Welcome to the Climate AI Nordics Newsletter April 2025.

Since the launch in October, the network has grown to 146 people spread over the Nordic countries (199 including international supporting affiliates). Do you know researchers who works in the intersection of AI and Climate Change? Tell them about Climate AI Nordics! climateainordics.com/join.

News

Will you join the #AIWeatherQuest?

2025-04-07 ECMWF invites AI/ML experts to improve sub-seasonal forecasts (days 19-25 & 26-31)—key for climate adaptation & planning. Open to all, any tools/datasets.
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Featured Paper: Efficiency is Not Enough: A Critical Perspective of Environmentally Sustainable AI

2025-03-27 This perspective paper discusses several reasons why it is crucial to look at the impact of machine learning (ML) using systems thinking, i.e. across the entire life cycle of models, from development to deployment.
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Climate AI Nordics co-founder in Swedish Public Radio

2025-03-26 Climate AI Nordics co-founder Olof Mogren in Swedish Public Radio show “Klotet”. “AI is part of the solution—but not the whole solution”.
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Partnership between Climate AI Nordics and ELLIS Machine Learning for Earth and Climate Sciences Program

2025-03-20 We are happy to share that we have partnered with ELLIS Machine Learning for Earth and Climate Sciences Program 🌍✨!
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Coming events

SatML for large-scale above-ground biomass estimation

This event takes place 2025-05-08. Webinar with Ghjulia Sialelli, ETH Zurich. The combination of remote sensing and machine learning has made it possible to map forest properties at an unprecedented scale and resolution. In this presentation, I will focus on the application of deep learning techniques to estimate above-ground biomass (AGB), a key metric for tracking forest carbon and ecosystem dynamics. I will begin by introducing our recently published, machine-learning-ready dataset. It features high-resolution (10m) multi-modal satellite imagery, paired with AGB reference values from NASA’s Global Ecosystem Dynamics Investigation (GEDI) mission. Key aspects include the carefully selected geographic coverage, thoughtful integration of diverse satellite data sources, and the establishment of performance baselines using standard deep learning models. Next, I will describe our ongoing efforts to build on said baselines. This was done both through feature and model engineering. I will also mention some promising yet unsuccessful approaches, highlighting some key challenges of the task at hand. Finally, I will discuss future directions, including incorporating uncertainty estimation and exploring the potential for generating a global above-ground biomass map.
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Recent events

Efficient and precise annotation of local structures in data

This event took place 2025-04-24. Webinar with John Martinsson, RISE and Lund University. Machine learning models now help scientists analyze vast datasets across every branch of science. These models typically improve with more data and larger architectures, mainly through supervised learning. Both training and evaluation therefore rely on labeled datasets. A main challenge is scaling the data labeling effort to the volumes required, because it is costly and label quality can vary. Methods that deliver inexpensive yet accurate labels are therefore essential. This talk examines how to lower annotation cost and increase label quality when labeling local structures in data—for example, a local structure can be a sound event in an audio recording. By detecting the boundaries of such structures automatically, we let annotators focus on supplying concise textual descriptions for the content within those boundaries. In this setting we analyze a widely used labeling method for audio where fixed and equal length audio segments are labeled with presence or absence of an event class. We benchmark it against an oracle method that defines an upper bound, and propose adaptive labeling techniques that achieve higher‑quality labels for the studied datasets at a lower cost.
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Optimising green infrastructure for climate-resilient cities: AI-driven approaches to urban cooling

This event took place 2025-04-03. Webinar with Abdul Shaamala, Queensland University of Technology. Green infrastructure (GI) is critical in enhancing urban resilience, mitigating heat stress, and improving environmental sustainability. However, optimising the placement and configuration of green elements such as trees, parks, and vegetative corridors, requires a data-driven approach that accounts for microclimate variations, urban morphology, and long-term ecosystem benefits. This talk explores how artificial intelligence (AI), machine learning (ML), and geospatial analysis can be leveraged to optimise GI for urban cooling and climate adaptation. Specifically, it delves into tree optimisation strategies that enhance shade provision, reduce urban heat islands (UHI), and improve outdoor thermal comfort. By utilising computational models, including optimisation algorithms and thermal analysis, cities can strategically position vegetation to maximise cooling benefits while balancing urban development needs.
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Reliable conclusions from remote sensing maps

This event took place 2025-03-13. Webinar with Sherrie Wang, MIT. Remote sensing maps are used to estimate regression coefficients relating environmental variables, such as the effect of conservation zones on deforestation. However, the quality of map products varies, and – because maps are outputs of complex machine learning algorithms that take in a variety of remotely sensed variables as inputs – errors are difficult to characterize. Thus, population-level estimates from such maps may be biased. We show how a small amount of randomly sampled ground truth data can correct for bias in large-scale remote sensing map products. Applying our method across multiple remote sensing use cases in regression coefficient estimation, we find that it results in estimates that are (1) more reliable than using the map product as if it were 100% accurate and (2) have lower uncertainty than using only the ground truth and ignoring the map product. Paper: https://arxiv.org/abs/2407.13659
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Machine learning for Earth system prediction and predictability

This event took place 2025-03-27. Webinar with María J. Molina, University of Maryland. Machine learning can be used for Earth system prediction, or to study the uppermost limit of prediction skill theoretically achievable given the system's initial state or other factors, otherwise known as predictability. In traditional numerical weather prediction frameworks, we solve the governing partial differential equations starting from an initial state. This initialized prediction framework usually involves three stages: 1) generating the initial conditions of the Earth system, 2) running the mathematical representation of the system on a computer forward in time, and 3) analyzing the output and converting it into a format that is useful for end users. Machine learning can be used to improve each of these individual stages, or to circumvent the three stage framework altogether, and examples of each will be given in this seminar. More time during the seminar will be dedicated to the challenges surrounding subseasonal prediction, which focuses on lead times of three to four weeks, and how we can use machine learning to both uncover potential biases in our initialized prediction systems and how we can bias-correct them.
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Spread the word!

Be sure to follow us on LinkedIn and BlueSky. Climate AI Nordics will have the most impact if you repost and like our stories! Also make sure to share your work with us, by sending us an email (contact@climateainordics.com), posting in our Slack or some other channel, and we’ll add it to the news feed! Take the chance of showcasing your work or your events to the community!**

Climate AI Nordics is a network of researchers working to harness AI in tackling the climate crisis through both mitigation and adaptation.

We promote AI-based tools and optimization methods that support sustainable decision-making—helping reduce emissions, restore ecosystems, and build climate resilience.

Our goals:

• Connect Nordic researchers through seminars and workshops
• Develop AI-driven solutions for sustainability, climate justice, and green innovation

Together, we aim to accelerate progress by sharing knowledge, tools, and expertise for impactful climate action.