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Consistent Evaluation of ACOS-GOSAT, BESD-SCIAMACHY, CarbonTracker, and MACC Through Comparisons to TCCONConsistent 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. Harmonizing satellite CO2 measurements is particularly important since the differences in instruments, observing geometries, sampling strategies, etc. imbue different measurement characteristics in the various satellite CO2 data products. 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(sub CO2)) for Greenhouse gases Observing SATellite (GOSAT) (Atmospheric CO2 Observations from Space, ACOS b3.5) and SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) (Bremen Optimal Estimation DOAS, BESD v2.00.08) as well as the CarbonTracker (CT2013b) simulated CO2 mole fraction fields and the Monitoring Atmospheric Composition and Climate (MACC) CO2 inversion system (v13.1) and compare these to Total Carbon Column Observing Network (TCCON) observations (GGG2012/2014). We find standard deviations of 0.9, 0.9, 1.7, and 2.1 parts per million vs. TCCON for CT2013b, MACC, GOSAT, and SCIAMACHY, respectively, with the single observation errors 1.9 and 0.9 times the predicted errors for GOSAT and SCIAMACHY, respectively. We quantify how satellite error drops with data averaging by interpreting according to (error(sup 2) equals a(sup 2) plus b(sup 2) divided by n (with n being the number of observations averaged, a the systematic (correlated) errors, and b the random (uncorrelated) errors). a and b are estimated by satellites, coincidence criteria, and hemisphere. Biases at individual stations have year-to-year variability of 0.3 parts per million, with biases larger than the TCCON predicted bias uncertainty of 0.4 parts per million at many stations. We find that GOSAT and CT2013b under-predict the seasonal cycle amplitude in the Northern Hemisphere (NH) between 46 and 53 degrees North latitude, MACC over-predicts between 26 and 37 degrees North latitude, and CT2013b under-predicts the seasonal cycle amplitude in the Southern Hemisphere (SH). The seasonal cycle phase indicates whether a data set or model lags another data set in time. We find that the GOSAT measurements improve the seasonal cycle phase substantially over the prior while SCIAMACHY measurements improve the phase significantly for just two of seven sites. The models reproduce the measured seasonal cycle phase well except for at Lauder_125HR (CT2013b) and Darwin (MACC). We compare the variability within 1 day between TCCON and models in June-July-August; there is correlation between 0.2 and 0.8 in the NH, with models showing 10-50 percent the variability of TCCON at different stations 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 degrees North latitude for GOSAT and CT2013b.
Document ID
20160005739
Acquisition Source
Goddard Space Flight Center
Document Type
Reprint (Version printed in journal)
External Source(s)
Authors
Kulawik, Susan
(Bay Area Environmental Research Inst. Sonoma, CA, United States)
Wunch, Debra
(California Inst. of Tech. Pasadena, CA, United States)
O’Dell, Christopher
(Colorado State Univ. Fort Collins, CO, United States)
Frankenberg, Christian
(California Inst. of Tech. Pasadena, CA, United States)
Reuter, Maximilian
(Bremen Univ. Germany)
Chevallier, Frederic
(Laboratoire de Meteorologie Dynamique du CNRS Palaiseau, France)
Oda, Tomohiro
(Universities Space Research Association Columbia, MD, United States)
Sherlock, Vanessa
(National Institute of Water and Atmospheric Research New Zealand)
Buchwitz, Michael
(Bremen Univ. Germany)
Osterman, Greg
(Jet Propulsion Lab., California Inst. of Tech. Pasadena, CA, United States)
Miller, Charles E.
(Jet Propulsion Lab., California Inst. of Tech. Pasadena, CA, United States)
Iraci, Laura T.
(NASA Ames Research Center Moffett Field, CA, United States)
Wolf, Joyce
(Jet Propulsion Lab., California Inst. of Tech. Pasadena, CA, United States)
Date Acquired
May 3, 2016
Publication Date
February 29, 2016
Publication Information
Publication: Atmospheric Measurement Techniques
Publisher: EGU
Volume: 9
Issue: 2
e-ISSN: 1867-8548
Subject Category
Meteorology And Climatology
Report/Patent Number
GSFC-E-DAA-TN31799
Funding Number(s)
CONTRACT_GRANT: LANL-LDRD, 20110081DR.
CONTRACT_GRANT: EU-H2020 No. 630080-MACC-III
CONTRACT_GRANT: NASA Roses ESDR-ERR 10/10-ESDR
CONTRACT_GRANT: NNG11HP16A
Distribution Limits
Public
Copyright
Other
Keywords
ACOS-GOSAT
TCCON
MACC

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