Tropical rainfall predictions from multiple seasonal forecast systems. (7th October 2018)

• Record Type: Journal Article
• Authors: Scaife, Adam A.; Ferranti, Laura; Alves, Oscar; Athanasiadis, Panos; Baehr, Johanna; Dequé, Michel; Dippe, Tina; Dunstone, Nick; Fereday, David; Gudgel, Richard G.; Greatbatch, Richard J.; Hermanson, Leon; Imada, Yukiko; Jain, Shipra; Kumar, Arun; MacLachlan, Craig; Merryfield, William; Müller, Wolfgang A.; Ren, Hong‐Li; Smith, Doug; Takaya, Yuhei; Vecchi, Gabriel; Yang, Xiaosong
• Is Part Of: International journal of climatology. Volume 39:Number 2(2019)
• Page Start: 974
• Page End: 988
• Publication Date: 2018-10-07
• Subjects: ensemble -- ENSO -- NAO -- PNA -- seasona prediction -- tropical rainfall; Climatology -- Periodicals; Climat -- Périodiques; Climatologie -- Périodiques; 551.605
• DOI: 10.1002/joc.5855
• Languages: English
• ISSNs: 0899-8418

Abstract

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. Abstract : Tropical rainfall and its year‐to‐year variability. Seasonal mean climatological rainfall (top left) and inter‐annual variability (top right) of global rainfall from GPCP observations. Tropical rainfall is analysed in the boxed regions for the Indian (45°–100°E, 5°S–10°N), West Pacific (110°–140°E, 5°S–25°N), East Pacific (200 –90 W, 5°S–10°N) and Atlantic (60°–0 W, 5°S–5°N) regions. Mean and standard deviation of ensemble member rainfall over the same years (1993–2012) from a single example prediction system (bottom panels, GloSea5). Note the logarithmic contour interval. Units are mm/day.