Detection of Spatiotemporal Extreme Changes in Atmospheric CO_2 Concentration Based on Satellite Observations

Abstract

Atmospheric CO_2 concentrations are sensitive to the effects of climate extremes on carbon sources and sinks of the land biosphere. Therefore, extreme changes of atmospheric CO_2 can be used to identify anomalous sources and sinks of carbon. In this study, we develop a spatiotemporal extreme change detection method for atmospheric CO_2 concentrations using column-averaged CO_2 dry air mole fraction (XCO_2) retrieved from the Greenhouse gases Observing SATellite (GOSAT) from 1 June 2009 to 31 May 2016. For extreme events identified, we attributed the main drivers using surface environmental parameters, including surface skin temperature, self-calibrating Palmer drought severity index, burned area, and gross primary production (GPP). We also tested the sensitivity of XCO_2 response to changing surface CO_2 fluxes using model simulations and Goddard Earth Observing System (GEOS)-Chem atmospheric transport. Several extreme high XCO_2 events are detected around mid-2010 over Eurasia and in early 2016 in the tropics. The magnitudes of extreme XCO_2 increases are around 1.5–1.8 ppm in the Northern Hemisphere and 1.2–1.4 ppm in Southern Hemisphere. The spatiotemporal pattern of detected high XCO_2 events are similar to patterns of local surface environmental parameter extremes. The extreme high XCO_2 events often occurred during periods of increased temperature, severe drought, increased wildfire or reduced GPP. Our sensitivity tests show that the magnitude of detectable anomalies varies with location, for example 25% or larger anomalies in local CO_2 emission fluxes are detectable in tropical forest, whereas anomalies must be half again as large in mid-latitudes (~37.5%). In conclusion, we present a method for extreme high XCO_2 detection, and large changes in land CO_2 fluxes. This provides another tool to monitor large-scale changes in the land carbon sink and potential feedbacks on the climate system.

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