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CO 2 Mixing/Advection by Fronts Aaron Wang Colorado State University.

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Presentation on theme: "CO 2 Mixing/Advection by Fronts Aaron Wang Colorado State University."— Presentation transcript:

1 CO 2 Mixing/Advection by Fronts Aaron Wang Colorado State University

2 Outline CO 2 concentration spikes when fronts pass WLEF tower Vertical Mixing vs. Horizontal Advection SiB – RAMS simulation Future work and problem

3 CO 2 concentration spikes Identify fronts by temperature, pressure, and wind direction – single-point identification Assume the first hour when temperature drops as relative time “0” – fronts are passing Take 10 days before and 10 days after the fronts and compute mean daily course Remove mean daily course from the original data hour by hour which corresponds to the time slot of a day Average over 14 cases

4 A spike right after fronts pass The jump is not as strong as most of the individual cases, because the jump happens around relative time 0 but not right at relative time 0.

5 A case of spike: CO 2 concentration has a 20-ppm spike. It happens when wind direction shifts, pressure has a dip, and temperature drops.

6 Wind Vector Field

7 When CO 2 concentration has a jump, it doesn’t mean there must be a front passing. But when there is front passing, CO 2 concentration definitely has a jump. http://weather.unisys.com/archive/sfc_map/

8 Mechanisms Strong convection – mixing between boundary layer and free troposphere Horizontal advection – rich-CO 2 air flow after fronts Influence of clouds – no solar radiation available for photosynthesis

9 SiB – RAMS simulation Regional Atmospheric Modeling System Simple Biosphere Model – SiB as the surface boundary condition for RAMS Lateral boundary condition for CO 2 is determined from TransCom global CO 2 concentration distribution New version of SiB and RAMS No internal nudging is used, since the physical processes in the domain are what we are concerned about most

10 Future work Introduce new variable, [CO 2 ] in RAMS Design the experiments for some representative cases: fronts, squall lines, lows, highs, etc. Initialize and run the model Analyze the results: vertical mixing, horizontal transport, ecosystem response, etc.

11 Inter-Lab Cooperation WLEF tower data (from PSU – Pennsylvania State University) Other related meteorological data Successful SiB-RAMS coupling (cooperation of CSU and USP – University of Sao Paulo, Brazil)


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