Seasonal‐Interannual Predictions of Summer Precipitation Over the Tibetan Plateau in North American Multimodel Ensemble

Abstract

Variations of precipitation over the Tibetan Plateau (TP) have great hydrometeorological impacts on local and surrounding areas. Focusing on the TP precipitation in the boreal summer, this study investigates the seasonal‐interannual predictions of eight dynamical models from the North‐American Multi‐Model Ensemble (NMME) using different observational data sets for verification. Results show dynamical prediction skill of the TP precipitation is regionally dependent with significantly high skills in the southwestern TP up to about three‐season ahead. Prediction skill of precipitation anomalies over the southwestern TP is significantly reliant on the models' ability in reproducing the observed relationship between the anomalous southwest‐TP precipitation and Pacific Sea surface temperature (SST). Using the different gridded observational data sets as verifications can lead to variations in prediction skills, and the multi‐observation mean tends to obtain the highest skills over the southwestern TP. These results provide a deeper understanding of dynamical seasonal‐interannual predictability of summer precipitation in TP.

Response of surface energy fluxes and hydrothermal characteristics to night‐time precipitation changes in the southeastern Tibetan Plateau

“…Second, in the valley area, the temperature drops faster on the mountain slopes at night and slower in the valley. Cold air sinks with the mountain slopes, causing the warm and wet air in the valley bottom to rise (Li et al, 2009; Wang et al, 2023; Yu et al, 2011). This makes the area an excellent research site for studying the effects of night‐time precipitation changes on surface energy and water characteristics.…”

Improving Predictions of Tibetan Plateau Summer Precipitation Using a Sea Surface Temperature Analog-Based Correction Method

“…Nevertheless, the current operational prediction skill for the TP precipitation still falls far from the expected level. For example, Wang et al (2022) demonstrated that climate models still exhibit limited skill in seasonal to inter-annual prediction for the TPSP, especially across the central-eastern TP [33]. Thus, improvements are urgently needed.…”