Contributions of Initial Conditions and Meteorological Forecast to Subseasonal-to-Seasonal Hydrological Forecast Skill in Western Tropical South America.

• Published in: Journal of Hydrometeorology
• Main Authors: Recalde-Coronel, G Cristina, Zaitchik, Benjamin, Pan, William K, Zhou, Yifan, Badr, Hamada
• Format: Article in Journal/Newspaper
• Language: English
• Published: 2024
• Subjects: Climate models; Forecast verification/skill; Land surface model; Seasonal forecasting; South America; Subseasonal variability; Antarc*; Antarctic
• Online Access: https://doi.org/10.1175/jhm-d-23-0064.1; https://pubmed.ncbi.nlm.nih.gov/38994349; https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11238824/

Hydrological predictions at subseasonal-to-seasonal (S2S) time scales can support improved decision-making in climate-dependent sectors like agriculture and hydropower. Here, we present an S2S hydrological forecasting system (S2S-HFS) for western tropical South America (WTSA). The system uses the global NASA Goddard Earth Observing System S2S meteorological forecast system (GEOS-S2S) in combination with the generalized analog regression downscaling algorithm and the NASA Land Information System (LIS). In this implementation study, we evaluate system performance for 3-month hydrological forecasts for the austral autumn season (March-May) using ensemble hindcasts for 2002-17. Results indicate that the S2S-HFS generally offers skill in predictions of monthly precipitation up to 1-month lead, evapotranspiration up to 2 months lead, and soil moisture content up to 3 months lead. Ecoregions with better hindcast performance are located either in the coastal lowlands or in the Amazon lowland forest. We perform dedicated analysis to understand how two important teleconnections affecting the region are represented in the S2S-HFS: El Niño-Southern Oscillation (ENSO) and the Antarctic Oscillation (AAO). We find that forecast skill for all variables at 1-month lead is enhanced during the positive phase of ENSO and the negative phase of AAO. Overall, this study indicates that there is meaningful skill in the S2S-HFS for many ecoregions in WTSA, particularly for long memory variables such as soil moisture. The skill of the precipitation forecast, however, decays rapidly after forecast initialization, a phenomenon that is consistent with S2S meteorological forecasts over much of the world.