Newest Research Highlights

Illustrating the build-up of a climatic signal in the upper snowpack

The article, published in JGR Earth Surface, presents the process of the formation of stable water isotope signals in surface snow on the Greenland Ice Sheet. The study covers a 2-month period during the summer of 2019. High-resolution information on surface topography was derived from near-daily digital elevation models and illustrates the timing and location of snowfall, erosion, and redeposition along a 40 m transect. Additional stable water isotope records were sampled at 30 positions on a 40 m long transect and 30 cm deep in the same area. The δ¹⁸O data show spatial variability of layers with low and high values, presumably winter and summer layers. We further observe that prevailing surface structures, such as dunes, influence the snow deposition and contribute to the variable structure of the climatic information. Eventually, snow accumulation alone cannot explain all the observed patterns in the isotopic data which is likely related to exchange processes between the snow and the atmosphere which modify the signal in the snow column after deposition.

A two-dimensional view on the proxy signal δ¹⁸O variability of each profile on a respective sampling day is shown in the figure. The snow height is derived from digital elevation models. The black line indicates the relative snow height for each individual sampling day and the gray line the snow height of the first sampling day, that is, 27 May 2019. These two-dimensional profiles of the isotopic variations (provide the first visualization of the δ¹⁸O signal and stratigraphic noise in snow from the Greenland Ice Sheet, similar to the findings from snow trenches in Antarctica. We find a pattern of top-down enriched-depleted-enriched isotopic composition, presumably indicative for seasonal layering in addition to horizontal isotopic variability which is caused by local stratigraphic noise.

Zuhr, A. M., Wahl, S., Steen-Larsen, H. C., Hörhold, M., Meyer, H., & Laepple, T. (2023). A snapshot on the buildup of the stable water isotopic signal in the upper snowpack at EastGRIP on the Greenland Ice Sheet. Journal of Geophysical Research: Earth Surface, 128, e2022JF006767. https://doi.org/10.1029/2022JF006767

How the ocean affects climate on land - lastest results from ERC SPACE

In a newly published in Nature Geoscience paper, we show that climate on land appears to be much more variable than currently simulated in climate models. While temperature in climate models tends towards a constant value as longer and longer periods are averaged, the pollen-based temperature reconstructions we studied suggest that temperature fluctuations on timescales longer than multi-decadal remain about 1℃, independent of the span of the averaging period, such that a typical century scale fluctuations is of similar magnitude to a millennial scale one. The reason for this is the oceanic influence, which shapes the fluctuations in land temperature on long time scales. The relationship we identified supports a dual role of oceans in influencing temperature variability. We know that oceanic climates are generally more stable, at least on annual to decadal timescales, but we found that the same regions became the most variable on millennial timescales. Therefore, while oceanic influence stabilizes short term climate, it appears to be the main driver of long-term variability.

The figure shows average spectral estimates of local land temperature over the Northern Hemisphere. a, PSD estimates of land air temperature (T land) from pollen-based reconstructions along instrumental data and model simulations, both extracted at the pollen record locations; logarithmically spaced axes were used. Also shown are estimates from timeseries detrended with a 23-kyr sinusoidal or with respect to log(CO2) (dashed). The number of pollen records contributing to each timescale is indicated below (brown axis). b, Average spectral estimates from reconstructed annual SSTs (Laepple & Huybers, 2014), and instrumental data at the corresponding locations. Observational spectra from a are reproduced. Linear combinations of power laws with slope β = 1.2 and white-noise series are shown as dashed-dotted lines (land in green, sea in blue and white-noise levels in grey). Shading indicates 90% confidence intervals around the mean.

For more information see the press release.

Hébert, R., Herzschuh, U., & Laepple, T. (2022). Millennial-scale climate variability over land overprinted by ocean temperature fluctuations. Nature Geoscience, 1–7. https://doi.org/10.1038/s41561-022-01056-4

Controversy over new climate reconstruction method

Global map of SAT correction method skill

Has the climate steadily warmed over the past 10,000 years, or was there a major warm period 10,000 to 6,000 years ago? This problem is an important research topic in (paleo)climate science as geologic records and climate simulations are contradictory. As marine sediment data can be shifted towards specific times of the year, comparing geologic data and physical models is fraught with many uncertainties and is an ongoing research debate. Dr. Samantha Bova and colleagues from the State University of New Jersey (USA) had presented a new method to correct data from marine sediments in the journal Nature in January 2021. In this work, they suggest that the climate has warmed continuously in both geological data and models, and that the previous discrepancy between climate models and geological data has been resolved. Together with colleagues from the United States, we questioned the conclusions of this work. We show that the new method oversimplifies the complexity of the climate system and that the method favours consistency between climate models and geological data. Our work shows not only that data from sedimentary records can be shifted towards a specific season, but that the climate also responds differently to solar radiation at different times of the year. Therefore, the question is again open, why climate models and climate reconstruction differ in this time period. Our research on proxy recording systems and the development and optimisation of seasonally resolving paleo-records (PhD thesis Jannis Viola) will help in solving this question.

Laepple, T., Shakun, J., He, F., & Marcott, S. (2022). Concerns of assuming linearity in the reconstruction of thermal maxima. Nature, 607(7920), E12–E14.

https://doi.org/10.1038/s41586-022-04831-w

Age-heterogeneity in marine sediments revealed by three-dimensional high-resolution radiocarbon measurements

We analysed three-dimensional profiles of foraminiferal radiocarbon from a marine boxcore from the South China Sea. The data set was created using 118 radiocarbon dates in 9 replicate cores, measured in the MICADAS laboratory in Bremerhaven. Our results show age-heterogeneity in marine sediments, and suggest that uncertainty in the age model of sediment cores is larger than that usually reported based on just radiocarbon measurement and calibration. We also provide suggestions for future studies, such as optimal sampling strategies and realistic uncertainty estimates for age models.

The image shows conceptual sketches of mixing processes and sediment heterogeneity in a marine environment. Following the traditional terms of the mixed and the historical layer, (A) shows a well-defined boundary between both layers while (B) shows more dynamicmixing processes including deep reaching burrows. (C) illustrates a potential two-dimensional view of the heterogeneous distribution of consistently increasing ages with depth. The panels on the right hand side show a zoom into local age-heterogeneity. Colored dots and areas indicate marks from mixing of fine materials, fossils and other detrital components.

Alexandra Zuhr

This work has been published in the following paper:

Zuhr, A. M., Dolman, A. M., Ho, S. L., Groeneveld, J., Löwemark, L., Grotheer, H., et al. (2022). Age-Heterogeneity in Marine Sediments Revealed by Three-Dimensional High-Resolution Radiocarbon Measurements. Frontiers in Earth Science, 10. https://doi.org/10.3389/feart.2022.871902

Measuring sediment mixing in the past.

Spread of radiocarbon dates decreases with number of foraminifera per sample

How old is my sediment sample? With radiocarbon dating this might seem like a simple question. Just pick out some carbon containing fossils, like foraminifera, and get a radiocarbon date. However, the upper few centimetres of the ocean floor are mixed together by burrowing animals so that as new sediment arrives it is mixed together with older sediment. The range of ages that get mixed together before they are fully buried depends on the depth of the mixed layer and on how fast new sediment piles up.

Until recently, we could only measure how much mixing is going on right now at the surface, but not what happened in the past. Now however we can radiocarbon date very small samples with just a few individual fossils in each. We see that the fewer individuals there are in each sample, the bigger the range in measured ages. So, for any sediment sample, modern or old, we can measure how variable its ages are and how much mixing took place. This is important information, because it tells us what kind of climate changes we can hope to see recorded in that sediment core, we can’t see fast 10-year changes if each sample has 100s of years of change mixed up together.

This figure shows how radiocarbon measurements made on different samples from the same depth indicate a range of ages, and how these ages are more variable when fewer individuals are included in each measurement.

Andrew Dolman.

Dolman, A. M., Groeneveld, J., Mollenhauer, G., Ho, S. L., & Laepple, T. (2021): Estimating Bioturbation From Replicated Small-Sample Radiocarbon Ages. Paleoceanography and Paleoclimatology, 36: e2020PA004142.

https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2020PA004142

Local-scale deposition of surface snow on the Greenland ice sheet

Firn and ice cores are used to infer past temperatures. However, the imprint of the climatic signal in stable water isotopes is influenced by depositional modifications. We present and use a photogrammetry structure-from-motion approach and find variability in the amount, the timing, and the location of snowfall. Depositional modifications of the surface are observed, leading to mixing of snow from different snowfall events and spatial locations and thus creating noise in the proxy record.

The figure shows a two-dimensional view of the internal structure of our study area based on DEM-derived snow height variations along the 2.5 m band for the last day of our observation period (DOP 78). Colours indicate the day of deposition during the season, namely when the DEM data recorded an increase in the snow height at the respective location. The grey background represents older snow and surface undulations prior to the first DEM on DOP 1. The long data gap between DOP 39 and DOP 56 does not cause an unrealistically thick snow layer, which suggests that the temporal resolution of our data set does not affect the derived internal structure.

Alexandra Zuhr

This work has been published in the following paper:

Zuhr, A. M., Münch, T., Steen-Larsen, H. C., Hörhold, M., & Laepple, T. (2021). Local-scale deposition of surface snow on the Greenland ice sheet. The Cryosphere, 15(10), 4873–4900. https://doi.org/10.5194/tc-15-4873-2021

Two-dimensional view of the internal structure of our study area based on DEM-derived snow height variations along the 2.5 m band for the last day of our observation period (DOP 78). Colours indicate the day of deposition during the season, namely when the DEM data recorded an increase in the snow height at the respective location. The grey background represents older snow and surface undulations prior to the first DEM on DOP 1. The long data gap between DOP 39 and DOP 56 does not cause an unrealistically thick snow layer, which suggests that the temporal resolution of our data set does not affect the derived internal structure. — (Photo: Alfred-Wegener-Institut)

How precipitation intermittency sets an optimal sampling distance for temperature reconstructions from Antarctic ice cores

The spatial decorrelation structures of both the climate (e.g. temperature) and the non-climatic noise signals recorded in nearby proxy records are important information, since they control how effectively the noise can be reduced upon spatially averaging together individual records. In this study, we analysed Holocene climate model simulation data to find the locations of Antarctic ice cores which are best suited to reconstruct the temperature at a certain location of interest. Doing so, we found that the spatial decorrelation scales of the temperature variations and of the noise from precipitation intermittency set an effective sampling length scale. Based on our results, a single ice core should be located at the site for which you want to obtain the temperature reconstruction, however, in the case of averaging two ice cores, a second core should optimally lie more than 500 km away from the first one, since this yields the best correlation with the temperature at the reconstruction site.

Depending on their distance from the temperature reconstruction site, the average of two ice cores exhibits a variable correlation with temperature. The figure shows an example where the optimal configuration of two ice cores (i.e. the one showing maximum correlation) is achieved for placing one core at the reconstruction site (zero distance) and the second one between 1000 and 1250 km away from this site.

Thomas Münch

This work has been published in the following paper:

Münch, T., Werner, M., & Laepple, T. (2021). How precipitation intermittency sets an optimal sampling distance for temperature reconstructions from Antarctic ice cores. Climate of the Past, 17(4), 1587–1605. https://doi.org/10.5194/cp-17-1587-2021

Archival processes and ice core records in Central Antarctica.

Figure 1: Description of the archival processes included in the forward model that lead to a loss of signal in the snow isotopic composition (Photo: Alfred-Wegener-Institut)

Archival processes leading to the isotopic signal in ice core records may limit the achievable resolution to centennial, or even millennial time scales in Central Antarctica. By studying the impact of precipitation intermittency and isotopic diffusion in a forward model for water isotopes in ice cores, we can predict the theoretical limits for the resolution at which an ice core can be interpreted. Indeed, precipitation intermittency reshuffles the large amount of variability associated with the seasonal cycle, creating a significant amount of white noise across all frequencies, while diffusion acts as a low pass filter, erasing high frequency variability.

In this manuscript, we make use of the spectral signature created by both these processes on synthetic ice cores created using ERA-interim temperature and precipitation time series to evaluate how much temperature reconstructions from ice core records are hampered. With a very simple approach, we manage to reproduce the spectra of the isotopic variability in near-surface ice cores, and predict that the limits of the resolution achievable can be multidecadal at best for some of the deep ice core sites from the East Antarctic Plateau.

Our results are providing lower bounds for the time scales at which ice core should be interpreted, and suggest that caution should be applied when interpreting high resolution of isotopic composition fluctuations from an individual ice core. This manuscript is under discussion in Climate Of the Past: www.clim-past-discuss.net/cp-2019-134/

The Power Spectrum of Climate Proxy Error

We can use temperature proxies, such as the Mg/Ca ratio in the shells of organisms buried in sediments, to estimate the past temperature of the ocean. As well as getting an estimate for the temperature in the past, it is important to know how certain we are about that temperature estimate. However, our uncertainty about temperatures reconstructed from proxies varies depending on the physical properties of the sediment, the number of measurements taken, and the time-period over which we average the measurements.

In a pair of discussion papers, we describe how we can represent these uncertainties as power-spectra of the errors. These error-spectra then allow us to calculate how the uncertainty changes when we average multiple estimates or smoothed (e.g. running mean) versions of the estimated temperature time-series. Part I introduces the theory behind the method and derives analytical expression for the error-spectra based on our understanding of the physical process of proxy creation and interpretation. Part II describes how the error-spectra method can be used by paleo-climate researchers and how appropriate values for the required parameters can be estimated from data. It gives examples of using the error-spectra approach, via the R package ‘psem’ github.com/earthsystemdiagnostics/psem

The image shows a conceptual representation of the Proxy Spectral Error Model (PSEM) for errors due to smoothing of the climate signal by bioturbation. The true climate signal is filtered (smoothed) by processes such as bioturbation. This modifies the power spectrum of the climate (red) in a frequency dependent way, producing the power spectrum of the climate signal after bioturbation (blue). Proxy records are assumed to represent the climate at a particular timescale, (e.g. centennial, millennial), the reference climate spectrum (purple) is the power spectrum of the true climate smoothed to this timescale. The error that bioturbation and other smoothing produces (dashed brown) is a function of the reference and bioturbated climate spectra.

Andrew Dolman / Torben Kunz

Kunz, T., Dolman, A. M., & Laepple, T. (2020). A spectral approach to estimating the timescale-dependent uncertainty of paleoclimate records – Part 1: Theoretical concept. Climate of the Past, 16(4), 1469–1492. https://doi.org/10.5194/cp-16-1469-2020

Dolman, A. M., Kunz, T., Groeneveld, J., & Laepple, T. (2021). A spectral approach to estimating the timescale-dependent uncertainty of paleoclimate records – Part 2: Application and interpretation. Climate of the Past, 17(2), 825–841. https://doi.org/10.5194/cp-17-825-2021

What climate signal is contained in decadal- to centennial-scale isotope variations from Antarctic ice cores?

Proxy data on climate variations contain noise from many sources. For reliable climate variability estimates, we hence need to determine those temporal scales at which the climate signal in the proxy record dominates the noise. In this contribution, we develop a method to derive timescale-dependent estimates of temperature proxy signal-to-noise ratios, which is applicable to a large set of palaeoclimate records. Specifically, we apply and discuss the method in the context of Antarctic ice-core records.

https://doi.org/10.5194/cp-14-2053-2018

Thomas Münch