Investigating dynamical features in the long-term daily maximum temperature time series recorded at Adrián Jara, Paraguay

2018 ◽  
Vol 66 (3) ◽  
pp. 393-403
Author(s):  
Luciano Telesca ◽  
Julián Báez Benitez
Keyword(s):  
Time Series ◽  
Temperature Time Series ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Dynamical Features ◽  
Temperature Time

Long-term variability of the temperature time series recorded in Lisbon

2009 ◽  
Vol 36 (3) ◽  
pp. 323-337 ◽  
Author(s):  
João Santos ◽  
Solange Leite
Keyword(s):  
Time Series ◽  
Temperature Time Series ◽  
Temperature Time

Detection of groundwater flux changes in response to two large earthquakes using long-term bedrock temperature time series

2020 ◽  
Vol 590 ◽  
pp. 125245
Author(s):  
Qiongying Liu ◽  
Shunyun Chen ◽  
Lichun Chen ◽  
Peixun Liu ◽  
Zhuzhuan Yang ◽  
...  
Keyword(s):  
Time Series ◽  
Temperature Time Series ◽  
Groundwater Flux ◽  
Temperature Time ◽  
Large Earthquakes

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.

Relationship between heating/cooling period and changing temperature conditions in the urban areas of Hungary

2020 ◽  
Author(s):  
Csenge Dian ◽  
Attila Talamon ◽  
Rita Pongrácz ◽  
Judit Bartholy
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Energy Consumption ◽  
Extreme Values ◽  
Building Design ◽  
Temperature Time Series ◽  
Maximum Temperature ◽  
Design Parameters ◽  
Daily Average ◽  
Temperature Time

<p>Climate change, extreme weather conditions, and local scale urban heat island (UHI) effect altogether have substantial impacts on people’s health and comfort. The urban population spends most of its time in buildings, therefore, it is important to examine the relationship between weather/climate conditions and indoor environment. The role of buildings is complex in this context. On the one hand UHI effect is mostly created by buildings and artificial surfaces. On the other hand they account for about 40% of energy consumption on European average. Since environmental protection requires increased energy efficiency, the ultimate goal from this perspective is to achieve nearly zero-energy buildings. When estimating energy consumption, daily average temperatures are taken into account. The design parameters (e.g. for heating systems) are determined using temperature-based criteria. However, due to climate change, these critical values are likely to change as well. Therefore, it is important to examine the temperature time series affecting the energy consumption of buildings. For the analysis focusing on the Carpathian region within central/eastern Europe, we used the daily average, minimum and maximum temperature time series of five Hungarian cities (i.e. Budapest, Debrecen, Szeged, Pécs and Szombathely). The main aim of this study is to investigate the effect of changing daily average temperatures and the rising extreme values on building design parameters, especially heating and cooling periods (including the length and average temperatures of such periods).</p>

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 Spatial-temporal analysis of temperature in Lake Shira based on long-term observations

2021 ◽  
Author(s):  
O.S. Volodko ◽  
L.A. Kompaniets ◽  
L.V. Gavrilova
Keyword(s):  
Principal Component Analysis ◽  
Time Series ◽  
Principal Component ◽  
Temporal Analysis ◽  
Temperature Time Series ◽  
Lake Shira ◽  
Temperature Time ◽  
Spatial Temporal Analysis

Long-term in-situ measurements of temperature were conducted in lake Shira during 2013-2015. The principal component analysis of temperature time series allowed to identify period of generation and propagation of internal waves. The spectral analysis revealed the dominance of the oscillations with periods of 21.3, 10.6 and 5.3 h.

scholarly journals Long term dynamics of OH<sup>∗</sup> temperatures over Middle Europe: Trends and solar correlations

2016 ◽  
Author(s):  
Christoph Kalicinsky ◽  
Peter Knieling ◽  
Ralf Koppmann ◽  
Dirk Offermann ◽  
Wolfgang Steinbrecht ◽  
...  
Keyword(s):  
Time Series ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Linear Trend ◽  
Temperature Time Series ◽  
Time Interval ◽  
Radio Flux ◽  
Solar Radio Flux ◽  
Temperature Time

Abstract. We present the analysis of annual average OH∗ temperatures in the mesopause region derived from measurements of the GRound based Infrared P-branch Spectrometer (GRIPS) at Wuppertal (51° N, 7° E) in the time interval 1988 to 2015. The current study uses a 7 year longer temperature time series compared to the latest analysis regarding the long term dynamics of OH* temperatures measured at Wuppertal. This additional time of observation leads to a change in characterisation of the observed long term dynamics. We perform a multiple linear regression using the solar radio flux F10.7cm (11-year cycle of solar activity) and time to describe the temperature evolution. The analysis leads to a linear trend of (−0.089&amp;pm;0.055) K year−1 and a sensitivity to the solar activity of (4.2&amp;pm;0.9) K (100 SFU)−1 (r2 of fit 0.6). However, one linear trend in combination with the 11-year solar cycle is not sufficient to explain all observed long term dynamics. Actually we find a clear trend break in the temperature time series in middle of 2006. Before this break point there is an explicit negative linear trend of (−0.22&amp;pm;0.08) K year−1 and after 2006 the linear trend turns positive with a value of (0.38&amp;pm;0.23) K year−1. This apparent trend break can also be described using a long periodic oscillation. One possibility is to use the 22-year solar cycle that describes the reversal of the solar magnetic field (Hale cycle). A multiple linear regression using the solar radio flux and the solar polar magnetic field as parameters leads to the regression coefficients Csolar = (5.0&amp;pm;0.7) K (100 SFU)−1 and Chale = (1.8&amp;pm;0.5) K (100 µT)−1 (r2 = 0.71). But the best way to describe the OH* temperature time series is to use the solar radio flux and a 24-year oscillation. A multiple linear regression using these parameters leads to a sensitivity to the solar activity of (4.3&amp;pm;0.7) K (100 SFU)−1 and an amplitude of the 24-year oscillation A = (1.95&amp;pm;0.43) K (r2 = 0.77). The most important finding here is that using these parameters for the multiple linear regression an additional linear trend is no longer needed. Moreover, with the knowledge of this 24-year oscillation the linear trends derived in this and in a former study of the Wuppertal data series can be reproduced by just fitting a line to the corresponding part (time interval) of the oscillation. This actually means that depending on the analysed time interval completely different linear trends with respect to magnitude and sign can be observed. This fact is of essential importance for any comparison between different observations and model simulations. After detrending the temperature time series regarding the 11-year solar cycle and the 24-year oscillation multi-annual oscillations (MAOs) remain. A harmonic analysis finds three pronounced oscillations with periods of (2.69&amp;pm;0.06) years, (3.15&amp;pm;0.07) years, and (4.54&amp;pm;0.17) years. The corresponding amplitudes are (1.03&amp;pm;0.33) K, (1.03&amp;pm;0.33) K, and (0.91&amp;pm;0.36) K, respectively.

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