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Global CO 2 distributions over land from the Greenhouse Gases Observing Satellite (GOSAT)
Author(s) -
Hammerling Dorit M.,
Michalak Anna M.,
O'Dell Christopher,
Kawa S. Randolph
Publication year - 2012
Publication title -
geophysical research letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.007
H-Index - 273
eISSN - 1944-8007
pISSN - 0094-8276
DOI - 10.1029/2012gl051203
Subject(s) - greenhouse gas , environmental science , satellite , climatology , latitude , meteorology , atmospheric sciences , geography , geology , geodesy , oceanography , aerospace engineering , engineering
January 2009 saw the successful launch of the first space‐based mission specifically designed for measuring greenhouse gases, the Japanese Greenhouse gases Observing SATellite (GOSAT). We present global land maps (Level 3 data) of column‐averaged CO 2 concentrations (X CO2 ) derived using observations from the GOSAT ACOS retrieval algorithm, for July through December 2009. The applied geostatistical mapping approach makes it possible to generate maps at high spatial and temporal resolutions that include uncertainty measures and that are derived directly from the Level 2 observations, without invoking an atmospheric transport model or estimates of CO 2 uptake and emissions. As such, they are particularly well suited for comparison studies. Results show that the Level 3 maps for July to December 2009 on a 1° × 1.25° grid, at six‐day resolution capture much of the synoptic scale and regional variability of X CO2 , in addition to its overall seasonality. The uncertainty estimates, which reflect local data coverage, X CO2 variability, and retrieval errors, indicate that the Southern latitudes are relatively well‐constrained, while the Sahara Desert and the high Northern latitudes are weakly‐constrained. A probabilistic comparison to the PCTM/GEOS‐5/CASA‐GFED model reveals that the most statistically significant discrepancies occur in South America in July and August, and central Asia in September to December. While still preliminary, these results illustrate the usefulness of a high spatiotemporal resolution, data‐driven Level 3 data product for direct interpretation and comparison of satellite observations of highly dynamic parameters such as atmospheric CO 2 .