Investigating causes of regional variations in atmospheric carbon dioxide concentrations
Date
2008
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Abstract
Atmospheric CO2 concentrations are rapidly increasing due to anthropogenic activities; however, only about half of the emissions have accumulated in the atmosphere, and the fate of the remaining half remains uncertain. Since atmospheric CO2 concentrations contain information regarding carbon sources and sinks, it is important to understand CO2 variability. This study investigated causes of atmospheric CO2 variability, focusing on the relationship between CO2 concentrations and clouds, the impact of heterogeneous land cover and agricultural production, and the effect of redistributing fossil fuel emissions. Due to global coverage and sheer data volume, satellite CO2 concentrations will be used in inverse models to improve carbon source and sink estimates. Satellite concentrations will only retrieve CO2 measurements in clear conditions, and it is important to understand how CO2 concentrations vary with cloud cover in order to optimally utilize these data. This study evaluated differences between clear-sky and mean concentrations on local, regional, and global scales. Analyses of in situ data, regional model simulations, and global model output all revealed clear-sky differences that were regionally coherent on sub-continental scales and that varied both with time and location. In the mid-latitudes, clear-sky CO2 concentrations were systematically lower than on average, and these differences were not due to biology, but rather to frontal convergence of large-scale gradients that were covered by clouds. Instead of using satellite data to represent temporal averages, inverse models and data assimilation systems that use satellite data to calculate carbon sources and sinks must be sampled consistently with the observations, including precise modeling of winds, clouds, fronts, and frontal timing. Just as CO2 concentrations vary with cloud cover, variability in atmospheric CO2 concentrations is also caused by heterogeneity in land cover and surface fluxes. This study focused on the impacts of land-cover heterogeneity and the effects of agricultural production on regional variations of atmospheric CO2 concentrations. Including sub-grid scale land cover heterogeneity improved simulated atmospheric CO2 concentrations by ~ 1 ppm. Implementing a crop-phenology model that explicitly simulated corn and soybeans into a coupled ecosystem-atmosphere model dramatically improved CO2 fluxes and concentrations over the mid-continent, with reductions in CO2 concentration root mean square errors of nearly 50% (over 10 ppm at some locations). Both the model and observations showed concentrations as low as 340 ppm over central Iowa, and a regional gradient of over 30 ppm in ~ 200 km occurred due to a combination of fluxes and meteorology. Since corn and soybeans have such a significant impact on both carbon fluxes and atmospheric concentrations, it is essential to model these crops accurately. In addition to biological surface fluxes, surface emissions due to fossil fuel combustion also cause variability in regional atmospheric CO2 concentrations. Using high-resolution fossil fuel emissions caused differences of over 10 ppm near the surface; and including temporal variability in the emissions impacted regional CO2 concentrations on monthly timescales, causing seasonal differences of more than 20 ppm in some locations. Using coarse spatial distributions and unaccounting for temporal variability in fossil fuel emissions created biases in the atmospheric CO2 concentrations and thus may cause significant errors in source and sink estimates from atmospheric inversions.
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Subject
atmospheric carbon dioxide
carbon cycle
carbon dioxide
fossil fuels
biogeochemistry
atmospheric sciences