Cross-equatorial extension of the Pacific-South American wave train enabled by Southeastern South American rainfall - Siyu Zhao; Rong Fu; Sen Zhao; Fei-Fei Jin; Hui Wang (Climate Dynamics, 2025)
The Pacific–South American (PSA) pattern is a key mode of climate variability in the mid-to-high latitudes of the Southern Hemisphere, impacting circulation and rainfall anomalies over South America. However, the effect of South American rainfall on the PSA has not been previously explored. This study focuses on the impact of rainfall over southeastern South American (SESA) during the austral summer (December–February). Observational analyses reveal that the PSA pattern remains confined to higher southern latitudes when SESA rainfall anomalies are weak. In contrast, strong SESA rainfall anomalies can generate a quasi-stationary Rossby wave train, which represents a cross-equatorial extension of the PSA. This wave train propagates along a southwest–northeast great circle path from higher latitudes, crosses the equator, and reaches the tropical Atlantic, southern Europe, and northern Africa, inducing a wet and cool weather condition over western and southern Europe. The observed wave train can be reproduced by the linear baroclinic model (LBM) simulations. Given the PSA’s connection to tropical forcing over the central Pacific, we examine differences in the wave response to central Pacific forcing alone versus combined central Pacific and SESA forcings. By incorporating SESA forcing, the wave train originally triggered by central Pacific forcing is amplified and extended. Our findings confirm the significant role of SESA rainfall anomalies in extending the PSA pattern to the Northern Hemisphere and highlight the South American continent as a land bridge that links circulation anomalies across the Pacific and Atlantic Oceans and the Southern and Northern Hemispheres.

Extended seasonal prediction of spring precipitation over the Upper Colorado River Basin - Siyu Zhao; Rong Fu; Michael L. Anderson; Sudip Chakraborty; Jonathan H. Jiang; Hui Su; Yu Gu (Climate Dynamics, 2023)
This study provides extended seasonal predictions for the Upper Colorado River Basin (UCRB) precipitation in boreal spring using an artificial neural network (ANN) model and a stepwise linear regression model, respectively. Sea surface temperature (SST) predictors are developed taking advantage of the correlation between the precipitation and SST over three ocean basins. The extratropical North Pacific has a higher correlation with the UCRB spring precipitation than the tropical Pacific and North Atlantic. For the ANN model, the Pearson correlation coefficient between the observed and predicted precipitation exceeds 0.45 ( p -value < 0.01) for a lead time of 12 months. The mean absolute percentage error (MAPE) is below 20% and the Heidke skill score (HSS) is above 50%. Such long-lead prediction skill is probably due to the UCRB soil moisture bridging the SST and precipitation. The stepwise linear regression model shows similar prediction skills to those of ANN. Both models show prediction skills superior to those of an autoregression model (correlation < 0.10) that represents the baseline prediction skill and those of three of the North American Multi-Model Ensemble (NMME) forecast models. The three NMME models exhibit different skills in predicting the precipitation, with the best skills of the correlation ~ 0.40, MAPE < 25%, and HSS > 40% for lead times less than 8 months. This study highlights the advantage of oceanic climate signals in extended seasonal predictions for the UCRB spring precipitation and supports the improvement of the UCRB streamflow prediction and related water resource decisions.