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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, the linear inverse model (LIM), to assimilate observations of SST, landT, 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. El Niño–Southern Oscillation (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. Significance StatementThe key advance in our reconstruction is that the ocean, atmosphere, and sea ice are dynamically consistent with each other and with observations across all components, thus forming a true climate reanalysis. Existing climate datasets are typically derived separately for each component (e.g., atmosphere, ocean, and sea ice), leading to spurious trends and inconsistencies in coupled climate variability. We use coupled data assimilation to unify observations and coupled dynamics across components. We combine forecasts from climate models with observations from ocean vessels and weather stations to produce monthly state estimates spanning 1850–2023 and a novel quantification of globally resolved uncertainty. This reconstruction provides insights into historical variability and trends while motivating future efforts to reduce uncertainties in the climate record. 

Abstract The traditional method for estimating weather forecast sensitivity to initial conditions uses adjoint models, which are limited to short lead times due to linearization around a control forecast. The advent of deep‐learning frameworks enables a new approach using backpropagation and gradient descent to iteratively optimize initial conditions, minimizing forecast errors. We apply this approach to the June 2021 Pacific Northwest heatwave using the GraphCast model, yielding over 90% reduction in 10‐day forecast errors over the Pacific Northwest. Similar improvements are found for Pangu‐Weather model forecasts initialized with the GraphCast‐derived optimal, suggesting that model error is an unimportant part of the perturbations. Eliminating small scales from the perturbations also yields similar forecast improvements. Extending the length of the optimization window, we find forecast improvement to about 23 days, suggesting atmospheric predictability at the upper end of recent estimates. 

Abstract Global deep learning weather prediction models have recently been shown to produce forecasts that rival those from physics-based models run at operational centers. It is unclear whether these models have encoded atmospheric dynamics or simply pattern matching that produces the smallest forecast error. Answering this question is crucial to establishing the utility of these models as tools for basic science. Here, we subject one such model, Pangu-Weather, to a set of four classical dynamical experiments that do not resemble the model training data. Localized perturbations to the model output and the initial conditions are added to steady time-averaged conditions, to assess the propagation speed and structural evolution of signals away from the local source. Perturbing the model physics by adding a steady tropical heat source results in a classical Matsuno–Gill response near the heating and planetary waves that radiate into the extratropics. A localized disturbance on the winter-averaged North Pacific jet stream produces realistic extratropical cyclones and fronts, including the spontaneous emergence of polar lows. Perturbing the 500-hPa height field alone yields adjustment from a state of rest to one of wind–pressure balance over ∼6 h. Localized subtropical low pressure systems produce Atlantic hurricanes, provided the initial amplitude exceeds about 4 hPa, and setting the initial humidity to zero eliminates hurricane development. We conclude that the model encodes realistic physics in all experiments and suggest that it can be used as a tool for rapidly testing a wide range of hypotheses. 

Abstract We evaluate Linear Inverse Models (LIMs) trained on last millennium model data to predict Arctic sea‐ice concentration, thickness, and other atmospheric and oceanic variables on monthly timescales. We find that more than 500 years of training data and 100 years of validation data are needed to reliably estimate LIM forecast skill. The best LIM has skill up to 8 months lead time and outperforms an autoregressive model of order one (AR1) forecast at all locations, with particularly large outperformance near the ice edge. However, for out‐of‐sample validation tests using data from various different model simulations and reanalysis products, they underperform an AR1 model due to differences in the location of the sea‐ice edge from the training data. We present a metric for predicting LIM forecast skill, based on the spatial correlation of the variance in the training and validation data sets.