Identifying coherent spatiotemporal modes in time-uncertain proxy paleoclimate records View Full Text


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Article Info

DATE

2012-08-26

AUTHORS

Kevin J. Anchukaitis, Jessica E. Tierney

ABSTRACT

High-resolution sedimentary paleoclimate proxy records offer the potential to expand the detection and analysis of decadal- to centennial-scale climate variability during recent millennia, particularly within regions where traditional high-resolution proxies may be short, sparse, or absent. However, time uncertainty in these records potentially limits a straightforward objective identification of broad-scale patterns of climate variability. Here, we describe a procedure for identifying common patterns of spatiotemporal variability from time uncertain sedimentary records. This approach, which we term Monte Carlo Empirical Orthogonal Function analysis, uses iterative age modeling and eigendecomposition of proxy time series to isolate common regional patterns and estimate uncertainties. As a test case, we apply this procedure to a diverse set of time-uncertain lacustrine proxy records from East Africa. We also perform a pseudoproxy experiment using climate model output to examine the ability of the method to extract shared anomalies given known signals. We discuss the advantages and disadvantages of our approach, including possible extensions of the technique. More... »

PAGES

1291-1306

References to SciGraph publications

  • 2000-01. Rainfall and drought in equatorial east Africa during the past 1,100 years in NATURE
  • 2006-10-18. Evaluating EOF modes against a stochastic null hypothesis in CLIMATE DYNAMICS
  • 2013-01-16. Multidecadal variability in East African hydroclimate controlled by the Indian Ocean in NATURE
  • 2004-01-01. Decadal and century-scale climate variability in tropical Africa during the past 2000 years in PAST CLIMATE VARIABILITY THROUGH EUROPE AND AFRICA
  • 2010. A Guide to Empirical Orthogonal Functions for Climate Data Analysis in NONE
  • 2010-05-16. Late-twentieth-century warming in Lake Tanganyika unprecedented since AD 500 in NATURE GEOSCIENCE
  • 1998-04-01. Global-scale temperature patterns and climate forcing over the past six centuries in NATURE
  • 1984-11. A 7,272-year tree-ring chronology for western Europe in NATURE
  • 2005-02. Solar variability and the levels of Lake Victoria, East Africa, during the last millenium in JOURNAL OF PALEOLIMNOLOGY
  • 2001-04. Reconstructing fluctuations of a shallow East African lake during the past 1800 yrs from sediment stratigraphy in a submerged crater basin in JOURNAL OF PALEOLIMNOLOGY
  • 2000-02. Warm-season temperatures since 1600 BC reconstructed from Tasmanian tree rings and their relationship to large-scale sea surface temperature anomalies in CLIMATE DYNAMICS
  • 2005-02. Highly variable Northern Hemisphere temperatures reconstructed from low- and high-resolution proxy data in NATURE
  • 2009-12. Half-precessional dynamics of monsoon rainfall near the East African Equator in NATURE
  • 2006-10-21. Solar and anthropogenic imprints on Lake Masoko (southern Tanzania) during the last 500 years in JOURNAL OF PALEOLIMNOLOGY
  • 1958-09. The varimax criterion for analytic rotation in factor analysis in PSYCHOMETRIKA
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    http://scigraph.springernature.com/pub.10.1007/s00382-012-1483-0

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    http://dx.doi.org/10.1007/s00382-012-1483-0

    DIMENSIONS

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