Potential predictability of summer mean precipitation over the globe is investigated using data obtained from seasonal prediction experiments for 21 yr from 1979 to 1999 using the Korea Meteorological Administration-Seoul National University (KMA-SNU) seasonal prediction system. This experiment is a part of the Climate Variability and Predictability Program (CLIVAR) Seasonal Model Intercomparison Project II (SMIP II). The observed SSTs are used for the external boundary condition of the model integration; thus, the present study assesses the upper limit of predictability of the seasonal prediction system. The analysis shows that the tropical precipitation is largely controlled by the given SST condition and is thus predictable, particularly in the ENSO region. But the extratropical precipitation is less predictable due to the large contribution of the internal atmospheric processes to the seasonal mean. The systematic error of the ensemble mean prediction is particularly large in the subtropical western Pacific, where the air-sea interaction is active and thus the two-tier approach of the present prediction experiment is not appropriate for correct predictions in the region. The statistical postprocessing method based on singular value decomposition corrects a large part of the systematic errors over the globe. In particular, about two-thirds of the total errors in the western Pacific are corrected by the postprocessing method. As a result, the potential predictability of the summer-mean precipitation is greatly enhanced over most of the globe by the statistical correction method; the 21-yr-averaged patterncorrelation value between the predictions and their observed counterparts is changed from 0.31 before the correction to 0.48 after the correction for the global domain and from 0.04 before the correction to 0.26 after the correction for the Asian monsoon and the western Pacific region.
A new northeast Asian summer monsoon index is introduced to investigate the characteristics of the northeast Asian summer rainfall variation, including Korea, Japan, and northeast China, and its possible connection to the tropical and midlatitude circulations. The summer precipitation over northeast Asia is separated into two components associated with tropical forcing and midlatitude dynamics using this monsoon index. The connection between the northeast Asian summer rainfall and ENSO is clearly identified by separating the Tropics-related component from the northeast Asian summer rainfall. That is, the Tropicsrelated precipitation over northeast Asia tends to be enhanced after the mature phase of El Niño. On the other hand, it is revealed that the extratropics-related component of summer precipitation is connected to the Eurasian wave pattern with no significant lag correlation.The intensity of the western North Pacific anticyclone modulated by ENSO is a key factor in the variation of the northeast Asian summer precipitation. It is found that the warm SST over the tropical eastern Pacific plays an important role in establishing the western North Pacific anticyclone during the preceding winter of strong northeast Asian summer monsoon years, whereas convective activities over the Bay of Bengal are contributed to the modulation of the anticyclonic circulation in the summer. The warming over the Indian Ocean in the summer of strong monsoon years induces the development of the anticyclone over the western North Pacific and the suppressed convection over the western Pacific tends to enhance the northeast Asian summer rainfall through the Pacific-Japan or East Asia-Pacific teleconnections.
A pattern projection downscaling method is applied to predict summer precipitation at 60 stations over Korea. The predictors are multiple variables from the output of six operational dynamical models. The hindcast datasets span a period of 21 yr from 1983 to 2003. A downscaled prediction was made for each model separately within a leave-one-out cross-validation framework. The pattern projection method uses a moving window, which scans globally, in order to seek the most optimal predictor for each station. The final forecast is the average of six model downscaled precipitation forecasts using the best predictors and will be referred to as ''DMME.'' It is found that DMME significantly improves the prediction skill by correcting the erroneous signs of the rainfall anomalies in coarse-resolution predictions of general circulation models. Although Korea's precipitation is strongly influenced by local mountainous terrain, DMME performs well at 59 stations with correlation skill significant at the 95% confidence level. The improvement of the prediction skill is attributed to three steps: coupled pattern selection, optimal predictor selection, and the multimodel downscaled precipitation ensemble. This study indicates that the large-scale circulation variables, which are predicted by the current operational dynamical models, if selected, can be used to make skillful predictions of the local precipitation by using appropriate statistical downscaling methods.
[1] This study assesses how well the East Asian monsoon index (EAMI), developed on the basis of zonal and meridional land -sea thermal contrasts over the AsiaPacific region, can represent the seasonal and interannual variations of the East Asian summer and winter monsoons (EASM and EAWM). It suggests that the EAMI can be used to estimate the timing of the onset and the relative intensity of the EASM, characterized by dominant meridional circulation and rainfall patterns over the Asia-Pacific region, as well as represent the EAWM, which is dominated by a nearly zonal dipole structure composed of Siberian high and Aleutian low prevailing in the middle and high latitudes. The EAMI is therefore of benefit in understanding the seasonal evolution of the East Asian monsoon circulation and interannual variation of the individual monsoons both in summer and in winter. Citation: Zhu, C., W.-S. Lee, H. Kang, and C.-K. Park (2005), A proper monsoon index for seasonal and interannual variations of the East Asian monsoon, Geophys. Res. Lett., 32, L02811,
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.