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What Do The Observations and Models Tell Us About Emissions?. BC at surface. BC C-130 #11 April 19th. RF16 highly processed plume. RF7 highly processed plume. Field Experiments Employ Mobile “Super-Sites” & Provide Opportunities to Characterize Emissions. The Informatics Problem:

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slide2

RF16 highly processed plume

RF7 highly processed plume

Field Experiments Employ Mobile “Super-Sites” & Provide Opportunities to Characterize Emissions

The Informatics Problem:

Observations

Source Information Air Mass Markers

Model Runs w/wo SourceSectors

Freidli et al., JGR submitted

slide4
Example of Use of Model with Emission Markers Frontal outflow of biomass burning plumes E of Hong Kong

100 ppb

Biomass burning

CO forecast

Observed CO –Sacshe et al.

Observed aerosol potassium

- Weber et al.

Longitude

slide5

Large-Scale Structure is Captured by Model– But Peaks are Underestimated

P-3B

SO2

Data: Kondo et al., Thorton, Sacshe

slide6

Under-predicted Points Are In The Yellow Sea

--- CO > 700

--- CO > 600

--- CO > 500

What does this tell us about the model?

-Model deficiency?

-Emissions problem?

Figure 7

slide7

Observed and Modeled Ratios Can Be Classified By Source Region Using Trajectories - Age Can Also Be Estimated

slide8

The BC and CO Concentrations are Underpredicted – but the Ratios are Accurately Captured!!

DBC/DCO

This analysis suggests we need to look for improvements in a specific sector

slide9

Domestic Sector??

DC-8 Flight 9

slide10

The Importance of Fossil, Biofuels and Open Burning Varies by Region -- Richness of Emissions Data Base Can be Exploited

slide11

Estimated Emissions

Observation Based Reconstruction

> 3.7

> 3.7

a

b

Can Useful Emissions Information Be Reconstructed Using Observed Ratios (or Concentrations) ?

regional emission signals can be identified and tested
Regional Emission Signals Can be Identified and Tested

Ethyne

Two Relationships Are Observed

Red Points Come From SE Asia –With Heavy Influence From Biomass Burning

slide13

?

Modeled

Observed

slide14
Top Down Approach (Inversion)emissions  model  predicted-fields  compare predicted with observed  emission inferences

Kasabhatla et al., GRL, 2003

u iowa kyushu argonne gfdl
U. Iowa/Kyushu/Argonne/GFDL

With support from NSF, NASA (ACMAP,GTE), NOAA, DOE, JST

improvements emissions require creative combination of bottom up and top down approaches
Improvements Emissions Require Creative Combination of Bottom-Up and Top-Down Approaches

Modeled

Observed

Bottom-Up Estimates

cfors stem 2k1 model data flow chart

Tracers/Markers:

SO2/Sulfate DMS

BC OC

Volcanic Megacities

CO fossil CO-Biomass

Ethane Ethene

Sea Salt Radon

Lightning NOx Dust 12 size bins

CFORS/STEM-2K1 Model Data Flow Chart

Large-scale

Meteorological

Fields

(JMA, NCEP, ECMWF

CFORS/RAMS

wind velocities, temperature, pressure,

water vapor content, cloud water

content, rain water content

and PV etc.

Dust, Sea Salt,

Lightning NOx

Biogenic

Emisisons

Biomass Emissions

Fuel/activity info

STEM-2K1

On-Line TUV

Emission

Preprocessor

Forecasts

Or Post

Analysis

Large Point

Sources

Volcanic SO2 Emissions

Anthropogenic Area Emissions

Satellite Observations

(fire counts, ozone columns, sea surface temperature, etc.)

slide19

Using Observed Megacity Ratios – Can We Cluster Cities?

Extension to particles

(b)

BEJ (Beijing), TIJ (Tianjin), QID (Qingdao), SHA (Shanghai), HOK (Hongkong), SEL (Seoul-Inchon), PUS (Pusan), TOK (Tokyo)

slide20
Speciation of NMVOC Reveals Great DifferencesAmong Countries with Different Levels ofEconomic Development

Cities

Countries

slide21

Biomass burning

Industry

Domestic

> 5.0

Ethyne

slide22
Use of Chemical Transport Models in Atmospheric Chemistry Field Experiments to Test/Improve Emissions