Open AccessPossible representation errors in inversions of satellite CO 2 retrievals
Author(s) -
Corbin Katherine D.,
Denning A. Scott,
Lu Lixin,
Wang JihWang,
Baker Ian T.
Publication year - 2008
Publication title -
journal of geophysical research: atmospheres
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.67
H-Index - 298
eISSN - 2156-2202
pISSN - 0148-0227
DOI - 10.1029/2007jd008716
Subject(s) - satellite , environmental science , column (typography) , sampling (signal processing) , grid , sky , temporal resolution , remote sensing , atmosphere (unit) , meteorology , atmospheric sciences , geology , computer science , geodesy , geography , physics , telecommunications , filter (signal processing) , frame (networking) , astronomy , quantum mechanics , computer vision
Owing to global spatial sampling and sheer data volume, satellite CO 2 concentrations can be used in inverse models to enhance our understanding of the carbon cycle. Using column measurements to represent a transport model grid column may introduce spatial, local clear‐sky, and temporal sampling errors into inversions: the footprint is smaller than a grid cell, total column concentrations are only retrieved in clear skies, and the mixing ratios are only sampled at one time. To investigate these errors, we used a coupled ecosystem‐atmosphere cloud‐resolving model to create CO 2 fields over fine (∼1° × 1°) and coarse (∼4° × 4°) grid columns from 1 km 2 and 25 km 2 pixels that utilized explicit microphysics. We performed two simulations in August 2001: one in central North America and one in the Brazilian Amazon. Differences between satellite and grid column concentrations were calculated by subtracting the domain mean column concentration from 10‐km‐wide simulated satellite measurements. Spatial and local clear‐sky errors were less than 0.5 ppm for the fine grid column; however, these errors became large and biased over the coarse grid column in North America. To avoid these errors, transport models should be run at high resolution. Using satellite measurements to represent bimonthly averages created large (>1 ppm) errors for all cases. The errors were negatively biased (approximately −0.4 ppm) in the North American simulation, indicating that inverse models cannot use satellite measurements to represent temporal averages. Simulated representation errors did not arise because of differences in ecosystem metabolism in cloudy versus sunny conditions; rather, they reflected large‐scale CO 2 gradients in midlatitudes that were organized along frontal boundaries and masked under regional cloud cover. Such boundaries were not found in the dry‐season tropical simulation presented here and may be less prevalent in the tropics in general. To avoid incurring errors, inversions must accurately model synoptic‐scale atmospheric transport and CO 2 concentrations must be assimilated at the time and place observed.