Webinar with Alp Kucukelbir, Columbia University. Artificial intelligence (AI) has the potential to make very significant contributions to climate change mitigation. The complexity and scale of the challenge is broad. In this talk, I break down opportunities for AI to effect incremental and transformational change across multiple sectors, focusing on industries with large carbon footprints. I highlight barriers and risks to the adoption of AI, including the carbon footprint of AI usage worldwide. I focus on the multiple definitions (and ultimate importance) of "trust in AI" and its impact on the integration of AI into complex workflows. This talk is for AI practitioners looking to understand how AI fits into the bigger picture of climate change. I highlight opportunities and challenges in each sector that I hope will motivate collaboration across academia and industry.
Webinar with Alireza Taheri Dehkordi, Lund University. The global decline in water quality, exacerbated by climate change and population growth, underscores the need for continuous and accurate monitoring of water quality parameters (WQPs). Remote sensing (RS) data, especially from multispectral satellites like Sentinel-2 and Landsat-8, offers large-scale, periodic observations for tracking WQPs. However, deriving accurate estimates solely from RS data is complex due to the intricate relationships between spectral bands and water quality indicators. This talk presents two novel machine learning approaches that leverage advanced RS data processing to enhance water quality monitoring.
The new Climate Change AI Nordics Featured Paper is "ONEKANA: Modelling Thermal Inequalities in African Cities" by Sabine Vanhuysse and colleagues. This research addresses the pressing issue of thermal disparities in rapidly urbanizing African cities, where vulnerable populations are disproportionately affected by extreme heat due to environmental and socioeconomic factors.
Welcome to the Climate Change AI Nordics Newsletter, November 2024! Read about recent and coming seminars, workshops, and publications from the network's researchers.
In Featured Preprints, preprints from affiliated researchers are summarized and featured at Climate Change AI Nordics. This one features "Continuous Ensemble Weather Forecasting with Diffusion Models", from Martin Andrae, Tomas Landelius, Joel Oskarsson, and Fredrik Lindsten.
Webinar with Joel Oskarsson, Linköping University. Recent years have seen rapid progress in using machine learning models for weather forecasting. These models show impressive performance, matching or even outperforming existing physics-based models, while running in a fraction of the time. This is fundamentally and rapidly changing the landscape of weather forecasting today. In this talk I will discuss the factors that enabled this paradigm shift, the core machine learning methods used and the research questions at the bleeding edge of machine learning for weather. In particular I will focus on how current methods can be extended to regional and probabilistic forecasting. For regional forecasting I will showcase graph-based methods for building limited area weather forecasting models. I will also discuss how generative machine learning methods can enable probabilistic forecasting, giving much-needed estimates of uncertainty and allowing for predicting extreme weather events.
Webinar with Alireza Taheri Dehkordi, Lund University. The global decline in water quality, exacerbated by climate change and population growth, underscores the need for continuous and accurate monitoring of water quality parameters (WQPs). Remote sensing (RS) data, especially from multispectral satellites like Sentinel-2 and Landsat-8, offers large-scale, periodic observations for tracking WQPs. However, deriving accurate estimates solely from RS data is complex due to the intricate relationships between spectral bands and water quality indicators. This talk presents two novel machine learning approaches that leverage advanced RS data processing to enhance water quality monitoring.
Webinar with Alp Kucukelbir, Columbia University. Artificial intelligence (AI) has the potential to make very significant contributions to climate change mitigation. The complexity and scale of the challenge is broad. In this talk, I break down opportunities for AI to effect incremental and transformational change across multiple sectors, focusing on industries with large carbon footprints. I highlight barriers and risks to the adoption of AI, including the carbon footprint of AI usage worldwide. I focus on the multiple definitions (and ultimate importance) of "trust in AI" and its impact on the integration of AI into complex workflows. This talk is for AI practitioners looking to understand how AI fits into the bigger picture of climate change. I highlight opportunities and challenges in each sector that I hope will motivate collaboration across academia and industry.
As COP29 is convening in Azerbajdzjan, Olof Mogren, co-founder of Climate Change AI Nordics was interviewed in Computer Sweden. "When it comes to climate change, we have to work broadly both with mitigation techniques and by adapting to the effects we are already seeing". "AI can be a tool to support these efforts".
The 2025 Nordic Workshop on AI for Tackling Climate Change will gather researchers from the Nordics. This one-day, in-person workshop, will take place in Gothenburg, Sweden, May 13th 2025. The workshop will feature a mix of keynotes, oral presentations, and posters around the topics of AI for climate change, including AI for biodiversity and the green transition. The workshop will be a meeting point for a wide range of researchers from (primarily) around the Nordic countries.
Climate Change AI Nordics is a network for researchers in the nordics working on problems related to tackling climate change using AI and machine learning. Our web site is now live.