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1.
In this study, the hierarchical clustering technique, called Ward method, was applied for grouping common features of air temperature series, precipitation total and relative humidity series of 244 stations in Turkey. Results of clustering exhibited the impact of physical geographical features of Turkey, such as topography, orography, land–sea distribution and the high Anatolian peninsula on the geographical variability. Based on the monthly series of nine climatological observations recorded for the period of 1970–2010, 12 and 14 clusters of climate zones are determined. However, from the comparative analyses, it is decided that 14 clusters represent the climate of Turkey more realistically. These clusters are named as (1) Dry Summer Subtropical Semihumid Coastal Aegean Region; (2) Dry-Subhumid Mid-Western Anatolia Region; (3 and 4) Dry Summer Subtropical Humid Coastal Mediterranean region [(3) West coast Mediterranean and (4) Eastern Mediterranean sub-regions]; (5) Semihumid Eastern Marmara Transition Sub-region; (6) Dry Summer Subtropical Semihumid/Semiarid Continental Mediterranean region; (7) Semihumid Cold Continental Eastern Anatolia region; (8) Dry-subhumid/Semiarid Continental Central Anatolia Region; (9 and 10) Mid-latitude Humid Temperate Coastal Black Sea Region [(9) West Coast Black Sea and (10) East Coast Black Sea sub-regions]; (11) Semihumid Western Marmara Transition Sub-region; (12) Semihumid Continental Central to Eastern Anatolia Sub-region; (13) Rainy Summer Semihumid Cold Continental Northeastern Anatolia Sub-region; and (14) Semihumid Continental Mediterranean to Eastern Anatolia Transition Sub-region. We believe that this study can be considered as a reference for the other climate-related researches of Turkey, and can be useful for the detection of Turkish climate regions, which are obtained by a long-term time course dataset having many meteorological variables.  相似文献   

2.
In the Sahel region, seasonal predictions are crucial to alleviate the impacts of climate variability on populations' livelihoods. Agricultural planning (e.g., decisions about sowing date, fertilizer application date, and choice of crop or cultivar) is based on empirical predictive indices whose accuracy to date has not been scientifically proven. This paper attempts to statistically test whether the pattern of rainfall distribution over the May–July period contributes to predicting the real onset date and the nature (wet or dry) of the rainy season, as farmers believe. To that end, we considered historical records of daily rainfall from 51 stations spanning the period 1920–2008 and the different agro-climatic zones in Burkina Faso. We performed (1) principal component analysis to identify climatic zones, based on the patterns of intra-seasonal rainfall, (2) and linear discriminant analysis to find the best rainfall-based variables to distinguish between real and false onset dates of the rainy season, and between wet and dry seasons in each climatic zone. A total of nine climatic zones were identified in each of which, based on rainfall records from May to July, we derived linear discriminant functions to correctly predict the nature of a potential onset date of the rainy season (real or false) and that of the rainy season (dry or wet) in at least three cases out of five. These functions should contribute to alleviating the negative impacts of climate variability in the different climatic zones of Burkina Faso.  相似文献   

3.
Rapidly accelerating climate change in the Himalaya is projected to have major implications for montane species, ecosystems, and mountain farming and pastoral systems. A geospatial modeling approach based on a global environmental stratification is used to explore potential impacts of projected climate change on the spatial distribution of bioclimatic strata and ecoregions within the transboundary Kailash Sacred Landscape (KSL) of China, India and Nepal. Twenty-eight strata, comprising seven bioclimatic zones, were aggregated to develop an ecoregional classification of 12 ecoregions (generally defined by their potential dominant vegetation type), based upon vegetation and landcover characteristics. Projected climate change impacts were modeled by reconstructing the stratification based upon an ensemble of 19 Earth System Models (CIMP5) across four Representative Concentration Pathways (RCP) emission scenarios (i.e. 63 impact simulations), and identifying the change in spatial distribution of bioclimatic zones and ecoregions. Large and substantial shifts in bioclimatic conditions can be expected throughout the KSL area by the year 2050, within all bioclimatic zones and ecoregions. Over 76 % of the total area may shift to a different stratum, 55 % to a different bioclimatic zone, and 36.6 % to a different ecoregion. Potential impacts include upward shift in mean elevation of bioclimatic zones (357 m) and ecoregions (371 m), decreases in area of the highest elevation zones and ecoregions, large expansion of the lower tropical and sub-tropical zones and ecoregions, and the disappearance of several strata representing unique bioclimatic conditions within the KSL, with potentially high levels of biotic perturbance by 2050, and a high likelihood of major consequences for biodiversity, ecosystems, ecosystem services, conservation efforts and sustainable development policies in the region.  相似文献   

4.
Summary Air temperature, absolute humidity and wind speed are the most important meteorological parameters that affect human thermal comfort. Because of heat loss, the human body feels air temperatures different to actual temperatures. Wind speed is the most practical element for consideration in terms of human comfort. In winter, due to the strong wind speeds, the sensible temperature is generally colder than the air temperature. This uncomfortable condition can cause problems related to tourism, heating and cooling. In this study, the spatial and temporal distributions of cooling temperatures and Wind Chill Index (WCI) are analyzed for Turkey, and their effect on the human body is considered. In this paper, monthly cooling temperatures between October and March in the years 1929 to 1990 are calculated by using measured temperature and wind speed at 79 stations in Turkey. The influence of wind chill is especially observed in the regions of the Aegean, west and middle Black Sea and east and central Anatolia. The wind chill in these regions has an uncomfortable effect on the human body. Usually, the WCI value is higher in western, northern and central Anatolia than in other regions.  相似文献   

5.
Accurate estimation of reference evapotranspiration (ET0) becomes imperative for better managing the more and more limited agricultural water resources. This study examined the feasibility of developing generalized artificial neural network (GANN) models for ET0 estimation using weather data from four locations representing different climatic patterns. Four GANN models with different combinations of meteorological variables as inputs were examined. The developed models were directly tested with climatic data from other four distinct stations. The results showed that the GANN model with five inputs, maximum temperature, minimum temperature, relative humidity, solar radiation, and wind speed, performed the best, while that considering only maximum temperature and minimum temperature resulted in the lowest accuracy. All the GANN models exhibited high accuracy under both arid and humid conditions. The GANN models were also compared with multivariate linear regression (MLR) models and three conventional methods: Hargreaves, Priestley–Taylor, and Penman equations. All the GANN models showed better performance than the corresponding MLR models. Although Hargreaves and Priestley–Taylor equations performed slightly better than the GANN models considering the same inputs at arid and semiarid stations, they showed worse performance at humid and subhumid stations, and GANN models performed better on average. The results of this study demonstrated the great generalization potential of artificial neural techniques in ET0 modeling.  相似文献   

6.
Measurements of surface ozone (O3), nitric oxide (NO), nitrogen dioxide (NO2), oxides of nitrogen (NOx=NO+NO2) and meteorological parameters have been made at Agra (North Central India, 27°10??N, 78°05??E) in post monsoon and winter season. The diurnal variation in O3 concentration shows daytime in situ photochemical production with diurnal maximum in noon hours ranging from 51 to 54 ppb in post monsoon and from 76 to 82 ppb in winter, while minimum (16?C24 ppb) during nighttime and early morning hours. Average 8-h O3 concentration varied from 12.4 to 83.9 ppb. The relationship between meteorological parameters (solar radiation intensity, temperature, relative humidity, wind speed and wind direction) and surface O3 variability was studied using principal component analysis (PCA), multiple linear regression (MLR) and correlation analysis (CA). PCA and MLR of daily mean O3 concentrations on meteorological parameters explain up to 80 % of day to day ozone variability. Correlation with meteorology is strongly emphasized on days having strong solar radiation intensity and longer sunshine time.  相似文献   

7.
Ground temperature is an important factor influencing ground source heat pumps, ground energy storage systems, land-atmosphere processes, and ecosystem dynamics. This paper presents an accurate development model (DM) based on a segment function: it can derive ground temperatures in permafrost regions of the Qinghai-Tibetan Plateau (QTP) from air temperature in case of shallow soil depths and without using air temperature data in case of deep soil depths. Here, we applied this model to simulate the active layer and permafrost ground temperature at the Tanggula observation station. The DM results were compared with those from the artificial neural network (ANN), support vector machine (SVM), and multiple linear regressions (MLR) models, which were based on climatic variables from prior models and on ground temperatures derived from observations at different depths. The results revealed that the effect of air temperature on simulated ground temperatures decreased with increasing depth; moreover, ground temperatures fluctuated greatly within the shallow layers but remained rather stable with deeper layers. The results also indicated that the DM has the best performance for the estimation of soil temperature compared to the MLR, SVM, and ANN models. Finally, we obtained the three average statistics indexes, i.e., mean absolute error (MAE), root mean square error (RMSE), and the normalized standard error (NSEE) at TGL site: they were equal to 0.51 °C, 0.63 °C, and 0.15 °C for the ground temperature of active layer and to 0.08 °C, 0.09 °C, and 0.07 °C for the permafrost temperature.  相似文献   

8.

Soil temperature is a meteorological data directly affecting the formation and development of plants of all kinds. Soil temperatures are usually estimated with various models including the artificial neural networks (ANNs), adaptive neuro-fuzzy inference system (ANFIS), and multiple linear regression (MLR) models. Soil temperatures along with other climate data are recorded by the Turkish State Meteorological Service (MGM) at specific locations all over Turkey. Soil temperatures are commonly measured at 5-, 10-, 20-, 50-, and 100-cm depths below the soil surface. In this study, the soil temperature data in monthly units measured at 261 stations in Turkey having records of at least 20 years were used to develop relevant models. Different input combinations were tested in the ANN and ANFIS models to estimate soil temperatures, and the best combination of significant explanatory variables turns out to be monthly minimum and maximum air temperatures, calendar month number, depth of soil, and monthly precipitation. Next, three standard error terms (mean absolute error (MAE, °C), root mean squared error (RMSE, °C), and determination coefficient (R 2)) were employed to check the reliability of the test data results obtained through the ANN, ANFIS, and MLR models. ANFIS (RMSE 1.99; MAE 1.09; R 2 0.98) is found to outperform both ANN and MLR (RMSE 5.80, 8.89; MAE 1.89, 2.36; R 2 0.93, 0.91) in estimating soil temperature in Turkey.

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9.
This study aims to put out on what ratio Bursa province, one of the important heavy industry regions of Turkey, has been affected climatic process called “Global Warming” or “Climate Change”. For this intend climatic measurement results from Bursa center, top of Uludağ Mount, Yenişehir and Keles meteorological stations were used. These measurements were taken as minimum temperature at night-time, maximum temperature at day-time, and mean temperature, mean pressure, insolation intensity, insolation duration, mean wind speed, minimum temperature above soil, soil temperatures at depths of 5, 10, and 20 cm rainfall. Overall, our statistical results showed that there was a considerable warming at statistically 1% and 5% levels in summer months, particularly in July Almost all performed measurements confirm this result. According to climatic data for thirty years (1975–2005), in the last twelve years contrary to previous 18 years, mean temperature values were higher than long-term mean value nine times (years) repetitively. Temperatures did not deviated higher than 0.5°C in six of these. At the temperatures below mean, The maximum deviation was −0.4°C.  相似文献   

10.

The purpose of this study is to revaluate the changing spatial and temporal trends of precipitation in Turkey. Turkey is located in one of the regions at greatest risk from the potential effects of climate change. Since the 1970s, a decreasing trend in annual precipitation has been observed, in addition to an increasing number of precipitation-related natural hazards such as floods, extreme precipitation, and droughts. An understanding of the temporal and spatial characteristics of precipitation is therefore crucial to hazard management as well as planning and managing water resources, which depend heavily on precipitation. The ordinary kriging method was employed to interpolate precipitation estimates using precipitation records from 228 meteorological stations across the country for the period 1976–2010. A decreasing trend was observed across the Central Anatolian region, except for 1996–2000 which saw an increase in precipitation. However, this same period is identified as the driest year in Eastern and South Eastern Anatolia. The Eastern Black Sea region has the highest precipitation in the country; after 1996, an increase in annual precipitation in this region is observed. An overall reduction is also seen in southwest Turkey, with less variation in precipitation.

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11.
In this study, the ability of two models of multi linear regression (MLR) and Levenberg–Marquardt (LM) feed-forward neural network was examined to estimate the hourly dew point temperature. Dew point temperature is the temperature at which water vapor in the air condenses into liquid. This temperature can be useful in estimating meteorological variables such as fog, rain, snow, dew, and evapotranspiration and in investigating agronomical issues as stomatal closure in plants. The availability of hourly records of climatic data (air temperature, relative humidity and pressure) which could be used to predict dew point temperature initiated the practice of modeling. Additionally, the wind vector (wind speed magnitude and direction) and conceptual input of weather condition were employed as other input variables. The three quantitative standard statistical performance evaluation measures, i.e. the root mean squared error, mean absolute error, and absolute logarithmic Nash–Sutcliffe efficiency coefficient $ \left( {\left| {{\text{Log}}({\text{NS}})} \right|} \right) $ were employed to evaluate the performances of the developed models. The results showed that applying wind vector and weather condition as input vectors along with meteorological variables could slightly increase the ANN and MLR predictive accuracy. The results also revealed that LM-NN was superior to MLR model and the best performance was obtained by considering all potential input variables in terms of different evaluation criteria.  相似文献   

12.
Projected shifts of wine regions in response to climate change   总被引：1，自引：1，他引：0  
This research simulates the impact of climate change on the distribution of the most important European wine regions using a comprehensive suite of spatially informative layers, including bioclimatic indices and water deficit, as predictor variables. More specifically, a machine learning approach (Random Forest, RF) was first calibrated for the present period and applied to future climate conditions as simulated by HadCM3 General Circulation Model (GCM) to predict the possible spatial expansion and/or shift in potential grapevine cultivated area in 2020 and 2050 under A2 and B2 SRES scenarios. Projected changes in climate depicted by the GCM and SRES scenarios results in a progressive warming in all bioclimatic indices as well as increasing water deficit over the European domain, altering the climatic profile of each of the grapevine cultivated areas. The two main responses to these warmer and drier conditions are 1) progressive shifts of existing grapevine cultivated area to the north–northwest of their original ranges, and 2) expansion or contraction of the wine regions due to changes in within region suitability for grapevine cultivation. Wine regions with climatic conditions from the Mediterranean basin today (e.g., the Languedoc, Provence, Côtes Rhône Méridionales, etc.) were shown to potentially shift the most over time. Overall the results show the potential for a dramatic change in the landscape for winegrape production in Europe due to changes in climate.  相似文献   

13.
 The Mark 2 version of the CSIRO coupled global climatic model has been used to generate a 1000-year simulation of natural (i.e. unforced) climatic variability representative of “present conditions”. The annual mean output from the simulation has been used to investigate the occurrence of decadal and longer trends over the globe for a number of climatic variables. Here trends are defined to be periods of years with a climatic anomaly of a given sign. The analysis reveals substantial differences between the trend characteristics of the various climatic variables. Trends longer than 12 years duration were unusual for rainfall. Such trends were fairly uniformly distributed over the globe and had an asymmetry in the rate of occurrence for wet or dry conditions. On the other hand, trends in surface wind stress, and especially the atmospheric screen temperature, were of longer duration but primarily confined to oceanic regions. The trends in the atmospheric screen temperature could be traced deep into the oceanic mixed layer, implying large changes in oceanic thermal inertia. This thermal inertia then constituted an important component of the `memory' of the climatic system. While the geographic region associated with a given trend could be identified over several adjacent grid boxes of the model, regional plots for individual years of the trend revealed a range of variations, suggesting that a consistent forcing mechanism may not be responsible for a trend at a given location. Typical return periods for 12-year rainfall trends were once in 1000 years, highlighting the rarity of such events. Using a looser definition of a trend revealed that drying trends up to 50 years duration were also possible, attributable solely to natural climatic variability. Significant (∼20% to 40%) rainfall reductions per year can be associated with a long-term drying trend, hence such events are of considerable climatic significance. It can take more than 100 years for the hydrologic losses associated with such a trend to be overcome. Overall, the simulation provides new and useful insights into climatic trends, and quantifies a number of poorly observed characteristics. The results highlight the extensive and pervasive influence of unforced natural climatic variability as an omnipresent generator of climatic trends. Received: 20 January 2000 / Accepted: 21 September 2000  相似文献   

14.
Summary ?The knowledge of the dependence pattern of daily weather variables, which tend to persist in time, is important in developing techniques for simulating weather data. The dependence of daily maximum and minimum temperatures is presented for 17 stations in the three main climatic zones (humid, sub-humid and semi-arid) of Nigeria. Daily maximum and minimum temperatures have an average lag-one serial correlation coefficient of 0.833 and 0.802, respectively, depending more on latitude followed by elevation of the site. The correlation between maximum and minimum temperature averaged 0.387 and also varied with season and location. The results of the Student’s T-test show that persistence within maximum and minimum temperatures and interdependence between the two meteorological variables did not differ significantly, 95% of the times, at most of the sites. The implication of these in developing limited area models for weather forecasting and regional climate studies has been stressed. Received July 6, 1999/Revised March 6, 2000  相似文献   

15.
Multi-criteria evaluation of CMIP5 GCMs for climate change impact analysis   总被引：1，自引：0，他引：1  
Climate change is expected to have severe impacts on global hydrological cycle along with food-water-energy nexus. Currently, there are many climate models used in predicting important climatic variables. Though there have been advances in the field, there are still many problems to be resolved related to reliability, uncertainty, and computing needs, among many others. In the present work, we have analyzed performance of 20 different global climate models (GCMs) from Climate Model Intercomparison Project Phase 5 (CMIP5) dataset over the Columbia River Basin (CRB) in the Pacific Northwest USA. We demonstrate a statistical multicriteria approach, using univariate and multivariate techniques, for selecting suitable GCMs to be used for climate change impact analysis in the region. Univariate methods includes mean, standard deviation, coefficient of variation, relative change (variability), Mann-Kendall test, and Kolmogorov-Smirnov test (KS-test); whereas multivariate methods used were principal component analysis (PCA), singular value decomposition (SVD), canonical correlation analysis (CCA), and cluster analysis. The analysis is performed on raw GCM data, i.e., before bias correction, for precipitation and temperature climatic variables for all the 20 models to capture the reliability and nature of the particular model at regional scale. The analysis is based on spatially averaged datasets of GCMs and observation for the period of 1970 to 2000. Ranking is provided to each of the GCMs based on the performance evaluated against gridded observational data on various temporal scales (daily, monthly, and seasonal). Results have provided insight into each of the methods and various statistical properties addressed by them employed in ranking GCMs. Further; evaluation was also performed for raw GCM simulations against different sets of gridded observational dataset in the area.  相似文献   

16.
May–July Standardized Precipitation Index (SPI) for the land area of most of Turkey and some adjoining regions are reconstructed from tree rings for the period 1251–1998. The reconstruction was developed from principal components analysis (PCA) of four Juniperus excelsa chronologies from southwestern and south-central Turkey and is based on reliable and replicable statistical relationships between climate and tree ring growth. The SPI reconstruction shows climate variability on both interannual and interdecadal time scales. The longest period of consecutive drought years in the reconstruction (SPI threshold ≤−1) is 2 yr. These occur in 1607–1608, 1675–1676, and 1907–1908. There are five wet events (SPI threshold ≥+1) of two consecutive years each (1330–1331, 1428–1429, 1503–1504, 1629–1630, and 1913–1914). A 5-yr moving average of the reconstructed SPI shows that two sustained drought periods occurred from the mid to late 1300s and the early to mid 1900s. Both episodes are characterized by low variability.  相似文献   

17.
For the 1980–2003 period, we analyzed the relationship between crop yield and three climatic variables (minimum temperature, maximum temperature, and precipitation) for 12 major Californian crops: wine grapes, lettuce, almonds, strawberries, table grapes, hay, oranges, cotton, tomatoes, walnuts, avocados, and pistachios. The months and climatic variables of greatest importance to each crop were used to develop regressions relating yield to climatic conditions. For most crops, fairly simple equations using only 2–3 variables explained more than two-thirds of observed yield variance. The types of variables and months identified suggest that relatively poorly understood processes such as crop infection, pollination, and dormancy may be important mechanisms by which climate influences crop yield. Recent climatic trends have had mixed effects on crop yields, with orange and walnut yields aided, avocado yields hurt, and most crops little affected by recent climatic trends. Yield-climate relationships can provide a foundation for forecasting crop production within a year and for projecting the impact of future climate changes.  相似文献   

18.
Summary  Degree-days as a measure of accumulated temperature deviations from a base temperature have many practical applications in various human related activities such as home cooling, heating, plant growth in agriculture and power generation in addition to energy requirement. Long temperature records are necessary for their reliable estimations at given stations. In this paper, degree-day measure has been applied to monthly temperature records for systematically changed base temperature values from − 25 °C to + 35 °C with 5 °C increments at 255 meteorology stations in Turkey. The results are represented in the form of spatial degree-day distribution maps, which are then related to various climatic, meteorological and topographic features of Turkey. For instance, free surface water bodies in forms of surrounding seas, lakes and rivers insert retardation in the expansion of heating degree-days over large regions. On the other hand, cold air penetration from polar regions in the northeastern Turkey originating from Siberia appears at moderate base temperature heating degree-days. Received August 20, 1998 Revised June 21, 1999  相似文献   

19.
A spectral analysis of Iberian Peninsula monthly rainfall   总被引：2，自引：0，他引：2  
Summary A spectral analysis of Iberian Peninsula monthly rainfall series was carried out. The data set consists of monthly precipitation records from 40 meteorological observatories over 74 years (1919–1992). The stations are representative of most of the Iberian Peninsula. The rainfall series were analyzed spatially by means of Principal Component Analysis (PCA) and temporally by means of the Multi-Taper Method (MTM) of spectral analysis of by Monte-Carlo Singular Spectrum Analysis (MCSSA). The PCA gave six dominant modes of variation which explain 75% of the variance with each component affecting a different region of the Peninsula. The spectral analysis showed 7 year oscillations for the dominant pattern and 2.7 and 16 years for the third pattern. The 7-year oscillation seems to be related to other climatic oscillations recorded in the northern hemisphere while the 2.7-year oscillation could be related to the ENSO phenomenon. Received July 18, 2000 Revised April 19, 2001  相似文献   

20.
To interpret past vegetation and climate changes from pollen data, we need to reveal the degree of similarity between modern analogues and fossil pollen spectra, which would help us predict the future climate and vegetation. Ninety surface pollen samples across six vegetation zones along an altitudinal gradient from 460 to 3510 m and 44 fossil samples at Caotan Lake were collected in the central Tianshan Mountains, northern Xinjiang, China. Discriminant analyses results, fossil pollen and phytolith assemblages were then used to reconstruct palaeovegetation and palaeoclimate in the area. The 90 surface samples were divided into six pollen zones (alpine cushion, alpine and subalpine meadow, montane Tianshan spruce forest, forest-steppe ecotone, Artemisia desert, typical desert), corresponding to the major vegetation types in the area. These zones follow a climatic gradient of increasing precipitation with increasing elevation. Paleovegetation reconstructed from 44 fossil pollen assemblages through discriminant analysis reflects the regional vegetation shifted from typical desert to Artemisia desert since 4640 cal. year BP in the Caotan Lake wetland. The fossil pollen and phytolith record also reveal the arid climate has not fundamentally changed in the period. But a dry-wet-dry local climate oscillation since 2700 cal. year BP has a fundamental influence on local wetland vegetation dynamics and peat accumulation of the Caotan wetland. Modern wetland landscape and surface pollen assemblages from the Ebinur Lake Wetland Nature Reserve provide further evidence for ferns and Betula growing in the Caotan Lake wetland during the historical period.  相似文献   

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