Future Land Precipitation Changes Over the North American Monsoon Region Using CMIP5 and CMIP6 Simulations

Abstract

The North American Monsoon (NAM) dominates the annual cycle of rainfall over the southwest United States and western Mexico, feeding into economic and environmental resources, including agricultural practices, water management, and fire season variability. The peak monsoon season is observed between July through September, with the largest rainfall occurrence over the elevated terrain of the Sierra Madre Occidental (SMO) and accounts for 70%-80% of the mean annual rainfall (Pascale et al., 2019). Variations in NAM timing and magnitude can have significant impacts on the region's hydroclimate and water resources, especially under anthropogenic climate change. However, uncertainty remains regarding the NAM's response to climate change, including its seasonal cycle, among previous generations of global climate models (GCMs) due, in part, to model parameterizations and coarse resolutions that can influence their ability to simulate complex terrain and dynamical features and produce striking biases within climate simulations such as the double Intertropical Convergence Zone (ITCZ)

Historical Evolution and Future Trends of Precipitation Based on Integrated Datasets and Model Simulations of Arid Central Asia

“…Our comprehensive analysis reveals that neither CMIP5 nor CMIP6 model simulations satisfactorily capture all aspects of the temporal and spatial characteristics of the CRU data across the entire ALL region and its two sub-regions. Moreover, most institutions employing CMIP6 showed CRI values that were lower or closer to the reference than those using CMIP5 for both the ALL region and its sub-regions in certain seasons, which is contrary to recent studies that have suggested overall improvements in the precipitation change simulations in CMIP6 models on a global scale [79,80]. Specifically, we observed that most model simulations tend to overestimate the magnitude of mean annual precipitation in the ALL region across all four seasons.…”

Prospective Evolution of Meteorological Drought in the Haihe River Basin and Its Connection With Large‐Scale Atmospheric Circulations Using CMIP6 Multimodel Ensemble

“…Additionally, the Taylor diagram was used to compare the capabilities of the CMIP6 models to reproduce monthly precipitation through the standard deviation (STD), the RMSE, and spatial correlation coefficient (SCC) during the validation years (Taylor, 2001). According to these metrics, a skill score was implemented to further evaluate the GCMs' accuracy and select optimal models for reproducing spatio‐temporal distribution of precipitation over the Haihe River Basin (L. Chen & Frauenfeld, 2014; Hernandez & Chen, 2022). The skill score was calculated by the following equation: $$$S=\frac{{(1+\text{SCC})}^{4}}{{4\left(\text{SDR}+\frac{1}{\text{SDR}}\right)}^{2}}$$$ where SDR is the ratio of STD between the model and observations.…”

“…Previous studies have pointed out that as the side length of the region approaches one hundred kilometers, the impact of this term on precipitation becomes negligible and can be disregarded (Akiyama, 1975; C.‐C. Chen et al., 2019; Hernandez & Chen, 2022; J. Jiang et al., 2020). The last term in Equation depicts the vertical integral moisture flux convergence (MFC).…”

“…The first term of Equation 2 in right side is the tendency term represents q's local change rate. Previous studies have pointed out that as the side length of the region approaches one hundred kilometers, the impact of this term on precipitation becomes negligible and can be disregarded (Akiyama, 1975;C.-C. Chen et al, 2019;Hernandez & Chen, 2022;J. Jiang et al, 2020).…”

“…This has resulted in a sustained reduction in annual precipitation, leading to more frequent droughts (Z. Jiang et al., 2017; R. Yang et al., 2020). Under the challenges of warming climate, future changes in the spatiotemporal heterogeneity of rainfall in monsoon regions will have significant changes, and the resulting evolution of future drought remains uncertain (Hernandez & Chen, 2022; Oh & Ha, 2015). Therefore, it is imperative to conduct a comprehensive examination of the spatiotemporal patterns and mechanisms of future drought to enhance the effectiveness of regional strategies for managing watershed water resources.…”