle meeting, Paris, November 2004

Explicit simulations of 6.7 m water vapour radiances in

HadAM3

Use John Edwards’ radiance solver within Hadley Centre climate model

Simulate HIRS 6.7 m radiance Account for inconsistent satellite

sampling of clear-skies See Allan et al. (2003) QJRMS p.3371

Trends in Water Cycle meeting, Paris, November 2004

Interannual monthly anomalies of 6.7 m radiance: HadAM3 vs HIRS (tropical oceans)

(Allan et al. 2003, QJRMS, p.3371)

Models and data both suggest only small changes in RH over decadal time-scale

Trends in Water Cycle meeting, Paris, November 2004

Changes in tropical radiation budget and

cloudiness Evidence suggests constant RH water

vapour feedback is robust and well simulated by models

Satellite and other data suggests the radiative effect of cloud is highly dynamic and poorly simulated by models

Trends in Water Cycle meeting, Paris, November 2004

Following: Wielicki et al. (2002); Allan & Slingo (2002)

+Altitude and orbit corrections (40S-40N)

Clear LW

LW

SW

Trends in Water Cycle meeting, Paris, November 2004

– Satellite data suggest large decadal variability of radiative energy balance

» 1980’s to 1990’s: increase in OLR of ~ 2 Wm-2 decrease in RSW of ~3 Wm-2 Clear-sky OLR variation small

– Models do not capture these changes changes in simulated OLR determined exclusively by the

changes in clear-sky OLR which are strongly influenced by the surface temperature variation due to constant RH

– Satellite data suggest reduced tropical cloudiness

» Evidence to suggest intensification of hydrological cycle (Chen et al. 2002, Science)

» surface heating and atmospheric cooling destabilising

Trends in Water Cycle meeting, Paris, November 2004

Additional evidence… ISCCP – reduction in cloud fraction

Cess and Udelhofen (2002) GRL

Consistent changes in ISCCP-derived radiation budget to ERBS

– Zhang et al. (2004) JGR; Hatzianastassiou et al. (2004) Atmos. Chem. Phys. Hatzidimitriou et al. (2004) Atmos. Chem. Phys.

SAGE II reduction in high cloud (Wang et al. 2002, GRL)

Earthshine measurements of reduced albedo Palle et al. (2004) Science

Surface obs – reduction in high cloud (J. Norris, pers. Comm.)

Trends in Water Cycle meeting, Paris, November 2004

What is the spatiotemporal signature of the changes in the radiative energy

balance?

Trends in Water Cycle meeting, Paris, November 2004

EOFs of May-June OLR using

altitude and orbit corrected

WFOVdata

(1985-1999)

EOF1 (ENSO-like)

EOF2 (“trend”-like?)

Trends in Water Cycle meeting, Paris, November 2004

EOF2 (ENSO-like)

EOF1 (“trend”-like?)

EOFs of May-June

Reflected Shortwav

e Radiation

(RSW) using

altitude and orbit- corrected

WFOVdata

(1985-1999)

Trends in Water Cycle meeting, Paris, November 2004

CONCLUSIONS Models can simulate the interannual

thermodynamic changes in low-altitude moisture– Reanalyses cannot

Changes in OLRc and RH small in models&data Climate models do not simulate observed

decadal changes in radiation budget 1979-99– OLR increases ~2 Wm-2 and RSW decreases ~3 Wm-2

Radiation Budget changes symptomatic of reduced low-latitude cloudiness from 1980’s-90’s

– Initially, changes in radiation budget should force surface heating and atmospheric cooling

– Radiation Budget / T-Lapse Rate / Dynamics interaction