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Tong-Wen Wu and Zheng-An Qian


Utilizing winter (November–March) accumulated snow depth data at 60 stations over the Tibetan Plateau (TP) for the period 1960–98, three typical patterns of the TP snow anomaly's spatial distribution were objectively classified by means of empirical orthogonal function (EOF) analysis. They are characterized by light snow over the entire Tibet region (LS pattern), by an eastern Tibet heavy snow (ETHS pattern), and by a southwestern Tibet heavy snow (SWTHS pattern), respectively.

The possible relations between various patterns of the Tibet winter snow anomaly and subsequent summer monsoon and rainfall over south, southeast, and east Asia are investigated using composite analysis. In ETHS and SWTHS years, the south and southeast Asian summer monsoon becomes weak and there is less summer rainfall over south and southeast Asia than in normal years. In LS years, the anomalies of the subsequent summer monsoon and rainfall are opposite to those in ETHS and SWTHS years. The physical mechanism is, in part, attributed to the impact of heavy snow on Tibet's atmospheric temperature, on the land–sea meridional thermal contrast, and also on the strength of the summer monsoon. The variation of summer rainfall over China associated with the preceding winter TP snow anomaly is also analyzed. There is a clear positive correlation between the Tibetan winter snow and the subsequent summer rainfall over the middle and lower reaches of the Yangtze River valley (central China). In contrast to the previous studies that use snow cover averaged over all of the Tibetan Plateau as a single number, the association between the winter snow and the subsequent summer precipitation over east China is much clearer.

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Chung-Chieng A. Lai, Wen Qian, and Scott M. Glenn

The Institute for Naval Oceanography, in cooperation with Naval Research Laboratories and universities, executed the Data Assimilation and Model Evaluation Experiment (DAMÉE) for the Gulf Stream region during fiscal years 1991–1993. Enormous effort has gone into the preparation of several high-quality and consistent datasets for model initialization and verification. This paper describes the preparation process, the temporal and spatial scopes, the contents, the structure, etc., of these datasets.

The goal of DAMÉE and the need of data for the four phases of experiment are briefly stated. The preparation of DAMÉE datasets consisted of a series of processes: 1) collection of observational data; 2) analysis and interpretation; 3) interpolation using the Optimum Thermal Interpolation System package; 4) quality control and reanalysis; and 5) data archiving and software documentation.

The data products from these processes included a time series of 3D fields of temperature and salinity, 2D fields of surface dynamic height and mixed-layer depth, analysis of the Gulf Stream and rings system, and bathythermograph profiles. To date, these are the most detailed and high-quality data for mesoscale ocean modeling, data assimilation, and forecasting research. Feedback from ocean modeling groups who tested this data was incorporated into its refinement.

Suggestions for DAMÉE data usages include 1) ocean modeling and data assimilation studies, 2) diagnosis and theorectical studies, and 3) comparisons with locally detailed observations.

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Cheng Qian, Wen Zhou, Soi Kun Fong, and Ka Cheng Leong


The Gaussian assumption has been widely used without testing in many previous studies on climate variability and change that have used traditional statistical methods to estimate linear trends, diagnose physical mechanisms, or construct statistical prediction/downscaling models. In this study, the authors carefully test the normality of two hot extreme indices in Macao, China, during the last 100 years based on consecutive daily temperature observational data and find that the occurrences of both hot day and hot night indices are non-Gaussian. Simple least squares fitting is shown to overestimate the linear trend when the Gaussian assumption is violated. Two approaches are further proposed to statistically predict non-Gaussian temperature extremes: one uses a multiple linear regression model after transforming the non-Gaussian predictant to a quasi-Gaussian variable and uses Pearson’s correlation test to identify potential predictors, and the other uses a generalized linear model when the transformation is difficult and uses a nonparametric Spearman’s correlation test to identify potential predictors. The annual occurrences of hot days and hot nights in Macao are used as examples of these two approaches, respectively. The physical mechanisms for these two hot extremes in Macao are also investigated, and the results show that both are related to the interannual and interdecadal variability of a coupled El Niño–Southern Oscillation (ENSO)–East Asian summer monsoon system. Finally, the authors caution other researchers to test the assumed distribution of climate extremes and to apply appropriate statistical approaches.

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Qian Zou, Ruiqiang Ding, Jianping Li, Yu-heng Tseng, Zhaolu Hou, Tao Wen, and Kai Ji


This study investigates the connection between the North Pacific Victoria mode (VM) during the boreal spring [February–April (FMA)] and the following boreal winter [January–March (JFM)] rainfall over South China (SC). The VM is defined as the second empirical orthogonal function mode (EOF2) of sea surface temperature (SST) anomalies (SSTAs) in the North Pacific poleward of 20°N. It is found that the boreal spring VM has a significant positive correlation with the following winter rainfall over SC. Analyses indicate that a strong positive VM during spring can induce El Niño during the following winter via an air–sea interaction, resulting in the generation of an anomalous anticyclone over the western North Pacific (WNPAC). The anomalous southwesterlies along the southeast coast of East Asia associated with the WNPAC favor an abundant supply of water vapor and anomalous ascending motion over SC. As a result, winter rainfall over SC increases. A linear regression model based on the VM shows that the VM can act as an effective predictor of winter rainfall over SC about 1 year in advance. It also has a higher prediction skill than ENSO in predicting winter rainfall over SC.

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