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Muted multidecadal climate variability in central Europe during cold stadial periods

Abstract

During the last ice age, the Northern Hemisphere experienced a series of abrupt millennial-scale climatic changes linked to variations in the strength of the Atlantic Meridional Overturning Circulation and sea-ice extent. However, our understanding of their impacts on decadal-scale climate variability in central Europe has been limited by the lack of high-resolution continental archives. Here, we present a near annual-resolution climate proxy record of central European temperature reconstructed from the Eifel maar lakes of Holzmaar and Auel in Germany, spanning the past 60,000 years. The lake sediments reveal a series of previously undocumented multidecadal climate cycles of around 20 to 150 years that persisted through the last glacial cycle. The periodicity of these cycles suggests that they are related to the Atlantic multidecadal climate oscillations found in the instrumental record and in other climate archives during the Holocene. Our record shows that multidecadal variability in central Europe was strong during all warm interstadials, but was substantially muted during all cold stadial periods. We suggest that this decrease in multidecadal variability was the result of the atmospheric circulation changes associated with the weakening of the Atlantic Meridional Overturning Circulation and the expansion of North Atlantic sea-ice cover during the coldest parts of the last ice age.

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Fig. 1: Location of the Auel Maar with respect to modern climatology.
Fig. 2: Temperature changes in Greenland and central Europe over the past 60,000 years.
Fig. 3: Reduced high-frequency climate variability in central Europe during the coldest parts of the last ice age.
Fig. 4: Evolution of decadal- to centennial-scale climate cycles across the stadial/interstadial events of the last ice age.
Fig. 5: Lomb–Scargle spectra for selected times of the Holocene, interstadial GI3 and Heinrich 3.

Data availability

The ELSA-20 data are available in the Supplementary Information and the PANGAEA data repository (https://doi.pangaea.de/10.1594/PANGAEA.932624). More information on ELSA is accessible from the ELSA webpage (http://www.ELSA-Project.de). Source data are provided with this paper.

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Acknowledgements

We thank the Stölben Drilling Company (Cochem, Germany) for years of fruitful cooperation in optimizing the drilling technique in the Eifel maar lakes and B. Stoll, F. Rubach, B. Hinenberg, C. Liebl, L. Marsiske, A. Blum, A. Mack, R. Deutsch, and P. Sigl for technical support. M. Zech (Dresden) provided Corg data for calibration of the ISRS670. The work was funded by the University of Mainz and Max Planck Institute for Chemistry, Mainz. We thank I. Obreht for very helpful suggestions and comments during the review process.

Author information

Affiliations

Authors

Contributions

F.S. coordinated the ELSA drillings and proposed and directed the research. F.S., A.M.-G. and G.H.H. led the interpretation of the data. F.S. and A.M.-G. wrote the manuscript and generated the figures. R.S. and R.F. provided 14C dates. R.M. and M.C. analysed the 10Be on the Auel samples. F.F., S.B., K.S. and J.A. worked on the age assignment. B.D. constructed the Auel stack by dynamic time warp. M.M. calculated the Bayesian age model. A.M.-G. and M.M. performed the spectral analysis of the records. Y.H. directed the µXRF analysis.

Corresponding author

Correspondence to Frank Sirocko.

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Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Geoscience thanks Igor Obreht and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: James Super.

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Extended data

Extended Data Fig. 1 The AU3/AU4 drilling site.

a, Photo of the infilled maar lake of Auel with drilling sites and locations of seismic lines. b, Seismic line 1 and core locations. Core AU2 is included in the figures, because this core was studied by Sirocko et al.15 for the pioneering work on the Auel maar.

Extended Data Fig. 2 Maar lake sediment cores SMf, HM4 and AU4.

a, Photo of freeze core SMf2 from Schalkenmehrener Maar with 137Cs profile and varve counts for the last 800 years. b, Core photo of sediment core HM4 with depth of palynostratigraphical fix-points for the Bayesian age model of the Holocene section. The ages for the palynological markers are taken from Litt et al.66, see also Supplementary Data 2. c, Core photo of sediment core AU4 with depth and age (all yr b2k) of interstadials from Rasmussen et al.17, see also Supplementary Data 2. d, Sedimentary disturbances between 75,5 and 78,0 m in core AU4. This interval represents GS12 and GI12 for which only selected parts of the Corg(chlorins) data have been used. All ages are given in the yr b2k notation.

Extended Data Fig. 3 The ISRS method.

a, reflectance of visible light wavelength, measured with a Gretag Spectrolino at 1 mm step size with 2x2 mm sensor field and spectral resolution of 10 nm. The red line characterizes the absorption at 670 nm. Examples are given for three samples: rich, intermediate, low in organic carbon content. Reflectance spectra from fresh and withered plant leaves and needles are given for comparison to the adsorption of organic carbon from diatoms at 670 nm16. b, Quantification of the Absorption Depth at wavelength of 670 nm. The approach follows the “In Situ Reflectance Spectroscopy – ISRS” method for chlorophyll derivates (mainly chlorins) in marine sediments off Peru16, but is now adapted to the lake sediments of the Eifel maar lakes. c, Organic carbon measurements of 10 discrete samples versus ISRS of the same samples. Each sample represents a homogenized 10 cm long section from core AU2. The homogenized sample was measured 10 times and averaged. The relation between ISRS at 670nm and Corg content is linear and can be expressed by the equation Corg = ISRS670 x 22.7.

Extended Data Fig. 4 Age-depth relation.

a, The Corg(chlorins) data for cores SMf, HM4, AU3 and AU4 are shown versus depth to document the basis for the Age-Depth model, calculated according to Fig. ED5.The ELSA-20 age is plotted together with 14C dates and 10Be data. The error is calculated with a Bayesian approach. The Age-Depth model is compared to other high resolution marine and terrestrial records. Cariaco Basin: ODP-1002C83; St. Barbara Basin ODP89384; Guatemala: Lake Petén-Itzá85; China: Jingyuan loess plateau86; Japan: Lake Suigetsu87; Italy: Lago Grande di Monticchio88; China: Sihailongwan Maar Lake89; S-Patagonia: Laguna Potrok Aike90. The sedimentation rate of the SMf and HM4 cores is similar to other global records, but the sedimentation rate of the Auel cores is globally exceptional during MIS3. b, The Corg(chlorins) are shown versus depth for cores AU3 and AU4, which have been drilled with an 0.5 m offset. Both cores reveal an identical interstadial pattern, which allows for the Dynamic Time Warp of short AU3 sections into the AU4 core. Background colors are the pattern of Landscape Evolution Zones (LEZ) as defined by Sirocko et al.15. 8 tephra have been observed in the cores of HM Förster & Sirocko54 and Förster et al.51. The ages for the Eltville Tephra (EVT), Wartgesberg Tephra (WBT), Dreiser Weiher Tephra (DWT) and Meefelder Maar Tephra (MMT) are presented here on the updated ELSA-20 timescale. c, The lithology of all ELSA-20 cores is presented with its main characteristics.

Extended Data Fig. 5 Ice core tuning, 23 - 36 ka.

δ18O and chronology of the NGRIP ice core1,17,80,81,82,91 in comparison to ELSA-20 Corg(chlorins) content and Si/Al. The ELSA-20 Corg(chlorins) data are also shown in the kernel detrended version used for the wavelet and spectra as shown in Figs. 3-5.

Extended Data Fig. 6 Ice core tuning, 36–48 ka.

δ18O and chronology of the NGRIP ice core1,17,80,81,82,91 in comparison to ELSA-20 Corg(chlorins) content and Si/Al. The ELSA-20 Corg(chlorins) data are also shown in the kernel detrended version used for the wavelet and spectra as shown in Figs. 3-5.

Extended Data Fig. 7 Ice core tuning, 48–59 ka.

δ18O and chronology of the NGRIP ice core1,17,80,81,82,91 in comparison to ELSA-20 Corg(chlorins) content and Si/Al. The ELSA-20 Corg(chlorins) data are also shown in the kernel detrended version used for the wavelet and spectra as shown in Figs. 3-5.

Extended Data Fig. 8 Uncertainty determination.

Control points for the Bayesian error calculation of the AU4 Corg(chlorins) to δ18O from NGRIP1 for selected GI events except the disturbed sections of GI12 - GS12.

Extended Data Fig. 9 Statistical Analysis.

(a-r) Corg(chlorins) Lomb-Scargle spectral power estimation for selected time intervals for the ELSA-20 Corg(chlorins) record. Employed were a Welch taper and following estimation parameters: a, number of segments (n50) = 10, 6-dB bandwidth (BW) = 8.0 × 10–4 yr–1; b, n50 = 2, BW = 9.9 × 10–3 yr–1; c, n50 = 2, BW = 8.0 × 10–3 yr–1; d, n50 = 4, BW = 8.6 × 10–3 yr–1; e, n50 = 3, BW = 8.4 × 10–3 yr–1; f, n50 = 5, BW = 2.9 × 10–3 yr–1; g, n50 = 2, BW = 2.2 × 10–3 yr–1; h, n50 = 10, BW = 1.7 × 10–3 –1; i, n50 = 6, BW = 1.9 × 10–3 yr–1; j, n50 = 3, BW = 3.2 × 10–3 yr–1; k, n50 = 6, BW = 2.8 × 10–3 yr–1; l, n50 = 3, BW = 8.0 × 10–3 yr–1; m, n50 = 5, BW = 4.8 × 10–3 yr–1; n, n50 = 7, BW = 4.2 × 10–3 yr–1; o, n50 = 4, BW = 5.7 × 10–3 yr–1; p, n50 = 5, BW = 4.8 × 10–3 yr–1; q, n50 = 4, BW = 5.0 × 10–3 yr–1; and r, n50 = 11, BW = 1.8 × 10–3 yr–1. Each panel indicates (italics) name and time interval (yr b2k) and shows spectral power (black line), AR(1) red noise upper 99% percentile (red line), white noise upper 99% percentile (grey line) and the periods for significant and (within BW) separable spectral peaks. The low-frequency peak marked by an asterisk (a) may have a biased power estimate due to the kernel detrending. The high-frequency spectrum parts (up to 0.5 yr–1) do not exhibit relevant peaks. (s-aj) Si/Al Lomb-Scargle spectral power estimation for selected time intervals for the ELSA-20 Si/Al record. Employed were a Welch taper and following estimation parameters: s, number of segments (n50) = 10, 6-dB bandwidth (BW) = 8.1 × 10–4 yr–1; t, n50 = 2, BW = 9.9 × 10–3 yr–1; u, n50 = 2, BW = 8.0 × 10–3 yr–1; v, n50 = 4, BW = 8.6 × 10–3 yr–1; w, n50 = 3, BW = 8.4 × 10–3 yr–1; x, n50 = 5, BW = 2.9 × 10–3 yr–1; y, n50 = 2, BW = 2.2 × 10–3 yr–1; z, n50 = 10, BW = 1.7 × 10–3 yr–1; aa, n50 = 6, BW = 1.9 × 10–3 yr–1; ab, n50 = 3, BW = 3.2 × 10–3 yr–1; ac, n50 = 6, BW = 2.8 × 10–3 yr–1; ad, n50 = 3, BW = 8.0 × 10–3 yr–1; ae, n50 = 5, BW = 4.8 × 10–3 yr–1; af, n50 = 7, BW = 4.2 × 10–3 yr–1; ag, n50 = 4, BW = 5.7 × 10–3 yr–1; ah, n50 = 5, BW = 4.8 × 10–3 yr–1; ai, n50 = 4, BW = 5.0 × 10–3 yr–1; and aj, n50 = 11, BW = 1.8 × 10–3 yr–1. Each panel indicates (italics) name and time interval (yr b2k) and shows spectral power (black line), AR(1) red noise upper 99% percentile (red line), white noise upper 99% percentile (grey line) and the periods for significant and (within BW) separable spectral peaks. The low-frequency peaks marked by an asterisk (a, f, r) may have biased power estimates due to the kernel detrending. The high-frequency spectrum parts (up to 0.5 yr–1) do not exhibit relevant peaks.

Supplementary information

Supplementary Information

Descriptions of Supplementary Data 1 and 2.

Supplementary Data 1

The ELSA-20 data.

Supplementary Data 2

Table of chronological control points.

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Sirocko, F., Martínez-García, A., Mudelsee, M. et al. Muted multidecadal climate variability in central Europe during cold stadial periods. Nat. Geosci. 14, 651–658 (2021). https://doi.org/10.1038/s41561-021-00786-1

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