## 3 Results
### Tropics/Subtropics Analysis
This parameterization scheme contains a series of new settings that result in a substantial increase in the evaporation of cloud ice crystals [35], leading to higher than expected specific humidity values throughout the upper troposphere (especially in the tropics). Figure 3: (**a**) C2 and ERA5 zonal specific humidity percentage differences, and (**b**) C2 and MERRA-2 zonal specific humidity percentage differences within 40\({}^{\circ}\)N-40\({}^{\circ}\)S from September–December 2019. Figure 5 looks closer at the strength of the relationship between pairs of C2 and reanalysis SH profiles by showing contours of the gridded Pearson correlation coefficients derived from these profile pairs. The correlation coefficients are calculated using the SH values at the selected altitudes from all collocated C2 and reanalysis profile pairs within 5\({}^{\circ}\)\(\times\) 5\({}^{\circ}\) grids. Correlations between C2 and each reanalysis are generally strong, with the best agreement in the subtropics and slightly weaker correlations in the tropics. Correlation coefficients between C2 and ERA5 pairs are often greater than 0.9 at 3 km (Figure 5a). However, slightly poorer correlations are observed throughout the deep tropics (10\({}^{\circ}\)N-10\({}^{\circ}\)S) along with some lower correlation hotspots in regions that see frequent deep convection, such as central Africa and northern South America (which will be shown in greater detail later in the study). C2 and MERRA-2 pairs at 3 km (Figure 5b) show slightly poorer correlations throughout the entire study region, and smaller coefficients (0.4-0.7) are more consistently observed throughout the entirety of the deep tropics. For C2 and ERA5 at 7 km (Figure 5c), agreement is even better compared to 3 km, with gridded coefficients in the deep tropics now above 0.8. However, correlations are again weaker for C2 and MERRA-2 (Figure 5d), with some gridded coefficients in the equatorial Pacific smaller by 0.3-0.5. Interestingly, correlations over many of the subtropical dry regions, such as northern Africa and Australia, are poorer at 7 km relative to 3 km, but the reason for this is unclear. Figure 6 looks more closely at the specific humidity profile pair sampling distributions within the deep tropics (10\({}^{\circ}\)N-10\({}^{\circ}\)S) due to the lower correlations observed there in the previous figure. Frequent deep convection is observed throughout the deep tropics. Since reanalysis assimilation systems often rely on moisture data sources that have larger uncertainties (or are unable to provide any moisture information) in the presence of deep convection, it is possible that this could be impacting SH variability between the profile  
Figure 5: (**a,c**) Gridded correlation coefficients for C2 and ERA5 profile pairs at 3 km and 7 km, and (**b,d**) gridded correlation coefficients for C2 and MERRA-2 profile pairs at 3 km and 7 km within 40\({}^{\circ}\)N-40\({}^{\circ}\)S from September–December 2019.  
pairs. Overall, the agreement between C2 and both ERA5 and MERRA-2 is very good, although large differences between profile pairs are also infrequently observed. At 3 km, a strong agreement is observed between C2 and ERA5 profile pairs (Figure 6a) as most pairs are clustered along the 1:1 line and the correlation coefficient is 0.908. The correlation is slightly weaker for C2 and MERRA-2 (Figure 6b) and a larger spread is observed within the sampling distribution. For example, extreme cases show SH pair differences of up to 9 g/kg, whereas extremes are not quite as large for ERA5, and these extremes will be explored in further detail in subsequent sections. Similar patterns are observed at 7 km. Agreement between C2 and ERA5 (Figure 6c) is again better than C2 and MERRA-2 (Figure 6d), as the correlation coefficients are 0.925 and 0.859, respectively. While C2 and ERA5 agree very well when moisture amounts are low, C2 exhibits larger SH values than ERA5 when moisture amounts become higher.