Consistent evaluation of ACOS-GOSAT, BESD-SCIAMACHY, CarbonTracker, and MACC through comparisons to TCCON
Consistent validation of satellite CO<sub>2</sub> estimates is a prerequisite for using multiple satellite CO<sub>2</sub> measurements for joint flux inversion, and for establishing an accurate long-term atmospheric CO<sub>2</sub> data record. Harmonizing satellit...
| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2016-02-01
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| Series: | Atmospheric Measurement Techniques |
| Online Access: | http://www.atmos-meas-tech.net/9/683/2016/amt-9-683-2016.pdf |
| Summary: | Consistent validation of satellite CO<sub>2</sub> estimates is a prerequisite for
using multiple satellite CO<sub>2</sub> measurements for joint flux inversion, and
for establishing an accurate long-term atmospheric CO<sub>2</sub> data record.
Harmonizing satellite CO<sub>2</sub> measurements is particularly important since
the differences in instruments, observing geometries, sampling strategies,
etc. imbue different measurement characteristics in the various satellite
CO<sub>2</sub> data products. We focus on validating model and satellite
observation attributes that impact flux estimates and CO<sub>2</sub> 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>CO<sub>2</sub></sub>) for Greenhouse gases Observing SATellite (GOSAT) (Atmospheric
CO<sub>2</sub> 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 CO<sub>2</sub> mole fraction fields and the Monitoring Atmospheric Composition and Climate (MACC) CO<sub>2</sub> 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 error<sup>2</sup> = <i>a</i><sup>2</sup> + <i>b</i><sup>2</sup>/<i>n</i>
(with <i>n</i> being the number of observations averaged, <i>a</i> the systematic (correlated)
errors, and <i>b</i> the random (uncorrelated) errors). <i>a</i> and <i>b</i> 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 TCCON-predicted 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. |
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| ISSN: | 1867-1381 1867-8548 |