1. Coupled Simulations of [CO2] with SiB-RAMS Aaron Wang, Kathy Corbin, Scott Denning, Lixin Lu, Ian Baker, John Kleist

2. “Average” Summer Cold Front Drops for a couple of days before the front Rises ~10 ppm just before Drops right afterward Why does this happen?! Composite of 17 summertime cold fronts over 5 years

3. Simple Biosphere Model: SiB2 Sellers et al., 1996 Single Vegetation Canopy Calculates transfer of energy, mass, and momentum between atmosphere and vegetation 13 Vegetation Types 3 Soil Layers 12 Soil Classes Two-Stream Approximation Model Photosynthesis Model of Farquhar et al. [1980] Stomatal model of Ball [1988] Water Vapor, Sensible Heat, and CO 2 Fluxes Expressed As Differences in Potentials Divided by Resistances

4. SiB2-RAMS 10-Day Simulation Centered on WLEF tall tower in Park Falls, WI 4 nested grids - Grid 3  450 by 450 km  5 km grid increment - Grid 4  97 by 97 km  1 km grid increment Explicitly represented cloud processes 18 LST August 10, 2001 to 18 LST August 20, 2001 - Cold front Aug 12 ~ 2 LST - Cold front Aug 15 ~ 23 LST - Cold front Aug 17 ~ 18 LST SiB-RAMS Simulations

10. SiB2-RAMS vs. WLEF

11. SiB2-RAMS NEE vs. WLEF

12. SiB2-RAMS 396 m [CO 2 ] vs. WLEF

16. Conclusions on Front Blobs! NEE blobs formed by weather anomalies acting on Re and GPP NEE blobs make regional CO2 blobs Weather acts on CO2 blobs, advecting them around and causing big variations at WLEF Why do we usually see positive blobs with summer cold fronts?!

17. Orbiting Carbon Observatory (OCO) Scheduled to launch in 2008 3 high-resolution spectrometers measuring reflected sunlight - 0.76 mm O 2 A-band - 1.61 and 2.06 mm CO 2 bands Column-average CO 2 dry air mole fraction (X CO2 ) Single shot precision of ~0.5% 1:15 PM equator crossing time 16-day repeat cycle 10 km-wide cross-track field of view (FOV) at nadir FOV divided into eight 1.25-km wide samples 2.25-km down-track resolution at nadir

18. Errors to Avoid 1)Spatial Representation Errors: To what degree can one satellite track from a heterogeneous domain accurately represent the average CO 2 concentration at the inversion resolution? 2) Temporal Representation Errors: Will measurements at 1:15 PM accurately capture the CO 2 diurnal average? 3) Clear-Sky Errors: What is the sign and magnitude of local clear-sky errors? Will the measurements have temporal sampling errors from under-sampling synoptic events? Satellite CO 2 in Transport Inversions?

19. Assessment of Clearsky Bias from Obs Select daytime values: - 1 PM (like seeing “holes between clouds”) - 11 AM – 4 PM (like big clear areas) Sub-divide the data into clear subsets Fit two harmonics to both the complete daytime datasets and the clear subsets Calculate the error: clear fit – total fit

20. Observed Clearsky Bias CO 2 NEE CO Negative errors year-round WLEF errors smaller than HF Mean 1 PM error at WLEF is –1.15 ppm Mean 1 PM error at HF is –2.57 ppm Negative summer errors No significant winter errors WLEF errors smaller than HF Anthropogenic emissions Negative errors year-round Similar shape to CO 2 errors 20 ppb of CO ~.5 ppm of CO 2

21. Explanatory Hypothesis: What could be going on? Year-round negative errors in both [CO] and [CO 2 ] has two possible implications: 1) Boundary layer is deeper on clear days diluting [CO 2 ] 2) Cloudy days have advection of high [CO 2 ] Investigated boundary layer depths using ECMWF re-analysis Clear-sky boundary layer depth errors In the summer, boundary layers are ~250 m deeper than on cloudy days In the winter, boundary layers are nearly the same on clear and cloudy days

22. SiB-RAMS Error Evaluation OCO Track “Transport Model” Grid Cell ~1-degree Grid 3  x = 5 km Grid 4  x = 1 km Sample total column [CO 2 ] along 10-km wide N-S “OCO tracks” Compare to “true” variation in domain Same case presented before (summer cold front, 2001)

23. Total Column [CO 2 ] & Cloud Cover Cloudy days have higher CO 2 Cloud cover and the total column modeled CO 2 concentration over WLEF. Cloud cover of 0 is clear sky, 1 is cloudy.

24. Total Column CO 2 Grid 4 1 PM total column CO 2 concentrations, in ppm. 97 km

25. Cloud Cover Grid 4 Daily cloud cover at 1 PM. Clear Cloudy 97 km

26. Total Column CO 2 Grid 3 Daily 1 PM total column CO 2 concentrations, in ppm. 450 km

27. Cloud Cover Grid 3 Daily cloud cover at 1 PM. Clear Cloudy 450 km

28. Spatial Representation Errors Grid 4 Grid 3 Nearly symmetrical between under and overestimation On grid 4, 95% of OCO tracks within 0.2 ppm of domain avg On grid 3, 95% of OCO tracks within 0.8 ppm of domain avg Simulated OCO at 1 PM – Corresponding 1 PM Domain Average Lower Higher

29. Temporal Representation Errors Temporal variability not well sampled with a single measurement Using satellite [CO 2 ] to optimize diurnally-averaged concentrations introduces large errors into the inversion Grid 4 Grid 3 OCO track at 1 PM – Domain-Average Diurnal Mean

30. “Local” Clear-Sky Errors Grid 4 Grid 3 Clear Grid Cells – All Grid Cells for Each Track Error is symmetrical between under and overestimation Main influence is advection, not biology On grid 4, 95% of the tracks are within 0.1 On grid 3, 95% of the tracks are within 1.0

31. Temporal Sampling Errors Grid 4 Grid 3 OCO clearsky mean – 10-Day Total Mean from Each Track All tracks have large bias of ~0.5 ppm Contributing factors: - Under-sampling of synoptic events - Local suppression of NEE: bias of ~0.5  mol/m 2 /s

32. Temporal Sampling Errors Grid 4 Grid 3 OCO at 1 PM – 10-Day Domain Average Error primarily negative due to lower CO 2 on clear days Each peak shows errors from a different day Largest errors come from completely clear days

33. Satellite Conclusions Spatial representation error not bad relative to spectroscopy Ditto for diurnal cycle “Local” clearsky error (due to  NEE) not bad HUGE errors associated with temporal undersampling of synoptic variability (because of frontal clouds!)