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

2015 ◽  
Vol 16 (3) ◽  
pp. 366-372 ◽  
Author(s):  
Ahmad Reza Ghasemi
2017 ◽  
Vol 30 (24) ◽  
pp. 9897-9914 ◽  
Author(s):  
Meng Gao ◽  
Christian L. E. Franzke

In this study, temporal trends and spatial patterns of extreme temperature change are investigated at 352 meteorological stations in China over the period 1956–2013. The temperature series are first examined for evidence of long-range dependence at daily and monthly time scales. At most stations there is evidence of significant long-range dependence. Noncrossing quantile regression has been used for trend analysis of temperature series. For low quantiles of daily mean temperature and monthly minimum value of daily minimum temperature (TNn) in January, there is an increasing trend at most stations. A decrease is also observed in a zone ranging from northeastern China to central China for higher quantiles of daily mean temperature and monthly maximum value of daily maximum temperature (TXx) in July. Changes of the large-scale atmospheric circulation partly explain the trends of temperature extremes. To reveal the spatial pattern of temperature changes, a density-based spatial clustering algorithm is used to cluster the quantile trends of daily temperature series for 19 quantile levels (0.05, 0.1, …, 0.95). Spatial cluster analysis identifies a few large clusters showing different warming patterns in different parts of China. Finally, quantile regression reveals the connections between temperature extremes and two large-scale climate patterns: El Niño–Southern Oscillation (ENSO) and the Arctic Oscillation (AO). The influence of ENSO on cold extremes is significant at most stations, but its influence on warm extremes is only weakly significant. The AO not only affects the cold extremes in northern and eastern China, but also affects warm extremes in northeastern and southern China.


2020 ◽  
Author(s):  
Dhais Peña-Angulo ◽  
Leire Sandonís-Pozo ◽  
Michele Brunetti ◽  
Santiago Beguería ◽  
José Carlos Gonzalez-Hidalgo

<p>We have finished the complete digitalization of Annual Books from the Spanish meteorological service (AEMET) between 1916 to 1949. Data retrieved included monthly means of maximum and minimum temperature. In the present contribution we are going to show the new MOTEDAS_Century dataset (MOnthly TEmperature Dataset of Spain century) which has been performed matching data from the annual books and data from the national climate data bank of AEMET. The amount of stations with temperature data vary from a minimum of 228 (1938) and 2.030 (1994). This length of the time series is sometimes very short. Since we aim to analyse the information with a highest spatial density as possible we decided, instead of reconstructing series, to reconstruct monthly fields independently by using all the information available month to month between 1916 and 2015. Monthly interpolated data were converted to a high-resolution grid (10x10 km) using the Angular Distance Weighting method, resulting into a 5000 pixels grid.</p><p> </p><p>The time series of annual mean temperature in Spanish mainland from 1916 to 2015 shows the well-known pattern of increase during the first decades, a slowdown in the middle of the 20<sup>th</sup> century, and the final rise since the 1970´s, including a final stage without significant trend for the last three decades.</p><p> </p><p>MOTEDAS_Century´s annual temperature average series has been compared with other analogous series from BEST (Berkelay Earth Surface Temperature) and SDAT (Spanish Daily Adjusted Temperature Series) datasets, as well as the twentieth century reanalysis for the Iberian Peninsula. The different versions resemble the global pattern, although differences exist particularly during the last three decades. The comparison of the annual mean temperature series with their counterparts in the BEST, AEMET and SDAT databases suggests that processing the newly retrieved information does not modify the behaviour patterns of mean annual temperatures in the Spanish mainland, and that the difference observed among the various sources can be attributed to a combination of effects from the different number of weather stations examined, which is very much higher in MOTEDAS_century, to the local characteristics of stations. The MOTEDAS_century grid in the anomalies format is available on request from the authors and will be in future on the website of the CLICES Project (http://clices.unizar.es).</p>


2011 ◽  
Vol 2 (4) ◽  
pp. 187-192 ◽  
Author(s):  
Xinyu Wen ◽  
Guoli Tang ◽  
Shaowu Wang ◽  
Jianbin Huang

2021 ◽  
Vol 14 (11) ◽  
pp. 57-63
Author(s):  
Abujam Manglem Singh

Understanding local climate variability and change is necessary for improving future climate forecasts and also aids preparation of informed area specific climate mitigation and adaptation strategies. Climate change at local scale is best revealed by studying observed variabilities and trends in rainfall and temperature data through statistical techniques. Therefore, this study employed standard deviation and coefficient of variability and Mann-Kendall test and Sen slope determination non-parametric techniques to perform variability and trends analyses across multiple temporal scales on climate data obtained at Imphal (Tulihal) station. The results indicate prevalence of different patterns between rainfall and temperature trends. Except for the positive trends in the month May (2mm/yr) and in the pre-monsoon season (9.49mm/yr), no other discernable patterns in rainfall data were observed. Temperature trends, on the other hand, witnessed significant positive increase in maximum, minimum and mean values. For mean temperature, all months registered significant increasing trends. At the annual and seasonal scales also, maximum, minimum and mean temperatures have increased although with varying rates. It is noteworthy to mention that temperature change has occurred at two distinct phases; before 1993 slow warming and after 1993 rapid warming. Temporal distribution of annual mean temperature captures this pattern more vividly as warming rate before 1993 was less than 0.01 compared to 0.450c/year in the latter phase. Overall, it can be said that rainfall has higher variability with very little or no pattern but temperature distribution suggests existence of strong trends in the observed data.


2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Xiaoqiong Liu ◽  
Yuyang Zhang ◽  
Yansui Liu ◽  
Xinzheng Zhao ◽  
Jian Zhang ◽  
...  

AbstractThe cumulative anomaly analysis, the ensemble empirical mode decomposition (EEMD), the Bernaola Galvan heuristic segmentation algorithm (BGSA), the Le Page test, the moving t test at different sub-series scales, and the quasi-periodic oscillations (QPOs) were used to demonstrate the statistical characteristics of the temperature changes in the study area from 1960 to 2015. The results were as follows: the temperatures varied obviously among subregions and seasons and they generally increased; the climate tendency rates of autumn mean temperatures were higher than those of summer and spring; additionally, the temperatures in the three subregions of the Three Rivers’ Headstream Region (THRHR) were relatively low in the 1960s, especially in the early 1960s, followed by those in the 1970s, and the annual mean temperature has been increasing since the mid-late 1980s, especially in the middle 1990s. The results of EEMD showed that the QPOs of the annual mean temperature series in the study area were mainly quasi-3 years, quasi-5–8 years, quasi-12–15 years, and quasi-35–38 years. The results of the annual mean temperature series mutational sites showed that a significant warming mutation began in approximately 1997; and the mutational sites of seasonal mean temperature series in the three subregions of the THRHR all began in the middle and late 1990s. The prediction result of the temperature series trend based on multiple methods showed that the warming persistence of annual and seasonal mean temperature series would be stronger, and their seasonal and regional differences were obvious.


MAUSAM ◽  
2021 ◽  
Vol 48 (1) ◽  
pp. 41-44
Author(s):  
R.P. KANE ◽  
N.B. TRIVEDI

ABSTRACT .Maximum Entropy spectral Analysis (MESA) of the 8IUlua1 mean temperature series for Central England for 1659-1991 indicated significant periodicilies at T = 7.8, 11.1, 12.5, 15, 18, 23, 32, 37, 68, 81, l09 and 203 years. These compare well with T = 22, 30, 80, 200 years obtained for China. Also, a good comparison is obtained with some periodicities in the sunspot number series.    


2012 ◽  
Vol 8 (1) ◽  
pp. 103-119
Author(s):  
Z.-X. Hao ◽  
J.-Y. Zheng ◽  
Q.-S. Ge ◽  
W.-C. Wang

Abstract. We present statistically reconstructed annual winter (December–February) mean temperature in the Middle and Lower Reaches of the Yangtze River (24–34° N, east of 108° E) back to 1736. The reconstructions are based on information from snowfall days from Yu-Xue-Fen-Cun archive (one of historical documents proxies) in Qing Dynasty (1644–1911). Those information are calibrated with regional winter temperature series spanning the period 1951 to 2007 period. The gap from 1912 to 1950 is filled using early instrumental observation. With respect to the 1951–2007 climatology, the 18th century was 0.6 °C colder, and the 19th century was 1.0 °C colder. But since the 20th century, climate entered into the warming phase, particular in the last 30 yr, the mean temperature from 1981 to 2007 is 0.25 °C higher than that of climatology, a highest level of the past 300 yr. The uncertainty is existed for the period prior of 1900, and possible causes have been discussed here.


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