David Reynolds is a Lecturer at the University of Exeter, based at the Penryn Campus in Cornwall, UK.

David is a climate scientist whose research focuses on understanding variability within the coupled climate system across annual to centennial timescales. His work investigates how interactions between the ocean, atmosphere, cryosphere, and carbon cycle have shaped past climate variability, with a particular focus on Atlantic Ocean dynamics, ocean circulation, and abrupt climate change.

To address these questions, David develops annually resolved climate reconstructions using long-lived marine organisms alongside other high-resolution palaeoclimate archives, including tree rings, ice cores, and sediment records. By integrating these diverse proxy records with quantitative and spatial reconstruction approaches, his research reconstructs past changes in ocean and climate variability beyond the instrumental era.

A major focus of this work is the development of climate field reconstruction techniques that allow sparse proxy records to be transformed into spatial reconstructions of sea surface temperature, sea ice variability, ocean circulation, and wider Earth-system dynamics. These datasets provide critical long-term context for understanding interannual to multidecadal climate variability and for evaluating how well climate models simulate coupled ocean-atmosphere processes.

Ultimately, David’s research aims to improve understanding of the mechanisms, impacts, and risks associated with climate variability and long-term climate change, helping to inform projections of future environmental change and climate risk.

Alongside his research, David develops open-source software and computational tools designed to accelerate the generation, analysis, and interpretation of high-resolution proxy climate records. These include RingdateR, a graphical and statistical crossdating platform for annually resolved growth increment archives from marine, freshwater, and terrestrial environments. His software development focuses on improving the accessibility, reproducibility, and scalability of palaeoclimate analysis workflows.