Multi-Model Ensemble Sub-Seasonal Forecasting of Precipitation over the Maritime Continent in Boreal Summer
The Maritime Continent (MC) is a critical region with unique geographical conditions and significant monsoon activities that plays a vital role in global climate variation. In this study, the weekly prediction of precipitation over the MC during boreal summer (from May to September) was analyzed usi...
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-05-01
|
| Series: | Atmosphere |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-4433/11/5/515 |
| _version_ | 1797567812662722560 |
|---|---|
| author | Yan Wang Hong-Li Ren Fang Zhou Joshua-Xiouhua Fu Quan-Liang Chen Jie Wu Wei-Hua Jie Pei-Qun Zhang |
| author_facet | Yan Wang Hong-Li Ren Fang Zhou Joshua-Xiouhua Fu Quan-Liang Chen Jie Wu Wei-Hua Jie Pei-Qun Zhang |
| author_sort | Yan Wang |
| collection | DOAJ |
| description | The Maritime Continent (MC) is a critical region with unique geographical conditions and significant monsoon activities that plays a vital role in global climate variation. In this study, the weekly prediction of precipitation over the MC during boreal summer (from May to September) was analyzed using the 12-year reforecasts data from five Sub-seasonal to Seasonal (S2S) models, including the China Meteorological Administration (CMA), the European Centre for Medium-Range Weather Forecasts (ECMWF), Environment and Climate Change Canada (ECCC), the National Centers for Environmental Prediction (NCEP), and the Met Office (UKMO). The result shows that, compared with the individual models, our newly derived median multi-model ensemble (MME) can significantly improve the prediction skill of sub-seasonal precipitation in the MC. Both the Temporal Correlation Coefficient (TCC) skill and the Pattern Correlation Coefficient (PCC) skill reached 0.6 in lead week 1, dropped the following week, did not exceed 0.2 in lead week 3, and then lost their significance. The results show higher prediction skill near the Equator than in the north at 10° N. It is difficult to make effective predictions with the models beyond three weeks. The prediction ability of the median MME improves significantly as the total number of model members increases. The prediction performance of the median MME depends not only on the diversity of models but also on the number of model members. Moreover, the prediction skill is particularly sensitive to the intensity and phase of Boreal Summer Intraseasonal Oscillation 1 (BSISO1) with the highest skills appearing at initial phases 1 and 5. |
| first_indexed | 2024-03-10T19:47:47Z |
| format | Article |
| id | doaj.art-98a8b813b2c347ca9b0bd53bdbce842b |
| institution | Directory Open Access Journal |
| issn | 2073-4433 |
| language | English |
| last_indexed | 2024-03-10T19:47:47Z |
| publishDate | 2020-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Atmosphere |
| spelling | doaj.art-98a8b813b2c347ca9b0bd53bdbce842b2023-11-20T00:41:23ZengMDPI AGAtmosphere2073-44332020-05-0111551510.3390/atmos11050515Multi-Model Ensemble Sub-Seasonal Forecasting of Precipitation over the Maritime Continent in Boreal SummerYan Wang0Hong-Li Ren1Fang Zhou2Joshua-Xiouhua Fu3Quan-Liang Chen4Jie Wu5Wei-Hua Jie6Pei-Qun Zhang7Plateau Atmosphere and Environment Key Laboratory of Sichuan Province, College of Atmospheric Science, Chengdu University of Information Technology, Chengdu 610225, ChinaState Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, ChinaClimate Change Research Center, Institute of Atmospheric Physics, and Nansen–Zhu International Research Centre, Chinese Academy of Sciences, Beijing 100029, ChinaDepartment of Atmospheric and Oceanic Sciences & Institute of Atmospheric Sciences, Fudan University, Shanghai 200433, ChinaPlateau Atmosphere and Environment Key Laboratory of Sichuan Province, College of Atmospheric Science, Chengdu University of Information Technology, Chengdu 610225, ChinaLaboratory for Climate Studies & CMA-NJU Joint Laboratory for Climate Prediction Studies, National Climate Center, China Meteorological Administration, Beijing 100081, ChinaLaboratory for Climate Studies & CMA-NJU Joint Laboratory for Climate Prediction Studies, National Climate Center, China Meteorological Administration, Beijing 100081, ChinaLaboratory for Climate Studies & CMA-NJU Joint Laboratory for Climate Prediction Studies, National Climate Center, China Meteorological Administration, Beijing 100081, ChinaThe Maritime Continent (MC) is a critical region with unique geographical conditions and significant monsoon activities that plays a vital role in global climate variation. In this study, the weekly prediction of precipitation over the MC during boreal summer (from May to September) was analyzed using the 12-year reforecasts data from five Sub-seasonal to Seasonal (S2S) models, including the China Meteorological Administration (CMA), the European Centre for Medium-Range Weather Forecasts (ECMWF), Environment and Climate Change Canada (ECCC), the National Centers for Environmental Prediction (NCEP), and the Met Office (UKMO). The result shows that, compared with the individual models, our newly derived median multi-model ensemble (MME) can significantly improve the prediction skill of sub-seasonal precipitation in the MC. Both the Temporal Correlation Coefficient (TCC) skill and the Pattern Correlation Coefficient (PCC) skill reached 0.6 in lead week 1, dropped the following week, did not exceed 0.2 in lead week 3, and then lost their significance. The results show higher prediction skill near the Equator than in the north at 10° N. It is difficult to make effective predictions with the models beyond three weeks. The prediction ability of the median MME improves significantly as the total number of model members increases. The prediction performance of the median MME depends not only on the diversity of models but also on the number of model members. Moreover, the prediction skill is particularly sensitive to the intensity and phase of Boreal Summer Intraseasonal Oscillation 1 (BSISO1) with the highest skills appearing at initial phases 1 and 5.https://www.mdpi.com/2073-4433/11/5/515Maritime Continentmulti-model ensemblesub-seasonal predictionprecipitation |
| spellingShingle | Yan Wang Hong-Li Ren Fang Zhou Joshua-Xiouhua Fu Quan-Liang Chen Jie Wu Wei-Hua Jie Pei-Qun Zhang Multi-Model Ensemble Sub-Seasonal Forecasting of Precipitation over the Maritime Continent in Boreal Summer Atmosphere Maritime Continent multi-model ensemble sub-seasonal prediction precipitation |
| title | Multi-Model Ensemble Sub-Seasonal Forecasting of Precipitation over the Maritime Continent in Boreal Summer |
| title_full | Multi-Model Ensemble Sub-Seasonal Forecasting of Precipitation over the Maritime Continent in Boreal Summer |
| title_fullStr | Multi-Model Ensemble Sub-Seasonal Forecasting of Precipitation over the Maritime Continent in Boreal Summer |
| title_full_unstemmed | Multi-Model Ensemble Sub-Seasonal Forecasting of Precipitation over the Maritime Continent in Boreal Summer |
| title_short | Multi-Model Ensemble Sub-Seasonal Forecasting of Precipitation over the Maritime Continent in Boreal Summer |
| title_sort | multi model ensemble sub seasonal forecasting of precipitation over the maritime continent in boreal summer |
| topic | Maritime Continent multi-model ensemble sub-seasonal prediction precipitation |
| url | https://www.mdpi.com/2073-4433/11/5/515 |
| work_keys_str_mv | AT yanwang multimodelensemblesubseasonalforecastingofprecipitationoverthemaritimecontinentinborealsummer AT hongliren multimodelensemblesubseasonalforecastingofprecipitationoverthemaritimecontinentinborealsummer AT fangzhou multimodelensemblesubseasonalforecastingofprecipitationoverthemaritimecontinentinborealsummer AT joshuaxiouhuafu multimodelensemblesubseasonalforecastingofprecipitationoverthemaritimecontinentinborealsummer AT quanliangchen multimodelensemblesubseasonalforecastingofprecipitationoverthemaritimecontinentinborealsummer AT jiewu multimodelensemblesubseasonalforecastingofprecipitationoverthemaritimecontinentinborealsummer AT weihuajie multimodelensemblesubseasonalforecastingofprecipitationoverthemaritimecontinentinborealsummer AT peiqunzhang multimodelensemblesubseasonalforecastingofprecipitationoverthemaritimecontinentinborealsummer |