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An improved statistical approach for reconstructing past climates from biotic assemblages.

Mengmeng Liu1, Iain Colin Prentice1,2,3, Cajo J F Ter Braak4

  • 1Department of Life Sciences, Imperial College London, Silwood Park Campus, Buckhurst Road, Ascot SL5 7PY, UK.

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Summary

This study introduces an improved weighted averaging partial least-squares regression (WA-PLS) method to reduce bias in quantitative climate reconstructions. The new approach enhances accuracy by accounting for taxon abundance distributions and climate variable frequency, improving climate model evaluation.

Keywords:
WA-PLSbias reductionclimate reconstructionmodel calibrationpalaeoclimatepollen data

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Area of Science:

  • Paleoclimatology
  • Statistical modeling
  • Biogeosciences

Background:

  • Quantitative climate reconstructions are crucial for validating climate models.
  • Weighted averaging partial least-squares regression (WA-PLS) is a common method for such reconstructions.
  • WA-PLS can suffer from compression bias, leading to inaccurate past climate estimates.

Purpose of the Study:

  • To develop an improved WA-PLS method to reduce compression bias.
  • To enhance the accuracy of quantitative climate reconstructions from fossil biotic assemblages.
  • To improve the evaluation of climate model performance.

Main Methods:

  • Developed a tolerance-weighted WA-PLS approach.
  • Incorporated assumptions of unimodal taxon abundance and multinomial distribution.
  • Included frequency correction (fx) based on the training dataset's climate variable distribution.

Main Results:

  • Tolerance-weighted WA-PLS with fx correction significantly reduces compression bias compared to standard WA-PLS.
  • The improved method enhances model performance in climate reconstructions.
  • Demonstrated effectiveness using an extensive modern pollen dataset.

Conclusions:

  • The enhanced WA-PLS method provides more accurate quantitative climate reconstructions.
  • This advancement aids in better evaluating climate model performance.
  • The approach offers a valuable tool for paleoclimate research.