Modeling extreme minimum air temperature series under climate change conditions
Considering the presence of non-stationary components, such as trends, in the extreme minimum air temperature series available from three locations of the State of São Paulo-Brazil, the aim of this research was to describe the probabilistic structure of this variable by using a non-stationary model (based on the general extreme value distribution; GEV model) in which the parameters are estimated as a function of time covariate. The Mann-Kendall test has proven the presence of significant increasing trends in all analyzed series. Furthermore, according to the Pettitt (changing-point) test, 1991 is the initial year of these trends (in the three locations). The applied selection criteria indicated that a GEV model in which the location parameter is estimated as a function of time is recommended to describe the probability structure of the variable under evaluation. The others two parameters of this model remained time-independent. According to this non-stationary model, the detected trends in the climate conditions of these locations have shown the same rate of change (0.04°C per year).
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Universidade Federal de Santa Maria
2011
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oai:scielo:S0103-847820110011000052011-11-21Modeling extreme minimum air temperature series under climate change conditionsBlain,Gabriel Constantino time-dependent model probability function non-stationary approach Considering the presence of non-stationary components, such as trends, in the extreme minimum air temperature series available from three locations of the State of São Paulo-Brazil, the aim of this research was to describe the probabilistic structure of this variable by using a non-stationary model (based on the general extreme value distribution; GEV model) in which the parameters are estimated as a function of time covariate. The Mann-Kendall test has proven the presence of significant increasing trends in all analyzed series. Furthermore, according to the Pettitt (changing-point) test, 1991 is the initial year of these trends (in the three locations). The applied selection criteria indicated that a GEV model in which the location parameter is estimated as a function of time is recommended to describe the probability structure of the variable under evaluation. The others two parameters of this model remained time-independent. According to this non-stationary model, the detected trends in the climate conditions of these locations have shown the same rate of change (0.04°C per year).info:eu-repo/semantics/openAccessUniversidade Federal de Santa MariaCiência Rural v.41 n.11 20112011-11-01info:eu-repo/semantics/articletext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-84782011001100005en10.1590/S0103-84782011001100005 |
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Blain,Gabriel Constantino |
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Blain,Gabriel Constantino Modeling extreme minimum air temperature series under climate change conditions |
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Blain,Gabriel Constantino |
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Blain,Gabriel Constantino |
| title |
Modeling extreme minimum air temperature series under climate change conditions |
| title_short |
Modeling extreme minimum air temperature series under climate change conditions |
| title_full |
Modeling extreme minimum air temperature series under climate change conditions |
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Modeling extreme minimum air temperature series under climate change conditions |
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Modeling extreme minimum air temperature series under climate change conditions |
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modeling extreme minimum air temperature series under climate change conditions |
| description |
Considering the presence of non-stationary components, such as trends, in the extreme minimum air temperature series available from three locations of the State of São Paulo-Brazil, the aim of this research was to describe the probabilistic structure of this variable by using a non-stationary model (based on the general extreme value distribution; GEV model) in which the parameters are estimated as a function of time covariate. The Mann-Kendall test has proven the presence of significant increasing trends in all analyzed series. Furthermore, according to the Pettitt (changing-point) test, 1991 is the initial year of these trends (in the three locations). The applied selection criteria indicated that a GEV model in which the location parameter is estimated as a function of time is recommended to describe the probability structure of the variable under evaluation. The others two parameters of this model remained time-independent. According to this non-stationary model, the detected trends in the climate conditions of these locations have shown the same rate of change (0.04°C per year). |
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Universidade Federal de Santa Maria |
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2011 |
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http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-84782011001100005 |
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AT blaingabrielconstantino modelingextrememinimumairtemperatureseriesunderclimatechangeconditions |
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1756406041589841920 |