Space‐based near‐infrared CO₂ measurements: Testing the Orbiting Carbon Observatory retrieval algorithm and validation concept using SCIAMACHY observations over Park Falls, Wisconsin

Abstract

[1] Space-based measurements of reflected sunlight in the near-infrared (NIR) region promise to yield accurate and precise observations of the global distribution of atmospheric CO 2 . The Orbiting Carbon Observatory (OCO) is a future NASA mission, which will use this technique to measure the column-averaged dry air mole fraction of CO 2 (X CO 2 ) with the precision and accuracy needed to quantify CO 2 sources and sinks on regional scales ($1000 Â 1000 km 2 ) and to characterize their variability on seasonal timescales. Here, we have used the OCO retrieval algorithm to retrieve X CO 2 and surface pressure from space-based Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) measurements and from coincident ground-based Fourier transform spectrometer (FTS) measurements of the O 2 A band at 0.76 mm and the 1.58 mm CO 2 band for Park Falls, Wisconsin. Even after accounting for a systematic error in our representation of the O 2 absorption cross sections, we still obtained a positive bias between SCIAMACHY and FTS X CO 2 retrievals of $3.5%. Additionally, the retrieved surface pressures from SCIAMACHY systematically underestimate measurements of a calibrated pressure sensor at the FTS site. These findings lead us to speculate about inadequacies in the forward model of our retrieval algorithm. By assuming a 1% intensity offset in the O 2 A band region for the SCIAMACHY X CO 2 retrieval, we significantly improved the spectral fit and achieved better consistency between SCIAMACHY and FTS X CO 2 retrievals. We compared the seasonal cycle of X CO 2 at Park Falls from SCIAMACHY and FTS retrievals with calculations of the Model of Atmospheric Transport and Chemistry/Carnegie-AmesStanford Approach (MATCH/CASA) and found a good qualitative agreement but with MATCH/CASA underestimating the measured seasonal amplitude. Furthermore, since SCIAMACHY observations are similar in viewing geometry and spectral range to those of OCO, this study represents an important test of the OCO retrieval algorithm and validation concept using NIR spectra measured from space. Finally, we argue that significant improvements in precision and accuracy could be obtained from a dedicated CO 2 instrument such as OCO, which has much higher spectral and spatial resolutions than SCIAMACHY. These measurements would then provide critical data for improving our understanding of the carbon cycle and carbon sources and sinks.

A method for colocating satellite <i>X</i><sub>CO<sub>2</sub></sub> data to ground-based data and its application to ACOS-GOSAT and TCCON

“…The ACOS-GOSAT data processing algorithm is based on the optimal estimation approach of Rodgers (2000) and is described in detail in O'Dell et al (2012). It is modified from the OCO retrieval algorithm (Bosch et al, 2006;Connor et al, 2008;Boesch et al, 2011) to account for the different physical viewing geometries and properties such as instrument line shapes and noise models.…”

“…We make a simplifying assumption that the annual and seasonal trends are constant over the hemispheres. We aggregate daily CarbonTracker X CO 2 values over both the Northern and Southern Hemisphere for the entire 2-year period, and we compute the trend coefficients {c 0 (s), c 1 (s), a(s), θ (s)} using variable transformation and linear regression (Artis et al, 2007). The resulting coefficients are displayed in Table 1.…”

The Total Carbon Column Observing Network

“…They have shown that SCIAMACHY can accurately capture the seasonal cycle in X CO 2 due to vegetation growth on monthly time scales. Bösch et al [33] use X CO 2 Park Falls data to reveal biases in the SCIAMACHY data. De Beek et al [34], Buchwitz et al [35] and Reuter et al [36,37] have significantly improved the SCIAMACHY retrieval algorithms, again using TCCON data as their ground reference.…”

Aerosol information content analysis of multi-angle high spectral resolution measurements and its benefit for high accuracy greenhouse gas retrievals

“…The greenhouse gas retrievals described in Bösch et al (2006) and O'Dell et al (2012) use this approach to derive column amounts of greenhouse gases alongside aerosol properties. In this particular case, x comprises elements related to aerosols (such as aerosol profiles for various types or microphysical properties as in Butz et al, 2009) Table 1.…”

“…As opposed to ground-based direct sun spectra such as from the TCCON network (Wunch et al, 2011), the exact light-path distribution of recorded photons is not known from the nadir space-borne viewpoint, causing ambiguities between light-path and trace-gas amount changes. To mitigate this general retrieval problem, full-physics algorithms (e.g., Bösch et al, 2006;Boesche et al, 2009;Butz et al, 2009;O'Dell et al, 2012) simultaneously fit multiple channels using radiative transfer calculations, concurrently retrieving X CO 2 , X CH 4 , surface albedos as well as a set of aerosol properties, the prime contributor to photon pathlength changes. Also for SCIAMACHY (Bovensmann et al, 1999), multi-band retrievals have been developed fitting the O 2 A-band and a weak CO 2 band at moderate spectral resolution (Reuter et al, 2010).…”