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Pao-Shin Chu, Zhi-Ping Yu, and Stefan Hastenrath

To detect climate change in the Amazon Basin, as possibly induced by deforestation, time series of monthly mean outgoing longwave radiation (OLR), an index of tropical convection, and monthly rainfall totals at Belem and Manaus for the past 15 years are analyzed. A systematic bias in the original OLR series was removed prior to the analysis. Linear regression analysis and nonlinear Mann-Kendall rank statistic are employed to detect trends. Over almost all of the basin, the OLR trend values are negative, indicating an increase of convection with time. The largest negative and statistically significant values are found in the western equatorial portion of Amazonia, where rainfall is most abundant. Consistent with this, the rainfall series at Belém and Manaus also feature upward trends. Small positive and statistically insignificant, OLR trend values are confined to the southern fringe of the basin, where deforestation has been most drastic. Thus, there is little indication for a rainfall increase associated with deforestation, but rather a strong signal of enhanced convection in the portion of Amazonia contributing most strongly to the total precipitation over the basin.

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Jien-Yi Tu, Chia Chou, and Pao-Shin Chu

Abstract

Bayesian analysis is applied to detect changepoints in the time series of seasonal typhoon counts in the vicinity of Taiwan. An abrupt shift in the typhoon count series occurs in 2000. On average, 3.3 typhoons per year have been noted before 2000 (1970–99), with the rate increasing to 5.7 typhoons per year since 2000 (2000–06). This abrupt change is consistent with a northward shift of the typhoon track over the western North Pacific–East Asian region and an increase of typhoon frequency over the Taiwan–East China Sea region. The northward shift of the typhoon track tends to be associated with typhoon-enhancing environmental conditions over the western North Pacific, namely, the weakening of the western North Pacific subtropical high, the strengthening of the Asian summer monsoon trough, and the enhanced positive vorticity anomalies in the lower troposphere. Based on observational analysis and model simulations, warm sea surface temperature anomalies over the equatorial western and central Pacific appear to be a major factor contributing to a northward-shifted typhoon track.

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Sung-Hun Kim, Il-Ju Moon, and Pao-Shin Chu

Abstract

A statistical–dynamical model for predicting tropical cyclone (TC) intensity has been developed using a track-pattern clustering (TPC) method and ocean-coupled potential predictors. Based on the fuzzy c-means clustering method, TC tracks during 2004–12 in the western North Pacific were categorized into five clusters, and their unique characteristics were investigated. The predictive model uses multiple linear regressions, where the predictand or the dependent variable is the change in maximum wind speed relative to the initial time. To consider TC-ocean coupling effects due to TC-induced vertical mixing and resultant surface cooling, new potential predictors were also developed for maximum potential intensity (MPI) and intensification potential (POT) using depth-averaged temperature (DAT) instead of sea surface temperature (SST). Altogether, 6 static, 11 synoptic, and 3 DAT-based potential predictors were used. Results from a series of experiments for the training period of 2004–12 using TPC and DAT-based predictors showed remarkably improved TC intensity predictions. The model was tested on predictions of TC intensity for 2013 and 2014, which are not used in the training samples. Relative to the nonclustering approach, the TPC and DAT-based predictors reduced prediction errors about 12%–25% between 24- and 96-h lead time. The present model is also compared with four operational dynamical forecast models. At short leads (up to 24 h) the present model has the smallest mean absolute errors. After a 24-h lead time, the present model still shows skill that is comparable with the best operational models.

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Pao-Shin Chu, Andrew J. Nash, and Fee-Yung Porter

Abstract

The circulation features that accompanied the dry January/February of 1981 and the wet January/February of 1982 in Hawaii are compared. The results indicate that surface and upper-air circulation features are very distinct during these two winter months with contrasting rainfall extremes. Four major synoptic patterns (frontal, kona, trade, and ridge) that influence Hawaiian rainfall have been described. The Kona storm pattern contributes to most of the rainfall in wet 1982, followed by the frontal pattern. No kona storm days occurred during dry 1981, and the rainfalls on frontal days in dry 1981 were less than half of those in wet 1982. The trade wind and ridge patterns are not Important for rainfall either in dry 1981 or wet 1982. A possible relationship between the PNA pattern and rainfall anomalies during these two non-ENSO winter months is suggested.

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Zhi-Ping Yu, Pao-Shin Chu, and Thomas Schroeder

Abstract

Drought and flooding are recurrent and serious problems in the U.S. Affiliated Pacific Islands (USAPI). Given the agricultural and water-dependent characteristics of the USAPI economies, accurate forecasts of seasonal to interseasonal rainfall variations have the potential to provide important information for decision makers involved in resource management issues and response strategies related to drought and flood events.

Climatology of rainfall and outgoing longwave radiation (OLR) cycle in the USAPI and the response of OLR to the El Niño–Southern Oscillation (ENSO) are addressed. Boxplot and harmonic analyses indicate that the annual cycles in rainfall and OLR are generally strong in USAPI except those stations close to the equator. Northern USAPI have positive (negative) OLR anomalies during El Niño (La Niña) winters.

Two statistical models, canonical correlation analysis (CCA) and a relatively new method called multivariate Principal Component Regression (PCR), are employed to forecast rainfall variations in 10 USAPI stations. Sea surface temperatures (SSTs) in the Pacific Ocean are used as predictors for both models. The results of this study indicate that both models are potentially useful in predicting seasonal rainfall variations in the USAPI region, especially in winter (DJF) and spring (MAM). CCA cross validation shows that at one and two seasons lead JFM is the most accurately forecast period in the northern USAPI stations, with average skills of 0.53 and 0.41, respectively. However, the authors’ analysis indicates a problem of lower predictive skill in summer (JJA) and fall (SON). One reason might be associated with the so-called spring barrier in predictive skill in the tropical ocean–atmosphere system. Another reason might be associated with the tropical cyclone activity during these seasons. Predictions using the PCR model yield similar predictive skill. Though simpler than He and Barnston’s model in term of the number of predictor variables used, the authors’ CCA and PCR provide comparable skills.

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Pao-Shin Chu, Ying Ruan Chen, and Thomas A. Schroeder

Abstract

For the first time, trends of five climate change indices related to extreme precipitation events in the Hawaiian Islands are investigated using daily observational records from the 1950s to 2007. Four indices [simple daily intensity index (SDII), total number of day with precipitation ≥25.4 mm (R25), annual maximum consecutive 5-day precipitation amount (R5d), and the fraction of annual total precipitation from events exceeding the 1961–90 95th percentile (R95p)] describe the intensity (SDII), frequency (R25), and magnitude (R5d and R95p) of precipitation extremes, and the fifth index [consecutive dry days (CDD)] describes drought conditions. The annual probability density functions (PDFs) of precipitation indices for two epochs (i.e., 1950–79 and 1980–2007) are analyzed. Since the 1980s, there has been a change in the types of precipitation intensity, resulting in more frequent light precipitation and less frequent moderate and heavy precipitation intensity. The other three precipitation-related indices (R25, R5d, and R95p) demonstrate a shift toward the left of the distribution over time, suggesting shorter annual number of days with intense precipitation and smaller consecutive 5-day precipitation amounts and smaller fraction of annual precipitation due to events exceeding the 1961–90 95th percentile in the recent epoch relative to the first epoch. The changes of PDF distribution for SDII, R25, R5d, and CDD are significant at the 5% level according to a two-sample Kolmogorov–Smirnov test.

A nonparametric trend analysis is then performed for four periods, with different starting years (e.g., the 1950s, the 1960s) but the same ending year (2007). Long-term downward trends are evident for four precipitation-related indices, and long-term upward trends are observed for CDD. Geographically, Kauai and Oahu are dominated by long-term decreasing trends for four precipitation-related indices, while increasing trends play the major role over the island of Hawaii. The upward trends of drought conditions in the long run are predominant on all the major Hawaiian Islands.

To investigate whether the trends are stable throughout the time, the derivatives of trends for each of the 30-yr running series are calculated (e.g., 1950–79, 1951–80, … , 1978–2007) for four precipitation-related indices at each station. For Kauai and Oahu, positive derivatives prevail for all indices in the presence of long-term negative trends, suggestive of a phase change in precipitation extremes and such extremes showing an upswing recently. For the island of Hawaii, there is also an indication of phase reversal over the last 60 yr, with negative derivatives occurring in the presence of the background positive trends.

A positive relationship is found between the precipitation indices and the Southern Oscillation index (SOI), implying more precipitation extremes during La Niña years and vice versa for El Niño years. Spatial patterns of standardized anomalies of indices are presented for the La Niña/−PDO minus El Niño/+PDO composites.

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Pao-Shin Chu, Xin Zhao, Ying Ruan, and Melodie Grubbs

Abstract

Heavy rainfall and the associated floods occur frequently in the Hawaiian Islands and have caused huge economic losses as well as social problems. Extreme rainfall events in this study are defined by three different methods based on 1) the mean annual number of days on which 24-h accumulation exceeds a given daily rainfall amount, 2) the value associated with a specific daily rainfall percentile, and 3) the annual maximum daily rainfall values associated with a specific return period. For estimating the statistics of return periods, the three-parameter generalized extreme value distribution is fit using the method of L-moments. Spatial patterns of heavy and very heavy rainfall events across the islands are mapped separately based on the aforementioned three methods. Among all islands, the pattern on the island of Hawaii is most distinguishable, with a high frequency of events along the eastern slopes of Mauna Kea and a low frequency of events on the western portion so that a sharp gradient in extreme events from east to west is prominent. On other islands, extreme rainfall events tend to occur locally, mainly on the windward slopes. A case is presented for estimating return periods given different rainfall intensity for a station in Upper Manoa, Oahu. For the Halloween flood in 2004, the estimated return period is approximately 27 yr, and its true value should be no less than 13 yr with 95% confidence as determined from the adjusted bootstrap resampling technique.

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Christopher F. O’Connor, Pao-Shin Chu, Pang-Chi Hsu, and Kevin Kodama

Abstract

Rainfall in Hawaii during La Niña years has undergone abnormal variability since the early 1980s. Traditionally, Hawaii receives greater-than-normal precipitation during the La Niña wet seasons. Recently, La Niña years have experienced less-than-normal rainfall. A drying trend in Hawaiian precipitation during La Niña years is evident. A changepoint analysis determined that the shift in precipitation occurred in 1983, forming the two epochs used for comparison in this study. The first epoch (E1) runs from 1956 to 1982 and the second epoch (E2) from 1983 to 2010. Location-specific changes in rainfall anomalies from E1 to E2 throughout the Hawaiian Islands are examined, illustrating that the greatest difference in rainfall between epochs is found on the climatologically drier sides (i.e., south and west) of the islands. Variations in tropical sea surface temperatures and circulation features in the northern Pacific Ocean have changed during La Niña wet seasons, thus changing La Niña–year rainfall.

The strengthening, broadening, and westward shifting of the eastern North Pacific subtropical high, coupled with an eastward elongation and intensification of the subtropical jet stream, are two main influences when considering the lack of precipitation during the recent La Niña wet seasons. Moisture transport analysis shows that variations in circulation structures play a dominant role in the reduction of moisture flux convergence in the Hawaiian region during the second epoch. Additionally, a storm-track analysis reveals that the changes found in the aforementioned circulation features are creating a less favorable environment for the development of Kona lows and midlatitude fronts in the vicinity of Hawaii.

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Hyeong-Seog Kim, Chang-Hoi Ho, Joo-Hong Kim, and Pao-Shin Chu

Abstract

Skillful predictions of the seasonal tropical cyclone (TC) activity are important in mitigating the potential destruction from the TC approach/landfall in many coastal regions. In this study, a novel approach for the prediction of the seasonal TC activity over the western North Pacific is developed to provide useful probabilistic information on the seasonal characteristics of the TC tracks and vulnerable areas. The developed model, which is termed the “track-pattern-based model,” is characterized by two features: 1) a hybrid statistical–dynamical prediction of the seasonal activity of seven track patterns obtained by fuzzy c-means clustering of historical TC tracks and 2) a technique that enables researchers to construct a forecasting map of the spatial probability of the seasonal TC track density over the entire basin. The hybrid statistical–dynamical prediction for each pattern is based on the statistical relationship between the seasonal TC frequency of the pattern and the seasonal mean key predictors dynamically forecast by the National Centers for Environmental Prediction Climate Forecast System in May. The leave-one-out cross validation shows good prediction skill, with the correlation coefficients between the hindcasts and the observations ranging from 0.71 to 0.81. Using the predicted frequency and the climatological probability for each pattern, the authors obtain the forecasting map of the seasonal TC track density by combining the TC track densities of the seven patterns. The hindcasts of the basinwide seasonal TC track density exhibit good skill in reproducing the observed pattern. The El Niño–/La Niña–related years, in particular, tend to show a better skill than the neutral years.

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Yi-Leng Chen, Pay-Liam Lin, Feng Hsiao, Pao-Shin Chu, and Mei-Huei Su
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