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Statistical challenges in estimating past climate changes

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We review the statistical methods currently in use to estimate past changes in climate. These methods encompass the full gamut of statistical modeling approaches, ranging from simple regression up to nonparametric spatiotemporal Bayesian models. Often the full inferential challenge is broken down into many submodels each of which may involve multiple stochastic components, and occasionally mechanistic or process‐based models too. We argue that many of the traditional approaches are simplistic in their structure, handling, and presentation of uncertainty, and that newer models (which incorporate mechanistic aspects alongside statistical models) provide an exciting research agenda for the next decade. We hope that policy‐makers and those charged with predicting future climate change will increasingly use probabilistic paleoclimate reconstructions to calibrate their forecasts, learn about key natural climatological parameters, and make appropriate decisions concerning future climate change. Remarkably few statisticians have involved themselves with paleoclimate reconstruction, and we hope that this article inspires more to take up the challenge. This article is categorized under: Applications of Computational Statistics > Computational Climate Change and Numerical Weather Forecasting
General overview of the various processes that lead to the proxy paleodata used in climate reconstructions. In the example of pollen, the sensor system is the plant ecosystem. The archive systems are the lakes or mires where pollen is deposited. The observation system includes the field and laboratory measures such as core sampling, pollen counting, and radiocarbon dating among others
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Schematic representations of the link between climate and proxies for four of the journal articles summarized in this paper. (a) Relates to Tingley and Huybers (), (b) to Li et al. (), (c) to Haslett et al. () and (d) to Garreta et al. ()
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Glendalough reconstruction of the mean temperature of the coldest month. The red region represents the 95% probability intervals for climate over time. The darker shading represents the 50% intervals. Overlain in green is a “most representative” climate history across all of the sampled climates
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Key components of a model for the link between climate forcings and pollen counts from lake sediments. Ellipses represent numerical input and output values. Rectangles represent components of the model for which detailed models need specifying and arrows represent the flow of numerical values from one part of the model to another and illustrate places in which conditional independence assumptions are typically made. For clarity, we do not show any feedbacks, but deciding which feedbacks to model and how to represent them is a key part of implementing such models
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Overview of some of the climatological processes, which lead to the proxy paleodata used in climate reconstructions, including an overview of the intermediate stages involved in data acquisition. The arrows represent the flow or causal direction of the steps, which lead to the proxy data. As an example, the processes that lead to fossil pollen data obtained from lake sediment are highlighted in red with two climate variables of interest identified. One is GDD5 (growing degree days above 5 °C), a measure of the length of the growing season (days above 5 °C), and the other is MTCO (mean temperature of the coldest month), a temperature measure which captures the harshness of winter
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Applications of Computational Statistics > Computational Climate Change and Numerical Weather Forecasting

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