The study on seasonal characteristics of water masses in the western East China Sea shelf area

On the basis of the CTD data and the modeling results in the winter and summer of 2009, the seasonal characteristics of the water masses in the western East China Sea shelf area were analyzed using a cluster analysis method. The results show that the distributions and temperature-salinity characteristics of the water masses in the study area are of distinct seasonal difference. In the western East China Sea shelf area, there are three water masses during winter, i.e., continental coastal water（CCW）, Taiwan Warm Current surface water（TWCSW） and Yellow Sea mixing water（YSMW）, but four ones during summer, i.e., the CCW, the TWCSW, Taiwan Warm Current deep water（TWCDW） and the YSMW. Of all, the CCW, the TWCSW and the TWCDW are all dominant water masses. The CCW, primarily characterized by a low salinity, has lower temperature, higher salinity and smaller spatial extent in winter than in summer. The TWCSW is warmer, fresher and smaller in summer than in winter, and it originates mostly from the Kuroshio surface water（KSW） northeast of Taiwan, China and less from the Taiwan Strait water during winter, but it consists of the strait water and the KSW during summer. The TWCDW is characterized by a low temperature and a high salinity, and originates completely in the Kuroshio subsurface water northeast of Taiwan.

Seasonal Characteristics and Interannual Variability of Monthly Scale Low-Frequency Oscillation in a Low-Order Global Spectral Model

Analysis is done of five-year low-pass filtered data by a five-layer low-order global spectral model, indicating that although any non-seasonal external forcing is not considered in the model atmosphere,monthly-scale anomaly takes place which is of remarkable seasonality and interannual variability.Analysis also shows that for the same seasonal external forcing the model atmosphere can exhibit two climatic states,similar in the departure pattern but opposite in sign, indicating that the anomaly is but the manifestation of the adverse states, which supports the theory of multi-equilibria proposed by Charney and Devore(1979) once again.Finally, the source for the low-frequency oscillation of the global atmosphere is found to be the convective heat source / sink inside the tropical atmosphere as discussed before in our study.Therefore, the key approach to the exploration of atmospheric steady low-frequency oscillation and the associated climatic effect lies in the examination of the distribution of convective heat sources / sinks and the variation in the tropical atmosphere.

CHARACTERISTICS OF MIDDLE EAST JET STREAM DURING SEASONAL TRANSITION AND ITS RELATION WITH INDIAN SUMMER MONSOON ONSET

By using the NCEP/NCAR pentad reanalysis data from 1968 to 2009, the variation characteristics of Middle East jet stream(MEJS) and its thermal mechanism during seasonal transition are studied. Results show that the intensity and south-north location of MEJS center exhibit obvious seasonal variation characteristics. When MEJS is strong, it is at 27.5°N from the 67 th pentad to the 24 th pentad the following year; when MEJS is weak, it is at 45°N from the 38 th pentad to the 44 th pentad. The first Empirical Orthogonal Function(EOF) mode of 200-hPa zonal wind field shows that MEJS is mainly over Egypt and Saudi Arabia in winter and over the eastern Black Sea and the eastern Aral Sea in summer. MEJS intensity markedly weakens in summer in comparison with that in winter. The 26th-31 st pentad is the spring-summer transition of MEJS, and the 54th-61 st pentad the autumn-winter transition. During the two seasonal transitions, the temporal variations of the 500-200 hPa south-north temperature difference(SNTD) well match with 200-hPa zonal wind velocity, indicating that the former leads to the latter following the principle of thermal wind. A case analysis shows that there is a close relation between the onset date of Indian summer monsoon and the transition date of MEJS seasonal transition. When the outbreak date of Indian summer monsoon is earlier than normal, MEJS moves northward earlier because the larger SNTD between 500-200 hPa moves northward earlier, with the westerly jet in the lower troposphere over 40°-90°E appearing earlier than normal, and vice versa.

Seasonal Short-Term Load Forecasting for Power Systems Based onModal Decomposition and Feature-FusionMulti-Algorithm Hybrid Neural NetworkModel

To enhance the refinement of load decomposition in power systems and fully leverage seasonal change information to further improve prediction performance,this paper proposes a seasonal short-termload combination prediction model based on modal decomposition and a feature-fusion multi-algorithm hybrid neural network model.Specifically,the characteristics of load components are analyzed for different seasons,and the corresponding models are established.First,the improved complete ensemble empirical modal decomposition with adaptive noise(ICEEMDAN)method is employed to decompose the system load for all four seasons,and the new sequence is obtained through reconstruction based on the refined composite multiscale fuzzy entropy of each decomposition component.Second,the correlation between different decomposition components and different features is measured through the max-relevance and min-redundancy method to filter out the subset of features with strong correlation and low redundancy.Finally,different components of the load in different seasons are predicted separately using a bidirectional long-short-term memory network model based on a Bayesian optimization algorithm,with a prediction resolution of 15 min,and the predicted values are accumulated to obtain the final results.According to the experimental findings,the proposed method can successfully balance prediction accuracy and prediction time while offering a higher level of prediction accuracy than the current prediction methods.The results demonstrate that the proposedmethod can effectively address the load power variation induced by seasonal differences in different regions.

VOC characteristics and their source apportionment in a coastal industrial area in the Yangtze River Delta, China

Volatile organic compounds(VOCs)are important precursors of secondary organic compounds and ozone,which raise major environmental concerns.To investigate the VOC emission characteristics,measurements of VOCs based on proton transfer reaction-mass spectrometry during 2017 were conducted in a coastal industrial area in Ningbo,Zhejiang Province,China.Based on seasonal variation in species concentration,the positive matrix factorization(PMF)receptor model was applied to apportion the sources of VOCs in each season.The PMF results revealed that unknown acetonitrile source,paint solvent,electronics industry,biomass burning,secondary formation and biogenic emission were mainly attributed to VOC pollution.Biomass burning and secondary formation were the major sources of VOCs and contributed more than 70%of VOC emissions in spring and autumn.Industry-related sources contributed 8.65%–31.2%of the VOCs throughout the year.The unknown acetonitrile source occurred in winter and spring,and contributed 7.6%–43.73%of the VOC emissions in the two seasons.Conditional probability function(CPF)analysis illustrated that the industry sources came from local emission,while biomass burning and biogenic emission mainly came from the northwest direction.The potential source contribution function(PSCF)model showed that secondary formation-related source was mainly from Jiangsu Province,northeastern China and the surrounding ocean.The potential source areas of unknown acetonitrile source were northern Zhejiang Province,southern Jiangsu Province and the northeastern coastal marine environments.

Seasonal variations in wave-induced stress over global ocean based on China France Oceanography Satellite

Due to the scarcity of simultaneous observations on global-scale wind and wave spectra,there has been limited research on the characteristics of global wave-induced stress and wind stress with wave effects using observed wave spectra,particularly their seasonal variations.The China France Oceanography Satellite(CFOSAT)for the first time can simultaneously observe global sea surface wind and wave spectra,providing a solid data basis for investigating this difficult issue.In this study,the seasonal characteristics of global sea surface wave-induced stress and wind stress were analyzed by combining one-year simultaneous wind and wave observations from CFOSAT with a wave boundary layer model.Waveinduced stress was divided into wind-wave-induced stress and swell-induced stress based on different wave forms.The results showed that the wave-induced stress presented a significant inverse correlation with swell index.A higher swell index corresponded to a larger proportion of swell-induced stress,resulting in a decrease in wind stress,and vice versa,wind-wave-induced stress was dominant,resulting in an increase in wind stress.From spring to winter in the Northern Hemisphere(NH),wind-wave-induced stress predominated in the westerly belt of the Southern Hemisphere(SH),while swell-induced stress predominated near the equator.Further analysis revealed that the seasonal variation in wind-waveinduced stress in the SH was not significant,however,wind-wave-induced stress during the boreal summer was significantly lower than that in other seasons.The absolute value of swell-induced stress in the SH showed a trend of decrease and then increase from spring to winter.The percentage of increase or decrease in wind stress after considering the waveinduced stress showed a roughly symmetrical pattern between the NH and SH during the spring and autumn seasons,while the summer and winter seasons showed an asymmetrical feature.Wave-induced stress significantly modulated wind stress,resulting in zonal mean variations by up to±30%.This finding further highlights the important modulation of surface waves on wind stress at the global scale.

Influence of Geological Factor on Indoor Radon Concentrations in Dwelling of Queretaro,Mexico

In this study, radon-222 concentrations were measured within 34 houses distributed in the northern, southern, and central areas of the city of Querétaro in 2007. The objective of this study was to establish the spatial distribution of radon with respect to geological faults and its relation to certain environmental variables. For each sampling site, the radon-222 concentration was analyzed as a function of various climate variables, relative humidity, temperature, and atmospheric pressure, in addition to the ventilation of the house and distance of each site from any geological faults in the area. A multivariate statistical technique was used to analyze these variables based on two components. For component 1, the relative humidity and temperature had the greatest weight, 0.677 and 0.492, respectively, while for component 2, the greatest weights corresponded to radon and the temperature with 0.693 and 0.609, respectively. The average radon concentration across the entire sampling period was 38.92 Bq/m3, the minimum detected value was 6.01 Bq/m3?for site 91GAG0607, and the maximum detected value was 225.95 Bq/m3?for site 91CAM20907. This last value is outside the standards permitted by the Environmental Protection Agency of the United Stated, which is 148 Bq/m3?for indoor environments. With respect to geological faults and their relationship to radon-222, the concentrations for the sampling sites had no relationship to the closeness or distance to these faults.

Chemical components of PM_(2.5)in different seasons in Harbin,China

The seasonal characteristics of fine particulate matter(PM_(2.5))were investigated from October 2020 to April 2021(spreading fall,winter and spring)in Harbin,a city located in northeast China.The mass concentrations of PM_(2.5)in winter were significantly higher than those in fall and spring.Moreover,our results indicated that various aerosol species had obvious seasonality.The proportions of secondary components were higher in winter than other two seasons.In contrast,the ratios of nitrate to sulfate(NO_(3)^(-)/SO_(4)^(2-))showed lower levels in winter,which was because both the ratios of nitrogen dioxide to sulfur dioxide(NO_(2)/SO_(2))and the ratios of nitrogen oxidation ratio to sulfur oxidation ratio(NOR/SOR)exhibited lower values in winter than in fall and spring.With PM_(2.5)increased,the NO_(3)^(-)/SO_(4)^(2-)ratios showed increasing trends in all three seasons,which was mainly attributed to the increase of NOR/SOR ratios in fall and spring,and the increase of both NO_(2)/SO_(2)and NOR/SOR ratios in winter.This result highlighted that nitrate was more important than sulfate as a driver for the growth of PM_(2.5)during the period of heavy air pollution.Additionally,the sources of organic aerosol(OA)in different seasons were also distinctly different.Overall,the sum of biomass burning OA(BBOA)and secondary OA(SOA)contributed>70%of OA in three seasons.The fractional contributions of BBOA to total OA,notably,exhibited higher levels in fall and spring,because of intensive open agricultural fires.The SOA fractions in OA were larger in winter,likely due to higher relative humidity which facilitated the secondary for-mation.A large increase in the proportions of BBOA was observed during polluted days in fall and spring compared to clean days.In comparison,during heavily-polluted periods,secondary formation made a dominant contribution to organic matter in winter.