1. Evaluating Spatial, Temporal, and Clear-Sky Errors in Satellite CO 2 Measurements Katherine Corbin, A. Scott Denning, Ian Baker, Aaron Wang, Lixin Lu TransCom Meeting June 13-16, 2005

2. Orbiting Carbon Observatory (OCO) Scheduled to launch in 2008 3 high-resolution spectrometers measuring reflected sunlight - 0.76  m O 2 A-band - 1.61 and 2.06  m 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

3. Sources of Error 1) Spatial Representativeness Errors OCO measurements will be representing a grid cell of at best 100 km by 100 km

4. Sources of Error 2) Temporal Representation Errors OCO’s 1:15 PM measurements will not be able to see significant near-surface diurnal variability Average July diurnal cycle at various levels on the WLEF tall tower.

5. Sources of Error 3) Clear-Sky Errors Local Clear-Sky Errors - NEE is enhanced on cloudy days (e.g. Freedman et al., 2002; Gu et al., 2002; Law et al., 2002) - For a given irradiance level, cloudy days have higher NEE than clear days (e.g. Gu et al., 2002; Baldocchi et al., 1997) Temporal Sampling Errors: - Clouds are frequently associated with fronts and changes of air masses

6. 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 10,1997 to 18 LST August 20, 1997 - Cold front Aug 12 ~ 2 LST - Cold front Aug 15 ~ 23 LST - Cold front Aug 17 ~ 18 LST

7. Methods Vertically integrate [CO 2 ] to create total column mixing ratio Emulate OCO - Assume satellite tracks due south - Clear-sky pixels only  No liquid water or ice condensates at any vertical level - Track width  10 km » Grid 4: 10 1-km pixels » Grid 3: 2 5-km pixels - Meridionally average [CO 2 ] OCO Track Transport Model ~1º Grid Cell

8. SiB-RAMS 396 m [CO 2 ] for Grid 1 0 GMT August 14 – 0 GMT August 17

9. Total Column [CO 2 ] vs. 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.

10. Total Column CO 2 Variability Daily 1 PM total column CO 2 concentrations, in ppm. Grid 3 Grid 4 450 km

11. Cloud Cover Daily cloud cover at 1 PM. Clear Cloudy

12. Spatial Representation Errors Fine GridCoarse Grid Nearly symmetrical between under and overestimation On grid 4, 95% of emulated satellite tracks capture domain average within 0.2 ppm On grid 3, 95% of emulated satellite tracks capture domain average within 0.8 ppm … (includes Great Lakes) Emulated Satellite [CO 2 ] at 1 PM – Corresponding 1 PM Domain Average Under Over

13. Temporal Representation Errors Strong dependence on timing of synoptic events Temporal variability not well sampled with a single measurement Using satellite [CO 2 ] to optimize diurnally-averaged concentrations introduces large errors into the inversion Emulated Satellite [CO 2 ] at 1 PM – Domain-Average Diurnal Mean Fine GridCoarse Grid

14. Local Clear-Sky Errors 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 ppm of the track value that includes all pixels On grid 3, 95% of the tracks are within 1.0 ppm of the track value that includes all pixels…. (much larger due to Great Lakes) Fine GridCoarse Grid

15. Temporal Sampling Errors Emulated Satellite [CO 2 ] 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 Fine GridCoarse Grid

16. Conclusions Strong synoptic variability! Satellite sampling errors due to small tracks, time of day, and clear-sky are small relative to under-sampling of synoptic events Inversions could benefit most from satellite data if: –Transport is modeled accurately at high resolution (synoptic events correctly simulated) –Model is sampled at same time/place as satellite obs

17. Extra Slides!!

18. Model: SiB2-RAMS SiB2 – Simple Biosphere Model [Sellers et al., 1996] – Calculates the transfer of energy, mass, and momentum between the atmosphere and the vegetated surface of the earth – Water vapor, sensible heat, and CO 2 fluxes are expressed as differences in potentials divided by resistances – Photosynthesis model of Farquhar et al. [1980] and stomatal model of Ball [1988] RAMS – Regional Atmospheric Modeling System – Comprehensive mesoscale meteorological modeling system (Cotton et al., 2002) – Telescoping, nested grid scheme – Bulk microphysics parameterization (Meyers et al., 1997 and Walko et al., 1995) – Meteorological fields initialized and lateral boundaries nudged using the NCEP mesoscale Eta–212 grid analysis (40-km resolution)

19. Vertical Profiles of Local Clear-Sky Errors Grid 4 Majority of the error is near the surface in the lowest 2 km Domain Average at each vertical level of the difference between the track [CO 2 ] using clear-sky pixels and the track mixing ratio using all pixels Grid 3 Prior to the front on the 15 th, the entire column had decreased [CO 2 ] compared to the domain-average

20. Local Clear-Sky Errors vs. Cloudiness Grid 4 Errors increase with increasing cloudiness Errors primarily due to advection rather than biology Grid 3 Similar to grid 4, spread of errors increases as cloud fraction decreases Errors again mostly due to advection Differences between emulated satellite concentration at 1 PM using only clear-sky pixels and satellite concentration at 1 PM using all pixels in the track

21. Local Clear-Sky NEE Errors Differences between emulated track NEE at 1 PM using only clear-sky pixels and NEE at 1 PM using all pixels in the track Grid 4 Small, random NEE errors that increase with clouds Grid 3 Random NEE errors

22. Temporal Sampling Errors vs. Cloudiness Differences between emulated satellite concentration from each track using only clear-sky pixels and the 10-day domain average Grid 4 Clear days have the largest errors Days following fronts typically have smaller errors Grid 3 Errors appear more random Two clear-sky pixels on August 15 over-estimate the domain average

23. Temporal Sampling Clear-Sky NEE Errors Differences between NEE value from each track using only clear-sky pixels and the 10-day domain average Grid 4 Shifted towards enhanced NEE on clear days Average NEE bias of –0.5  mol/m 2 /s Grid 3 NEE errors are random