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Page 1© Crown copyright 2006 Modelled & Observed Atmospheric Radiation Balance during the West African Dry Season. Sean Milton, Glenn Greed, Malcolm Brooks, R.Allan* & A. Slingo* Met Office, Exeter, UK *ESSC, Reading University AMMA SOP0 Meeting 15 May 2007
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Page 2© Crown copyright 2006 Outline 1.Systematic Errors in current NWP models & Role of Aerosols Global Model evaluation vs ARM (Niamey) & GERB –Dec 05 to Mar 06 2.Evaluating a dust parametrization. 6-9 March Saharan Dust event Performance in Saharan regional Model
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Page 3© Crown copyright 2006 OLR Errors & Saharan Dust Conclusion: OLR errors over the Western Sahara at 1200 UTC during May-August 2004 can be explained by failure to include the strong greenhouse effect of high amounts of desert dust in the model … see Haywood et al. (2004) JGR Aerosol Optical Depth
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Page 4© Crown copyright 2006 Observations available over Africa during 2006 SOP1&2 SOP3 Jan-Feb 2006 DABEX/DODO Aug 2006 DODO2 ARM mobile facility - Niamey RADAGAST
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Page 5© Crown copyright 2006 NWP Forecast Systems – Unified Model UK 4km Regional 12km Global 40km 60 hour forecast twice/day 144 hour forecast twice/day +EPS 24member, 90km 50 levels Saharan Regional Model 20km
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Page 6 Atmospheric Radiation Balance © Crown copyright 2006
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Page 7© Crown copyright 2006 SW Radiation Balance – Surface Mid Dec 2005 – mid Apr 2006 Accuracy +/- 9Wm-2
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Page 8© Crown copyright 2007 Surface Albedo (Annual): Model – MODIS UM too dark UM too bright
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Page 9© Crown copyright 2006 Importance of Aerosols for NWP Global UM - ARM AOT 440nm Banizoumbou AOT 440nm 8 Mar Dust Event
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Page 10© Crown copyright 2006 Near surface Temperatures – Model vs ARM
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Page 11© Crown copyright 2006 Dust Parametrization – Woodward 2001 Threshold friction velocity defined for particles from.06 to 2000 microns (previously.06 to 60) 9 Bins in Horizontal Flux (3 sand) Inhibition of dust from steep slopes – numerical issues Use of IGBP (1km) soil source data in place of Wilson- Henderson-Sellers (1985) 1 deg data (NWP only). Reduce threshold friction velocity by 0.15 m/s in NWP Use global “nudged” soil moisture to initialise Saharan CAMM. Soils Data Uplift (v*, soil moisture) Transport Deposition Six dust sizes 0.03 -30 microns Conv BL mixing Gravitational Settling Wet
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Page 12© Crown copyright 2006 06 March 00Z 06 March 12Z07 March 12Z 08 March 12Z SEVERI Images of Dust 6-9 March 2006
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Page 13© Crown copyright 2006 1-5 day global model forecast N216 L38 Initialised 12UTC 4 March 2006
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Page 14© Crown copyright 2006 Impact of Dust on Radiation balance – Day 4 Forecasts 12 UTC 8 th March
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Page 15© Crown copyright 2006 Net SW Net LW ARM 200 Wm -2 Reduction SW Model 100 Wm -2 Reduction In SW due to inclusion of dust
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Page 16© Crown copyright 2006
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Page 17© Crown copyright 2006 Saharan CAMM Dust loading – Aug 2006 Saharan CAMM TOMS AI (1980-92) A B C A B C (TOMS AI from Engelstaedter et. al, Earth Science Reviews, 2006)
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Page 18© Crown copyright 2006 Comparisons of Saharan CAMM with AERONET Andreas Keil Glenn Greed
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Page 19© Crown copyright 2006 Summary Aerosols – Failure to account for dust (and biomass burning?) in current NWP versions causes significant errors in the radiation balance over Africa. Specification of surface albedo over Africa is poor leading to systematic underestimates of reflected SW- review Mineral Dust Parametrization Performs well in both Global and Saharan Model – major Saharan dust outbreak predictable 4 days in advance. Uncertainties Dust optical properties Dust uplift Vertical distribution Predicted Size distributions
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Page 20© Crown copyright 2006 Future Further comparison of Dust in model with aircraft data from DODO/DABEX and GERBIL. Tuning of dust radiative properties – single scatter albedo. More work on dust uplift (PDRA Reading D.Ackerley) Impacts on the circulation – test in THORPEX. Dust operational in Southern Asia Model by 2008. Global NWP Model? – simplified dust parametrization.
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Page 21© Crown copyright 2006 UM vs ARM – Surface Energy Balance Mid Dec – mid Apr 3 hourly averaged fluxes for UM (12-36hr forecasts) & ARM data
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Page 22© Crown copyright 2006 Reflected SW TOA – Model vs GERB Jan 2006 – mid Mar 2006 Surface reflectivity ? Aerosols – Dust, Biomass ? Cloud Biases?
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Page 23© Crown copyright 2006 OLR – Model vs GERB Mid Dec 2005 – mid Apr 2006
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Page 24© Crown copyright 2006 TOA Radiation Balance UM vs GERB Mid Jan 2006 – mid Mar 2006 3 hourly averaged fluxes for UM (12-36hr forecasts) & GERB data
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Page 25© Crown copyright 2006 Observed Surf. Radiation Balance – ARM, Niamey Dust Outbreak – 8 th March 250 Wm -2 Reduction SW Increased Greenhouse Effect
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Page 26© Crown copyright 2006
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Page 27© Crown copyright 2006 Predicted Dust Evaluation : 6-8 March 2006 MODEL AOD OMI AI
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Page 28© Crown copyright 2006
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Page 29© Crown copyright 2006
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Page 30© Crown copyright 2006 SUMMARY Radiative Forcing from Aerosol (Dust, Biomass)- Significant component of surface (&TOA) radiation errors in the global model measured at Niamey during Dec-Apr 2005/6. Poor specification of surface albedo & seasonality of Vegetation – errors in reflected SW
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Page 31 Mean Diurnal Cycle – Model vs ARM 13 Jan – 16 March 2006 © Crown copyright 2006
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Page 32© Crown copyright 2006 Predicted Dust Evaluation : 7-8 March 2006 MODEL AOD OMI AI 7 March – T+728 March – T+96
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Page 33© Crown copyright 2006 Surface SW fluxes - Cloud Free days Overestimate SW ~ 50Wm -2 Lack of Aerosol? Systematic underestimate SW ~ 50Wm -2 Poor Surf. Albedo Biomass?
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Page 34© Crown copyright 2006 12Z 8 Mar 2006 500m 12Z 9 Mar 2006 500m Day 4 Forecast Day 5 Forecast
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Page 35© Crown copyright 2006
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Page 36© Crown copyright 2006 Predicting Dust During DODO2 Flight B238 23 August 2006
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Page 37© Crown copyright 2006 Reflected SW TOA– Clear sky sampling GERB (ARG)ModelModel - GERB R.Allan ESSC May 2003-May 2006
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Page 38© Crown copyright 2006
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Page 39© Crown copyright 2006 Uncertainties in modelling Dust radiative properties Dust – NoDust Impacts on Zonal Temperatures SSA=0.9 “More absorbing” SSA=0.96 “Less absorbing”
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