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ASL Basics Global Lower-Tropospheric Measurements Calibration of CO 2 with AIRS Results Comparison Conclusion Breno Imbiriba, Larrabee Strow, Sergio de Souza-Machado, and Scott Hannon. Atmospheric Spectroscopy Laboratory (ASL) University

  1. ASL Basics Global Lower-Tropospheric Measurements Calibration of CO 2 with AIRS Results Comparison Conclusion Breno Imbiriba, Larrabee Strow, Sergio de Souza-Machado, and Scott Hannon. Atmospheric Spectroscopy Laboratory (ASL) University of Maryland Baltimore County Physics Department and the Joint Center for Earth Systems Technology Airs Science Team Meeting - Pasadena - CA May 5, 2009 1 / 28

  2. ASL Overview Try to sense as low in the atmosphere as possible. Complements Chahine’s 250 mbar retrievals. AIRS 791.7 wn Jacobian (dBT/dCO 2 ) Basics 0 Calibration 100 Results 200 Comparison 300 Conclusion 400 mbar 500 600 700 800 900 1000 − 0.025 − 0.02 − 0.015 − 0.01 − 0.005 0 dK/dppm Must handle surface carefully. Clear only. May try cloud-cleared radiances in the future. Ocean zonal CO 2 derived using this algorithm extensively validated in our 2007 JGR paper. This work: Validate land CO 2 measurements. Nominal reporting grid is 1-2 months, 5 degree grid boxes. 2 / 28

  3. ASL Data FOV Selection Used AIRS ACDS clear FOVs Removed about 7% of FOVS due to cirrus Basics ECMWF (with adjustments) used for atmospheric state. Calibration Results Atmospheric State Comparison Atmospheric state from ECMWF adjusted for T sfc and total Conclusion column water. Some FOVs removed due to poor water vapor. Sea surface emissivity - Masuda. Land surface emissivity: UW MODIS-based model. Further adjustments to the ǫ T s product done simultaneously with CO 2 retrieval. 3 / 28

  4. ASL How Good is ECMWF? ECMWF strongly ties temperature to sondes, dynamic bias adjustment procedure applied to satellite data Difference of Std of bias between AIRS and ECMWF and Basics Calibration AIRS NEDT is ∼ 0.03 to 0.05K, equivalent to ∼ 1-2 ppm of Results CO 2 . Comparison Conclusion 0.65 ECMWF Std AIRS NEDT 0.6 0.55 0.5 Std, Noise in K 0.45 0.4 0.35 0.3 0.25 0.2 700 720 740 760 780 800 Wavenumber (cm − 1 ) 4 / 28

  5. ASL CO 2 Retrieval 790 cm − 1 (surface channel, no CO 2 sensitivity) 791 cm − 1 (temperature insensitive CO 2 channel) Basics B 790 obs − B 790 J 790 Calibration T s δ T s = calc Results B 791 obs − B 791 J 791 T s δ T s + J 791 CO 2 δ CO 2 = Comparison calc Conclusion Assume emissivity constant between 790 and 791 cm − 1 . Jacobians J computed for each FOV CO 2 also retrieved similarly using SW channels (2395 cm − 1 region). These are much more temperature sensitive and provide a diagnostic on errors in ECMWF T(z). 5 / 28

  6. Bias Adjustment Needed for LW and SW CO 2 ASL Retrieval Spectroscopy plus radiometric errors could easily reach 5-10 ppm Basics Used NOAA’s GlobalView data set Calibration Results Comparison Conclusion 400-500 mbar sensitivity limited validation to 11 aircraft sites (all US). Hope to find more validation data sets in Russia, Amazonia. 6 / 28

  7. ASL NOAA’s GlobalView Aircraft Sites Limited CO 2 profile information even with aircraft sites. Simple approach; use the highest altitude flight only Basics (usually 5-8 km). Calibration Results GlobalView smooths the raw data. Form time series → and Comparison linearly interpolate to AIRS measurement times. Conclusion Coincidence criteria: 4 degrees lat/lon and 4 days. Beaver Crossing, Nebraska, United States (BNE) (40.80N,97.18W) USA sites − Highest altitude airplanes 390 386 384 385 382 380 NWR 3526m 378 380 LEF 4000m CO 2 ppm CO 2 ppm NHA 5500m 376 BNE 7000m DND 7000m 374 375 HIL 7000m RIA 7000m 372 HAA 7500m HFM 7500m 370 1000m 370 TGC 7500m 3000m 368 CAR 8000m 5000m 7000m 366 365 2002 2003 2004 2005 2006 2007 2008 2002 2003 2004 2005 2006 2007 2008 Date Date 7 / 28

  8. Sample Histograms of Obs-Calc CO 2 , Day ASL Std due to AIRS Noise should be 7-9 ppm CO 2 HAA − Day − Mean offset = 8.11 ppm − Standard Deviation 7.03 ppm BNE − Day − Mean offset = 7.68 ppm − Standard Deviation 7.44 ppm TGC − Day − Mean offset = 7.57 ppm − Standard Deviation 7.18 ppm 2000 1400 3000 1800 Basics 1200 1600 2500 1400 1000 Calibration 2000 1200 800 Count Count Count 1000 Results 1500 600 800 1000 Comparison 600 400 400 500 200 Conclusion 200 0 0 0 − 30 − 20 − 10 0 10 20 30 40 50 − 30 − 20 − 10 0 10 20 30 40 50 − 30 − 20 − 10 0 10 20 30 40 50 Δ CO 2 ppm Δ CO 2 ppm Δ CO 2 ppm RIA − Day − Mean offset = 7.89 ppm − Standard Deviation 7.67 ppm HIL − Day − Mean offset = 7.64 ppm − Standard Deviation 7.70 ppm HFM − Day − Mean offset = 8.35 ppm − Standard Deviation 8.63 ppm 1500 2000 500 1800 450 1600 400 1400 350 1000 1200 Count 300 Count Count 1000 250 800 200 500 600 150 400 100 200 50 0 0 0 − 30 − 20 − 10 0 10 20 30 40 50 − 30 − 20 − 10 0 10 20 30 40 50 − 30 − 20 − 10 0 10 20 30 40 50 Δ CO 2 ppm Δ CO 2 ppm Δ CO 2 ppm 8 / 28

  9. ASL Sample Histograms of Obs-Calc CO 2 , Night HAA − Night − Mean offset = 8.19 ppm − Standard Deviation 6.86 ppm BNE − Night − Mean offset = 7.05 ppm − Standard Deviation 7.72 ppm TGC − Night − Mean offset = 6.72 ppm − Standard Deviation 7.30 ppm 3000 1800 Basics 2500 2000 1600 1400 Calibration 2000 1500 1200 Count Count Count 1000 1500 Results 1000 800 1000 Comparison 600 500 400 500 Conclusion 200 0 0 0 − 30 − 20 − 10 0 10 20 30 40 50 − 30 − 20 − 10 0 10 20 30 40 50 − 30 − 20 − 10 0 10 20 30 40 50 Δ CO 2 ppm Δ CO 2 ppm Δ CO 2 ppm HFM − Night − Mean offset = 4.73 ppm − Standard Deviation 8.10 ppm RIA − Night − Mean offset = 5.81 ppm − Standard Deviation 7.59 ppm HIL − Night − Mean offset = 5.29 ppm − Standard Deviation 7.53 ppm 1800 800 1500 1600 700 1400 600 1200 1000 500 1000 Count Count Count 400 800 300 500 600 200 400 100 200 0 0 0 − 30 − 20 − 10 0 10 20 30 40 50 − 30 − 20 − 10 0 10 20 30 40 50 − 30 − 20 − 10 0 10 20 30 40 50 Δ CO 2 ppm Δ CO 2 ppm Δ CO 2 ppm 9 / 28

  10. ASL Bias Calibration USA − Day − Mean offset = 7.70 ppm − Standard deviation = 7.62 ppm USA − Night − Mean offset = 6.28 ppm − Standard deviation = 7.76 ppm 14000 12000 Basics 12000 10000 Calibration 10000 8000 Count 8000 Count Results 6000 6000 Comparison 4000 4000 Conclusion 2000 2000 0 0 − 30 − 20 − 10 0 10 20 30 40 50 − 30 − 20 − 10 0 10 20 30 40 50 Δ CO 2 ppm Δ CO 2 ppm Errors appear to be relatively gaussian Mean bias derived from ∼ 200-500 AIRS FOVs per site Daytime (Nighttime) Bias: 7.70 (6.28) ppm Individual site Std: ∼ 6 ppm. √ Uncertainty = (mean over 11 sites)/ 11 ≈ 0 . 4 ppm . Roughly the same as single site statistical uncertainty. Too low; US only sites too homogeneous. 10 / 28

  11. ASL Time series Hard to examine AIRS versus aircraft CO 2 time series since single FOV noise high. Basics So, fit AIRS data with the a simle function: Calibration Results f ( t ) = A + Rt + C 1 sin (ω y t + φ 1 ) + C 2 sin ( 2 ω y t + φ 2 ), Comparison Conclusion Two examples: HAA (7500 m) and BNE (7000 m) longwave − daytime − HAA longwave − daytime − BNE 400 400 AIRS − obs AIRS − obs GV − obs GV − obs 395 395 AIRS − fit AIRS − fit 390 390 385 385 380 380 375 375 370 370 365 365 360 360 355 355 350 350 2002 2003 2004 2005 2006 2007 2008 2009 2002 2003 2004 2005 2006 2007 2008 2009 11 / 28

  12. Southern Hemisphere Independent Data Set ASL Rarotonga, Cook Islands (RTA) - Cape Grim, Tasmania, Australia (AIA) RTA − Night − Mean offset = 9.20 ppm − Standard Deviation 6.91 ppm RTA − Day − Mean offset = 9.96 ppm − Standard Deviation 7.12 ppm 1600 1400 1400 Basics 1200 1200 1000 Calibration 1000 800 Count Count 800 Results 600 600 Comparison 400 400 200 Conclusion 200 0 0 − 30 − 20 − 10 0 10 20 30 40 50 − 30 − 20 − 10 0 10 20 30 40 50 Δ CO 2 ppm Δ CO 2 ppm AIA − Day − Mean offset = 12.60 ppm − Standard Deviation 8.23 ppm AIA − Night − Mean offset = 8.65 ppm − Standard Deviation 7.48 ppm 600 1000 500 800 400 Count Count 600 300 400 200 200 100 0 0 − 30 − 20 − 10 0 10 20 30 40 50 − 30 − 20 − 10 0 10 20 30 40 50 Δ CO 2 ppm Δ CO 2 ppm RTA: 4500 m, ocean, good agreement AIA: 6500 m, daytime bias implies we are a few ppm low 12 / 28

  13. AIRS Trends ASL Northern Hemisphere (30-50 deg) zonal avg 12 Basics Calibration 10 Night Results Day Comparison 8 Conclusion 6 CO 2 − 370 (ppm) 4 2 0 − 2 − 4 − 6 2003 2004 2005 2006 2007 2008 Time 13 / 28

  14. ASL Jacobians - Day and Night differences Basics Calibration Results Comparison Conclusion Weighed mean of the pressure field - using the calculated Jacobians as the weighing function. Overall, Daytime sees lower over continental areas. Fill in blancks with surrounding averaged data (Sahara/Poles). For now we use night only climatological Jacobians for CT comparisons 14 / 28

  15. Yearly mean (Fall to Fall) - 2002 to 2006 ASL CO2 mean over all 5 years Basics Calibration Results Comparison Conclusion 15 / 28

  16. ASL 5-Year Seasonal Mean Basics Calibration Results Comparison Conclusion 16 / 28

  17. AIRS Growth Rate ASL Very rough estimate, just raw differences Basics Calibration Results Comparison Conclusion Mean is around 2.5ppm/year Will fit each grid point to rate equation in future Higher rates for high-latitude land? Southern Africa anomaly is Kalahari Desert - will investigate. 17 / 28

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