Opportunities and Barriers for Skillful Subseasonal Prediction of East Asian Summer Precipitation
Year: 2024
Type: article
Abstract: Accurate subseasonal (2–8 weeks) prediction of monsoon precipitation is crucial for mitigating flood and heatwave disasters caused by intraseasonal variability (ISV). However, current state-of-the-art subseasonal-to-seasonal (S2S) models have limited prediction skills beyond 1 week when predicting weekly precipitation. Our findings suggest that predictability primarily arises from strong ISV events, and the prediction skills for ISV events depend on the propagation stability of preceding signals, regardless of models. This allows us to identify opportunities and barriers (OBs) within S2S models, clarifying what the models can and cannot achieve in ISV event prediction. Focusing on the complex East Asian summer monsoon (EASM), we discover that stable propagation of Eurasian and tropical atmospheric wave trains toward East Asia serves as an opportunity. This opportunity offers a 1-week leading prediction skill of up to 0.85 and skillful prediction up to 13 days ahead for 43% of all ISV events. However, the Tibetan Plateau barrier highlights the limitation of EASM predictability. Identifying these OBs will help us gain confidence in making more accurate subseasonal prediction. Significance Statement Accurate subseasonal prediction (2–8 weeks) is urgently needed to facilitate effective disaster prevention, application of renewable energy, logistical planning, agricultural production, and decision-making. Current scientific research and practical applications typically focus on predicting all ISV events, which renders the low-average prediction skill relatively less helpful. In response, we propose an innovative idea for performing subseasonal prediction: predicting only what the models can accurately predict. The central idea is to identify the types of ISV events in which the model exhibits high prediction skills (opportunities) and those where the model’s skills are limited (barriers). Compared to the average forecast skill of all events, accurate predictions of specific events can be more beneficial to us.
Institutions: Sun Yat-sen University, Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), China Meteorological Administration, University of Hawaiʻi at Mānoa, Institute of Tropical and Marine Meteorology, China Meteorological Administration, University of Gothenburg, Tsinghua University, Chinese Academy of Sciences, Institute of Atmospheric Physics, State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Ministry of Natural Resources, Second Institute of Oceanography, State Key Laboratory of Satellite Ocean Environment Dynamics, Chinese Academy of Meteorological Sciences, State Key Laboratory of Severe Weather, Nanjing University of Information Science and Technology
Language: English
Subfield: Global and Planetary Change
Field: Environmental Science
Domain: Physical Sciences
SDG: Climate action
Open Access status: hybrid
OpenAlex ID: W4403204718