1. Investigating Synoptic Variations in Atmospheric CO2 Using Continuous Observations and a Global Transport Model Nicholas Parazoo, Scott Denning, Randy Kawa, Ravindra Lokupitia 8 th TransCom workshop program (v3) April 24 th -27 th, 2007

2. Synoptic Variations Large day-to-day variations likely associated with transport events such as cold fronts Synoptic variations contain upstream source/sink information -> target cold fronts

3. Experimental Design Assimilation of radiosondes, aircraft measurements, satellite data (precipitable water, surface winds) Physics determined from NCAR CCM3 Vegetation Indices Biome type Soil properties Weather Reanalysis Weather Reanalysis (GEOS4) 12 year spinup Hourly [CO2] OBS Terrestrial CO2 surface flux Winds, cloud mass fluxes, PBL Parameters Forward Transport Model Fossil Fuels, Ocean Flux Lin and Rood (1996) dynamical core 1.25°x1°,  t=7.5m 3 year spinup (2000-02) Model Evaluation 6-hourly Land Surface Model (SiB) Transport Model (PCTM)

4. Frontal Identification The time at which magnitude of gradient of  changes the most rapidly defines the trough (minimum GG  ) and ridge (maximum GG  ) Apply to reanalysis, retain events with minima in both water vapor and temperature Filter out events without clockwise wind shift or pressure minimum GG   (water vapor) pressure Cold Front! Frontal Locator Function wind direction

5. Frontal CO2 Composites Multiple cold fronts averaged together (diurnal & seasonal cycle removed) Error bars are sd of ave Unique patterns, shape and phase match observations Amplitude mismatch 1)Upstream sinks/sources? 2)Vegetation stress? 3)Improper response to frontal weather?

6. Why do large day-to-day variations of 10-20ppm exist during frontal passage?

8. Run 1: Global surfaces fluxes Run 2: Eastern Hemisphere surface fluxes only Correlation of the 2 Runs in July (mid-day values only) shows the importance of lateral flow over NA (R 2 = 35-70% in SE!) Large Scale Influence Correlation Coefficient

9. Horizontal Transport is Key Horizontal transport suggested in coherent events Transport acts on gradients How are gradients established? Mid-day [CO2] LEFFRS SGPWKT OBS PCTM July 18 July 19 Aug 27 Aug 28

10. Time Mean [CO2] Gradients In the Midwest, ignoring fossil fuels, weak gradients in the time mean –dC/dx ~ 1-3ppm/3-5° July Average PCTM [CO2] at 50m

11. Deformational Flow Anomalies organize along cold front dC/dx ~ 10-15ppm/3-5° shear deformation - tracer field rotated by shear vorticity stretching deformation - tracer field deformed by stretching gradient strength PCTM [CO2] at 50m

12. PBL CO2 Budget 1)Time rate of change of [CO2] in the PBL due to transport and surface fluxes, daytime only 2)Average local biogenic and fossil fuel flux 3)Advection by vertical wind, w = -w/(rg), w derived from mass flux divergence 4)Advection by horizontal wind 5)PBL transport by moist convective mass fluxes      Bakwin et al., 2004

13. Monthly Budget Monthly average 1-hr tendencies (daytime only) Pattern and sign match, magnitude error in some places Horizontal advection negligible (except near cities) Monthly average 1-hr tendency reflection of uptake east of Rockies and cloud transport in SE DC/DT (Model)DC/DT (Budget) Cloud TransportVertical Advection Horizontal AdvectionSurface Flux

14. Budget for Frontal Case DC/DT (Model)DC/DT (Budget) Vertical AdvectionCloud Transport Horizontal AdvectionSurface Flux All terms important, including horizontal advection Transport biggest driver along front Balance between vertical advection and surface flux behind

15. Conclusions Phase of synoptic [CO2] is good, but amplitude too strong Synoptic variations of CO2 caused by deformational flow along fronts Regional models need to properly account for large scale inflow at boundaries Monthly PBL balance is between biology & vertical mixing Synoptic PBL balance also strongly affected by horizontal advection

16. Thanks to the following for continuous CO2 data: Ken Davis, Arlyn Andrews, Sebastien Biraud, Doug Worthy, Bill Munger, NOAA-GMD This Research is funded by NASA Contracts #NNX06AC75G and #NNG05GD15G and NOAA Contract #NA17RJ1228 Amend. 97

17. Performance of Frontal Locator OklahomaWisconsinCanada Wind Direction Wind Speed Water Vapor Temp Surface Pressure Events identified in the reanalysis match observations in shape and phase Mismatch of magnitude of water vapor at northern sites Events not classified in terms of other weather (ppt, radiation, etc.)

18. Regression of transport model onto continuous observations 2004 only, seasonal cycle removed R 2 from 17-39% Good agreement, some room for improvement of surface fluxes and/or transport Synoptic CO2 Variations PCTM Observations