Page 1© Crown copyright 2006 Modelled & Observed Atmospheric Radiation Balance during the West African Dry Season. Sean Milton, Glenn Greed, Malcolm Brooks,

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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

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

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

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

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

Page 6 Atmospheric Radiation Balance © Crown copyright 2006

Page 7© Crown copyright 2006 SW Radiation Balance – Surface Mid Dec 2005 – mid Apr 2006 Accuracy +/- 9Wm-2

Page 8© Crown copyright 2007 Surface Albedo (Annual): Model – MODIS UM too dark UM too bright

Page 9© Crown copyright 2006 Importance of Aerosols for NWP Global UM - ARM AOT 440nm Banizoumbou AOT 440nm 8 Mar Dust Event

Page 10© Crown copyright 2006 Near surface Temperatures – Model vs ARM

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 microns Conv BL mixing Gravitational Settling Wet

Page 12© Crown copyright March 00Z 06 March 12Z07 March 12Z 08 March 12Z SEVERI Images of Dust 6-9 March 2006

Page 13© Crown copyright day global model forecast N216 L38 Initialised 12UTC 4 March 2006

Page 14© Crown copyright 2006 Impact of Dust on Radiation balance – Day 4 Forecasts 12 UTC 8 th March

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

Page 16© Crown copyright 2006

Page 17© Crown copyright 2006 Saharan CAMM Dust loading – Aug 2006 Saharan CAMM TOMS AI ( ) A B C A B C (TOMS AI from Engelstaedter et. al, Earth Science Reviews, 2006)

Page 18© Crown copyright 2006 Comparisons of Saharan CAMM with AERONET Andreas Keil Glenn Greed

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

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  Global NWP Model? – simplified dust parametrization.

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

Page 22© Crown copyright 2006 Reflected SW TOA – Model vs GERB Jan 2006 – mid Mar 2006 Surface reflectivity ? Aerosols – Dust, Biomass ? Cloud Biases?

Page 23© Crown copyright 2006 OLR – Model vs GERB Mid Dec 2005 – mid Apr 2006

Page 24© Crown copyright 2006 TOA Radiation Balance UM vs GERB Mid Jan 2006 – mid Mar hourly averaged fluxes for UM (12-36hr forecasts) & GERB data

Page 25© Crown copyright 2006 Observed Surf. Radiation Balance – ARM, Niamey Dust Outbreak – 8 th March 250 Wm -2 Reduction SW Increased Greenhouse Effect

Page 26© Crown copyright 2006

Page 27© Crown copyright 2006 Predicted Dust Evaluation : 6-8 March 2006 MODEL AOD OMI AI

Page 28© Crown copyright 2006

Page 29© Crown copyright 2006

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

Page 31 Mean Diurnal Cycle – Model vs ARM 13 Jan – 16 March 2006 © Crown copyright 2006

Page 32© Crown copyright 2006 Predicted Dust Evaluation : 7-8 March 2006 MODEL AOD OMI AI 7 March – T+728 March – T+96

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?

Page 34© Crown copyright Z 8 Mar m 12Z 9 Mar m Day 4 Forecast Day 5 Forecast

Page 35© Crown copyright 2006

Page 36© Crown copyright 2006 Predicting Dust During DODO2 Flight B August 2006

Page 37© Crown copyright 2006 Reflected SW TOA– Clear sky sampling GERB (ARG)ModelModel - GERB R.Allan ESSC May 2003-May 2006

Page 38© Crown copyright 2006

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”