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We present an age model for the 651 m deep Skytrain Ice Rise ice core (79°44.5'S, 78°32.7'W). The top 2000 years have previously been dated using age markers interpolated through annual layer counting. Below this, we align the Skytrain core to the AICC2012 age model using tie points in the ice and air phase, and apply the Paleochrono program to obtain the best fit to the tie points and glaciological constraints. In the gas phase, ties are made using methane and, in critical sections, δ18Oair; in the ice phase ties are through 10Be across the Laschamps Event, and through ice chemistry related to long-range dust transport and deposition. This strategy provides a good outcome to about 108 ka (~605 m). Beyond that there are signs of flow disturbance, with a section of ice probably repeated. Nonetheless values of CH4 and δ18Oair confirm that part of the last interglacial (LIG), from about 117-126 ka (617-628 m), is present and in chronological order. Below this there are clear signs of stratigraphic disturbance, with rapid oscillation of values in both the ice and gas phase at the base of the LIG section. Based on methane values, the warmest part of the LIG and the coldest part of the penultimate glacial are missing from our record. Ice below 631 m appears to be of age >150 ka.

There is a growing need for past weather and climate data to support science and decision-making. This paper describes the compilation and the construction of a global multivariable (air temperature, pressure, precipitation sum, number of precipitation days) monthly instrumental climate database that encompasses a substantial body of the known early instrumental time series. The dataset contains series compiled from existing databases that start before 1890 (though continuing to the present) as well as a large amount of newly rescued data. All series underwent a quality control procedure and subdaily series were processed to monthly mean values. An inventory was compiled, and the collection was deduplicated based on coordinates and mutual correlations. The data are provided in a common format accompanied by the inventory. The collection totals 12452 meteorological records in 118 countries. The data has been merged from 18250 original data files. The data can be used for climate reconstructions and analyses. It is the most comprehensive global monthly climate data set for the preindustrial period.

This MATLAB code is intended to help end-users who wish to work with the sea-level rise projections of Kopp et al. (2014) and successor papers in greater detail than provided by the supplementary tables accompanying that table but without re-running the full global analysis using the supplementary code accompanying the paper. Key functionality these routines provide include: 1. Local sea-level rise projections at decadal time points and arbitrary quantiles 2. Localized Monte Carlo samples, disaggregatable by contributory process 3. Localized variance decomposition plots This code was originally developed to accompany the results of R. E. Kopp, R. M. Horton, C. M. Little, J. X. Mitrovica, M. Oppenheimer, D. J. Rasmussen, B. H. Strauss, and C. Tebaldi (2014). Probabilistic 21st and 22nd century sea-level projections at a global network of tide gauge sites. Earth's Future 2: 287–306, doi:10.1002/2014EF000239. Please cite that paper, along with the paper(s) associated with the specific projections you are using, when using any sea-level rise projections generated with this code.

This MATLAB code is intended to help end-users who wish to work with the sea-level rise projections of Kopp et al. (2014) and successor papers in greater detail than provided by the supplementary tables accompanying that table but without re-running the full global analysis using the supplementary code accompanying the paper. Key functionality these routines provide include: 1. Local sea-level rise projections at decadal time points and arbitrary quantiles 2. Localized Monte Carlo samples, disaggregatable by contributory process 3. Localized variance decomposition plots This code was originally developed to accompany the results of R. E. Kopp, R. M. Horton, C. M. Little, J. X. Mitrovica, M. Oppenheimer, D. J. Rasmussen, B. H. Strauss, and C. Tebaldi (2014). Probabilistic 21st and 22nd century sea-level projections at a global network of tide gauge sites. Earth's Future 2: 287–306, doi:10.1002/2014EF000239. Please cite that paper, along with the paper(s) associated with the specific projections you are using, when using any sea-level rise projections generated with this code.

This study presents simulations of Greenland surface melt for the Eemian interglacial period (∼130 000 to 115 000 years ago) derived from regional climate simulations with a coupled surface energy balance model. Surface melt is of high relevance due to its potential effect on ice core observations, e.g., lowering the preserved total air content (TAC) used to infer past surface elevation. An investigation of surface melt is particularly interesting for warm periods with high surface melt, such as the Eemian interglacial period. Furthermore, Eemian ice is the deepest and most compressed ice preserved on Greenland, resulting in our inability to identify melt layers visually. Therefore, simulating Eemian melt rates and associated melt layers is beneficial to improve the reconstruction of past surface elevation. Estimated TAC, based on simulated melt during the Eemian, could explain the lower TAC observations. The simulations show Eemian surface melt at all deep Greenland ice core locations and an average of up to ∼30 melt days per year at Dye-3, corresponding to more than 600 mm water equivalent (w.e.) of annual melt. For higher ice sheet locations, between 60 and 150 mmw.e.yr-1 on average are simulated. At the summit of Greenland, this yields a refreezing ratio of more than 25 % of the annual accumulation. As a consequence, high melt rates during warm periods should be considered when interpreting Greenland TAC fluctuations as surface elevation changes. In addition to estimating the influence of melt on past TAC in ice cores, the simulated surface melt could potentially be used to identify coring locations where Greenland ice is best preserved.

High resolution aerosol data from Greenland NGRIP and NEEM ice cores. All data was measured using continuous flow analysis with the Bern CFA system during the respective field campaigns. A detailed description of the measurement procedures can be found in Röthlisberger et al. (2000) and Kaufmann et al. (2008) and are summarised in the accompanying ESSD publication (Erhardt et al 2022). Data is provided at 1mm depth resolution and 10yr averages on the GICC05 age scale of the respective core (Andersen et al, 2006; Rasmussen et al., 2006; Svensson et al., 2008; Wolff et al., 2010; Rasmussen et al., 2013). For a detailed description of the uncertainties in the presented data refer to the accompanying ESSD publication. If you use this data please cite Röthlisberger et al. (2000), Kaufmann et al. (2008) and Erhardt et al. (2022).

This dataset captures the yearlong evolution of physical properties of the snow cover over Arctic sea ice during the MOSAiC expedition (October 2019-September 2020). It also includes the surface scattering layer that is typical of the melting summer sea ice surface. This dataset is specifically for measurements that were logged as “snowpit events” during MOSAiC. The snowpit events were either detailed point-measurements of vertical snow profiles or horizontally repeated transects, measured at selected locations in designated undisturbed areas. One snowpit event corresponds to one site visit. The snowpits are often co-located with measurements from other MOSAiC teams to improve our understanding of how snow cover affects and interacts with the atmosphere-sea ice-ocean-ecology system. Most snowpits were measured at least bi-weekly to capture the temporal evolution of physical properties of snow. Some snowpits were one-off events to capture interesting and unplanned-for surface conditions. This dataset includes 576 snowpit events, and describes the snow conditions during the entire expedition. Please direct inquiries to; David Wagner (PS122/1), Martin Schneebeli (PS122/2), Amy Macfarlane (PS122/3 and PS122/4), Ruzica Dadic (PS122/5).

As part of the Model Intercomparison Project on the climatic response to Volcanic forcing (VolMIP), several climate modeling centers performed a coordinated pre-study experiment with interactive stratospheric aerosol models simulating the volcanic aerosol cloud from an eruption resembling the 1815 Mt. Tambora eruption (VolMIP-Tambora ISA ensemble). The pre-study provided the ancillary ability to assess intermodel diversity in the radiative forcing for a large stratospheric-injecting equatorial eruption when the volcanic aerosol cloud is simulated interactively. An initial analysis of the VolMIP-Tambora ISA ensemble showed large disparities between models in the stratospheric global mean aerosol optical depth (AOD). In this study, we now show that stratospheric global mean AOD differences among the participating models are primarily due to differences in aerosol size, which we track here by effective radius. We identify specific physical and chemical processes that are missing in some models and/or parameterized differently between models, which are together causing the differences in effective radius. In particular, our analysis indicates that interactively tracking hydroxyl radical (OH) chemistry following a large volcanic injection of sulfur dioxide (SO2) is an important factor in allowing for the timescale for sulfate formation to be properly simulated. In addition, depending on the timescale of sulfate formation, there can be a large difference in effective radius and subsequently AOD that results from whether the SO2 is injected in a single model grid cell near the location of the volcanic eruption, or whether it is injected as a longitudinally averaged band around the Earth.