Abstract
The Yangtze-Huaihe River basin (YHRB) is the core region of sultry heat wave occurrence over China during peak summer [July and August (JA)]. The extremely hot and muggy weather is locally controlled by a descending high pressure anomaly connected to the western Pacific subtropical high. During 1961-2015, the heat wave days (HWDs) in JA over the YHRB exhibit large year-to-year and decadal variations. Prediction of the total number of HWDs in JA is of great societal and scientific importance. The summer HWDs are preceded by a zonal dipole SST tendency pattern in the tropical Pacific and a meridional tripole SST anomaly pattern over the North Atlantic. The former signifies a rapid transition from a decaying central Pacific El Niño in early spring to a developing eastern Pacific La Niña in summer, which enhances the western Pacific subtropical high and increases pressure over the YHRB by altering the Walker circulation. The North Atlantic tripole SST anomalies persist from the preceding winter to JA and excite a circumglobal teleconnection pattern placing a high pressure anomaly over the YHRB. To predict the JA HWDs, a 1-month lead prediction model is established with the above two predictors. The forward-rolling hindcast achieves a significant correlation skill of 0.66 for 1981-2015, and the independent forecast skill made for 1996-2015 reaches 0.73. These results indicate the source of predictability of summer HWDs and provide an estimate for the potential predictability, suggesting about 55% of the total variance may be potentially predictable. This study also reveals greater possibilities for dynamical models to improve their prediction skills.
| Original language | English |
|---|---|
| Pages (from-to) | 2185-2196 |
| Number of pages | 12 |
| Journal | Journal of Climate |
| Volume | 31 |
| Issue number | 6 |
| DOIs | |
| State | Published - Mar 1 2018 |
| Externally published | Yes |
Funding
Miaoni Gao and Jing Yang were supported by funds from the National Key Research and Development Program-Global Change and Mitigation Project: Global Change Risk of Population and Economic System: Mechanism and Assessment (Grant 2016YFA0602401) and the National Natural Science Foundation ofChina (Grants 41775071 and 41621061). Bin Wang acknowledges the support from the National Natural Science Foundation of China (Grant 41420104002) and the National Research Foundation (NRF) of Korea through a Global Research Laboratory (GRL) grant of the Korean Ministry of Education, Science and Technology (MEST; 2011-0021927). This study was supported by the National Key Research and Development Program project 2016YFA0602503. This is the NUIST-Earth System ModelingCenter Publication 198, the School of Ocean and Earth Science and Technology Publication 10282, and the International Pacific Research Center Publication 1298 Acknowledgments. Miaoni Gao and Jing Yang were supported by funds from the National Key Research and Development Program-Global Change and Mitigation Project: Global Change Risk of Population and Economic System: Mechanism and Assessment (Grant 2016YFA0602401) and the National Natural Science Foundation of China (Grants 41775071 and 41621061). Bin Wang acknowledges the support from the National Natural Science Foundation of China (Grant 41420104002) and the National Research Foundation (NRF) of Korea through a Global Research Laboratory (GRL) grant of the Korean Ministry of Education, Science and Technology (MEST; 2011-0021927). This study was supported by the National Key Research and Development Program project 2016YFA0602503. This is the NUIST–Earth System Modeling Center Publication 198, the School of Ocean and Earth Science and Technology Publication 10282, and the International Pacific Research Center Publication 1298.
Keywords
- Atmosphere-ocean interaction
- Extreme events
- Interannual variability
- Seasonal forecasting
- Statistical forecasting