Interpretable Relations between Tropical Sea Surface Temperature and U.S. Precipitation in Winter Season Forecasts

We explore the large-scale relations between anomalies of global tropical sea surface temperature (SST) and U.S. precipitation to assess the sources of December-February (DJF) predictability and skill. Canonical Correlation Analysis (CCA) is applied to forecasts from NOAA's latest seasonal prediction system, the Seamless System for Prediction and EArth System Research (SPEAR). We find that DJF skill can largely be recreated using 2 SST principal components (PC) and 4 precipitation Principal Components (PCs). However, the leading CCA modes based on these PCs are a blend of two climate signals, El Nino-Southern Oscillation (ENSO) and linear trends, which makes them difficult to interpret. We separate the trend and ENSO signals using partial CCA whereby CCA is applied twice: once to data with linear trends removed and once to data with Nino-3.4 index linearly removed. After both signals are removed, CCA find no consequential relations. Therefore, ENSO and linear trends alone explain the predictable parts of the winter forecast of tropical SST and US precipitation anomalies. Despite SPEAR model predictions representing both linear and non-linear variability, skill is mostly explained by these simpler linear CCA patterns.