Seasonal United Forecast System Performance with a Focus on El Niño

Seasonal forecasting primarily consists of predicting changes in large scale weather patterns months in advance, which is important for numerous sectors, including energy and insurance. A main driver of seasonal predictive skill is forecasting El Niño, which has significant implications on weather patterns across the globe. One of the strongest El Niño events on record occurred from late 2015 through early 2016, likely contributing to a severe drought in the Caribbean and the most active hurricane season on record in the central Pacific. This research explores how well a prototype seasonal application of the Unified Forecast System (UFS) does in predicting sea surface temperature (SST), cloud cover, and other related variables in the Nino3.4 region. Based on ten year-long model runs, initialized every May 1 from 2010 through 2019, the UFS captured the strong El Niño of 2015 but has significant challenges forecasting SST, cloud cover, precipitation, and other variables at a seasonal time scale. The UFS has a wet and cloudy bias globally, which becomes worse when focusing on the El Niño3.4 region. Finally, a quasi-real time seasonal UFS simulation was initialized on May 1, 2023 and run through April 2024; verification of the first 6 months of the forecast will be shown.