Abstract
Column-averaged dry air mole fraction of atmospheric CO2 (XCO2), obtained by multiple satellite observations since 2003 such as ENVISAT/SCIAMACHY, GOSAT, and OCO-2 satellite, is valuable for understanding the spatio-temporal variations of atmospheric CO2 concentrations which are related to carbon uptake and emissions. In order to construct long-term spatio-temporal continuous XCO2 from multiple satellites with different temporal and spatial periods of observations, we developed a precision-weighted spatio-temporal kriging method for integrating and mapping multi-satellite observed XCO2. The approach integrated XCO2 from different sensors considering differences in vertical sensitivity, overpass time, the field of view, repeat cycle and measurement precision. We produced globally mapped XCO2 (GM-XCO2) with spatial/temporal resolution of 1 × 1 degree every eight days from 2003 to 2016 with corresponding data precision and interpolation uncertainty in each grid. The predicted GM-XCO2 precision improved in most grids compared with conventional spatio-temporal kriging results, especially during the satellites overlapping period (0.3–0.5 ppm). The method showed good reliability with R2 of 0.97 from cross-validation. GM-XCO2 showed good accuracy with a standard deviation of bias from total carbon column observing network (TCCON) measurements of 1.05 ppm. This method has potential applications for integrating and mapping XCO2 or other similar datasets observed from multiple satellite sensors. The resulting GM-XCO2 product may be also used in different carbon cycle research applications with different precision requirements.
Highlights
Spatio-temporal variation of atmospheric CO2 concentration reflects the balance between anthropogenic carbon emissions and terrestrial and oceanic carbon uptake or emissions [1]
To create the longest possible time series of XCO2 and leverage multiple measurements to improve precision when possible, we developed a precision-weighted spatio-temporal kriging method for gap filling of integrated XCO2 from multiple satellite observations
We developed a precision-weighted spatio-temporal kriging interpolation method of multiple satellite observed XCO2
Summary
Spatio-temporal variation of atmospheric CO2 concentration reflects the balance between anthropogenic carbon emissions and terrestrial and oceanic carbon uptake or emissions [1]. The seasonal variations of terrestrial carbon uptake and emission contribute most to the seasonal cycle in atmospheric CO2 [6], which varies spatially due to non-uniform land-biosphere CO2 exchange [7]. There is spatial-temporal variability of atmospheric CO2 concentrations that can be used to study changes in regional land biosphere net CO2 fluxes, for example, seasonal cycle amplitude increase [8,9] and regional effects of extreme weather patterns like droughts [10,11]. Model simulations can provide continuous maps of CO2 using estimated surface fluxes and atmospheric mixing transport in addition to the previously noted sparse validation stations [17].
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