The temperature of Europe during the Holocene reconstructed from pollen data
Introduction
A number of attempts have recently been made to develop dynamic regional and global time series temperature reconstructions for the last 1000 years (Mann et al., 1999; Shaopeng et al., 2000; Briffa et al., 2001). These reconstructions have been used to investigate the role of various natural and anthropogenic forcing on the climate system, and the ability of climate models to reproduce them (Jones et al., 1998). The development of these time series has mainly been based on annually resolved proxies, particularly tree-rings, effectively limiting such studies to the last millennia when annual archives are widely available. On longer time scales, non-annually resolved proxies such as pollen data occur more extensively, but the problem of chronological control has led to the adoption of a different non-dynamic approach to regional synthesis. Typically, these have been based on a broad time slice with samples assimilated within a 500–1000-year time window around the target time, such as the ‘mid-Holocene’ 6000±500 years 14C BP (COHMAP Members, 1988; Huntley and Prentice, 1993; Cheddadi et al., 1997). These static map-based reconstructions have been widely applied to data-model comparisons using climate models run to equilibrium (Prentice et al., 1997; Masson et al., 1999).
As computing power continues to increase, then standard models (AGCMs/CGCMs) can be run for progressively longer periods. Also, a new type of climate model has recently been developed called Earth system models of intermediate complexity (EMICs) (Claussen et al., 2002) which allow the simulation of climate over much longer time periods, including the whole Holocene (Crucifix et al., 2002). These allow the dynamic time-dependent response of the atmosphere to be investigated against a variety of internal (ice, ocean circulation, biosphere, trace gases) and external (orbital) forcing mechanisms (Brovkin et al., 1999; Ganopolski and Rahmstorf, 2001; Weber, 2001). Evaluation of these model simulations against actual climate change requires palaeoclimate data at a comparable temporal and spatial scale. This requires not only a long-term (Holocene) time frame and grid-box (continental) scale, but also a dynamic approach that allows data-model comparison through time.
In this study, we present an innovative new approach to non-annual (pollen-based) palaeoclimate data assimilation and presentation that provides a dynamic and quantitative view of Continental-scale climate change compatible with climate model output. This approach uses a new four-dimensional gridding procedure to assimilate data from hundreds of sites and thousands of samples onto a regular spatial grid and time step. We have applied this method to a palaeo-temperature dataset derived from pollen samples from sites across Europe. This dataset was created using an improved modern-analogue pollen-climate transfer function that can accommodate non-analogous fossil pollen assemblages. The reconstructions include seasonal (coldest month/warmest month) and annual mean temperatures, providing an all-year perspective on temperature trends. We present the results as area-average time series calculated for the whole of Europe and six sub-regions at a 100-year pseudo-resolution (time step) over the last 12,000 years.
Section snippets
Modern pollen data and climate
The modern pollen surface sample dataset used in the transfer function consisted of 2363 samples from throughout North Africa and Europe west of the Urals. This was based on data from the European Pollen Database (EPD), the authors, the PANGAEA data archive, H.J.B. Birks and S. Peglar. All samples were composed of original raw counts of the full assemblage. Each sample site was assigned a modern climate based on interpolation from station data using an artificial neural network (Guiot et al.,
Pollen-climate reconstruction
Fossil pollen samples were assigned a palaeoclimate using a modern analogue matching technique based on a training set of modern pollen samples (Guiot, 1990). This method has been employed in a large number of studies at both single site (Cheddadi et al., 1998) and continental (Cheddadi et al., 1997) scales, and is discussed in detail in many previous papers (e.g. Magny et al., 2001). In this study we have applied a modification to the technique, using PFT (Plant Functional Type) scores (
Results and discussion
All results are shown as anomalies compared to the 60 BP (1890) reconstruction. The modern −40 BP (1990) reconstruction was not used as the baseline because this time step was not based on a balance of samples both forward and back in time. Reconstructions are represented by six regional time series (Fig. 3, Fig. 4), together with summary reconstructions for the whole European area (Fig. 5). In comparing these results with data from individual sites or smaller local regions it is important to
Conclusions
We have shown that by assimilating many thousands of individual pollen-based proxy-climate observations through four-dimensions using a GIS, it is possible to provide an entirely new quantitative and dynamic perspective on Holocene climate change. The internal consistency of the results and their agreement with other proxy records suggests that the influence of local climatic and non-climatic factors on the reconstruction method has been limited. This can be attributed to both the large
Acknowledgments
The authors would like to acknowledge all those who have contributed pollen data to this project, and the facilities offered by the EPD from which the majority of this data has been accessed. The PANGAEA database was also utilized in this study, and we would also like to acknowledge the data and facilities it provides. The authors would particularly like to thank Rachid Cheddadi and Jaques-Louis de Beaulieu for their support and access to the resources of IMEP, John Birks and Silvia Peglar for
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- 1
The data contributors have all provided pollen data for this study. The subsequent analysis and interpretation is the work and responsibility of the first four authors. The contributors include: Allen, J., Almqvist-Jacobson, H., Ammann, B., Andreev, A.A., Argant, J., Atanassova, J., Balwierz, Z., Barnosky, C.D., Bartley, D.D., Beaulieu, JL de, Beckett, S.C., Behre, K.E., Bennett, K.D., Berglund, B.E.B., Beug, H-J., Bezusko, L., Binka, K., Birks, H.H., Birks, H.J.B., Björck, S., Bliakhartchouk, T., Bogdel I., Bonatti, E., Bottema, S., Bozilova, E.D.B., Bradshaw, R., Brown, A.P., Brugiapaglia, E., Carrion, J., Chernavskaya, M., Clerc, J., Clet, M., Coûteaux, M., Craig, A.J., Cserny, T., Cwynar, L.C., Dambach, K., De Valk, E.J., Digerfeldt, G., Diot, M.F., Eastwood, W., Elina, G., Filimonova, L., Filipovitch, L., Gaillard-Lemdhal, M.J., Gauthier, A., Göransson, H., Guenet, P., Gunova, V., Hall, V.A.H., Harmata, K., Hicks, S., Huckerby, E., Huntley, B., Huttunen, A., Hyvärinen, H., Ilves, E., Jacobson, G.L., Jahns, S., Jankovská, V., Jóhansen, J., Kabailiene, M., Kelly, M.G., Khomutova, V.I., Königsson, L.K., Kremenetski, C., Kremenetskii, K.V., Krisai, I., Krisai, R., Kvavadze, E., Lamb, H., Lazarova, M.A., Litt, T., Lotter, A.F., Lowe, J.J., Magyari, E., Makohonienko, M., Mamakowa, K., Mangerud, J., Mariscal, B., Markgraf, V., McKeever, Mitchell, F.J.G., Munuera, M., Nicol-Pichard, S., Noryskiewicz, B., Odgaard, B.V., Panova, N.K., Pantaleon-Cano, J., Paus, A.A., Pavel, T., Peglar, S.M., Penalba, M.C., Pennington, W., Perez-Obiol, R., Pushenko, M., Ralska-Jasiewiszowa, M., Ramfjord, H., Regnéll, J., Rybnickova, E., Rybnickova, M., Saarse, L., Sanchez Gomez, M.F., Sarmaja-Korjonen, K., Sarv, A., Seppa, H., Sivertsen, S., Smith, A.G., Spiridonova, E.A., Stancikaite, M., Stefanova, J., Stewart, D.A., Suc, J-P., Svobodova, H., Szczepanek, K., Tarasov, P., Tobolski, K., Tonkov, Sp., Turner, J., Van der Knaap, W.O., Van Leeuwen, J.F.N., Vasari, A., Vasari, Y., Verbruggen, C., Vergne, V., Veski, S, Visset, L., Vuorela, I., Wacnik, A., Walker, M.J.C., Waller, M.P., Watson, C.S., Watts, W.A., Whittington, G., Willis, K.J., Willutzki, H., Yelovicheva, Ya., Yll, E.I., Zelikson, E.M., Zernitskaya, V.P.