Evaluating the Met Office global forecast model using Geostationary Earth Radiation Budget (GERB) data Richard Allan, Tony Slingo Environmental Systems.

Evaluating the Met Office global forecast model using

Geostationary Earth Radiation Budget (GERB) data

Richard Allan, Tony Slingo

Environmental Systems Science Centre, University of Reading

Sean Milton, Malcolm Brooks

Met Office, Exeter

Thanks to the GERB International Science Team

Objectives

• Improve experience with satellite datasets including GERB

• Timely Model Evaluation– using geostationary data independent of the

assimilation system

• Understanding of physical processes

Sinergee project: www.nerc-essc.ac.uk/~rpa/GERB/gerb.html

GERB June 2007 OLR Animation Model

Harries et al. (2005) BAMS; Allan et al. (2005) JGR

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Mean model bias: 2006

All-sky Clear-sky

All-sky Clear-sky

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Mineral dust aerosol

Surface albedo

Dust impact on longwave radiation

• Large perturbation to Met Office model OLR during summer over west Sahara– Correlates with high mineral dust aerosol optical depth

(see also Haywood et al. 2005, JGR)

– GERBIL aircraft campaign (Jim Haywood)

Model minus GERB OLR: July 2006, 12-18 UTC

All-sky Clear-sky dust aerosol

All-sky Clear-sky

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Marine stratocumulus

Convective cloud

Radiative biases in the Met Office global model

Convective outflow

Marine Stratocumulus

• Curious banding structure– Transition across model

levels (see Lock et al. 2001, MWR)

• Cloud reflectivity bias– Model low-altitude

stratiform clouds are too reflective

Cloud liquid water path

Bias: model minus GERB; SSM/I; SEVIRI

Albedo Liquid Water Path Cloud

Reduction in model bias from June to July 2006 - relates to cloud liquid water

…but see also Horvath and Davies (2007) JGR

Convective cloud

5th June 2006

Convective Decay Time-scale

• Unrealistically low levels of convective cloud

• On-off; common problem in models

• Simple fix…

Improved shortwave reflectivity

• Increased convective cloud cover

• But is the physics any better?

• Future work: Comparisons with CloudSat

Conclusions

• Top down-bottom up approach– Satellite data independent of assimilation system– Good feedback for modellers and satellite team

• Mineral dust aerosol over Sahara– Monthly longwave radiative effect up to 50 Wm-2

– Large effect of single events (Slingo et al. 2006, GRL)

• Marine stratocumulus– Reflectivity and seasonal variability: issues

• Deep convection– Intermittent in models; issues with detrainment