Detecting trends that are nonlinear and asymmetric on diurnal and seasonal timescales.

Trends in climate time series are often nonlinear and temporally-asymmetric, i.e. the trend is different for different seasons and/or hours of the day. Here a method is developed that allows the nonlinearity and temporal asymmetry of a trend to be investigated simultaneously. First, nonlinear trend...

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Bibliographic Details
Published in:Climate Dynamics
Main Authors: Fischer, M, Paterson, A
Format: Article in Journal/Newspaper
Language:English
Published: Springer 2014
Subjects:
CLIMATIC CHANGE
FUNCTIONS
WEATHER
BIOLOGICAL VARIABILITY
EXTRACTION
ASYMMETRY
Grim
Law Dome
Macquarie Island
Online Access:http://apo.ansto.gov.au/dspace/handle/10238/6059
https://doi.org/10.1007/s00382-014-2086-8
Description
Summary:Trends in climate time series are often nonlinear and temporally-asymmetric, i.e. the trend is different for different seasons and/or hours of the day. Here a method is developed that allows the nonlinearity and temporal asymmetry of a trend to be investigated simultaneously. First, nonlinear trend components are extracted from a univariate time series, by adapting a nonparametric dimension-reduction method. Then, the nonlinear trend components are substituted into a regression model in which the periodic mean component and the periodic variation in the amplitude of the nonlinear trend are modeled using harmonic functions of the seasonal and diurnal periods. Third, trend patterns in the positive and negative anomalies are investigated, by extending the nonlinear trend model using indicator variables. Fourth, a non-local inferential test is developed to test the statistical significance of the trend patterns. The nonlinear trend model is applied to a simulated time series, as well as to long-term high-resolution temperature records from five Southern Hemisphere sites: Lucas Heights, Sydney Airport, Cape Grim, Macquarie Island and Law Dome. Our method should be generally useful for identifying the effect of both climate-related factors and observation/site-related factors on seasonal and diurnal trends in meteorological data series. © 2014, Springer.

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