## 6 Conclusions  
Here we present a novel method for detecting large regions of extreme of carbon source and sink changes in time and space using satellite retrievals of extreme high atmospheric total column CO\({}_{2}\) concentrations. Mean dXCO\({}_{2}\) within detected units are around 1.4-1.8 ppm over NH and 1.2-1.4 ppm over SH, and it is different over various land-cover types. Detected XCO\({}_{2}\) anomalies can be generally attributed with surface parameters, related to heatwave, drought, fire, and reductions in vegetation photosynthesis. However, different combinations of main drivers cause different events. Periods with many extreme grid detections are identified using the spatial pattern of dXCO\({}_{2}\)'s Z score. We find that western Russia and tropical land areas are the main region of carbon release for 2009-2010 and 2015-2016, respectively. We can use this method to examine the spatial patterns of CO\({}_{2}\) flux changes due to EI Nino or La Nina events. The detection method could be sensitive for 25% or more local flux emission enhancements over tropical forest areas, and 37.5% for boreal forest area, as determined by the GEO-Chem sensitivity test. We show the detected temporal and spacial distribution can be influenced by the magnitude of CO\({}_{2}\) emission and atmospheric transmission. Furthermore, the current detection method can be extended to identify periods of extreme low XCO\({}_{2}\). More extreme XCO\({}_{2}\) could be detected and better evaluated with long time series and more precise observations and data aggregation becoming available from various greenhouse gas satellites, such as current the Orbiting Carbon Observatory-2 (OCO-2) and TanSat, forthcoming GOSAT-2 and the Geostationary Carbon Cycle Observatory (GeoCarb), and previous observations from the Scanning Imaging Absorption Spectrometer for Atmospheric Chatrography (SCIAMACHY). In addition, it could be applied to carbon monoxide (CO) or other data for extreme fire or anthropogenic emission detection. The following are available online at [[http://www.mdpi.com/2072-4292/10/6/839/s1](http://www.mdpi.com/2072-4292/10/6/839/s1)]([http://www.mdpi.com/2072-4292/10/6/839/s1](http://www.mdpi.com/2072-4292/10/6/839/s1)), Figure S1: The relationship between predicted XCO\({}_{2}\) and observed XCO\({}_{2}\) values in cross-validation of global land mapping of XCO\({}_{2}\). The color grids represent the density of data distribution. The dotted line is derived from linear regression of predicted values of XCO\({}_{2}\) (Y) and the observed values of XCO\({}_{2}\) (X), which shows a significant linear relationship with R\({}^{2}\) equals 0.94 (\(p\)-value < 0.01) and good consistency of observed XCO\({}_{2}\) and predicted XCO\({}_{2}\) with MAPE equal to 0.85. The solid line shows the one-to-one line. Figure S2: Temporal variation comparison for the 13 TCCON site. As shown in these panels, the original ACOS-GOSAT XCO\({}_{2}\) retrievals within 500 km of the TCCON site are in gray dots. The TCCON data, smoothed by applying the ACOS-GOSAT averaging kernel, are indicated by blue dots. The data are chosen using coincidence criteria of within \(\pm\)2 h of GOSAT overpass time, and a 3-day (one time-unit) mean is calculated for the comparison. The predicted TCCON site XCO\({}_{2}\) time series using the mapping approach are indicated by the red dots. Figure S3: Local biosphere CO\({}_{2}\) flux for GEOS-Chem model simulation. They are mean bisospheric CO\({}_{2}\) flux and corresponding XCO\({}_{2}\) output shown in (a) and (b). Different enhancements of local biospheric CO\({}_{2}\) flux as emission input for simulating different carbon sources/sinks changes are shown in (c). Different XCO\({}_{2}\) output for different CO\({}_{2}\) flux change areshown in (d). Figure S4: Latitudinal-temporal Z score of XCO\({}_{2}\) fitting residuals from original ACOS-XCO\({}_{2}\) (a) and GM-XCO\({}_{2}\) (b). Red represents high possibility of extreme highly increased XCO\({}_{2}\).