1. © Crown copyright Met Office Aerosol Research Group Current staff:- Jim Haywood (50%): Aerosol Research Manager Ben Johnson: Aerosol-radiation, analysis of OPCs, model validation Kate Szpek (nee Turnbull): Aerosol-radiation, analysis of OPCs, wet neph, MOCCA Franco Marenco: Aerosol lidar expert, airborne lidar retrievals, radiative transfer Purpose: to develop better representation of aerosols and their impacts on:- Climate (direct, indirect and semi-direct effects) NWP forecasts Air quality Visibility

2. Research Topics: 1)Climate: Many parameterisations / optical parameters in the UM are derived from MRF/FAAM: SAFARI, SHADE, DABEX, GERBILS. 2)NWP: Improving representation of aerosols in operational NWP models in conjunction with GMED 3)Air quality: PM2.5 & PM10 monitored by DEFRA. Forecasts required via AQUM. 4)Visibility: Hygroscopic growth is essential. Wet-neph is a very important instrument but has (a lot) of limitations. 5)MOCCA: Volcanic ash measurements 6)Radiative transfer: Impact of dust on military operations © Crown copyright Met Office

3. Simple Land/Sea climatologies Improved CLASSIC aerosol climatologies Replacement of climatologies with prognostic schemes based on CLASSIC Saharan dust* Sea-salt Biomass burning Little resemblance to reality Reasonable monthly means but no relation to meteorology MACC/GEMS Assimilated aerosol for initial conditions DA of fires for biomass burning Prognostic UKCA-MODE upgrades for other aerosol species 2001-2008 2008-2011 Current Fully prognostic driven by meteorology Mid-Term Upgrades 2. Global NWP

4. UKCA Science Exploitation meeting Why go from monthly mean climatologies to prognostic aerosols? Milton et al (2008) Global UM - ARM AOT 440nm Banizoumbou The aerosol optical depth and global NWP model bias in surface SW radiation in W Africa Mean surface radiation bias = 56 Wm -2 ~50% due to dust, ~50% due to smoke

5. Simple Land/Sea climatologies Improved CLASSIC aerosol climatologies Replacement of climatologies with prognostic schemes based on CLASSIC Saharan dust* Sea-salt Biomass burning Little resemblance to reality Reasonable monthly means but no relation to meteorology MACC/GEMS Assimilated aerosol for initial conditions DA of fires for biomass burning Prognostic UKCA-MODE upgrades for other aerosol species 2001-2008 2008-2011 Current Fully prognostic driven by meteorology Mid-Term Upgrades NOW: This is the proposed order: Dust: DABEX, DODO, GERBILS Sea salt: GFDex Biomass burning: SAMBBA

6. © Crown copyright Met Office Example of dust forecast from global NWP model 2-bin version of UM dust scheme became fully operational in global NWP model this year. Used for WMO Sand and Dust Storm Warning and military.

7. Dust forecast behind warm front Vertical distribution: well mixed B>G>R, little signal Dusty profile B~G~R, up to 20x10 -6 m -1 Well mixed to ~FL120: in agreement with model MOCCA

8. Direction for next 5 years: SAMBBA (South American Biomass burning analysis): Incorporation into the global NWP model NAMSTRABBA: Sept 2015/16? Interaction of biomass burning aerosols and stratocumulus clouds Walvis Bay, Namibia). Met Office + large NERC grant + NASA + NSF. Drivers of Variability in the South Asian Monsoon: Aerosols are implicated in various studies. Aircraft deployment in India. NERC + Met Office + India Development of ‘aerosol’ rack: Accurate measurements of extinction, absorption, and hygroscopic growth Validation of AQUM and visibility

9. Critical Instruments: 1)Lidar: for remote sensing of clouds above stratocumulus for NAMSTRABBA/SAMMA 2)SWS/SHIMS: NAMSTRABBA 3)Aerosol rack. Need a better controlled airborne hygroscopic growth instrument for visibility, and development of parameterisations for DA of visibility. Cavity ring down methods far more precise and accurate. 4)OPCs (PCASPs, CAS, etc.): Better understanding of these instruments and how to interpret the data 5)AMS/SP2: Reliance on University of Manchester will continue.

10. Questions & answers

11. UKCA Science Exploitation meeting 1. Climate. CLASSIC – AEROSOL OPTICAL DEPTHS (or, what UKCA-MODE has to beat) Climate Model Aerosol Optical Depths compared against AERONET observations