Search

Three recently published papers including Napier et al. (2022, https://doi.org/10.1029/2021PA004355) utilize novel microanalytical approaches with varved marine sediments to demonstrate the potential to reconstruct seasonal and inter-annual climate variability. Obtaining paleoclimate data at a resolution akin to the observational record is vitally important for improving our understanding of climate phenomena such as monsoons and modes of variability such as the El Niño Southern Oscillation, for which appraisals of past inter-annual variability is critical. The ability to generate seasonal and inter annual resolution sea surface temperature proxy time series spanning a thousand years or more is revolutionary and has the potential to fill gaps in our knowledge of climate variability. Although generally limited to sediments from regions with oxygen depleted bottom waters, there is great potential to integrate shorter seasonal resolution climate “snap shots” from other archives such as annually banded corals into composite time series. But as paleoceanographic data are used more by the observational and modeling fields, we make the case for conducting a thorough case-by-case assessment of the processes that influence the climate signal recovered from proxies, using careful replication to validate new approaches. Understanding or exploring the potential influence of processes which effectively filter the climate signal will lead to more quantitative paleoceanographic data that will better serve the broader climate science community.

Key Points: - Novel micro-analytical techniques allow seasonally resolved climate proxy data from varved marine sediments - Potential to generate seasonal and inter annual resolution sea surface temperature proxy time series spanning >1,000 years - Thorough assessment of processes that influence the climate signal recovered from proxies, validated with careful replication, is required Three recently published papers including Napier et al. (2022, https://doi.org/10.1029/2021PA004355) utilize novel microanalytical approaches with varved marine sediments to demonstrate the potential to reconstruct seasonal and inter-annual climate variability. Obtaining paleoclimate data at a resolution akin to the observational record is vitally important for improving our understanding of climate phenomena such as monsoons and modes of variability such as the El Niño Southern Oscillation, for which appraisals of past inter-annual variability is critical. The ability to generate seasonal and inter annual resolution sea surface temperature proxy time series spanning a thousand years or more is revolutionary and has the potential to fill gaps in our knowledge of climate variability. Although generally limited to sediments from regions with oxygen depleted bottom waters, there is great potential to integrate shorter seasonal resolution climate “snap shots” from other archives such as annually banded corals into composite time series. But as paleoceanographic data are used more by the observational and modeling fields, we make the case for conducting a thorough case-by-case assessment of the processes that influence the climate signal recovered from proxies, using careful replication to validate new approaches. Understanding or exploring the potential influence of processes which effectively filter the climate signal will lead to more quantitative paleoceanographic data that will better serve the broader climate science community.

We present a chronology framework named LegacyAge 1.0 containing harmonized chronologies for 2831 pollen records (downloaded from the Neotoma Paleoecology Database and the supplementary Asian datasets) together with their age control points and metadata in machine-readable data formats. All chronologies use the Bayesian framework implemented in Bacon version 2.5.3. Optimal parameter settings of priors (accumulation.shape, memory.strength, memory.mean, accumulation.rate, and thickness) were identified based on information in the original publication or iteratively after preliminary model inspection. The most common control points for the chronologies are radiocarbon dates (86.1ĝ€¯%), calibrated by the latest calibration curves (IntCal20 and SHCal20 for the terrestrial radiocarbon dates in the Northern Hemisphere and Southern Hemisphere and Marine20 for marine materials). The original publications were consulted when dealing with outliers and inconsistencies. Several major challenges when setting up the chronologies included the waterline issue (18.8ĝ€¯% of records), reservoir effect (4.9ĝ€¯%), and sediment deposition discontinuity (4.4ĝ€¯%). Finally, we numerically compare the LegacyAge 1.0 chronologies to those published in the original publications and show that the reliability of the chronologies of 95.4ĝ€¯% of records could be improved according to our assessment. Our chronology framework and revised chronologies provide the opportunity to make use of the ages and age uncertainties in synthesis studies of, for example, pollen-based vegetation and climate change. The LegacyAge 1.0 dataset, including metadata, datings, harmonized chronologies, and R code used, is open-access and available at PANGAEA (10.1594/PANGAEA.933132; Li et al., 2021) and Zenodo (10.5281/zenodo.5815192; Li et al., 2022), respectively.

The East Australian Current (EAC) is the western boundary current of the South Pacific Subtropical Gyre that transports warm tropical waters to higher southern latitudes and significantly impacts the climate of Australia and New Zealand. Modern observations show that the EAC has strengthened with rising global temperatures. However, little is known about the pre-industrial variability of the EAC and the forcing mechanisms. Planktic foraminifera Globigerinoides ruber (white) Mg/Ca-based sea surface temperature reconstructions offshore northeastern Australia between 15° and 26°S reveal an increase by ∼1.2°C after ∼1400 CE. We infer that the increase in temperature is related to a stronger EAC heat transport that is likely driven by a strengthening of the Southern Hemisphere subtropical gyre circulation due to a progressive shift of the Southern annular mode toward its positive phase and of El Niño-Southern Oscillation toward more El Niño-like conditions.

Marine sedimentary archives are routinely used to reconstruct past environmental changes. In many cases, bioturbation and sedimentary mixing affect the proxy time-series and the age-depth relationship. While idealized models of bioturbation exist, they usually assume homogeneous mixing, thus that a single sample is representative for the sediment layer it is sampled from. However, it is largely unknown to which extent this assumption holds for sediments used for paleoclimate reconstructions. To shed light on 1) the age-depth relationship and its full uncertainty, 2) the magnitude of mixing processes affecting the downcore proxy variations, and 3) the representativity of the discrete sample for the sediment layer, we designed and performed a case study on South China Sea sediment material which was collected using a box corer and which covers the last glacial cycle. Using the radiocarbon content of foraminiferal tests as a tracer of time, we characterize the spatial age-heterogeneity of sediments in a three-dimensional setup. In total, 118 radiocarbon measurements were performed on defined small- and large-volume bulk samples ( ∼ 200 specimens each) to investigate the horizontal heterogeneity of the sediment. Additionally, replicated measurements on small numbers of specimens (10 × 5 specimens) were performed to assess the heterogeneity within a sample volume. Visual assessment of X-ray images and a quantitative assessment of the mixing strength show typical mixing from bioturbation corresponding to around 10 cm mixing depth. Notably, our 3D radiocarbon distribution reveals that the horizontal heterogeneity (up to 1,250 years), contributing to the age uncertainty, is several times larger than the typically assumed radiocarbon based age-model error (single errors up to 250 years). Furthermore, the assumption of a perfectly bioturbated layer with no mixing underneath is not met. Our analysis further demonstrates that the age-heterogeneity might be a function of sample

Although numerous pollen records are available worldwide in various databases, their use for synthesis works is limited as the chronologies are, as yet, not harmonized globally, and temporal uncertainties are unknown. We present a chronology framework named LegacyAge 1.0 that includes harmonized chronologies of 2831 palynological records (out of 3471 available records), downloaded from the Neotoma Paleoecology Database (last access: April 2021) and 324 additional Asian records. All chronologies use the Bayesian framework implemented in Bacon version 2.5.3. Optimal parameter settings of priors (accumulation.shape, memory.strength, memory.mean, accumulation.rate, thickness) were identified based on previous experiences or iteratively after preliminary model inspection. The most common control points for the chronologies are radiocarbon dates (86.1 %), calibrated by the latest calibration curves (IntCal20 and SHcal20 for the terrestrial radiocarbon dates in the northern and southern hemispheres; Marine20 for marine materials). The original literature was consulted when dealing with obvious outliers and inconsistencies. Several major challenges when setting up the chronologies included the waterline issue (18.8 % of records), reservoir effect (4.9 %), and sediment deposition discontinuity (4.4 %). Finally, we numerically compare the LegacyAge 1.0 chronologies to the original ones and show that the chronologies of 95.4 % of records could be improved according to our assessment. Our chronology framework and revised chronologies provide the opportunity to make use of the ages and age uncertainties in synthesis studies of, for example, pollen-based vegetation and climate change. The LegacyAge 1.0 dataset and R code used are open-access and available at PANGAEA (https://doi.pangaea.de/10.1594/PANGAEA.933132) and Github (https://github.com/LongtermEcology/LegacyAge-1.0), respectively.

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 to providing realistic error estimates for smoothed or stacked records, detection of 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 II, 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.

Marine sedimentary records are a key archive when reconstructing past climate; however, mixing at the seabed (bioturbation) can strongly influence climate records, especially when sedimentation rates are low. By commingling the climate signal from different time periods, bioturbation both smooths climate records, by damping fast climate variations, and creates noise when measurements are made on samples containing small numbers of individual proxy carriers, such as foraminifera. Bioturbation also influences radiocarbon-based age-depth models, as sample ages may not represent the true ages of the sediment layers from which they were picked. While these effects were first described several decades ago, the advent of ultra-small-sample $^{14}$C dating now allows samples containing very small numbers of foraminifera to be measured, thus enabling us to directly measure the age-heterogeneity of sediment for the first time. Here, we use radiocarbon dates measured on replicated samples of 3-30 foraminifera to estimate age-heterogeneity for five marine sediment cores with sedimentation rates ranging from 2-30 cm kyr$^{-1}$. From their age-heterogeneities and sedimentation rates we infer mixing depths of 10-20 cm for our core sites. Our results show that when accounting for age-heterogeneity, the true error of radiocarbon dating can be several times larger than the reported measurement. We present estimates of this uncertainty as a function of sedimentation rate and the number of individuals per radiocarbon date. A better understanding of this uncertainty will help us to optimise radiocarbon measurements, construct age models with appropriate uncertainties and better interpret marine paleo records.

Marine sedimentary records are a key archive when reconstructing past climate; however, mixing at the seabed (bioturbation) can strongly influence climate records, especially when sedimentation rates are low. By commingling the climate signal from different time periods, bioturbation both smooths climate records, by damping fast climate variations, and creates noise when measurements are made on samples containing small numbers of individual proxy carriers, such as foraminifera. Bioturbation also influences radiocarbon-based age-depth models, as sample ages may not represent the true ages of the sediment layers from which they were picked. While these effects were first described several decades ago, the advent of ultra-small-sample $^{14}$C dating now allows samples containing very small numbers of foraminifera to be measured, thus enabling us to directly measure the age-heterogeneity of sediment for the first time. Here, we use radiocarbon dates measured on replicated samples of 3-30 foraminifera to estimate age-heterogeneity for five marine sediment cores with sedimentation rates ranging from 2-30 cm kyr$^{-1}$. From their age-heterogeneities and sedimentation rates we infer mixing depths of 10-20 cm for our core sites. Our results show that when accounting for age-heterogeneity, the true error of radiocarbon dating can be several times larger than the reported measurement. We present estimates of this uncertainty as a function of sedimentation rate and the number of individuals per radiocarbon date. A better understanding of this uncertainty will help us to optimise radiocarbon measurements, construct age models with appropriate uncertainties and better interpret marine paleo records.

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.

Compound-specific radiocarbon (C-14) dating often requires working with small samples of < 100 mu g carbon (mu gC). This makes the radiocarbon dates of biomarker compounds very sensitive to biases caused by extraneous carbon of unknown composition, a procedural blank, which is introduced to the samples during the steps necessary to prepare a sample for radiocarbon analysis by accelerator mass spectrometry (i.e., isolating single compounds from a heterogeneous mixture, combustion, gas purification and graphitization). Reporting accurate radiocarbon dates thus requires a correction for the procedural blank. We present our approach to assess the fraction modern carbon ((FC)-C-14) and the mass of the procedural blanks introduced during the preparation procedures of lipid biomarkers (i.e. n-alkanoic acids) and lignin phenols. We isolated differently sized aliquots (6-151 mu gC) of n-alkanoic acids and lignin phenols obtained from standard materials with known (FC)-C-14 values. Each compound class was extracted from two standard materials (one fossil, one modern) and purified using the same procedures as for natural samples of unknown (FC)-C-14. There is an inverse linear relationship between the measured (FC)-C-14 values of the processed aliquots and their mass, which suggests constant contamination during processing of individual samples. We use Bayesian methods to fit linear regression lines between (FC)-C-14 and 1/mass for the fossil and modern standards. The intersection points of these lines are used to infer (FCblank)-C-14 and m(blank) and their associated uncertainties. We estimate 4.88 +/- 0.69 mu gC of procedural blank with (FC)-C-14 of 0.714 +/- 0.077 for n-alkanoic acids, and 0.90 +/- 0.23 mu gC of procedural blank with (FC)-C-14 of 0.813 +/- 0.155 for lignin phenols. These (FCblank)-C-14 and m(blank)( )can be used to correct AMS results of lipid and lignin samples by isotopic mass balance. This method may serve as a standardized procedure for blank assessment

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 to providing realistic error estimates for smoothed or stacked records, detection of anomalies and identifying trend, 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 II, 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.

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 is 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 as well as 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, 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.

Here we show how the frequency scaling of a climate reconstruction from sediment proxy records is affected by various sources of uncertainty. Specifically, analytic expressions are derived and illustrated for the power spectral density of a climate reconstruction, based on a simple model that takes into account: the spectral structure of the true climate, uneven recording throughout the year, precession-like orbital modulations of the seasonal cycle, bioturbation, sampling of a finite number of signal carriers from discontinuous slices of sediment material, uncorrelated measurement noise; and that includes the effects of spectral aliasing and leakage. The basic behaviour and the properties of the model are demonstrated, and the implications for the interpretation of climate reconstructions are discussed.

Due to mixing processes, sediment samples taken from a single depth can contain particles (e.g. foraminifera) with a wide range of ages. When radiocarbon measurements are made on samples containing many of these individual particles, the resulting values can hide a lot of internal age variation. Furthermore, if the measured samples contain a material from only a small number of individual particles, the resulting 14C ages will be noisy estimates of the true mean age of material from that depth. Similarly, for proxies such as Mg/Ca, or d18O, the range of ages contained in a single sample results in measurements that represent average values for an extended time period. Again, these values will be noisy if the number of particles per sample is small, but even with large samples, the resulting proxy records are “smoothed out” and the reconstructed amplitude of climate transitions is reduced. The advent of ultra-small-sample 14C dating means that samples consisting of very small numbers of foraminiferal shells now can be dated. This poses both a problem, as individual 14C ages will be less representative of their layer, but also an opportunity as it allows for a direct estimate of the heterogeneity in the age of material at a given depth. We used 14C measurements on samples of 3-30 foraminifera to estimate the underlying standard deviation in the age of individuals picked from the same depth. We repeated this for cores with sedimentation rates ranging from 3-30 cm/kyr and found age-variances consistent with simple sediment mixing models and typical bioturbation depths. These direct estimates of age-variance allow for more realistic estimates of age uncertainty and have already proven useful to use in reconciling apparently inconsistent age-depth profiles from adjacent sediment cores. They also allow for a better-informed interpretation of proxy records, both in terms of the relative timing of events and in terms of the amount of amplitude reduction of the climate signal

The statistical properties of climate variability are often reconstructed and interpreted from single proxy records. However, variation in the proxy record is influenced by both climate and non-climate factors, and these must be understood for climate inferences to be reliable.