The Supplementary Information for - Nature

SUPPLEMENTARY INFORMATION doi: 10.1038/ngeo439

The Supplementary Information for: Rapid oceanic and atmospheric changes during the Younger Dryas cold period 1,2

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Jostein Bakke , Øyvind Lie , Einar Heegaard , Trond Dokken 1, Gerald H. Haug4, Hilary H. Birks1,3, Peter Dulski5 and Trygve Nilsen6 1

Bjerknes Centre for Climate Research, Allégaten 55, N-5007 Bergen, Norway

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Department of Geography, University of Bergen, Fosswinckelsgt 6, N-5020 Bergen, Norway

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Department of Biology, University of Bergen, Allégaten 41, N-5007 Bergen, Norway

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Section 3.3., GeoForschungsZentrum Potsdam, Telegrafenberg, D-14473 Potsdam, Germany

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Section 3.3., GeoForschungsZentrum Potsdam, Telegrafenberg, D-14473 Potsdam, Germany

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Department of Mathematics, University of Bergen, J. Brunsgate 12, N-5007 Bergen, Norway

Site map adapted from Larsen and Stalsberg 2004  

Figure S1. (A) Map of Norway. Kråkenes is located in the western part of the framed area. (B) Southern Norway with shading showing the area covered by the Younger Dryas ice sheet. The study site, Lake Kråkenes, is indicated. (C) Map of the area with cirque moraine, melt-water channel and lake at Kråkenes. Note that the melt-water channel from the glacier enters the lake in the southern part. The dotted line shows the former extent of the lake before partial drainage about a century ago. The bedrock consists of homogenous gneiss without any major changes in lithology within the catchment. This figure was originally published by Larsen and Mangerud in 1 and slightly modified by Larsen and Stalsberg 2 and slightly modified again in this study. Red dot marks the coring site for this study.

Age-depth in Lake Kråkenes We have converted all climate records used in this paper into the NGRIP age model during the Younger Dryas 3. This was done in order to make the comparison between the different nature geoscience | www.nature.com/naturegeoscience

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supplementary information

doi: 10.1038/ngeo439

archives easier. The shift in variability occurred at 12,150 yr BP shortly after the Vedde ash was deposited at 12,171 yr BP in NGRIP years. The tephra horizon is therefore a robust marker for comparison between the records in the North Atlantic region. The age-model for the marine core MD99-2284 is discussed below.

Details of the sedimentation environment and the sediment accumulation rates in Lake Kråkenes are discussed in several papers 2,4-7. The Vedde ash was precisely radiocarbon dated8 and the high resolution radiocarbon dating of other sediments in Lake Kråkenes is described by Gulliksen et al.5,7. All together 96 radiocarbon dates are reported from earlier studies on the lake sediments. The dating was done mainly on Salix herbacea leaves through the late-glacial and earliest Holocene. Other terrestrial plant material (seeds, fruits, Sphagnum) and bulk gyttja were dated later in the Holocene 5. The dates were recalibrated by Oxcal 4.0.9 (IntCal04) and the age-depth relationship was plotted (Fig. S2). We used the same sedimentation rate derived from these radiocarbon dates for the core used in this study. We then established an age-depth relationship using the transitions (onset and exit of YD) and the Vedde Ash as tie points. The transitions were defined both statistical and visually based on the sediment lithology.

Figure S2 – Red dots show the relationship of calibrated radiocarbon dates (calculated using OXCAL and IntCal 04) and sediment depth at Lake Kråkenes. There are 34 radiocarbon dates from the Younger Dryas section. The figure shows that there is a relatively stable sedimentation rate during the YD with a slightly faster rate above the Vedde Ash.

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doi: 10.1038/ngeo439


For converting the GISP2 record into the NGRIP age model we identified the onset and the end the Younger Dryas10 and gave these transitions the same ages as in NGRIP. We applied the non-linear, existing age model from the GISP2 3,10,11 ice core to the new inception and termination ages of the Younger Dryas stadial. There will naturally be some offsets in this age model (Fig. S3). However, these offsets are not larger than the original standard deviations on the original GISP2 age model.

Figure S3. Each bar shows the offset from a linear age model in the GISP2 ice core. The variance derived here was transformed to percentage deviation from the linear age-depth model applied using the new NGRIP ages for the start and end of the Younger Dryas.

The boundaries of the YD in the Kråkenes core are visually obvious in the lithostratigraphy4,5,8,12,13. To define the onset and exit of the YD more precisely, we used a piecewise-regression of the XRF data across these boundaries to obtain a statistically robust definition of the transitions. A piecewise regression14 detects the speed and position of transitions. Our two transitions were defined separately, but both are recognised by the sequence of a stable period - linear transition - stable period (Fig. S4). Thus, we need three pieces or two knots to characterise the transition. The knots separate the pieces; the stable pieces are both characterised by their averages and the central transition piece is defined by a linear term. With these constraints we optimised the position of the knots from all possibilities through a maximum likelihood approach. In this study we applied a Linear Mixed-Effect Model10 approach which takes into account the random contribution by the chemical elements in the XRF-analysis. 3


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The annually varved lake sediment record for Meerfelder Maar15 has its own independent age model through the Younger Dryas. We therefore used the inception age at the onset of the Younger Dryas from NGRIP to adjust the age model. As there is a hiatus in the annual layering towards the end of the Younger Dryas it is difficult to validate the model. Based on the differences in duration of the YD in Meerfelder Maar (c. 1100 yr) and NGRIP (c. 1200 yr) we used a factor of 1.14 to match Meerfelder Maar to the NGRIP age model 15.

The transfer of independent age-depth models from Lake Kråkenes, MD99-2284, GISP2, and Meerfelder Maar on to the NGRIP age scale can potentially provide a source of error. Despite of the quality of the recalculated age-depth models, the important point in this paper is the change of amplitude and heterogeneity in the different archives occurring close to 12,150 yr BP in all the records. In those records where Vedde ash is present, it can be used as a time marker to define the transition between the two modes described in the paper.

Figure S4. The rate and the slope of the transition when calculating the onset and exit of the YD by the use of a piecewise regression model on the Kråkenes XRF data. This shows that the sedimentary archive in Lake Kråkenes responded rapidly when the glacier formed and melted down in the catchment.

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XRF analyses The geochemical analysis was done with a Micro X-ray fluorescence spectrometer equipped with low power Rh X-ray tube, capillary lens to focus the X-ray beam onto the sample surface, Si-Li semiconductor detector, two cameras (magnification ten- and hundred fold) for sample-site viewing, and EDAX vision software. The XRF core scanner is a non-destructive logging instrument applying energy dispersive X-ray fluorescence spectrometry for the determination of major element concentrations in split sediment-core samples. The sediment cores were sub-sampled in U-channels (2x2 cm in diameter). The sediment surface in the Uchannels was cleaned and covered with a thin plastic film to protect the sediments from drying during analysis. For the statistical analyses of the XRF results we have used the following geochemical element concentrations measured in the XRF analyses: aluminium (Al), calcium (Ca), iron (Fe), inverse rhodium (invr-Rh(L-line)), rhodium (Rh(K-line)), potassium (K), log transformed sulphur (log S), manganese (Mn), silicon (Si), titanium (Ti) and vanadium (V). These elements were chosen as they showed a near-normal distribution or they could be transformed into a near- normal distribution prior to analysis. Other elements were in low quantity so that signal could not be distinguished from noise. The reason for using inverse rhodium(L-line) is to obtain sign-similarities with the other elements, i.e. in raw format this variable was negatively correlated with the other variables. Note that we use two rhodium estimates measured along different lines (K-line and L-line). The logarithm of S is used here to reduce the skewed raw-distribution, i.e. the logarithmic form appears more normalised.

Figure S5: The BLUP (Best Linear Unbiased Prediction; X= deviation from the mean) of the element-specific trends in time. The different colours correspond to different elements. These eleven elements constitute the basis for the calculation of relative precision. Note the anomalous geochemical composition close to the deposition of the Vedde ash (11 171 yr BP). 5


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Physical sediment proxies To quantify the sedimentation processes in Lake Kråkenes we performed several laboratory analyses on the cores. These are 1) % loss-on-ignition (LOI) at 550oC, 2) dry bulk density (DBD), 3) water content (WC), (Fig. S7) and 4) grain size analyses (GS; Fig. S6). LOI and WC show very distinct responses to the presence of a glacier in the catchment. During the YD, LOI was stable around 2% in contrast to the start of the Holocene where the values reached 20% during the first 500 yrs after the YD. WC is low during the YD with higher values during the Allerød and after the transition into the Holocene. DBD is also presented in Fig. 1 in the paper. It is relatively stable in the early part of the YD but shows high frequency variability with larger amplitudes after 12,150 yr BP. A previous comparison between a massbalance model and DBD in a distally fed glacial lake down-stream from the glacier Folgefonna in south Norway showed that there is a close relationship between variations in ELA and DBD in distal-fed glacial lakes 16.

Figure S6. Grain size distribution plotted against age for Lake Kråkenes (N=157). The grain size distribution is mainly controlled by the strength of the river that enters Lake Kråkenes. This is best illustrated in the very coarse silt fraction that increases towards the end of the YD. The period with ´flickering´ towards the end of YD is characterized by high content of very coarse silt with high variability. This is interpreted to reflect a high mass turnover at the cirque glacier with higher winter accumulation and higher summer ablation leading to more melt water coming out from the glacier snout.

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Figure S7. Water content (WC), %Loss-on-Ignition (LOI) and Dry Bulk Density (DBD) in Lake Kråkenes plotted against age. The WC and LOI show little variability during the YD, as both parameters are below their sensitivity thresholds as long as the glacier exists in the cirque throughout the YD. DBD show an increased variability during the latest phase of the YD with values peaking into 2.5 g/cm3. This is reflecting higher sedimentation of very coarse silt as a function of the increased strength of the current through the lake. nature geoscience | www.nature.com/naturegeoscience

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LLMM – approach The LLMM is a local version of a Linear Mixed-Effect Model 10 using similar principals to a LOESS regression 10. Along the time trajectory (cageij = Calibrated age) we identify t target points, and for each of these we perform a locally weighted linear mixed model for the XRF data. The results from all t target points are amalgamated into one model describing the trend as a function of time. For each target point we define a local weight (W = KTK), where K is the square-root of the weight matrix. The window width of our estimation, i.e. roughness of the estimated response, is defined by the parameters of the weight function11,12, and the window width was optimized based on the sum mean Bayesian Information Criteria (smBIC). The model with the lowest smBIC was given priority12. For the relationship between the elements (yij) and the calibrated age (cageij) at the t'th target point we use the square root of the weights (diag(K) = kij): kijyij = kijβ0 + kijb0i + kij (β1 + b1i)cageij + εij

(1)

where β´s are fixed effects, and b´s are element-specific random effects. The bqi ~N (0, σq2 ) and element-specific covariance is diag(Ψ) = [σ02 ; σ1 2] indicating independence between the random effects. Further, as the model is autoregressive, the residual covariance is Σ r = σ2R, where R is a correlation matrix of AR1 10. We used an AR1-process because the observations are evenly separated in time.

The LLMM provides local estimates of the temporal trend shared by all elements, and the deviations of the individual elements as a random effect (element-specific random variance). The element-specific random variance consists of two components; the random variability in average ion concentration and the random variability of the rate of change (slope) in ion concentration. These elements define in combination the relative precision, i.e. ∆ = Ψ −1/σ−1. Due to the in-window orthogonal property of the stochastic contributors we may identify the relative precision at the target point by the element-specific random average and the residual variability. The relative precision is important for two aspects in our study; by being inferred directly, and as an important part of calculating the best linear unbiased prediction (BLUP) (Fig. S5). The latter is the prediction of the individual elements. This is a prediction as we do not estimate its coefficients directly but infer them from the estimated spread parameters, which constitutes the relative precision. 8


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Equally importantly the relative precision is an estimate of the combined distribution of the stochastic precision and an estimate of the combined distribution of the stochastic effects. The element-specific random average describes the magnitude of difference among elements. This variability is true for all observations of a specific element, i.e. it captures a broader temporal scale. On the other hand, the residual contribution is related to the individual observations, i.e. variability at a shorter time-scale. As the relative precision is defined as the fraction of residual variance over element-specific variance, a high relative precision means that a major part of the stochastic processes constituting the variance is found among the residual contribution. In other words, it is dominated by a short-term effect or a higher degree of flickering. The reason for using LLMM in this study is that it is explicitly designed for handling multiple time-series, in decomposing the variability into trends and cluster-specific random effects and residual effects. The procedure is based on well-established statistical tools, by combining the Linear mixed effect modelling10 with locally weighting procedures as found in classic local regression11. In combinations the LLMM provides a procedure that can be used to evaluate trends common to individual time-series (the elements) and to describe explicitly the temporal development of the stochastic processes as they change during the time span of the study. For each target point we obtain information about the trend, the cluster-specific random effects (how the elements are different) and the residual structure (residual variance and correlation). Thus, for the time-span of our study, we do not need to assume a stationarity, as LLMM provide tools for explicitly describing how all the components of the model vary. This full flexibility and orthogonality among the elements of the model is imperative for this study as we focus on the how the stochastic processes are contributing to the variance, how this changes with time, and whether such processes/patterns are general to this set of geochemical elements.

Marine core MD99-2284 from the Faeroe-Shetland passage The core is located north of the Faeroe-Shetland channel at 62o22 `48`N and 00o588`1`W, under 1500 m water depth. The total length of the core is 33 m, and the lowest part is estimated to have an age of 35,000 years. The core is well located for tracking changes in the inflow of Atlantic Water into the Nordic Seas, as well as deepwater outflow through the Faeroe-Shetland channel. A large number of AMS 14C dates have been obtained from the core. The YD is defined between about 420cm and 250cm in MD99-2284. A well-defined peak with rhyolitic ash grains is clear at a core depth of 360cm. It is assumed to correlate with 9


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the Vedde ash layer (12,171 in NGRIP years). An AMS 14C date at 400cm has a calendar age of 12,460 years and confirms that the ash layer at 360cm is the Vedde Ash layer. Two proxies from MD99-2284 are used in this study. Planktonic foraminifera: an indirect measure of the temperature of the upper 100 meters of the sea surface (SST – sea surface temperature). Using transfer functions and modern analogue techniques we are able to make a good estimate of SST. The max and min values of sub-polar foraminifera in core MD99-2284 represent a 6-8oC difference in sea-surface temperature. Stable oxygen isotopes (δ18O): these are temperature dependent, but are also affected by the amount of ice stored on land and the salinity of the water in which the foraminifera are living.

The MD99-2284 age model is fine tuned against the NGRIP ice core to allow for a better comparison with the NGRIP data. Two ash-layers, the Vedde ash and Saksunarvatn ash, are identified both in NGRIP 3 and MD99-2284, which perfectly constrains the ages of these depths at 10,347yrs (Saksunarvatn Ash) and at 12,171yrs (Vedde Ash). Age model The age model for MD99-2284 is based on calibrated 14C ages (Table S1, Figure S8) that were calibrated using the computer software CALIB 5.10 using the marine04 calibration curve13, assuming a modern ocean reservoir age of 400 years. However, an extra 400 14C years have been added to ages within the YD interval (ΔR) 17. The spans of the dates are plotted in red in Figure S8a. The age model was constrained by using two ash layers (Saksunarvatn ash and Vedde ash), which are both identified in the NGRIP ice core18, allowing absolute age comparison between the marine core and the ice core3 at these points. As parts of the North Atlantic and the Nordic Seas during the deglaciation may also be subject to reservoir-age offset outside YD, the ash layers represent important time markers to evaluate the age model derived from calibrated 14C ages only. We have chosen to synchronize the age model with the NGRIP age-scale at two more tuning points. One is based on our interpretation of the onset of the Bølling warm period in the core, and the other at the transition from the YD to the Holocene (both transitions based on rate of change calculations). This causes the age model based on 14C ages only to deviate a bit from the tuned age model at the transition into the Bølling. The blue line in Figure S8a indicates the tuned age model used in this study. 10


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Table S1 Radiocarbon dated intervals and tephra horizons used in the age model for marine core MD99-2284. All AMS 14C ages are measured on N. pachyderma sin. The ages given for Saksunarvatn ash and Vedde ash are from Rasmussen et al.3 based on b2k (before year A.D. 2000). Samples with laboratory reference “TUa-“ were measured at the Uppsala Accelerator in Sweden, but the samples were prepared at the radiocarbon laboratory in Trondheim, Norway. KIA-samples were measured in the radiocarbon laboratory in Kiel, Germany. 14

Laboratory Reference

Interval (cm)

Mean depth (cm)

C Age, uncorrected (yr BP)

Error 1σ (yr)

TUa-3301 Saksunarvatn Ash TUa-3302 TUa-3304

100-101

100,5

8680

85

185,5 213-214 249-250

Vedde Ash KIA-10677 TUa-3305 TUa-3987 TUa-3988 TUa-3989 KIA-10678 TUa-3306

213,5 249,5

89 10050 10700

362,5 398-403 450-451 472-474 502-504 542-545 598-603 650-651

400,5 450,5 473,0 503,0 543,5 600,5 650,5

95 90 114

11150 11955 12235 12595 12980 13150 13550

60 90 75 130 130 70 100

Calendar age intercept (yr) 9356 10347 yr b2k 11037 12100 12171 yr b2k 12802 13378 13699 14034 14729 15050 15539

Max. cal. age (yr BP)

Min. cal. age (yr BP)

9451

9261

11172 12283

10901 11917

12844 13474 13785 14221 15010 15175 15746

12759 13281 13613 13846 14447 14924 15332

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Figure S8: a) Blue line represents the tuned age model used in this study. The age model is constructed using the 14

C ages (see text for details). Calibrated maximum and minimum ages of these are indicated by red triangles,

and red lines connect the maximum and minimum ages. Two ash layers (Saksunarvatn ash at 185.5 cm depth and Vedde ash at 362.5 cm) are identified in the core and indicated by green vertical lines. These ash layers are also present in the NGRIP core. Red vertical lines indicate the two other tuning points used for the age model – onset of Bolling and the Younger Dryas/Holocene transition. b) δ18O record from NGRIP in grey, and #lithic grains/(#lithic grains+#foraminifera) in the size fraction 150µm - 0.5mm (# = Number of). c) δ18O record from 12

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NGRIP in grey, and the sea-surface temperature (SST) reconstruction in MD99-2284. d) Sea-level data from Barbados, Sunda shelf, Tahiti and Bonaparte. Blue line is a smoother between reconstructed sea-level data, converted into δ18O. e) δ18O record from NGRIP in grey, and the δ18O record in MD99-2284 converted to standard mean ocean water (SMOW).

Sea surface temperature (SST) estimates For the foraminiferal data, raw census data of the planktonic assemblages were compiled and the transfer function technique Maximum Likelihood (ML) was used to calculate the seasurface temperature (Figure S1c). This ML method operates with a statistical error close to ±1ºC, and is proven to give the least autocorrelation compared to other statistical methods19.

Reconstructed salinity Foraminiferal calcite δ18O is dependent upon both seawater δ18O and calcification temperature. Foraminiferal based SST estimates described above provides an estimate of the calcification temperature and therefore the temperature effects on calcite δ18O, caused by equilibrium fractionation, can be factored out. The temperature-δ18O calibration used is based on Kim and O´Neil’s (1997) equation: T = 16.1 - 4.64(δ18Ocalcite- δ18Osw) + 0.09 (δ18Ocalcite - δ18Osw)2 From this equation, knowing the calcification temperature and the measured δ18Ocalcite we can extract the isotopic composition of the ambient sea water - δ18Osw. The evaporation-precipitation balance in the world's ocean basins allows a linear relationship between salinity and seawater δ18O to be established20. However, before transforming deglacial δ18O data into salinity the effect of cryosphere changes throughout the deglaciation has to be accounted for. The fractionation processes of O-isotopes when seawater is trapped into ice causes an ocean-wide δ18O shift of about 1.1o/oo in the full-glacial compared to present. When this water is transferred back to the ocean during the deglaciation, the change in δ18O has to be adjusted for using reconstructed sea-level records (Figure S8-D). Calculation of standard deviation (30 yrs window) – mirrored structure between Lake Kråkenes and GISP2/ Meerfelder Maar In order to visualize anti-phasing in variability between Ti-values in Lake Kråkenes against varve thickness in Meerfelder Maar and geochemical element variations in GISP2, we have applied a 30 point moving window measuring the standard deviation in the different records. The result is shown in Fig. S9 and clearly shows the anti-phasing of Lake Kråkenes against the other two archives. In Lake Kråkenes the highest standard deviation is reached towards 13


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the late YD whereas the records of varve thickness and GISP2 show the opposite; the largest variability is shown in the earliest part of the YD. This illustrates the non-stationary behaviour of the atmosphere over the North Atlantic region during the YD.

Figure S9. Calculated standard deviation (Stadv.) through a 30 year moving window of Lake Kråkenes Ti count rates, the Meerfelder Maar varve thickness, and the GISP2 geochemical records (Ca in red; Na in blue). This was done in order to show the mirrored structure between the sites exploring the suggested shift in atmospheric circulation during the YD.

Mean July air temperature reconstructed from the pollen data at Kråkenes Mean July air temperatures were reconstructed from the pollen data at Kråkenes5 using Weighted Average Partial Least Squares regression (WA-PLS)21. The 2-component WA-PLS model was based on a 384 sample pollen/climate data-set from Norway and Svalbard (H.J.B. 14


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Birks et al. unpublished) and square-root transformed pollen percentages of all terrestrial pollen and spores.

Figure S9. Mean July air temperature reconstructed from the pollen data at Kråkenes. The red dots show the data points in the record. The time resolution hampers the possibility to study the high frequent “flickering” seen in the Ti count rates (Fig 1 paper). Note also that the age scale is in calibrated calendar years BP and not in NGRIP years as the rest of the records in this paper. Despite this, the Vedde Ash is a robust time marker during the YD, dividing the reconstruction into on early phase where it is relatively cold whereas the later part of YD shows in general a warming before the cooling at the exit of the YD. This is in accordance with our observed higher glacial mass turnover gradient, seen in the Ti count rates. This increase in glacial mass turnover is explained by an increase in winter accumulation and higher ablation season temperatures.

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