scholarly journals Trend Detection in Regional-Mean Temperature Series: Maximum, Minimum, Mean, Diurnal Range, and SST

1997 ◽  
Vol 10 (2) ◽  
pp. 317-326 ◽  
Author(s):  
Xiaogu Zheng ◽  
Reid E. Basher ◽  
Craig S. Thompson
Keyword(s):  
Temperature Series ◽  
Trend Detection ◽  
Mean Temperature ◽  
Diurnal Range ◽  
Maximum Minimum

scholarly journals Quantile Regression–Based Spatiotemporal Analysis of Extreme Temperature Change in China

2017 ◽  
Vol 30 (24) ◽  
pp. 9897-9914 ◽  
Author(s):  
Meng Gao ◽  
Christian L. E. Franzke
Keyword(s):  
Quantile Regression ◽  
Temperature Change ◽  
Long Range ◽  
Large Scale ◽  
Extreme Temperature ◽  
Temperature Series ◽  
Long Range Dependence ◽  
Temperature Extremes ◽  
Mean Temperature ◽  
Cold Extremes

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.

scholarly journals MOTEDAS Century database, Part 1: temperature evolution in Spanish Mainland (1916-2015).

2020 ◽  
Author(s):  
Dhais Peña-Angulo ◽  
Leire Sandonís-Pozo ◽  
Michele Brunetti ◽  
Santiago Beguería ◽  
José Carlos Gonzalez-Hidalgo
Keyword(s):  
Time Series ◽  
Data Bank ◽  
Angular Distance ◽  
Temperature Series ◽  
Spatial Density ◽  
Climate Data ◽  
Present Contribution ◽  
Weighting Method ◽  
Global Pattern ◽  
Mean Temperature

<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>

scholarly journals Comparison of Global Mean Temperature Series

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

Climate Variability and Trend Analyses of Rainfall and Temperature Data of Imphal, Manipur

2021 ◽  
Vol 14 (11) ◽  
pp. 57-63
Author(s):  
Abujam Manglem Singh
Keyword(s):  
Climate Variability ◽  
Monsoon Season ◽  
Temperature Data ◽  
Climate Mitigation ◽  
Climate Data ◽  
Local Climate ◽  
Mitigation And Adaptation ◽  
Temperature Trends ◽  
Mean Temperature ◽  
Maximum Minimum

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.

scholarly journals Characteristics of temperature evolution from 1960 to 2015 in the Three Rivers’ Headstream Region, Qinghai, China

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Xiaoqiong Liu ◽  
Yuyang Zhang ◽  
Yansui Liu ◽  
Xinzheng Zhao ◽  
Jian Zhang ◽  
...  
Keyword(s):  
Regional Differences ◽  
Ensemble Empirical Mode Decomposition ◽  
Temperature Series ◽  
Statistical Characteristics ◽  
Temperature Changes ◽  
Mean Temperature ◽  
Mode Decomposition ◽  
Anomaly Analysis ◽  
The 1960S ◽  
Three Rivers

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.

Maxent modelling for predicting climate change effects on the potential planting area of tuber mustard in China

2019 ◽  
Vol 157 (5) ◽  
pp. 375-381 ◽  
Author(s):  
H. Q. Li ◽  
X. H. Liu ◽  
J. H. Wang ◽  
L.G. Xing ◽  
Y. Y. Fu
Keyword(s):  
Suitable Habitat ◽  
Maxent Model ◽  
Climate Change Effects ◽  
Mean Temperature ◽  
Chongqing City ◽  
Diurnal Range ◽  
Maxent Modelling ◽  
Shanghai City ◽  
Tuber Mustard ◽  
Model Training

AbstractPotential planting area for tuber mustard was simulated using the Maxent model under current and future conditions based on 591 coordinates and 22 environmental layers. Model accuracy was excellent, with area under the receiving operator curve values of 0.967 and 0.958 for model training and testing, respectively. Dominant factors were mean diurnal range, mean temperature of the coldest quarter, annual mean temperature and minimum temperature of the coldest month, with thresholds of 6.5–7.5, 5.5–9, 16–19 and 2.0–6.5 °C, respectively. Under current conditions, suitable habitat areas (2.16% of total land in China) were concentrated mainly in Central, Southwest and East China, which can be defined as three occurrence and diffusion centres. In the 2050s and 2070s, suitable habitat areas are predicted to change to 3.72 and 3.92%, and 3.60 and 3.73% under scenarios RCP4.5 and RCP6.0, respectively, indicating that suitable habitat areas will increase slightly. However, future distribution of tuber mustard was predicted to differ among provinces or cities, i.e. predicted suitable habitat areas in Sichuan Province increased up to the 2050s but remained relatively unchanged between the 2050s and 2070s; in Chongqing city they first increased and then decreased; in Hunan, Anhui, Jiangsu, Zhejiang and Fujian Provinces they increased continuously; and in Guizhou, Hubei, Jiangxi Provinces and Shanghai city they first decreased, and then increased. The results from the current study provide useful information for management decisions of tuber mustard.

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