Abstract

Abstract. Reconstructions of summer, winter or annual mean temperatures based on the species composition of bio-indicators such as pollen, foraminifera or chironomids are routinely used in climate model–proxy data comparison studies. Most reconstruction algorithms exploit the joint distribution of modern spatial climate and species distribution for the development of the reconstructions. They rely on the space-for-time substitution and the specific assumption that environmental variables other than those reconstructed are not important or that their relationship with the reconstructed variable(s) should be the same in the past as in the modern spatial calibration dataset. Here we test the implications of this “correlative uniformitarianism” assumption on climate reconstructions in an ideal model world, in which climate and vegetation are known at all times. The alternate reality is a climate simulation of the last 6000 years with dynamic vegetation. Transient changes of plant functional types are considered as surrogate pollen counts and allow us to establish, apply and evaluate transfer functions in the modeled world. We find that in our model experiments the transfer function cross validation r2 is of limited use to identify reconstructible climate variables, as it only relies on the modern spatial climate–vegetation relationship. However, ordination approaches that assess the amount of fossil vegetation variance explained by the reconstructions are promising. We furthermore show that correlations between climate variables in the modern climate–vegetation relationship are systematically extended into the reconstructions. Summer temperatures, the most prominent driving variable for modeled vegetation change in the Northern Hemisphere, are accurately reconstructed. However, the amplitude of the model winter and mean annual temperature cooling between the mid-Holocene and present day is overestimated and similar to the summer trend in magnitude. This effect occurs because temporal changes of a dominant climate variable, such as summer temperatures in the model's Arctic, are imprinted on a less important variable, leading to reconstructions biased towards the dominant variable's trends. Our results, although based on a model vegetation that is inevitably simpler than reality, indicate that reconstructions of multiple climate variables based on modern spatial bio-indicator datasets should be treated with caution. Expert knowledge on the ecophysiological drivers of the proxies, as well as statistical methods that go beyond the cross validation on modern calibration datasets, are crucial to avoid misinterpretation.

Highlights

  • Continental-scale climate reconstructions (Bartlein et al, 2011; Davis et al, 2003; Mauri et al, 2014) are frequently used as a paleodata target to evaluate and benchmark climate models (e.g., Harrison et al, 2014; Fischer and Jungclaus, 2011)

  • We find that in our model experiments the transfer function cross validation r2 is of limited use to identify reconstructible climate variables, as it only relies on the modern spatial climate–vegetation relationship

  • One specific requirement is that environmental variables other than those considered in the calibration are not important or that their relationship with the reconstructed variable(s) was the same in the past as it is in the modern spatial calibration dataset (Birks and Seppä, 2005; Birks et al, 2010)

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Summary

Introduction

Continental-scale climate reconstructions (Bartlein et al, 2011; Davis et al, 2003; Mauri et al, 2014) are frequently used as a paleodata target to evaluate and benchmark climate models (e.g., Harrison et al, 2014; Fischer and Jungclaus, 2011). Climate models and proxy data disagree on the annual mean temperature changes over the course of the Holocene (Liu et al, 2014; Marcott et al, 2013). K. Rehfeld et al.: Pollen-based reconstructions in a perfect model world tions might be the root of the observed proxy–model divergence (Liu et al, 2014). To arrive at quantitative assessments of past climate changes from pollen assemblages, transfer function algorithms are used to establish a link between modern climate and vegetation composition across space. A basic assumption underlying these transfer functions is methodological uniformitarianism (Scott, 1963; Gould, 1965), namely that modern spatial relationships between species, vegetation and environmental conditions can be applied to past conditions (e.g., Birks et al, 2010)

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