Coupled Seasonal Data Assimilation of Sea Ice, Ocean, and Atmospheric Dynamics over the Last Millennium
Zilu Meng, Gregory J. Hakim, Eric J. Steig
Abstract
Abstract “Online” data assimilation (DA) is used to generate a 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 Niño events, seasonal temperature trends that are consistent with orbital forcing over the last millennium, and polar-amplified cooling in the transition from the medieval climate anomaly to the little ice age. Significance Statement This paper introduces a new seasonal-resolution reanalysis of the last millennium, based on an “online” data assimilation method using a linear inverse model to assimilate paleoclimate proxies. We find good agreement when verifying the reconstruction against modern instrumental reanalyses and out-of-sample proxies. Results show that seasonal temperature trends are similar to predictions from orbital-insolation trends, and seasonal variability of modern El Niño events is similar to instrumental reanalyses. This framework offers a dynamically consistent, seasonally resolved view of past climate variability that supports broader applications in paleoclimate research.