Variability of the North Atlantic Oscillation over the past 5,200 years

Transcription

1 SUPPLEMENTARY INFORMATION DOI: 1.138/NGEO1589 Variability of the North Atlantic Oscillation over the past 5,2 years Jesper Olsen a N. John Anderson b Mads F. Knudsen c a 14 CHRONO Centre for Climate, the Environment and Chronology, School of Geography, Archaeology and Palaeoecology, Queen s University Belfast BT7 1NN, UK, j.olsen@qub.ac.uk. b Department of Geography, Loughborough University, Loughborough LE11 3TU, UK, n.j.anderson@lboro.ac.uk. c Department of Geoscience, Aarhus University, Høegh-Guldbergs Gade 2, DK-8 Aarhus C, mfk@geo.au.dk Correspondence and requests for materials should be sent to J.Olsen (jesper.olsen@phys.au.dk) NATURE GEOSCIENCE 1

2 1. Location Lake SS122 is located northeast of Kangerlussuaq (67 3 N, W) at an altitude of 338 m above sea level (Fig. S1). SS122 is a shallow lake with a mean depth <5m and a small, deep basin at the centre (maximum depth ~17 m). The lake is dimictic, oligotrophic, and hydrologically open with an outflow that drains the lake on the north side.the present day lake has a conductivity of ~23 μs cm 1, a ph of 7.9, and an alkalinity of 1536 µeql -1 1,2. a b SS122 Greenland North Kangerlussuaq 35 7 kilometers Fig. S1. Map showing the study area in West Greenland (a) and the Kangerlussuaq area with Lake SS122 positioned in the continental climate region close to the Greenland ice sheet (b). The position of the Kangerlussuaq weather stations is shown for reference. 2. Instrumental data The instrumental data series consist of meteorological weather data, covering the period from 1976 to 29, from the Kangerlussaq Airport weather station retrieved from the Danish Meteorological Institute (DMI), lake water temperatures, and on-site weather data retrieved from an automated weather station (AWS) deployed between June 21 and August 24. Monthly NAO data were taken from the NOAA website 3 and cover the period from 195 to 212. Summary statistics of the DMI and AWS datasets are provided in Table S1. Based on the average daily temperatures, the onset, termination and length of the growing season have been calculated. The growth season is defined as starting when the temperature during five consecutive days exceeds 5 C, and ends after five consecutive days of temperatures below 5 C. There is a strong correlation between the AWS and Kangerlussuaq temperature data (.94) and between lake water temperatures and the AWS air temperature (.86) (Table S1). The Kangerlussuaq weather series show a strong temperature correlation with NAO during all seasons, but the strongest correlations are observed for the winter (DJF) and summer (JJA) NAO, both yielding correlation coefficients of.64 (Table S2) 4. Further, there is strong correlation between the length of the growing season and the summer (JJA) NAO index (.66) as well as between the Kangerlussuaq winter (DJF) temperatures and the length of the growing season (.57) (Table S2). 2

3 NAO + years in West Greenland are characterised by below-average temperatures and NAO - with above-average temperatures. To further investigate the effect of positive and negative NAO modes on the West Greenland climate, unbalanced one-way ANOVA (using Matlab 7.8) was applied to the monthly, seasonal and yearly Kangerlussuaq weather data by selecting weather data associated with either clearly positive/ negative NAO modes (Table S3). Clear positive (negative) monthly NAO modes are selected using NAO 1 (NAO 1), for seasonal data NAO.5 (NAO.5) are used, whereas yearly NAO modes are selected using NAO.25 (NAO.25). Mean weather data are then calculated for months characterized by clear positive and negative NAO modes and tested against the remaining weather data (F complete ) to characterize if they are significantly different. A test against the weather data not included by either clearly positive/negative NAO modes was also conducted (, Table S3). F values lower than 5% indicate a significant difference at the 95% confidence level. Numbers in parentheses show the percentage included of the complete dataset that are characterized by positive / negative NAO modes (Table S3). Using the monthly datasets indicates that NAO + events are on average c. 3 C lower and NAO about 2 C higher than normal (Table S3). The seasonal data indicate that the NAO temperature impact is greatest during winter (DJF), yielding temperatures around 21 C in contrast to NAO yielding temperatures around 15 C. The NAO impact on precipitation, wind speed and wind direc direction during the winter months appears to be minimal (Table S3). The winter precipitation appears to show the largest variability with NAO + precipitation (around 14 mm), whereas the NAO precipitation variability is significantly smaller (around 9 mm) (Table S3). Wind speed and wind direction show no significant variability with the NAO configuration. The onset and length of the growing season is linked with the onset and length of the ice free period in Lake SS122 (Fig. 1, Table S1). Both the summer (JJA) NAO and yearly NAO index appear to influence the length and onset of the growing season such that NAO circulation modes result in a length and onset of ~9 days and ~6. months, respectively, in contrast to NAO + circulation modes resulting in a length and onset of 55 6 days and 6.6 months, respectively (Table S3). In summary, the NAO index appears to have a large influence on climate in SW Greenland. In particular, the monthly average precipitation, atmospheric air pressure and temperature are controlled by the monthly NAO index (Table S2, S3). The length and onset of the growing season appears to be influenced by the summer (JJA) and the yearly NAO index, whereas the winter (DJF) NAO index exerts the greatest influence on winter air temperatures (DJF) (Table S2, S3) 3. Chronology The high-resolution age model derived for SS122 is based on a total of C dated moss and bulk (gyttja) samples. As terrestrial macrofossils were not found in the SS122 sediments, it was necessary to primarily date bulk samples. In total, 1 bulk samples have been 14 C dated and the remaining 4 14 C dated samples were from aquatic mosses Calliergon trifarium and Scorpidium scorpioides (Table S4). The calibrated age probability distributions as a function of depth are shown in Fig. S2. As bulk 14 C samples are integrating organic material (OM) from different sources, their 14 C results may show a significant 14 C reservoir offset. In order to check if a 14 C reservoir age offset applies to SS122 bulk samples, the bulk 14 C age at depth cm (AAR-7857) was compared with the 3

4 aquatic moss sample at depth cm (AAR-13327). The 14 C age difference between these two samples amounts to 48±49 14 C yr and they are thus statistically indistinguishable. No reservoir age correction of bulk 14 C has therefore been applied. The age model was constructed using depositional models in OxCal and the IntCal9 calibration curve 6. The model parameter k has been estimated to 15, yielding a model agreement index A of 63.%. The resulting age model uncertainty has a mean of 54 years (range 2 13 years, Fig. S2). Depth (cm) Age model error (years) a 15 b Aquatic moss Bulk, gytje Age (calbp) c Oxcal MCMC model2 Oxcal MCMC model1 Tuned model Age (calbp) Depth (cm) Fig. S2. a, The 14 C chronology of SS122 and the age model constructed using OxCal together with the 14 C dates calibrated using IntCal9 6. b, Age model errors of the age constructed using OxCal 4.1. c, The upper most 14 C calibrated probability distributions together with the constructed OxCal 4.1 age model (black line, error envelope shown in grey). The red and blue are the two OxCal trial age models (single trial age models saved during OxCal depositional analysis) providing the highest correlation between PCA3 and NAO ms 7. The shaded blue is the resulting fine-tuned age model representing the optimal correlation of PCA3 onto the NAO ms age scale. Note that the top of the core is only limited by being younger than the uppermost 14 C dates and older than the year of coring 4. Principal component analysis (PCA) Principal component analysis (PCA) was conducted using MatLab 7.8 on a normalised dataset. Normalisation was done by dividing each parameter with its standard deviation and by log transforming all percentage values. The temporal resolution of the high-resolution data was reduced to fit the low resolution data by using average values according to the bin size of the low resolution data. A 5-yr moving average was then applied to smooth the high-resolution data in order to make general trends more apparent. Eigenvalues and loadings of the PCA analysis are provided in Table S5. Broken stick and scree plot analysis indicated that 3 principal components were significant. The PCA components (PCA1 and 2) show two major groups explaining most of the data variability. PCA1 reflects organic constituents, while PCA2 consists mainly of minerogenic parameters (Table S5). PCA3 integrates parameters reflecting redox variability (e.g. Mn, Mn/Fe, Ca/Ti and grey scale 4

5 with the Mn/Fe ratio exhibiting the largest loading), which can be associated with NAO-like atmospheric circulation patterns, and explains 9.4% of the total observed variability (Table S5). 5. MCMC tuning of age model and normalised NAO indices A constant lag correlation between PCA3 and NAO 7 ms yields a correlation coefficients of.77 for a constant lag of 14 and +47 years. To obtain the most optimal correlation, with the purpose of normalising PCA3 to NAO ms, PCA3 was correlated to NAO ms using 48 trial age models saved during the OxCal depositional age model construction. Two models (model-1 & model-2, Fig. S2) yield high correlation coefficients (ρ model1 =.77, ρ model2 =.77). These two models were then adopted as starting points for fine tuning the PCA3 record onto the NAO ms age scale for the overlapping period. The fine tuning was subjected to three requirements: 1) the model must be constrained by the calibrated probability distribution of the 14 C data; 2) the correlation coefficient between PCA3 and NAO ms must be ρ.8; and 3) the squared difference (dev 2 ) between PCA3 and NAO ms should be minimised. The fine tuning was performed using a Monte-Carlo-Markov-Chain (MCMC) analy- Frequency ( 1,) a Frequency ( 1,) b Oxcal MCMC model1 Oxcal MCMC model Correlation coefficient (ρ) Deviation (dev 2 ) 2 c 1 z-score NAO ms PCA3 on tuned model Age (calbp) Fig. S3. a, Probability distributions of correlation coefficients r of the fine-tuned model OxCal models model-1 and model-2. b, Probability distributions of square difference (dev 2 ) between PCA3 and NAO ms of the finetuned model OxCal models model-1 and model-2. c, PCA3 and NAO ms 7 on the resulting fine-tuned model using model-2 as the most optimal correlation. 5

6 sis implemented using the Metropolis-Hasting sampling algorithm in MatLab 7.8. Each of the two fine-tuned models consists of 1 iterations and the outcome of r and dev 2 is shown in Fig. S3. Due to the significantly lower dev 2 of model-2, this model was chosen as the optimal correlation of PCA3 onto the NAO ms age scale. The OxCal depositional age model was therefore modified in accordance with the fine-tuned model for the overlapping period (ages > 1 cal BP). The resulting fine-tuned model was calculated as the average of all 1 MCMC age model outcomes and the error estimated by the standard deviation of all 1 iterations (Fig. S2). The resulting correlation is shown Fig. S3. Due to significantly lower dev 2 values of model-2 this was chosen as the best fit with NAO ms, resulting in a correlation coefficient of r =.84±.2. The aim of the MCMC-tuned age model analysis is to derive NAO normalized values from PCA3 using the NAO ms record (Fig. S4). The linear fit transfer function provides NAO errors on the order of.11, whereas the standard deviation of the difference between NAO ms (PCA3) and NAO 7 ms is.6. The error of our PCA3- derived NAO index is therefore taken to be.6 (Fig. S4). 2 a 4 3 b 1 2 NAO ms -1-2 NAO ms PCA3 1.5 c 1. NAO ms - NAO PCA Age (calbp) Fig. S4. a, Scatterplot of the PCA3 and NAO ms 7 data. A linear fit (R 2 =.74) to the data is used to normalise PCA3 onto NAO ms values. b, The multiproxy reconstructed NAO ms index 7 together with the normalised NAO ms (PCA3) data. c, The difference between the normalised PCA3 values and the NAO ms index together with the errors resulting from the linear fit. Also shown are the mean difference (blue) and the standard deviation on the difference (light blue). 6. Spectral and wavelet analyses Global power spectra were calculated by use of the Lomb-Scargle Fourier transform 8,9 as this approach allows direct computation of spectral amplitudes without first having to interpolate the data onto an evenly spaced time grid. The proxy records were first detrended to suppress spectral leakage from low-frequency variations (~1 yr and longer periods) prior to computation of the power spectra by use of a Welch parabolic weight function and the publicly available software program REDFIT 1. The univariate spectra were also bias-corrected using 1 Monte-Carlo simulations 6

7 following the procedure outlined by Schulz and Mudelsee 1. REDFIT automatically computes rednoise false-alarm levels, based on a first-order auto-regressive model (AR1), which were used for identification of the dominant periodicities in the global power spectra (Fig. S5). Wavelet analysis was performed using the publicly available wavelet software for MatLab 11,12. Prior to wavelet analysis, the data were normalised by subtracting the mean value and dividing by the standard deviation. The unevenly spaced data were re-sampled to an evenly spaced timescale by interpolation using the minimum time difference in the data, i.e. oversampling was used in order to ensure maximum resolution (Table S5). The applied wavelet parameters are provided in Table S5. Power Power 2. a yr 17 yr yr 9 yr 55 yr 39 yr Frequency (yr -1 ) c Power (x1) Power (x1) yr 47 yr 44 yr 24-2 yr 39 yr Frequency (yr -1 ).3 37yr d yr b.5.1 Frequency (yr -1 ) 7 yr.1.2 Frequency (yr -1 ) Fig. S5. Lomb-Scargle Fourier transform global power spectrum of Mn/Fe (a), Ca/Ti (b), NAO ms 7 (c) and PCA3 (d). Significant global periods are indicated by numbers. Light green areas indicate periods between 7 11 years, dark green 45 7 years. 7

8 7. References 1 Anderson, N. J., Fritz, S. C., Gibson, C. E., Hasholt, B. & Leng, M. J. Lake-catchment interactions with climate in the low Artic of Southern West Greenland. Geology of Greenland Survey Bulletin 191, (22). 2 Lindeberg, C. et al. Natural Fluctuations of Mercury and Lead in Greenland Lake Sediments. Environmental Science & Technology 4, 9-95 (26). 3 ascii 4 Box, J. E. Survey of Greenland instrumental temperature records: International Journal of Climatology 22, , doi:1.12/joc.852 (22). 5 Ramsey, C. B. Deposition models for chronological records. Quaternary Sci. Rev. 27, 42-6 (28). 6 Reimer, P. J. et al. Intcal9 and Marine9 Radiocarbon Age Calibration Curves, -5, Years Cal BP. Radiocarbon 51, (29). 7 Trouet, V. et al. Persistent Positive North Atlantic Oscillation Mode Dominated the Medieval Climate Anomaly. Science 324, 78-8 (29). 8 Lomb, N. R. Least-squares frequency analysis of unequally spaced data. Astrophys. Space Sci. 39, (1976). 9 Scargle, J. D. Studies in astronomical time series analysis. II. Statistical aspects of spectral analysis of unevenly spaced data. Astrophys. J. 263, (1982). 1 Schulz, M. & Mudelsee, M. REDFIT: estimating red-noise spectra directly from unevenly spaced paleoclimatic time series. Computers & Geosciences 28, (22). 11 Torrence, C. & Compo, G. P. A Practical Guide to Wavelet Analysis. Bulletin of the American Meterological Society 79, (1998)

9 Table S1: Instrumental weather and NAO data Weather data summary statistics Kangerlussuaq ( ) AWS (21 24) Average snow depth (cm) 7.8 ±4.9 (1.8 / 26.) Precipitation (mm) 2. ±2.8 ( / 122) 16.7 ±32.4 ( / 125) Relative humidity (%) 7.4 ±7.9 (45 / 88) T (ºC) -5.3 ±11.9 (-36.9 / 12.3) -3.4 ±1.8 (-23.6 / 1.7) Air pressure (Pa) 18 ±5 (994 / 123) Wind speed (m/s) 3.6 ±.6 (1.2 / 6.) 2.3 ±.4 (1.4 / 3.1) Water temperature (ºC) 7.4 ±5.6 ( / 15.8) Correlation between Kangerlussuaq weather data and AWS data Precipitation (mm) Wind speed (m/s) T (ºC) Correlation between AWS and water temperature T (ºC) NAO Monthly (August) Yearly DJF.4.79 JJA

10 Table S2: NAO and temperature correlations Monthly NAO index, n= 62, 195 to 211 Probability (significant if >95%) NAO DJF MAM JJA SON DJF MAM JJA SON DJF % 86% 56% 35% MAM % 1% 34% 95% JJA % 12% 1% 35% SON % 6% 83% 1% Monthly total precipitation, n= 34, 1976 to 29 Probability (significant if >95%) NAO DJF MAM JJA SON DJF MAM JJA SON DJF % 2% 89% 66% MAM % 1% 4% 69% JJA % 37% 82% 7% SON % 39% 62% 95% Monthly average T, n= 34, 1976 to 29 Probability (significant if >95%) NAO DJF MAM JJA SON DJF MAM JJA SON DJF % 97% 3% 63% MAM % 1% 59% 55% JJA % 4% 1% 86% SON % 2% 76% 99% Growth season, n= 36, 1974 to 29 Probability (significant if >95%) NAO length start end length start end DJF % 45% 5% MAM % 44% 28% JJA % 1% 1% SON % 89% 11% Growth season Probability (significant if >95%) Temperature length start end length start end DJF % 1% 86% MAM % 73% 47% JJA % 73% 12% SON % 64% 4% 1

11 Table S3: Average climate data with NAO + / NAO - circulation modes NAO monthly data (deviation from normal monthly mean) N total =432 Clear negative NAO modes Clear positive NAO modes Control NAO - ( -1, 15.5%) NAO + ( 1, 18.5%) 1> NAO >-1 F NAO+/- m ±1s Precipitation (mm) -1.7 ±17.3 n= % 64.4% 4.5 ±2. n=77.9% 1.3% 5.% -.7 ±15.1 n=274 Relative humidity (%) -1 ±6.6 n= % 44.5% -.5 ±7.1 n=8 22.2% 18.4% 66.7% -1.7 ±7.6 n=285 T (ºC) 2.2 ±3.5 n=67.%.% -3 ±3.8 n=8.%.%.%.4 ±3.2 n=285 Air pressure (Pa) 4.4 ±3.2 n=67.%.% -3.7 ±3.7 n=8.%.%.% ±3.6 n=285 Wind speed (m/s) ±.6 n= % 95.9% ±.5 n=8 58.7% 59.5% 66.5% ±.5 n=285 Wind direction -3.3 ±19.4 n= % 28.6% 1.9 ±17.9 n=8 14.8% 22.1% 9.2% -.7 ±17.1 n=285 Winter NAO index (DJF): N total =36 NAO - ( -.5, 13.9%) NAO + (.5, %).5> NAO >-.5 F NAO+/- m ±1s Precipitation (mm) 27.6 ±16.7 n= % 91.7% 31.1 ±12.5 n=14 4.% 4.1% 62.8% 26.7 ±15.3 n=17 Relative humidity (%) 77.9 ±4.2 n=5 2.42% 11.5% 74.4 ±9. n= % 34.% 41.9% 71.3 ±8.5 n=17 T (ºC) ±3.4 n=5 3.9% 15.9% ±3.8 n=14.1%.5%.4% ±3.4 n=17 Air pressure (Pa) 18 ±1.8 n=5.94% 1.6% 14 ±3.1 n= % 88.3% 1.7% 14 ±3. n=17 Wind speed (m/s) 3.3 ±1.1 n= % 3.7% 3.5 ±.3 n= % 44.% 51.4% 3.6 ±.4 n=17 Wind direction 67.4 ±21.7 n= % 31.4% 71.6 ±14.6 n=14 8.1% 53.6% 62.6% 74.5 ±1.5 n=17 Growth season length (days) Growth season onset (month) Growth season end (month) Summer NAO index (JJA): N total = ±14.5 n=5 8.47% 2.4% 74.5 ±19.7 n= % 23.9% 3.3% 81.8 ±14.2 n= ±.4 n=5 9.2% 2.7% 6.4 ±.5 n=14 39.% 12.5% 36.% 6.2 ±.4 n= ±.5 n= % 5.5% 8.8 ±.3 n= % 94.3% 57.9% 8.8 ±.3 n=17 NAO - ( -.5, 22.2%) NAO + (.5, 13.9%).5> NAO >-.5 F NAO+/- m ±1s Precipitation (mm) 89.5 ±62.2 n=8 4.22% 67.7% 143 ±43.5 n=5 3.2% 2.4% 12.2% 97.2 ±37.8 n=23 Relative humidity (%) 59.6 ±8.2 n=8 15.3% 27.9% 66.3 ±3.7 n=5 8.7% 8.2% 12.% 62.2 ±4.7 n=23 T (ºC) 1.1 ±.6 n=8 1.8% 5.% 8.3 ±.7 n=5.1%.3%.% 9.5 ±.8 n=23 Air pressure (Pa) 112 ±1.1 n=8.%.% 17 ±1.4 n=5.1%.3%.% 19 ±1.4 n=23 Wind speed (m/s) 3.8 ±.3 n= % 3.7% 4 ±.6 n=5 61.8% 78.9% 39.9% 4 ±.4 n=23 Wind direction ±11.9 n=8 5.38% 7.% ±17.5 n=5 31.5% 54.% 1.1% ±13.5 n=23 Growth season length (days) Growth season onset (month) Growth season end (month) Yearly NAO data N total = ±11.1 n=8.29%.4% 54 ±2.4 n=5.1%.4%.1% 76.1 ±12.5 n= ±.3 n=8 7.1% 1.1% 6.7 ±.6 n=5 3.9% 9.% 4.7% 6.3 ±.4 n= ±.2 n=8 1.38% 1.5% 8.4 ±.5 n=5.8% 3.3%.9% 8.8 ±.3 n=23 NAO - ( -.25, 13.9%) NAO + (.25, 27.8%).25> NAO >-.25 F NAO+/- m ±1s Precipitation (mm) ±69.8 n=5 97.7% 77.3% ±56.8 n=1 27.% 26.5% 53.1% 22.4 ±83.7 n=21 Relative humidity (%) 7.5 ±5.1 n=5 5.8% 3.1% 71.7 ±2.6 n=1 7.5% 4.8% 56.7% 67.2 ±6.6 n=21 T (ºC) -3.3 ±.7 n=5 1.% 4.3% -6.5 ±1.3 n=1.2%.8%.% -4.9 ±1.6 n=21 Air pressure (Pa) 11 ±.5 n=5 9.2% 22.8% 18 ±1.6 n=1.3% 1.1% 1.% 19 ±1.4 n=21 Wind speed (m/s) 3.5 ±.4 n=5 5.1% 5.1% 3.6 ±.3 n=1 93.3% 78.2% 66.8% 3.6 ±.3 n=21 Wind direction 12.3 ±11.7 n=5 6.2% 69.9% 98.2 ±1.4 n=1 6.8% 69.5% 5.5% 1 ±11.9 n=21 Growth season length (days) Growth season onset (month) Growth season end (month) 91 ±11.7 n=5 4.1% 11.7% 62.4 ±16.8 n=1.1%.5%.5% 79.8 ±14.2 n=21 6 ±.3 n=5 3.9% 1.7% 6.6 ±.4 n=1 2.5% 7.2%.9% 6.3 ±.4 n= ±.2 n=5 45.6% 83.1% 8.6 ±.3 n=1 2.3% 4.3% 5.5% 8.9 ±.4 n=21 11

12 Table S4: Chronological information Lab. No. Material Depth (cm) δ 13 C ( VPDB) 14 C Age (BP) Model age calbp AAR-7852 Bulk, gyttja ± ±34 AAR Calliergon trifarium ± ±23 AAR-7853 Bulk, gyttja ± ±23 AAR-7854 Bulk, gyttja ± ±24 AAR-7855 Bulk, gyttja ±41 13 ±21 AAR Calliergon trifarium ± ±29 AAR-7856 Bulk, gyttja ± ±42 AAR-7857 Bulk, gyttja ± ±49 AAR Calliergon trifarium, Scorpidium scorpioides ± ±49 AAR-7858 Bulk, gyttja ±45 31 ±6 AAR-7859 Bulk, gyttja ± ±55 AAR Calliergon trifarium ± ±45 AAR-786 Bulk, gyttja ± ±61 AAR-7861 Bulk, gyttja ± ±112 12

13 Table S5: PCA analysis PCA1 PCA2 PCA3 Eigenvalue Variance explained 42.6% 19.8% 9.4% Accumulated variance 42.6% 62.4% 71.8% Loadings Ca Ca/Ti CaCO Cl C\N C\S δ 13 C ORG δ 15 N ORG Fe Grey scale Minerogenic matter Mn Mn/Fe S Si Sr Magnetic susceptibility Ti TN TOC TS Zr

14 Table S6: Wavelet parameters Dataset Resolution Δt (years) N original N interpolated Smoothing Wavelet d j s j 1 Ca/Ti Mn/Fe Grey scale PCA none Morlet 1/ /d j NAO ms none Morlet 1/32 4 7/d j The interpolated resolution Δt based on minimum time difference in data except for the grey scale data where the average time difference is used as Δt. 14