WP4: Scaling-up activities (Edinburgh): summary and preliminary analysis Time: 24 months (years 2 & 3 of ACES) Science Obj #1: Identify weaknesses in model processes (physical and chemical) Science Obj #2: Assess regional impact of aerosols on atmospheric chemistry Tools: nGLOMAP, satellite and in situ observations of trace gases and aerosols.

Cloud effective radius re [mm] Preliminary analysis of aerosol optical properties and associated first indirect effect over Borneo using ATSR from 1997 28-day rolling mean Cloud effective radius re [mm] r = -0.46 IE Corr DJF -0.19±0.05 0.86 MAM -0.07±0.07 0.50 JJA -0.15±0.14 0.29 SON 0.43±0.3 -0.76 AOD ta/AI [unitless] Seasonal cycle dominated by biomass burning in Austral summer Aerosol index (inferred from the Angstrom exponent) provides a measure of the fine fraction (aerosol most likely to act as CCN) Strongest (unphysical) IE during BB peak C/o Claire Bulgin

The InterTropical Convergence Zone acts as an effective barrier for inter-hemispheric transport ITCZ climatology Borneo is in the SH in Austral winter and on the NH/SH divide in Austral summer. Implication: Likely to be a large seasonal cycle in trace gases and aerosols determined by different continental and marine sources.

Flexpart runs c/o Richard Damoah Observed airmasses at Danum Valley GAW tower (100m) during OP3 months have very different origins April 2007 July 2007 For illustrative purposes 10-day back trajectories with Zmax  2km are shown Flexpart runs c/o Richard Damoah Height ASL [m]

nGLOMAP – Delivery to ACES in early 2008 O(Global CTM scale) nGLOMAP grid = 0.6 degrees Uses established ECMWF meteorology. Availability of these data for 2008 TBC [Action item] Global grid is 2.8o x 2.8o Horizontal resolution of nested grid will be 1/5 of global grid (per comm: Chipperfield) Nested grid will focus over Southeast Asia (see fig inset) Tagged capability will allow source attribution without additional sensitivity runs

Emission estimates from Southeast Asia in 2008 Biofuel, OC and BC emissions from Tami Bond (nominally for 2005) Ongoing work with NCAR MEGAN group to improve BVOC emission inventory Work with Global Fire Emission Database consortium to build 2008 inventory www.carma.org Estimating large-point sources and other anthropogenic sectors builds on work developed during TRACE-P and ACE-Asia from 2001. Input from David Streets sought [Argonne National Lab].

Chemistry Schemes Mechanisms will closely follow those outlined in Spracklen et al GLOMAP papers ??SOA scheme?? CittyCat follows early work by Jenkin and this may be a good starting point for GLOMAP if nothing else is available. [Definite discussion point]

EO data MODIS, MISR, CALIPSO, CloudSat + trace gases Available data OP3/ACES EO data MODIS, MISR, CALIPSO, CloudSat + trace gases {Height, optical depth, extinction profiles, particle distribution} GAW Intermittent data about GHGs and aerosols Concentration(z) Three/four prong attack: Chemistry; Emissions: magnitude and controls; Transport OP3/ACES

Using data assimilation to quantify model error The object of assimilation is to minimize a cost function J that describes the mismatch between model and data (linear case): x = what we want to estimate; subscript a (s) denotes the prior guess (solution). In this problem x = AOD. y = observations. In this problem y = AOD same as x. J = (xs-xa)Sa-1(xs-xa)T+ (y-Kxa) Sy-1(y - Kxa)T S denotes the error covariance matrix of the prior (Sa) or the measurements (Sy) K is the Jacobian describing how y changes with x, ie dy/dx.

Using data assimilation to quantify model error The object of assimilation is to minimize a cost function J that describes the mismatch between model and data (linear case): x = what we want to estimate; subscript a (s) denotes the prior guess (solution). In this problem x = AOD. y = observations. In this problem y = AOD same as x. J = (xs-xa)Sa-1(xs-xa)T+ (y-Kxa) Sy-1(y - Kxa)T S denotes the error covariance matrix of the prior (Sa) or the measurements (Sy) K is the Jacobian describing how y changes with x, ie dy/dx.

Using data assimilation to quantify model error The object of assimilation is to minimize a cost function J that describes the mismatch between model and data (linear case): x = what we want to estimate; subscript a (s) denotes the prior guess (solution). In this problem x = AOD. y = observations. In this problem y = AOD same as x. J = (xs-xa)Sa-1(xs-xa)T+ (y-Kxa) Sy-1(y - Kxa)T S denotes the error covariance matrix of the prior (Sa) or the measurements (Sy) K is the Jacobian describing how y changes with x, ie dy/dx.

Using data assimilation to quantify model error The object of assimilation is to minimize a cost function J that describes the mismatch between model and data (linear case): x = what we want to estimate; subscript a (s) denotes the prior guess (solution). In this problem x = AOD. y = observations. In this problem y = AOD same as x. J = (xs-xa)Sa-1(xs-xa)T+ (y-Kxa) Sy-1(y - Kxa)T S denotes the error covariance matrix of the prior (Sa) or the measurements (Sy) K is the Jacobian describing how y changes with x, ie dy/dx.

Errors: Emissions, Chemistry and Transport Play “games” to assess relative importance of different errors, for example: Only assimilate data immediately over Danum Valley and monitor model improvements [emissions vs chemistry] Correlate aerosol optical properties and trace gases related to particular sources [chemistry vs transport] Use post-assimilation 3-D fields to provide: recommendations for model development – feedback mechanism to rest of ACES? local and regional impacts of aerosols

Estimated Timeline Edinburgh PDRA due to start May 2008 2009 2010 OP3/ ACES Model EO data Assimil ation OP3/ACES intensives Model development and preparation EO data preparation and off-line data analysis Assimilation scheme preparation and application EO data/model comparison Model impact studies

Estimated Timeline Edinburgh PDRA due to start May 2008 2009 2010 OP3/ ACES Model EO data Assimil ation OP3/ACES intensives Model development and preparation EO data preparation and off-line data analysis Assimilation scheme preparation and application EO data/model comparison Model impact studies

Estimated Timeline Edinburgh PDRA due to start May 2008 2009 2010 OP3/ ACES Model EO data Assimil ation OP3/ACES intensives Model development and preparation EO data preparation and off-line data analysis Assimilation scheme preparation and application EO data/model comparison Model impact studies

Estimated Timeline Edinburgh PDRA due to start May 2008 2009 2010 OP3/ ACES Model EO data Assimil ation OP3/ACES intensives Model development and preparation EO data preparation and off-line data analysis Assimilation scheme preparation and application EO data/model comparison Model impact studies

Estimated Timeline Edinburgh PDRA due to start May 2008 2009 2010 OP3/ ACES Model EO data Assimil ation OP3/ACES intensives Model development and preparation EO data preparation and off-line data analysis Assimilation scheme preparation and application EO data/model comparison Model impact studies

Estimated Timeline Edinburgh PDRA due to start May 2008 2009 2010 OP3/ ACES Model EO data Assimil ation OP3/ACES intensives Model development and preparation EO data preparation and off-line data analysis Assimilation scheme preparation and application EO data/model comparison Model impact studies