Seasonal and Spatial Variation of Radiative Effects of Anthropogenic Sulfate Aerosol

On the basis of the emission data of the industrial sulphur dioxide (SO_2) and observed climate fields over East Asia, the distribution of anthropogenic sulfate aerosol(SO_4~2-) with seasonal variation in the troposphere is simulated and analyzed by a regional sulfur transport model, and the direct radiative effects of SO_4~2- under different weather conditions are also calculated using the discrete ordinate method. The results show that the concentration of SO_4~2- has significant seasonal and spatial variations resulting from the effects of SO_2 emission source and precipitation and wind fields. Both the concentration of SO_2 and its radiative forcing have the largest values in October and the lowest in July. SO_4~2- causes the decrease of the radiation flux absorbed by earth-atmosphere and the cooling of air temperature by scattering more solar radiation back into space. Besides, the radiative and climatic effects of SO_4~2- are related to the types and height and optical thickness, etc., of the clouds.

Historical Phase-Locked El Nino Episodes

Using a newly reported Pacific sea surface temperature data set, we extend a prior study that assigned El Ni&#241o episodes to distinct sequences. Within these sequences the episodes are phase-locked to subharmonics of the annual solar irradiance cycle having two- or three-year periodicity. There are 40 El Ni&#241o episodes occurring since 1872, each found within one of eighteen such sequences. Our list includes all previously reported events. Three El Ni&#241o episodes have already been observed in boreal winters of 2009, 2012 and 2015, illustrating a sequence of 3-year intervals that began in 2008. If the climate system remains in this state, the next El Ni&#241o is likely to occur in boreal winter of 2018.

Statistical Study of foF2 Diurnal Variation at Dakar Station from 1971 to 1996:Effect of Geomagnetic Classes of Activity on Seasonal Variation at Solar Minimum and Maximum

The statistical study of F2 layer critical frequency at Dakar station from 1971 to 1996 is carried out. This paper shows foF2 statistical diurnal for all geomagnetic activities and all seasons and that during solar maximum and minimum phases. It emerges that foF2 diurnal variation graphs at Dakar station exhibits the different types of foF2 profiles in African EIA regions. The type of profile depends on solar activity, season and solar phase. During solar minimum and under quiet time condition, data show?the signature of a strength electrojet that is coupled with intense counter electrojet in the afternoon. Under disturbed conditions,?mean intense electrojet is observed in winter?during fluctuating and recurrent activities. Intense counter electrojet is seen under fluctuating and shock activities in all seasons coupled with strength electrojet in autumn. In summer?and spring under all geomagnetic activity condition, there is intense counter electrojet. During solar maximum, in summer and spring there is no electrojet under geomagnetic activity conditions.?Winter shows a mean intense electrojet. Winter and autumn are marked by the signature of the reversal electric field.

Seasonal compensation implied no weakening of the land carbon sink in the Northern Hemisphere under the 2015/2016 El Niño

The recurrent extreme El Niño events are commonly linked to reduced vegetation growth and the land carbon sink over many but discrete regions of the Northern Hemisphere(NH).However,we reported here a pervasive and continuous vegetation greening and no weakened land carbon sink in the maturation phase of the 2015/2016 El Niño event over the NH(mainly in the extra-tropics),based on multiple evidences from remote sensing observations,global ecosystem model simulations and atmospheric CO_(2)inversions.We discovered a significant compensation effect of the enhanced vegetation growth in spring on subsequent summer/autumn vegetation growth that sustained vegetation greening and led to a slight increase in the land carbon sink over the spring and summer of 2015(average increases of 23.34%and 0.63%in net ecosystem exchange from two independent datasets relative to a 5-years average before the El Niño event,respectively)and spring of 2016(6.82%),especially in the extra-tropics of the NH,where the water supply during the pre-growing-season(November of the previous year to March of the current year)had a positive anomaly.This seasonal compensation effect was much stronger than that in 1997 and 1998 and significantly alleviated the adverse impacts of the 2015/2016 El Niño event on vegetation growth during its maturation phase.The legacy effect of water supply during the pre-growing-season on subsequent vegetation growth lasted up to approximately six months.Our findings highlight the role of seasonal compensation effects on mediating the land carbon sink in response to episodic extreme El Niño events.

Effects of Season, Variety, and Processing Method on Ellagic Acid Content in Pomegranate Leaves

Ellagic acid （EA） has aroused great interest worldwide owing to its antioxidant, anti-inflammatory, and anticarcinogenetic properties. The EA content in pomegranate leaf was measured in this study using high performance liquid chromatography to investigate the effects of season, variety, and processing method on the EA level. The results show that the EA content in 11 varieties of pomegranate from the Zaozhuang region in China range from 1.30 mg · g^-1 to 6.46 mg · g^-1 of dry weight in five consecutive seasons from June to October. An analysis of variance （ANOVA） shows that the EA content is significantly dependent on the season （p〈0.05）. The EA content increases significantly during the growing season to the highest level in September and October. The effect of the leaf variety on the EA content is less significant than the season. The processing methods have different effects on the EA content. Soaking for 24 hours slightly increases the EA content （p〈0.05）. Heating at 80℃ or 100℃ for 1 h after soaking has little influence on the EA content, while slow-fired cooking at high temperature significantly elevates the EA content （p〈0.05）. To improve quality and stability, several parameters such as leaf collection time, slow-fired cooking, and cooking time should be strictly controlled during the processing of pomegranate leaf tea and its extract.

Seasonal effects of impervious surface estimation in subtropical monsoon regions

Accurate impervious surface estimation(ISE)is challenging due to the diversity of land covers and the vegetation phenology and climate.This study investigates the variation of impervious surfaces estimated from different seasons of satellite images and the seasonal sensitivity of different methods.Four Landsat ETM?images of four different seasons and two popular methods(i.e.artificial neural network(ANN)and support vector machine(SVM))are employed to estimate the impervious surface on the pixel level.Results indicate that winter(dry season)is the best season to estimate impervious surface even though plants are not in their growing season.Less cloud and less variable source areas(VSA)(seasonal water body)become the major advantages of winter for the ISE,as cloud is easily confusedwith bright impervious surfaces,andwater in VSA is confusedwith dark impervious surfaces due to their similar spectral reflectance.For the seasonal sensitivity of methods,ANN appears more stable as its accuracy varied less than that obtained with SVM.However,both the methods showed a general consistency of the seasonal changes of the accuracy,indicating that winter time is the best season for impervious surfaces estimation with optical satellite images in subtropical monsoon regions.

Total Electricity Consumption Forecasting Based on Temperature Composite Index and Mixed-Frequency Models

The total electricity consumption(TEC)can accurately reflect the operation of the national economy,and the forecasting of the TEC can help predict the economic development trend,as well as provide insights for the formulation of macro policies.Nowadays,high-frequency and massive multi-source data provide a new way to predict the TEC.In this paper,a"seasonal-cumulative temperature index"is constructed based on high-frequency temperature data,and a mixed-frequency prediction model based on multi-source big data(Mixed Data Sampling with Monthly Temperature and Daily Temperature index,MIDAS-MT-DT)is proposed.Experimental results show that the MIDAS-MT-DT model achieves higher prediction accuracy,and the"seasonal-cumulative temperature index"can improve prediction accuracy.

High-resolution peak demand estimation using generalized additive models and deep neural networks

This paper covers predicting high-resolution electricity peak demand features given lower-resolution data.This is a relevant setup as it answers whether limited higher-resolution monitoring helps to estimate future high-resolution peak loads when the high-resolution data is no longer available.That question is particularly interesting for network operators considering replacing high-resolution monitoring by predictive models due to economic considerations.We propose models to predict half-hourly minima and maxima of high-resolution(every minute)electricity load data while model inputs are of a lower resolution(30 min).We combine predictions of generalized additive models(GAM)and deep artificial neural networks(DNN),which are popular in load forecasting.We extensively analyze the prediction models,including the input parameters’importance,focusing on load,weather,and seasonal effects.The proposed method won a data competition organized by Western Power Distribution,a British distribution network operator.In addition,we provide a rigorous evaluation study that goes beyond the competition frame to analyze the models’robustness.The results show that the proposed methods are superior to the competition benchmark concerning the out-of-sample root mean squared error(RMSE).This holds regarding the competition month and the supplementary evaluation study,which covers an additional eleven months.Overall,our proposed model combination reduces the out-of-sample RMSE by 57.4%compared to the benchmark.

Leaves of Field-Grown Mastic Trees Suffer Oxidative Stress at the Two Extremes of their Lifespan

Leaf senescence is a complex phenomenon occurring in all plant species, but it is still poorly understood in plants grown in Mediterranean field conditions and well-adapted to harsh climatic conditions. To better understand the physiological processes underlying leaf senescence in mastic trees （Pistacia lentiscus L.）, we evaluated leaf growth, water and N content, photosystem II （PSII） photochemistry, lipid peroxidation and levels of photosynthetic pigments, antioxidants, abscisic acid, and salicylic acid and jasmonic acid during the complete leaf lifespan, from early expansion to late senescence in relation to natural climatic conditions in the field. While mature leaves suffered from water and N deficit during late spring and summer, both young （emerging） and old （senescing） leaves were most sensitive to photo- oxidative stress, as indicated by reductions in the Fv/Fm ratio and enhanced lipid peroxidation during late autumn and winter. Reductions in the FvlFm ratio were associated with low ^-tocopherol （vitamin E） levels, while very old, senescing leaves additionally showed severe anthocyanin losses. We have concluded that both young （emerging） and old （senescing） leaves suffer oxidative stress in mastic trees, which may be linked in part to suboptimal temperatures during late autumn and winter as well as to low vitamin E levels.

Effect of seasonal changes on predictive model of bovine babesiosis transmission

In this paper,we present an attempt at analyzing the seasonal pattern of the Babesiosis transmission dynamics in bovine and tick populations.The infestation rate plays an important role in the variation of infection.In this logic,we set out a mathematical model with variable infestation rate for the evolution of babesiosis disease.Using the(0,2)-Jacobi multi-wavelets method combined with the decoupling and quasi-linearization technique,we demonstrate the validity and applicability of our model.Then,a set of experimental data is used to validate the proposed model under the same operating conditions.The results of numerical simulations are provided to show the impact of seasonality on the transmission dynamics of Babesiosis.Eventually,a numerical study of the model varying the control parameters of babesiosis shows different scenarios about the spread of the disease.