Dynamical Predictability of Leading Interannual Variability Modes of the Asian-Australian Monsoon in Climate Models

The dynamical prediction of the Asian-Australian monsoon(AAM)has been an important and long-standing issue in climate science.In this study,the predictability of the first two leading modes of the AAM is studied using retrospective prediction datasets from the seasonal forecasting models in four operational centers worldwide.Results show that the model predictability of the leading AAM modes is sensitive to how they are defined in different seasonal sequences,especially for the second mode.The first AAM mode,from various seasonal sequences,coincides with the El Niño phase transition in the eastern-central Pacific.The second mode,initialized from boreal summer and autumn,leads El Niño by about one year but can exist during the decay phase of El Niño when initialized from boreal winter and spring.Our findings hint that ENSO,as an early signal,is conducive to better performance of model predictions in capturing the spatiotemporal variations of the leading AAM modes.Still,the persistence barrier of ENSO in spring leads to poor forecasting skills of spatial features.The multimodel ensemble(MME)mean shows some advantage in capturing the spatiotemporal variations of the AAM modes but does not provide a significant improvement in predicting its temporal features compared to the best individual models in predicting its temporal features.The BCC_CSM1.1M shows promising skill in predicting the two AAM indices associated with two leading AAM modes.The predictability demonstrated in this study is potentially useful for AAM prediction in operational and climate services.

A SEASONAL INFLUENZA THEORY AND MATHEMATICAL MODEL INCORPORATING METEOROLOGICAL AND SOCIO-BEHAVIORAL FACTORS

On the basis of a comprehensive literature review and data analysis of global influenza surveillance, a transmission theory based numerical model is developed to understand the causative factors of influenza seasonality and the biodynamical mechanisms of seasonal flu. The model is applied to simulate the seasonality and weekly activity of influenza in different areas across all continents and climate zones around the world. Model solution and the good matches between model output and actual influenza indexes affirm that influenza activity is highly auto-correlative and relies on determinants of a broad spectrum. Internal dynamic resonance; variations of meteorological elements (solar radiation, precipitation and dewpoint); socio-behavioral influences and herd immunity to circulating strains prove to be the critical explanatory factors of the seasonality and weekly activity of influenza. In all climate regions, influenza activity is proportional to the exponential of the number of days with precipitation and to the negative exponential of quarter power of sunny hours. Influenza activity is a negative exponential function of dewpoint in temperate and arctic regions and an exponential function of the absolute deviation of dewpoint from its annual mean in the tropics. Epidemics of seasonal influenza could be deemed as the consequence of the dynamic resonance and interactions of determinants. Early interventions (such as opportune vaccination, prompt social distancing, and maintaining incidence well below a baseline) are key to the control and prevention of seasonal influenza. Moderate amount of sunlight exposure or Vitamin D supplementation during rainy and short-day photoperiod seasons, more outdoor activities, and appropriate indoor dewpoint deserve great attention in influenza prevention. To a considerable degree, the study reveals the mechanism of influenza seasonality, demonstrating a potential for influenza activity projection. The concept and algorithm can be explored for further applications.

Long-term Prediction and Verification of Rainfall Based on the Seasonal Model

Using the seasonal cross-multiplication trend model, monthly precipitation of eight national basic weather stations of Shaanxi Province from 2005 to 2010 was predicted, and the forecast results were verified using the rainfall scoring rules of China Meteorological Administration. The verification results show that the average score of annual precipitation prediction in recent six years is higher than that made by a professional forecaster, so this model has a good prospect of application. Moreover, the level of making prediction is steady, and it can be widely used in long-term prediction of rainfall.

Seasonal Prediction of Summer Temperature over Northeast China Using a Year-to-Year Incremental Approach

We present a model for predicting summertime surface air temperature in Northeast China（NESSAT） using a year-to-year incremental approach.The predicted value for each year＇s increase or decrease of NESSAT is added to the observed value within a particular year to yield the net forecast NESSAT.The seasonal forecast model for the year-to-year increments of NESSAT is constructed based on data from 1975- 2007.Five predictors are used：an index for sea ice cover over the East Siberian Sea,an index for central Pacific tropical sea surface temperature,two high latitude circulation indices,as well as a North American pressure index.All predictors are available by no later than March,which allows for compilation of a seasonal forecast with a two-month lead time.The prediction model accurately captures the interannual variations of NESSAT during 1977-2007 with a correlation coefficient between the predicted and observed NESSAT of 0.87（accounting for 76%of total variance） and a mean absolute error（MAE） of 0.3℃.A cross-validation test during 1977-2008 demonstrates that the model has good predictive skill,with MAE of 0.4℃and a correlation coefficient between the predicted and observed NESSAT of 0.76.

CALENDAR EFFECTS IN MONTHLY TIME SERIES MODELS

It is not unusual for the level of a monthly economic time series, such as industrial production, retail and wholesale sales, monetary aggregates, telephone calls or road accidents, to be influenced by calendar effects. Such effects arise when changes occur in the level of activity resulting from differences in the composition of calendar between years. The two main sources of calendar effects are trading day variations and moving festivals. Ignoring such calendar effects will lead to substantial distortions in the identification stage of time series modeling. Therefore, it is mandatory to introduce calendar effects, when they are present in a time series, as the component of the model which one wants to estimate.

Dam deformation analysis based on BPNN merging models

Hydropower has made a significant contribution to the economic development of Vietnam,thus it is important to monitor the safety of hydropower dams for the good of the country and the people.In this paper,dam horizontal displacement is analyzed and then forecasted using three methods:the multi-regression model,the seasonal integrated auto-regressive moving average(SARIMA)model and the back-propagation neural network(BPNN)merging models.The monitoring data of the Hoa Binh Dam in Vietnam,including horizontal displacement,time,reservoir water level,and air temperature,are used for the experiments.The results indicate that all of these three methods can approximately describe the trend of dam deformation despite their different forecast accuracies.Hence,their short-term forecasts can provide valuable references for the dam safety.

A Hybrid Dynamical-Statistical Approach for Predicting Winter Precipitation over Eastern China

Correlation analysis revealed that winter precipitation in six regions of eastern China is closely related not only to preceding climate signals but also to synchronous atmospheric general circulation fields. It is therefore necessary to use a method that combines both dynamical and statistical predictions of winter precipitation over eastern China （hereinafter called the hybrid approach）, in this connection, seasonal real-time prediction models for winter precipitation were established for the six regions. The models use both the preceding observations and synchronous numerical predictions through a multivariate linear regression analysis. To improve the prediction accuracy, the systematic error between the original regression model result and the corresponding observation was corrected. Cross-validation analysis and real-time prediction experiments indicate that the prediction models using the hybrid approach can reliably predict the trend, sign, and interannual variation of regionally averaged winter precipitation in the six regions of concern. Averaged over the six target regions, the anomaly correlation coefficient and the rate with the same sign of anomaly between the cross-validation analysis and observation during 1982-2008 are 0.69 and 78%, respectively. This indicates that the hybrid prediction approach adopted in this study is applicable in operational practice.

Decontamination efficiency and root structure change in the plant-intercropping model in vertical-flow constructed wetlands

Subtropical climatic conditions can contribute to the death of the aerial parts of constructed wetland plants in winter. This presents a barrier to the widespread application of constructed wetland and is an issue that urgently needs to be solved. Three contrasting experi- ments, the plant-intercropping model （A）, the warm- seasonal plant model （B）, and the non-plant model （C）, were studied in terms of their efficiency in removing pollutants, and the change in root structure of plants in the plant-intercropping model within the vertical-flow con- structed wetlands. The results indicate that model A was able to solve the aforementioned problem. Overall, average removal rates of three pollutants （CODcr, total nitrogen （TN） and total phosphorous （TP）） using model A were significantly higher than those obtained using models B and C （P 〈 0.01）. Moreover, no significant differences in removal rates of the three pollutants were detected between A and B during the higher temperature part of the year （P〉 0.05）. Conversely, removal rates of the three pollutants were found to be significantly higher using model A than those observed using model B during the lower temperature part of the year （P 〈 0.01）. Furthermore, the morphologies and internal structures of plant roots further demonstrate that numerous white roots, whose distribution in soil was generally shallow, extend further under model A. The roots of these aquatic plants have an aerenchyma structure composed of parenchyma cells, therefore, roots of the cold-seasonal plants with major growth advantages used in A were capable of creating a more favorable vertical-flow constructed wetlands media- microenvironment. In conclusion, the plant-intercropping model （A） is more suitable for use in the cold environment experienced by constructed wetland during winter.

Application of Time-Series Model to Predict Groundwater Dynamic in Sanjiang Plain, Northeast China

To study the groundwater dynamic in the typical region of Sanjiang Plain, long-term groundwater level observation data in the Honghe State Farm were collected and analyzed in this paper. The seasonal and long-term groundwater dynamic was explored. From 1996 to 2008, groundwater level kept declining due to intensive exploitation of groundwater resources for rice irrigation. A decline of nearly 5 m was found for almost all the monitoring wells. A time-series method was established to model the groundwater dynamic. Modeled results by time-series model showed that the groundwater level in this region would keep declining according to the current exploitation intensity. A total dropdown of 1.07 m would occur from 2009 to 2012. Time-series model can be used to model and forecast the groundwater dynamic with high accuracy. Measures including control on groundwater exploitation amount and application of water saving irrigation technique should be taken to prevent the continuing declining of groundwater in the Sanjiang Plain.