1. Inter-comparison of retrieved CO 2 from TCCON, combining TCCON and TES to the overpass flight data Le Kuai 1, John Worden 1, Susan Kulawik 1, Kevin Bowman 1, Christian Frankenberg 1, Edward Olsen 1, Debra Wunch 3, Run-Lie Shia 3, Brian Connor 2, Charles Miller 1, and Yuk Yung 3 1.Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Drive, Mail stop: 233-200, Pasadena, CA 91109 2.BC Consulting Ltd., 6 Fairway Dr, Alexandra 9320, New Zealand 3.California Institute of Technology, 1200 E. California Blvd., Pasadena, CA, 91125 The comparison of CO 2 from TCCON profile retrievals and aircraft overpass data 1.Comparison of dry mole fraction (DMF) profile: f CO2 (z) 2.Comparison of total column-averaged DMF: X CO2 3.Comparison of partial column-averaged DMF in boundary layer: pX CO2 BDL (combining TCCON and TES) Time window selection : Short enough to measure a same air parcel by flight and TCCON. Long enough for sufficient number of profiles from TCCON for a good statistical treatment. Overpass time windows: Lamont: Lat=36°, Lon=-97° Parkfalls: Lat=46°, Lon=-90° TCCON site DateFlight time window (UTC) Retrieval time window (UTC) Number of retrievals Park falls2004/07/1416:18:17 – 16:48:1915:00 – 17:3036 Parkf alls2004/07/1513:11:09 – 15:49:0013:00 – 16:0081 Park falls2004/08/1419:46:37 – 22:24:2919:00 – 23:0044 Park falls2008/05/1217:1918 – 17:55:2916:00 – 20:0036 Lamont2009/01/3019:43:18 – 20:46:3019:00 – 21:0042 Lamont2009/07/3114:37:00 – 17:31:0014:00 – 18:00111 Lamont2009/08/0215:05:00 – 17:57:0015:00 – 18:0085 Lamont2009/08/0315:14:00 – 18:00:0015:00 – 19:0090 Lamont2010/07/1816:15:39 – 20:27:5416:00 – 20:3079 Convert to dry profile A priori: Retrieved: Park falls: Apply averaging kernel FLT_AK: 2004/07/12 2004/07/15 2004/08/02 2008/05/12 2009/01/30 2009/07/31 2009/08/02 2009/08/03 2010/07/18 Lamont: Abstract: Characterizing the global carbon budget requires mapping the global distribution and variability of CO 2 sources and sinks. Measurements of the total column of CO 2 by ground or by satellite have the potential to estimate global sources and sinks (Rayner and O’Brien, GRL, 2001, Olsen and Randerson, JGR, 2004) but are less sensitive to regional scale sources and sinks because CO 2 is a long-lived gas which makes it challenging to disentangle local sources from CO 2 transported into the observed air parcel (Keppel-Aleks et al., BGD, 2011). In our poster we explore the use of total column measurements with estimates of the free tropospheric CO 2 by TES to distinguish boundary layer CO 2 and free tropospheric CO 2 because quantify the vertical gradient between the free troposphere and boundary layer is critical for estimating CO 2 fluxes (Stephens, Science, 2007) and near surface CO 2 should be more sensitive to local fluxes than the total column CO 2. In this study, CO 2 profiles are estimated from the Total Carbon Column Observing Network (TCCON) measurements and integrated into a column-averaged concentration. These column amount agree with aircraft data within 0.46 ppm, consistent with the uncertainties due to measurement noise and temperature. There is bias of about -5 ppm, consistent with Wunch et al. (Atmos. Meas. Tech. 2010). Free troposphere estimates of CO 2 are obtained from the GEOS-Chem model that has assimilated CO 2 measurements from Aura Tropospheric Spectrometer. The boundary layer CO 2 estimates are calculated by subtracting TES free troposphere CO 2 from TCCON column CO 2. This estimate of boundary layer CO 2 agrees well with aircraft data with RMS of 1.46 ppm for the sixteen PBL CO 2 estimates we compared. This work shows that total column from NIR measurements (GOSAT, TCCON and OCO-2) and free troposphere measurement from TIR (e.g. TES and AIRS) can be used to profile CO 2 and obtain PBL CO 2 with precision necessary to capture the atmospheric CO 2 variability. It also shows potential of joint retrieval of NIR and TIR. With a long term boundary layer CO 2 record, the CO 2 surface flux can be better quantified. Without O 2 Correction: With O 2 Correction 2. Comparison of total column-averaged CO2 from the TCCON profile estimate to aircraft CO2 3. Characterizing boundary layer partial column estimates using TCCON, TES and aircraft tropospheric data P cuf-off P cut-off =600 hPaRMS TCCON0.42 ppm TCCON & TES0.70 ppm TCCON A priori2.61 ppm P cut-off = 600 hPaRMS TCCON0.46 ppm TCCON & TES1.46 ppm TCCON a priori3.2 ppm Estimates of dry X CO2 much account for the water vapor concentrations and light path errors. We used O 2 retrieved amount and co-retrieved H 2 O to estimate dry X CO2. The RMS of dry Xco2 without O 2 correction is ** ppm and with O 2 correction is 0.46 ppm. ‘Flt_AK’: a profile accounts for the TCCON sensitivity and vertical resolution. It represents the profile that would be retrieved from TCCON measurements in the absence of other errors. The comparison should performed between the TCCON profile (Ret) and ‘Flt_AK’. These profile are used to obtain total column CO 2 estimates. Combining TCCON and TES assimilated data, the boundary layer partial column CO 2 is determined by subtracting the partial column amount within and above free troposphere from the total column amount by TCCON. The remained partial column amount in boundary layer is weighted by the partial column amount of dry air in the boundary layer for. The comparison of to those by integral the flight profile within boundary layer shows small bias and high precision. The knowledge of boundary layer CO 2 was greatly improved by combining TCCON and TES assimilated CO 2 data compared to the climatology a priori. Characterizing boundary layer partial column estimates using TCCON, TES and partial tropospheric data from SGP aircraft aircraft TES SGP FLT There are additional flight measurements at Lamont in 2009 but these CO 2 profiles only go up to 5 to 6 Km. However, it still allow us to compare the boundary layer CO 2 from combining TCCON and TES to the flight data. For the comparison of total column, a priori CO 2 apply to the profile above the ceiling of the flight measurements. Figure 7 shows sixteen days’ comparison in 2009 from January to December when flight measurements are available. The bias and precision of X CO2 are both consistent with previous results. The bias in boundary layer CO 2 stay small but root mean square (RMS) is increased due to two outliers. These two outliers are because of flight boundary layer CO 2 falling outside the a priori. Units (ppm)BiasSTD 20090108-5.150.280.230.68 20090116-5.180.30.280.70 20090129-5.30.30.290.72 20090204-5.050.260.280.70 20090211-4.90.540.280.71 20090219-5.391.080.280.72 20090221-4.970.360.290.73 20090304-4.393.490.270.65 20090407-5.490.260.280.68 20090517-5.870.320.270.66 20090604-5.650.380.270.65 20090701-5.790.860.270.62 20090822-5.571.240.270.62 20090901-4.810.450.270.63 20091101-6.110.360.270.65 20091218-5.860.470.280.71 Error analysis The measurement random noise ( ) is consistent with the RMS of estimates within a day. Large variability is due to the cloud coverage (e.g. 2009/03/04, 2009/08/22, etc.). The average of calculated temperature errors is about 0.7 ppm which is larger than the actual error (RMS of TCCON minus aircraft over several days) of 0.46 ppm. The calculated error is larger than the actual error because the temperature error is likely over estimated. CALIFORNIA INSTITUTE OF TECHNOLOGY Profile retrieval set up 10 retrieved levels for CO 2. Interference gases: H 2 O, HDO and CH 4. SNR = 200 Figure 1. Covariance for CO 2 scaling factor. Figure 2. Table 1. Figure 3. Figure 4. Figure 5. Table 2. Table 3. Table 4. Figure 6. 1.Profile comparison: Time window (STD for clear sky: 0.36 ppm) Day-to-day RMS (0.46 ppm) Actual error Expected error Figure 7.