Observed trends in daily temperature extreme indices in Aguascalientes, Mexico

Ruíz-Álvarez, Osías; Singh, Vijay P.; Enciso, Juan; Ontiveros-Capurata, Ronald Ernesto; Santos, Carlos Antônio Costa dos

doi:10.1007/s00704-020-03391-1

Cited by 7 publications

(5 citation statements)

References 54 publications

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“…It runs in the R (http://www.rproject.org, accessed on 27 August 2021) programming environment. In order to ensure the reliability of the data, Rclimdex1.0 software is used to screen the outliers and the daily data's error values [38]. As climate characteristics are complex and diverse in Yunnan, Rclimdex1.0 software will be used to calculate all 27 extreme climate indices to evaluate the changing characteristics of extreme weather in Yunnan.…”

Section: Extreme Climate Indicesmentioning

confidence: 99%

Analysis of Spatiotemporal Variability in Extreme Climate and Potential Driving Factors on the Yunnan Plateau (Southwest China) during 1960–2019

Yan

Cai

et al. 2021

Atmosphere

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Global warming is increasing the frequency and intensity of extreme weather events around the world. The extreme climate in plateau and mountainous areas is sensitive and fragile. Based on the software Rclimdex 1.0, the spatio-temporal variation characteristics of 27 extreme climate indices at 120 meteorological stations were calculated in Yunnan from 1960 to 2019. The results show that the extreme temperature is rising, and the warming rate at night is higher than that in the daytime. It showed a trend of warming and drying, and precipitation was concentrated into more intense bursts. Extreme temperature cold indices (TX10p, TN10p, FD0, ID0, and CSDI) were negatively correlated with extreme precipitation indices (R × 5day, PRCPTOT, R10 mm, R20 mm, and R25 mm). Extreme temperature warmth indices (TX90p and TN90p) were positively correlated with extreme precipitation indices (R × 5day, CWD, PRCPTOT, R10 mm, R20 mm, and R25 mm). The change rate of extreme temperature does not increase linearly with altitude. The increase in middle-altitude and high-altitude areas is higher than that in low-altitude areas. Compared with ENSO and AO, NAO is a vital circulation pattern affecting the extreme climate in Yunnan. The influence of NAO on Yunnan’s extreme climate indices is most significant in the current month and the second month that follows. NAO was negatively correlated with extreme temperature warm indices (TN90p, TX90p, SU25, and TR20). NAO positively correlates with the extreme cold temperature indices (TN10p and TX10p). Except that ENSO has a significant effect on CDD, the effect of the general circulation patterns on the extreme temperature indices was more significant than that on the extreme precipitation indices in Yunnan. The results of this study are helpful to further understand and predict the characteristics of extreme climatic events and the factors affecting their geographical locations and atmospheric circulation patterns in Yunnan.

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Section: Extreme Climate Indicesmentioning

confidence: 99%

Analysis of Spatiotemporal Variability in Extreme Climate and Potential Driving Factors on the Yunnan Plateau (Southwest China) during 1960–2019

Yan

Cai

et al. 2021

Atmosphere

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“…(2018) used classic statistical approaches (Annual Maxima) to define the extreme temperature series and their trends globally. A plethora of regional studies have recently surfaced aiming to assess extreme temperature trends mainly in Asia (Ahmad et al., 2023; Ding et al., 2021; Ma et al., 2022; Om et al., 2022; Wang et al., 2021; Yatim et al., 2019; Yosef et al., 2019; S. Zhang et al., 2022), but also Latin America (Meseguer‐Ruiz et al., 2019; Ruiz‐Alvarez et al., 2020; Sanches et al., 2023), Europe (Tošić et al., 2023) Antarctica (Wei et al., 2019), and New Zealand (Caloiero, 2017), among other regions. Here we use an alternative method (H1‐L1) for obtaining the extreme value series and assess global changes in extreme temperature.…”

Section: Discussionmentioning

confidence: 99%

“…A plethora of regional studies have recently surfaced aiming to assess extreme temperature trends mainly in Asia (Ahmad et al, 2023;Ding et al, 2021;Ma et al, 2022;Om et al, 2022;Wang et al, 2021;Yatim et al, 2019;Yosef et al, 2019;S. Zhang et al, 2022), but also Latin America (Meseguer-Ruiz et al, 2019;Ruiz-Alvarez et al, 2020;Sanches et al, 2023), Europe (Tošić et al, 2023) Antarctica (Wei et al, 2019), and New Zealand (Caloiero, 2017), among other regions.…”

Section: Discussionmentioning

confidence: 99%

Nonstationarity in High and Low‐Temperature Extremes: Insights From a Global Observational Data Set by Merging Extreme‐Value Methods

Nerantzaki,

Papalexiou,

Rajulapati

et al. 2023

Earth's Future

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We merge classical extreme value methods to extract high (high temperatures (HT)) and low (low temperatures (LT)) temperatures and form time series having at least one extreme value per year. Observed daily maximum and minimum temperature records are used from 4,797 quality‐controlled, global, surface stations over 1970–2019. We assess changes in the magnitude and frequency of extreme temperatures by introducing and applying novel methods that exploit the definition of stationarity. Analysis shows significant increasing (40.6% of the stations) and decreasing (41.1%) trends in the frequency of high and LT, respectively, and increasing trends in both high‐ and low‐temperature values (35.6% and 49.7%). Globally, HT and LT frequencies are increasing and decreasing, respectively, by 0.9% and 1.1% per year, relative to the expected frequencies under the assumption of stationarity. The global mean annual HT and LT magnitudes are increasing by 0.004 and 0.016°C/year compared to the expected ones under stationarity. The results indicate that the assumption of stationarity fails to explain the observed changes. The proposed methods are an alternative approach to classical extreme value methods and a useful tool to reveal changes in extremes in the era of earth‐system change.

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“…Some studies have observed signs of opposite air temperature trends (increases and decreases) and nonuniformity, both in time (between months and/or between seasons) and in space (when comparing the same region). Tis behavior has been observed in Ireland [11], China [17,40], the USA [20], Aguascalientes, Mexico [41], and Chhattisgarh, India [42], although positive trends have been more common and widespread.…”

Section: Introductionmentioning

confidence: 99%

Statistical Analysis for the Detection of Change Points and the Evaluation of Monthly Mean Temperature Trends of the Moulouya Basin (Morocco)

Addou,

Obda,

Krakauer

et al. 2024

Advances in Meteorology

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This study examines the spatiotemporal variability of mean monthly temperature in the Moulouya watershed of northeastern Morocco, highlighting associated trends. To this end, statistical methods widely recommended by climate researchers were adopted. We used monthly mean temperature data for the period 1980–2020 from 9 measuring stations belonging to the Moulouya Watershed Agency (ABHM). These stations were rigorously selected, taking into account their reliability, the length of their records, and their geographical position in the basin. In addition, a quality test and homogenization of the temperature series were carried out using the Climatol tool. The results obtained show a significant upward trend in mean monthly temperature, mainly pronounced during the summer months, in the Moulouya watershed. In fact, Z values generally exceeded the 0.05 significance level at all stations during April, May, June, July, August, and October. According to the results of Sen’s slope test, mean monthly temperatures show an annual increase ranging from 0 to 0.13°C. The maximum magnitude of warming is recorded in July, specifically at Oujda Station. On an overall watershed scale, May, August, and July show a rapid warming trend, with average rates of 0.093, 0.086, and 0.08°C per year, respectively. By contrast, the series for the other months show no significant trend. Significant trend change points were also identified at watershed and station scales, mainly around 2000, primarily for accelerated warming of the summer months.

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Observed trends in daily temperature extreme indices in Aguascalientes, Mexico

Cited by 7 publications

References 54 publications

Analysis of Spatiotemporal Variability in Extreme Climate and Potential Driving Factors on the Yunnan Plateau (Southwest China) during 1960–2019

Analysis of Spatiotemporal Variability in Extreme Climate and Potential Driving Factors on the Yunnan Plateau (Southwest China) during 1960–2019

Nonstationarity in High and Low‐Temperature Extremes: Insights From a Global Observational Data Set by Merging Extreme‐Value Methods

Statistical Analysis for the Detection of Change Points and the Evaluation of Monthly Mean Temperature Trends of the Moulouya Basin (Morocco)

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