|2016|405|405|
|---|---|---|
|2017|406|407|
|2018|409|409|
|2019|411|412|
|2020|414|414|

# **4.2 Hemispheric Seasonal Cycles**

As previously noted, global CO2 concentrations fluctuate seasonally with CO2 absorption and respiration by vegetation over the annual cycle. Seasonal fluctuations are more pronounced in the Northern Hemispheric because it has more plant life. To measure the amplitude of CO2 cycles in the OCO-2 data, we regress measured CO2 on a time trend and compute monthly mean residuals for the Northern and Southern Hemispheres. Figure 1 displays mean residuals by month. The cycle is pronounced in the Northern Hemisphere, with the peak in April, the trough in August, and an annual amplitude of 7.5 ppm. The Southern Hemisphere cycle is much flatter, with the peak in July, the trough in March, and an annual amplitude of 1.5 ppm.

# **4.3 City Concentration Anomalies**

In our model, differences in cities’ economic, demographic and geographic conditions affect residual variations in measured CO2 once global trend growth and seasonal fluctuations are accounted for. We assess the scale of these concentration anomalies by computing residuals from a regression of CO2 on a time trend and hemispheric dummy variables for months. Figure 2 displays the distribution of mean anomalies for the cities in our sample, with a standard deviation of 1.0 ppm, 94.8% of cities in the range [-2,2] ppm and 98.5% in the range [-3,3] ppm. For comparison, human activity currently generates about 40 gigatons of CO2 emissions each year, increasing the atmospheric CO2 concentration by about 2 ppm. The city concentration anomalies in Figure 2 have the same order of magnitude, thus highlighting the global significance of inter-city variation.

Methods for direct conversion of city residuals to CO2 emissions are still in the research phase. Several recent studies (e.g. Ye et al. 2020; Wu et al. 2020) compare OCO-2-based city concentration anomalies (∆CO2OCO2) with anomalies (∆CO2E) estimated by combining atmospheric transport models with city-level data from global emissions inventories (principally ODIAC (Oda, Maksyutov and Andres 2018)). For the scaling factor [R=∆CO2OCO2/∆CO2E], Ye et al. (2020) find values of 1.6-1.9 for Riyadh, 2.4-2.9 for Cairo, and 2.9-3.2 for Los Angeles. In all three cases, city concentration anomalies calculated from standard emissions inventories significantly underestimate the anomalies in OCO-2 observations. These discrepancies may incorporate errors in sector-level activity data or emissions parameters employed by emissions inventories, as well as exclusion of some sectors from the inventories. For each city studied, a mid-range R-factor could be used to adjust its inventory-based emissions estimate.

Over time, accurate estimation of R-factors for more cities may permit larger-scale adjustment of urban CO2 emissions inventory estimates. The research reported in this paper contributes by quantifying the incremental contributions of multiple sectors to city-level OCO-2 concentration anomalies. Follow-on research could construct sector-level R-factors for adjusting emissions inventories at the sector level. Longer-term, R-factor research may succeed in dropping its current dependence on emissions inventories and produce methods for direct estimation of CO2 emissions 10