How predictable is the anomaly pattern of the Indian summer rainfall?
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Century-long efforts have been devoted to seasonal forecast of Indian summer monsoon rainfall (ISMR). Most studies of seasonal forecast so far have focused on predicting the total amount of summer rainfall averaged over the entire India (i.e., all Indian rainfall index-AIRI). However, it is practically more useful to forecast anomalous seasonal rainfall distribution (anomaly pattern) across India. The unknown science question is to what extent the anomalous rainfall pattern is predictable. This study attempted to address this question. Assessment of the 46-year (1960–2005) hindcast made by the five state-of-the-art ENSEMBLE coupled dynamic models’ multi-model ensemble (MME) prediction reveals that the temporal correlation coefficient (TCC) skill for prediction of AIRI is 0.43, while the area averaged TCC skill for prediction of anomalous rainfall pattern is only 0.16. The present study aims to estimate the predictability of ISMR on regional scales by using Predictable Mode Analysis method and to develop a set of physics-based empirical (P–E) models for prediction of ISMR anomaly pattern. We show that the first three observed empirical orthogonal function (EOF) patterns of the ISMR have their distinct dynamical origins rooted in an eastern Pacific-type La Nina, a central Pacific-type La Nina, and a cooling center near dateline, respectively. These equatorial Pacific sea surface temperature anomalies, while located in different longitudes, can all set up a specific teleconnection pattern that affects Indian monsoon and results in different rainfall EOF patterns. Furthermore, the dynamical models’ skill for predicting ISMR distribution primarily comes primarily from these three modes. Therefore, these modes can be regarded as potentially predictable modes. If these modes are perfectly predicted, about 51 % of the total observed variability is potentially predictable. Based on understanding the lead–lag relationships between the lower boundary anomalies and the predictable modes, a set of P–E models is established to predict the principal component of each predictable mode, so that the ISMR anomaly pattern can be predicted by using the sum of the predictable modes. Three validation schemes are used to assess the performance of the P–E models’ hindcast and independent forecast. The validated TCC skills of the P–E model here are more than doubled that of dynamical models’ MME hindcast, suggesting a large room for improvement of the current dynamical prediction. The methodology proposed here can be applied to a wide range of climate prediction and predictability studies. The limitation and future improvement are also discussed.
KeywordsPredictability Predictable mode analysis (PMA) Indian summer monsoon rainfall Seasonal prediction Physics-based empirical prediction model
This work was jointly supported by NOAA/MAPP Project Award Number NA10OAR4310247, the US National Science Foundation awards #AGS-1005599, the National Research Foundation (NRF) of Korea through a Global Research Laboratory grant (MEST, #2011-0021927) and the APEC Climate Center. We also acknowledge support from the US-China Atmosphere–Ocean Research Center sponsored by Nanjing University of Information Science and Technology (NUIST). This is publication No.9451 of the School of Ocean and Earth Science and Technology, the publication No.1129 of the International Pacific Research Center and the publication No.55 of NUIST Earth System Modelling Center.
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