Primary Convener:
Dan Lunt, University of Bristol

Convener:
Junxuan Fan, Nanjing University
Xin Ren, University of Bristol

Early Career Convener:
Tianyi Chu, University of Bristol

Chair:
Dan Lunt, University of Bristol
Junxuan Fan, Nanjing University
Xin Ren, University of Bristol
Tianyi Chu, University of Bristol

Machine Learning (ML) offers exciting techniques for addressing long-standing problems in the fields of paleoclimates and paleoecology, through improving computational efficiency and accuracy, and enabling the management of large, complex datasets. In particular, ML techniques are capable of handling non-linear relationships and high-dimensional data, allowing for a more sophisticated analysis than using traditional approaches. We aim to bring together those from the modelling and proxy communities, along with experts in machine learning, to showcase existing work applying ML to paleoclimate/ecology problems. Topics include, but are not limited to:Emulators as surrogate models for ESMs to enable efficient paleoclimate/ecology simulations; Efficient model-tuning to enhance the performance in simulating paleoclimate/ecology;New model parameterisations developed through ML; Improvements in management and stratigraphic calibration of large proxy dataset; Advances in proxy calibration and quantifying uncertainties; Data assimilation and field reconstruction;Proxy system modelling;Downscaling model results for model-data comparisons

Index Terms
4928 Global climate models
4950 Paleoecology
4999 General or miscellaneous

Suggested Itineraries:
Machine Learning and AI

Cross-Listed:
B - Biogeosciences

Neighborhoods:
3. Earth Covering