Meet PLOS Climate Academic Editor Alaa Al Khourdajie

We are delighted to introduce Alaa Al Khourdajie (Imperial College London, UK) as an Academic Editor in PLOS Climate‘s new Machine Learning and AI section.

Could you tell us about your research background and your current work?

My work focuses on long-term climate change mitigation scenarios and the integrated assessment models (IAMs) that generate them. Two threads currently occupy most of my attention. The first concerns how these scenarios are affected by climate physical impacts and disruptive events. The second examines applications of machine learning and AI in climate science, both for evidence synthesis and for the generation and assessment of scenario data. Underpinning both is my ongoing involvement in IPCC assessments in AR6 and AR7, where the question of how scenario evidence is produced, communicated, and trusted is never far from view.

Why did you decide to join PLOS Climate’s editorial board?

Since its launch in 2021, PLOS Climate’s open-access model and its explicit commitment to inter- and multidisciplinary work resonate with my own emphasis on inclusivity across scientific communities, policymakers, and the public. Joining the editorial board is an opportunity to contribute to that openness, supporting work across the diverse methods and applications now active at the intersection of climate and AI.

What excites you about the new Machine Learning & AI section of the journal?

AI and ML applications in climate science are expanding rapidly, in volume and in the diversity of methods. A dedicated section is timely in ensuring such applications are robust, transparent, and reproducible. What strikes me as particularly worth this editorial attention is distinguishing between applications that genuinely extend what scientific methods can do, and those that automate steps where transparent expert reasoning is still needed. Equally, I would hope the section becomes a venue that publishes critical and negative findings alongside successes, given the selection-bias risks inherent in a fast-moving methods literature. 

What kinds of submissions would you particularly like to see?

I would welcome submissions across two broad directions. The first is work that uses AI and ML to address scientific questions previously considered intractable, whether because of data volume, dimensionality, or computational cost. The second is work that augments established scientific tools and methods to address existing inquiries more robustly. Across both, serious engagement with validation, reproducibility, and the provenance of training data is very welcome, as are submissions reporting negative or null results.

Ready to submit your work to PLOS Climate? Follow our step-by-step guide to the submission process.