Changes and trends in maximum, minimum and mean temperature series in Iran

Ghasemi, Ahmad

doi:10.1002/asl2.569

Cited by 86 publications

(48 citation statements)

References 24 publications

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“…While the annual trends of the three variables are not significantly different, the seasonal averages of T min show greater increases (and smaller decreases) than those of T max except for in the spring. Ghasemi (2015) presented similar findings in Iran. The largest increase was in the value of T min during the summer, and the largest decrease was in the value of T max during the winter.…”

Section: Historical Trendssupporting

confidence: 92%

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Spatial variations in temperature in a mountainous region of Jeju Island, South Korea

Kim

2016

Intl Journal of Climatology

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The goal of this study was to better understand the spatial variations in the daily average, maximum and minimum air temperatures on Jeju Island, South Korea, by interpolating between observed temperature data that are distributed across a mountainous region. This spatial interpolation was performed in two steps: filling in the ungauged station data using principal component regression, and then downscaling the station‐based data to obtain a spatially gridded data set using the Precipitation‐elevation Regressions on Independent Slopes Model. Whereas the conventional practice of interpolating data does not include filling in ungauged data, our approach is particularly useful because most ungauged data correspond to high‐elevation regions. Using the proposed approach, we were able to reasonably construct the spatial distribution of the temperatures on Jeju Island. Our trend analysis showed that the temperatures increased significantly in the summer and decreased in the winter. Furthermore, the positive temperature trends generally become larger at higher elevation when the average positive trends are significant in the summer.

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Section: Historical Trendssupporting

confidence: 92%

“…For example, the station‐based observed temperature data in Egypt from 1971 to 2000 show increasing and decreasing temperature trends in the summer and winter, respectively (Domroes and El‐tantawi, ). Ghasemi () identified significant summer and spring warming trends and insignificant winter and fall temperature trends in Iran.…”

Section: Resultsmentioning

confidence: 89%

Spatial variations in temperature in a mountainous region of Jeju Island, South Korea

Kim

2016

Intl Journal of Climatology

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“…SSVTT forecasting has important results regarding rainfall patterns [42]. There are two widely used SSVTT models in climate, viz., multiplicative (trended and nontrended) and additive (trended and non-trended) models [46,47]. SSVTT models have high importance in modeling and forecasting precipitation [48].…”

Section: Measurement Methodsmentioning

confidence: 99%

Spatial-temporal Variability of Seasonal Precipitation in Iran

Javari¹

2016

TOASCJ

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Spatial-seasonal variability and temporal trends has essential importance to climatic prediction and analysis. The aim of this research is the seasonal variations and temporal trends in the Iran were predicted by using rainfall series. The exploratoryconfirmatory method, and seasonal time series procedure (STSP), temporal trend (TT), seasonal least squares (SLS) and spatial (GIS) methods (STSP¬-SLS-GIS) were employed to bring to light rainfall spatial-seasonal variability and temporal trends (SSVTT). To explore the spatial-seasonal variability and temporal trends during the period over 1975 to 2014 at 140 stations. To investigate the spatial-seasonal variability and temporal trends amount of each series was studied using ArcGIS 10.3 on different time scale. New climatic findings for the region: the investigates and predictions revealed that: (a) range of monthly and seasonal changes of rainfall tends to be highest (increasing trend) during winter (Winter Seasonal Index or WUSI=137.83 mm); (b) lowest (decreasing trend) during summer (Summer Seasonal Index or SUSI=20.8l mm) and (c) the coefficient of rainfall seasonal pattern variations in winter to 5.94 mm, in spring to 11.13 mm, in summer to 4.44 mm and in autumn to 8.05 mm with seasonality being the most effective of all. Mean annual rainfall changed from 51.45 mm (at Bafgh) to 1834.9 mm (at Bandar Anzali). Maximum decrease in annual rainfall was obtained at Miandeh Jiroft (-143.83%) and minimum at Abali (-0.013%) station. The most apparent year of variation was 2007 in annual rainfall.

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“…The average temperature trend over the basin is 0.012°C year-1 and the highest and the lowest magnitude of trends are seen in central and western basin respectively. According to Ghasemi (2015), the average warming trend in Iran is 0.023°C year-1 and in comparison to the country as a whole, the warming trend over Zayanderud basin is generally weaker. In addition to urbanization effect, the reason for regional differences in the magnitude of warming over the basin may refer to physical geographic (e.g., topography, exposure), vegetation, land cover, land use, atmospheric circulation etc.…”

Section: Trends In Stationsmentioning

confidence: 99%

“…Several studies have done to detect temperature trend in Iran and Zayanderud basin (Tabari and HosseinzadehTalaee, 2011;Gandomkar, 2011;Ataei et al, 2013;Ghasemi, 2015) but few researches (types of research) have been conducted on the effect of urbanization upon air temperature trend and it is unclear at present, how much effect the increased UHI magnitude has had on the long-term trends in Zayanderud basin.…”

Section: Introductionmentioning

confidence: 99%

Urbanization Effect on Air Temperature in Iran's Zayanderud River Basin

Zahraei¹,

Saadati²,

Eslamian³

et al. 2018

American Journal of Engineering and Applied Sciences

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Detection of urbanization effect to understand climate change is important. Urbanization effect on air temperature in Zayanderud river basin was evaluated using weather stations network. Map of the temperature trend was generated using full dataset and rural stations to visualize warming trends. A comparative analysis was carried out to estimate urban heat island effect. The result indicated that overall warming trend in the basin is 0.012°C year-1 and the contribution of urban warming to total temperature change was estimated to be 17%. Variation of urban warming throughout the basin was 0-0.016°C year-1. The centre of a basin with a large UHI effect by 0.006-0.016°C year-1 is more influenced by urbanization. Records of hemispheric mean temperatures from ground areas for the past 100 years prepare pivotal input to the argument over global warming. Contrary to precise apply of the basic station data in some of these collections of hemispheric temperature, there have been proposals that a ration of the 0.5°C warming seen on a century timescale may be related to urbanization affections-local warming caused by the effects of urban enlargement. The consequences display that the urbanization affection in two of the most widely used hemispheric data sets is, at most, an order of quantity less than the warming seen on a century timescale. The climatic alternations of the temperature are considered. Gridded temperature data are necessary to run ecological models at regional scales for climate impact studies and have been generated by spatially incorporating preceded values at synoptic stations.

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Changes and trends in maximum, minimum and mean temperature series in Iran

Cited by 86 publications

References 24 publications

Spatial variations in temperature in a mountainous region of Jeju Island, South Korea

Spatial variations in temperature in a mountainous region of Jeju Island, South Korea

Spatial-temporal Variability of Seasonal Precipitation in Iran

Urbanization Effect on Air Temperature in Iran's Zayanderud River Basin

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