Seasonal Mean Temperature Research Articles

ИЗМЕНЕНИЯ КЛИМАТА И ЦВЕТЕНИЕ АРКТИЧЕСКОГО ОЗЕРА ИМАНДРА

Trends of meteorological parameters potentially implicated in harmful algal blooms regularly occurring since early 2000-ies in a major Arctic freshwater basin the lake Imandra have been analyzed. Compared with the preceding period, increases occurred in mean annual and seasonal air temperatures, sunshine duration, and solar radiation, whereas decreases occurred in spring cloudiness and winter precipitates. These changes resulted in altered light regimens in the lake, which were associated with an increased availability of photosynthetically relevant radiation in springtime. In combination with an increased biogenic matter load, this could lead to drastic changes in the structure of phytoplanktonic communities and in their seasonal successions, as well as in biogenic matter dynamics. Based on these considerations and published data, a conceptual model of the emergence and development of regular harmful algal blooms in the lake Imandra is suggested.

Information entropy‐based projection of temperature variability under the warming trend in China

AbstractQuantifying the predictability of temperature in the future is a priority to inform policy and adaptation planning. Based on information entropy theory, this study uses global climate models within the Coupled Model Intercomparison Project Phase 6 (CMIP6) to project spatiotemporal changes and variability of temperature in China in the 21st century under different shared socio‐economic pathway scenarios (SSP2‐4.5 and SSP5‐8.5). Results show that the multimodel ensembled values can accurately capture the spatiotemporal pattern of temperature in China. The warming rates of annual mean temperature in China under SSP2‐4.5, SSP5‐8.5 scenarios are 0.42 and 0.61°C·decade−1 in the near‐term (2021–2050), 0.20 and 0.87°C·decade−1 in the long‐term (2071–2100), respectively. Warming is found to be the greatest in winter. Northeast, northwest, and the first region of southwest are anticipated to increase particularly rapidly, with larger increases than the rest of China. The intrayear variability of temperature (the variability of monthly mean temperature within the year or different seasons) in China shows obvious latitudinal differences. The variability of monthly mean temperature exhibits a decreasing trend in parts of northeast and the first region of southwest, and an increasing trend in northwest and parts of north. This is mainly resultant from the variability of monthly mean temperatures within spring and autumn. And this trend is more significant under the SSP5‐8.5 scenario than that under the SSP2‐4.5 scenario. The interannual variability of temperature (the variability of annual or seasonal mean temperature) in China shows significant regional difference, and two obvious regions with extreme variability (northeast and the first region of southwest). The trend of variability increases and then decreases under the SSP2‐4.5 scenario, whereas it keeps increasing under the SSP5‐8.5 scenario. This study helps to understand the spatiotemporal characteristics of temperature variability in China in the 21st century.

Impact of Riverine Fresh Water on Indian Summer Monsoon: Coupling a Runoff Routing Model to a Global Seasonal Forecast Model

Rivers form an essential component of the earth system, with ~36,000 km3 of riverine freshwater being dumped into the global oceans every year. The role of rivers in controlling the sea-surface salinity and ensuing air-sea interactions in the Bay of Bengal (BoB) is well-known from observational studies; however, attempts to include rivers in coupled models used for seasonal prediction have been limited. This study reports the benefits of river routing in coupled models over prescribing observational river discharge and the impact on the Indian Summer Monsoon (ISM) simulation. Seasonal hindcasts are carried out using a state-of-the-art global coupled ocean-atmosphere-land-sea ice model, Climate Forecast System version 2, coupled to a runoff routing model. It is demonstrated that such a coupling leads to a better representation of the upper ocean stratification in northern BoB, causes mixed layer warming during July-August, and imparts a significant inter-annual variability to the mixed layer heat budget. The rainfall-runoff coupled feedback associated with ISM is captured better, and remote teleconnections with the equatorial Pacific are enhanced. Improved seasonal mean temperature and salinity profiles in the northern BoB lead to the formation of a thicker barrier layer, which is closely tied to the freshwater from rivers. These processes result in an overall enhancement of the ISM rainfall simulation skill, which stems from scale interactions between the sub-seasonal and seasonal variability of ISM. A significant community effort is required to reduce biases in land-surface processes to improve streamflow simulations, along with better parameterization of mixing of river water with the ocean.

Spatio-Statistical Analysis of Temperature and Trend Detection in Baluchistan, Pakistan

The assessment of temperature variability in any region of the world is important for prior planning in the context of current climate change. The mean monthly, seasonal and annual temperature data of nine meteorological stations was analysed in this study to determine the temperature variability and trend in Baluchistan province, Pakistan. The non-parametric Mann–Kendall and Sen’s slope estimator were applied to investigate the temperature trend pattern in the study Temperature data from the relevant meteorological stations (met-stations) were collected for this purpose from the Pakistan Meteorological Department and the National Center for Environmental Information. The result revealed an increasing significant trend (IST) in the mean annual temperature at Dalbandin, Kalat, Quetta, Zhob and Gawadar meteorological stations. While the seasonal minimum, maximum and mean temperature showed trend fluctuations in winter, spring and autumn seasons. In the seasonal analysis, the increasing trend has been detected in most of the meteorological stations (Dalbandin, Kalat, Quetta, Zhob, Khuzdar, Lasbella, Gawadar) in all four seasons. Whereas in Sibi met-station, a decreasing significant trend (DST) was recorded in summer (minimum, maximum and mean) and the met-station Barkhan recorded decreasing trend in maximum temperature in the autumn season. The Baluchistan province is totally spread over an arid and semiarid area of Pakistan where temperature fluctuations further aggravate the deprived agriculture and sparse natural vegetation.

The effect of climatic factors on the number of malaria cases in an inland and a coastal setting from 2011 to 2017 in the equatorial rain forest of Cameroon

BackgroundWeather fluctuation affects the incidence of malaria through a network of causuative pathays. Globally, human activities have ultered weather conditions over time, and consequently the number of malaria cases. This study aimed at determining the influence of humidity, temperature and rainfall on malaria incidence in an inland (Muyuka) and a coastal (Tiko) settings for a period of seven years (2011–2017) as well as predict the number of malaria cases two years after (2018 and 2019).MethodsMalaria data for Muyuka Health District (MHD) and Tiko Health District (THD) were obtained from the Regional Delegation of Public Health and Tiko District Health service respectively. Climate data for MHD was obtained from the Regional Delegation of Transport while that of THD was gotten from Cameroon Development Coorporation. Spearman rank correlation was used to investigate the relationship between number of malaria cases and the weather variables and the simple seasonal model was used to forecast the number of malaria cases for 2018 and 2019.ResultsThe mean monthly rainfall, temperature and relative humidity for MHD were 200.38 mm, 27.050C, 82.35% and THD were 207.36 mm, 27.57 °C and 84.32% respectively, with a total number of malaria cases of 56,745 and 40,160. In MHD, mean yearly humidity strongly correlated negatively with number of malaria cases (r = − 0.811, p = 0.027) but in THD, a moderate negative yearly correlation was observed (r = − 0.595, p = 0.159). In THD, the mean seasonal temperature moderately correlated (r = 0.599, p = 0.024) positively with the number of malaria cases, whereas MHD had a very weak negative correlation (r = − 0.174, p = 0.551). Likewise mean seasonal rainfall in THD moderately correlated (r = − 0.559, p = 0.038) negatively with malaria cases, contrary to MHD which showed a very weak positive correlation (r = 0.425, p = 0.130). The simple seasonal model predicted 6,842 malaria cases in Muyuka, for 2018 and same number for 2019, while 3167 cases were observed in 2018 and 2848 in 2019. Also 6,738 cases of malaria were predicted for MHD in 2018 likewise 2019, but 7327 cases were observed in 2018 and 21,735 cases in 2019.ConclusionHumidity is the principal climatic variable that negatively influences malaria cases in MHD, while higher seasonal temperatures and lower seasonal rain fall significantly increase malaria cases in THD.

Climate variability and trends in the Endorheic Lake Hayk basin: implications for Lake Hayk water level changes in the lake basin, Ethiopia

For sustainable climate hazard management, climate information at the local level is crucial for developing countries like Ethiopia, which has an exceptionally low adaptive capacity to changes in climate. As a result, rather than making local decisions based on findings from national-scale studies, the study area’s research gap of local level climate variability and trend was investigated within the endorheic Lake Hayk basin for the historic series from 1986 to 2015 to scientifically show climate change/variability implications on Lake Hayk water level variations. To achieve the study objective, the precipitation, temperature (station and ERA5 reanalysis product) and Lake water level (patchy lake level fused with remotely sensed water areas) were examined using various statistical approaches with the integration of remote sensing and geographical information system. The major findings revealed less variable (CV = 16.66%) and statistically nonsignificant declining trends (− 33.94 mm/decade) in annual precipitation. Belg and bega seasonal precipitations showed highly variable (CV = 43.94–47.46%) nonsignificant downward trends of 4.57 mm and 22 mm per decade respectively, whereas kiremt precipitation showed moderate variability (CV = 23.80%) and increased nonsignificantly at a rate of 21.89 mm per decade. Annual and seasonal mean temperatures exhibited less variable but statistically significant rising trends ranging from 0.20 to 0.45 °C each decade. The lake’s water level showed less variable nonsignificant rising trends (70 mm to 120 mm per year) on annual and seasonal scales between 1999 and 2005. The lake's surface area has decreased from 2243.33 ha in 2005 to 2165.33 ha in 2011. During 2011–2015, it demonstrated a high variable (CV > 30%) statistically significant dropping trend ranging from − 250 mm to − 340 mm per year on annual and seasonal periods. The study discovered that the endorheic lake Hayk basin was dominated by variable and diminishing precipitation, as well as continuous warming trends, particularly in recent years. It is argued that this climatic state was having a negative impact on Lake Hayk’s substantially dropping water level, necessitating an immediate basin-based water management decision to save Lake Hayk from extinction.

Guiding Conservation for Mountain Tree Species in Lebanon

The objective of this study is to contribute to the conservation of upland tree species in the face of climate change. We used a conservation index to prioritize the areas and populations of three conifer species in the mountains of Lebanon. This conservation index integrates (1) mountain topography to identify areas that could provide a suitable microclimate, (2) genetic diversity to assess the adaptive capacity of populations in these mountain areas, and (3) a hypothetical climate change scenario that could affect this Mediterranean region. The idea of this index is to prioritize protected areas based on a match between the relevance of the area to be protected and the populations that need local and long-term protection. The stronger the match, the higher the priority of the area to be protected. We applied this conservation index to 36 populations of 15 fir, 15 cedar, and 6 juniper. These populations were genotyped by different authors whose published data we used. The results show that 10 populations of the 3 species have a very high index and 9 others have a lower but still high index, indicating a high conservation priority. These 19 populations occur in 5 different areas that we delineated and that form a network along the Lebanon Mountains. We hypothesize that the conservation of these 19 populations across the Lebanon Mountains could contribute to the long-term sustainability of the 3 species in the face of a 2 °C increase in mean seasonal temperature and a 20% decrease in seasonal precipitation compared to the current climate.

Interactive effect of irrigation and temperature regimes on growth and development of kidney bean (Phaseolus vulgaris L.)

Approximately 60 percent of kidney bean (Phaseolus vulgaris L.) produced in developing countries is cultivated under conditions of drought stress and low fertility/ low input. Coincidence of water and heat stress, which is projected to occur more in changing climates, cause severe yield loss. To quantify the response of kidney bean to water and temperature stress, an experiment was carried under Randomized Block Design in field and temperature gradient tunnel conditions. Temperature treatments included high temperature for entire crop growth period (~4.6℃, ~3.1℃, ~2.8℃ above mean ambient, 19.6℃), ambient temperature, elevated temperature during pre-flowering phase (~3.7℃ above mean ambient during that period, 19.6℃), flowering phase (~5.3℃ above mean ambient during that period, 23.1℃) and pod-filling phase (~5.2℃ above mean ambient during that period, 28.9℃). These were combined with i) two irrigations (applied at sowing and seedling phase) and ii) three irrigations (additional irrigation at pod-filling phase). Results indicated that plants under seasonal mean temperatures of 36.7/11.7℃, 33.9/11.6℃ and 33.1/11.7℃ had shortened crop duration with 73-78 DAS. Findings this study indicated that kidney bean exhibits phenological plasticity response to temperature and water regimes. Seasonal mean temperature of 24.2℃ lead to better performance of the crop while temperature shocks during pre-flowering and flowering significantly reduced seed yield. Additionally, exposure of kidney bean plants to water stress (available soil moisture content <10%) significantly reduced seed yield. Coincidence of temperature stress and water stress had more detrimental effects on seed yield. Given the climate scenarios indicating increased variability in temperature and rainfall, findings from this study indicate that climatic risks mayaffect kidney bean seed yield in future. Strategies such as providing supplemental irrigation can alleviate the negative effects of temperature stress and improve kidney bean yield.

Assessment of the TREELIM model in predicting present treeline along a longitudinal continentality-maritimity gradient in south-western Norway

ABSTRACT Treelines driven by climatic conditions are characteristic of alpine areas. This study assesses the degree of accuracy with which the TREELIM model predicts present treeline elevation along a regional continentality-maritimity gradient in southwest Norway. The study area consists of 15 sites chosen along this gradient, wherein observed treelines range from nearly sea level to over 1200 m a.s.l. The TREELIM model calculates treeline position based solely on meteorological parameters obtained from gridded climate datasets. The model assumes that tree growth occurs if the length of the growing season (LGS) exceeds a minimum duration and the seasonal mean temperature (SMT) does not fall below a certain value. Six different scenarios were developed in order to determine the effects of changing precipitation and drought within TREELIM. Within these scenarios, three different SMTs were used in order to model the treeline position. Model performance was evaluated by comparing resultant treeline elevation with observed values. Results of experiments using lapse rate-based temperature input data were compared to those using the gridded climate data. Statistical analysis determined that the lapse rate-based temperature scenario removing drought constraints and using a SMT of 6.4°C gave the most accurate prediction of treeline position, and a relatively uniform growing season ranging from 110 to 128 days. Precipitation and snow cover do not significantly affect treeline elevation. The results suggest that TREELIM accurately predicts the treeline in most cases, but is less suited in areas with steeply varying topography.

Aerosol indirect effects in complex-orography areas: a numerical study over the Great Alpine Region

Abstract. Aerosols play a crucial role in climate through different feedback mechanisms, affecting radiation, clouds, and air column stability. This study focuses on the altitude dependence of the cloud-mediated indirect effects of aerosols in the Great Alpine Region (GAR), an area characterized by high pollution levels from anthropic activities in the Po Valley and a complex orography with some of the highest mountains in Europe. Using a regional atmospheric model, 5-year-long convective-permitting sensitivity experiments have been run with different surface aerosol fluxes. The results show that seasonal mean cloud cover, temperature, and precipitations are affected by the aerosol concentrations in the air column and that the response to pollution is both elevation- and season-dependent. The overall cloud cover increase with aerosol levels leads to either surface cooling or warming depending on the surface albedo (snow covered or not). Furthermore, different types of clouds have a different response: while the lifetime of low-pressure-system clouds and orographic clouds is generally increased at high levels of aerosols, convective clouds (typical of the summer season) can decrease at high levels of pollution due to the reduction in strong updrafts associated with an increased air column stability.

A Trend Analysis of Climatic Variables in the Karimganj District of Assam, India

Objectives: The main goal of this research is to analyse the trends of climatic variables in the Karimganj district of Assam. The study examines the trend of seasonal and annual temperature maximum, temperature minimum, rainfall and relative humidity for the period 1981-2020. Methods: A non-parametric Mann-Kendall (MK) technique has been employed to establish statistically significant trend in the series. The magnitude of a time series trend has been evaluated by Sen’s slope test. Trend lines and time series graphs are used for better understanding of trends. Findings: It has been found that annual and seasonal temperature maximum (Tmax) have a significant negative trend over the period except post monsoon season. There is no significant trend in the annual temperature minimum (Tmin) series but seasonal mean temperature minimum is showing a significant positive trend at different levels of significance during all four seasons. Except winter season, annual rainfall series and all other seasonal average rainfall series are showing significant positive trends. The seasonal averages and annual relative humidity are showing statistically significant positive trends over the period. Novelty: The study is unique and innovative for Karimganj district of Assam to best of our knowledge. From the analysis, the study claims that climate change is occurring in the district. To combat the impact of climate change, participation of people in adaptations practices and government intervention in the form of mitigation policies are necessary. JEL Classification: Q50, Q54 Keywords: Karimgani; climate change; rainfall; temperature; relative humidity; Assam

Dominant species as biological indicators to predict the changes of trace element in different types of rangeland

Alpine meadow, typical steppe and desert are globally important rangeland ecosystems. However, the seasonal variations in concentrations of trace elements, which are important nutrients for grazing livestock, are unclear in these types of rangeland and it is difficult to prevent disorders in grazing livestock caused by trace element excesses and deficiencies. In order to identify which forage species might serve as indicators of the trace element status of livestock forage, we investigated the seasonal trends in Fe, Mn, Cu, and Zn concentrations in the plant communities and dominant species from diverse rangeland types in northwest China in relation to livestock sufficiency levels. The trace element concentrations in the plant communities changed significantly with the season, with the highest Cu, Mn and Zn concentrations and the lowest Fe and Mn concentrations detected in spring and summer, respectively. The seasonal mean temperature, seasonal precipitation, and above-ground biomass significantly negatively correlated with the seasonal variation of Fe and Mn concentrations in the plant community. However, there was almost no significant correlation between these factors and the seasonal Cu and Zn concentrations. The plant concentrations of Fe and Mn were much higher than the recommended levels for livestock in all rangeland types, but the concentrations of Cu in the alpine meadow and the Zn concentrations in all the rangelands barely met the nutritional requirements for animal growth. Based on these deficiencies, we suggest that some forage species could be used as biological indicators to predict changes in the concentrations of trace elements. A decrease in the relative biomass of Kobresia capillifolia and Kobresia humilis in alpine meadows implies a decrease in the levels of Cu in the plant community. A decrease in the relative biomass of K. humilis in alpine meadows, an increase in Stipa bungeana in typical steppes and an increase in Nitraria tangutorum and Salsola passertine in desert rangelands lead to decreased concentrations of Zn in the plant community.

Seasonal circulation regimes in the North Atlantic: Towards a new seasonality

AbstractEuropean climate variability is shaped by atmospheric dynamics over the North Atlantic and local processes. Better understanding their future seasonality is essential to anticipate changes in weather conditions for human and natural systems. We explore atmospheric seasonality over 1979–2017 and 1979–2100 with seasonal circulation regimes (SCRs), by clustering year‐round daily fields of Z500 from the ERA‐Interim reanalysis and 12 Coupled Model Intercomparison Project phase 5 (CMIP5) climate models (historical and RCP8.5 runs). The spatial and temporal variability of SCR structures and associated patterns of surface air temperature are investigated. Climate models have biases but reproduce structures and evolutions of SCRs similar to the reanalysis over 1979–2017: decreasing frequency of winter conditions (starting later and ending earlier in the year) and the opposite for summer conditions. These changes are stronger over 1979–2100 than over 1979–2017, associated with a large increase of North Atlantic seasonal mean Z500 and temperature. When using more SCRs (more freedom in definition of seasonality), the changes over 1979–2100 correspond to a long‐term swap between SCRs, resulting in similar structures (annual cycle and spatial patterns) relative to the evolution of seasonal mean Z500 and temperature. To understand whether the evolution of SCRs is linked to uniform warming, or to changes in circulation patterns, we remove the calendar trend in the Z500 regional average to define SCRs based on detrended data (d‐SCRs). The temporal properties of d‐SCRs appear almost constant whereas their spatial patterns change, indicating that the calendar Z500 regional trend drives the evolutions of SCRs, and that changing spatial patterns in d‐SCRs account for the heterogeneity of this trend. Our study suggests that historical winter conditions will continue to decrease in the future while historical summer conditions continue to increase. However, it also suggests that the spatial and temporal patterns of SCRs would remain similar, relatively to the year‐round Z500 increase.

Molecular phylogeography and species distribution modelling evidence of \u2018oceanic\u2019 adaptation for Actinidia eriantha with a refugium along the oceanic\u2013continental gradient in a biodiversity hotspot

BackgroundRefugia is considered to be critical for maintaining biodiversity; while discerning the type and pattern of refugia is pivotal for our understanding of evolutionary processes in the context of conservation. Interglacial and glacial refugia have been studied throughout subtropical China. However, studies on refugia along the oceanic–continental gradient have largely been ignored. We used a liana Actinidia eriantha, which occurs across the eastern moist evergreen broad-leaved forests of subtropical China, as a case study to test hypotheses of refugia along the oceanic–continental gradient and ‘oceanic’ adaptation.ResultsThe phylogeographic pattern of A. eriantha was explored using a combination of three cpDNA markers and 38 nuclear microsatellite loci, Species distribution modelling and dispersal corridors analysis. Our data showed intermediate levels of genetic diversity [haplotype diversity (hT) = 0.498; unbiased expected heterozygosity (UHE) = 0.510] both at the species and population level. Microsatellite loci revealed five clusters largely corresponding to geographic regions. Coalescent time of cpDNA lineages was dated to the middle Pliocene (ca. 4.03 Ma). Both geographic distance and climate difference have important roles for intraspecific divergence of the species. The Zhejiang-Fujian Hilly Region was demonstrated to be a refugium along the oceanic–continental gradient of the species and fit the ‘refugia in refugia’ pattern. Species distribution modelling analysis indicated that Precipitation of Coldest Quarter (importance of 44%), Temperature Seasonality (29%) and Mean Temperature of Wettest Quarter (25%) contributed the most to model development. By checking the isolines in the three climate layers, we found that A. eriantha prefer higher precipitation during the coldest quarter, lower seasonal temperature difference and lower mean temperature during the wettest quarter, which correspond to ‘oceanic’ adaptation. Actinidia eriantha expanded to its western distribution range along the dispersal corridor repeatedly during the glacial periods.ConclusionsOverall, our results provide integrated evidence demonstrating that the Zhejiang-Fujian Hilly Region is a refugium along the oceanic–continental gradient of Actinidia eriantha in subtropical China and that speciation is attributed to ‘oceanic’ adaptation. This study gives a deeper understanding of the refugia in subtropical China and will contribute to the conservation and utilization of kiwifruit wild resources in the context of climate change.

ТЕМПЕРАТУРНЫЙ РЕЖИМ Г. МУРМАНСК ЗА ПОСЛЕДНИЕ 70 ЛЕТ: ИЗМЕНЧИВОСТЬ И ЭКСТРЕМАЛЬНЫЕ СОБЫТИЯ

Extreme temperature events and their long-term variability are investigated using daily air temperature data from Murmansk weather station for the period of 1942-2018. It was found that the variability of extremely warm and cold events in Murmansk in winter is mainly explained rather by the dynamics of storm tracks than by the local processes of air mass transformation in the region. It is shown that the main characteristics of extreme events demonstrate a clearly pronounced seasonality. It was revealed that the positive trend in seasonal mean temperatures is mainly due to the reduced number of extremely cold events in Murmansk. The study shows that the leading factor determining the variability of extreme events is the Arctic Oscillation (AO). In turn, the AO anomalies are formed as a result of the dynamic stratosphere-troposphere coupling, which is expressed in the response of storm tracks to sudden stratospheric warmings.

Water Demand in Maize Is Projected to Decrease under Changing Climate in India

Crop stage-specific information on the impacts of projected climate change on crop and irrigation water requirements are essential for improving productivity. This study investigated the possible implications of projected climate change on the phenology, effective rainfall (Peff), crop (CWR) and irrigation water requirements (IWR) of maize in eight locations in India. CWR, Peff and IWR were estimated for seven crop stages viz., emergence, 5th leaf stage, tasseling, silking, milking, dough and maturity during the baseline (1980–2009) and near-century (2022–39) using climate data derived from a subset of 29 general circulation models. The results indicated that mean seasonal maximum temperature, minimum temperature and rainfall were projected to increase in all the locations. Hence, the total crop duration (3–7 days), CWR (8–69 mm) and IWR (1–54 mm) were projected to decrease. The study could identify the specific stages in which the greatest reduction in crop duration, CWR and IWR would occur. Such information will be of immense help to farmers and varietal improvement programs in the study regions in the near future.

Testing mean air temperature trends in southern Greece: A Bayesian approach

AbstractThis study investigates the variability and trends of the mean annual and seasonal surface air temperature in southern Greece, namely in Crete and in the islands of the southern Aegean Sea. The climatic data used are the mean monthly homogenized air temperature values of seven meteorological stations of the Hellenic Meteorological Service, for the period 1960–2004. A Bayesian trend analysis is performed by fitting alternative stochastic‐trend models based on the Log‐normal, the Normal, the Generalized Extreme Value and the Gumbel distributions. Posterior simulations results based on the best fitting model reveal the locations and time‐periods with the strongest and the weakest interannual trends for both mean and variance. Furthermore, a model comparison experiment based on Bayes Factors compares the above stochastic‐trend models with the corresponding ones with no‐time trends. The results are very significant since they reveal the locations and the seasons where interannual trends for both mean and variance have real grounds.

The Gavkhouni Wetland Dryness and Its Impact on Air Temperature Variability in the Eastern Part of the Zayandeh-Rud River Basin, Iran

The Gavkhouni wetland provides many environmental and economic benefits for the central region of Iran. In recent decades, it has completely dried up several times with substantial impacts on local ecosystems and climate. Remote sensing-based Land Surface Temperature (LST), Normalized Difference Water Index (NDWI) and Normalized Difference Vegetation Index (NDVI) in combination with in-situ data were used to investigate the trend of the Gavkhouni wetland dryness and the associated impact on the variability of local air temperature. The results indicate that the wetland has increasingly experienced drier conditions since the year 2000. The wetland was almost completely dry in 2009, 2011, 2015 and 2017. In addition, the results show that Gavkhouni wetland dryness has a significant impact on local climate, increasing the mean seasonal air temperature by ~1.6 °C and ~1 °C in spring and summer, respectively. Overall, this study shows that remote sensing imagery is a valuable source for monitoring dryness and air temperature variations in the region. Moreover, the results provide a basis for effective water allocation decisions to maintain the hydrological and ecological functionality of the Gavkhouni wetland. Considering that many factors such as latitude, cloud cover, and the direction of prevailing winds affect land surface and air temperatures, it is suggested to use a numerical climate model to improve a regional understanding of the effects of wetland dryness on the surrounding climate.

Climate Change and Its Impact on Agriculture with Special Reference to Production System in India

Agriculture production is directly dependent on climate change and weather. Climate change can bring positive and negative effects on Indian agriculture, India’s agriculture is more dependent on monsoon from the ancient periods. Any change in monsoon trend drastically affects agriculture. Even the increasing temperature is affecting the Indian agriculture. The term “weather” refers to the short-term (daily) changes in temperature, wind, and/or rainfall of a region. In the long run, the climatic change could affect agriculture in several ways such as quantity and quality of crops in terms of productivity, growth rates, photosynthesis and transpiration taxes, wetness accessibility etc. Climate change is likely to directly impact food production across the globe. Increase in the mean seasonal temperature can reduce the duration of many crops and hence reduce the yield. In areas where temperatures are already close to the physiological maxima for crops, warming will impact produces more immediately. Motorists of climate change through alterations in atmospheric arrangement can also influence food production directly by its impacts on plant physiology. The consequences of agriculture’s contribution to climate change, and of climate change’s negative impact on agriculture, are severe which is projected to have a great impact on food production and may threaten the food security and hence, need special agricultural measures to struggle with. In the Indo-Gangetic Plain, these pre monsoon changes will primarily affect the wheat crop (>0.5oC increase in time slice 2010-2039). In the states of Jharkhand, Odisha and Chhattisgarh alone, rice production losses during severe deficiencies (about one year in five) average about 40% of total production, with an estimated value of $800 million. The consequence of climate change poses many threats; one of the important significances is bringing about changes in the quality and quantity water resources and agriculture productivity This study focus on study about the climate change how affecting the agriculture production in India different regions. Keywords: Climate Change: Impact: Agriculture Production: Temperature

Patterns and driving factors of leaf C, N, and P stoichiometry in two forest types with different stand ages in a mid-subtropical zone

Carbon (C), nitrogen (N), and phosphorus (P) stoichiometry is a key indicator of nutrient utilization in plants, and C/N/P ratios are related to the life histories and adaptation strategies of tree species. However, no consensus has been reached on how leaf stoichiometric characteristics are affected by forest type and stand ages. The relationships between leaf stoichiometry and geographical, meteorological, and soil factors also remain poorly understood. Leaf and soil were sampled from forest stands of different age groups (young, middle-aged, near-mature, and mature) in two forest types (Chinese fir (Cunninghamia lanceolata) forests and evergreen broadleaved forests). The relationships between leaf C, N, and P stoichiometric parameters and geographical, meteorological, and soil factors were analysed by using redundancy analysis (RDA) and stepwise linear regression analysis. Leaf C concentrations peaked in the near-mature stands with increasing age irrespective of forest type. Leaf N and P concentrations fluctuated with a rising trend in Chinese fir forests, while decreased first and increased later from young to mature phases in natural evergreen broadleaved forests. Chinese fir forests were primarily limited by N and P, while natural evergreen broadleaved forests were more susceptible to P limitation. Leaf C, N, and P stoichiometric characteristics in Chinese fir forests were mainly affected by the soil total P concentration (SP), longitude (LNG), growing season precipitation (GSP) and mean temperature in July (JUT). The leaf C concentration was mainly affected by GSP and JUT; leaf N and P concentrations were both positively correlated with LNG; and leaf P was positively correlated with SP. In evergreen broadleaved forests, however, leaf stoichiometric parameters displayed significant correlations with latitude (LAT) and mean annual precipitation (MAP). Leaf stoichiometry differed among forest stands of different age groups and forest types. Leaf C, N, and P stoichiometry was primarily explained by the combinations of SP, LNG, GSP and JUT in Chinese fir forests. LAT and MAP were the main controlling factors affecting the variations in the leaf C, N, and P status in natural evergreen broadleaved forests, which supports the temperature-plant physiological hypothesis. These findings improve the understanding of the distribution patterns and driving mechanisms of leaf stoichiometry linked with stand age and forest type.