Moving Spectral Variance and Coherence Analysis and Some Applications on Long Air Temperature Series

The tree-ring width and the maximum wood density of Norway spruce (Picea abies (L.) Karst.) have been examined in order to reconstruct air temperature of the summer half-year during the period 1804 - 1989. The trees examined come from a natural spruce stand of Labský důl (Elbe Valley) in Krkonoše (Giant Mts.), North Bohemia. The results obtained by this way have been compared with a similar reconstruction made for Central Europe and with air temperature records from the Prague-Klementinum station. Both temperature series (reconstructed and measured), however, show only 36 % of commonly clarified variability. Differences may follow from the standardization of dendrochronologies as well as from other factors which may have influenced the growth of spruce. The quality of air temperature measurement may play also role.

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Incorporating climate trends in the stochastic modeling of extreme minimum air temperature series of Campinas, state of São Paulo, Brazil

Bragantia ◽

10.1590/s0006-87052011000400031 ◽

2011 ◽

Vol 70 (4) ◽

pp. 952-957 ◽

Cited By ~ 3

Author(s):

Gabriel Constantino Blain

Keyword(s):

Air Temperature ◽

Extreme Value Distribution ◽

Temperature Series ◽

Time Dependent ◽

Ratio Test ◽

Climate Trends ◽

Probabilistic Structure ◽

Dependent Model ◽

Time Dependent Model ◽

Minimum Air Temperature

Under the hypothesis that the presence of climate trends in the annual extreme minimum air temperature series of Campinas (Tminabs; 1891-2010; 22º54'S; 47º05'W; 669 m) may no longer be neglected, the aim of the work was to describe the probabilistic structure of this series based on the general extreme value distribution (GEV) with parameters estimated as a function of a time covariate. The results obtained by applying the likelihood ratio test and the percentil-percentil and quantil-quantil plots, have indicated that the use of a time-dependent model provides a feasible description of the process under evaluation. In this non-stationary GEV model the parameters of location and scale were expressed as time-dependent functions. The shape parameter remained constant. It was also verified that although this non-stationary model has indicated an average increase in the values of the analyzed data, it does not allow us to conclude that the region of Campinas is now free from frost occurrence since this same model also reveals an increasing trend in the dispersions of the variable under evaluation. However, since the parameters of location and scale of this probabilistic model are significantly conditioned on time, the presence of climate trends in the analyzed time series is proven.

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Climate trends in the municipality of Pelotas, state of Rio Grande do Sul, Brazil

Engenharia Agrícola ◽

10.1590/1809-4430-eng.agric.v35n4p769-777/2015 ◽

2015 ◽

Vol 35 (4) ◽

pp. 769-777 ◽

Cited By ~ 1

Author(s):

Izabele B. Kruel ◽

Monica C. Meschiatti ◽

Gabriel C. Blain ◽

Ana M. H. de Ávila

Keyword(s):

Air Temperature ◽

Rio Grande ◽

Location Parameter ◽

Temperature Series ◽

Likelihood Method ◽

Daily Maximum ◽

Rio Grande Do Sul ◽

The Mean ◽

Maximum Air Temperature ◽

Over Time

ABSTRACT Changes in the frequency of occurrence of extreme weather events have been pointed out as a likely impact of global warming. In this context, this study aimed to detect climate change in series of extreme minimum and maximum air temperature of Pelotas, State of Rio Grande do Sul, (1896 - 2011) and its influence on the probability of occurrence of these variables. We used the general extreme value distribution (GEV) in its stationary and non-stationary forms. In the latter case, GEV parameters are variable over time. On the basis of goodness-of-fit tests and of the maximum likelihood method, the GEV model in which the location parameter increases over time presents the best fit of the daily minimum air temperature series. Such result describes a significant increase in the mean values of this variable, which indicates a potential reduction in the frequency of frosts. The daily maximum air temperature series is also described by a non-stationary model, whose location parameter decreases over time, and the scale parameter related to sample variance rises between the beginning and end of the series. This result indicates a drop in the mean of daily maximum air temperature values and increased dispersion of the sample data.

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A Remote-Sensing Method of Selecting Reference Stations for Evaluating Urbanization Effect on Surface Air Temperature Trends

Journal of Climate ◽

10.1175/2010jcli3658.1 ◽

2011 ◽

Vol 24 (13) ◽

pp. 3179-3189 ◽

Cited By ~ 42

Author(s):

Yuyu Ren ◽

Guoyu Ren

Keyword(s):

Remote Sensing ◽

Air Temperature ◽

Mainland China ◽

Surface Air Temperature ◽

Image Data ◽

Temperature Series ◽

Urbanization Effect ◽

Temperature Reference ◽

Weather Stations ◽

Reference Climate

Abstract In the global lands, the bias of urbanization effects still exits in the surface air temperature series of many city weather stations to a certain extent. Reliable reference climate stations need to be selected for the detection and correction of the local manmade warming bias. The underlying image data of remote sensing retrieval is adopted in this study to obtain the spatial distribution of surface brightness temperature, and the surface air temperature reference stations are determined based on the locations of the weather stations in the remote sensing surface thermal fields. Among the 672 national reference climate stations and national basic weather stations of mainland China, for instance, 113 surface air temperature reference stations are selected for applying this method. Compared with the average surface air temperature series of the reference stations obtained by a more sophisticated method developed in China, this method is proven to be robust and applicable, and can be adopted for the evaluation and adjustment study on the urbanization bias of the currently used air temperature records of surface climate stations in the global lands.

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