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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 km²during the 1920s. 

Characterized by similar-to-today CO2 (∼400 ppm) and surface temperatures approximately 3°–4°C warmer than the preindustrial, the mid-Pliocene warm period (mPWP) has often been used as an analog for modern CO2-driven climate change and as a constraint on the equilibrium climate sensitivity (ECS). However, model intercomparison studies suggest that non-CO2boundary conditions—such as changes in ice sheets—contribute substantially to the higher global mean temperatures and strongly shape the pattern of sea surface warming during the mPWP. Here, we employ a set of CESM2 simulations to quantify mPWP effective radiative forcings, study the role of ocean circulation changes in shaping the patterns of sea surface temperatures, and calculate radiative feedbacks during the mPWP. We find that the non-CO2boundary conditions of the mPWP, enhanced by changes in ocean circulation, contributed to larger high-latitude warming and less-stabilizing feedbacks relative to those induced by CO2alone. Accounting for differences in feedbacks between the mPWP and the modern (greenhouse gas–driven) climate provides stronger constraints on the high-end of modern-day ECS. However, a quantification of the forcing of non-CO₂boundary condition changes combined with the distinct radiative feedbacks that they induce suggests that Earth system sensitivity may be higher than previously estimated. 

Abstract As the last time period when concentrations were near 400 ppm, the Pliocene Epoch (5.33–2.58 Ma) is a useful paleoclimate target for understanding future climate change. Existing estimates of global warming and climate sensitivity during the Pliocene rely mainly on model simulations. To reconstruct Pliocene climate and incorporate paleoclimate observations, we use data assimilation to blend sea‐surface temperature (SST) proxies with model simulations from the Pliocene Modeling Intercomparison Project 2 and the Community Earth System Models. The resulting reconstruction, “plioDA,” suggests that the mid‐Pliocene (3.25 Ma) was warmer than previously thought (on average 4.1°C warmer than preindustrial, 95% CI = 3.0°C–5.3°C), leading to a higher estimate of climate sensitivity (4.8°C per doubling of , 90% CI = 2.6°C–9.9°C). In agreement with previous work, the tropical Pacific zonal SST gradient during the mid‐Pliocene was moderately reduced (°C, 95% CI = –0.4°C). However, this gradient was more reduced during the early Pliocene (4.75 Ma, -2.3°C, 95% CI = -3.9t to -1.1°C), a time period that is also warmer than the mid‐Pliocene (4.8°C above preindustrial, 95% CI = 3.6°C–6.2°C). PlioDA reconstructs a fresh North Pacific and salty North Atlantic, supporting Arctic gateway closure and contradicting the presence of Pacific Deep Water formation. Overall, plioDA updates our view of global and spatial climate change during the Pliocene, as well as raising questions about the state of ocean circulation and the drivers of differences between the early and mid‐Pliocene. 

Here, we show that the Last Glacial Maximum (LGM) provides a stronger constraint on equilibrium climate sensitivity (ECS), the global warming from increasing greenhouse gases, after accounting for temperature patterns. Feedbacks governing ECS depend on spatial patterns of surface temperature (“pattern effects”); hence, using the LGM to constrain future warming requires quantifying how temperature patterns produce different feedbacks during LGM cooling versus modern-day warming. Combining data assimilation reconstructions with atmospheric models, we show that the climate is more sensitive to LGM forcing because ice sheets amplify extratropical cooling where feedbacks are destabilizing. Accounting for LGM pattern effects yields a median modern-day ECS of 2.4°C, 66% range 1.7° to 3.5°C (1.4° to 5.0°C, 5 to 95%), from LGM evidence alone. Combining the LGM with other lines of evidence, the best estimate becomes 2.9°C, 66% range 2.4° to 3.5°C (2.1° to 4.1°C, 5 to 95%), substantially narrowing uncertainty compared to recent assessments. 

Model output from experiments described in Cooper et al 2022.</p> Includes WW3 output of ice concentration and significant wave height (hourly) for 2018-01-01 through 2018-12-31.</p> Includes WW3 1-D wave spectra (hourly) for 2018-07.</p> Includes CICE output of representative radius (daily) for 2018-01-01 through 2018-12-31.</p> Note: uncompressed file size is 2x the tar.gz file size.</p> 

Model output from experiments described in Cooper et al 2022.</p> Includes WW3 output of ice concentration and significant wave height (hourly) for 2018-01-01 through 2018-12-31.</p> Includes WW3 1-D wave spectra (hourly) for 2018-07.</p> Includes CICE output of representative radius (daily) for 2018-01-01 through 2018-12-31.</p> Note: uncompressed file size is 2x the tar.gz file size.</p> 

Model output from experiments described in Cooper et al 2022.</p> Includes WW3 output of ice concentration and significant wave height (hourly) for 2018-01-01 through 2018-12-31.</p> Includes WW3 1-D wave spectra (hourly) for 2018-07.</p> Includes CICE output of representative radius (daily) for 2018-01-01 through 2018-12-31.</p> Note: uncompressed file size is 2x the tar.gz file size.</p> 

Model output from experiments described in Cooper et al 2022.</p> Includes WW3 output of ice concentration and significant wave height (hourly) for 2018-01-01 through 2018-12-31.</p> Includes WW3 1-D wave spectra (hourly) for 2018-07.</p> Includes CICE output of representative radius (daily) for 2018-01-01 through 2018-12-31.</p> Note: uncompressed file size is 2x the tar.gz file size.</p> 

Model output from experiments described in Cooper et al 2022.</p> Includes WW3 output of ice concentration and significant wave height (hourly) for 2018-01-01 through 2018-12-31.</p> Includes WW3 1-D wave energy spectra (hourly) for 2018-07.</p> Includes CICE output of representative radius (daily) for 2018-01-01 through 2018-12-31.</p> Note: uncompressed file size is 2x the tar.gz file size.</p> 

Model output from experiments described in Cooper et al 2022.</p> Includes WW3 output of ice concentration and significant wave height (hourly) for 2018-01-01 through 2018-12-31.</p> Includes WW3 1-D wave spectra (hourly) for 2018-07.</p> Includes CICE output of representative radius (daily) for 2018-01-01 through 2018-12-31.</p> Note: uncompressed file size is 2x the tar.gz file size.</p>