Climate AI Nordics

Climate AI Nordics

Nordic network for collaborations around tackling climate change using AI and machine learning.

Climate AI Nordics Newsletter, February 2025

Welcome to the Climate AI Nordics Newsletter February 2025.

Email us at contact@climateainordics.com to contribute your own relevant news! We will include news on our web page, climateainordics.com, and in our newsletter. Some announcements will also be communicated in social media. Take the chance of showcasing your work or your events to the community!

Since the launch in October, the network has grown to 111 people spread over the Nordic countries (142 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


2025-02-28 Read about an exciting project involving an both artificial intelligence and an artificial rock shelf!
(Read more)


Climate AI Nordics Core Team

2025-02-27 We are proud to announce the amazing Core Team of Climate AI Nordics! Ten people spread over the Nordics will take place in the Core Team, to share the responsibility of developing the network and run our activities.
(Read more)


Partnership between Climate AI Nordics and Klimatkollen

2025-02-10 We are thrilled to share our partnership with Klimatkollen 🌍📈!
(Read more)


Partnership between Climate AI Nordics and Global Wetland Center

2025-02-07 We are excited to share our partnership with Global Wetland Center, 🌍✨!
(Read more)


Coming events


Edge AI and IoT-Driven Water Quality Monitoring

This event takes place 2025-03-06. Webinar with Atakan Aral, University of Vienna. The integration of Edge Computing and Artificial Intelligence (i.e., Edge AI) unlocks new possibilities for remote monitoring of water quality across diverse environments. This talk will explore the dual aspects of "remote" monitoring: (1) collecting water quality data in geographically remote regions with limited energy and connectivity and (2) using connected water quality sensors to gain real-time insights remotely. By deploying low-power or energy-harvesting sensors and learning from data in close proximity to sensors, we can improve efficiency, reduce latency, and maintain learning performance in challenging environments without reliable communication and energy infrastructures. The presentation will showcase two real-world case studies in river pollution source identification and monitoring of water distribution systems.
(Read more)


Recent events


Tech-informed nature conservation - are we there yet?

This event took place 2025-02-20. Webinar with Abdulhakim Abdi, Lund University. In the face of a global biodiversity crisis, the ability to efficiently monitor ecological change across space and time has never been more accessible. Advances in satellite remote sensing and AI have opened new frontiers for biodiversity monitoring by offering powerful tools to analyze vast datasets, automate species detection, and improve disturbance tracking. While these technologies hold great promise, their widespread adoption faces key challenges, including data accessibility, environmental costs, model interpretability, and the need for stronger interdisciplinary collaboration. This presentation explores the opportunities and limitations of tech-driven nature conservation, assessing whether current developments are sufficient to bridge critical knowledge gaps. The integration of remote sensing with AI brings us closer to a more data-informed approach to managing the living world - but are we truly ready to harness its full potential?
(Read more)

Spread the word!

Climate AI Nordics is present on LinkedIn and BlueSky. Follow us and repost our stories so that we get the most impact! Also make sure to share your work with us, by sending us an email (contact@climateainordics.com) or some other channel, and we’ll add it to the news feed!

Climate AI Nordics is a network of researchers dedicated to developing and utilizing AI technologies to address the urgent global challenge of climate change. Our researchers focus on creating and promoting AI solutions that support both climate change mitigation, reducing the severity of climate change, and adaptation, adjusting to the effects of climate change. We are already in a climate emergency which is causing biodiversity loss, extreme weather events, and human suffering, and this necessitates a multifaceted approach involving both policy change, limitations on activities contributing to climate change, and bolstering societal resilience against climate-related events.

Climate AI Nordics promotes the development of AI-based analytical tools and optimization techniques that can inform decision-making processes crucial for ensuring a sustainable future for generations to come. In particular, we recognize that technology, particularly AI-based solutions, can play a role in supporting efforts such as rewilding, rewetting, and reducing emissions and the reliance on fossil fuels.

By bringing together researchers in the Nordic countries, Climate AI Nordics aims to:

  • Foster collaboration and knowledge exchange through seminars and workshops.
  • Help develop AI-driven solutions that contribute to the creation of climate-friendly products and services, optimize processes for efficiency and sustainability, and promote justice in addressing the impacts of climate change.

We hope that the collaborative nature of Climate AI Nordics will accelerate progress in this critical field, enabling the sharing of expertise and resources to develop impactful AI solutions for climate change mitigation and adaptation.

You May Also Enjoy

Reliable conclusions from remote sensing maps

Event date:

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

Machine learning for Earth system prediction and predictability

Event date:

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.

Climate AI Nordics Core Team

Published:

We are proud to announce the amazing Core Team of Climate AI Nordics! Ten people spread over the Nordics will take place in the Core Team, to share the responsibility of developing the network and run our activities.