## 4 Results
### Comparing Satellite Observations and Model Simulations  
Here we compare the number of extreme high XCO\({}_{2}\) grids identified from GM-XCO\({}_{2}\) and CarbonTracker 2016 with the monthly Southern Ocillation Index (SOI), related to El Nino and La Nina conditions (Figure 6). The largest extreme event in a high XCO\({}_{2}\) grid number of satellite and model simulations are distributed over summer (around August to September) of 2010. The anomalous grid number of GM-XCO\({}_{2}\) increases from 300 around July to 1100 around September, and dropped to lower than 300 in October. A relatively narrow extreme XCO\({}_{2}\) can be found over late spring and early summer of 2013 both in GM-XCO\({}_{2}\) and CarbonTracker. Another extreme XCO\({}_{2}\) period was found in January-May 2016, which is up to 1300 grid number, from GM-XCO\({}_{2}\) satellite observations. Note that CarbonTracker fluxes were available through 2015 and are not comparable to the 2016 events. Moreover, high values of dXCO\({}_{2}\) Z scores, likely to be extreme, were found over NH in mid-2010, SH and low latitude of early 2016 both in original ACOS-GOSAT XCO\({}_{2}\) and GM-XCO\({}_{2}\), shown in Figure S4. Extreme XCO\({}_{2}\) events in 2009-2010 in the northern hemisphere occurred during a weak El Nino transition to a strong La Nina. The 2013 event was during weak La Nina conditions, and the 2015-2016 events in the southern hemisphere followed strong El Nino conditions. This analysis shows the global pattern of influence of these characteristic climate patterns on extreme CO\({}_{2}\) fluxes. Figure 6: Southern Oscillation Index is shown in the top panel. Time series statistics of extreme XCO\({}_{2}\) grid number of retrievals from GOSAT (black) and CarbonTracker (green) are shown in the bottom. Sensitivity Test of XCO\({}_{2}\) Response to Local Biosphere Flux Change with Goddard Earth Observing System (GEOS)-Chem  
We designed a sensitivity test for XCO\({}_{2}\) response to local CO\({}_{2}\) flux changes over six regions shown in Figure S3. The extreme detection method was applied for evaluating how much local CO\({}_{2}\) flux change is required to make the increased XCO\({}_{2}\) detectable. Increased XCO\({}_{2}\) are predicted using the GEOS-Chem atmospheric transport model by assuming different carbon flux inputs, described in Section 3.3. CO\({}_{2}\) fluxes with emission increases of 25%, 37.5%, 50%, 62.5% and 75% were added to the model input to create different XCO\({}_{2}\) simulations. The increased XCO\({}_{2}\) (\(\Delta\)XCO\({}_{2}\)) response to different local land biosphere CO\({}_{2}\) flux changes are shown in Figure 7. Increased XCO\({}_{2}\) is linear to the local CO\({}_{2}\) change with atmospheric transport over different regions, but the slope varies by regions. The slopes ranged from 0.32 ppm/ (10\({}^{-8}\) kg/m\({}^{2}\)/s) in North America to 1.05 ppm/ (10\({}^{-8}\) kg/m\({}^{2}\)/s) in South Africa. The shallower slopes from South America, North America, Eurasia and South Asia flux anomalies, compared to South Africa and Australia shows that CO\({}_{2}\) anomalies in the atmosphere are spread out faster in the northern mid-latitudes. Therefore, larger flux anomalies are required to detect extreme events in XCO\({}_{2}\) in mid-latitudes. The strong linear relationship between XCO\({}_{2}\) increase response to local CO\({}_{2}\) flux change, shows that XCO\({}_{2}\) anomalies can be used for local-surface CO\({}_{2}\) flux change detection. We show the spatial extent of extreme XCO\({}_{2}\) detected results for different emission enhancements added in Figure 8, with the sensitivity test method shown in Section 3.3. Detected grids number increase with local CO\({}_{2}\) flux increase. The period of detected anomalous XCO\({}_{2}\) could last a long time, even after the increased CO\({}_{2}\) release stopped. The flux enhancement stopped in September, but the anomaly was still detectable through December.