Implementation of Sulfate and Sea-Salt Aerosol Microphysics in GEOS-Chem Hi everyone. My name is Win Trivitayanurak… I’m a PhD student working with Peter Adams at Carnegie Mellon. I am going to talk about aerosol microphysics implementation in GEOSChem. Win Trivitayanurak & Peter Adams Carnegie Mellon University 3rd GEOS-Chem Users’ Meeting April 11, 2007

Radiative Forcing Estimates Global-average estimates for 2005, relative to 1750 IPCC (2007) +2.6 W/m2 +1.6 W/m2 -1.2 W/m2 Level of scientific understanding Our project is driven by the need to improve aerosol indirect effect estimate. This chart from IPCC forth assessment report shows low level of scientific understanding for aerosol indirect effect. Notice the error bar. And the cloud lifetime/distribution effect (or the 2nd effect) is not even in the chart yet. [click] So how do improve the estimate? Must improve aerosol indirect effect estimate!

Improving Aerosol Microphysics GISS GCM-II’ Produce its own meteorology Cannot predict at a specific time GEOSCHEM Driven by assimilated meteorology Good for predicting at specific times for comparison with observations The goal is to improve the estimate of cloud condensation nuclei (CCN) in the model by improving the aerosol microphysics. Originally there is this aerosol microphyics [click] in GISS GCM and this package would be for climate change and forcing estimate [click]. But the problem with GISS is that it cannot predict meteorogy accurately at a specific time – so I can’t compare aerosol prediction from GISS GCM to field campaign data like ACE-Asia. So I had to put this microphysics [click] into GEOSCHEM so that I can compare it with field campaign data. [click] With GEOSCHEM hosting this microphysics, this package is for aerosol model evaluation purpose. [click] Now let’s get to know more about the microphysics Microphysics Microphysics Climate change & Forcing estimate purpose Aerosol model evaluation purpose

Size-resolved Microphysics TOMAS (TwO-Moment Aerosol Sectional microphysics algorithm) [Adams and Seinfeld, 2002] Moments = 1) aerosol number and 2) aerosol mass 30 bins segregated by dry mass per particle Size range is about 10 nm – 10 μm Size Distribution The microphysical algorithm is called TOMAS, which stands for Two Moment Aerosol Sectional microphysics. The first moment is aerosol number and the second moment is aerosol mass. There are 30 size sections segregated by dry mass per particle covering size range of about 10nm to 10um dry diameter. This picture shows what TOMAS tracks, in each bin, we have mass of each component such as sulfate, sea-salt, and so on… and also aerosol number. So we tracks three critical information, composition, amount, and size. [click] N7 … N6 N4 N5 NaCl N30 NaCl N1 N3 N2 SO42- … SO42- Dp

TOMAS processes Coagulation Condensation/ evaporation Nucleation In-cloud sulfur oxidation Size-resolved dry deposition Size-resolved wet deposition These are the microphysical processes. Apart from adding them, I also had to modify existing processes to work properly with aerosol size distribution. [click]

TOMAS Status Species: Number, SO42-, Sea-salt Advection Convection Problem: TPCORE/ PPM parabolic interpolation artificially grows/shrinks particles Fixed (see me for detail) Convection Negative entrainment at some time steps Now let’s talk about the status of TOMAS and the GEOS-CHEM processes that were modified accordingly. Right now the aerosol species we have number, sulfate, and sea-salt. For advection, there was a problem with the piecewise parabolic method inside TPCORE module doing parabolic interpolation and grow or shrink particles artificially. This problem is fixed and you can talk to me for more detail. Convection, I observed some negative entrainment but this is probably insignificant errors and still unresolved.

TOMAS Status Emission Nucleation : Binary nucleation (H2SO4-H2O) Sulfate: Same total, apply size distribution Sea-salt: Clarke et al (2006) parameterization Number: calculated from emitted mass and assumed size distribution Nucleation : Binary nucleation (H2SO4-H2O) Aqueous oxidation of sulfate Distribute over activated size range using condensation/evaporation algorithm Emissions: I apply assumed size distribution for the same total of sulfate. Sea-salt is emitted using Clarke 2006 param. Number is emitted according to mass and assumed size dist. Another source of aerosol particles is nucleation, I used the binary nucleation of sulfuric acid and water. Next, another source of sulfate mass from aqueous oxidation; this portion of mass is distributed over activated size range using condensation algorithm.

TOMAS Status Wet deposition In-cloud: Activated particles are removed same way as the original rainout (1st order loss). Interstitial particles are assumed inert. Below-cloud: Apply size-resolved washout rate For wet deposition. The in-cloud deposition is treated with the assumption that only particles larger than the activation diameter gets to be removed using the original rainout rate and interstitial particles are not removed. As for below cloud deposition, I applied size-resolved washout rate.

Dry particle diameter (mm) TOMAS Status Dry deposition Size-resolved scheme of Zhang et al (2001) as in later versions of G-C 0.01 0.1 1 10 Dry particle diameter (mm) 10 1 0.1 0.01 Deposition Velocity (cm s-1) Original New

Aerosol Prediction from TOMAS

Model Results Total Number Concentration (cm-3)

Model Results CCN(0.2%) (cm-3) Notice the scale! Total Number Concentration (cm-3)

Model Results Total Number Concentration (cm-3) CCN(0.2%) (cm-3) Tropical convection  CCN removed with rain Nucleation region Emitted as well as grown into CCN sizes Pressure [mbar] Heavy pollution region Latitude Latitude Total Number Concentration (cm-3) CCN(0.2%) (cm-3)

Number Size Distribution Model Results Remote marine, Southern Ocean Industrial region, China Nucleated particles Sulfate primary emission Grow by condensation as they descend Cloud processing From cloud top down to surface  Bimodal dist. Sea-salt emission Significant mass (Larger particle) Fewer numbers. Number Size Distribution dN/dLogDp (cm-3)

Number Size Distribution L19 Model Results Remote marine, Southern Ocean L17 L10 L1 Number Size Distribution dN/dLogDp (cm-3)

Upcoming Tasks & Challenges Introducing 30-bin OC, EC, and dust Running 2x2.5  memory need for (30*(n+2)) aerosol tracers, where n=number of mass species OR … Running nested grid over ACE-Asia domain? Comparing aerosol prediction with number size distribution measured from ACE-Asia

Question? Check out my poster about … Comparison with Heintzenberg marine aerosol data Inter-model comparison : G-C, GISS GCM-II’ and GLOMAP