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Trends in Water Cycle meeting, Paris, November 2004 Cloud and water vapour variability: models, reanalyses and observations Richard P. Allan and Tony Slingo.

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Presentation on theme: "Trends in Water Cycle meeting, Paris, November 2004 Cloud and water vapour variability: models, reanalyses and observations Richard P. Allan and Tony Slingo."— Presentation transcript:

1 Trends in Water Cycle meeting, Paris, November 2004 Cloud and water vapour variability: models, reanalyses and observations Richard P. Allan and Tony Slingo Environmental Systems Science Centre, University of Reading

2 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)?

3 Trends in Water Cycle meeting, Paris, November 2004 Decadal variability of Column Water Vapour (see Allan et al. 2003, QJRMS, p.3371) SST CWV

4 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?

5 Trends in Water Cycle meeting, Paris, November 2004 Allan et al. 2004, JGR, vol. 109 Variability in low latitude column integrated water vapour ( ) Reanalyses (ERA40 and NCEP), HadAM3 model and microwave observations (SMMR; SSM/I)

6 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)

7 Trends in Water Cycle meeting, Paris, November 2004 dOLRc/dTs~2 Wm -2 K -1 doesnt indicate consistent water vapour feedback? Allan et al. 2002, JGR, 107(D17), HadAM3 GFDL dTa(p)/dTsdq(p)/dTs HadAM3 GFDL

8 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

9 Trends in Water Cycle meeting, Paris, November 2004 Interannual monthly anomalies of 6.7 mm 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

10 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

11 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

12 Trends in Water Cycle meeting, Paris, November 2004 –Satellite data suggest large decadal variability of radiative energy balance » 1980s to 1990s: 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

13 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.)

14 Trends in Water Cycle meeting, Paris, November 2004 What is the spatiotemporal signature of the changes in the radiative energy balance?

15 Trends in Water Cycle meeting, Paris, November 2004 EOFs of May-June OLR using altitude and orbit corrected WFOV data ( ) EOF1 (ENSO- like) EOF2 (trend -like?)

16 Trends in Water Cycle meeting, Paris, November 2004 EOF2 (ENSO- like) EOF1 (trend -like?) EOFs of May-June Reflected Shortwave Radiation (RSW) using altitude and orbit- corrected WFOV data ( )

17 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 –OLR increases ~2 Wm -2 and RSW decreases ~3 Wm -2 Radiation Budget changes symptomatic of reduced low-latitude cloudiness from 1980s-90s –Initially, changes in radiation budget should force surface heating and atmospheric cooling –Radiation Budget / T-Lapse Rate / Dynamics interaction


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