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Linking climate dynamics and ecological modelling

Determining the relationship between climate and ecosystems is crucial to our understanding of the environment processes such as desertification and the loss of species. However, the multi-component and multi-scale nature of the climate system and ecosystems makes them difficult to easily understand.
Linking climate dynamics and ecological modelling
The aim of the SMSEE (Stochastic modeling of spatially extended ecosystems and ecological and climate data analysis) project was to provide a better understanding of how changes to ecosystems occur. The initiative used non-linear, stochastic modelling to study the relationship between climate and ecosystems.

SMSEE combined methods taken from physics, non-linear dynamics and game theory to investigate the various effects of ‘noise’ on climate and ecosystem dynamics. The intention was to fill in the knowledge gaps in several fields including, climate dynamics and the dynamics of spatially extended ecosystems.

Learning algorithms were used to give significantly improved predictions of the climate in the future and reduce the level of uncertainty. In addition, the team developed a theory for the relationship between wind statistics and the statistics of ocean open currents.

Researchers also introduced a new concept in the field of ecosystem regime shifts. These are often conceived as abrupt global transitions, from one stable state to another, triggered by slow environmental changes or by disturbances. The team suggested that these transitions may be gradual, rather than abrupt.

The failure of the conventional indicators to identify gradual regime shifts led to the suggestion that a combination of abrupt-shift and gradual-shift indicators may be needed to accurately identify regime shifts. The results were particularly relevant to desertification, where the process often involves a transition from a patterned state to bare soil due to repeated droughts or a permanent reduction of the precipitation.

SMSEE increased understanding of regime shifts in spatially extended ecosystems and highlighted the crucial role of temporal correlations in wind-driven ocean current statistics and provided a practical tool for the improvement of climate predictions over a scale of decades.

Project results will therefore help to improve understanding of climate and ecosystem dynamics. It will also advance research in fundamental physics, such as the relationship between complex non-linear dynamics and stochastic effects.

Related information


Climate, ecosystems, SMSEE, stochastic modelling, learning algorithms, ocean currents, early-warning signals, desertification
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