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Climate Dynamics

, Volume 13, Issue 1, pp 1–9 | Cite as

The climate of Europe 6000 years ago

  • R. Cheddadi
  • G. Yu
  • J. Guiot
  • S. P. Harrison
  • I. Colin Prentice

Abstract.

Palaeoclimates across Europe for 6000 y BP were estimated from pollen data using the modern pollen analogue technique constrained with lake-level data. The constraint consists of restricting the set of modern pollen samples considered as analogues of the fossil samples to those locations where the implied change in annual precipitation minus evapotranspiration (PE) is consistent with the regional change in moisture balance as indicated by lakes. An artificial neural network was used for the spatial interpolation of lake-level changes to the pollen sites, and for mapping palaeoclimate anomalies. The climate variables reconstructed were mean temperature of the coldest month (T c ), growing degree days above 5  °C (GDD), moisture availability expressed as the ratio of actual to equilibrium evapotranspiration (α), and PE. The constraint improved the spatial coherency of the reconstructed palaeoclimate anomalies, especially for PE. The reconstructions indicate clear spatial and seasonal patterns of Holocene climate change, which can provide a quantitative benchmark for the evaluation of palaeoclimate model simulations. Winter temperatures (T c ) were 1–3 K greater than present in the far N and NE of Europe, but 2–4 K less than present in the Mediterranean region. Summer warmth (GDD) was greater than present in NW Europe (by 400–800 K day at the highest elevations) and in the Alps, but >400 K day less than present at lower elevations in S Europe. PE was 50–250 mm less than present in NW Europe and the Alps, but α was 10–15% greater than present in S Europe and PE was 50–200 mm greater than present in S and E Europe.

Keywords

Holocene Spatial Coherency Holocene Climate Change Pollen Analogue Analogue Technique 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1996

Authors and Affiliations

  • R. Cheddadi
    • 1
  • G. Yu
    • 3
  • J. Guiot
    • 2
  • S. P. Harrison
    • 3
  • I. Colin Prentice
    • 4
  1. 1. European Pollen Database, Centre Universitaire d'Arles, F-13200 Arles, FranceFR
  2. 2. Laboratoire de Botanique Historique et Palynologie, CNRS UA 1152, Faculté de St-Jérôme, Case 451, F-13397 Marseille cedex 20, FranceFR
  3. 3. Dynamic Palaeoclimatology, Lund University, Box 117, S-221 00 Lund, SwedenSE
  4. 4. Global Systems Group, Department of Ecology, Lund University, Ecology Building, Sölvegatan 37, S-22362 Lund, SwedenSE

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