Observational evidence of 3‐D cloud effects in OCO‐2 CO₂ retrievals

Abstract

The standard deviations of the distributions of Orbiting Carbon Observatory (OCO‐2) measurements of CO2 (i.e., XCO2) increase in size in the presence of clouds. XCO2 and Moderate Resolution Imaging Spectroradiometer (MODIS) radiance and cloud fields, and OCO‐2 A‐band radiances, are analyzed in order to determine if this behavior is best described as a radiance‐level retrieval artifact or by 3‐D radiative transfer effects. Observations in clear‐sky and fair weather cumulus scenes are analyzed. Scatter diagrams of XCO2 versus MODIS (and OCO‐2) radiances are presented, and averages are calculated for each scene for several radiance bins. The averages vary little in clear skies but decrease markedly for cloudy scenes as radiances increase. These decreases are consistent with an interpretative framework provided by 3‐D SHDOM radiative transfer calculations. Two 3‐D metrics, ΔXCO2 and Have, are calculated and applied. ΔXCO2 is the difference in XCO2 for the smallest and largest radiance bins. Have is a measure of the heterogeneity of the cloud radiance field. Lines of XCO2 and MODIS radiance for four target mode scenes have different slopes for clear and cloudy scenes, contrary to the radiance‐level retrieval artifact interpretation. In contrast, the graph of ΔXCO2 and MODIS Have for the various scenes has a linear correlation coefficient of 0.92, consistent with the 3‐D interpretation. Since the OCO‐2 measurement requirement is 1 ppmv, the cloudy scene XCO2 standard deviations between 1.2 and 2.6 ppmv indicate that 3‐D cloud effects add an important component to the XCO2 error budget.

Improved retrievals of carbon dioxide from Orbiting Carbon Observatory-2 with the version 8 ACOS algorithm

“…For instance, the topography-related biases noted by Wunch et al (2017) still exist in B8, but have recently been tracked to a misspecification of the satellite-to-ground pointing vector, and will be corrected in a forthcoming version 9. Also, there are still cloud-related errors in the OCO-2 data, for instance as noted by Massie et al (2016). It is believed that these are often related to three-dimensional cloud effects, for instance as discussed in Merrelli et al (2015).…”

The 2015–2016 carbon cycle as seen from OCO-2 and the global in situ network

“…OCO-2 observations have the potential to significantly improve our understanding of the tropical carbon cycle, given their relatively frequent coverage in a region that is poorly observed by the existing in situ network. However, persistent cloudiness during the wet season and biomass burning aerosol in the dry season in the tropics can lead to both fewer observations and residual bias in those that occur in the vicinity of clouds and aerosols (Merrelli et al, 2015;Massie et al, 2017). Examining Fig.…”

“…The TM5 4DVAR system is a Bayesian inverse modeling framework that infers surface fluxes of a tracer given mea-sured tracer mole fractions in the atmosphere (Meirink et al, 2008). It uses the TM5 atmospheric chemistry transport model to connect atmospheric measurements with surface fluxes (Krol et al, 2005).…”

Improved Retrievals of Carbon Dioxide from the Orbiting Carbon Observatory-2 with the version 8 ACOS algorithm

“…For instance, the topography-related biases noted by Wunch et al (2017) still exist in B8, but have recently been tracked to a misspecification of the satellite-to-ground pointing vector, and will be corrected in a forthcoming version 9. Also, there are still cloud-related errors in the OCO-2 data, for instance as noted by Massie et al (2016).…”