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Modelling Long-Term Variability in Daily Air Temperature Time Series for Southern Hemisphere Stations

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Abstract

Customarily, climate studies of long-range temperature variability have been carried out using annual or monthly averages. The approach mixes the details of short- and long-range variability that are different for air temperature series. This work shows that a useful method for eliminating short-range variability on long-range variability is to apply a sufficiently long (about 2 months) time step to the daily series. An autoregressive integrated moving average model is fitted to daily maximum and minimum temperature anomalies from the mean seasonal cycle, using data from a number of Australian and New Zealand weather stations. The fitted model can be considered as a sum of random walk plus white noise. This enables us to obtain a quantitative long-term description of the temperature variability.

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Authors and Affiliations

  1. Tartu Observatory, Toravere, Estonia

    Olavi Kärner

  2. School of Environment, University of Auckland, Auckland, New Zealand

    Chris R. de Freitas

Authors
  1. Olavi Kärner
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  2. Chris R. de Freitas
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Correspondence to Olavi Kärner.

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Kärner, O., de Freitas, C.R. Modelling Long-Term Variability in Daily Air Temperature Time Series for Southern Hemisphere Stations. Environ Model Assess 17, 221–229 (2012). https://doi.org/10.1007/s10666-011-9269-z

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  • DOI: https://doi.org/10.1007/s10666-011-9269-z

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