The Coupled Model Predictability of the Western North Pacific Summer Monsoon with Different Leading Times

Transcription

1 ATMOSPHERIC AND OCEANIC SCIENCE LETTERS, 2012, VOL. 5, NO. 3, The Coupled Model Predictability of the Western North Pacific Summer Monsoon with Different Leading Times LU Ri-Yu 1, LI Chao-Fan 1, Se-Hwan YANG 1, and Buwen DONG 2 1 State Key Laboratory of Numerical Modelling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing , China 2 National Centre for Atmospheric Science-Climate, Department of Meteorology, University of Reading, Reading, United Kingdom Received 31 January 2012; revised 23 February 2012; accepted 27 February 2012; published 16 May 2012 Abstract Leading time length is an important issue for modeling seasonal forecasts. In this study, a comparison of the interannual predictability of the Western North Pacific (WNP) summer monsoon between different leading months was performed by using one-, four-, and sevenmonth lead retrospective forecasts (hindcasts) of four coupled models from Ensembles-Based Predictions of Climate Changes and Their Impacts (ENSEMBLES) for the period of It is found that the WNP summer anomalies, including lower-tropospheric circulation and precipitation anomalies, can be well predicted for all these leading months. The accuracy of the four-month lead prediction is only slightly weaker than that of the one-month lead prediction, although the skill decreases with the increase of leading months. Keywords: seasonal forecast, leading month, Western North Pacific, coupled models, ENSEMBLES Citation: Lu, R.-Y., C.-F. Li, S.-H. Yang, et al., 2012: The coupled model predictability of the western North Pacific Summer Monsoon with different leading times, Atmos. Oceanic Sci. Lett., 5, Introduction East Asia suffers frequently from severe floods and droughts in summer, and the seasonal prediction of East Asian summer climate remains a challenging task, although the predictability can be moderately improved by some statistical approaches (Zhu et al., 2008; Wang and Fan, 2009; Lang and Wang, 2010; Ke et al., 2011; Lang, 2011). The summer climate anomaly in the tropical and subtropical Western North Pacific (WNP), however, shows a relatively much higher predictability (e.g., Lee et al., 2011; Li et al., 2011). Because lower-tropospheric circulation anomalies over the WNP affect moisture transport into East Asia and thus have an important role in influencing precipitation in East Asia (e.g., Nitta, 1987; Huang and Sun, 1992; Lau et al., 2000; Lu, 2001b), the predictability of the WNP summer anomalies is of central importance to the seasonal prediction of East Asian summer rainfall. In addition to the accuracy, the prediction lead time is also an important issue in the practice of seasonal prediction. Recently, Lee et al. (2011) suggested that the WNP monsoon index (WNPMI) prediction with a February Corresponding author: LU Ri-Yu, lr@mail.iap.ac.cn initial condition is even better than that with a May or June initial condition. They obtained this result by analyzing retrospective forecast data for a 26-year period ( ) from two coupled models. In this study, we attempt to investigate the prediction skill of the WNP summer monsoon with different leading months, using more coupled models and a longer period of retrospective forecasts made by the Ensembles-Based Predictions of Climate Changes and Their Impacts (ENSEMBLES) project. ENSEMBLES is an EU-funded integrated project that intends to develop an ensemble prediction system. Four coupled models in ENSEMBLES were used to perform retrospective forecasts for the 46-year period of , with initial conditions from the preceding November, February, and May. These leading months are roughly consistent with those of operational prediction currently performed by the National Climate Center of China, which makes seasonal forecasts of summer rainfall in China in the preceding November, March, and June. 2 Hindcasts and observational datasets The models used in this study are the four fully coupled atmosphere-ocean-land prediction systems that come from a new seasonal-to-annual multi-model project named ENSEMBLES, including the European Centre for Medium-Range Weather Forecasts (ECMWF), the Leibniz Institute of Marine Sciences at Kiel University (IFM-GEOMAR), the Météo-France (MF), and the UK Met Office (UKMO) models. These four models were used to perform one-, four-, and seven-month lead retrospective forecasts for the 46-year period of For each year, the seasonal forecasts were initialized on the 1st of May, February, and the preceding November with nine members for each model. The June-July-August (JJA) predicted results are analyzed in this study. The multi-model ensemble (MME) results are calculated through a simple composite by applying equal weights to the four models. Further details of the ENSEMBLES multi-model project and the models are referred to Doblas-Reyes et al. (2009, 2010) and van der Linden and Mitchell (2009). The observed datasets used for model verification include monthly mean National Centers for Environmental Prediction/National Center for Atmospheric Research

2 220 ATMOSPHERIC AND OCEANIC SCIENCE LETTERS VOL. 5 (NCEP/NCAR) reanalysis data (Kalnay et al., 1996) and the National Oceanic and Atmospheric Administration (NOAA) Extended Reconstructed monthly mean SST V3 dataset (Smith and Reynolds, 2004) from 1960 to The observational monthly precipitation data are obtained from the Global Precipitation Climatology Project (GPCP) during (Adler et al., 2003). 3 Results Following Wang and Fan (1999), a WNPMI, defined as the difference of the 850-hPa zonal wind anomalies between (5 15 N, E) and (20 30 N, E), is used to quantify the interannual variation of the lowertropospheric circulation. This index is a good indicator for interannual variability in the WNP summer monsoon. Figure 1 shows the one-, four-, and seven- month lead retrospective forecasts of the WNPMI and the observations. Basically, the WNPMI is well predicted, even for the seven-month lead. The correlation coefficients between the MME predictions and observations are 0.69, 0.66, and 0.59 for the one-, four-, and seven-month leads, respectively (Table 1), all being significant at the 99.9% confidence level. For 1998, the year with the strongest negative WNPMI anomaly, all the MME predictions with the one-, four-, and seven-month leads are excellent. The prediction skill for the WNPMI decreases with the length of prediction lead, and the one-month lead prediction is best, while the seven-month is worst. This result is true not only for MME, but also for each model. For MME, the four-month lead prediction is only slightly worse than the one-month lead prediction, while the seven-month lead prediction is remarkably worse than the four-month lead prediction. This case is also true for the ECMWF model. For this model, the correlation coefficient between the observations and predictions decreases by 0.03 from the one-month lead to the four-month lead, Figure 1 Normalized time series of the JJA-mean WNPMI anomalies for prediction (lines) and NCEP/NCAR reanalysis data (bars). (a) one-month lead; (b) four-month lead; (c) seven-month lead.

3 NO. 3 LU ET AL.: PREDICTABILITY OF WESTERN NORTH PACIFIC MONSOON 221 Table 1 Correlation coefficients of the WNPMI between the observations and predictions. one-month lead four-month lead seven-month lead MME ECMWF IFM-GEOMAR MF UKMO but decreases by 0.16 from the four-month lead to the seven-month lead. For the other models, the correlation coefficient between the observations and predictions decreases equivalently with the increase of leading months. The lower-tropospheric circulation anomaly over the WNP is closely related to the precipitation anomaly in the Philippine Sea (Lu, 2001a, b; Lu and Dong, 2001), and thus, the WNPMI prediction skill can be depicted by the prediction skill of precipitation in this region. Figure 2 shows the temporal correlation coefficient (TCC) between the predicted and observed precipitation at each grid point. Here, for brevity, only the MME prediction results are shown. The results for the individual models are roughly similar to the MME results. For a one-month lead prediction, the high correlation coefficients are basically concentrated in the tropical Pacific and southwest part of the maritime continent (Fig. 2a). The region of high correlation coefficient shrinks in the equatorial area for the fourmonth lead prediction (Fig. 2b), and a negative TCC appears in the equatorial eastern Pacific for the seven-month lead prediction (Fig. 2c). The TCC essentially decreases with the increase of leading months. In the Indian Ocean, the TCC of the precipitation is relatively much lower for all the leading months, with only some scattered regions of significant TCC. In this region, however, the TCC does not exhibit an appreciable drop with the increase of the leading months. Figure 2 also indicates that the TCC is high over the Philippine Sea for the one-month lead prediction (Fig. 2a). Figure 2 Temporal correlation coefficients (TCC) for JJA-mean precipitation between observation and MME prediction. The contours represent the statistical significance of the correlation coefficients at the 95% and 99% confidence levels, respectively.

4 222 ATMOSPHERIC AND OCEANIC SCIENCE LETTERS VOL. 5 Although the TCC decreases over this region with the increase of leading months, the decreasing amplitudes are weak. In the central and eastern Pacific, at latitudes similar to the Philippine Sea (10 20 N), the TCC is significant for the one-month lead prediction but is remarkably weakened for the four-month lead prediction and further weakened for the seven-month lead prediction. The above-mentioned results can be more clearly illustrated by the TCC differences between the different leading months (Figs. 4a and 4b). With the increase of leading months, the TCC is not considerably weakened in the Philippine Sea, particularly from the one-month to the four-month lead predictions, while it is remarkably weakened in most areas of the tropical Pacific. It is interesting that over East Asia, the TCC difference from the one-month to four-month lead predictions shows signs that are opposite the TCC difference from the four-month to seven-month lead predictions. The TCC decreases in a belt-shaped region extending northeastward from southern China into southern Japan, and it increases in north- eastern Asia from the one-month to four-month lead prediction (Fig. 4a). From the four-month to seven-month lead prediction, in contrast, the TCC decreases in northeastern Asia and increases in the region from South China into southern Japan. It should be noted, however, that there is almost no significant TCC of precipitation in East Asia for these leading months (Fig. 2), suggesting that the seasonal forecast of East Asian climate remains a challenging task. Figure 3 shows the TCC between the predicted and observed SSTs. The TCC of the SST is high and significant except some regions in the western North Pacific for the one-month lead prediction (Fig. 3a). With an increase of leading months, the TCC becomes weaker in the Pacific but does not change appreciably in the Indian Ocean, particularly in the northern Indian Ocean (Figs. 3, 4c, and 4d). The high TCC in the Indian Ocean may be related to the delayed effects of wintertime ENSO events on the summer SSTs in this region (Xie et al., 2009). The good prediction of Indian Ocean SSTs may con- Figure 3 Same as Fig. 2, but for SST and during

5 NO. 3 LU ET AL.: PREDICTABILITY OF WESTERN NORTH PACIFIC MONSOON 223 Figure 4 Differences in TCC of precipitation (a) between one-month lead and four-month lead prediction and (b) between four-month lead and seven-month lead prediction; (c) and (d): same as (a) and (b), but for SST. tribute to the good prediction of the WNP climate anomaly. The Indian Ocean SSTs can affect the WNP climate anomaly via the equatorial Kelvin wave and the subtropical westerly jet (Chowdary et al., 2010). Furthermore, the prediction skill of the leading principal component drops significantly without the interactive role of the Indian Ocean (Chowdary et al., 2009). 4 Summary This study compares the seasonal forecasts of the WNP summer monsoon anomaly with different leading months using the one-, four-, and seven-month lead retrospective forecasts of four coupled models from ENSEMBLES for the period of It is found that the summer WNP climate anomaly can be well predicted for all these leading months. The correlation coefficients between the MME predicted and the observed WNPMI are 0.69, 0.66, and 0.59 for the one-, four-, and seven-month lead forecasts, respectively. The TCC of precipitation remains high and significant in the Philippine Sea with the increase of leading months. The good prediction of the WNP climate anomaly may be partially associated with the prediction of the Indian Ocean SSTs. The present results suggest that the summer climate anomaly over the WNP can be predicted well even for a relatively longer lead time, such as the preceding November. The present study also indicates that the prediction skill decreases with the increase of leading months, but the skill for the four-month lead prediction is only slightly weaker than that for the one-month lead prediction. This result is partially in agreement with Lee et al. (2011), who suggested that the WNPMI prediction shows the highest skill with a February initial condition. However, it should be noted that the models used to compare the impacts of prediction lead time are inadequate: there are four models in this study and two in Lee et al. (2011). Therefore, retrospective forecasts by more coupled models are needed to confirm this result. Acknowledgements. This study was supported by the Special Scientific Research Project for Public Interest (Grant No. GYHY ). Buwen DONG was supported by the U.K. National Centre for Atmospheric Science-Climate (NCAS-Climate) at the University of Reading. References Adler, R. F., G. J. Huffman, A. Chang, et al., 2003: The Version-2 Global Precipitation Climatology Project (GPCP) monthly precipitation analysis (1979 Present), J. Hydrometeor., 4, Chowdary, J. S., S.-P. Xie, J.-Y. Lee, et al., 2010: Predictability of summer Northwest Pacific climate in eleven coupled model hindcasts: Local and remote forcing, J. Geophys. Res., 115, D22121, doi: /2010jd Chowdary, J. S., S.-P. Xie, J.-J. Luo, et al., 2009: Predictability of Northwest Pacific climate during summer and the role of the tropical Indian Ocean, Climate Dyn., 36, Doblas-Reyes, F. J., A. Weisheimer, M. Déqué, et al., 2009: Addressing model uncertainty in seasonal and annual dynamical ensemble forecasts, Quart. J. Roy. Meteor. Soc., 135, Doblas-Reyes, F. J., A. Weisheimer, T. N. Palmer, et al., 2010: Forecast Quality Assessment of the ENSEMBLES Seasonal-to- Decadal Stream 2 Hindcasts, ECMWF Technical Memorandum No. 621, ECMWF, Reading, 45pp. Huang, R., and F. Sun, 1992: Impacts of the tropical western Pacific on the East Asian summer monsoon, J. Meteor. Soc. Japan, 70, Kalnay, E., M. Kanamitsu, R. Kistler, et al., 1996: The NCEP/ NCAR 40-year reanalysis project, Bull. Amer. Meteor. Soc., 77, Ke, Z.-J., P.-Q. Zhang, L.-J. Chen, et al., 2011: An experiment of a statistical downscaling forecast model for summer precipitation over China, Atmos. Oceanic Sci. Lett., 4, Lang, X.-M., 2011: An effective approach for improving the real-time prediction of summer rainfall over China, Atmos. Oceanic Sci. Lett., 4, Lang, X.-M., and H.-J. Wang, 2010: Improving extraseasonal summer rainfall prediction by merging information from GCMs and observations, Wea. Forecasting, 25, Lau, K., K. Kim, and S. Yang, 2000: Dynamical and boundary forcing characteristics of regional components of the Asian summer monsoon, J. Climate, 13,

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