Oriented Generation of Novel Thermo-Sensitive Genic Male Sterile Lines with Improved Grain Shape and Outcrossing Rate in Early-Season Rice

Two-line hybrid rice with excellent quality is preferred in the Chinese market.However,there is a trade-off between reducing costs for hybrid seed production and lowering the outcrossing rate of the sterile line,which is largely determined by the stigma exsertion rate(SER).In this study,we constructed mutants of male sterility lines with improved grain length(GL)and SER in three elite early-season indica rice varieties through targeted manipulation of the TMS5 and GS3 genes using CRISPR/Cas9-mediated multiplex systems.We obtained a series of marker-free gs3 single mutants and gs3tms5 double mutants with significantly higher SER,longer grains,and increased 1000-grain weight compared with the wild type(WT).Importantly,the typically thermo-sensitive genic male sterile(TGMS)trait with a higher SER was observed in gs3tms5 mutants,and their F1 hybrids exhibited remarkable improvements in grain shape and yield-related traits.Our findings provided an efficient method to generate new valuable TGMS germplasm with improved SER through the mutagenesis of GS3 and TMS5 synergistically,and demonstrated that GS3 had pleiotropic effects on grain size,SER,and grain quality in early-season indica rice.

Seasonal variation of mesoscale eddy intensity in the global ocean

Mesoscale eddies are a prominent oceanic phenomenon that plays an important role in oceanic mass transport and energy conversion.Characterizing by rotational speed,the eddy intensity is one of the most fundamental properties of an eddy.However,the seasonal spatiotemporal variation in eddy intensity has not been examined from a global ocean perspective.In this study,we unveil the seasonal spatiotemporal characteristics of eddy intensity in the global ocean by using the latest satellite-altimetry-derived eddy trajectory data set.The results suggest that the eddy intensity has a distinct seasonal variation,reaching a peak in spring while attaining a minimum in autumn in the Northern Hemisphere and the opposite in the Southern Hemisphere.The seasonal variation of eddy intensity is more intense in the tropical-subtropical transition zones within latitudinal bands between 15°and 30°in the western Pacific Ocean,the northwestern Atlantic Ocean,and the eastern Indian Ocean because baroclinic instability in these areas changes sharply.Further analysis found that the seasonal variation of baroclinic instability precedes the eddy intensity by a phase of 2–3 months due to the initial perturbations needing time to grow into mesoscale eddies.

Impacts of Human Activities and Season on Species Diversity and Abundance of Butterflies in Mpanga Kipengere Game Reserve and Surrounding Farmlands, Tanzania

Maintaining natural habitats is crucial for the preservation of insects and other species that indicate environmental changes. However, the Mpanga/Kipengere Game Reserve and its surrounding farmlands are facing disturbance due to human activities, which is putting many wildlife species, particularly larger mammals, at risk. To determine the impact of human activities on butterfly species diversity and abundance in the reserve and its surrounding areas, we conducted a study from November 2021 to October 2023. We collected butterfly data using transect walks and baited traps in two habitat types. Our study yielded 2799 butterfly Individuals ranging in 124 species divided into five families habitat, season, and anthropogenic factors are significant environmental variables influencing species diversity and abundance of butterflies. Therefore, it’s important to protect habitat and dry-season water for the conservation of invertebrates such as butterflies. Our study findings provide essential information for ecological monitoring and future assessment of the Mpanga/Kipengere Game Reserve ecosystem health.

Downscaling Seasonal Precipitation Forecasts over East Africa with Deep Convolutional Neural Networks

This study assesses the suitability of convolutional neural networks(CNNs) for downscaling precipitation over East Africa in the context of seasonal forecasting. To achieve this, we design a set of experiments that compare different CNN configurations and deployed the best-performing architecture to downscale one-month lead seasonal forecasts of June–July–August–September(JJAS) precipitation from the Nanjing University of Information Science and Technology Climate Forecast System version 1.0(NUIST-CFS1.0) for 1982–2020. We also perform hyper-parameter optimization and introduce predictors over a larger area to include information about the main large-scale circulations that drive precipitation over the East Africa region, which improves the downscaling results. Finally, we validate the raw model and downscaled forecasts in terms of both deterministic and probabilistic verification metrics, as well as their ability to reproduce the observed precipitation extreme and spell indicator indices. The results show that the CNN-based downscaling consistently improves the raw model forecasts, with lower bias and more accurate representations of the observed mean and extreme precipitation spatial patterns. Besides, CNN-based downscaling yields a much more accurate forecast of extreme and spell indicators and reduces the significant relative biases exhibited by the raw model predictions. Moreover, our results show that CNN-based downscaling yields better skill scores than the raw model forecasts over most portions of East Africa. The results demonstrate the potential usefulness of CNN in downscaling seasonal precipitation predictions over East Africa,particularly in providing improved forecast products which are essential for end users.

Seasonal Variation of the Sea Surface Temperature Growth Rate of ENSO

El Ni?o–Southern Oscillation(ENSO) exhibits a distinctive phase-locking characteristic, first expressed during its onset in boreal spring, developing during summer and autumn, reaching its peak towards winter, and decaying over the next spring. Several studies have demonstrated that this feature arises as a result of seasonal variation in the growth rate of ENSO as expressed by the sea surface temperature(SST). The bias towards simulating the phase locking of ENSO by many state-of-the-art climate models is also attributed to the unrealistic depiction of the growth rate. In this study, the seasonal variation of SST growth rate in the Ni?o-3.4 region(5°S–5°N, 120°–170°W) is estimated in detail based on the mixed layer heat budget equation and recharge oscillator model during 1981–2020. It is suggested that the consideration of a variable mixed layer depth is essential to its diagnostic process. The estimated growth rate has a remarkable seasonal cycle with minimum rates occurring in spring and maximum rates evident in autumn. More specifically, the growth rate derived from the meridional advection(surface heat flux) is positive(negative) throughout the year. Vertical diffusion generally makes a negative contribution to the evolution of growth rate and the magnitude of vertical entrainment represents the smallest contributor. Analysis indicates that the zonal advective feedback is regulated by the meridional immigration of the intertropical convergence zone, which approaches its southernmost extent in February and progresses to its northernmost location in September, and dominates the seasonal variation of the SST growth rate.

Fish Assemblage and Abundance Distribution in Nanji Islands Marine Nature Reserve in Relation to Season Change

The fishing survey station located in the warm waters of the East China Sea is one of the abundant fishery resources of waters;with a large number of economic fish breeding grounds and economic juvenile feeding grounds. Several fish species according to this bottom trawl survey results which appear in the annual survey from winter to autumn give us a total of 58 species, which have appeared throughout the year. But the vast majority of species appears only in certain seasons, according to the sea fish survey and based on seasonal migratory species or species mainly small endemic species. The survey of fish is tropical and subtropical warm water species and warm temperate species, and the history of the area has several types of 397 different species. There are many reasons, in addition to recent years, human disturbance and adverse consequences of environmental changes brought about, but also with the relevant sampling methods and sampler time, the survey sampled only trawl based, from Ping yang Ocean and Fisheries Bureau, the waters are still other jobs method net, gill nets, fishing industry, etc. Survey results show that the type of fish in the summer (58 species) > spring (55 species) > winter (51 species) > autumn (42 species). The type of season was among spring to summer rose, summer to autumn decline. The survey found that in the reef area of the Nanji Islands, no one species of fish is the dominant species throughout the year (according to the dominant species, each species is dominant only in one season). From the dominant species turnover accounting for the four seasons, in winter it has 53.72% of the total biomass or annual biomass, spring it has 41.53% of the overall biomass, summer it has 31.85% and autumn it has 38.56% of total biomass. Visible, seasonal succession of dominant species of fish phenomenon is very obvious, especially in the spring and winter, summer transfers dominate species at this stage show the greatest change, but winter, the transfer of this dominant species changes in minimal. Seasonal changes from biomass and the average annual marine fish survey biomass were 4832.25 g/h. From different seasons, the average biomass in winter (10779.88 g/h) > of the average biomass in spring (3624.1 g/hour) > of the mean biomass in autumn (3158 g/h) > the average Biomass in summer (1767 g/h). All of them show significant seasonal variation of the biomass, in winter and spring, the biomass is generally not high, but significantly higher than in the summer and autumn seasons. Each season, around the coast of southern Zhejiang, is less than the biomass and other catches off, which involves investigating sea location, size range, and the survey ship different network related tools. Another change from the area of biomass, some studies found that the survey of four-season fish of waters above the biological capacity of several kilograms only in some stations, namely stations and the highest biomass in summer appeared in stations 3, 7 and 11. In autumn the highest biomass appeared in stations 3, 6, 20 and 23.

Seasonal Performance of Solar Power Plants in the Sahel Region: A Study in Senegal, West Africa

The main objective of this study is to evaluate the seasonal performance of 20 MW solar power plants in Senegal. The analysis revealed notable seasonal variations in the performance of all stations. The most significant yields are recorded in spring, autumn and winter, with values ranging from 5 to 7.51 kWh/kWp/day for the reference yield and 4.02 to 7.58 kWh/kWp/day for the final yield. These fluctuations are associated with intense solar activity during the dry season and clear skies, indicating peak production. Conversely, minimum values are recorded during the rainy season from June to September, with a final yield of 3.86 kWh/kW/day due to dust, clouds and high temperatures. The performance ratio analysis shows seasonal dynamics throughout the year with rates ranging from 77.40% to 95.79%, reinforcing reliability and optimal utilization of installed capacity. The results of the capacity factor vary significantly, with March, April, May, and sometimes October standing out as periods of optimal performance, with 16% for Kahone, 16% for Bokhol, 18% for Malicounda and 23% for Sakal. Total losses from solar power plants show similar seasonal trends standing out for high loss levels from June to July, reaching up to 3.35 kWh/kWp/day in June. However, using solar trackers at Sakal has increased production by up to 25%, demonstrating the operational stability of this innovative technology compared with the plants fixed panel. Finally, comparing these results with international studies confirms the outstanding efficiency of Senegalese solar power plants, other installations around the world.

Trend in seasonal amplitude of northern net ecosystem production:Simulated results from IAP DGVM in CAS-ESM2

北方陆地净生态系统生产力(NEP)具有明显的季节变化特征,这是大气CO_(2)季节变化的关键驱动.研究这些碳循环过程并理解潜在的驱动因素是气候研究的一个关键问题.本文利用第二代中国科学院地球系统模式(CAS-ESM2)中的全球植被动态模型(IAPDGVM),研究了1990-2014年北方NEP(40°-90°N)的季节振幅及其变化趋势.在初始化试验的基础上,本文开展了一个控制试验来评估模拟的北方NEP季节幅度的变化趋势,同时开展了三个敏感性试验来研究气候和大气CO_(2)的贡献.结果表明:1990-2014年,模拟的北方NEP季节振幅显著增加,趋势为9.69万吨碳/月/年,这主要是由于最大NEP增加所致.当分别排除CO_(2)施肥效应和气候效应时,上述增加趋势大大减弱.这些显著的减少表明大气CO_(2)和气候变化对北方NEP的季节性振幅有重要影响.尽管模式存在不确定性,但这些结果有利于进一步提升IAPDGVM对陆地碳循环的精确模拟,也为CAS-ESM研究碳-气候相互作用的应用提供了重要参考.

Climate prediction of the seasonal sea-ice early melt onset in the Bering Sea

基于大尺度环流异常对海冰消融的影响过程,本文采用年际增量预测方法研制了白令海季节性海冰早期消融开始日期(EMO)的统计预测模型.预测模型选取了3个具有明确物理意义的预测因子:1月波弗特高压,前期11月东西伯利亚地区海平面气压,以及11月东欧平原积雪覆盖率。1月波弗特高压可以通过海气相互作用影响白令海地区海温异常,该海温异常能够从1月持续到3月,进而影响白令海EMO.11月东西伯利亚地区海平面气压与11月至次年2月北太平洋中纬度东部海温密切相关。伴随着北太平洋中纬度东部冷海温异常的出现,白令海地区会出现暖海温异常,进而导致白令海海冰范围减少,EMO较晚.1月北极偶极子异常是11月东欧平原积雪覆盖率影响次年白令海EMO的桥梁之一.1981-2022年的交叉检验结果表明:统计模型对白令海EMO具有较好的预测能力,预测与观测的EMO之间时间相关系数达到了0.45,超过了99%的置信水平.统计模型对白令海EMO正常年份和异常年份的预测准确率分别为60%和41%.

Seasonal Prediction of the Record-Breaking Northward Shift of the Western Pacific Subtropical High in July 2021

The unprecedented Zhengzhou heavy rainfall in July 2021 occurred under the background of a northward shift of the western Pacific subtropical high(WPSH).Although the occurrence of this extreme event could not be captured by seasonal predictions,a skillful prediction of the WPSH variation might have warned us of the increased probability of extreme weather events in Central and Northern China.However,the mechanism for the WPSH variation in July 2021 and its seasonal predictability are still unknown.Here,the observed northward shift of the WPSH in July 2021 is shown to correspond to a meridional dipole pattern of the 850-hPa geopotential height to the east of China,the amplitude of which became the strongest since 1979.The meridional dipole pattern is two nodes of the Pacific–Japan pattern.To investigate the predictability of the WPSH variation,a 21-member ensemble of seasonal predictions initiated from the end of June 2021 was conducted.The predictable and unpredictable components of the meridional dipole pattern were identified from the ensemble simulations.Its predictable component is driven by positive precipitation anomalies over the tropical western Pacific.The positive precipitation anomalies are caused by positive horizonal advection of the mean moist enthalpy by southwesterly anomalies to the northwestern flank of anticyclonic anomalies excited by the existing La Niña,which is skillfully predicted by the model.The leading mode of the unpredictable component is associated with the atmospheric internal intraseasonal oscillations,which are not initialized in the simulations.The relative contributions of the predictable and unpredictable components to the observed northward shift of the WPSH at 850 hPa are 28.0%and 72.0%,respectively.

Seasonal Prediction of Extreme High-Temperature Days in Southwestern China Based on the Physical Precursors

Extreme high temperatures frequently occur in southwestern China,significantly impacting the local ecological system and economic development.However,accurate prediction of extreme high-temperature days(EHDs)in this region is still an unresolved challenge.Based on the spatiotemporal characteristics of EHDs over China,a domain-averaged EHD index over southwestern China(SWC-EHDs)during April-May is defined.The simultaneous dynamic and thermodynamic fields associated with the increased SWC-EHDs are a local upper-level anticyclonic(high-pressure)anomaly and wavy geopotential height anomaly patterns over Eurasia.In tracing the origins of the lower boundary anomalies,two physically meaningful precursors are detected for SWC-EHDs.They are the tripolar SST change tendency from December-January to February-March in the northern Atlantic and the February-March mean snow depth in central Asia.Using these two selected predictors,a physics-based empirical model prediction was applied to the training period of 1961–2005 to obtain a skillful prediction of the EHDs index,attaining a correlation coefficient of 0.76 in the independent prediction period(2006–19),suggesting that 58%of the total SWC-EHDs variability is predictable.This study provides an estimate for the lower bound of the seasonal predictability of EHDs as well as for the hydrological drought over southwestern China.

Stable seasonal migration patterns in giant pandas

A critical function of animal movement is to maximize access to essential resources in temporally fluctuating and spatially heterogeneous environments.Seasonally mediated resource fluctuations may influence animal movements,enabling them to track changing resource distributions,resulting in annual migration patterns.The conservation-dependent giant panda(Ailuropoda melanoleuca) displays seasonal movement patterns;however,the key factor driving these seasonal migration patterns remains poorly understood.Here,we used GPS tracking collars to monitor the movements of six giant pandas over a 12-year period across different elevations,and performed statistical analysis of seasonal migration directions,routes,habitat revisitation,home range overlap,first arrival events,and stability.Our results revealed a compelling pattern of seasonal migrations that facilitated the ability of the pandas to forage at the appropriate time and place to maximize nutritional intake.Our results indicated that pandas utilize spatial memory to locate reliable food resources,as evidenced by their annual return to the same or similar winter and summer home ranges and the consistently maintained percentage of home range overlap.These novel insights into giant panda foraging and movement ecology not only enhance our understanding of its ability to adapt to nutritionally poor dietary resources but also provide important information for the development of resource utilization-based protection and management strategies.

Influence of Surface Types on the Seasonality and Inter-Model Spread of Arctic Amplification in CMIP6

A robust phenomenon termed the Arctic Amplification(AA)refers to the stronger warming taking place over the Arctic compared to the global mean.The AA can be confirmed through observations and reproduced in climate model simulations and shows significant seasonality and inter-model spread.This study focuses on the influence of surface type on the seasonality of AA and its inter-model spread by dividing the Arctic region into four surface types:ice-covered,ice-retreat,ice-free,and land.The magnitude and inter-model spread of Arctic surface warming are calculated from the difference between the abrupt-4×CO_(2)and pre-industrial experiments of 17 CMIP6 models.The change of effective thermal inertia(ETI)in response to the quadrupling of CO_(2) forcing is the leading mechanism for the seasonal energy transfer mechanism,which acts to store heat temporarily in summer and then release it in winter.The ETI change is strongest over the ice-retreat region,which is also responsible for the strongest AA among the four surface types.The lack of ETI change explains the nearly uniform warming pattern across seasons over the ice-free(ocean)region.Compared to other regions,the ice-covered region shows the maximum inter-model spread in JFM,resulting from a stronger inter-model spread in the oceanic heat storage term.However,the weaker upward surface turbulent sensible and latent heat fluxes tend to suppress the inter-model spread.The relatively small inter-model spread during summer is caused by the cancellation of the inter-model spread in ice-albedo feedback with that in the oceanic heat storage term.

Short-Term Wind Power Prediction Based on ICEEMDAN-SE-LSTM Neural Network Model with Classifying Seasonal

Wind power prediction is very important for the economic dispatching of power systems containing wind power.In this work,a novel short-term wind power prediction method based on improved complete ensemble empirical mode decomposition with adaptive noise(ICEEMDAN)and(long short-term memory)LSTM neural network is proposed and studied.First,the original data is prepossessed including removing outliers and filling in the gaps.Then,the random forest algorithm is used to sort the importance of each meteorological factor and determine the input climate characteristics of the forecast model.In addition,this study conducts seasonal classification of the annual data where ICEEMDAN is adopted to divide the original wind power sequence into numerous modal components according to different seasons.On this basis,sample entropy is used to calculate the complexity of each component and reconstruct them into trend components,oscillation components,and random components.Then,these three components are input into the LSTM neural network,respectively.Combined with the predicted values of the three components,the overall power prediction results are obtained.The simulation shows that ICEEMDAN-SE-LSTM achieves higher prediction accuracy ranging from 1.57%to 9.46%than other traditional models,which indicates the reliability and effectiveness of the proposed method for power prediction.

Seasonal Prediction Skill and Biases in GloSea5 Relating to the East Asia Winter Monsoon

The simulation and prediction of the climatology and interannual variability of the East Asia winter monsoon(EAWM),as well as the associated atmospheric circulation,was investigated using the hindcast data from Global Seasonal Forecast System version 5(GloSea5),with a focus on the evolution of model bias among different forecast lead times.While GloSea5 reproduces the climatological means of large-scale circulation systems related to the EAWM well,systematic biases exist,including a cold bias for most of China’s mainland,especially for North and Northeast China.GloSea5 shows robust skill in predicting the EAWM intensity index two months ahead,which can be attributed to the performance in representing the leading modes of surface air temperature and associated background circulation.GloSea5 realistically reproduces the synergistic effect of El Niño–Southern Oscillation(ENSO)and the Arctic Oscillation(AO)on the EAWM,especially for the western North Pacific anticyclone(WNPAC).Compared with the North Pacific and North America,the representation of circulation anomalies over Eurasia is poor,especially for sea level pressure(SLP),which limits the prediction skill for surface air temperature over East Asia.The representation of SLP anomalies might be associated with the model performance in simulating the interaction between atmospheric circulations and underlying surface conditions.

Chemical Composition of Sphyraena barracuda with Different Area, Size and Season, in Sudanese Red Sea Coast

This study aims to determine the effect of area, size and season on chemical composition of Sphyraena barracuda from Sudanese Red Sea Coast. A total of 180 fish samples were collected from Port Sudan and Sawakin fishing sites during the winter and summer of 2020-2021. The fishes sample ranged between (30 - 50) cm in total length and (250 - 1000) gm in body weight of small size, and ranged between (60 - 90) cm in total length and (1250 - 250) gm in body weight for large size. Fish flesh was subject to gross chemical analysis. Moisture, protein, fat, ash, acidity and pH were determined. The results indicated a significant effect by area, season and size. The different areas had a significant effect on crude protein and ash;higher parameter value is found in Sawakin. Higher chemical parameters were showed in winter than in summer. Also, size had a significant effect in all parameters (except moisture), fat and ash were high in large size. The study has concluded that the large size fishes of S. barracuda with high nutritional value when caught in winter from the southern region of Sawakin.

Time series analysis-based seasonal autoregressive fractionally integrated moving average to estimate hepatitis B and C epidemics in China

BACKGROUND Hepatitis B(HB)and hepatitis C(HC)place the largest burden in China,and a goal of eliminating them as a major public health threat by 2030 has been set.Making more informed and accurate forecasts of their spread is essential for developing effective strategies,heightening the requirement for early warning to deal with such a major public health threat.AIM To monitor HB and HC epidemics by the design of a paradigmatic seasonal autoregressive fractionally integrated moving average(SARFIMA)for projections into 2030,and to compare the effectiveness with the seasonal autoregressive integrated moving average(SARIMA).METHODS Monthly HB and HC incidence cases in China were obtained from January 2004 to June 2023.Descriptive analysis and the Hodrick-Prescott method were employed to identify trends and seasonality.Two periods(from January 2004 to June 2022 and from January 2004 to December 2015,respectively)were used as the training sets to develop both models,while the remaining periods served as the test sets to evaluate the forecasting accuracy.RESULTS There were incidents of 23400874 HB cases and 3590867 HC cases from January 2004 to June 2023.Overall,HB remained steady[average annual percentage change(AAPC)=0.44,95%confidence interval(95%CI):-0.94-1.84]while HC was increasing(AAPC=8.91,95%CI:6.98-10.88),and both had a peak in March and a trough in February.In the 12-step-ahead HB forecast,the mean absolute deviation(15211.94),root mean square error(18762.94),mean absolute percentage error(0.17),mean error rate(0.15),and root mean square percentage error(0.25)under the best SARFIMA(3,0,0)(0,0.449,2)12 were smaller than those under the best SARIMA(3,0,0)(0,1,2)12(16867.71,20775.12,0.19,0.17,and 0.27,respectively).Similar results were also observed for the 90-step-ahead HB,12-step-ahead HC,and 90-step-ahead HC forecasts.The predicted HB incidents totaled 9865400(95%CI:7508093-12222709)cases and HC totaled 1659485(95%CI:856681-2462290)cases during 2023-2030.CONCLUSION Under current interventions,China faces enormous challenges to eliminate HB and HC epidemics by 2030,and effective strategies must be reinforced.The integration of SARFIMA into public health for the management of HB and HC epidemics can potentially result in more informed and efficient interventions,surpassing the capabilities of SARIMA.

Using eye drops in compliance may improve the symptoms and quality of life in patients with seasonal allergic conjunctivitis: analysis of a two-season web survey

Dear Editor,Allergic conjunctivitis is the most common ocular surface allergic disease, affecting more than 30% of the population[1-2]. In the immediate phase of the allergic reactions(type Ⅰ) in patients with seasonal allergic conjunctivitis(SAC), the binding of antigen with the reaginic antibody leads to the formation and release of chemical mediators, such as histamines, in the local anaphylactic reaction[3].

Seasonal Forecasts of Precipitation during the First Rainy Season in South China Based on NUIST-CFS1.0

Current dynamical models experience great difficulties providing reliable seasonal forecasts of regional/local rainfall in South China.This study evaluates seasonal forecast skill for precipitation in the first rainy season(FRS,i.e.,April–June)over South China from 1982 to 2020 based on the global real-time Climate Forecast System of Nanjing University of Information Science and Technology(NUIST-CFS1.0,previously known as SINTEX-F).The potential predictability and the practical forecast skill of NUIST-CFS1.0 for FRS precipitation remain low in general.But NUIST-CFS1.0 still performs better than the average of nine international models in terms of correlation coefficient skill in predicting the interannual precipitation anomaly and its related circulation index.NUIST-CFS1.0 captures the anomalous Philippines anticyclone,which transports moisture and heat northward to South China,favoring more precipitation in South China during the FRS.By examining the correlations between sea surface temperature(SST)and FRS precipitation and the Philippines anticyclone,we find that the model reasonably captures SST-associated precipitation and circulation anomalies,which partly explains the predictability of FRS precipitation.A dynamical downscaling model with 30-km resolution forced by the large-scale circulations of the NUIST-CFS1.0 predictions could improve forecasts of the climatological states and extreme precipitation events.Our results also reveal interesting interdecadal changes in the predictive skill for FRS precipitation in South China based on the NUIST-CFS1.0 hindcasts.These results help improve the understanding and forecasts for FRS precipitation in South China.

The Influence of Local Rainy and Dry Seasons on the Diurnal Temperature Range in Nigeria

This study analyzed the impact of the local dry and rainy seasons on diurnal temperature range (DTR), for each major climatic zone of Nigeria namely the tropical monsoon, tropical savannah and semi-arid, using meteorological data from thirteen observation stations for the period 1981 to 2021. DTR was computed from the difference of minimum temperature from maximum temperature and yearly and forty one years’ monthly averages of DTR and rainfall were computed and plotted in different graphs. The overall results from each climatic zone showed that DTR fluctuates with the seasons and there is an inverse relationship between DTR and rainfall whereby the value of DTR decreases as the rainy season approaches but increases as the rainy season departs ushering in the dry season or conversely DTR increases as the dry season approaches and decreases as the dry season departs ushering-in the rainy season. Secondly, the average yearly patterns of rainfall and DTR are roughly and oppositely shaped parabolas where the peak value of rainfall is diametrically opposite to the trough value of DTR and the least or nil volume of rainfall corresponds to the highest value of DTR. Thirdly, due to the yearly seasonal cycle of dry and rainy seasons in Nigeria coupled with the inverse relationship between DTR and Rainfall, the seasonal plot of DTR and rainfall is also cyclic in pattern with DTR cycle lagging 180 degrees with the rainfall cycle and the intersection of the two cycles represents the departure of one season and onset of another season while each half-cycle represents either the dry or rainy season. Fourthly, the dependence of DTR on any season at hand in Nigeria makes DTR season-forcing. This fourth result is underpinned by a result that showed that the 1981 and 2021 patterns of DTR and 1981 and 2021 patterns of rainfall when compared were similar, the differences were in the volume of rainfall which was due to climate change that has taken place over the four decades and which also impacted DTR since DTR varies inversely with rainfall. Finally and notwithstanding the common grounds of the results stated above, the result further showed that each climatic zone of Nigeria reacts differently to the local and global climate changes leading to the magnitude of DTR and the volume of rainfall being different across climatic zones, with rainfall volume and duration decreasing towards the arid North from the Coastal South while contrariwise DTR increases towards the arid North from the Coastal South.