Climate field reconstructions from pollen and macrofossil syntheses using Bayesian hierarchical models

Abstract

Studying past climate states is important to understand climate changes and climate variability on centennial to orbital timescales, to analyze the reaction of the Earth system to large-scale changes in external forcings, and to identify physical and biogeochemical processes that drive these changes. It improves not just the understanding of the climate system but can also lead to more accurate projections of future climate conditions. As the instrumental record is restricted to approximately the last 150 years, paleoclimatology has to rely on indirect observations of climate variables, so-called proxy data. Examples are isotope compositions in ice cores, pollen counts from lake sediment records, and geochemical indices measured in marine sediment cores. In addition, numerical Earth system models can run simulations with adjusted boundary conditions to test their ability to reproduce past climate states and to study mechanisms which control them.<br /> Probabilistic spatial or spatio-temporal reconstructions of past climate states, known as climate field reconstructions, are an important tool to quantitatively study the climate system under different forcing conditions because they combine the information contained in a proxy synthesis in a comprehensible product. Unfortunately, they are subject to a complex uncertainty structure due to complicated proxy-climate relations and sparse data, which makes interpolation between samples difficult. Therefore, advanced statistical methods are required which are robust under sparse and noisy data. In this thesis, Bayesian hierarchical models are developed for spatial and spatio-temporal reconstructions from terrestrial proxy networks. The focus is on pollen and macrofossil records, which provide information on the past vegetation composition and indirectly on past temperature and precipitation distributions. Bayesian hierarchical models feature promising properties for climate field reconstructions like the possibility to include multiple sources of information and to quantify uncertainties in a statistically rigorous way. To interpolate between the proxy samples, this study combines geostatistical and data assimilation methods. While data assimilation techniques facilitate the use of physically consistent estimates of past climate states on regional scales provided by Earth system models, geostatistical methods are required to account for the small number of available state-of-the-art simulations and potentially large biases in the produced climate states. Bayesian inference is performed using Markov chain Monte Carlo methods following a Metropolis-within-Gibbs strategy. The Bayesian frameworks produce spatially or spatio-temporally distributed probability distributions that facilitate quantitative analyses which account for uncertainties.<br /> The first application of this study is a reconstruction of European summer and winter temperature during the mid-Holocene using a published pollen and macrofossil synthesis in combination with a multi-model climate simulation ensemble from the Paleoclimate Modelling Intercomparison Project Phase III. To transfer the pollen and macrofossil data into climate information, a forward version of the probabilistic indicator taxa method is applied. Different ways to incorporate the climate simulations in the Bayesian reconstruction framework are compared using identical twin and cross-validation experiments. The spatial reconstruction features dipole structures with warming in Northern Europe and cooling in Southern Europe in concordance with previous results from the literature. The reconstruction performs well in cross-validation experiments and exhibits a reasonable degree of spatial smoothing.<br /> In a second application of the spatial reconstruction framework, summer temperature and mean annual precipitation during the Last Glacial Maximum in Siberia are reconstructed. A compilation of local reconstructions from pollen samples, provided by the Polar Terrestrial Environmental Systems Division at the Alfred-Wegener-Institute, is combined with the Last Glacial Maximum multi-model ensemble from the Paleoclimate Modelling Intercomparison Project Phase III. The reconstruction features a strong summer cooling in the mid latitudes but only moderate cooling in high latitudes perhaps due to the impact of lower sea levels. Our findings provide new insights into explanations for the absence of a Siberian ice sheet during the last Glacial.<br /> To understand the climate evolution from the Last Glacial Maximum to the mid-Holocene, this study develops a data-driven Bayesian hierarchical model for reconstructing the spatio-temporal temperature evolution during the Last Deglaciation on continental scales. Eurasia is chosen as reconstruction domain because more proxy records are available compared to other continents. The Last Deglaciation features millennial-scale trends with a shift from Glacial to Interglacial climate conditions and additional abrupt climate changes. Therefore, a statistical model is required which can recover temporal and spatial non-stationarities. The Bayesian hierarchical model is tested in a controlled environment using pseudo-proxy experiments with a reference climate simulation. These experiments show that the model is recovering non-linear millennial-scale trends with high accuracy and abrupt climate changes are detected even though the magnitude of events tends to be slightly underestimated. Thus, the Bayesian hierarchical model is well-suited for future applications with new and existing proxy syntheses.