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 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.

Sign in / Sign up

Export Citation Format

Share Document