Mapping of CO 2 at high spatiotemporal resolution using satellite observations: Global distributions from OCO‐2

Satellite observations of CO 2 offer new opportunities to improve our understanding of the global carbon cycle. Using such observations to infer global maps of atmospheric CO 2 and their associated uncertainties can provide key information about the distribution and dynamic behavior of CO 2 , through comparison to atmospheric CO 2 distributions predicted from biospheric, oceanic, or fossil fuel flux emissions estimates coupled with atmospheric transport models. Ideally, these maps should be at temporal resolutions that are short enough to represent and capture the synoptic dynamics of atmospheric CO 2 . This study presents a geostatistical method that accomplishes this goal. The method can extract information about the spatial covariance structure of the CO 2 field from the available CO 2 retrievals, yields full coverage (Level 3) maps at high spatial resolutions, and provides estimates of the uncertainties associated with these maps. The method does not require information about CO 2 fluxes or atmospheric transport, such that the Level 3 maps are informed entirely by available retrievals. The approach is assessed by investigating its performance using synthetic OCO‐2 data generated from the PCTM/GEOS‐4/CASA‐GFED model, for time periods ranging from 1 to 16 days and a target spatial resolution of 1° latitude × 1.25° longitude. Results show that global CO 2 fields from OCO‐2 observations can be predicted well at surprisingly high temporal resolutions. Even one‐day Level 3 maps reproduce the large‐scale features of the atmospheric CO 2 distribution, and yield realistic uncertainty bounds. Temporal resolutions of two to four days result in the best performance for a wide range of investigated scenarios, providing maps at an order of magnitude higher temporal resolution relative to the monthly or seasonal Level 3 maps typically reported in the literature.