Breakthrough in forecasting El Niño phenomenon in the Atlantic

Welcome to Sogndal – conference is held in Norwegian Årets klimaomstillingskonferanse er i Sogndal 6.-7. mai. Programmet er nå sluppet – påmeldingsfrist 11. april https://klimaomstilling.no/ Velkommen til vakre Sogndal og givende møter med forskere, offentlige aktører og næringsliv.

Abstract With the modern availability of data and computing resources, machine learning is becoming an important tool in solving problems in many domains, from robotics and medicine to weather and climate. In this talk I will attempt to give an accessible introduction to core machine learning concepts, aimed at beginners in the topic. I’ll walk through ideas like supervised, unsupervised and representation learning and share some examples from my own research. I will end by looking at recent developments in large general-purpose foundation models, such as AURORA, and how they’re being applied in climate forecasting and beyond. Speaker information Linus Ericsson is a postdoctoral researcher at the University of Edinburgh, specialising in representation learning, neural architectures, and domain adaptation. His current research explores efficient ways of finding the right neural network structure for the right task and how to make existing networks cheaper to run. Linus has published in top venues such as NeurIPS, CVPR, and the IEEE Signal Processing Magazine. His interests span a wide array of topics, including multimodal learning and responsible AI applications in climate and healthcare.

Abstract The rapid warming of the Arctic where permafrost is prevalent is threatening to release carbon which would accelerate global warming if it reaches the atmosphere. There are many unknowns regarding carbon cycling and budgets in Arctic watersheds. This presentation shows that active layer soil above permafrost functions as a thin but extensive unconfined aquifer made up of mostly of peat. The supra-permafrost aquifer has relatively high porosity and permeability, creating efficient subsurface flow paths above otherwise impermeable permafrost. Observations and modeling reveal that much of the water and carbon going through Imnavait Creek, a headwater river in the North Slope of Alaska, has passed through the supra-permafrost aquifer. Remote sensing showed that supra-permafrost groundwater is prevalent during summer while extensive sampling showed that there is substantial carbon within the supra-permafrost aquifers, as much as those estimated for permafrost. The crucial task of predicting the fate of carbon in Arctic watersheds depends on knowing the subsurface flow properties and processes. Speaker information 2025 Birdsall-Dreiss Distinguished Lecturer on a worldwide lecture tour M. Bayani Cardenas is a hydrology professor in the Department of Earth and Planetary Sciences of the Jackson School of Geosciences at the University of Texas at Austin. His research seeks to understand flow and transport processes across different hydrologic settings, water quality and quantity problems, and scales, using a combination of theoretical, computational, and observational methods. He received his education from the University of the Philippines-Diliman, the University of Nebraska-Lincoln, and the New Mexico Institute of Mining and Technology.