Journal
CLIMATE DYNAMICS
Volume 41, Issue 3-4, Pages 573-587Publisher
SPRINGER
DOI: 10.1007/s00382-012-1588-5
Keywords
Asian winter monsoon; Seasonal climate prediction; DJF 2 m air temperature variability; Monsoon-ENSO relationship; Statistical model; Multi-model ensemble (MME)
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Funding
- GRL grant of the National Research Foundation (NRF)
- Korean Government [MEST 2011-0021927]
- APEC Climate Center
- IPRC
- JAMSTEC
- NOAA
- NASA
- Korea Meteorological Institute (KMI) [APCC12-01] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
- National Research Foundation of Korea [2011-0021927] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
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Efforts have been made to appreciate the extent to which we can predict the dominant modes of December-January-February (DJF) 2 m air temperature (TS) variability over the Asian winter monsoon region with dynamical models and a physically based statistical model. Dynamical prediction was made on the basis of multi-model ensemble (MME) of 13 coupled models with the November 1 initial condition for 21 boreal winters of 1981/1982-2001/2002. Statistical prediction was performed for 21 winters of 1981/1982-2001/2002 in a cross-validated way and for 11 winters of 1999/2000-2009/2010 in an independent verification. The first four observed modes of empirical orthogonal function analysis of DJF TS variability explain 69 % of the total variability and are statistically separated from other higher modes. We identify these as predictable modes, because they have clear physical meaning and the MME reproduces them with acceptable criteria. The MME skill basically originates from the models' ability to capture the predictable modes. The MME shows better skill for the first mode, represented by a basin-wide warming trend, and for second mode related to the Arctic Oscillation. However, the statistical model better captures the third and fourth modes, which are strongly related to El Nio and Southern Oscillation (ENSO) variability on interannual and interdecadal timescales, respectively. Independent statistical forecasting for the recent 11-year period further reveals that the first and fourth modes are highly predictable. The second and third modes are less predictable due to lower persistence of boundary forcing and reduced potential predictability during the recent years. In particular, the notable decadal change in the monsoon-ENSO relationship makes the statistical forecast difficult.
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