Inter-annual variability of summertime CO2 exchange in Northern Eurasia inferred from GOSAT XCO2

Abstract

Northern Eurasia is one of the largest terrestrial carbon reservoirs on the Earth’s surface. However, since the coverage of surface CO _2 observations is still limited, the response to the climate variability remains uncertain. We estimated monthly CO _2 fluxes for three sub-regions in Northern Eurasia (north of ∼60°N), Northeastern Europe, Western Siberia and Eastern Siberia, using CO _2 retrievals from the Japanese Greenhouse Gases Observing SATellite (GOSAT). The variations of estimated CO _2 fluxes were examined in terms of the regional climate variability, for the three consecutive growing seasons of 2009–2011. The CO _2 fluxes estimated using GOSAT data are highly correlated with the surface temperature anomalies in July and August ( r > 0.8) while no correlation is found in the CO _2 fluxes estimated only using surface observations. The estimated fluxes from GOSAT data exhibit high negative correlations with one-month lagged positive precipitation anomalies in late summer ( r > −0.7) through surface temperature and the Normalized Difference Vegetation Index (NDVI). The results indicate that GOSAT data reflects the changes in terrestrial biospheric processes responding to climate anomalies. In 2010, a large part of Eurasia experienced an extremely hot and dry summer, while cold and wet weather conditions were recorded in Western Siberia. The CO _2 fluxes estimated from GOSAT data showed a reduction of net CO _2 uptake in Northeastern Europe and Eastern Siberia, but the enhancement of net CO _2 uptake in Western Siberia. These opposite sub-regional flux anomalies can be explained by the different climate anomalies on a sub-regional scale in Northern Eurasia. Thus, this study demonstrates that space-based observations by GOSAT compensate for the lack of ground-based observational coverage so as to better capture the inter-annually varying atmosphere-terrestrial biosphere CO _2 exchange on a regional scale.

Keywords

• carbon cycle
• climate variability
• inverse modeling
• satellite-based observations