Coupled Seasonal Data Assimilation of Sea Ice, Ocean, and Atmospheric Dynamics over the Last Millennium

TL;DR

This study develops a coupled data assimilation method to produce a high-resolution, millennium-scale reanalysis of sea ice, ocean, and atmospheric dynamics, demonstrating improved accuracy and robustness over previous paleo-reanalyses.

Contribution

It introduces a novel coupled linear inverse model-based data assimilation approach that integrates climate proxy records for the last millennium.

Findings

Achieves highest correlation skill in surface temperature reconstructions

Reconstructs ocean and sea-ice variables with high correlation to observed data

Effectively captures seasonal evolution and amplitude of El Niño events

Abstract

``Online" data assimilation (DA) is used to generate a new seasonal-resolution reanalysis dataset over the last millennium by combining forecasts from an ocean--atmosphere--sea-ice coupled linear inverse model with climate proxy records. Instrumental verification reveals that this reconstruction achieves the highest correlation skill, while using fewer proxies, in surface temperature reconstructions compared to other paleo-DA products, particularly during boreal winter when proxy data are scarce. Reconstructed ocean and sea-ice variables also have high correlation with instrumental and satellite datasets. Verification against independent proxy records shows that reconstruction skill is robust throughout the last millennium. Analysis of the results reveals that the method effectively captures the seasonal evolution and amplitude of El Ni\~{n}o events. Reconstructed seasonal temperature variations are consistent with trends in orbital forcing over the last millennium.