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

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. 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. 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. 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. 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

Joint state-parameter estimation of a nonlinear stochastic energy balance model from sparse noisy data

While nonlinear stochastic partial differential equations arise naturally in spatiotemporal modeling, inference for such systems often faces two major challenges: sparse noisy data and ill-posedness of the inverse problem of parameter estimation. To overcome the challenges, we introduce a strongly regularized posterior by normalizing the likelihood and by imposing physical constraints through priors of the parameters and states. We investigate joint parameter-state estimation by the regularized posterior in a physically motivated nonlinear stochastic energy balance model (SEBM) for paleoclimate reconstruction. The high-dimensional posterior is sampled by a particle Gibbs sampler that combines MCMC with an optimal particle filter exploiting the structure of the SEBM. In tests using either Gaussian or uniform priors based on the physical range of parameters, the regularized posteriors overcome the ill-posedness and lead to samples within physical ranges, quantifying the uncertainty in estimation. Due to the ill-posedness and the regularization, the posterior of parameters presents a relatively large uncertainty, and consequently, the maximum of the posterior, which is the minimizer in a variational approach, can have a large variation. In contrast, the posterior of states generally concentrates near the truth, substantially filtering out observation noise and reducing uncertainty in the unconstrained SEBM

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

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. 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. 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. 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. 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

Investigating Adoption Determinants of Service-Oriented Architectures (SOA)

Service-Oriented Architectures (SOA) gain an increasing focus in the academic literature as well as in practice. According to Forrester Research at least 63% of the North-American, European and Asian-Pacific enterprises have adopted SOA by the end of 2008 (Heffner 2008). However, there is still a lack of a coherent picture of why firms adopt SOA. As Vitharana et al. point out future research should investigate organizational and industry characteristics that influence adoption of the service paradigm (2007). Thus, the aim of this research is to investigate different proposed drivers of SOA adoption, such as technological advantages such as reduced costs due to modularity and reuse, business advantages such as increased business flexibility, or environmental determinants, such as management fashion

Combining a pollen and macrofossil synthesis with climate simulations for spatial reconstructions of European climate using Bayesian filtering

Probabilistic spatial reconstructions of past climate states are valuable to quantitatively study the climate system under different forcing conditions because they combine the information contained in a proxy synthesis into 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. Bayesian hierarchical models feature promising properties to handle these issues, like the possibility to include multiple sources of information and to quantify uncertainties in a statistically rigorous way. We present a Bayesian framework that combines a network of pollen and macrofossil samples with a spatial prior distribution estimated from a multi-model ensemble of climate simulations. The use of climate simulation output aims at a physically reasonable spatial interpolation of proxy data on a regional scale. To transfer the pollen data into (local) climate information, we invert a forward version of the probabilistic indicator taxa model. The Bayesian inference is performed using Markov chain Monte Carlo methods following a Metropolis-within-Gibbs strategy. Different ways to incorporate the climate simulations into the Bayesian framework are compared using identical twin and cross-validation experiments. Then, we reconstruct the mean temperature of the warmest and mean temperature of the coldest month during the mid-Holocene in Europe using a published pollen and macrofossil synthesis in combination with the Paleoclimate Modelling Intercomparison Project Phase III mid-Holocene ensemble. The output of our Bayesian model is a spatially distributed probability distribution that facilitates quantitative analyses that account for uncertainties

Strategic Sourcing in Banking - A Framework

The banking landscape in Europe and Germany has a polypolistic market structure with an average vertical range of integration of 80%, implying high process redundancies. Accumulating processes for standardized products is one way to achieve efficiency improvements. “Smart sourcing” can change the production mix of banks lead to a concentration of processes. A challenge is the lack of consistent frameworks for the banking industry to systematically support the sourcing decision process. In this paper, drawing on the resource based view and transaction cost economics it is shown that, in addition to a make or buy decision, co-operation between competitors should be included into a sourcing analysis as the third alternative. To support the decision on making, buying or sharing an activity, a quantitative analysis of each activity of the banking value chain is necessary. We propose an analysis framework based on a generic banking value chain. Since the recent literature mostly focuses only on the outsourcers’ case, a formalized model is developed to consider both the objective function of the out- and the insourcer. Since the market might restrict sourcing options, as there may be no providers of relevant activities, it can be shown that “sharing” as part of a co-opetition concept instead of buy or make can be a way to break up the value chain. The framework thus supports decision makers to approach an optimal degree of vertical integration for both market sides

A Social Linkage View on the Business Value of IT

Our research intends to explore whether a social perspective on IT business alignment can help shed light on the IT value creation process by considering different facets of interpersonal linkage. In this paper, we develop a theoretical model which could be discussed at the JAIS workshop. Further, we use some empirical data from 149 US banks in order to find first empirical evidence whether our research focus represents a promising direction. We find initial support for our main hypotheses that communication, cross-domain knowledge and mutuality among and between IT and business staff significantly impact IT usage and business process outcomes. The final results of our research could contribute to our understanding of how the IT resource should be understood and used to measurably contribute to firm goals. The initial findings support the caveat of recent studies suggesting that informal aspects of alignment might be quite notable (e.g. Chan, 2002) and show that our theoretical understanding of alignment should be extended to better incorporate social aspects of daily work life

The deglacial forest conundrum

How fast the Northern Hemisphere (NH) forest biome tracks strongly warming climates is largely unknown. Regional studies reveal lags between decades and millennia. Here we report a conundrum: Deglacial forest expansion in the NH extra-tropics occurs approximately 4000 years earlier in a transient MPI-ESM1.2 simulation than shown by pollen-based biome reconstructions. Shortcomings in the model and the reconstructions could both contribute to this mismatch, leaving the underlying causes unresolved. The simulated vegetation responds within decades to simulated climate changes, which agree with pollen-independent reconstructions. Thus, we can exclude climate biases as main driver for differences. Instead, the mismatch points at a multi-millennial disequilibrium of the NH forest biome to the climate signal. Therefore, the evaluation of time-slice simulations in strongly changing climates with pollen records should be critically reassessed. Our results imply that NH forests may be responding much slower to ongoing climate changes than Earth System Models predict

Identification of a BRCA2-Specific modifier locus at 6p24 related to breast cancer risk

Common genetic variants contribute to the observed variation in breast cancer risk for BRCA2 mutation carriers; those known to date have all been found through population-based genome-wide association studies (GWAS). To comprehensively identify breast cancer risk modifying loci for BRCA2 mutation carriers, we conducted a deep replication of an ongoing GWAS discovery study. Using the ranked P-values of the breast cancer associations with the imputed genotype of 1.4 M SNPs, 19,029 SNPs were selected and designed for inclusion on a custom Illumina array that included a total of 211,155 SNPs as part of a multi-consortial project. DNA samples from 3,881 breast cancer affected and 4,330 unaffected BRCA2 mutation carriers from 47 studies belonging to the Consortium of Investigators of Modifiers of BRCA1/2 were genotyped and available for analysis. We replicated previously reported breast cancer susceptibility alleles in these BRCA2 mutation carriers and for several regions (including FGFR2, MAP3K1, CDKN2A/B, and PTHLH) identified SNPs that have stronger evidence of association than those previously published. We also identified a novel susceptibility allele at 6p24 that was inversely associated with risk in BRCA2 mutation carriers (rs9348512; per allele HR = 0.85, 95% CI 0.80-0.90, P = 3.9×10−8). This SNP was not associated with breast cancer risk either in the general population or in BRCA1 mutation carriers. The locus lies within a region containing TFAP2A, which encodes a transcriptional activation protein that interacts with several tumor suppressor genes. This report identifies the first breast cancer risk locus specific to a BRCA2 mutation background. This comprehensive update of novel and previously reported breast cancer susceptibility loci contributes to the establishment of a panel of SNPs that modify breast cancer risk in BRCA2 mutation carriers. This panel may have clinical utility for women with BRCA2 mutations weighing options for medical prevention of breast cancer