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Abstract:
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. 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 dryair
mole fraction (XCO2 / 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 Compositionand 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 ppm 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 error2 D a2 Cb2=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 ppm, with biases larger than the TCCONpredicted
bias uncertainty of 0.4 ppm at many stations. We
find that GOSAT and CT2013b underpredict the seasonal cycle
amplitude in the Northern Hemisphere (NH) between 46
and 53 N, MACC overpredicts between 26 and 37 N, and
CT2013b underpredicts 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 JJA; there
is correlation between 0.2 and 0.8 in the NH, with models
showing 10–50% 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 N for GOSAT and CT2013b.
