# Influence of Droughs on Mid-Tropospheric CO\({}_{2}\)
## 3 Results  
To identify wet and dry years, we calculate averaged TRMM precipitation over the southwestern USA (32\({}^{\circ}\)N-42\({}^{\circ}\)N, 235\({}^{\circ}\)E-249\({}^{\circ}\)E) from June through September (JJAS) in 2003-2013, as well as the mean value and the standard deviation of these data. When the precipitation is 0.5 times standard deviation above (below) the mean value (35.1 mm), it is classified as a wet (dry) year. Red dashed lines in Figure 1 represent mean precipitation \(\pm\)0.5 times standard deviation of TRMM precipitation. As shown by red dots in Figure 1, 2006, 2008, 2011, and 2012 are wet years. Dry years (2003, 2007, and 2010) are shown by green dots in Figure 1.  
To investigate the influence of droughts on AIRS mid-tropospheric CO\({}_{2}\), we first remove the linear trend [(42)] from the AIRS mid-tropospheric CO\({}_{2}\) data. We then calculate mean values of AIRS detrended CO\({}_{2}\) for dry years (JJAS of 2003, 2007, and 2010) and wet years (JJAS of 2006, 2008, 2011, and 2012) separately. Figure 2a,b demonstrate spatial patterns of the detrended AIRS mid-tropospheric CO\({}_{2}\) for dry years and wet years, respectively. The difference of the detrended AIRS mid-tropospheric CO\({}_{2}\) between dry years (JJAS of 2003, 2007, and 2010) and wet years (JJAS of 2006, 2008, 2011, and 2012) is shown in Figure 2c. There is more CO\({}_{2}\) over the southwestern USA during dry years than wet years. The CO\({}_{2}\) difference between dry and wet years is \(\sim\)1 ppm. There are \(\sim\)4000 AIRS CO\({}_{2}\) retrievals in each grid box for dry and wet months. Since the error for an individual CO\({}_{2}\) retrieval follows a Gaussian distribution and is random [(31)], the CO\({}_{2}\) error in each grid box is equal to the CO\({}_{2}\) standard error (\(\sim\)1-2 ppm) divided by the square root of number of data [(22)]. As a result, the error for the CO\({}_{2}\)  
Figure 1: TRMM precipitation (black solid line) averaged over southwestern USA (32\({}^{\circ}\)N–42\({}^{\circ}\)N, 235\({}^{\circ}\)E–249\({}^{\circ}\)E) from June to September in 2003–2013. Red dashed lines represent mean precipitation \(\pm\)0.5 times standard deviation of precipitation. Units for precipitation are mm/month. Red dots, green dots, and black dots indicate wet years, dry years, and normal years, respectively. is less than 0.1 ppm, which is significantly less than the 1 ppm difference between dry and wet years shown in Figure 2c. A bootstrap method is used to calculate the statistical significance of CO\({}_{2}\) differences between dry years and wet years [43; 44]. The bootstrap method can be used for any distribution function of the data [43]. We generate 3000 bootstrap samples for the data in each grid. For each bootstrap sample, we repeat random re-sampling of the data, which will be further utilized to estimate the confidence interval at each grid. The CO\({}_{2}\) differences statistically significant at the 5% significance level and 1% significance level are plotted as light green and dark green areas in Figure 2d. Results are considered to be statistically significant when the significance level is 5% or less, which corresponds to two standard deviations [42]. As shown in Figure 2d, the CO\({}_{2}\) difference is within the 5% significance level in all regions. To explore possible mechanisms for high CO\({}_{2}\) concentrations during dry years, we investigate the 500 hPa vertical pressure velocity and CO\({}_{2}\) surface emissions during dry and wet years. Vertical pressure velocity during dry years and wet years are shown in Figure 3a,b, respectively. Negative values of vertical pressure velocity represent rising air, while positive values of vertical pressure velocity represent sinking air. As shown in Figure 3a,b, there is rising air over most regions for both dry years and wet years. The difference of 500 hPa vertical pressure velocity between dry years and wet years is shown in Figure 3c.