# Numerical Fit Modeling for Temperature Mitigation in
## 4 Discussion  
Figures 5 and 6 show dramatic differences in the expected temperatures depending on the region. The explanation is that the calcite plume caused a significant reduction in cloud cover in humid regions, but there are very few convective clouds in arid regions.  
[PERSON] [6] predicted these results by extrapolating adjustments to the humidity and cloud cover. It was found that when there were fewer condensation clouds at low altitudes, the interaction with clouds decreased. Incremental reduction in humidity increased the performance of the mitigation. Eventually, with low humidity, there was no interaction with clouds. This shows that plumes of calcite are much more effective in areas of lower humidity.  
Other methods of solar radiation management have a much lower local effect because they are intended to reduce average global temperatures. Stratospheric aerosol injection with sulfur could have an effect of 3.71 W/m\({}^{2}\)[37]. Small-scale experiments with MCB have achieved about 1 W/m\({}^{2}\)[38]. In comparison, the models show that mitigation with calcite at low altitudes can regularly reduce solar radiation by 100 W/m\({}^{2}\). However, it has only local effects, so it does not mitigate climate change globally; rather, it reduces the urban heat island effect in cities.  
Other strategies for the mitigation of urban heat islands can reduce temperatures. Planting a dense canopy of trees in Montreal could reduce air temperatures by 4 C [39]. This is significantly less than the projected improvements for arid cities in Figure 6, but it matches the results for humid cities as shown in Figure 3. A full canopy of trees mitigates air temperatures in the immediate vicinity, but it does not have a significant effect on the solar heat gain felt in buildings.  
A limitation of the modeling approach is that it is difficult to use for prediction of future weather. The numerical fit method uses historical data. Therefore, it is only a theoretical tool for evaluating scenarios. A bias would occur if it was applied to the future if it was assumed that the air mass properties would stay the same for the future.  
Further validation of this work can be carried out with experimental methods. Also, surrogate data sources can be used. It is well documented that water clouds reduce solar radiation and temperatures. However, a complication is that clouds are correlated to changing atmospheric conditions as air masses change. Aerosols have varying effects on air temperatures depending upon the source of the aerosol. High temperatures increase the likelihood of dust storms [40]. Therefore, any correlations between temperatures and naturally occurring dust can confuse cause and effect. It would be difficult to isolate the effect that natural dust has on temperatures.  
Experimental validation should be explored through application of the design of experiments methodology. One of the challenges will be real-time validation. In addition to standard meteorological data, during experimentation and implementation, it may be necessary for aircraft to collect aerial data about numerous factors such as solar radiation rates and dust plume elevations and concentrations. This may result in on-the-fly modification of procedures.