Abstract
Abstract. We present two new products from near-infrared Greenhouse Gases Observing Satellite (GOSAT) observations: lowermost tropospheric (LMT, from 0 to 2.5 km) and upper tropospheric–stratospheric (U, above 2.5 km) carbon dioxide partial column mixing ratios. We compare these new products to aircraft profiles and remote surface flask measurements and find that the seasonal and year-to-year variations in the new partial column mixing ratios significantly improve upon the Atmospheric CO2 Observations from Space (ACOS) and GOSAT (ACOS-GOSAT) initial guess and/or a priori, with distinct patterns in the LMT and U seasonal cycles that match validation data. For land monthly averages, we find errors of 1.9, 0.7, and 0.8 ppm for retrieved GOSAT LMT, U, and XCO2; for ocean monthly averages, we find errors of 0.7, 0.5, and 0.5 ppm for retrieved GOSAT LMT, U, and XCO2. In the southern hemispheric biomass burning season, the new partial columns show similar patterns to MODIS fire maps and MOPITT multispectral CO for both vertical levels, despite a flat ACOS-GOSAT prior, and a CO–CO2 emission factor comparable to published values. The difference of LMT and U, useful for evaluation of model transport error, has also been validated with a monthly average error of 0.8 (1.4) ppm for ocean (land). LMT is more locally influenced than U, meaning that local fluxes can now be better separated from CO2 transported from far away.
Highlights
Remote ocean sites have been selected because (a) the vertical air mass observed by Gases Observing Satellite (GOSAT) lowermost troposphere (LMT) will not match the vertical air mass observed by the surface site, the long correlation length scales of remote locations should make the comparisons useful, and (b) these sites are not used in development of the bias correction terms and so are an independent test of bias correction for observations over ocean
To compare LMT and U sensitivity to surface fluxes, we look at 10-day back-trajectory footprints created using the Weather Research and Forecasting (WRF) model combined with the Stochastic Time-Inverted Lagrangian Transport (STILT) model (WRF-STILT; Nehrkorn et al, 2010)
The emission ratio seen by the Measurement of Pollution in the Troposphere (MOPITT) and GOSAT LMT products are compared to those estimated from aircraft observations over tropical forests by Akagi et al (2011, Table 1), which is 8.8 %
Summary
An important goal of carbon cycle research is to improve top-down estimates of CO2 fluxes, which assimilate data into models to trace the observed variability in the long-lived tracer backwards to sources and sinks. Such topdown flux estimates have relied on surface observations (e.g., Peters et al, 2007; Chevallier et al, 2010), though it was postulated 15 years ago that satellite-based measurements of column CO2 could dramatically reduce top-down-based flux uncertainties (Rayner and O’Brien, 2001; O’Brien and Rayner, 2002).
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