Carbon source/sink information provided by column CO 2 measurements from the Orbiting Carbon Observatory

By David Baker, H. Bösch, S. Doney, Denis M. O'Brien, David S. Schimel. 2008

142 citations recorded by the source corpus.

Abstract. We quantify how well column-integrated CO2 measurements from the Orbiting Carbon Observatory (OCO) should be able to constrain surface CO2 fluxes, given the presence of various error sources. We use variational data assimilation to optimize weekly fluxes at a 2°×5° resolution (lat/lon) using simulated data averaged across each model grid box overflight (typically every ~33 s). Grid-scale simulations of this sort have been carried out before for OCO using simplified assumptions for the measurement error. Here, we more accurately describe the OCO measurements in two ways. First, we use new estimates of the single-sounding retrieval uncertainty and averaging kernel, both computed as a function of surface type, solar zenith angle, aerosol optical depth, and pointing mode (nadir vs. glint). Second, we collapse the information content of all valid retrievals from each grid box crossing into an equivalent multi-sounding measurement uncertainty, factoring in both time/space error correlations and data rejection due to clouds and thick aerosols. Finally, we examine the impact of three types of systematic errors: measurement biases due to aerosols, transport errors, and mistuning errors caused by assuming incorrect statistics. When only random measurement errors are considered, both nadir- and glint-mode data give error reductions over the land of ~45% for the weekly fluxes, and ~65% for seasonal fluxes. Systematic errors reduce both the magnitude and spatial extent of these improvements by about a factor of two, however. Improvements nearly as large are achieved over the ocean using glint-mode data, but are degraded even more by the systematic errors. Our ability to identify and remove systematic errors in both the column retrievals and atmospheric assimilations will thus be critical for maximizing the usefulness of the OCO data.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/f4af57d42cf493b599093f73caff8bdee27a70ad

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Atmospheric Science

In Global and Planetary Change

Citations in the corpus

“Variable localization” in an ensemble Kalman filter: Application to the carbon cycle data assimilation (2011, methodology): “Baker et al. [2006, 2010] have applied a four‐dimensional variational (4D‐Var) method to their OSSEs while Peters et al. [2005, 2007] and Feng et al. [2009] have used EnKFs.”
Carbon monitoring system flux estimation and attribution: impact of ACOS-GOSAT XCO2 sampling on the inference of terrestrial biospheric sources and sinks (2014, methodology): “We also ignore any systematic biases in GOSAT observations, whose impact on flux estimation has been discussed before (e.g. Baker et al., 2010; Basu et al., 2013).”
The ACOS CO 2 retrieval algorithm – Part 1: Description and validation against synthetic observations (2011, background): “Accurate, global measurements of column CO2 concentration, coupled with atmospheric trans5 port models, should complement ground-based and aircraft measurements and allow for the “top-down” monitoring of regional-scale carbon sources and sinks on timescales of weeks to months (e.g. Rayner and O’Brie”
Improved retrievals of carbon dioxide from Orbiting Carbon Observatory-2 with the version 8 ACOS algorithm (2018, background): “Such measurements are critical for inferring sources and sinks of carbon, and how these sources and sinks change over time due to both anthropogenic and natural causes (e.g., Rayner and O’Brien, 2001; Chevallier et al., 2007; Baker et al., 2010).”