  Climate Change (CC) is a pressing topic of global importance, attracting
increasing attention across research fields, from social sciences to Natural
Language Processing (NLP). CC is also discussed in various settings and
communication platforms, from academic publications to social media forums.
Understanding who and what is mentioned in such data is a first critical step
to gaining new insights into CC. We present CLIMATELI (CLIMATe Entity LInking),
the first manually annotated CC dataset that links 3,087 entity spans to
Wikipedia. Using CLIMATELI (CLIMATe Entity LInking), we evaluate existing
entity linking (EL) systems on the CC topic across various genres and propose
automated filtering methods for CC entities. We find that the performance of EL
models notably lags behind humans at both token and entity levels. Testing
within the scope of retaining or excluding non-nominal and/or non-CC entities
particularly impacts the models' performances.
