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Reconstructions of past climates in both time and space provide important insight into the range and rate of change within the climate system. However, producing a coherent global picture of past climates is difficult because indicators of past environmental changes (proxy data) are unevenly distributed and uncertain. In recent years, paleoclimate data assimilation (paleoDA), which statistically combines model simulations with proxy data, has become an increasingly popular reconstruction method. Here, we describe advances in paleoDA to date, with a focus on the offline ensemble Kalman filter and the insights into climate change that this method affords. PaleoDA has considerable strengths in that it can blend multiple types of information while also propagating uncertainty. Drawbacks of the methodology include an overreliance on the climate model and variance loss. We conclude with an outlook on possible expansions and improvements in paleoDA that can be made in the upcoming years. 

A long-term record of global mean surface temperature (GMST) provides critical insight into the dynamical limits of Earth’s climate and the complex feedbacks between temperature and the broader Earth system. Here, we present PhanDA, a reconstruction of GMST over the past 485 million years, generated by statistically integrating proxy data with climate model simulations. PhanDA exhibits a large range of GMST, spanning 11° to 36°C. Partitioning the reconstruction into climate states indicates that more time was spent in warmer rather than colder climates and reveals consistent latitudinal temperature gradients within each state. There is a strong correlation between atmospheric carbon dioxide (CO₂) concentrations and GMST, identifying CO₂as the dominant control on variations in Phanerozoic global climate and suggesting an apparent Earth system sensitivity of ~8°C. 

Abstract. Paleoclimate data assimilation (DA) is a tool for reconstructing past climates that directly integrates proxy records with climate model output. Despite the potential for DA to expand the scope of quantitative paleoclimatology, these methods remain difficult to implement in practice due to the multi-faceted requirements and data handling necessary for DA reconstructions, the diversity of DA methods, and the need for computationally efficient algorithms. Here, we present DASH, a MATLAB toolbox designed to facilitate paleoclimate DA analyses. DASH provides command line and scripting tools that implement common tasks in DA workflows. The toolbox is highly modular and is not built around any specific analysis, and thus DASH supports paleoclimate DA for a wide variety of time periods, spatial regions, proxy networks, and algorithms. DASH includes tools for integrating and cataloguing data stored in disparate formats, building state vector ensembles, and running proxy (system) forward models. The toolbox also provides optimized algorithms for implementing ensemble Kalman filters, particle filters, and optimal sensor analyses with variable and modular parameters. This paper reviews the key components of the DASH toolbox and presents examples illustrating DASH's use for paleoclimate DA applications. 

Abstract Efforts to estimate past global mean temperature and latitudinal gradients must contend with spatial heterogeneity in sea surface temperatures (SSTs). Here, we use modern SSTs to show that the environments from which most paleoclimatic data are drawn, shallow epeiric seas and continental margins, are systematically offset from zonal mean temperatures. Epeiric seas are warmer and more seasonal than open‐ocean values from the same latitudes, while continental margins exhibit consistent and predictable deviations related to gyre circulation. Warm temperatures inferred from Paleozoic proxy data may largely reflect that these data derive almost entirely from epeiric seas. Moreover, pseudoproxy analysis using Paleogene sampling localities demonstrates how undersampling of the full range of dynamical environments associated with gyre circulation can generate spurious estimates of latitudinal temperature gradients. Recognition of these global patterns permits a predictive framework within which to more robustly interpret proxy data, improve Earth system models, and reconstruct ancient dynamic regimes.