Detection of Spatiotemporal Extreme Changes in Atmospheric CO2 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.

Spatio-Temporal Mapping of Multi-Satellite Observed Column Atmospheric CO2 Using Precision-Weighted Kriging Method

“…Because GM-XCO 2 is from instantaneous satellite observations, it could capture detailed and abnormal XCO 2 change which could be related to local carbon uptake and emission [10,55]. It could also be an important dataset for biosphere-atmosphere interactions by relating its changes to local biosphere parameter variations [7,11]. In addition, we provide XCO 2 precision and interpolation uncertainty for each XCO 2 .…”

“…XCO 2 observations from different satellites/sensors, observing conditions and inversion methods have different data precision. So, the varying data precision should be considered in building the loss function for optimizing spatio-temporal correlation structure with precision weighting factor as shown in Equation (11).…”

“…The seasonal variations of terrestrial carbon uptake and emission contribute most to the seasonal cycle in atmospheric CO 2 [6], which varies spatially due to non-uniform land-biosphere CO 2 exchange [7]. In addition, there is spatial-temporal variability of atmospheric CO 2 concentrations that can be used to study changes in regional land biosphere net CO 2 fluxes, for example, seasonal cycle amplitude increase [8,9] and regional effects of extreme weather patterns like droughts [10,11]. Atmospheric CO 2 concentrations have also been used for carbon flux or to estimate carbon uptake/emission changes using atmospheric inversion models [12,13] and a data-driven method [14].…”

“…With the development of remote sensing technology, there are several satellites used for atmospheric CO 2 concentration observations [20,21]. Leveraging all available XCO 2 datasets to construct a long time series, continuous, and comparable global CO 2 concentrations would be useful.Satellite-observed column-averaged dry air mole fraction of CO 2 (XCO 2 ) have been wildly used for carbon cycle studies, including CO 2 enhancement detection induced by anthropogenic emissions [22,23], constraining model simulations of carbon fluxes [24,25], investigating carbon cycle responses to weather extremes [11,26], and improving understanding of vegetation uptake [27]. Satellites measuring XCO 2 are the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) onboard the Environmental Satellite (ENVISAT) [28], Greenhouse Gases Observing Satellite (GOSAT) [29], and Orbiting Carbon Observatory-2 (OCO-2) [30].…”

Evidence of Carbon Uptake Associated with Vegetation Greening Trends in Eastern China

“…In step 3, we applied a gap-filling interpolation method to the integrated XCO 2 to produce continuous global XCO 2 maps in time and space. The spatio-temporal geostatistical mapping method, GM-XCO 2 , applied in GOSAT observations has been previously used in global carbon research studies [28,31,63,64]. Considering the spatial connectivity used in variogram modelling, we divided global land area (within 45 • S-60 • N) into five continents.…”