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Understanding water level (WL) fluctuations in river deltas is important for managing water resources and minimizing the impacts of floods and droughts. Here, we demonstrate the competing effects of atmospheric and oceanic forcing on multi-time-scale variability and changes in the Pearl River Delta (PRD) WLs in southern China, using 52 years (1961–2012) of in situ observations at 13 hydrological stations. PRD WL presents significant seasonal to decadal variations, with large amplitudes upstream related to strong variability of southern China rainfall, and with relatively small amplitudes at the coastal stations determined by sea level (SL) fluctuations of the northern South China Sea. We find that the strengths of atmospheric and oceanic forcing in PRD are not mutually independent, leading to a distinct contrast of WL–forcing relationships at upstream and coastal stations. In the transition zone, because of the counteraction of atmospheric and oceanic forcing, no robust relationships are identified between WL and either of the forcing. We further show that in the drought season of the warm ENSO and PDO epochs, the effect of atmospheric (oceanic) forcing on PRD WL is largely enhanced (weakened), due to increased southern China rainfall and negative SL anomalies. Over the observation period, WL significantly decreased at upstream stations, by up to 28–42 mm yr−1 for flood season, contrasting with the upward trends of <4.3 mm yr−1 at coastal stations across all seasons. Southern China rainfall explains little of the observed WL trends, while SL rise is mostly responsible for the WL trends at coastal stations. [ABSTRACT FROM AUTHOR]

This study assesses the forecast skill of eight North American Multimodel Ensemble (NMME) models in predicting Niño-3/-3.4 indices and improves their skill using Bayesian updating (BU). The forecast skill that is obtained using the ensemble mean of NMME (NMME-EM) shows a strong dependence on lead (initial) month and target month and is quite promising in terms of correlation, root-mean-square error (RMSE), standard deviation ratio (SDRatio), and probabilistic Brier skill score, especially at short lead months. However, the skill decreases in target months from late spring to summer owing to the spring predictability barrier. When BU is applied to eight NMME models (BU-Model), the forecasts tend to outperform NMME-EM in predicting Niño-3/-3.4 in terms of correlation, RMSE, and SDRatio. For Niño-3.4, the BU-Model outperforms NMME-EM forecasts for almost all leads (1-12; particularly for short leads) and target months (from January to December). However, for Niño-3, the BU-Model does not outperform NMME-EM forecasts for leads 7-11 and target months from June to October in terms of correlation and RMSE. Last, the authors test further potential improvements by preselecting 'good' models (BU-Model-0.3) and by using principal component analysis to remove the multicollinearity among models, but these additional methodologies do not outperform the BU-Model, which produces the best forecasts of Niño-3/-3.4 for the 2015/16 El Niño event. [ABSTRACT FROM AUTHOR]

This paper describes the study of the relationship between the thermal configuration of the Bay of Bengal (BOB)-Tibetan Plateau (TP) region and the precipitation anomaly in Yunnan, a province in China, in May using ERA-Interim data and precipitation data for May from 125 meteorological stations across Yunnan for 1979-2014. Results from the analysis indicate that the interannual variability of May precipitation in Yunnan is significantly modulated by the BOB-TP thermal configuration. Model runs with a linear baroclinic model suggest physical consistency. The thermal conditions over the BOB mainly impact the May precipitation anomaly in Yunnan via changes in water vapor transport from the eastern BOB northeastward to southwestern Yunnan. The second factor influencing precipitation anomalies relates to the characteristics and variability of cold air transport from the TP to northeastern Yunnan. When the BOB (the TP) is occupied by positive (negative) diabatic heating, a thermal gradient with a warmer (colder) center over the BOB and a colder (warmer) center over the TP is established, and more-than-normal (less than normal) precipitation in Yunnan will occur in May. This relationship can persist from April to the following May to some extent; therefore, the BOB-TP thermal configuration in April could be used to forecast May precipitation in Yunnan. [ABSTRACT FROM AUTHOR]

This study aims to assess whether, and the extent to which, an increase in atmospheric resolution of the Geophysical Fluid Dynamics Laboratory (GFDL) Forecast-Oriented Low Ocean Resolution version of CM2.5 (FLOR) with 50-km resolution and the High-Resolution FLOR (HiFLOR) with 25-km resolution improves the simulation of the El Niño-Southern Oscillation (ENSO)-tropical cyclone (TC) connections in the western North Pacific (WNP). HiFLOR simulates better ENSO-TC connections in the WNP including TC track density, genesis, and landfall than FLOR in both long-term control experiments and sea surface temperature (SST)- and sea surface salinity (SSS)-restoring historical runs (1971-2012). Restoring experiments are performed with SSS and SST restored to observational estimates of climatological SSS and interannually varying monthly SST. In the control experiments of HiFLOR, an improved simulation of the Walker circulation arising from more realistic SST and precipitation is largely responsible for its better performance in simulating ENSO-TC connections in the WNP. In the SST-restoring experiments of HiFLOR, more realistic Walker circulation and steering flow during El Niño and La Niña are responsible for the improved simulation of ENSO-TC connections in the WNP. The improved simulation of ENSO-TC connections with HiFLOR arises from a better representation of SST and better responses of environmental large-scale circulation to SST anomalies associated with El Niño or La Niña. A better representation of ENSO-TC connections in HiFLOR can benefit the seasonal forecasting of TC genesis, track, and landfall; improve understanding of the interannual variation of TC activity; and provide better projection of TC activity under climate change. [ABSTRACT FROM AUTHOR]

Tropical cyclone (TC) intensity prediction, especially in the warning time frame of 24-48 h and for the prediction of rapid intensification (RI), remains a major operational challenge. Sea surface temperature (SST) based empirical or theoretical maximum potential intensity (MPI) is the most important predictor in statistical intensity prediction schemes and rules derived by data mining techniques. Since the underlying SSTs during TCs usually cannot be observed well by satellites because of rain contamination and cannot be produced on a timely basis for operational statistical prediction, an ocean coupling potential intensity index (OC_PI), which is calculated based on pre-TC averaged ocean temperatures from the surface down to 100 m, is demonstrated to be important in building the decision tree for the classification of 24-h TC intensity change Δ V24, that is, RI (Δ V24 ≥ 25 kt, where 1 kt = 0.51 m s−1) and non-RI (Δ V24 < 25 kt). Cross validations using 2000-10 data and independent verification using 2011 data are performed. The decision tree with the OC_PI shows a cross-validation accuracy of 83.5% and an independent verification accuracy of 89.6%, which outperforms the decision tree excluding the OC_PI with corresponding accuracies of 83.2% and 83.9%. Specifically for RI classification in independent verification, the former decision tree shows a much higher probability of detection and a lower false alarm ratio than the latter example. This study is of great significance for operational TC RI prediction as pre-TC OC_PI can skillfully reduce the overestimation of storm potential intensity by traditional SST-based MPI, especially for the non-RI TCs. [ABSTRACT FROM AUTHOR]

This study investigates the classification of developing and nondeveloping tropical disturbances in the western North Pacific (WNP) through the C4.5 algorithm. A decision tree is built based on this algorithm and can be used as a tool to predict future tropical cyclone (TC) genesis events. The results show that the maximum 800-hPa relative vorticity, SST, precipitation rate, divergence averaged between 1000- and 500-hPa levels, and 300-hPa air temperature anomaly are the five most important variables for separating the developing and nondeveloping tropical disturbances. This algorithm also unravels the thresholds of the five variables (i.e., 4.2 × 10−5 s−1 for maximum 800-hPa relative vorticity, 28.2°C for SST, 0.1 mm h−1 for precipitation rate, −0.7 × 10−6 s−1 for vertically averaged convergence, and 0.5°C for 300-hPa air temperature anomaly). Six rules are derived from the decision tree. The classification accuracy of this decision tree is 81.7% for the 2004-10 cases. The hindcast accuracy for the 2011-13 dataset is 84.6%. [ABSTRACT FROM AUTHOR]

The China Meteorological Administration (CMA)'s tropical cyclone (TC) database includes not only the best-track dataset but also TC-induced wind and precipitation data. This article summarizes the characteristics and key technical details of the CMA TC database. In addition to the best-track data, other phenomena that occurred with the TCs are also recorded in the dataset, such as the subcenters, extratropical transitions, outer-range severe winds associated with TCs over the South China Sea, and coastal severe winds associated with TCs landfalling in China. These data provide additional information for researchers. The TC-induced wind and precipitation data, which map the distribution of severe wind and rainfall, are also helpful for investigating the impacts of TCs. The study also considers the changing reliability of the various data sources used since the database was created and the potential causes of temporal and spatial inhomogeneities within the datasets. Because of the greater number of observations available for analysis, the CMA TC database is likely to be more accurate and complete over the offshore and land areas of China than over the open ocean. Temporal inhomogeneities were induced primarily by changes to the nature and quality of the input data, such as the development of a weather observation network in China and the use of satellite image analysis to replace the original aircraft reconnaissance data. Furthermore, technical and factitious changes, such as to the wind-pressure relationship and the satellite-derived current intensity (CI) number-intensity conversion, also led to inhomogeneities within the datasets. [ABSTRACT FROM AUTHOR]

This study examines whether there exist significant differences in tropical cyclone (TC) landfall between central Pacific (CP) El Niño, eastern Pacific (EP) El Niño, and La Niña during the peak TC season (June-October) and how and to what extent CP El Niño influences TC landfall over East Asia for the period 1961-2009. The peak TC season is subdivided into summer [June-August (JJA)] and autumn [September-October (SO)]. The results are summarized as follows: (i) during the summer of CP El Niño years, TCs are more likely to make landfall over East Asia because of a strong easterly steering flow anomaly induced by the westward shift of the subtropical high and northward-shifted TC genesis. In particular, TCs have a greater probability of making landfall over Japan and Korea during the summer of CP El Niño years. (ii) In the autumn of CP El Niño years, TC landfall in most areas of East Asia, especially Indochina, the Malay Peninsula, and the Philippines, is likely to be suppressed because the large-scale circulation resembles that of EP El Niño years. (iii) During the whole peak TC season [June-October (JJASO)] of CP El Niño years, TCs are more likely to make landfall over Japan and Korea. TC landfall in East Asia as a whole has an insignificant association with CP El Niño during the peak TC season. In addition, more (less) TCs are likely to make landfall in China, Indochina, the Malay Peninsula, and the Philippines during the peak TC season of La Niña (EP El Niño) years. [ABSTRACT FROM AUTHOR]

Retrospective seasonal forecasts of North Atlantic tropical cyclone (TC) activity over the period 1980-2014 are conducted using a GFDL high-resolution coupled climate model [Forecast-Oriented Low Ocean Resolution (FLOR)]. The focus is on basin-total TC and U.S. landfall frequency. The correlations between observed and model predicted basin-total TC counts range from 0.4 to 0.6 depending on the month of the initial forecast. The correlation values for U.S. landfalling activity based on individual TCs tracked from the model are smaller and between 0.1 and 0.4. Given the limited skill from the model, statistical methods are used to complement the dynamical seasonal TC prediction from the FLOR model. Observed and predicted TC tracks were classified into four groups using fuzzy c-mean clustering to evaluate the model's predictability in observed classification of TC tracks. Analyses revealed that the FLOR model has the highest skill in predicting TC frequency for the cluster of TCs that tracks through the Caribbean and the Gulf of Mexico. New hybrid models are developed to improve the prediction of observed basin-total TC and landfall TC frequencies. These models use large-scale climate predictors from the FLOR model as predictors for generalized linear models. The hybrid models show considerable improvements in the skill in predicting the basin-total TC frequencies relative to the dynamical model. The new hybrid model shows correlation coefficients as high as 0.75 for basinwide TC counts from the first two lead months and retains values around 0.50 even at the 6-month lead forecast. The hybrid model also shows comparable or higher skill in forecasting U.S. landfalling TCs relative to the dynamical predictions. The correlation coefficient is about 0.5 for the 2-5-month lead times. [ABSTRACT FROM AUTHOR]

Temporal scaling properties of the monthly sea surface temperature anomaly (SSTA) in global ocean basins are examined by the power spectrum and detrended fluctuation analysis methods. Analysis results show that scaling behaviors of the SSTA in most ocean basins (e.g., global average, South Pacific, eastern and western tropical Pacific, tropical Indian Ocean, and tropical Atlantic) are separated into two distinct regimes by a common crossover time scale of 52 months (i.e., 4.3 yr). It is suggested that this crossover is modulated by the El Niño/La Niña-Southern Oscillation (ENSO), indicating different scaling properties at different time scales. The SSTA time series is nonstationary and antipersistent at the small scale (i.e., crossover). It is, however, stationary and long range correlated at the large scale (i.e., crossover). For both time scales, scaling behaviors of SSTA are heterogeneously distributed over the ocean, and the fluctuation of SSTA intensifies with decreasing latitude. Stronger fluctuation appears over the tropical regions (e.g., central-eastern tropical Pacific, tropical Atlantic, tropical Indian Ocean, and South China Sea), which are directly or indirectly linked to ENSO. Weaker fluctuation and stronger persistence are found in mid- and high-latitude areas, coinciding with the 'reemergence' areas. [ABSTRACT FROM AUTHOR]