Abstract Historical observations of Earth’s climate underpin our knowledge and predictions of climate variability and change. However, the observations are incomplete and uncertain, and existing datasets based on these observations typically do not assimilate observations simultaneously across different components of the climate system, yielding inconsistencies that limit understanding of coupled climate dynamics. Here we use coupled data assimilation, which synthesizes observational and dynamical constraints across all climate fields simultaneously, to reconstruct globally resolved sea-surface temperature (SST), near-surface air temperature (T), sea-level pressure (SLP), and sea-ice concentration (SIC), over 1850–2023. We use a Kalman filter and forecasts from an efficient emulator (Linear Inverse Model; LIM) to assimilate observations of SST, land T, marine SLP, and satellite-era SIC. We account for model error by training LIMs on eight CMIP6 models, and we use the LIMs to generate eight independent reanalyses with 200 ensemble members, yielding 1600 total members. Key findings in the Tropics include post-1980 trends in the Walker circulation that are consistent with past variability, whereas the tropical SST contrast (the difference between warmer and colder SSTs) shows a distinct strengthening since 1975. ENSO amplitude exhibits substantial low-frequency variability and a local maximum in variance over 1875–1910. In polar regions, we find a muted cooling trend in the Southern Ocean post-1980 and substantial uncertainty. Changes in Antarctic sea ice are relatively small between 1850 and 2000, while Arctic sea ice declines by 0.5±0.1 (1σ) million km 2 during the 1920s.
Cooper et al. (Fri,) studied this question.