Consistent evaluation of GOSAT, SCIAMACHY, carbontracker, and MACC through comparisons to TCCON

Journal Article · Mon Jun 22 00:00:00 EDT 2015 · Atmospheric Measurement Techniques Discussions (Online)

DOI:https://doi.org/10.5194/amtd-8-6217-2015· OSTI ID:1208847

Kulawik, S. S. ^[1]; Wunch, D. ^[2]; O'Dell, C. ^[3]; Frankenberg, C. ^[2]; Reuter, M. ^[4]; Oda, T. ^[3]; Chevallier, F. ^[5]; Sherlock, V. ^[6]; Buchwitz, M. ^[4]; Osterman, G. ^[2]; Miller, C. ^[2]; Wennberg, P. ^[2]; Griffith, D. W. T. ^[7]; Morino, I. ^[8]; Dubey, M. ^[9]; Deutscher, N. M. ^[10]; Notholt, J. ^[4]; Hase, F. ^[11]; Warneke, T. ^[4]; Sussmann, R. ^[11] ; Robinson, J. ^[12]; Strong, K. ^[13]; Schneider, M. ^[9]; Wolf, J. ^[2]

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1. Bay Area Environmental Research Institute, Sonoma, CA (United States)
2. California Institute of Technology, Pasadena, CA (United States)
3. Cooperative Institute for Research in the Atmosphere (CIRA), Fort Collins, CO (United States)
4. University of Bremen, Bremen (Germany)
5. Laboratoire des Sciences du Climat et de l'Environment, LSCE (France)
6. The National Institute of Water and Atmospheric Research, Wellington and Lauder (New Zealand); Lab. de Meteorologie Dynamique, Palaiseau (France)
7. University of Wollongong, New South Wales (Australia)
8. National Institute for Environmental Studies (NIES), Tsukuba, Ibaraki (Japan)
9. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
10. University of Bremen, Bremen (Germany); University of Wollongong, New South Wales (Australia)
11. Institute for Meteorology and Climate Research (IMK-ASF), Karlsruhe (Germany)
12. The National Institute of Water and Atmospheric Research, Wellington and Lauder (New Zealand)
13. University of Toronto, Toronto, ON (Canada)

Consistent validation of satellite CO₂ estimates is a prerequisite for using multiple satellite CO₂ measurements for joint flux inversion, and for establishing an accurate long-term atmospheric CO₂ data record. We focus on validating model and satellite observation attributes that impact flux estimates and CO₂ assimilation, including accurate error estimates, correlated and random errors, overall biases, biases by season and latitude, the impact of coincidence criteria, validation of seasonal cycle phase and amplitude, yearly growth, and daily variability. We evaluate dry air mole fraction (X_CO2) for GOSAT (ACOS b3.5) and SCIAMACHY (BESD v2.00.08) as well as the CarbonTracker (CT2013b) simulated CO₂ mole fraction fields and the MACC CO₂ inversion system (v13.1) and compare these to TCCON observations (GGG2014). We find standard deviations of 0.9 ppm, 0.9, 1.7, and 2.1 ppm versus TCCON for CT2013b, MACC, GOSAT, and SCIAMACHY, respectively, with the single target errors 1.9 and 0.9 times the predicted errors for GOSAT and SCIAMACHY, respectively. When satellite data are averaged and interpreted according to error² = a²+ b² /n (where n are the number of observations averaged, a are the systematic (correlated) errors, and b are the random (uncorrelated) errors), we find that the correlated error term a = 0.6 ppm and the uncorrelated error term b = 1.7 ppm for GOSAT and a = 1.0 ppm, b = 1.4 ppm for SCIAMACHY regional averages. Biases at individual stations have year-to-year variability of ~ 0.3 ppm, with biases larger than the TCCON predicted bias uncertainty of 0.4 ppm at many stations. Using fitting software, we find that GOSAT underpredicts the seasonal cycle amplitude in the Northern Hemisphere (NH) between 46–53° N. In the Southern Hemisphere (SH), CT2013b underestimates the seasonal cycle amplitude. Biases are calculated for 3-month intervals and indicate the months that contribute to the observed amplitude differences. The seasonal cycle phase indicates whether a dataset or model lags another dataset in time. We calculate this at a subset of stations where there is adequate satellite data, and find that the GOSAT retrieved phase improves substantially over the prior and the SCIAMACHY retrieved phase improves substantially for 2 of 7 sites. The models reproduce the measured seasonal cycle phase well except for at Lauder125 (CT2013b), Darwin (MACC), and Izana (+ 10 days, CT2013b), as for Bremen and Four Corners, which are highly influenced by local effects. We compare the variability within one day between TCCON and models in JJA; there is correlation between 0.2 and 0.8 in the NH, with models showing 10–100 % the variability of TCCON at different stations (except Bremen and Four Corners which have no variability compared to TCCON) and CT2013b showing more variability than MACC. This paper highlights findings that provide inputs to estimate flux errors in model assimilations, and places where models and satellites need further investigation, e.g. the SH for models and 45–67° N for GOSAT

• Research Organization: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
• Sponsoring Organization: USDOE
• Grant/Contract Number: AC52-06NA25396
• OSTI ID: 1208847
• Journal Information: Atmospheric Measurement Techniques Discussions (Online), Journal Name: Atmospheric Measurement Techniques Discussions (Online) Journal Issue: 6 Vol. 8; ISSN 1867-8610
• Publisher: European Geosciences Union
• Country of Publication: United States
• Language: English

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54 ENVIRONMENTAL SCIENCES