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Conventional methods of palaeoclimate reconstruction provide estimates of climatic parameters using proxy data which have originated from individual sites. These reconstructions yield information on the local environment but only limited information on spatial scales that are required for model-data intercomparisons. We present here a new approach that connects these different scales by an upscaling of the local palaeoinformation together with a dynamically consistent spatial smoothing. A probabilistic data-based method for local reconstructions is combined with a dynamic constraint on the reconstructed climate parameter which stabilises the reconstruction on the target scale. The variational analysis leads to climatological fields being optimised with respect to the proxy data and to the prescribed dynamics in a statistically consistent way. This method allows a probabilistic approach of quality control of the palaeodata in terms of their spatial consistency and homogeneity and for an estimation of reconstruction errors. The method was applied to palaeobotanical data to reconstruct near-surface temperature fields constrained by simple linear dynamics. An approximate approach was used to estimate the magnitude of reconstruction errors in terms of standard deviations. Reconstructed January and July mean temperature of the early Eemian (∼ 125,000 years bp) have errors with a median value of about 1.8°C in January and about 1.1°C in July. Reconstructions across Europe show positive temperature anomalies for Scandinavia and near the East coast of the Baltic Sea. In contrast, early Eemian temperatures were apparently quite similar to those found today in Central Europe, as no drastic differences were reconstructed between the Eemian and modern (1961–1990) climate. This implies somewhat stronger temperature gradients in the Eemian than are observed today. [ABSTRACT FROM AUTHOR]

We present a new procedure, the pdf method (pdf=probability density function), for reconstructing Quaternary climate utilizing botanical data. The procedure includes the advantages of the indicator species method by considering the fossil and modern presence and absence of taxa rather than their frequencies, thus avoiding the need for modern analog plant communities. Overcoming the problematic use of absolute limits to describe climate response ranges is the main progress of the pdf method in comparison to the indicator species method. This advantage results from estimating probability density functions (pdfs) for monthly mean January and July temperature conditional on the present day occurrence of single taxa. Gaussian distributions sufficiently approximate pdfs of many, although not all, studied taxa. On the assumption of statistical independence, the procedure calculates a joint pdf as the product of the pdfs of the individual taxa. This algorithm weights each taxon according to the extent of its climate response range expressed by its covariance structure. We interpret the maximum of the resulting pdf as the most likely climate and its confidence interval as the uncertainty range. To avoid an artificial reduction of uncertainty arising from the use of numerous similar pdfs, a preselection method is proposed based on the Mahalanobis distance between pdfs. The pdf method was applied to the Carpinus phase of a profile from Gro¨bern, Germany, that spans the last interglaciation (Eemian). The reconstructed most probable January and July temperatures of about 0.0°C and 18.4°C barely differ from the modern values of −0.5°C and 18.3°C. [Copyright &y& Elsevier]

Fossil pollen records are well-established indicators of past vegetation changes. The prevalence of pollen across environmental settings including lakes, wetlands, and marine sediments, has made palynology one of the most ubiquitous and valuable tools for studying past environmental and climatic change globally for decades. A complementary research focus has been the development of statistical techniques to derive quantitative estimates of climatic conditions from pollen assemblages. This paper reviews the most commonly used statistical techniques and their rationale and seeks to provide a resource to facilitate their inclusion in more palaeoclimatic research. To this end, we first address the fundamental aspects of fossil pollen data that should be considered when undertaking pollen-based climate reconstructions. We then introduce the range of techniques currently available, the history of their development, and the situations in which they can be best employed. We review the literature on how to define robust calibration datasets, produce high-quality reconstructions, and evaluate climate reconstructions, and suggest methods and products that could be developed to facilitate accessibility and global usability. To continue to foster the development and inclusion of pollen climate reconstruction methods, we promote the development of reporting standards. When established, such standards should 1) enable broader application of climate reconstruction techniques, especially in regions where such methods are currently underused, and 2) enable the evaluation and reproduction of individual reconstructions, structuring them for the evolving open-science era, and optimising the use of fossil pollen data as a vital means for the study of past environmental and climatic variability. We also strongly encourage developers and users of palaeoclimate reconstruction methodologies to make associated programming code publicly available, which will further help disseminate these techniques to interested communities. [ABSTRACT FROM AUTHOR]