Terachet Rojrachsombat, Salvatore Pascale, Morgan E. O’Neill
Past comprehensive climate models exhibit large biases in their simulation of the North American Monsoon (NAM), a major contributor to annual rainfall in a vast area of the North American Southwest (Geil et al., 2013; Liang et al., 2008). As such, future projections of the NAM remain uncertain, limiting future adaptation planning needed to manage water resources. This study analyzes the model depictions of NAM precipitation in the core monsoon domain (25-29ºN 105-109ºW) and the sea surface temperature (SST) in adjacent oceans, using the recently released Coupled Model Intercomparison Project Phase 6 (CMIP6). Compared to satellite and reanalysis data, coupled CMIP6 models tend to overestimate both the NAM precipitation and the SST off the coast of Mexico. However, uncoupled CMIP6 models, which use observed SST instead of model-run SST, depict NAM precipitation more realistically. Therefore, SST biases reduce the accuracy of NAM precipitation depictions. However, in-depth investigations into the relationship between local SST biases and NAM precipitation yields no statistically significant results yet. Spatial resolution also influences model accuracy, but the effect is small compared to SST biases. When global warming is replicated using five different model experiments, in four of them, most climate models predict a decrease in NAM precipitation. The results are consistent with Cook & Seager, 2013, Pascale et al., 2017, and Pascale et al., 2019. This future decrease in precipitation also correlates with model precipitation biases. The more biased-dry the model, the greater the precipitation decrease due to global warming, which is consistent with Bukovsky et al., 2015. Thus, when studying the NAM, especially when dealing with global warming, one must use models that are low in precipitation bias, low in SST bias, and high resolution.