Higher-order Internal Modes Of Variability Imprinted In Year-to-year California Streamflow Changes

This article discusses a study that quantifies the impact of internal variability on seasonal peak flow in California using CMIP6 simulations and a quantitative framework. The study found that ML models trained with CMIP6 simulations and reanalysis/observations as a testing set performed better with higher-order EOF modes, such as PNA-5 and PDO-5. The study also highlights the potential for this workflow to be applied to other hydrological components and regions.