scholarly journals Nonlinear Trends, Long-Range Dependence, and Climate Noise Properties of Surface Temperature

2012 ◽  
Vol 25 (12) ◽  
pp. 4172-4183 ◽  
Author(s):  
Christian Franzke
Keyword(s):  
Time Series ◽  
Climate Variability ◽  
Null Model ◽  
Warming Trend ◽  
Temperature Time Series ◽  
Trend Detection ◽  
Detection Methods ◽  
Nonlinear Trends ◽  
Temperature Time ◽  
Climate Noise

Abstract This study investigates the significance of trends of four temperature time series—Central England Temperature (CET), Stockholm, Faraday-Vernadsky, and Alert. First the robustness and accuracy of various trend detection methods are examined: ordinary least squares, robust and generalized linear model regression, Ensemble Empirical Mode Decomposition (EEMD), and wavelets. It is found in tests with surrogate data that these trend detection methods are robust for nonlinear trends, superposed autocorrelated fluctuations, and non-Gaussian fluctuations. An analysis of the four temperature time series reveals evidence of long-range dependence (LRD) and nonlinear warming trends. The significance of these trends is tested against climate noise. Three different methods are used to generate climate noise: (i) a short-range-dependent autoregressive process of first order [AR(1)], (ii) an LRD model, and (iii) phase scrambling. It is found that the ability to distinguish the observed warming trend from stochastic trends depends on the model representing the background climate variability. Strong evidence is found of a significant warming trend at Faraday-Vernadsky that cannot be explained by any of the three null models. The authors find moderate evidence of warming trends for the Stockholm and CET time series that are significant against AR(1) and phase scrambling but not the LRD model. This suggests that the degree of significance of climate trends depends on the null model used to represent intrinsic climate variability. This study highlights that in statistical trend tests, more than just one simple null model of intrinsic climate variability should be used. This allows one to better gauge the degree of confidence to have in the significance of trends.

Interdecadal oscillations and the warming trend in global temperature time series

Nature ◽  
1991 ◽  
Vol 350 (6316) ◽  
pp. 324-327 ◽  
Author(s):  
M. Ghil ◽  
R. Vautard
Keyword(s):  
Time Series ◽  
Warming Trend ◽  
Temperature Time Series ◽  
Global Temperature ◽  
Temperature Time

scholarly journals Modelling and Forecasting of Monthly Rainfall and Temperature Time Series Using SARIMA for Trend Detection- A Case Study of Umiam, Meghalaya (India)

2020 ◽  
pp. 155-172
Author(s):  
P. P. Dabral ◽  
Issac Tabing
Keyword(s):  
Time Series ◽  
Minimum Temperature ◽  
Moving Average ◽  
Temperature Time Series ◽  
Monthly Rainfall ◽  
Trend Detection ◽  
Maximum Temperature ◽  
Lm Test ◽  
Temperature Time ◽  
Sarima Models

Seasonal Auto Regressive Integrative Moving Average Models (SARIMA) were developed for monthly rainfall, mean monthly maximum and minimum temperature time series for Umiam (Barapani), Meghalaya (India). The best model was selected based on the minimum values of AIC and BIC criteria as well as based on observing the ACF and PACF plot of residuals. SARIMA (5,1,2) x (1,1,1)12, SARIMA (2,1,2) x (2,1,1)12, SARIMA (6,1,4) x (2,1,3)12 models were found to be the best fit model for the monthly rainfall, mean monthly maximum  and minimum temperatures time series respectively. The adequacy of the SARIMA models was also verified using the Ljung-Box (Q) statistic test. McLeod-Li test and Engle’s ARCH LM test were carried out for residuals. The results indicated that there was no Arch effect in the established SARIMA models and models can be used for forecasting the future values for the year 2013 to 2028. The determination of trend in monthly rainfall, mean maximum and minimum temperatures in the forecasted series were done using different trend analysis techniques. For monthly rainfall and mean monthly minimum temperature time series, all the selected methods supported no significant trend. However, in the case of mean monthly maximum temperature time series, three selected methods supported falling trend.

scholarly journals Long-Range Dependence and Climate Noise Characteristics of Antarctic Temperature Data

2010 ◽  
Vol 23 (22) ◽  
pp. 6074-6081 ◽  
Author(s):  
Christian Franzke
Keyword(s):  
Time Series ◽  
Long Range ◽  
Short Range ◽  
Null Model ◽  
Null Models ◽  
Environmental Research ◽  
Noise Characteristics ◽  
Dependent Process ◽  
Nonlinear Trends ◽  
Climate Noise

Abstract This study examines the long-range dependency, climate noise characteristics, and nonlinear temperature trends of eight Antarctic stations from the Reference Antarctic Data for Environmental Research (READER) dataset. Evidence is shown that Antarctic temperatures are long-range dependent. To identify possible nonlinear trends, the ensemble empirical mode decomposition (EEMD) method is used, and then the question of whether the observed trends can arise from internal atmospheric fluctuations is examined. To answer this question, surrogate data are generated from two paradigmatic null models: a standard first-order autoregressive process representing a short-range dependent process and a fractional integrated process representing a long-range dependent process. It is found that three of the eight stations show statistically significant trends when tested against the short-range dependent process while only the Faraday–Vernadsky station temperature time series shows a significant trend when tested against the long-range dependent null model. All other considered stations show no trends that are statistically significant against the two null models, and thus they can be explained by internal atmospheric variability. These results imply that more attention should be given to assessing the correlation structure of climate time series.

scholarly journals The effect of streambed heterogeneity on groundwater-surface water exchange fluxes inferred from temperature time series

2015 ◽  
Vol 51 (1) ◽  
pp. 198-212 ◽  
Author(s):  
Dylan J. Irvine ◽  
Roger H. Cranswick ◽  
Craig T. Simmons ◽  
Margaret A. Shanafield ◽  
Laura K. Lautz
Keyword(s):  
Time Series ◽  
Surface Water ◽  
Water Exchange ◽  
Temperature Time Series ◽  
Temperature Time ◽  
Streambed Heterogeneity

scholarly journals The Simulation of Daily Temperature Time Series from GCM Output. Part I: Comparison of Model Data with Observations

1997 ◽  
Vol 10 (10) ◽  
pp. 2497-2513 ◽  
Author(s):  
J. P. Palutikof ◽  
J. A. Winkler ◽  
C. M. Goodess ◽  
J. A. Andresen
Keyword(s):  
Time Series ◽  
Temperature Time Series ◽  
Daily Temperature ◽  
Model Data ◽  
Temperature Time

The Use of Satellite TIR Time Series for Thermal Anomalies’ Detection on Natural and Urban Areas

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Malvina Silvestri ◽  
Federico Rabuffi ◽  
Massimo Musacchio ◽  
Sergio Teggi ◽  
Maria Fabrizia Buongiorno
Keyword(s):  
Time Series ◽  
Land Surface Temperature ◽  
Satellite Data ◽  
Land Surface ◽  
Urban Areas ◽  
Temperature Time Series ◽  
Campi Flegrei ◽  
Thermal Anomalies ◽  
Pca Method ◽  
Temperature Time

In this work, the land surface temperature time series derived using Thermal InfraRed (TIR) satellite data offers the possibility to detect thermal anomalies by using the PCA method. This approach produces very detailed maps of thermal anomalies, both in geothermal areas and in urban areas. Tests were conducted on the following three Italian sites: Solfatara-Campi Flegrei (Naples), Parco delle Biancane (Grosseto) and Modena city.

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