Paleoclimate Data Assimilation: unlocking past climate dynamics to better constrain the future

Instrumental observations only capture a short interval of the climate history of the Earth, and are insufficient to fully constrain low-frequency variability, internal dynamics, and the response of the climate system to changing background states. Paleoclimate archives, by contrast, document a wide range of past climate changes, yet translating this information into Earth System Models (ESMs) to enhance their performance and future projections remains a major challenge. Paleoclimate Data Assimilation (PDA) provides a promising pathway to bridge this gap by combining proxy records and ESMs within a physically consistent framework. Here we present a paleo reanalysis based on an adaptation of the Norwegian Climate Prediction Model (NorCPM), in which an ensemble Kalman filter is used to assimilate hundreds of annually resolved proxy records (including coral, tree-ring, and ice-core records) back to 1600 CE. Unlike many existing paleo reanalyses for past centuries, which primarily constrain the atmospheric state and only indirectly represent ocean variability, our approach explicitly accounts for ocean dynamics. By nudging three dimensional atmospheric wind fields derived from the paleo atmospheric reanalysis, we generate a dynamically consistent coupled reanalysis, by simulating the response of the ocean to large-scale wind variability, as well as their associated impacts through thermodynamical feedbacks. The climate reanalysis represents both forced and internal variability over the last four centuries and shows good agreement with independent instrumental observations. By construction, this approach yields a dynamically coherent, multivariate reconstruction that goes beyond traditional proxy reconstructions and enables direct investigation of climate dynamics and feedback. Here, we focus on methodological aspects and perspectives of PDA, highlighting how paleo reanalyses can (i) constrain modes of low-frequency variability and their stability across different climate states, and (ii) evaluate and refine the calibration of the ESMs beyond the instrumental period. Such approaches are essential for improving confidence in future climate projections, particularly with respect to long-timescale variability, feedback, and the potential for abrupt transitions in the Earth system.