1. Carbon dioxide from TES Susan Kulawik F. W. Irion Dylan Jones Ray Nassar Kevin Bowman Thanks to Chip Miller, Mark Shephard, Vivienne Payne S. Kulawik – February, 2009

2. Challenges More accuracy is required than for any other TES species –Year over year increase in CO 2 ~2 ppm (0.5%) –Seasonal variability ~5-15 ppm in the Northern Hemisphere (1-3%) –Plumes at ground stations are ~20 ppm (4%) T ATM and CO 2 information is entangled –A +1K temperature error propagates into about +25 ppm CO 2 error –CO 2 retrieved by itself in the TES 2B1 filter has 2 DOF. When co- retrieved with T ATM, the CO 2 DOF drops to 0.6. GMAO T ATM is not adequate for TES CO 2 retrievals –T ATM errors propagate into CO 2 unless T ATM is retrieved

3. Strategy Retrieve using 670-725, 970-990, 1070-1120 cm -1 spectral regions Remove spectral areas showing poor fit, spikes, or spectroscopic issues Co-retrieve T ATM, H 2 O, CO 2, cloud parameters, and surface temperature Use a loose CO 2 constraint, expecting averaging data AIRS selected windows S. Kulawik – February, 2009

4. Measurements American Samoa Macquarie Island CONTRAIL TES Mauna Loa TES TES measurement box –13-35N x 128-158E –2006 - 2008 –Peak information at 511 hPa –Ocean, low cloud OD CONTRAIL aircraft –9-11 km, Northern & Southern hemisphere. –Through 2006 Mauna Loa –19.5N, 155.6E, 3.4km –Through 2009 American Samoa –54.5S, 159E, surface site –Through 2008 S. Kulawik – February, 2009

5. TES Northern Hemisphere results Individual targets Predicted error 7.9 ppm Standard deviation 8.1 ppm Monthly averages of ~200 targets Approx 1 ppm errors compared to validation data 5.6 ppm low bias, constant value Highly correlated with Mauna Loa observatory 1.5 +- 0.5 ppm yearly increase – Mauna Loa 1.8 +- 0.2 ppm Uniform CO 2 prior TES monthly ave CONTRAIL aircraft data Mauna Loa S. Kulawik – February, 2009

6. Southern hemisphere results Monthly averages of ~130 targets –Approx 2 ppm errors compared to validation data –5 ppm low bias compared to American Samoa ground station –1.7 +- 0.3 ppm yearly increase -- Samoa 1.9 +- 0.1 ppm Uniform CO 2 prior CONTRAIL aircraft data Samoa ground station S. Kulawik – February, 2009 TES monthly ave

7. Observing System Simulation Experiment (OSSE) - Ray Nassar TES 20 x 30 degree x 1 month averages –0.3 – 1.1 DO0.5 – 2.0 ppm errors GLOBALVIEW 76 surface stations MODEL GEOS-Chem with NASA GMAO met. fields, specialized CO2 source/sink inputs FLUXES 14 regions of combustion and terrestrial exchange + “rest of world” (29 elements) A priori flux uncertainty: –100% for terrestrial biosphere –30% for combustion S. Kulawik – February, 2009

8. OSSE - Ray Nassar -cont A posteriori flux uncertainties: TES: improves flux uncertainty from 100% to 15- 28% for biosphere fluxes 76 surface stations: comparable to TES if 0.1 ppm errors assumed; worse performance if 1 ppm errors assumed TES and surface station sensitivities are complementary and future work will explore combinations of TES, surface stations, OCO, and GOSAT S. Kulawik – February, 2009

9. S. Kulawik – January, 2009 Spectral view Signal apparent in residual –Working with AER (Vivienne Payne, Mark Shephard) to resolve and test spectroscopy improvements –Also Javier Martin-Torres is going to compare radiances with FUTBOLIN forward model

10. Conclusions TES observes CO 2 –Correct seasonal cycles and patterns for NH and SH ocean –Yearly increase seen –OSSE shows TES data will improve flux estimates Future plans –Validation– land, bias characterization, global comparisons to AIRS, Carbontracker, spectroscopy updates –Generate 3 months of TES CO 2 for assimilation and flux estimates S. Kulawik – February, 2009 Work at JPL was carried out under contract to NASA with funds from ROSES 2007. Work by Nassar et al. funded by Natural Sciences and Engineering Research Council (NSERC) of Canada. We acknowledge use of GLOBALVIEW-CO 2, Mauna Loa, and Samoa data from NOAA-ESRL and CONTRAIL data from World Data Centre for Greenhouse Gases (WDCGG).World Data Centre for Greenhouse Gases (WDCGG)

11. References Matsueda, H., H. Y. Inoue, and M. Ishii (2002), Aircraft observation of carbon dioxide at 8 ‑ 13 km altitude over the western Pacific from 1993 to 1999. Tellus, 54B(1), 1 ‑ 21, doi: 10.1034/j. 1600 ‑ 0889.2002.00304.x U.S. Department of Commerce | National Oceanic and Atmospheric Administration Earth System Research Laboratory | Global Monitoring Division http://www.esrl.noaa.gov/gmd/dv/site/SMO.html S. Kulawik – February, 2009

12. Future plans Validation –Global characterization of bias –Land targets (comparing to airplane flights, e.g. SGP) –Effects of strategy on T ATM, other species –Comparisons to AIRS and CarbonTracker Science –3 months of TES CO 2 output for assimilation and inversion by Dylan Jones’ group to estimate fluxes –CO 2 data in Atlantic/Pacific near US for REAM model comparisons (Y.Choi) –CO 2 /CO ratios for elevated CO regions Work at JPL was carried out under contract to NASA with funds from ROSES 2007. Work by Nassar et al. funded by Natural Sciences and Engineering Research Council (NSERC) of Canada. We acknowledge use of GLOBALVIEW-CO 2, Mauna Loa, and Samoa data from NOAA-ESRL and CONTRAIL data from World Data Centre for Greenhouse Gases (WDCGG).World Data Centre for Greenhouse Gases (WDCGG) S. Kulawik – February, 2009

13. S. Kulawik – January, 2009 TATM Effects Retrieve CO 2 with TATM fixed at GMAO and selected windows –Signal too large –Peak too late Co-retrieved TES T ATM vs GMAO shows seasonal fluctuations

14. S. Kulawik – January, 2009 Spectral view (cont) Idea from Mark Shephard –Compare radiance change from dCO2 and dTATM –Select radiances most influenced by dCO2 (red) –Would mean a sequential retrieval