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

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Presentation on theme: "Evaluating the Met Office global forecast model using Geostationary Earth Radiation Budget (GERB) data Richard Allan, Tony Slingo Environmental Systems."— Presentation transcript:

1 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

2 Objectives Improve experience with satellite datasets including GERB Timely Model Evaluation –using geostationary data independent of the assimilation system Understanding of physical processes

3 Sinergee project: GERB June 2007 OLR Animation Model Harries et al. (2005) BAMS; Allan et al. (2005) JGR

4 ShortwaveLongwave Mean model bias: 2006 All-sky Clear-sky

5 ShortwaveLongwave Mineral dust aerosol Surface albedo

6 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, UTC All-skyClear-skydust aerosol

7 All-sky Clear-sky ShortwaveLongwave Marine stratocumulus Convective cloud Radiative biases in the Met Office global model Convective outflow

8 Marine Stratocumulus

9 Curious banding structure –Transition across model levels (see Lock et al. 2001, MWR) Cloud reflectivity bias –Model low-altitude stratiform clouds are too reflective

10 Cloud liquid water path Bias: model minus GERB; SSM/I; SEVIRI Albedo Liquid Water Path Cloud Reduction in model bias from June to July relates to cloud liquid water …but see also Horvath and Davies (2007) JGR

11 Convective cloud 5 th June 2006

12 Convective Decay Time-scale Unrealistically low levels of convective cloud On-off; common problem in models Simple fix…

13 Improved shortwave reflectivity

14 Increased convective cloud cover But is the physics any better? Future work: Comparisons with CloudSat

15 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


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