Sub‐Seasonal Predictability of North American Monsoon Precipitation. Issue 9 (28th April 2022) - Prein, Andreas F.; Towler, Erin; Ge, Ming; Llewellyn, Dagmar; Baker, Sarah; Tighi, Shana; Barrett, Lucas
Abstract: North American Monsoon (NAM) rainfall is a vital water resource in the United States Southwest, providing 60–80% of the region's annual precipitation. However, NAM rainfall is highly variable and water managers lack skillful guidance on summer rainfall that could help inform their management decisions and operations. Here we show that NAM season (June–October) precipitation can be forecasted by the European Centre for Medium‐Range Weather Forecast's model months ahead at catchment scales. This is possible by identifying the frequency of days with synoptic‐scale moisture advection into the NAM region, which greatly improves predictability over directly utilizing modeled precipitation. Other forecasting systems fail to provide useful guidance due to deficiencies in their data assimilation systems and biases in representing key synoptic features of the NAM including its teleconnections. Plain Language Summary: Managing freshwater resources in the United States (U.S.) Southwest is difficult due to the region's large year‐to‐year precipitation variability and increasing population density. Additionally, climate change is expected to reduce the region's water availability making the efficient management of water a necessity. However, the lack of skill in predicting regional precipitation on sub‐seasonal to seasonal timescales makes it extremely difficult to reach this goal. We use state‐of‐the‐art seasonal forecasts to investigate their skill in predicting North American Monsoon season rainfall. Consistent with previous studies, we find that the models fail in forecasting rainfall in the region. However, using the frequency of atmospheric moisture surges into the U.S. Southwest as a proxy for rainfall results in skillful predictions starting in April. This novel forecasting method will allow regional water managers to make more informed decisions and can mitigate the impacts of droughts and floods in the future. Key Points: Seasonal forecasting models do not have skill in simulating North American Monsoon (NAM) rainfall We use a data driven weather type (WT) algorithm to identify synoptic situations associated with NAM rainfall on catchment scales The frequency of monsoonal flow WTs days allows for the skillful forecast of NAM season rainfall starting in April