Schedule

Schedule

10:30am - 10:45am: Florian Schwarz: Summary of the 2023 IPCC report

11:00am - 11:30am: Daniel Dylewski: Deep Learning for Early Warning Signals of Climate Tipping Points: Spatiotemporal Phase Transitions

11:45am -12:45pm: Francis Poulin: Complexity on Earth

 1:00pm - 2:00pm: Lunchbreak with pizza 🍕

2:15pm - 2:45pm:  Maryam Mahmoudi Gharaie: A spatiotemporal framework and its application in modeling wind power ramps across different wind farms

 3:00pm - 4:00pm: Evan Wilson: A Discussion on the role of Renewables In Battling Climate Change

Abstracts

Florian Schwarz: Summary of the IPCC report

In March the intergovernmental panel on climate change (IPCC) published their AR6 Synthesis report. This report is a summary of what we know about global warming and ways to handle it. Thisis not a mathematics talk. It's goal is to give context to the current climate emergency and where we currently are.

Daniel Dylewski: Deep Learning for Early Warning Signals of Climate Tipping Points: Spatiotemporal Phase Transitions

The potential for complex systems to exhibit tipping points in which an equilibrium state undergoes a sudden and often irreversible shift is well established, but prediction of these events using standard forecast modeling techniques is quite difficult. This has led to the development of an alternative suite of methods that seek to identify signatures of critical phenomena in data, which are expected to occur in advance of many classes of dynamical bifurcation. Crucially, the manifestations of these critical phenomena are generic across a variety of systems, meaning that data-intensive deep learning methods can be trained on (abundant) synthetic data and plausibly prove effective when transferred to (more limited) empirical data sets. In this talk I will present results from my recent paper in which I trained neural networks to perform Early Warning Signal (EWS) detection for lattice phase transitions. This departure from existing methods for one-dimensional time series is intended to produce classifiers better suited to spatiotemporal climate data. A model trained exclusively on 2D Ising model phase transitions is tested on a number of real and simulated climate systems with considerable success. Its accuracy frequently surpasses that of conventional statistical indicators, with performance shown to be consistently improved by the inclusion of spatial indicators. Tools such as this may offer valuable insight into climate tipping events, as remote sensing measurements provide increasingly abundant data on complex geospatially-resolved Earth systems.

Francis Poulin: Complexity on Earth

The climate problem is determined by many different components, and perhaps the two most important are the atmosphere and oceans. This talk will give a broad overview of the dynamics of these geophysical fluids and the difficulties we face in simulating their dynamics. Subsequently, I will give an introduction to the Lorentz attractor, give some background of how it arises, show some interesting solutions and how it is connected to chaos. If time permits, I will also discuss how the power spectrum can be used to determine whether a system is chaotic.

Maryam Mahmoudi Gharaie: A spatiotemporal framework and its application in modeling wind power ramps across different wind farms

In this presentation, spatiotemporal models are discussed. Applications of these models to estimating the ramping of future wind farms based on data from existing ones are presented.

Evan Wilson: A Discussion on the role of Renewables In Battling Climate Change

In this informal session, Evan Wilson, Alberta and Federal Policy Director for the Canadian Renewable Energy Association, will share his insights on the progress of renewables in Alberta and key Provincial and Federal policies that will be affecting the contribution of renewables to contribute to GHG emission reductions.