Reconstructing past climates

Quantitative reconstructions of past climate change on time scales that are longer than those of instrumental data are important aspects of palaeoclimatology and need to be underpinned by robust numerical methods.

Ice coring

Coring on a frozen lake
Coring on a frozen lake
Photo: 
Anne Bjune

Richard Telford, John Birks, Gaute Velle, Anne Bjune

There are two strands to our past climate research - palaeoecological reconstructions and developing robust numerical methods for palaeoecology

Palaeoecological Research

Knowledge of the full range of variability in the natural climate system is necessary to allow us to separate out human-induced climate changes. There is thus a need for quantification of Holocene (last 11 500 years) climate change as input into models for modelling past, present, and future climate change.

A palaeoecological approach has been used widely to study past climate change by analysing changes in the occurrence and abundance of several biological proxies through time, for example, fossil pollen, mite, chironomid, and macrofossil assemblages preserved in sediments. Lake sediments are some of the most useful archives for past climate change as they often span long periods and can give high temporal resolution. When reconstructions are performed at several sites it is possible to see changes in both time and space.

The basic idea of quantitative climate reconstructions involves transfer or calibration functions. To estimate the past climate variables, the responses of taxa found today are modelled in relation to modern climate variables of interest. This involves compiling a modern “training set” or “calibration set” of taxa preserved in surface sediments with an associated set of modern climatic variables. The modern relationships between the assemblages in the surface samples and the modern climate are modelled numerically and the resulting function is used as a transfer or calibration function to transform fossil assemblages into quantitative estimates of the past climate variables.

Pollen-based transfer functions are a valuable tool for reconstructing mean July temperatures in Scandinavia, but are less suitable for reconstructing precipitation, another important climatic variable. However, winter ppt can be reconstructed if estimated mean July temperatures are combined with glacial equilibrium line altitude (ELA) variations, as there is an established relationship between ELA, winter precipitation, and summer temperature.

The basis for all reconstructions is methodological uniformitarianism and assumes that there have been no changes in organism-environment relationships through time.

Key papers

Bjune, A.E., Birks, H.J.B, Peglar, S.M. & Odland, A. 2010. Developing a modern pollen-climate calibration data-set for Norway. Boreas 674-688.

Bjune, A.E., Birks, H.J.B. & Seppä, H. 2009. Quantitative summer-temperature reconstructions for the last 2000 years based on pollen-stratigraphical data from northern Fennoscandia. Journal of Palaeolimnology 1: 43-56.

Bjune, A.E., Bakke, J., Nesje, A. and Birks, H.J.B. 2005. Holocene mean July temperature and winter precipitation in western Norway inferred from lake sediment proxies. The Holocene 15: 177-189.

Birks, H.J.B. 2003. Quantitative palaeoenvironmental reconstructions from Holocene biological data. In: Mackay, A., Battarbee, R.W., Birks, H.J.B. & Oldfield, F. (eds.) Global Change in the Holocene, pp. 107-123.

Numerical Methods for Palaeoecology

Testing the robustness of numerical methods used to analyse data and ensuring the interpretation is ecologically meaningful.


Key papers

Telford, R.J. & Birks, H.J.B. 2009. Evaluation of transfer functions in spatially structured environments. Quaternary Science Reviews 28: 1309-1316. 10.1016/j.quascirev.2008.12.020

Telford, R.J. & Birks, H.J.B. 2005. The secret assumption of transfer functions: problems with spatial autocorrelation in evaluating model performance. Quaternary Science Reviews 24: 2173-2179. DOI: 10.1016/j.quascirev.2005.05.001

Telford, R.J., Heegaard, E. & Birks, H.J.B. 2004. All age-depth models are wrong: but how badly? Quaternary Science Reviews 23: 1-5.