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Accurate estimates of water isotope diffusion lengths are crucial when reconstructing and interpreting water isotope records from ice cores. This is especially true in the deepest, oldest sections of deep ice cores, where thermally enhanced diffusive processes have acted over millennia on extremely thinned ice. Previous statistical estimation methods, used with great success in shallower, younger ice cores, falter when applied to these deep sections, as they fail to account for the statistics of the climate on millennial timescales. Here, we present a new method to estimate the diffusion length from water isotope data and apply it to the Marine Isotope Stage 19 (MIS 19) interglacial at the bottom of the EPICA Dome C (EDC, Dome Concordia) ice core. In contrast to the conventional estimator, our method uses other interglacial periods taken from further up in the ice core to estimate the structure of the variability before diffusion. Through use of a Bayesian framework, we are able to constrain our fit while propagating the uncertainty in our assumptions. We estimate a diffusion length of 31±5 cm for the MIS 19 period, which is significantly smaller than previously estimated (40–60 cm). Similar results were obtained for each interglacial used to represent the undiffused climate signal, demonstrating the robustness of our estimate. Our result suggests better preservation of the climate signal at the bottom of EDC and likely other deep ice cores, offering greater potentially recoverable temporal resolution and improved reconstructions through deconvolution.

Stable water isotopes stored in snow, firn and ice are used to reconstruct climatic parameters. The imprint of these parameters at the snow surface and their preservation in the upper snowpack are determined by a number of processes influencing the recording of the environmental signal. Here, we present a dataset of approximately 3800 snow samples analysed for their stable water isotope composition, which were obtained during the summer season next to the deep drilling site of the East Greenland Ice Core Project in northeast Greenland (75.635411° N, 36.000250° W). Sampling was carried out every third day between 14 May and 3 August 2018 along a 39 m long transect. Three depth intervals in the top 10 cm were sampled at 30 positions with a higher resolution closer to the surface (0–1 and 1–4 cm depth vs. 4–10 cm). The sample analysis was carried out at two renowned stable water isotope laboratories that produced isotope data with the overall highest uncertainty of 0.09 ‰ for δ18O and 0.8 ‰ for δD. This unique dataset shows the strongest δ18O variability closest to the surface, damped and delayed variations in the lowest layer, and a trend towards increasing homogeneity towards the end of the season, especially in the deepest layer. Additional information on the snow height and its temporal changes suggests a non-uniform spatial imprint of the seasonal climatic information in this area, potentially following the stratigraphic noise of the surface. The data can be used to study the relation between snow height (changes) and the imprint and preservation of the isotopic composition at a site with 10–14 cm w.e. yr−1 accumulation. The high-temporal-resolution sampling allows additional analyses on (post-)depositional processes, such as vapour–snow exchange. The data can be accessed at https://doi.org/10.1594/PANGAEA.956626 (Zuhr et al., 2023a).

Abstract Tree rings are the most widely‐used proxy records for reconstructing Common Era temperatures. Tree‐ring records correlate strongly with temperature on an interannual basis, but studies have found discrepancies between tree rings and climate models on longer timescales, indicating that low‐frequency noise could be prevalent in these archives. Using a large network of temperature‐sensitive tree‐ring records, we partition timeseries variance into a common (i.e., “signal”) and non‐climatic (i.e., “noise”) component using a frequency‐resolved signal‐to‐noise ratio (SNR) analysis. We find that the availability of stored resources from prior years (i.e., biological “memory”) dampens the climate signal at high‐frequencies, and that independent noise reduces the SNR on long timescales. We also find that well‐replicated, millennial‐length records had the strongest common signal across centuries. Our work suggests that low‐frequency noise models are appropriate for use in pseudoproxy experiments, and speaks to the continued value of high‐quality data development as a top priority in dendroclimatology.

Stable water isotopologues of snow, firn and ice cores provide valuable information on past climate variations. Yet single profiles are generally not suitable for robust climate reconstructions. Stratigraphic noise, introduced by the irregular deposition, wind-driven erosion and redistribution of snow, impacts the utility of high-resolution isotope records, especially in low-accumulation areas. However, it is currently unknown how stratigraphic noise differs across the East Antarctic Plateau and how it is affected by local environmental conditions. Here, we assess the amount and structure of stratigraphic noise at seven sites along a 120 km transect on the plateau of Dronning Maud Land, East Antarctica. Replicated oxygen isotope records of 1 m length were used to estimate signal-to-noise ratios as a measure of stratigraphic noise at sites characterised by different accumulation rates (43–64 mm w.e. a−1), snow surface roughnesses and slope inclinations. While we found a high level of stratigraphic noise at all sites, there was also considerable variation between sites. At sastrugi-dominated sites, greater stratigraphic noise coincided with stronger surface roughnesses, steeper slopes and lower accumulation rates, probably related to increased wind speeds. These results provide a first step to modelling stratigraphic noise and might guide site selection and sampling strategies for future expeditions to improve high-resolution climate reconstructions from low-accumulation regions.

The EPICA Dome C (EDC) ice core provides the longest continuous climatic record, covering the last 800 000 years (800 kyr). A unique opportunity to investigate decadal to millennial variability during past glacial and interglacial periods is provided by the high-resolution water isotopic record (δ18O and δD) available for the EDC ice core. We present here a continuous compilation of the EDC water isotopic record at a sample resolution of 11 cm, which consists of 27 000 δ18O measurements and 7920 δD measurements (covering, respectively, 94 % and 27 % of the whole EDC record), including published and new measurements (2900 for both δ18O and δD) for the last 800 kyr. Here, we demonstrate that repeated water isotope measurements of the same EDC samples from different depth intervals obtained using different analytical methods are comparable within analytical uncertainty. We thus combine all available EDC water isotope measurements to generate a high-resolution (11 cm) dataset for the past 800 kyr. A frequency decomposition of the most complete δ18O record and a simple assessment of the possible influence of diffusion on the measured profile shows that the variability at the multi-decadal to multi-centennial timescale is higher during glacial than during interglacial periods and higher during early interglacial isotopic maxima than during the Holocene. This analysis shows as well that during interglacial periods characterized by a temperature optimum at the beginning, the multi-centennial variability is strongest over this temperature optimum.

Ice cores from polar ice sheets and glaciers are an important climate archive. Snow layers, consecutively deposited and buried, contain climatic information from the time of their formation. However, particularly low-accumulation areas are characterised by temporally intermittent precipitation, which can be further redistributed after initial deposition, depending on the local surface features at different spatial scales. Therefore, the accumulation conditions at an ice core site influence the quantity and quality of the recorded climate signal in proxy records. This study aims to characterise the local accumulation patterns and the evolution of the snow height to describe the contribution of the snow (re-)deposition to the overall noise level in climate records from ice cores. To this end, we applied a structure-from-motion photogrammetry approach to generate near-daily elevation models of the surface snow for a 195 m2 area in the vicinity of the deep drilling site of the East Greenland Ice-core Project in northeast Greenland. Based on the snow height information we derive snow height changes on a day-to-day basis throughout our observation period from May to August 2018 and find an average snow height increase of ∼ 11 cm. The spatial and temporal data set also allows an investigation of snow deposition versus depositional modifications. We observe irregular snow deposition and erosion causing uneven snow accumulation patterns, a removal of more than 60 % of the deposited snow, and a negative relationship between the initial snow height and the amount of accumulated snow. Furthermore, the surface roughness decreased by approximately a factor of 2 throughout the spring and summer season at our study site. Finally, our study shows that structure from motion is a relatively simple method to demonstrate the potential influences of depositional processes on proxy signals in snow and ice.

Characterizing the variability across timescales is important for understanding the underlying dynamics of the Earth system. It remains challenging to do so from palaeoclimate archives since they are more often than not irregular, and traditional methods for producing timescale-dependent estimates of variability, such as the classical periodogram and the multitaper spectrum, generally require regular time sampling. We have compared those traditional methods using interpolation with interpolation-free methods, namely the Lomb–Scargle periodogram and the first-order Haar structure function. The ability of those methods to produce timescale-dependent estimates of variability when applied to irregular data was evaluated in a comparative framework, using surrogate palaeo-proxy data generated with realistic sampling. The metric we chose to compare them is the scaling exponent, i.e. the linear slope in log-transformed coordinates, since it summarizes the behaviour of the variability across timescales. We found that, for scaling estimates in irregular time series, the interpolation-free methods are to be preferred over the methods requiring interpolation as they allow for the utilization of the information from shorter timescales which are particularly affected by the irregularity. In addition, our results suggest that the Haar structure function is the safer choice of interpolation-free method since the Lomb–Scargle periodogram is unreliable when the underlying process generating the time series is not stationary. Given that we cannot know a priori what kind of scaling behaviour is contained in a palaeoclimate time series, and that it is also possible that this changes as a function of timescale, it is a desirable characteristic for the method to handle both stationary and non-stationary cases alike.

Many palaeoclimate proxies share one challenging property: they are not only driven by the climatic variable of interest, e.g. temperature, but they are also influenced by secondary effects which cause, among other things, increased variability, frequently termed noise. Noise in individual proxy records can be reduced by averaging the records, but the effectiveness of this approach depends on the correlation of the noise between the records and therefore on the spatial scales of the noise-generating processes. Here, we review and apply this concept in the context of Antarctic ice-core isotope records to determine which core locations are best suited to reconstruct local- to regional-scale temperatures. Using data from a past-millennium climate model simulation equipped with stable isotope diagnostics we intriguingly find that even for a local temperature reconstruction the optimal sampling strategy is to combine a local ice core with a more distant core ∼ 500–1000 km away. A similarly large distance between cores is also optimal for reconstructions that average more than two isotope records. We show that these findings result from the interplay of the two spatial scales of the correlation structures associated with the temperature field and with the noise generated by precipitation intermittency. Our study helps to maximize the usability of existing Antarctic ice cores and to optimally plan future drilling campaigns. It also broadens our knowledge of the processes that shape the isotopic record and their typical correlation scales. Finally, many palaeoclimate reconstruction efforts face the similar challenge of spatially correlated noise, and our presented method could directly assist further studies in also determining optimal sampling strategies for these problems.

Proxy climate records are an invaluable source of information about the earth's climate prior to the instrumental record. The temporal and spatial coverage of records continues to increase; however, these records of past climate are associated with significant uncertainties due to non-climate processes that influence the recorded and measured proxy values. Generally, these uncertainties are timescale dependent and correlated in time. Accounting for structure in the errors is essential for providing realistic error estimates for smoothed or stacked records, detecting anomalies, and identifying trends, but this structure is seldom accounted for. In the first of these companion articles, we outlined a theoretical framework for handling proxy uncertainties by deriving the power spectrum of proxy error components from which it is possible to obtain timescale-dependent error estimates. Here in Part 2, we demonstrate the practical application of this theoretical framework using the example of marine sediment cores. We consider how to obtain estimates for the required parameters and give examples of the application of this approach for typical marine sediment proxy records. Our new approach of estimating and providing timescale-dependent proxy errors overcomes the limitations of simplistic single-value error estimates. We aim to provide the conceptual basis for a more quantitative use of paleo-records for applications such as model–data comparison, regional and global synthesis of past climate states, and data assimilation.

The isotopic signal (δ18O and δD) imprinted in ice cores from Antarctica is not solely generated by the temperature sensitivity of the isotopic composition of precipitation, but it also contains the signature of the intermittency of the precipitation patterns, as well as of post-deposition processes occurring at the surface and in the firn. This leads to a proxy signal recorded by the ice cores that may not be representative of the local climate variations. Due to precipitation intermittency, the ice cores only record brief snapshots of the climatic conditions, resulting in aliasing of the climatic signal and thus a large amount of noise which reduces the minimum temporal resolution at which a meaningful signal can be retrieved. The analyses are further complicated by isotopic diffusion, which acts as a low-pass filter that dampens any high-frequency changes. Here, we use reanalysis data (ERA-Interim) combined with satellite products of accumulation to evaluate the spatial distribution of the numerical estimates of the transfer function that describes the formation of the isotopic signal across Antarctica. As a result, the minimum timescales at which the signal-to-noise ratio exceeds unity range from less than 1 year at the coast to about 1000 years further inland. Based on solely physical processes, we are thus able to define a lower bound for the timescales at which climate variability can be reconstructed from the isotopic composition in ice cores.

Proxy records represent an invaluable source of information for reconstructing past climatic variations, but they are associated with considerable uncertainties. For a systematic quantification of these reconstruction errors, however, knowledge is required not only of their individual sources but also of their auto-correlation structure as this determines the timescale dependence of their magnitude, an issue that has been often ignored until now. Here a spectral approach to uncertainty analysis is provided for paleoclimate reconstructions obtained from single sediment proxy records. The formulation in the spectral domain rather than the time domain allows for an explicit demonstration and quantification of the timescale dependence that is inherent in any proxy-based reconstruction uncertainty. This study is published in two parts. In this first part, the theoretical concept is presented, and analytic expressions are derived for the power spectral density of the reconstruction error of sediment proxy records. The underlying model takes into account the spectral structure of the climate signal, seasonal and orbital variations, bioturbation, sampling of a finite number of signal carriers, and uncorrelated measurement noise, and it includes the effects of spectral aliasing and leakage. The uncertainty estimation method, based upon this model, is illustrated by simple examples. In the second part of this study, published separately, the method is implemented in an application-oriented context, and more detailed examples are presented.

Proxy records from climate archives provide evidence about past climate changes, but the recorded signal is affected by non-climate-related effects as well as time uncertainty. As proxy-based climate reconstructions are frequently used to test climate models and to quantitatively infer past climate, we need to improve our understanding of the proxy record signal content as well as the uncertainties involved. In this study, we empirically estimate signal-to-noise ratios (SNRs) of temperature proxy records used in global compilations of the middle to late Holocene (last 6000 years). This is achieved through a comparison of the correlation of proxy time series from nearby sites of three compilations and model time series extracted at the proxy sites from two transient climate model simulations: a Holocene simulation of the ECHAM5/MPI-OM model and the Holocene part of the TraCE-21ka simulation. In all comparisons, we found the mean correlations of the proxy time series on centennial to millennial timescales to be low (R), even for nearby sites, which resulted in low SNR estimates. The estimated SNRs depend on the assumed time uncertainty of the proxy records, the timescale analysed, and the model simulation used. Using the spatial correlation structure of the ECHAM5/MPI-OM simulation, the estimated SNRs on centennial timescales ranged from 0.05 – assuming no time uncertainty – to 0.5 for a time uncertainty of 400 years. On millennial timescales, the estimated SNRs were generally higher. Use of the TraCE-21ka correlation structure generally resulted in lower SNR estimates than for ECHAM5/MPI-OM. As the number of available high-resolution proxy records continues to grow, a more detailed analysis of the signal content of specific proxy types should become feasible in the near future. The estimated low signal content of Holocene temperature compilations should caution against over-interpretation of these multi-proxy and multisite syntheses until further studies are able to facilitate a better characterisation of the signal content in paleoclimate records.

Ice-core-based records of isotopic composition are a proxy for past temperatures and can thus provide information on polar climate variability over a large range of timescales. However, individual isotope records are affected by a multitude of processes that may mask the true temperature variability. The relative magnitude of climate and non-climate contributions is expected to vary as a function of timescale, and thus it is crucial to determine those temporal scales on which the actual signal dominates the noise. At present, there are no reliable estimates of this timescale dependence of the signal-to-noise ratio (SNR). Here, we present a simple method that applies spectral analyses to stable-isotope data from multiple cores to estimate the SNR, and the signal and noise variability, as a function of timescale. The method builds on separating the contributions from a common signal and from local variations and includes a correction for the effects of diffusion and time uncertainty. We apply our approach to firn-core arrays from Dronning Maud Land (DML) in East Antarctica and from the West Antarctic Ice Sheet (WAIS). For DML and decadal to multi-centennial timescales, we find an increase in the SNR by nearly 1 order of magnitude ( ∼ 0.2 at decadal and ∼ 1.0 at multi-centennial scales). The estimated spectrum of climate variability also shows increasing variability towards longer timescales, contrary to what is traditionally inferred from single records in this region. In contrast, the inferred variability spectrum for WAIS stays close to constant over decadal to centennial timescales, and the results even suggest a decrease in SNR over this range of timescales. We speculate that these differences between DML and WAIS are related to differences in the spatial and temporal scales of the isotope signal, highlighting the potentially more homogeneous atmospheric conditions on the Antarctic Plateau in contrast to the marine-influenced conditions on WAIS. In general, our approach provides a methodological basis for separating local proxy variability from coherent climate variations, which is applicable to a large set of palaeoclimate records.

Climate reconstructions based on proxy records recovered from marine sediments, such as alkenone records or geochemical parameters measured on foraminifera, play an important role in our understanding of the climate system. They provide information about the state of the ocean ranging back hundreds to millions of years and form the backbone of paleo-oceanography.However, there are many sources of uncertainty associated with the signal recovered from sediment-archived proxies. These include seasonal or depth-habitat biases in the recorded signal; a frequency-dependent reduction in the amplitude of the recorded signal due to bioturbation of the sediment; aliasing of high-frequency climate variation onto a nominally annual, decadal, or centennial resolution signal; and additional sample processing and measurement error introduced when the proxy signal is recovered.Here we present a forward model for sediment-archived proxies that jointly models the above processes so that the magnitude of their separate and combined effects can be investigated. Applications include the interpretation and analysis of uncertainty in existing proxy records, parameter sensitivity analysis to optimize future studies, and the generation of pseudo-proxy records that can be used to test reconstruction methods. We provide examples, such as the simulation of individual foraminifera records, that demonstrate the usefulness of the forward model for paleoclimate studies. The model is implemented as an open-source R package, sedproxy, to which we welcome collaborative contributions. We hope that use of sedproxy will contribute to a better understanding of both the limitations and potential of marine sediment proxies to inform researchers about earth's past climate.

The oldest ice core records are obtained from the East Antarctic Plateau. Water isotopes are key proxies to reconstructing past climatic conditions over the ice sheet and at the evaporation source. The accuracy of climate reconstructions depends on knowledge of all processes affecting water vapour, precipitation and snow isotopic compositions. Fractionation processes are well understood and can be integrated in trajectory-based Rayleigh distillation and isotope-enabled climate models. However, a quantitative understanding of processes potentially altering snow isotopic composition after deposition is still missing. In low-accumulation sites, such as those found in East Antarctica, these poorly constrained processes are likely to play a significant role and limit the interpretability of an ice core's isotopic composition.By combining observations of isotopic composition in vapour, precipitation, surface snow and buried snow from Dome C, a deep ice core site on the East Antarctic Plateau, we found indications of a seasonal impact of metamorphism on the surface snow isotopic signal when compared to the initial precipitation. Particularly in summer, exchanges of water molecules between vapour and snow are driven by the diurnal sublimation–condensation cycles. Overall, we observe in between precipitation events modification of the surface snow isotopic composition. Using high-resolution water isotopic composition profiles from snow pits at five Antarctic sites with different accumulation rates, we identified common patterns which cannot be attributed to the seasonal variability of precipitation. These differences in the precipitation, surface snow and buried snow isotopic composition provide evidence of post-deposition processes affecting ice core records in low-accumulation areas.

Stable isotope ratios δ18O and δD in polar ice provide a wealth of information about past climate evolution. Snow-pit studies allow us to relate observed weather and climate conditions to the measured isotope variations in the snow. They therefore offer the possibility to test our understanding of how isotope signals are formed and stored in firn and ice. As δ18O and δD in the snowfall are strongly correlated to air temperature, isotopes in the near-surface snow are thought to record the seasonal cycle at a given site. Accordingly, the number of seasonal cycles observed over a given depth should depend on the accumulation rate of snow. However, snow-pit studies from different accumulation conditions in East Antarctica reported similar isotopic variability and comparable apparent cycles in the δ18O and δD profiles with typical wavelengths of ∼ 20 cm. These observations are unexpected as the accumulation rates strongly differ between the sites, ranging from 20 to 80 mm w. e. yr−1 ( ∼ 6–21 cm of snow per year). Various mechanisms have been proposed to explain the isotopic variations individually at each site; however, none of these are consistent with the similarity of the different profiles independent of the local accumulation conditions.Here, we systematically analyse the properties and origins of δ18O and δD variations in high-resolution firn profiles from eight East Antarctic sites. First, we confirm the suggested cycle length (mean distance between peaks) of ∼ 20 cm by counting the isotopic maxima. Spectral analysis further shows a strong similarity between the sites but indicates no dominant periodic features. Furthermore, the apparent cycle length increases with depth for most East Antarctic sites, which is inconsistent with burial and compression of a regular seasonal cycle. We show that these results can be explained by isotopic diffusion acting on a noise-dominated isotope signal. The firn diffusion length is rather stable across the Antarctic Plateau and thus leads to similar power spectral densities of the isotopic variations. This in turn implies a similar distance between isotopic maxima in the firn profiles.Our results explain a large set of observations discussed in the literature, providing a simple explanation for the interpretation of apparent cycles in shallow isotope records, without invoking complex mechanisms. Finally, the results underline previous suggestions that isotope signals in single ice cores from low-accumulation regions have a small signal-to-noise ratio and thus likely do not allow the reconstruction of interannual to decadal climate variations.

The isotopic composition of water in ice sheets is extensively used to infer past climate changes. In low-accumulation regions their interpretation is, however, challenged by poorly constrained effects that may influence the initial isotope signal during and after deposition of the snow. This is reflected in snow-pit isotope data from Kohnen Station, Antarctica, which exhibit a seasonal cycle but also strong interannual variations that contradict local temperature observations. These inconsistencies persist even after averaging many profiles and are thus not explained by local stratigraphic noise. Previous studies have suggested that post-depositional processes may significantly influence the isotopic composition of East Antarctic firn. Here, we investigate the importance of post-depositional processes within the open-porous firn (≳ 10 cm depth) at Kohnen Station by separating spatial from temporal variability. To this end, we analyse 22 isotope profiles obtained from two snow trenches and examine the temporal isotope modifications by comparing the new data with published trench data extracted 2 years earlier. The initial isotope profiles undergo changes over time due to downward advection, firn diffusion and densification in magnitudes consistent with independent estimates. Beyond that, we find further modifications of the original isotope record to be unlikely or small in magnitude (≪ 1 ‰ RMSD). These results show that the discrepancy between local temperatures and isotopes most likely originates from spatially coherent processes prior to or during deposition, such as precipitation intermittency or systematic isotope modifications acting on drifting or loose surface snow.

Reconstructions of summer, winter or annual mean temperatures based on the species composition of bio-indicators such as pollen, foraminifera or chironomids are routinely used in climate model–proxy data comparison studies. Most reconstruction algorithms exploit the joint distribution of modern spatial climate and species distribution for the development of the reconstructions. They rely on the space-for-time substitution and the specific assumption that environmental variables other than those reconstructed are not important or that their relationship with the reconstructed variable(s) should be the same in the past as in the modern spatial calibration dataset. Here we test the implications of this “correlative uniformitarianism” assumption on climate reconstructions in an ideal model world, in which climate and vegetation are known at all times. The alternate reality is a climate simulation of the last 6000 years with dynamic vegetation. Transient changes of plant functional types are considered as surrogate pollen counts and allow us to establish, apply and evaluate transfer functions in the modeled world. We find that in our model experiments the transfer function cross validation r2 is of limited use to identify reconstructible climate variables, as it only relies on the modern spatial climate–vegetation relationship. However, ordination approaches that assess the amount of fossil vegetation variance explained by the reconstructions are promising. We furthermore show that correlations between climate variables in the modern climate–vegetation relationship are systematically extended into the reconstructions. Summer temperatures, the most prominent driving variable for modeled vegetation change in the Northern Hemisphere, are accurately reconstructed. However, the amplitude of the model winter and mean annual temperature cooling between the mid-Holocene and present day is overestimated and similar to the summer trend in magnitude. This effect occurs because temporal changes of a dominant climate variable, such as summer temperatures in the model's Arctic, are imprinted on a less important variable, leading to reconstructions biased towards the dominant variable's trends. Our results, although based on a model vegetation that is inevitably simpler than reality, indicate that reconstructions of multiple climate variables based on modern spatial bio-indicator datasets should be treated with caution. Expert knowledge on the ecophysiological drivers of the proxies, as well as statistical methods that go beyond the cross validation on modern calibration datasets, are crucial to avoid misinterpretation.

Along a traverse through North Greenland in May 2015 we collected snow cores up to 2 m depth and analyzed their density and water isotopic composition. A new sampling technique and an adapted algorithm for comparing data sets from different sites and aligning stratigraphic features are presented. We find good agreement of the density layering in the snowpack over hundreds of kilometers, which allows the construction of a representative density profile. The results are supported by an empirical statistical density model, which is used to generate sets of random profiles and validate the applied methods. Furthermore we are able to calculate annual accumulation rates, align melt layers and observe isotopic temperatures in the area back to 2010. Distinct relations of δ18O with both accumulation rate and density are deduced. Inter alia the depths of the 2012 melt layers and high-resolution densities are provided for applications in remote sensing.

In low-accumulation regions, the reliability of δ18O-derived temperature signals from ice cores within the Holocene is unclear, primarily due to the small climate changes relative to the intrinsic noise of the isotopic signal. In order to learn about the representativity of single ice cores and to optimise future ice-core-based climate reconstructions, we studied the stable-water isotope composition of firn at Kohnen Station, Dronning Maud Land, Antarctica. Analysing δ18O in two 50 m long snow trenches allowed us to create an unprecedented, two-dimensional image characterising the isotopic variations from the centimetre to the 100-metre scale. Our results show seasonal layering of the isotopic composition but also high horizontal isotopic variability caused by local stratigraphic noise. Based on the horizontal and vertical structure of the isotopic variations, we derive a statistical noise model which successfully explains the trench data. The model further allows one to determine an upper bound for the reliability of climate reconstructions conducted in our study region at seasonal to annual resolution, depending on the number and the spacing of the cores taken.

Particulate organic matter (POM) derived from permafrost soils and transported by the Lena River represents a quantitatively important terrestrial carbon pool exported to Laptev Sea sediments (next to POM derived from coastal erosion). Its fate in a future warming Arctic, i.e., its remobilization and remineralization after permafrost thawing as well as its transport pathways to and sequestration in marine sediments, is currently under debate. We present one of the first radiocarbon (14C) data sets for surface water POM within the Lena Delta sampled in the summers of 2009–2010 and spring 2011 (n = 30 samples). The bulk Δ14C values varied from −55 to −391 ‰ translating into 14C ages of 395 to 3920 years BP. We further estimated the fraction of soil-derived POM to our samples based on (1) particulate organic carbon to particulate nitrogen ratios (POC : PN) and (2) on the stable carbon isotope (δ13C) composition of our samples. Assuming that this phytoplankton POM has a modern 14C concentration, we inferred the 14C concentrations of the soil-derived POM fractions. The results ranged from −322 to −884 ‰ (i.e., 3060 to 17 250 14C years BP) for the POC : PN-based scenario and from −261 to −944 ‰ (i.e., 2370 to 23 100 14C years BP) for the δ13C-based scenario. Despite the limitations of our approach, the estimated Δ14C values of the soil-derived POM fractions seem to reflect the heterogeneous 14C concentrations of the Lena River catchment soils covering a range from Holocene to Pleistocene ages better than the bulk POM Δ14C values. We further used a dual-carbon-isotope three-end-member mixing model to distinguish between POM contributions from Holocene soils and Pleistocene Ice Complex (IC) deposits to our soil-derived POM fraction. IC contributions are comparatively low (mean of 0.14) compared to Holocene soils (mean of 0.32) and riverine phytoplankton (mean of 0.55), which could be explained with the restricted spatial distribution of IC deposits within the Lena catchment. Based on our newly calculated soil-derived POM Δ14C values, we propose an isotopic range for the riverine soil-derived POM end member with Δ14C of −495 ± 153 ‰ deduced from our δ13C-based binary mixing model and δ13C of −26.6 ± 1 ‰ deduced from our data of Lena Delta soils and literature values. These estimates can help to improve the dual-carbon-isotope simulations used to quantify contributions from riverine soil POM, Pleistocene IC POM from coastal erosion, and marine POM in Siberian shelf sediments.

A stratigraphy-based chronology for the North Greenland Eemian Ice Drilling (NEEM) ice core has been derived by transferring the annual layer counted Greenland Ice Core Chronology 2005 (GICC05) and its model extension (GICC05modelext) from the NGRIP core to the NEEM core using 787 match points of mainly volcanic origin identified in the electrical conductivity measurement (ECM) and dielectrical profiling (DEP) records. Tephra horizons found in both the NEEM and NGRIP ice cores are used to test the matching based on ECM and DEP and provide five additional horizons used for the timescale transfer. A thinning function reflecting the accumulated strain along the core has been determined using a Dansgaard–Johnsen flow model and an isotope-dependent accumulation rate parameterization. Flow parameters are determined from Monte Carlo analysis constrained by the observed depth-age horizons. In order to construct a chronology for the gas phase, the ice age–gas age difference (Δage) has been reconstructed using a coupled firn densification-heat diffusion model. Temperature and accumulation inputs to the Δage model, initially derived from the water isotope proxies, have been adjusted to optimize the fit to timing constraints from δ15N of nitrogen and high-resolution methane data during the abrupt onset of Greenland interstadials. The ice and gas chronologies and the corresponding thinning function represent the first chronology for the NEEM core, named GICC05modelext-NEEM-1. Based on both the flow and firn modelling results, the accumulation history for the NEEM site has been reconstructed. Together, the timescale and accumulation reconstruction provide the necessary basis for further analysis of the records from NEEM.

We compare the ocean temperature evolution of the Holocene as simulated by climate models and reconstructed from marine temperature proxies. We use transient simulations from a coupled atmosphere–ocean general circulation model, as well as an ensemble of time slice simulations from the Paleoclimate Modelling Intercomparison Project. The general pattern of sea surface temperature (SST) in the models shows a high-latitude cooling and a low-latitude warming. The proxy dataset comprises a global compilation of marine alkenone- and Mg/Ca-derived SST estimates. Independently of the choice of the climate model, we observe significant mismatches between modelled and estimated SST amplitudes in the trends for the last 6000 yr. Alkenone-based SST records show a similar pattern as the simulated annual mean SSTs, but the simulated SST trends underestimate the alkenone-based SST trends by a factor of two to five. For Mg/Ca, no significant relationship between model simulations and proxy reconstructions can be detected. We test if such discrepancies can be caused by too simplistic interpretations of the proxy data. We explore whether consideration of different growing seasons and depth habitats of the planktonic organisms used for temperature reconstruction could lead to a better agreement of model results with proxy data on a regional scale. The extent to which temporal shifts in growing season or vertical shifts in depth habitat can reduce model–data misfits is determined. We find that invoking shifts in the living season and habitat depth can remove some of the model–data discrepancies in SST trends. Regardless whether such adjustments in the environmental parameters during the Holocene are realistic, they indicate that when modelled temperature trends are set up to allow drastic shifts in the ecological behaviour of planktonic organisms, they do not capture the full range of reconstructed SST trends. Results indicate that modelled and reconstructed temperature trends are to a large degree only qualitatively comparable, thus providing a challenge for the interpretation of proxy data as well as the model sensitivity to orbital forcing.

The Agulhas Current transport of heat and salt from the Indian Ocean into the South Atlantic around South Africa (Agulhas leakage), can affect the Atlantic meridional overturning circulation (AMOC) and, thus, influence global climate. However, efforts to elucidate forcing mechanisms connecting the Agulhas leakage with the upstream dynamics of the current have been hampered by a lack of climate records extracted from the area where the Agulhas current originates. We determine 800-kyr sea surface temperature (SST) and salinity (SSS) records from the "precursor" region of the Agulhas current and show that these records contain strong 100-kyr and 41-kyr cycles. This latter obliquity-driven cycle is nearly in phase with changes in the annual mean insolation and air temperature at high southern latitudes. In contrast, our SST and SSS records did not reveal precession-driven cycles, which is surprising given the low-latitude location of the upstream Agulhas current. Together, this indicates that the dynamics of the Agulhas current system is mainly controlled by high latitude obliquity through its influence on the position of the Southern Hemisphere subtropical front (STF) and its associated westerlies. Our study demonstrates that obliquity may drive an important part of the 100 kyr cycles observed in the system rather than precession. Our results also suggest that a stronger Agulhas current, associated with a northward shift of the wind system during glacial periods, leads to reduced leakage, in accordance with the theory. We argue that during terminations, stronger Agulhas leakage of heat and salt was triggered by increased obliquity exerting a positive feedback on the global climate system through modulating long-term AMOC variations.

In this study, we analyse the climatic impacts on the grape harvest date (GHD) in Burgundy (France) on interannual and decadal time scales. We affirm that the GHD is mainly influenced by the local April-to-August temperature (AAT) and provide the spatial expansion of this relationship. The spatial correlation pattern yields similar results for the instrumental and pre-instrumental period, indicating the consistency of the pre-instrumental field data with the instrumental GHD-spring/summer relationship. We find a previously undocumented second climate impact on the GHD. The winter temperature is significantly correlated with the GHD on decadal-to-multidecadal time scales and affects the GHD independently of the AAT. A multiple linear regression model, with AAT and decadal winter temperature as predictors, was found to be the best model to describe the GHD time series for the instrumental period. Stability tests of the correlations over time yield that both impacts on the GHD, AAT and decadal winter temperature, strengthen during the instrumental period. Using partial correlation analysis, we demonstrate that this is partly caused by a change in the winter–spring/summer temperature relationship. Summarising, the GHD is well suited to reconstruct interannual variations of the spring/summer temperature over large parts of Europe, even if the changing winter–spring/summer relation might affect the reconstruction in a second order. For decadal time scales, the December-to-August temperature shows the strongest relationship to the GHD and, therefore, proposes that the GHD can be used for European temperature reconstructions beyond the spring/summer season. Finally, we argue that our findings regarding the changed winter–spring/summer relation are relevant for physical and biological systems in several ways and should be analysed by other long-term proxy data and available model simulations.

The Model for Ozone and Related chemical Tracers, version 4 (MOZART-4) is an offline global chemical transport model particularly suited for studies of the troposphere. The updates of the model from its previous version MOZART-2 are described, including an expansion of the chemical mechanism to include more detailed hydrocarbon chemistry and bulk aerosols. Online calculations of a number of processes, such as dry deposition, emissions of isoprene and monoterpenes and photolysis frequencies, are now included. Results from an eight-year simulation (2000–2007) are presented and evaluated. The MOZART-4 source code and standard input files are available for download from the NCAR Community Data Portal (http://cdp.ucar.edu).

This paper presents a procedure for performing and optimizing inversions for DOAS tomography and its application to measurement data. DOAS tomography is a new technique to determine 2- and 3-dimensional concentration fields of air pollutants or other trace gases by combining differential optical absorption spectroscopy (DOAS) with tomographic inversion techniques. Due to the limited amount of measured data, the resulting concentration fields are sensitive to the inversion process. Therefore detailed error estimations are needed to determine the quality of the reconstruction. In this paper we compare different row acting methods for the inversion, present a procedure for optimizing the parameters of the reconstruction process and propose a way to estimate the error-fields by numerical studies. The procedure was applied to data from the motorway emission campaign BAB II. Two dimensional NO2 cross sections at right angles to the motorway could be reconstructed qualitatively well at different meteorological situations. Additionally we present error fields for the reconstructions which show the problems and skills of the used measurement setup. Numerical studies on an improved setup for future motorway campaigns show, that DOAS tomography is able to produce high quality concentration maps.

Global climatic changes expected in the next centuries are likely to cause unparalleled vegetation disturbances, which in turn impact ecosystem services. To assess the significance of disturbances, it is necessary to characterize and understand typical natural vegetation variability on multi-decadal timescales and longer. We investigate this in the Holocene vegetation by examining a taxonomically harmonized and temporally standardized global fossil pollen dataset. Using principal component analysis, we characterize the variability in pollen assemblages, which are a proxy for vegetation composition, and derive timescale-dependent estimates of variability using the first-order Haar structure function. We find, on average, increasing fluctuations in vegetation composition from centennial to millennial timescales, as well as spatially coherent patterns of variability. We further relate these variations to pairwise comparisons between biome classes based on vegetation composition. As such, higher variability is identified for open-land vegetation compared to forests. This is consistent with the more active fire regimes of open-land biomes fostering variability. Needleleaf forests are more variable than broadleaf forests on shorter (centennial) timescales, but the inverse is true on longer (millennial) timescales. This inversion could also be explained by the fire characteristics of the biomes as fire disturbances would increase vegetation variability on shorter timescales, but stabilize vegetation composition on longer timecales by preventing the migration of less fire-adapted species., Competing Interests: The authors declare no competing interests., (© 2023 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.)

Proxy-based reconstructions and modeling of Holocene spatiotemporal precipitation patterns for China and Mongolia have hitherto yielded contradictory results indicating that the basic mechanisms behind the East Asian Summer Monsoon and its interaction with the westerly jet stream remain poorly understood. We present quantitative reconstructions of Holocene precipitation derived from 101 fossil pollen records and analyse them with the help of a minimal empirical model. We show that the westerly jet-stream axis shifted gradually southward and became less tilted since the middle Holocene. This was tracked by the summer monsoon rain band resulting in an early-Holocene precipitation maximum over most of western China, a mid-Holocene maximum in north-central and northeastern China, and a late-Holocene maximum in southeastern China. Our results suggest that a correct simulation of the orientation and position of the westerly jet stream is crucial to the reliable prediction of precipitation patterns in China and Mongolia.

Foraminifera are commonly used in paleoclimate reconstructions as they occur throughout the world's oceans and are often abundantly preserved in the sediments. Traditionally, foraminifera-based proxies like δ 18 O and Mg/Ca are analyzed on pooled specimens of a single species. Analysis of single specimens of foraminifera allows reconstructing climate variability on timescales related to El Niño-Southern Oscillation or seasonality. However, quantitative calibrations between the statistics of individual foraminifera analyses (IFA) and climate variability are still missing. We performed Mg/Ca and δ 18 O measurements on single specimens from core top sediments from different settings to better understand the signal recorded by individual foraminifera. We used three species of planktic foraminifera (Globigerinoidesruber (s.s.), T . sacculifer , and N . dutertrei ) from the Indo-Pacific Warm Pool and one species (G. ruber (pink)) from the Gulf of Mexico. Mean values for the different species of Mg/Ca versus calculated δ 18 O temperatures agree with published calibration equations. IFA statistics (both mean and standard deviation) of Mg/Ca and δ 18 O between the different sites show a strong relationship indicating that both proxies are influenced by a common factor, most likely temperature variations during calcification. This strongly supports the use of IFA to reconstruct climate variability. However, our combined IFA data for the different species only show a weak relationship to seasonal and interannual temperature changes, especially when seasonal variability increases at a location. This suggests that the season and depth habitat of the foraminifera strongly affect IFA variability, such that ecology needs to be considered when reconstructing past climate variability.

This corrects the article DOI: 10.1038/nature25454.

Changes in climate variability are as important for society to address as are changes in mean climate. Contrasting temperature variability during the Last Glacial Maximum and the Holocene can provide insights into the relationship between the mean state of the climate and its variability. However, although glacial-interglacial changes in variability have been quantified for Greenland, a global view remains elusive. Here we use a network of marine and terrestrial temperature proxies to show that temperature variability decreased globally by a factor of four as the climate warmed by 3-8 degrees Celsius from the Last Glacial Maximum (around 21,000 years ago) to the Holocene epoch (the past 11,500 years). This decrease had a clear zonal pattern, with little change in the tropics (by a factor of only 1.6-2.8) and greater change in the mid-latitudes of both hemispheres (by a factor of 3.3-14). By contrast, Greenland ice-core records show a reduction in temperature variability by a factor of 73, suggesting influences beyond local temperature or a decoupling of atmospheric and global surface temperature variability for Greenland. The overall pattern of reduced variability can be explained by changes in the meridional temperature gradient, a mechanism that points to further decreases in temperature variability in a warmer future.

Broad-scale climate control of vegetation is widely assumed. Vegetation-climate lags are generally thought to have lasted no more than a few centuries. Here our palaeoecological study challenges this concept over glacial-interglacial timescales. Through multivariate analyses of pollen assemblages from Lake El'gygytgyn, Russian Far East and other data we show that interglacial vegetation during the Plio-Pleistocene transition mainly reflects conditions of the preceding glacial instead of contemporary interglacial climate. Vegetation-climate disequilibrium may persist for several millennia, related to the combined effects of permafrost persistence, distant glacial refugia and fire. In contrast, no effects from the preceding interglacial on glacial vegetation are detected. We propose that disequilibrium was stronger during the Plio-Pleistocene transition than during the Mid-Pliocene Warm Period when, in addition to climate, herbivory was important. By analogy to the past, we suggest today's widespread larch ecosystem on permafrost is not in climate equilibrium. Vegetation-based reconstructions of interglacial climates used to assess atmospheric CO2-temperature relationships may thus yield misleading simulations of past global climate sensitivity.

The variability of sea surface temperatures (SSTs) at multidecadal and longer timescales is poorly constrained, primarily because instrumental records are short and proxy records are noisy. Through applying a new noise filtering technique to a global network of late Holocene SST proxies, we estimate SST variability between annual and millennial timescales. Filtered estimates of SST variability obtained from coral, foraminifer, and alkenone records are shown to be consistent with one another and with instrumental records in the frequency bands at which they overlap. General circulation models, however, simulate SST variability that is systematically smaller than instrumental and proxy-based estimates. Discrepancies in variability are largest at low latitudes and increase with timescale, reaching two orders of magnitude for tropical variability at millennial timescales. This result implies major deficiencies in observational estimates or model simulations, or both, and has implications for the attribution of past variations and prediction of future change.

The Milankovitch theory states that global climate variability on orbital timescales from tens to hundreds of thousands of years is dominated by the summer insolation at high northern latitudes. The supporting evidence includes reconstructed air temperatures in Antarctica that are nearly in phase with boreal summer insolation and out of phase with local summer insolation. Antarctic climate is therefore thought to be driven by northern summer insolation. A clear mechanism that links the two hemispheres on orbital timescales is, however, missing. We propose that key Antarctic temperature records derived from ice cores are biased towards austral winter because of a seasonal cycle in snow accumulation. Using present-day estimates of this bias in the 'recorder' system, here we show that the local insolation can explain the orbital component of the temperature record without having to invoke a link to the Northern Hemisphere. Therefore, the Antarctic ice-core-derived temperature record, one of the best-dated records of the late Pleistocene temperature evolution, cannot be used to support or contradict the Milankovitch hypothesis that global climate changes are driven by Northern Hemisphere summer insolation variations.