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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. 

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. 

Abstract The Southern Annular Mode (SAM) is the leading mode of atmospheric variability in the extratropical Southern Hemisphere and has wide ranging effects on ecosystems and societies. Despite the SAM’s importance, paleoclimate reconstructions disagree on its variability and trends over the Common Era, which may be linked to variability in SAM teleconnections and the influence of specific proxies. Here, we use data assimilation with a multi-model prior to reconstruct the SAM over the last 2000 years using temperature and drought-sensitive climate proxies. Our method does not assume a stationary relationship between the SAM and the proxy records and allows us to identify critical paleoclimate records and quantify reconstruction uncertainty through time. We find no evidence for a forced response in SAM variability prior to the 20th century. We do find the modern positive trend falls outside the 2 σ range of the prior 2000 years at multidecadal time scales, supporting the inference that the SAM’s positive trend over the last several decades is a response to anthropogenic climate change. 

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. 

This paper explores a novel approach to dexterous manipulation, aimed at levels of speed, precision, robustness, and simplicity suitable for practical deployment. The enabling technology is a Direct-drive Hand (DDHand) comprising two fingers, two DOFs each, that exhibit high speed and a light touch. The test application is the dexterous manipulation of three small and irregular parts, moving them to a grasp suitable for a subsequent assembly operation, regardless of initial presentation. We employed four primitive behaviors that use ground contact as a “third finger”, prior to or during the grasp process: pushing, pivoting, toppling, and squeeze- grasping. In our experiments, each part was presented from 30 to 90 times randomly positioned in each stable pose. Success rates varied from 83% to 100%. The time to manipulate and grasp was 6.32 seconds on average, varying from 2.07 to 16 seconds. In some cases, performance was robust, precise, and fast enough for practical applications, but in other cases, pose uncertainty required time-consuming vision and arm motions. The paper concludes with a discussion of further improvements required to make the primitives robust, eliminate uncertainty, and reduce this dependence on vision and arm motion. 

We found that temperature-dependent infrared spectroscopy measurements (i.e., reflectance or transmittance) using a Fourier-transform spectrometer can have substantial errors, especially for elevated sample temperatures and collection using an objective lens. These errors can arise as a result of partial detector saturation due to thermal emission from the measured sample reaching the detector, resulting in nonphysical apparent reduction of reflectance or transmittance with increasing sample temperature. Here, we demonstrate that these temperature-dependent errors can be corrected by implementing several levels of optical attenuation that enable convergence testing of the measured reflectance or transmittance as the thermal-emission signal is reduced, or by applying correction factors that can be inferred by looking at the spectral regions where the sample is not expected to have a substantial temperature dependence.