Seasonal Variability of the Yellow Sea/East China Sea Surface Fluxes and Thermohaline Structure

We use the U.S. Navy's Master Oceanographic Observation Data Set (MOODS) forthe Yellow Sea/ East China Sea (YES) to investigate the climatological water mass features and theseasonal and non-seasonal variabilities of the thermohaline structure, and use the ComprehensiveOcean-Atmosphere Data Set (COADS) from 1945 to 1989 to investigate the linkage between the fluxes(momentum, heat, and moisture) across the air-ocean interface and the formation of the water massfeatures. After examining the major current systems and considering the local bathymetry and watermass properties, we divide YES into five regions: East China Sea (ECS) shelf, Yellow Sea (YS) Basin,Cheju bifurcation (CB) zone, Taiwan Warm Current (TWC) region, Kuroshio Current (KC) region. Thelong term mean surface heat balance corresponds to a heat loss of 30 W m^(-2) in the ESC and CBregions, a heat loss of 65 W m^(-2) in the KC and TWC regions, and a heat gain of 15 W m^(-2) in theYS region. The surface freshwater balance is defined by precipitation minus evaporation. The annualwater loss from the surface for the five subareas ranges from 1.8 to 4 cm month^(-1). The freshwater loss from the surface should be compensated for from the river run-off. The entire watercolumn of the shelf region (ECS, YS, and CB) undergoes an evident seasonal thermal cycle withmaximum values of temperature during summer and maximum mixed layer depths during winter. However,only the surface waters of the TWC and KC regions exhibit a seasonal thermal cycle.. We also foundtwo different relations between surface salinity and the Yangtze River run-off, namely, out-of-phasein the East China Sea shelf and in-phase in the Yellow Sea. This may confirm an earlier study thatthe summer fresh water discharge from the Yangtze River forms a relatively shallow, low salinityplume-like structure extending offshore on average towards the northeast.

Seasonal variability of the zooplankton community in the southwest of the Huanghai Sea (Yellow Sea) Cold Water Mass

Samples were collected with a plankton net in the four seasonal cruises during 2006-2007 to study the seasonal variability of the zooplankton community in the southwest part of Huanghai Sea Cold Water Mass （HSCWM, Yellow Sea Cold Water Mass）. The spatial and temporal variations of zooplankton species composition, biomass, abundance and biodiversity were examined. A total of 122 zooplankton species and 30 pelagic larvae were identified in the four cruises. Calanus sinicus and Aidanosagitta crassa were the most dominant species, and Themisto gaudichaudi and Euphau- sia pacifica were widely distributed in the HSCWM area. The spatial patterns of non-gelatinous zooplankton （removing the high water content groups） were similar to those of the total zooplank- ton biomass in autumn, but different significantly in the other three seasons. The seasonal means of zooplankton biomass in spring and summer were much higher than that in autumn and win- ter. The total zooplankton abundance averaged 283.5 ind./m3 in spring （highest）, 192.5 ind./m3 in summer, 165.5 ind./m3 in autumn and 65.9 ind./m3 in winter （lowest）, and the non-gelatinous groups contributed the most total abundance. Correlation analysis suggests that the non-gelatinous zooplankton biomass and abundance had a significant positive correlation in the whole year, but the relationship was insignificant between the total zooplankton biomass and abundance in spring and summer. The diversity index HI of zooplankton community averaged 1.88 in this study, which was somewhat higher than historical results. Relatively low diversity in summer was related to the high dominance of Calanus sinicus, probably due to the strongest effect of the HSCWM in this season.

Adaptive load forecasting of the Hellenic electric grid

Designers are required to plan for future expansion and also to estimate the grid's future utilization. This means that an effective modeling and forecasting technique, which will use efficiently the information contained in the available data, is required, so that important data properties can be extracted and projected into the future. This study proposes an adaptive method based on the multi-model partitioning algorithm (MMPA), for short-term electricity load forecasting using real data. The grid's utilization is initially modeled using a multiplicative seasonal ARIMA (autoregressive integrated moving average) model. The proposed method uses past data to learn and model the normal periodic behavior of the electric grid. Either ARMA (autoregressive moving average) or state-space models can be used for the load pattern modeling. Load anomalies such as unexpected peaks that may appear during the summer or unexpected faults (blackouts) are also modeled. If the load pattern does not match the normal be-havior of the load, an anomaly is detected and, furthermore, when the pattern matches a known case of anomaly, the type of anomaly is identified. Real data were used and real cases were tested based on the measurement loads of the Hellenic Public Power Cooperation S.A., Athens, Greece. The applied adaptive multi-model filtering algorithm identifies successfully both normal periodic behavior and any unusual activity of the electric grid. The performance of the proposed method is also compared to that produced by the ARIMA model.

Nitrogen pollution and source identification of urban ecosystem surface water in Beijing

Nitrogen contamination of surface water is a worldwide environmental problem with intensive agricul- ture and high population densities. We assessed the spatial and seasonal variation in concentrations of total nitrogen and different nitrogen species present in surface-water in Beijing, China. Also, chemical （NO3-N/C1-） and isotopic （615Nnitrate） indicators were used to identify nitrate sources. The results showed that, during 2009 and 2010, nitrate nitrogen concentrations ranged from 0.7 to 7.6 mg· L^-1, ammonium nitrogen from 0. I to 3.4 mg· L^-1, and total nitrogen from 2.4 to 17.0mg· L^-1. Inorganic nitrogen accounted for between 60 and 100% of total nitrogen at the ten monitoring sites. Nitrate nitrogen, ammonium nitrogen, and total nitrogen concentrations at the 2 downstream monitoring sites in south-eastern Beijing were significantly higher than those at the other eight upstream monitoring sites （P 〈 0.01）. Examination of seasonal variation showed that there was a significant inverse relationship between nitrate nitrogen concentrations and precipitation, and that nitrate nitrogen concentrations peaked in the dry seasons. The information given by the 15Nnitrate values and nitrate nitrogen concentrations, combined with the NO3-N/C1- ratio distribution, showed that domestic sewage was the major source of nitrate in Beijing. Methods to control and reduce sewage pollution are urgently needed to help manage surface water quality in Beijing.

Inertial gravity wave parameters for the lower stratosphere from radiosonde data over China

Characterization of gravity wave （GW） parameters for the stratosphere is critical for global atmospheric circulation models. These parameters are mainly determined from measurements. Here, we investigate variation in inertial GW activity with season and latitude in the lower stratosphere （18-25 km） over China, using radiosonde data with a high vertical resolution over a 2-year period. Eight radiosonde stations were selected across China, with a latitudinal range of 22°-49°N. Analyses show that the GW energy in the lower stratosphere over China has obvious seasonal variation and a meridional distribution, similar to other regions of the globe. The GW energy is highest in winter, and lowest in summer; it decreases with increasing latitude. Velocity perturbations with longitude and latitude are almost the same, indicating that GW energy is horizontally isotropic. Typically, 85% of the vertical wavelength distribution is concentrated between elevations of 1 and 3 km, with a mean value of 2 kin; it is almost constant with latitude. Over 80% of all the horizontal wavelengths occur in the range 100-800 km, with a mean value of 450 km; they show a weak decrease with increasing latitude, yielding a difference of about 40 km over the 22°-49°N range. The ratio of horizontal wavelength over vertical wavelength is about 200：1, which implies that inertial GWs in the lower stratosphere propagate along nearly horizontal planes. Ratios of their intrinsic frequency to the Coriolis parameter decrease with increasing latitude; most values are between 1 and 2, with a mean value of 1.5. Study of the propagation directions of GW energy shows that upward fractions account for over 60% at all stations. In contrast, the horizontal propagation direction is significantly anisotropic, and is mainly along prevailing wind directions; this anisotropy weakens with increasing latitude.

Seasonal variation of the global mixed layer depth： comparison between Argo data and FIO-ESM

The present study evaluates a simulation of the global ocean mixed layer depth （MLD） using the First Institute of Oceanography-Earth System Model （FIO- ESM）. The seasonal variation of the global MLD from the FIO-ESM simulation is compared to Argo observational data. The Argo data show that the global ocean MLD has a strong seasonal variation with a deep MLD in winter and a shallow MLD in summer, while the spring and fall seasons act as transitional periods. Overall, the FIO-ESM simula- tion accurately captures the seasonal variation in MLD in most areas. It exhibits a better performance during summer and fall than during winter and spring. The simulated MLD in the Southern Hemisphere is much closer to observations than that in the Northern Hemisphere. In general, the simulated MLD over the South Atlantic Ocean matches the observation best among the six areas. Additionally, the model slightly underestimates the MLD in parts of the North Atlantic Ocean, and slightly overestimates the MLD over the other ocean basins.