Dynamic responses of atmospheric carbon dioxide concentration to global temperature changes between 1850 and 2010

Changes in Earth's temperature have significant impacts on the global carbon cycle that vary at different time scales, yet to quantify such impacts with a simple scheme is traditionally deemed difficult. Here, we show that, by incorporating a temperature sensitivity parameter(1.64 ppm yr~(-1) ?C~(-1)) into a simple linear carbon-cycle model, we can accurately characterize the dynamic responses of atmospheric carbon dioxide(CO_2) concentration to anthropogenic carbon emissions and global temperature changes between 1850 and 2010(r~2 0.96 and the root-mean-square error 1 ppm for the period from 1960onward). Analytical analysis also indicates that the multiplication of the parameter with the response time of the atmospheric carbon reservoir(～12 year) approximates the long-term temperature sensitivity of global atmospheric CO_2concentration(～15 ppm?C~(-1)), generally consistent with previous estimates based on reconstructed CO_2 and climate records over the Little Ice Age. Our results suggest that recent increases in global surface temperatures, which accelerate the release of carbon from the surface reservoirs into the atmosphere, have partially offset surface carbon uptakes enhanced by the elevated atmospheric CO_2 concentration and slowed the net rate of atmospheric CO_2 sequestration by global land and oceans by ～30%since the 1960 s. The linear modeling framework outlined in this paper thus provides a useful tool to diagnose the observed atmospheric CO_2 dynamics and monitor their future changes.     

Identifying geospatial services across heterogeneous taxonomies

Geospatial services with different functions are assembled together to solve complex problems. Different taxonomies are developed to categorize these services into classes. As differences in granularity and semantics exist among these taxonomies, the identification of services across different taxonomies has become a challenge. In this paper, an approach to identify geospatial services across heterogeneous taxonomies is proposed. Using formal concept analysis, existing heterogeneous taxonomies are decomposed into semantic factors and their various combinations. With these semantic factors, a super taxonomy is established to integrate the original heterogeneous taxonomies. Finally, with the super taxonomy as a cross-referencing system, geospatial services with classes in original taxonomies are identifiable across taxonomies. Experiments in service registries and a social media-based spatial-temporal analysis project are presented to illustrate the effectiveness of this approach.