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

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.

[1]  Zhaohua Wu,et al.  On the trend, detrending, and variability of nonlinear and nonstationary time series , 2007, Proceedings of the National Academy of Sciences.

[2]  P. Robinson.  Long memory time series , 2003 .

[3]  S. Havlin,et al.  Indication of a Universal Persistence Law Governing Atmospheric Variability , 1998 .

[4]  C. Hurvich,et al.  Plug‐in Selection of the Number of Frequencies in Regression Estimates of the Memory Parameter of a Long‐memory Time Series , 1998 .

[5]  H. Storch,et al.  Statistical Analysis in Climate Research , 2000 .

[6]  Josefino C. Comiso,et al.  Warming of the Antarctic ice-sheet surface since the 1957 International Geophysical Year , 2009, Nature.

[7]  D. Turcotte,et al.  Self-Affine Time Series: I. Generation and Analyses , 1999 .

[8]  T. Bracegirdle,et al.  Ice core evidence for significant 100‐year regional warming on the Antarctic Peninsula , 2009 .

[9]  J. Geweke,et al.  THE ESTIMATION AND APPLICATION OF LONG MEMORY TIME SERIES MODELS , 1983 .

[10]  J. Turner,et al.  Antarctic climate change during the last 50 years , 2005 .

[11]  Jan Beran,et al.  Statistics for long-memory processes , 1994 .

[12]  Peter Huybers,et al.  Links between annual, Milankovitch and continuum temperature variability , 2005, Nature.

[13]  R. A. Madden,et al.  Estimates of the Natural Variability of Time-Averaged Sea-Level Pressure , 1976 .

[14]  L. Gil‐Alana.  Statistical Modeling of the Temperatures in the Northern Hemisphere Using Fractional Integration Techniques , 2005 .

[15]  Douglas W. Nychka,et al.  Statistical significance of trends and trend differences in layer-average atmospheric temperature time series , 2000 .

[16]  Murad S. Taqqu,et al.  On estimating the intensity of long-range dependence in finite and infinite variance time series , 1998 .

[17]  M. Ghil,et al.  Interdecadal oscillations and the warming trend in global temperature time series , 1991, Nature.

[18]  P. Robinson,et al.  Statistical inference for a random coefficient autoregressive model , 1978 .

[19]  Norden E. Huang,et al.  Ensemble Empirical Mode Decomposition: a Noise-Assisted Data Analysis Method , 2009, Adv. Data Sci. Adapt. Anal..

[20]  Donald B. Percival,et al.  Interpretation of North Pacific Variability as a Short- and Long-Memory Process* , 2001 .

[21]  Hans von Storch,et al.  Long‐term persistence in climate and the detection problem , 2006 .

[22]  C. Velasco,et al.  Non-stationary log-periodogram regression , 1999 .

[23]  Christian Franzke,et al.  Multi-scale analysis of teleconnection indices: climate noise and nonlinear trend analysis , 2009 .

[24]  Norden E. Huang,et al.  A review on Hilbert‐Huang transform: Method and its applications to geophysical studies , 2008 .

[25]  C. Gardiner.  Stochastic Methods: A Handbook for the Natural and Social Sciences , 2009 .

[26]  L. Kornblueh,et al.  Advancing decadal-scale climate prediction in the North Atlantic sector , 2008, Nature.

[27]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[28]  H. von Storch,et al.  Modelling the variability of midlatitude storm activity on decadal to century time scales , 2005 .

[29]  John Turner,et al.  The SCAR READER Project: toward a high-quality database of mean Antarctic meteorological observations , 2004 .

[30]  M. Taqqu,et al.  Simulation methods for linear fractional stable motion and farima using the fast fourier transform , 2004 .

[31]  Paul J. Kushner,et al.  Power-Law and Long-Memory Characteristics of the Atmospheric General Circulation , 2009 .

[32]  D. Nychka,et al.  Consistency of modelled and observed temperature trends in the tropical troposphere , 2008 .

[33]  C. Granger.  Long memory relationships and the aggregation of dynamic models , 1980 .

[34]  Donald B. Percival,et al.  “Eyeballing” Trends in Climate Time Series: A Cautionary Note , 2005 .

[35]  U. Ulbrich,et al.  On the relationship between cyclones and extreme windstorm events over Europe under climate change , 2004 .

[36]  H. Künsch.  Discrimination between monotonic trends and long-range dependence , 1986 .

[37]  P. Robinson.  Log-Periodogram Regression of Time Series with Long Range Dependence , 1995 .

[38]  S. Feldstein.  The Timescale, Power Spectra, and Climate Noise Properties of Teleconnection Patterns , 2000 .

[39]  Peter C. B. Phillips,et al.  Log Periodogram Regression: The Nonstationary Case , 2006 .

[40]  David B. Stephenson,et al.  Is the North Atlantic Oscillation a random walk , 2000 .

[41]  S. Schneider,et al.  Climate Change 2007 Synthesis report , 2008 .

[42]  S. Feldstein.  The Recent Trend and Variance Increase of the Annular Mode , 2002 .