Temperature sensitivity of soil respiration (Q10) is an important parameter in modeling the effects of global warming on ecosystem carbon release. Experimental studies of soil respiration have ubiquitously indicated that Q10 has high spatial heterogeneity. However, most biogeochemical models still use a constant Q10 in projecting future climate change and no spatial pattern of Q10 values at large scales has been derived. In this study, we conducted an inverse modeling analysis to retrieve the spatial pattern of Q10 in China at 8 km spatial resolution by assimilating data of soil organic carbon into a proc-ess-based terrestrial carbon model (CASA model). The results indicate that the optimized Q10 values are spatially heterogeneous and consistent to the values derived from soil respiration observations. The mean Q10 values of different soil types range from 1.09 to 2.38, with the highest value in volcanic soil, and the lowest value in cold brown calcic soil. The spatial pattern of Q10 is related to environmental factors, especially precipitation and top soil organic carbon content. This study demonstrates that inverse modeling is a useful tool in deriving the spatial pattern of Q10 at large scales, with which being incorporated into biogeochemical models, uncertainty in the projection of future carbon dynamics could be potentially reduced.
ZHOU Tao1,2, SHI PeiJun1,2, HUI DaFeng3 & LUO YiQi4 1 State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China
The global rate of fossil fuel combustion continues to rise, but the amount of CO2 accumulating in the atmosphere has not increased accordingly. The causes for this discrepancy are widely debated. Par- ticularly, the location and drivers for the interannual variability of atmospheric CO2 are highly uncertain. Here we examine links between global atmospheric CO2 growth rate (CGR) and the climate anomalies of biomes based on (1986―1995) global climate data of ten years and accompanying satellite data sets. Our results show that four biomes, the tropical rainforest, tropical savanna, C4 grassland and boreal forest, and their responses to climate anomalies, are the major climate-sensitive CO2 sinks/sources that control the CGR. The nature and magnitude by which these biomes respond to climate anomalies are generally not the same. However, one common influence did emerge from our analysis; the ex- tremely high CGR observed for the one extreme El Nio year was caused by the response of the tropical biomes (rainforest, savanna and C4 grassland) to temperature.
