Climate AI Nordics Core Team

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.

Olof Mogren, Research Director, RISE Research Institutes of Sweden / Co-founder of Climate AI Nordics

Aleksis Pirinen, Senior Researcher, RISE Research Institutes of Sweden / Co-founder of Climate AI Nordics

Francesca Larosa, PhD, postdoc at KTH Royal Institute of Technology

Ankit Kariryaa, tenure-track assistant professor at University of Copenhagen

Stefano Puliti, senior researcher at Norwegian Institute of Bioeconomy Research (NIBIO)

Hilda Sandström, postdoc at Aalto University

John Martinsson, PhD candidate at RISE Research Institutes of Sweden & Lund University

Sigrid Passano Hellan, senior researcher at NORCE Norwegian Research Centre / Bjerknes Centre for Climate Research

Nico Lang, Assistant Professor, Global Wetland Center / University of Copenhagen

Alouette van Hove, PhD Student, University of Oslo

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Reliable conclusions from remote sensing maps

Event date: 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

Machine learning for Earth system prediction and predictability

Event date: 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.