scholarly journals Trends of the daily maximum temperatures in relation with the climatic change and the urbanization in the Athens basin

2013 ◽  
Vol 8 (3) ◽  
pp. 186-194
Keyword(s):  
Time Series ◽  
Comparative Study ◽  
Statistical Significance ◽  
Extreme Temperature ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Societal Implications ◽  
Daily Maximum ◽  
Time Period ◽  
Maximum Temperatures

It is well known that the studies that associate the climatic changes with the greenhouse effect, as a sequence of uninterruptedly ongoing figures in the concentration mainly of carbon dioxide, have been focused on the trends of the mean temperature. On the other hand the variability and the trends of the extreme temperature values have not been considered sufficiently. We notice that the variability of the maximum and minimum temperature values and generally of the extreme weather has direct economic and societal implications. The interest in this paper is focused on the study of the trends of the daily and the monthly maximum temperature during the warm months July and August for the time period from 1955 to 2000 in the wide Athens area and specifically measurements of the Nea Philadelphia and Helliniko meteorological stations. Nea Philadelphia represents an immiscibly urban area station, while Helliniko a coastal suburban area one. The specific sites were selected for the comparative study of the temperature maximum trends in a time period which covers the population growth in the area of Athens. For the whole time period, the differences of the daily maximum temperature from the corresponding 10-days period mean maximum temperatures per month were calculated for each site. Then, the days with positive difference per month and per year as well as the trends of the time-series for each station were recorded along with the statistical significance of the regression slope’s value using the Student t-test distribution. Furthermore, in order to identify the “warmest decade” in the time-series, a study of the daily maximum temperature trend for the months July and August was performed for each decade followed by a test for the statistical significance of the slope coefficient. It is known that the presumable differences of the temperature time-series depend on the influence of the urbanization, the modification of the natural suburban environment and / or on the stations’ displacement. Based on these facts, we present more in this paper the conclusions of a comparative study of the results regarding each station analytically as well as the interpretation of the results concerning all the stations as an ensemble.

scholarly journals Monthly Temperature Observations for Uganda

2013 ◽  
Vol 52 (10) ◽  
pp. 2363-2372 ◽  
Author(s):  
John R. Christy
Keyword(s):  
Time Series ◽  
Public Access ◽  
Maximum Temperature ◽  
Daily Minimum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Record Keeping ◽  
Monthly Average ◽  
Weather Observations ◽  
Temperature Observations

AbstractThe International Surface Temperature Initiative is a worldwide effort to locate weather observations, digitize them for public access, and attach provenance to them. As part of that effort, this study sought documents of temperature observations for the nation of Uganda. Although scattered reports were found for the 1890s, consistent record keeping appears to have begun in 1900. Data were keyed in from images of several types of old forms as well as accessed electronically from several sources to extend the time series of 32 stations with at least 4 yr of data back as far as data were available. Important gaps still remain; 1979–93 has virtually no observations from any station. Because many stations were represented by more than one data source, a scheme is described to extract the “best guess” values for each station of monthly averages of the daily maximum, minimum, and mean temperature. A preliminary examination of the national time series indicates that, since the early twentieth century, it appears that Uganda experienced essentially no change in monthly-average daily maximum temperature but did experience a considerable rise in monthly-average daily minimum temperature, concentrated in the last three decades. Because there are many gaps in the data, it is hoped that readers with information on extant data that were not discovered for this study will contact the author or the project so that the data may be archived.

scholarly journals Time Series Construction of Summer Surface Temperatures for Alabama, 1883–2014, and Comparisons with Tropospheric Temperature and Climate Model Simulations

2016 ◽  
Vol 55 (3) ◽  
pp. 811-826 ◽  
Author(s):  
John R. Christy ◽  
Richard T. McNider
Keyword(s):  
Time Series ◽  
Climate Model ◽  
Spatial Scales ◽  
Heat Content ◽  
Time Frame ◽  
Maximum Temperature ◽  
Daily Minimum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Tropospheric Temperature

AbstractThree time series of average summer [June–August (JJA)] daily maximum temperature (TMax) are developed for three interior regions of Alabama from stations with varying periods of record and unknown inhomogeneities. The time frame is 1883–2014. Inhomogeneities for each station’s time series are determined from pairwise comparisons with no use of station metadata other than location. The time series for the three adjoining regions are constructed separately and are then combined as a whole assuming trends over 132 yr will have little spatial variation either intraregionally or interregionally for these spatial scales. Varying the parameters of the construction methodology creates 333 time series with a central trend value based on the largest group of stations of −0.07°C decade−1 with a best-guess estimate of measurement uncertainty from −0.12° to −0.02°C decade−1. This best-guess result is insignificantly different (0.01°C decade−1) from a similar regional calculation using NOAA’s divisional dataset based on daily data from the Global Historical Climatology Network (nClimDiv) beginning in 1895. Summer TMax is a better proxy, when compared with daily minimum temperature and thus daily average temperature, for the deeper tropospheric temperature (where the enhanced greenhouse signal is maximized) as a result of afternoon convective mixing. Thus, TMax more closely represents a critical climate parameter: atmospheric heat content. Comparison between JJA TMax and deep tropospheric temperature anomalies indicates modest agreement (r2 = 0.51) for interior Alabama while agreement for the conterminous United States as given by TMax from the nClimDiv dataset is much better (r2 = 0.86). Seventy-seven CMIP5 climate model runs are examined for Alabama and indicate no skill at replicating long-term temperature and precipitation changes since 1895.

scholarly journals A Stepwise-Clustered Simulation Approach for Projecting Future Heat Wave Over Guangdong Province

2021 ◽  
Vol 9 ◽  
Author(s):  
Jiayan Ren ◽  
Guohe Huang ◽  
Yongping Li ◽  
Xiong Zhou ◽  
Jinliang Xu ◽  
...  
Keyword(s):  
Heat Wave ◽  
Climate Models ◽  
Extreme Event ◽  
Heat Waves ◽  
Extreme Temperature ◽  
Global Climate Models ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Simulation Approach

A heat wave is an important meteorological extreme event related to global warming, but little is known about the characteristics of future heat waves in Guangdong. Therefore, a stepwise-clustered simulation approach driven by multiple global climate models (i.e., GCMs) is developed for projecting future heat waves over Guangdong under two representative concentration pathways (RCPs). The temporal-spatial variations of four indicators (i.e., intensity, total intensity, frequency, and the longest duration) of projected heat waves, as well as the potential changes in daily maximum temperature (i.e., Tmax) for future (i.e., 2006–2095) and historical (i.e., 1976–2005) periods, were analyzed over Guangdong. The results indicated that Guangdong would endure a notable increasing annual trend in the projected Tmax (i.e., 0.016–0.03°C per year under RCP4.5 and 0.027–0.057°C per year under RCP8.5). Evaluations of the multiple GCMs and their ensemble suggested that the developed approach performed well, and the model ensemble was superior to any single GCM in capturing the features of heat waves. The spatial patterns and interannual trends displayed that Guangdong would undergo serious heat waves in the future. The variations of intensity, total intensity, frequency, and the longest duration of heat wave are likely to exceed 5.4°C per event, 24°C, 25 days, and 4 days in the 2080s under RCP8.5, respectively. Higher variation of those would concentrate in eastern and southwestern Guangdong. It also presented that severe heat waves with stronger intensity, higher frequency, and longer duration would have significant increasing tendencies over all Guangdong, which are expected to increase at a rate of 0.14, 0.83, and 0.21% per year under RCP8.5, respectively. Over 60% of Guangdong would suffer the moderate variation of heat waves to the end of this century under RCP8.5. The findings can provide decision makers with useful information to help mitigate the potential impacts of heat waves on pivotal regions as well as ecosystems that are sensitive to extreme temperature.

scholarly journals Changes of Extreme Temperature and Its Influencing Factors in Shiyang River Basin, Northwest China

Atmosphere ◽  
2020 ◽  
Vol 11 (11) ◽  
pp. 1171
Author(s):  
Junju Zhou ◽  
Jumei Huang ◽  
Xi Zhao ◽  
Li Lei ◽  
Wei Shi ◽  
...  
Keyword(s):  
Global Warming ◽  
River Basin ◽  
Minimum Temperature ◽  
Extreme Temperature ◽  
Maximum Temperature ◽  
Daily Minimum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Extreme Temperatures ◽  
Shiyang River Basin

The increase in the frequency and intensity of extreme weather events around the world has led to the frequent occurrence of global disasters, which have had serious impacts on the society, economic and ecological environment, especially fragile arid areas. Based on the daily maximum temperature and daily minimum temperature data of four meteorological stations in Shiyang River Basin (SRB) from 1960 to 2015, the spatio-temporal variation characteristics of extreme temperature indices were analyzed by means of univariate linear regression analysis, Mann–Kendall test and correlation analysis. The results showed that the extreme temperatures warming indices and the minimum of daily maximum temperature (TXn) and the minimum of daily minimum temperature (TNn) of cold indices showed an increasing trend from 1960 to 2016, especially since the 1990s, where the growth rate was fast and the response to global warming was sensitive. Except TXn and TNn, other cold indices showed a decreasing trend, especially Diurnal temperature (DTR) range, which decreased rapidly, indicating that the increasing speed of daily min-temperature were greater than of daily max-temperature in SRB. In space, the change tendency rate of the warm index basically showed an obvious altitude gradient effect that decreased with the altitude, which was consistent with Frost day (FD0) and Cool nights (TN10p) in the cold index, while Ice days (ID0) and Cool days (TX10p) are opposite. The mutation of the cold indices occurred earlier than the warm indices, illustrating that the cold indices in SRB were more sensitive to global warming. The change in extreme temperatures that would have a significant impact on the vegetation and glacier permafrost in the basin was the result of the combined function of different atmospheric circulation systems, which included the Arctic polar vortex, Western Pacific subtropical high and Qinghai-tibet Plateau circulation.

Variability of Temperature Extremes in Northwest Himalayas during Early 21st Century.

2021 ◽  
Author(s):  
Farhan Aziz ◽  
Nadeem Tariq ◽  
Akif Rahim ◽  
Ambreen Mahmood
Keyword(s):  
Climate Change ◽  
21St Century ◽  
Extreme Temperature ◽  
Northern Region ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Temperature Extremes ◽  
Early 21St Century ◽  
Northwest Himalayas

<p>In recent years, extreme events and their severe damage have become more common around the world. It is widely known that atmospheric greenhouse gases have contributed to global warming. <br>A set of appropriate indicators describing the extremes of climate change can be used to study the extent of climate change. This study reveals the trends of temperature extreme indices on the spatial scale in the western part of Northwest Himalayas. The study is conducted at 13 climate stations lies at a different altitude of the study area.The Daily maximum and minimum temperature data during 2000--2018 of stations obtained from the Pakistan Meteorological Department (PMD) and Water and Power Development Authority (WAPDA). The 12 extreme temperature indices (FD, SU, TXx , TXn., TNx, TNn, TN10p , TN90p, TX10p , TX90p, CSDI, WSDI) recommended by ETCCDI (Expert Team on Climate Change Detection and Indices) are used to study the variabilities in temperature extremes. These indices are characterized based on amplitude, persistence, and frequency. The analysis is performed by using R package of extremes “RClimDEX”. The analysis shows the frequency of summer days (Su) and warm spells (WSDI) have increasing trends in the Southwest region, whereas the frequency of cold spells and frost days have decreasing trends observed in the Northern region of the study areas. The maximum and minimum values of daily maximum temperature (TXX, TXN) increase in the foothill area of the region and decreasing trends in the high elevation region. The day and night get cool in the Northwest region, whereas the days and nights are showing warmer trends in low elevation regions of the study area. Overall, the study concludes that the Northwestern parts have cool trends while South West and South eastern parts have warm trends during the early 21st century.</p><p><strong>Key words:</strong>  Temperature Extremes, Northwest Himalayas, Trends, R-Climdex, Climate Change</p>

Seasonal analysis of warm extreme events in Serbia from 1949 to 2017

2020 ◽  
Author(s):  
Ivana Tosic ◽  
Suzana Putniković ◽  
Milica Tošić
Keyword(s):  
Extreme Events ◽  
Heat Waves ◽  
Extreme Temperature ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Extreme Temperature Events ◽  
Air Temperatures ◽  
Seasonal Analysis ◽  
Novi Sad

<p>Worldwide studies revealed a general increase in frequency and severity of warm extreme temperature events. In this study, extreme temperature events including Heat waves (HWs) are examined. Extreme indices are calculated based on daily maximum temperature (Tx). The following definitions are employed: SU - number of days with Tx > 25 °C, umber of days with Tx > 90<sup>th</sup> percentile, and WSDI - number of days in intervals of at least six consecutive days for which Tx is higher than the calendar day 90<sup>th</sup> percentile. Daily values of air temperatures from 11 meteorological stations distributed across Serbia were used for the period 1949–2017.</p><p>Trends of extreme temperature events and their frequencies are examined. The period 1949–2017 are characterised by a warming of extreme temperature indices (SU, Tx90, HWs). It is found that maximum air temperatures increased at all stations, but statistically significant at 6 stations in winter, 4 stations in summer and two stations in spring. The average number of SU per station was between 63.1 in Novi Sad to 73.5 in Negotin during the summer season. Significant increase of SU is recorded in summer for 10 out of 11 stations. Positive trends of SU and Tx90 are observed for all stations and seasons, except in Novi Sad. The average number of Tx90 is about 9 for all stations in all seasons. The longest heat waves prevailed in 2012, but the most severe are recorded in 2007. Increasing of warm extreme events in Serbia are in agreement with studies for different regions of the world.</p>

Detection of Anthropogenic Influence on Fixed Threshold Indices of Extreme Temperature

2018 ◽  
Vol 31 (16) ◽  
pp. 6341-6352 ◽  
Author(s):  
Hong Yin ◽  
Ying Sun
Keyword(s):  
Dominant Role ◽  
Extreme Temperature ◽  
Poor Performance ◽  
Internal Variability ◽  
Global Scale ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Anthropogenic Forcing ◽  
Fixed Threshold

Threshold indices of extreme temperature are defined based on temperature values that fall above or below fixed thresholds and thus have important implications for agriculture, engineering, and human health. Here, we focus on four extreme temperature fixed threshold indices and their detection and attribution at the global and continental scales, as well as within China. These indices include the number of days with daily minimum temperatures below 0°C [frost days (FD)] and above 20°C [tropical nights (TR)] and the number of days with daily maximum temperatures below 0°C [ice days (ID)] and above 25°C [summer days (SU)]. We employ an optimal fingerprinting method to compare the spatial and temporal changes in these fixed threshold indices assessed from observations and simulations performed with multiple models. We find that an anthropogenic signal can be robustly detected in these fixed threshold indices at scales of over the globe, most of the continents, and China. A natural signal cannot be identified in the changes in most of the indices, thus indicating the dominant role of anthropogenic forcing in producing these changes. In North and South America, the models show poor performance in reproducing the fixed threshold indices related to daily maximum temperature. The changes in summer days are not clearly related to their responses to external forcing over these two continents. This study provides a useful complement to other detection studies and sheds light on the importance of anthropogenic forcing in determining most of the fixed threshold indices at the global scale and over most of the continents, compared with internal variability.

Effects of patchy shade on stream water temperature: how quickly do small streams heat and cool?

2004 ◽  
Vol 55 (8) ◽  
pp. 737 ◽  
Author(s):  
J. Christopher Rutherford ◽  
Nicholas A. Marsh ◽  
Peter M. Davies ◽  
Stuart E. Bunn
Keyword(s):  
Travel Time ◽  
Dynamic Equilibrium ◽  
Stream Water ◽  
Rate Of Change ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Small Streams ◽  
Maximum Temperatures ◽  
Strong Linear Relationship

Summer field observations in five 2nd order streams (width 1–2 m, depth 5–15 cm, velocity 5–10 cm s–1) in Western Australia and south-east Queensland showed that daily maximum temperatures changed by ±4°C over distances of 600–960 m (travel time 2–3 h) immediately downstream from 40–70% step changes in riparian shade. There was a strong linear relationship between the rate of change of daily maximum temperature and the change of shade such that downstream from a 100% change of shade the heating/cooling rates are ±4°C h–1 and ±10°C km–1 (upper bound ±6°C h–1 and ±15°C km–1) respectively. These high rates only apply over short distances and travel times because downstream water temperatures adjust to the new level of shade and reach a dynamic equilibrium. Shade was too patchy in the study streams to measure how long water takes to reach equilibrium, however, using an existing computer model, we estimate that this occurs after ~1200 m (travel time 4 h). Further modelling work is desirable to predict equilibrium temperatures under given meteorological, flow and shade conditions. Nevertheless, landowners and regulators can use this information to determine whether the presence/absence of certain lengths of bankside shade are likely to cause desirable/undesirable temperature decreases/increases.

Prediction of temperatures in cattle dung for estimating development times of coprophilous organisms

1988 ◽  
Vol 78 (2) ◽  
pp. 235-240 ◽  
Author(s):  
J. N. Matthiessen ◽  
M. J. Palmer
Keyword(s):  
Air Temperature ◽  
Meteorological Data ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Threshold Temperature ◽  
Cattle Dung ◽  
Daily Maximum ◽  
Daily Minimum ◽  
Maximum Temperatures ◽  
Minimum Air Temperature

AbstractIn studies in Western Australia, temperatures in air and one- and two-litre pads of cattle dung set out weekly and ranging from one to 20 days old were measured hourly for 438 days over all seasons, producing 1437 day x dung-pad observations. Daily maximum temperatures (and hence thermal accumulation) in cattle dung pads could not be accurately predicted using meteorological data alone. An accurate predictor of daily maximum dung temperature, using multiple regression analysis, required measurement of the following factors: maximum air temperature, hours of sunshine, rainfall, a seasonal factor (the day number derived from a linear interpolation of day number from day 0 at the winter solstice to day 182 at the preceding and following summer solstices) and a dung-pad age-specific intercept term, giving an equation that explained a 91·4% of the variation in maximum dung temperature. Daily maximum temperature in two-litre dung pads was 0·6°C cooler than in one-litre pads. Daily minimum dung temperature equalled minimum air temperature, and daily minimum dung temperatures occurred at 05.00 h and maximum temperatures at 14.00 h for one-litre and 14.30 h for two-litre pads. Thus, thermal summation in a dung pad above any threshold temperature can be computed using a skewed sine curve fitted to daily minimum air temperature and the calculated maximum dung temperature.

scholarly journals Neural Networks for Environmental Problems: Data Quality Control and Air Pollution Nowcasting

2013 ◽  
Vol 2 (3) ◽  
pp. 281-292 ◽  
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Quality Control ◽  
Air Pollutant ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Data Quality Control ◽  
Distributed Information ◽  
Pollutant Concentrations

This work illustrates the use and some related results of Artificial Neural Networks (ANNs) for data quality control of environmental time series and for reconstruction of missing data. ANNs are applied to the following problems: i) short and medium-term predicting of air pollutant concentrations in urban areas, ii) interpolating and extrapolating daily maximum temperature, iii) replacing time distribution with spatial distributed information (pollutant concentrations at different measuring sites). Observed versus predicted data are compared to test the efficacy of ANNs in simulating environmental processes. Results confirm ANNs as an improvement of classical models and show the utility of ANNs for restoration of time series..

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