Climate Theory Group
MSc projects in Climate Theory.
Can we identify and predict catastrophic tipping points in the climate system?(60 ECTS)
Ongoing greenhouse gas emissions put several sub-systems of the climate at risk of crossing critical thresholds (so-called tipping points), leading to abrupt irreversible climate change. These include Arctic sea ice and permafrost, the Amazon rainforest, the polar ice sheets, as well as the Atlantic ocean circulation. To avoid catastrophic changes, measures for reducing emissions should keep Earth in the safe operating space away from tipping points. Given that this safe operating space is poorly constrained, the following two projects explore how tipping points may be predicted when the critical thresholds in greenhouse gas forcing are unknown. Taking advantage of the similarity in the mathematical description of tipping points and phase transitions, early-warning signals associated with critical dynamics close to the phase transition may be observed.
Project 1: Early-warning of tipping points in historical observation
Several candidates for early-warning signals have been tested for climate changes in the past and for model simulations. However, it has not been tested systematically whether such fingerprints exist in historical observations leading up to the present-day. These might alarm us that an approach to one or more tipping points is already underway. In this project, big data sets of important climate parameters (e.g. Greenland melt rates) will be analyzed for potential early-warning signals using advanced statistical tools. Since these data sets often contain seasonality, as well as trends in statistical properties due to changes in the measurement process, there is the potential to expand the theory of early-warning signals during the project.
Project 2: Predicting a collapse of tropical rainforests using statistical mechanics
The crossing of a tipping point in one climate sub-systems might increase the likelihood of tipping in another, potentially leading to a domino-effect with a cascade of subsequent tipping points. This phenomenon might also occur locally within a climate sub-system, which has not been studied in much detail yet. E.g., in tropical rainforests, where full forest cover and savanna compete locally, local-scale tipping interacts with a regional-scale moisture feedback to determine the stability of the rainforest. Perturbations, such as forest fires, may cause a cascading tipping of the ecosystem.
This may be analyzed with tools from statistical mechanics, using a model similar to the Ising model of ferromagnetism. Here, a tipping cascade is analogous to a phase transition and an early-warning can be obtained due to critical slowing down. In the project a mathematical model could be constructed in order to identify the preconditions for crossing stability thresholds, as well as to find novel early-warning signals. As an extension of the Ising model, results of the project may also be of interest to the general physics community.
Causality and climate change (60 ECTS)
While there is overwhelming scientific consensus that the rise in global temperatures since the pre-industrial period is caused by human emissions of greenhouse gases into the atmosphere, there had also been changes in temperature as well as greenhouse gas concentrations in the past. Analyses of ice core data concluded that while CO2 levels and temperatures are strongly correlated over time scales of millennia and longer, changes in CO2 lag behind temperatures by roughly 500 years. This is often used as an argument by skeptics questioning the causality of anthropogenic climate change. The seemingly passive reaction of CO2 to temperatures is explainable by simple feedbacks, such as increased outgassing by the oceans, and does not contest the accepted mechanisms of anthropogenic climate change. Still, due to simplistic methods used to analyze the data until now, it is an outstanding question to what degree CO2 was also a driver of large climate changes in the past. This project revisits the analysis of ice core data by inferring actual causality (in a mathematical sense) between CO2, temperature proxies, and changes in incoming solar radiation.
Questions of causality in applied sciences are typically investigated by simple lagged correlation analysis in between two variables. In reality, in highly coupled and systems like the climate, causality is much more subtle and requires careful statistical treatment. By using advanced methods from information theory, as well as focusing on different time periods and time scales, this project will contribute to a more nuanced discussion of which causalities exist in the Earth system and how they can be best measured.
Chaos theory of abrupt climate change (60 ECTS)
Even though anthropogenic climate change is already causing considerable damage across the globe, increases in global temperature so far seem consistent with a linear response to rising CO2 levels. In contrast, reconstructions of past climate show very non-linear changes in climate in response to slowly changing solar forcing. This could lead to future climate change that is even more abrupt than presently observed, and than predicted by the current generation of climate models. Thus, our confidence in model projections need to be improved by testing them against past abrupt climate changes.
The latter have been re-occurring repeatedly during the last ice age, but in a very irregular manner. While this suggests a stochastic nature of the underlying driver, several lines of evidence now point at a deterministic mechanism. In this project, it shall be attempted to reconcile these two views by deriving dynamical systems models for past abrupt climate changes from idealized feedback processes in between components of the climate system. It will be explored how the models can display chaotic dynamics and explain the fundamental characteristics of past abrupt climate change in a deterministic way. The project furthermore gives opportunities to perform fundamental research in dynamical systems theory.