globalchange  > 全球变化的国际研究计划
DOI: 10.1002/joc.5855
Scopus ID: 2-s2.0-85054581983
Title:
Tropical rainfall predictions from multiple seasonal forecast systems
Author: Scaife A.A.; Ferranti L.; Alves O.; Athanasiadis P.; Baehr J.; Dequé M.; Dippe T.; Dunstone N.; Fereday D.; Gudgel R.G.; Greatbatch R.J.; Hermanson L.; Imada Y.; Jain S.; Kumar A.; MacLachlan C.; Merryfield W.; Müller W.A.; Ren H.-L.; Smith D.; Takaya Y.; Vecchi G.; Yang X.
Source Publication: International Journal of Climatology
ISSN: 8998418
Publishing Year: 2019
Volume: 39, Issue:2
pages begin: 974
pages end: 988
Language: 英语
Keyword: ensemble ; ENSO ; NAO ; PNA ; seasona prediction ; tropical rainfall
Scopus Keyword: Atmospheric pressure ; Climatology ; Forecasting ; Tropics ; ensemble ; ENSO ; Extratropical circulation ; Interannual variability ; Linear relationships ; Model representation ; Rainfall variability ; Tropical rainfall ; Rain ; accuracy assessment ; climate prediction ; El Nino-Southern Oscillation ; ensemble forecasting ; North Atlantic Oscillation ; rainfall ; seasonal variation ; teleconnection ; tropical region ; Atlantic Ocean ; Atlantic Ocean (Tropical) ; Indian Ocean ; Indian Ocean (Tropical) ; Pacific Ocean ; Pacific Ocean (East)
English Abstract: We quantify seasonal prediction skill of tropical winter rainfall in 14 climate forecast systems. High levels of seasonal prediction skill exist for year-to-year rainfall variability in all tropical ocean basins. The tropical East Pacific is the most skilful region, with very high correlation scores, and the tropical West Pacific is also highly skilful. Predictions of tropical Atlantic and Indian Ocean rainfall show lower but statistically significant scores. We compare prediction skill (measured against observed variability) with model predictability (using single forecasts as surrogate observations). Model predictability matches prediction skill in some regions but it is generally greater, especially over the Indian Ocean. We also find significant inter-basin connections in both observed and predicted rainfall. Teleconnections between basins due to El Niño–Southern Oscillation (ENSO) appear to be reproduced in multi-model predictions and are responsible for much of the prediction skill. They also explain the relative magnitude of inter-annual variability, the relative magnitude of predictable rainfall signals and the ranking of prediction skill across different basins. These seasonal tropical rainfall predictions exhibit a severe wet bias, often in excess of 20% of mean rainfall. However, we find little direct relationship between bias and prediction skill. Our results suggest that future prediction systems would be best improved through better model representation of inter-basin rainfall connections as these are strongly related to prediction skill, particularly in the Indian and West Pacific regions. Finally, we show that predictions of tropical rainfall alone can generate highly skilful forecasts of the main modes of extratropical circulation via linear relationships that might provide a useful tool to interpret real-time forecasts. © 2018 Crown copyright, Met Office Weather © 2018 Royal Meteorological Society This article is published with the permission of the Controller of HMSO and the Queen\u2019s Printer for Scotland
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被引频次[WOS]:14   [查看WOS记录]     [查看WOS中相关记录]
Document Type: 期刊论文
Identifier: http://119.78.100.158/handle/2HF3EXSE/116563
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Affiliation: Met Office Hadley Centre, Met Office, Exeter, United Kingdom; College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, United Kingdom; European Centre for Medium-Range Weather Forecast (ECMWF), Reading, United Kingdom; Bureau of Meteorology, Melbourne, Australia; Centro Euro-Mediterraneo sui Cambiamenti Climatici, Bologna, Italy; Center for Earth System Research and Sustainability, Institute of Oceanography, Universität Hamburg, Hamburg, Germany; Centre National de Recherches Météorologiques (UMR 3589), Toulouse, France; GEOMAR Helmholtz Centre for Ocean Research Kiel Düsternbrooker Weg 20, Kiel, Germany; Geophysical Fluid Dynamics Laboratory, National Oceanic and Atmospheric Administration, Princeton, NJ, United States; Climate Research Department, Meteorological Research Institute, Japan Meteorological Agency, Tsukuba, Japan; National Centre for Medium Range Weather Forecasting (NCMRWF), Ministry of Earth Sciences, Noida, India; NOAA/National Centers for Environmental Prediction, College Park, MD, United States; Canadian Centre for Climate Modelling and Analysis, Environment and Climate Change Canada, University of Victoria, Victoria, BC, Canada; Max Planck Institute for Meteorology, Hamburg, Germany; Laboratory for Climate Studies, National Climate Center, China Meteorological Administration, Beijing, China; Geosciences Department and Princeton Environmental Institute, Princeton University, Princeton, NJ, United States; University Corporation for Atmospheric Research, Boulder, CO, United States

Recommended Citation:
Scaife A.A.,Ferranti L.,Alves O.,et al. Tropical rainfall predictions from multiple seasonal forecast systems[J]. International Journal of Climatology,2019-01-01,39(2)
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