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Statistical analysis of climate change

Research project To understand climate variation over long time periods, lake sediment records may be used. We will develop statistical tools that can analyse several thousand years old varved lake sediments with respect to climate change.

Climate and environmental changes are today widely discussed, and in particular the impact of human activity. To understand variations in the climate over longer time periods, historical documents, year rings from trees, ice cores from glaciers as well as lake and sea sediments are being used. In this project, we develop statistical models and tools to analyse varved lake sediments. The tools aim at understanding the natural variation in the climate and the environment, and are developed based on a 6000 years old time series from the lake Kassjön.

Project overview

Project period:

2007-06-30 2009-06-30

Participating departments and units at Umeå University

Department of Mathematics and Mathematical Statistics, Faculty of Science and Technology

Research subject


Project description

In this project we develop statistical models and tools to analyse varved lake sediments, in particular with respect to environmental and climate variation. The tools will be developed base on digital images from a 6000 years old time series of varved lake sediments from Kassjön and has relevance for environmental and climate research. A varved lake sediment core, with its seasonal patterns in an unbroken line over several thousands of years constitutes a unique research material.

The statistical tools we develop aim at answering questions concerning climate variation on a yearly and seasonal level over several thousands years, whether breaks and shifts are present, and if there are periodicities e.g. connections to periodicities on the NAO (North Atlantic Oscillation) scale. We also want to relate knowledge on climate variation on a yearly/seasonal level to effects on ecosystems in lakes.

To study, analyse and evaluate these types of data with respect to trends, cycles, and seasonal profiles, is a big statistical challenge. The project requires a close cooperation between my research group (mathematical statistics) and Professor Ingemar Renbergs research group (Environmental change assessment, EMG).