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Stable water isotopes recorded in Antarctic ice cores have traditionally been used to infer past surface air temperatures (SATs). During the historical period (1850 onward), observational data and good-quality ice core records overlap, yielding an opportunity to investigate key relationships between ice core stable water isotope (δ18O) measurements and the Antarctic climate. We present a new ensemble of climate model simulations covering 1851–2004 using the UK Met Office HadCM3 general circulation model equipped with stable water isotopes. Our ensemble captures observed historical SAT and precipitation trends and weak δ18O trends. The weak δ18O trends mean there is no significant relationship between SAT and δ18O over one-third of Antarctica, and also half of our considered ice core sites, though relationships are stronger when using regional averages. The strongest regional relationships occur in the West Antarctic Ice Sheet (WAIS) region. This decoupling between SAT and δ18O occurs primarily because of the impact of autumnal sea ice loss during the simulated warming. The warming and sea ice loss are associated with (i) changes in near-coastal air mass intrusions (synoptic effects) induced by changes in the large-scale circulation and/or sea ice; (ii) direct sea-ice-driven changes in moisture pathways (especially lengths) to Antarctica; and (iii) precipitation seasonality changes, again mostly driven by sea ice changes. Consequently, when reconstructing temperatures over these timescales, changes in sea ice need to be considered, both to determine the most appropriate SAT and δ18O relationship and to understand how uncertainties affect the inference of past temperature from ice core δ18O measurements.

Dansgaard–Oeschger (DO) events occurred throughout the last glacial period. Greenland ice cores show a rapid warming during each stadial to interstadial transition, alongside an abrupt loss of sea ice and a major reorganisation of the atmospheric circulation. Other records also indicate simultaneous abrupt changes to the oceanic circulation. Recently, an advanced Bayesian ramp-fitting method has been developed and used to investigate time lags between transitions in these different climate elements with a view to determining the relative order of these changes. Here, we critically review this method in both its original implementation and a new, extended implementation. Using ice core data, climate model output, and carefully synthesised data representing DO events, we demonstrate that both implementations of the method suffer from biases of up to 15 years. These biases mean that the method will tend to yield transition onsets that are too early. Further investigation of DO warming event records in climate models and ice core data reveals that the biases are on the same order of magnitude as potential timing differences between the abrupt transitions of different climate elements. Additionally, we find that higher-resolution records would not reduce these biases. We conclude that decadal-scale leads and lags between climate elements across DO events cannot be reliably detected, as we cannot exclude the possibility that they result solely from the biases we present here.

Improving our understanding of the controls on Antarctic precipitation is critical for gaining insights into past and future polar and global environmental changes. Here we develop innovative water tracing diagnostics in the atmospheric general circulation model ECHAM6. These tracers provide new detailed information on moisture source locations and properties of Antarctic precipitation. In the preindustrial simulation, annual mean Antarctic precipitation originating from the open ocean has a source latitude range of 49–35∘ S, a source sea surface temperature range of 9.8–16.3 ∘C, a source 2 m relative humidity range of 75.6 %–83.3 %, and a source 10 m wind velocity (vel10) range of 10.1 to 11.3 m s−1. These results are consistent with estimates from existing literature. Central Antarctic precipitation is sourced from more equatorward (distant) sources via elevated transport pathways compared to coastal Antarctic precipitation. This has been attributed to a moist isentropic framework; i.e. poleward vapour transport tends to follow constant equivalent potential temperature. However, we find notable deviations from this tendency especially in the lower troposphere, likely due to radiative cooling. Heavy precipitation is sourced by longer-range moisture transport: it comes from 2.9∘ (300 km, averaged over Antarctica) more equatorward (distant) sources compared to the rest of precipitation. Precipitation during negative phases of the Southern Annular Mode (SAM) also comes from more equatorward moisture sources (by 2.4∘, averaged over Antarctica) compared to precipitation during positive SAM phases, likely due to amplified planetary waves during negative SAM phases. Moreover, source vel10 of annual mean precipitation is on average 2.1 m s−1 higher than annual mean vel10 at moisture source locations from which the precipitation originates. This shows that the evaporation of moisture driving Antarctic precipitation occurs under windier conditions than average. We quantified this dynamic control of Southern Ocean surface wind on moisture availability for Antarctic precipitation. Overall, the innovative water tracing diagnostics enhance our understanding of the controlling factors of Antarctic precipitation.

Dansgaard–Oeschger (D–O) events, millennial-scale climate oscillations between stadial and interstadial conditions (of up to 10–15 ∘C in amplitude at high northern latitudes), occurred throughout the Marine Isotope Stage 3 (MIS3; 27.8–59.4 ka) period. The climate modelling community up to now has not been able to answer the question of whether our climate models are too stable to simulate D–O events. To address this, this paper lays the ground-work for a MIS3 D–O protocol for general circulation models which are used in the International Panel for Climate Change (IPCC) assessments. We review the following: D–O terminology, community progress on simulating D–O events in these IPCC-class models (processes and published examples), and evidence about the boundary conditions under which D–O events occur. We find that no model exhibits D–O-like behaviour under pre-industrial conditions. Some, but not all, models exhibit D–O-like oscillations under MIS3 and/or full glacial conditions. Greenhouse gases and ice sheet configurations are crucial. However most models have not run simulations of long enough duration to be sure which models show D–O-like behaviour, under either MIS3 or full glacial states. We propose a MIS3 baseline protocol at 34 ka, which features low obliquity values, medium to low MIS3 greenhouse gas values, and the intermediate ice sheet configuration, which our review suggests are most conducive to D–O-like behaviour in models. We also provide a protocol for a second freshwater (Heinrich-event-preconditioned) experiment, since previous work suggests that this variant may be helpful in preconditioning a state in models which is conducive to D–O events. This review provides modelling groups investigating MIS3 D–O oscillations with a common framework, which is aimed at (1) maximising the chance of the occurrence of D–O-like events in the simulations, (2) allowing more precise model–data evaluation, and (3) providing an adequate central point for modellers to explore model stability.

The Last Interglacial (LIG) period, which had higher summer solar insolation than today, has been suggested as the last time that Arctic summers were ice free. However, the latest suite of Coupled Modelling Intercomparison Project 6 Paleoclimate (CMIP6-PMIP4) simulations of the LIG produce a wide range of Arctic summer minimum sea ice area (SIA) results, with a 30 % to 96 % reduction from the pre-industrial (PI) period. Sea ice proxies are also currently neither abundant nor consistent enough to determine the most realistic state. Here we estimate LIG minimum SIA indirectly through the use of 21 proxy records for LIG summer surface air temperature (SSAT) and 11 CMIP6-PMIP4 models for the LIG. We use two approaches. First, we use two tests to determine how skilful models are at simulating reconstructed ΔSSAT from proxy records (where Δ refers to LIG-PI). This identifies a positive correlation between model skill and the magnitude of ΔSIA: the most reliable models simulate a larger sea ice reduction. Averaging the two most skilful models yields an average SIA of 1.3×106 km2 for the LIG. This equates to a 4.5×106 km2 or 79 % SIA reduction from the PI to the LIG. Second, across the 11 models, the averaged ΔSSAT at the 21 proxy locations and the pan-Arctic average ΔSSAT are inversely correlated with ΔSIA (r=-0.86 and −0.79, respectively). In other words, the models show that a larger Arctic warming is associated with a greater sea ice reduction. Using the proxy-record-averaged ΔSSAT of 4.5±1.7 K and the relationship between ΔSSAT and ΔSIA suggests an estimated sea ice reduction of 4.2±1.4×106 km2 or about 74 % less sea ice than the PI period. The mean proxy-location ΔSSAT is well correlated with the Arctic-wide ΔSSAT north of 60∘ N (r=0.97), and this relationship is used to show that the mean proxy record ΔSSAT is equivalent to an Arctic-wide warming of 3.7±1.5 K at the LIG compared to the PI period. Applying this Arctic-wide ΔSSAT and its modelled relationship to ΔSIA, results in a similar estimate of LIG sea ice reduction of 4.1±1.2×106 km2. These LIG climatological minimum SIA of 1.3 to 1.5×106 km2 are close to the definition of a summer ice-free Arctic, which is a maximum sea ice extent of less than 1×106 km2. The results of this study thus suggest that the Arctic likely experienced a mixture of ice-free and near-ice-free summers during the LIG.

The lig127k-H11 simulation of the Paleoclimate Modelling Intercomparison Project (PMIP4) is run using the HadGEM3-GC3.1 model. We focus on the coupled system response to the applied meltwater forcing. We show here that the coupling between the atmosphere and the ocean is altered in the hosing experiment compared to a Last Interglacial simulation with no meltwater forcing applied. Two aspects in particular of the atmosphere–ocean coupling are found to be affected: Northern Hemisphere (NH) gyre heat transport and Antarctic sea ice area. We apply 0.2 Sv of meltwater forcing across the North Atlantic during a 250-year-long simulation. We find that the strength of the Atlantic Meridional Overturning Circulation (AMOC) is reduced by 60 % after 150 years of meltwater forcing, with an associated decrease of 0.2 to 0.4 PW in meridional ocean heat transport at all latitudes. The changes in ocean heat transport affect surface temperatures. The largest increase in the meridional surface temperature gradient occurs between 40–50∘ N. This increase is associated with a strengthening of 20 % in 850 hPa winds. The jet stream intensification in the Northern Hemisphere in return alters the temperature structure of the ocean by increasing the gyre circulation at the mid-latitudes and the associated heat transport by +0.1–0.2 PW, and it decreases the gyre circulation at high latitudes with a decrease of ocean heat transport of −0.2 PW. The changes in meridional surface temperature and pressure gradients cause the Intertropical Convergence Zone (ITCZ) to move southward, leading to stronger westerlies and a more positive Southern Annual Mode (SAM) in the Southern Hemisphere (SH). The positive SAM influences sea ice formation, leading to an increase in Antarctic sea ice.

Antarctic sea ice plays a critical role in the Earth system, influencing energy, heat and freshwater fluxes, air–sea gas exchange, ice shelf dynamics, ocean circulation, nutrient cycling, marine productivity and global carbon cycling. However, accurate simulation of recent sea-ice changes remains challenging and, therefore, projecting future sea-ice changes and their influence on the global climate system is uncertain. Reconstructing past changes in sea-ice cover can provide additional insights into climate feedbacks within the Earth system at different timescales. This paper is the first of two review papers from the Cycles of Sea Ice Dynamics in the Earth system (C-SIDE) working group. In this first paper, we review marine- and ice core-based sea-ice proxies and reconstructions of sea-ice changes throughout the last glacial–interglacial cycle. Antarctic sea-ice reconstructions rely mainly on diatom fossil assemblages and highly branched isoprenoid (HBI) alkenes in marine sediments, supported by chemical proxies in Antarctic ice cores. Most reconstructions for the Last Glacial Maximum (LGM) suggest that winter sea ice expanded all around Antarctica and covered almost twice its modern surface extent. In contrast, LGM summer sea ice expanded mainly in the regions off the Weddell and Ross seas. The difference between winter and summer sea ice during the LGM led to a larger seasonal cycle than today. More recent efforts have focused on reconstructing Antarctic sea ice during warm periods, such as the Holocene and the Last Interglacial (LIG), which may serve as an analogue for the future. Notwithstanding regional heterogeneities, existing reconstructions suggest that sea-ice cover increased from the warm mid-Holocene to the colder Late Holocene with pervasive decadal- to millennial-scale variability throughout the Holocene. Studies, supported by proxy modelling experiments, suggest that sea-ice cover was halved during the warmer LIG when global average temperatures were ∼2 ∘C above the pre-industrial (PI). There are limited marine (14) and ice core (4) sea-ice proxy records covering the complete 130 000 year (130 ka) last glacial cycle. The glacial–interglacial pattern of sea-ice advance and retreat appears relatively similar in each basin of the Southern Ocean. Rapid retreat of sea ice occurred during Terminations II and I while the expansion of sea ice during the last glaciation appears more gradual especially in ice core data sets. Marine records suggest that the first prominent expansion occurred during Marine Isotope Stage (MIS) 4 and that sea ice reached maximum extent during MIS 2. We, however, note that additional sea-ice records and transient model simulations are required to better identify the underlying drivers and feedbacks of Antarctic sea-ice changes over the last 130 ka. This understanding is critical to improve future predictions.

Environmental conditions during Marine Isotope Stage (MIS) 5e (130–116 ka) represent an important “process analogue” for understanding the climatic responses to present and future anthropogenic warming. The response of Antarctic sea ice to global warming is particularly uncertain due to the short length of the observational record. Reconstructing Antarctic winter sea-ice extent during MIS 5e therefore provides insights into the temporal and spatial patterns of sea-ice change under a warmer-than-present climate. This study presents new MIS 5e records from nine marine sediment cores located south of the Antarctic Polar Front between 55 and 70∘ S. Winter sea-ice extent and sea-surface temperatures are reconstructed using marine diatom assemblages and a modern analogue technique transfer function, and changes in these environmental variables between the three Southern Ocean sectors are investigated. The Atlantic and East Indian sector records show much more variable MIS 5e winter sea-ice extent and sea-surface temperatures than the Pacific sector records. High variability in the Atlantic sector winter sea-ice extent is attributed to high glacial meltwater flux in the Weddell Sea, indicated by increased abundances of the diatom species Eucampia antarctica and Fragilariopsis cylindrus. The high variability in the East Indian sector winter sea-ice extent is conversely believed to result from large latitudinal migrations of the flow bands of the Antarctic Circumpolar Current, inferred from latitudinal shifts in the sea-surface temperature isotherms. Overall, these findings suggest that Pacific sector winter sea ice displays a low sensitivity to warmer climates. The different variability and sensitivity of Antarctic winter sea-ice extent in the three Southern Ocean sectors during MIS 5e may have significant implications for the Southern Hemisphere climatic system under future warming.

The Hadley Centre Global Environment Model version 3 (HadGEM3) is the first coupled climate model to simulate an ice-free Arctic during the Last Interglacial (LIG), 127 000 years ago. This simulation appears to yield accurate Arctic surface temperatures during the summer season. Here, we investigate the causes and impacts of this extreme simulated ice loss. We find that the summer ice melt was predominantly driven by thermodynamic processes: atmospheric and ocean circulation changes did not significantly contribute to the ice loss. We demonstrate these thermodynamic processes were significantly impacted by melt ponds, which formed on average 8 d earlier during the LIG than during the pre-industrial control (PI) simulation. This relatively small difference significantly changed the LIG surface energy balance and impacted the albedo feedback. Compared to the PI simulation: in mid-June, of the absorbed flux at the surface over ice-covered cells (sea-ice concentration > 0.15), ponds accounted for 45 %–50 %, open water 35 %–45 %, and bare ice and snow 5 %–10 %. We show that the simulated ice loss led to large Arctic sea surface salinity and temperature changes. The sea surface temperature and salinity signals we identify here provide a means to verify, in marine observations, if and when an ice-free Arctic occurred during the LIG. Strong LIG correlations between spring melt pond and summer ice area indicate that, as Arctic ice continues to thin in future, the spring melt pond area will likely become an increasingly reliable predictor of the September sea-ice area. Finally, we note that models with explicitly modelled melt ponds seem to simulate particularly low LIG sea-ice area. These results show that models with explicit (as opposed to parameterised) melt ponds can simulate very different sea-ice behaviour under forcings other than the present day. This is of concern for future projections of sea-ice loss.

The Last Interglacial period (LIG) is a period with increased summer insolation at high northern latitudes, which results in strong changes in the terrestrial and marine cryosphere. Understanding the mechanisms for this response via climate modelling and comparing the models' representation of climate reconstructions is one of the objectives set up by the Paleoclimate Modelling Intercomparison Project for its contribution to the sixth phase of the Coupled Model Intercomparison Project. Here we analyse the results from 16 climate models in terms of Arctic sea ice. The multi-model mean reduction in minimum sea ice area from the pre industrial period (PI) to the LIG reaches 50 % (multi-model mean LIG area is 3.20×106 km2, compared to 6.46×106 km2 for the PI). On the other hand, there is little change for the maximum sea ice area (which is 15–16×106 km2 for both the PI and the LIG. To evaluate the model results we synthesise LIG sea ice data from marine cores collected in the Arctic Ocean, Nordic Seas and northern North Atlantic. The reconstructions for the northern North Atlantic show year-round ice-free conditions, and most models yield results in agreement with these reconstructions. Model–data disagreement appear for the sites in the Nordic Seas close to Greenland and at the edge of the Arctic Ocean. The northernmost site with good chronology, for which a sea ice concentration larger than 75 % is reconstructed even in summer, discriminates those models which simulate too little sea ice. However, the remaining models appear to simulate too much sea ice over the two sites south of the northernmost one, for which the reconstructed sea ice cover is seasonal. Hence models either underestimate or overestimate sea ice cover for the LIG, and their bias does not appear to be related to their bias for the pre-industrial period. Drivers for the inter-model differences are different phasing of the up and down short-wave anomalies over the Arctic Ocean, which are associated with differences in model albedo; possible cloud property differences, in terms of optical depth; and LIG ocean circulation changes which occur for some, but not all, LIG simulations. Finally, we note that inter-comparisons between the LIG simulations and simulations for future climate with moderate (1 % yr−1) CO2 increase show a relationship between LIG sea ice and sea ice simulated under CO2 increase around the years of doubling CO2. The LIG may therefore yield insight into likely 21st century Arctic sea ice changes using these LIG simulations.

Changes in the Greenland ice sheet (GIS) affect global sea level. Greenland stable water isotope (δ18O) records from ice cores offer information on past changes in the surface of the GIS. Here, we use the isotope-enabled Hadley Centre Coupled Model version 3 (HadCM3) climate model to simulate a set of last interglacial (LIG) idealised GIS surface elevation change scenarios focusing on GIS ice core sites. We investigate how δ18O depends on the magnitude and sign of GIS elevation change and evaluate how the response is altered by sea ice changes. We find that modifying GIS elevation induces changes in Northern Hemisphere atmospheric circulation, sea ice and precipitation patterns. These climate feedbacks lead to ice-core-averaged isotopic lapse rates of 0.49 ‰ (100 m)−1 for the lowered GIS states and 0.29 ‰ (100 m)−1 for the enlarged GIS states. This is lower than the spatially derived Greenland lapse rates of 0.62–0.72 ‰ (100 m)−1. These results thus suggest non-linearities in the isotope–elevation relationship and have consequences for the interpretation of past elevation and climate changes across Greenland. In particular, our results suggest that winter sea ice changes may significantly influence isotope–elevation gradients: winter sea ice effect can decrease (increase) modelled core-averaged isotopic lapse rate values by about −19 % (and +28 %) for the lowered (enlarged) GIS states, respectively. The largest influence of sea ice on δ18O changes is found in coastal regions like the Camp Century site.

Palaeoclimate model simulations are an important tool to improve our understanding of the mechanisms of climate change. These simulations also provide tests of the ability of models to simulate climates very different to today. Here we present the results from two brand-new simulations using the latest version of the UK's physical climate model, HadGEM3-GC3.1; they are the mid-Holocene (∼6 ka) and Last Interglacial (∼127 ka) simulations, both conducted under the auspices of CMIP6/PMIP4. This is the first time this version of the UK model has been used to conduct palaeoclimate simulations. These periods are of particular interest to PMIP4 because they represent the two most recent warm periods in Earth history, where atmospheric concentration of greenhouse gases and continental configuration are similar to the pre-industrial period but where there were significant changes to the Earth's orbital configuration, resulting in a very different seasonal cycle of radiative forcing. Results for these simulations are assessed firstly against the same model's pre-industrial control simulation (a simulation comparison, to describe and understand the differences between the pre-industrial – PI – and the two palaeo simulations) and secondly against previous versions of the same model relative to newly available proxy data (a model–data comparison, to compare all available simulations from the same model with proxy data to assess any improvements due to model advances). The introduction of this newly available proxy data adds further novelty to this study. Globally, for metrics such as 1.5 m temperature and surface rainfall, whilst both the recent palaeoclimate simulations are mostly capturing the expected sign and, in some places, magnitude of change relative to the pre-industrial, this is geographically and seasonally dependent. Compared to newly available proxy data (including sea surface temperature – SST – and rainfall) and also incorporating data from previous versions of the model shows that the relative accuracy of the simulations appears to vary according to metric, proxy reconstruction used for comparison and geographical location. In some instances, such as mean rainfall in the mid-Holocene, there is a clear and linear improvement, relative to proxy data, from the oldest to the newest generation of the model. When zooming into northern Africa, a region known to be problematic for models in terms of rainfall enhancement, the behaviour of the West African monsoon in both recent palaeoclimate simulations is consistent with current understanding, suggesting a wetter monsoon during the mid-Holocene and (more so) the Last Interglacial, relative to the pre-industrial era. However, regarding the well-documented “Saharan greening” during the mid-Holocene, results here suggest that the most recent version of the UK's physical model is still unable to reproduce the increases suggested by proxy data, consistent with all other previous models to date.

When the same weather or climate simulation is run on different high-performance computing (HPC) platforms, model outputs may not be identical for a given initial condition. While the role of HPC platforms in delivering better climate projections is to some extent discussed in the literature, attention is mainly focused on scalability and performance rather than on the impact of machine-dependent processes on the numerical solution. Here we investigate the behaviour of the Preindustrial (PI) simulation prepared by the UK Met Office for the forthcoming CMIP6 (Coupled Model Intercomparison Project Phase 6) under different computing environments. Discrepancies between the means of key climate variables were analysed at different timescales, from decadal to centennial. We found that for the two simulations to be statistically indistinguishable, a 200-year averaging period must be used for the analysis of the results. Thus, constant-forcing climate simulations using the HadGEM3-GC3.1 model are reproducible on different HPC platforms provided that a sufficiently long duration of simulation is used. In regions where El Niño–Southern Oscillation (ENSO) teleconnection patterns were detected, we found large sea surface temperature and sea ice concentration differences on centennial timescales. This indicates that a 100-year constant-forcing climate simulation may not be long enough to adequately capture the internal variability of the HadGEM3-GC3.1 model, despite this being the minimum simulation length recommended by CMIP6 protocols for many MIP (Model Intercomparison Project) experiments. On the basis of our findings, we recommend a minimum simulation length of 200 years whenever possible.

Latitudinal shifts in the Southern Ocean westerly wind jet could drive changes in the glacial to interglacial ocean CO2 inventory. However, whilst CMIP5 model results feature consistent future-warming jet shifts, there is considerable disagreement in deglacial-warming jet shifts. We find here that the dependence of pre-industrial (PI) to Last Glacial Maximum (LGM) jet shifts on PI jet position, or state dependency, explains less of the shifts in jet simulated by the models for the LGM compared with future-warming scenarios. State dependence is also weaker for intensity changes, compared to latitudinal shifts in the jet. Winter sea ice was considerably more extensive during the LGM. Changes in surface heat fluxes, due to this sea ice change, probably had a large impact on the jet. Models that both simulate realistically large expansions in sea ice and feature PI jets which are south of 50° S show an increase in wind speed around 55° S and can show a poleward shift in the jet between the PI and the LGM. However, models with the PI jet positioned equatorwards of around 47° S do not show this response: the sea ice edge is too far from the jet for it to respond. In models with accurately positioned PI jets, a +1° difference in the latitude of the sea ice edge tends to be associated with a −0.85° shift in the 850 hPa jet. However, it seems that around 5° of expansion of LGM sea ice is necessary to hold the jet in its PI position. Since the Gersonde et al. (2005) data support an expansion of more than 5°, this result suggests that a slight poleward shift and intensification was the most likely jet change between the PI and the LGM. Without the effect of sea ice, models simulate poleward-shifted westerlies in warming climates and equatorward-shifted westerlies in colder climates. However, the feedback of sea ice counters and reverses the equatorward trend in cooler climates so that the LGM winds were more likely to have also been shifted slightly poleward.

We compare the present and last interglacial periods as recorded in Antarctic water stable isotope records now available at various temporal resolutions from six East Antarctic ice cores: Vostok, Taylor Dome, EPICA Dome C (EDC), EPICA Dronning Maud Land (EDML), Dome Fuji and the recent TALDICE ice core from Talos Dome. We first review the different modern site characteristics in terms of ice flow, meteorological conditions, precipitation intermittency and moisture origin, as depicted by meteorological data, atmospheric reanalyses and Lagrangian moisture source diagnostics. These different factors can indeed alter the relationships between temperature and water stable isotopes. Using five records with sufficient resolution on the EDC3 age scale, common features are quantified through principal component analyses. Consistent with instrumental records and atmospheric model results, the ice core data depict rather coherent and homogenous patterns in East Antarctica during the last two interglacials. Across the East Antarctic plateau, regional differences, with respect to the common East Antarctic signal, appear to have similar patterns during the current and last interglacials. We identify two abrupt shifts in isotopic records during the glacial inception at TALDICE and EDML, likely caused by regional sea ice expansion. These regional differences are discussed in terms of moisture origin and in terms of past changes in local elevation histories, which are compared to ice sheet model results. Our results suggest that elevation changes may contribute significantly to inter-site differences. These elevation changes may be underestimated by current ice sheet models.