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1 Holocene carbon dynamics at the forest – steppe ecotone of southern Siberia
2
3 Running head: Holocene carbon dynamics in southern Siberia
4
5 1Mackay, A.W*., 2Seddon, A.W.R., 3,4Leng, M.J., 5Heumann, G., 1Morley, D.W.,
6 6Piotrowska, N., 7Rioual, P., 8Roberts, S., 8Swann, G.E.A.
7
8 1: *Environmental Change Research Centre, Department of Geography, UCL, London
9 UK, WC1E 6BT.
10 Tel: +44 (0)20 7679 0558; Fax: +44 (0)20 7679 0565; email: [email protected]
11
12 2: Department of Biology, University of Bergen, PO Box 7803, Bergen N 5020, Norway
13
14 3: NERC Isotope Geosciences Facilities, British Geological Survey, Nottingham NG12
15 5GG, UK
16
17 4: Centre for Environmental Geochemistry, University of Nottingham, Nottingham, NG7
18 2RD, UK
19
20 5: University of Bonn, Steinmann Institute of Geology, Mineralogy and Paleontology,
21 Nussallee 8, 53115 Bonn, Germany
22
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23 6: Department of Radioisotopes, Institute of Physics CSE, Silesian University of
24 Technology, Konarskiego 22B, 44 100 Gliwice, Poland
25
26 7: Key Laboratory of Cenozoic Geology and Environment, Institute of Geology &
27 Geophysics, Chinese Academy of Sciences, P.O. box 9825, Beijing 100029, China
28
29 8: School of Geography, University of Nottingham, University Park, Nottingham, NG7
30 2RD, UK
31
32
33
34 Keywords: abrupt climate change , carbon, forest – steppe ecotone; Holocene, Lake
35 Baikal, palaeolimnology, permafrost
36
37 Type of paper: Primary Research Article
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38
39 Abstract
40
41 The forest – steppe ecotone in southern Siberia is highly sensitive to climate change;
42 global warming is expected to push the ecotone northwards, at the same time resulting in
43 degradation of the underlying permafrost. To gain a deeper understanding of long term
44 forest – steppe carbon dynamics, we use a highly resolved, multiproxy,
45 palaeolimnological approach, based on sediment records from Lake Baikal. We
46 reconstruct proxies that are relevant to understanding carbon dynamics including carbon
47 mass accumulation rates (CMAR; g C m 2 yr 1) and isotope composition of organic matter
13 48 (δ CTOC ). Forest – steppe dynamics were reconstructed using pollen, and diatom records
49 provided measures of primary production from near and off shore communities. We
50 used a Generalized Additive Model (GAM) to identify significant change points in
51 temporal series, and by applying generalised linear least squares regression modelling to
52 components of the multiproxy data, we address: (1) what factors influence carbon
53 dynamics during early Holocene warming and late Holocene cooling?; (2) how did
54 carbon dynamics respond to abrupt sub Milankovitch scale events?; and (3) what is the
55 Holocene carbon storage budget for Lake Baikal.
56
57 CMAR values range between 2.8 – 12.5 g C m 2 yr 1. Peak burial rates (and greatest
58 variability) occurred during the early Holocene, associated with melting permafrost and
59 retreating glaciers, while lowest burial rates occurred during the neoglacial. Significant
60 shifts in carbon dynamics at 10.3, 4.1 and 2.8 kyr BP, provide compelling evidence for
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61 the sensitivity of the region to sub Milankovitch drivers of climate change. We estimate
62 that 1.03 Pg C were buried in Lake Baikal sediments during the Holocene, almost one
63 quarter of which was buried during the early Holocene alone. Combined, our results
64 highlight the importance of understanding the close linkages between carbon cycling and
65 hydrological processes, not just temperatures, in southern Siberian environments.
66
67
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68 Introduction
69
70 Permafrost is highly vulnerable to global warming, and in recent decades has experienced
71 temperature increases of up to 3ºC, with multiple, complex impacts on vegetation,
72 hydrology and the biogeochemical cycling of carbon (Vaughan et al. , 2013). Sporadic –
73 isolated permafrost regions are especially at risk, including those in southern Siberia –
74 northern Mongolia, from degradation through warming, human impact and increased
75 wildfires (Sharkuu, 1998; Romanovsky et al. , 2010; Zhao et al. , 2010; Törnqvist et al. ,
76 2014). Globally, permafrost contains one of the largest pools of organic carbon, and
77 warming ultimately results in the release of this carbon pool to the atmosphere via
78 microbial degradation (Schuur et al. , 2008). Old organic carbon liberated from melting
79 permafrost may also be exported to headwater streams and rivers as dissolved organic
80 carbon (DOC) (Spencer et al. , 2015). In central Siberia, large amounts of DOC are
81 transported from catchments into lakes, especially via rivers at more southerly latitudes
82 where sporadic and isolated permafrost is extensive (Prokushkin et al. , 2011).
83
84 Over long timescales, the nature of carbon release from permafrost soils is rather
85 uncertain (Schuur et al. , 2008), but one potential, under utilised tool for understanding
86 how climate change has influenced carbon dynamics is by lacustrine sediment records of
87 organic geochemistry. These records reflect long term interactions between lakes and
88 their catchments (Anderson, 2014), especially regions underlain by permafrost (Vonk et
89 al. , 2012). Lakes in general act as an important control on the global carbon cycle, despite
90 occupying only a small percentage of the surface of the earth. Carbon burial to the bottom
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91 of lakes is substantial, especially considering the quantities of sediment that have
92 accumulated since the end of the last glaciation, which likely represents more than two
93 fifths (42 Tg C yr 1) of the amount of organic carbon buried in ocean sediments (c. 100
94 Tg C yr 1) (Dean and Gorham 1998).
95
96 Within lake sediments, a number of different indicators can be used to record the
97 responses of carbon cycling to extrinsic drivers such as climate. For example,
98 sedimentary total organic carbon (TOC) provides a first order estimate of the amount of
99 bulk organic matter that escapes remineralization during sedimentation (Meyers &
100 Lallier Verges, 1999). However, TOC is sensitive to changes in sediment accumulation
101 rates, and so arguably a better estimate of organic carbon burial is achieved through the
102 calculation of carbon burial (or mass accumulation) rates (CMAR; g C m 2 yr 1) (Meyers
103 & Teranes, 2001) which are closely associated with the delivery of allochthonous carbon
104 to lakes (e.g. Watanabe et al., 2009; Hyodo & Longstaffe, 2011; Moy et al. , 2011).
105 Sources of organic carbon sequestered into lake sediments may be further discriminated
13 106 through their carbon isotope composition (δ CTOC ) and TOC/total nitrogen (C/N) ratios
107 (Leng & Marshall, 2004). Lake sediment records can also reveal major vegetation
108 changes in the forest steppe ecotone (through pollen analysis, e.g. Bezrukova et al. ,
109 2010; Iglesias et al. , 2014), as well as shifts between primary producers (e.g. diatoms),
110 linked to climate variability (Weckström et al. , 2014). Multiproxy palaeolimnology is a
111 powerful approach to gain deep insight into ecosystem dynamics in permafrost regions
112 over long timescales.
113
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114 One of the most important ecosystems in southern Siberia is Lake Baikal and its
115 catchment. It is the world’s largest lake by volume, but it is also the deepest and oldest
116 lake, with sedimentary records spanning at least 20 million years. Its catchment spans
117 almost 450,000 km 2, from the southern limit of the boreal forest into the steppe regions of
118 northern Mongolia. About 80% of Baikal’s catchment belongs to its largest tributary, the
119 Selenga River, which alone accounts for over half of all river input into the lake.
120 Catchment permafrost is extensive continuous and discontinuous permafrost dominate
121 the east and west portions of the basin (ca. 30%), while sporadic and isolated permafrost
122 dominate the south (Sharku, 1998; Törnqvist et al. , 2014). Annual air temperature trend
123 maps for the past 50 years show southern Siberia to be experiencing some of the largest
124 increases globally (Jones et al. 2012), threatening vulnerable carbon pools including
125 permafrost (Schuur et al. , 2008; Romanovsky et al. , 2010) and the hemi boreal forests
126 (Wu et al. , 2012; DeLuca & Boisvenue, 2012). Lake Baikal itself is also responding to
127 regional warming; surface water temperatures and summer stratification have increased
128 in recent decades (Hampton et al. 2014) while ice cover duration and thickness have
129 declined (Todd and Mackay 2003). Its long sedimentary record contains an estimated
130 4,500 Pg of organic carbon, more than 400 times that contained in its catchment soils
131 (Alin & Johnson 2007), which is essentially locked away permanently. More relevant for
132 understanding contemporary lake catchment interactions is the amount of organic carbon
133 sequestered since the last deglaciation, which is currently unknown, and the role that
134 climate may have played in this process. Understanding how climate change influenced
135 carbon dynamics in the past has the potential to provide important insights for
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136 understanding how global warming may influence lake catchment carbon dynamics into
137 the future.
138
139 Here, we apply a palaeolimnological, multiproxy approach to understand Holocene
140 carbon dynamics in the Baikal Selenga catchment at a multidecadal resolution. Global
141 temperatures during the early Holocene were at least as warm as today (Marcott et al .
142 2013), and rates of permafrost warming during the early Holocene were also comparable
143 to rates estimated for present day (Anisimov et al. , 2002). Therefore, comparisons
144 between early and late Holocene periods may provide useful insights into understanding
145 long term carbon dynamics at the forest – steppe ecotone. The Holocene also experienced
146 several centennial scale abrupt events (Mayewski et al. , 2004; Wanner et al. , 2014), such
147 as the 8.2 kyr cold event (Kleiven et al. , 2008) and the 4.1 kyr arid event (Cullen et al. ,
148 2000) but the extent to which these can influence Holocene carbon dynamics in
149 permafrost regions remains unknown. The multi decadal, multi proxy dataset offered in
150 this study has potential to provide several key insights into carbon dynamics in a climate
151 sensitive, permafrost region. To analyse these data, we use a Generalised Additive
152 Modelling version of a SiZer analysis (Chaudhuri & Marron, 1999; Korhola et al. 2000)
153 for pinpointing significant points of change in the different temporal series, and use
154 generalised least squares regression to investigate how key components of carbon cycling
155 in the lake respond to long term changes in climate variability. The dataset and methods
156 we have developed and applied in this study presents a unique opportunity to address
157 three principal questions:
158
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159 (1) what are the factors influencing carbon dynamics during early Holocene warming,
160 and how do they compare to the late Holocene?
161 (2) how did carbon dynamics respond to abrupt sub Milankovitch scale (e.g. 8.2 and
162 4.1 kyr) events?
163 (3) what is the carbon storage budget for Lake Baikal during the Holocene, and how
164 does this compare with other lakes?
165
166
167 Materials and methods
168
169 Study site
170
171 The Lake Baikal basin is situated in one of the world’s most continental regions;
172 summers are short, warm and wet while winters are long, dry and cold. Summer rainfall
173 stems from the progression of cyclones moving in from west Siberia. In autumn, cold
174 Arctic air intrudes from the Kara Sea to central Asia, which leads to the growth of the
175 Siberian High, a high pressure cell which intensifies during winter, and leads to cold air
176 passing into Asia (Gong & Ho, 2002) influencing the intensity of the East Asian Winter
177 Monsoon (EAWM) (Wu & Wang, 2002).
178
179 The Vydrino Shoulder (51.58°N, 104.85°E) is an isolated high in the south basin of Lake
180 Baikal (Fig. 1). It forms an upper to mid slope, underwater terrace of mostly fine
181 grained sediments, free from turbidites and unaffected by bottom water currents which
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182 can cause sediment focussing (Charlet et al. 2005). The Shoulder sits off shore from
183 several major south basins tributaries (including the Snezhnaya and Vydrinaya rivers,
184 which have their source in the neighbouring Khamar Daban mountain range) and is
185 approximately 130 km from where the Selenga River enters Lake Baikal. Sidescan sonar
186 mosaics and seismic data (Charlet et al. , 2005) show the upper terrace sediments to be
187 relatively undisturbed by tectonic activity and reworking and are therefore suitable for
188 Holocene reconstructions. In the summer of 2001, a suite of cores was extracted from an
189 off shore ridge crest location of continuous sedimentation (>600 m water depth)
190 including a box core (CON01 605 5) and a piston core (CON01 605 3). During retrieval,
191 the upper 12.5 cm of surface sediment were lost from the box core, representing the past
192 c. 800 years. To provide context for carbon dynamics related to recent regional warming,
193 carbon mass accumulation rates were calculated for the past 50 years from a UWITEC
194 gravity core (BAIK13 7) taken in 2013 to the west of CON01 605 cores. Full details of
195 the various core codes, their locations and relevant analyses are given in Table 1.
196
197 Dating
198
199 Radiocarbon dates were obtained by accelerated mass spectrometry (AMS) from pollen
200 and spore concentrates from twelve box core (CON01 605 5) samples (Piotrowska et al. ,
201 2004) (Table S1). All radiocarbon dates were calibrated using IntCal13 radiocarbon
202 calibration curve (Reimer et al. , 2013). Age depth modelling was done using ‘Bacon2.2’,
203 allowing for variable sediment accumulation rates (Blaauw & Christen, 2011; see Fig. 2).
204 The core was divided in 38 five cm sections, and prior parameters used for calculations
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205 were: 50 years per cm for accumulation rate with gamma distribution shape 1.5, and
206 default settings for memory (see Fig. 2). The results of Markov Chain Monte Carlo
207 iterations plotted in the upper left corner of Fig. 2 indicate good performance of the
208 model. Sediment samples from BAIK13 7 were dated using 210 Pb analyses by non
209 destructive gamma spectrometry. Chronologies were calculated using the CRS (constant
210 rate of 210 Pb supply) dating model, after corrections were made for the effect of self
211 absorption of low energy gamma rays within samples (Appleby, 2001).
212
213 Palaeoecology
214
215 Pollen and diatom analyses were undertaken on two different cores extracted from the
216 Vydrino Shoulder (Table 1). Pollen data were analysed at 10 mm intervals from the box
217 core CON01 605 5 and were used to represent long term vegetation changes in the
218 surrounding landscape. Pollen were counted at magnifications of 400 to 600x, with
219 critical identifications made at 1000x (see Demske et al. , 2005 for full details). Here we
220 report on total arboreal pollen (AP) and Pinus sylvestris pollen (PynSylv) (Scots Pine) as
221 indicators of forest dynamics. A steppe – boreal forest index was also calculated:
222 [( Artemisia +chenopods+ Ephedra )/AP]*100 (Traverse, 1998 in Bezrukova et al. , 2005).
223
224 We used a principal components analysis (PCA) on the pollen data to summarise long
225 term vegetation trends in around the lake (Fig SI). The pollen percentage data were
226 Hellinger transformed prior to analysis. For all subsequent analyses, we multiplied PC1
227 by 1 so that increases in the values of PC1 reflect expansion of boreal forest.
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228
229 Diatoms were analysed at 5 mm resolution from the piston core (CON01 605 3) and
230 represent a proxy for the main contributions of primary productivity within the lake. For
231 each sample at least 300 valves were counted using oil immersion phase contrast light
232 microscopy at x1000 magnification. Diatom cell fluxes (total and benthic) (cm 2 yr 1
233 x10 6) were estimated by the addition of divinylbenzene microspheres (Battarbee &
234 Kneen, 1982), together with calculated sedimentation rates (cm yr 1).
235
236 Isotope geochemistry
237
238 Isotope geochemistry was undertaken on the box core (CON01 605 5) on contiguous 5
239 mm samples and was used to understand different components of carbon cycling (Leng &
240 Marshall, 2004). Sediments were placed in 5% HCl to remove any CaCO 3 (assumed
241 negligible), then washed over Whatman 41 filter papers with deionised water and dried at
242 40°C in a drying cabinet. When dry, samples were ground to a fine powder and stored in
13 243 glass vials. Carbon isotope ratios (δ CTOC ), percentage total organic carbon (%TOC) and
244 percentage total nitrogen (%TN) (used to calculate C/N) were analysed during
245 combustion in a Carlo Erba 1500 on line to a VG Triple Trap and dual inlet mass
13 246 spectrometer. δ CTOC values were converted to the V PDB scale using a within run
247 laboratory standard calibrated against NBS 19 and NBS 22, with C/N ratios calibrated
248 against an Acetanilide standard. Replicate analysis of sample material indicated a
13 249 precision of ±0.1‰ for δ CTOC and ±0.1 for C/N. %TOC was also calculated for the past
250 50 years on BAIK13 7 sediments, using the methods outlined above.
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251
252 Carbon mass accumulation rates
253
254 Only sediment samples from the piston core (CON01 605 3) were routinely analysed for
255 wet densities and % dry weight at 105 °C, from which dry bulk density (DBD) values
256 could be calculated (Table 1). Therefore, mean piston core DBD values for 100 year
257 intervals during the Holocene were calculated for the piston core. These were used
258 alongside mean %TOC values for 100 year intervals of the Holocene box core (CON01
259 605 5) to derive organic matter densities (g cm 3). Using the Box core calibrated age
260 model (cm yr 1), organic carbon mass accumulation rates (CMAR; g C m 2 yr 1) were
261 calculated on the centennial scale averages of %TOC and DBDs. CMAR were also
262 calculated for the past 50 years using %TOC, DBD and sediment accumulation rates
263 calculated for BAIK13 7.
264
265 Statistical modelling of the Vydrino datasets
266
267 Ecological dynamics are subject to modes of variability across a variety of temporal
268 scales (Jackson & Overpeck, 2000), and so one curve may not be sufficient to capture the
269 complete components of variability within a temporal series. Therefore, for a full
270 appreciation of the long term dynamics of carbon cycling in Lake Baikal over the
271 Holocene approaches that can take multiple temporal dynamics into account are needed.
272 SiZer analyses (e.g. Chaudhuri & Marron, 1999) can capture such dynamics, by
273 identifying significant trends at different modes of variability. In this study we developed
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274 our own version of a SiZer analysis and applied it to each of the variables using
275 Generalized Additive Modelling (GAM) (Wood 2006). Our method allows temporal
276 autocorrelation to be fitted within each model, which should result in more conservative
277 tests when testing for significant trends (e.g. Park et al. 2004).
278
279 To develop our GAM SiZer method, we used the following procedure combining
280 functions within the package mgcv (Wood 2006), and a script developed by Simpson
281 (2014) in R (R Development Core Team, 2016) on each of the variables:
282 i) fix the smoothing parameter k to a given value using the option in the
283 smoothing term ‘ fx = TRUE ’;
284 ii) test for temporal autocorrelation in the residuals in the model assuming an
285 exponential decay function (e.g. Seddon et al. 2014);
286 iii) re fit the GAM model with an appropriate variance covariance matrix
287 reflected by the temporal autocorrelation using the stable multiple smoothing
288 parameter estimation method (Wood 2004);
289 iv) test for the significance of the slope of the GAM spline using a simultaneous
290 confidence interval method described by Simpson (2014);
291 v) identify which periods contain significantly increasing/ decreasing trends;
292 vi) repeat for different values of k (k = 5, 10, …, kmax );
293 vii) map the time periods of significantly increasing or decreasing trends in a
294 SiZer plot, with positive trends identified in red and negative trends identified
295 in blue.
296
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297 The value kmax is dependent on sample size, and the different sample resolution and
298 temporal structures of our datasets mean that overfitting may be an issue at higher values
299 of k. Therefore, to estimate the maximum value of k we used the ‘ gam.check() ’
300 function in the mcgv package to test whether the smoothing basis dimension for a GAM
301 spline was too high. This command employs a test to compare the residual variance of a
302 model fit with the difference of residuals between neighbours, and then randomly
303 reshuffles the residuals 1000 times to find a null distribution of variance differences (see
304 help file for gam.check() function in mgcv, Wood 2006). For each dataset, our value
305 kmax was selected according to when the variance differences moved above p = 0.05 from
306 the null distribution. Information on the data transformations used (to enable our models
307 to be run using Gaussian error distributions, the kmax values and the mean and median
308 sample resolutions for the different datasets) are provided in Table S2.
309
310 The GAM SiZer methodology presented here is useful for identifying periods of major
311 change within individual temporal series, but our multiproxy study design also means that
312 we were able to use statistical modelling to investigate whether longer term changes in
313 organic geochemistry were linked to changes in climate. A piecewise linear regression
314 revealed a breakpoint in PC1 axis representing long term forest climate responses at c.
315 6051 ± 241 cal yr BP (Fig S2). Therefore, we split the data into early Holocene (EH, 11.6
316 – 6.1 kyr) and late Holocene (LH, 6.1 0.8 kyr) periods, and ran linear regressions to
317 check for relationships between long term landscape/ climate changes and organic
318 geochemistry. Since the CMAR dataset had a different age model to the pollen data, the
319 pollen data were linearly interpolated to the sample ages of the CMAR dataset. We then
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320 used a generalised least squares regression to test for relationships between climate and
321 the different within lake proxies for the two time periods. We checked for the presence of
322 temporal autocorrelation in the residuals, and then fitted a new model assuming
323 exponential decay function to describe the degree of association between samples if
324 required (e.g. Seddon et al. 2014). The models including autocorrelation were compared
325 using the Akaike Information Criterion (AIC) and the best model (lowest AIC) was used
326 to interpret drivers of the changes of carbon cycling over time.
327
328
329 Results
330
331 Sediment sample ages calculated on modelled weighted means shows that the box core
332 sediments were deposited between c. 11.6 – 0.8 cal kyr BP (Fig. 2). Sediment
333 accumulation rates (SAR) range between 30.9 – 9.8 cm kyr 1 (mean 16.3 cm kyr 1), with
334 peak values calculated at 9.8 kyr BP. Thereafter, SAR decline to a low between 4.5 – 4.4
335 kyr BP.
336
337 The stratigraphic data are presented in Fig. 3 and the individual SiZer plots in Fig. 4.
338 Assessment of the SiZer plots help to identify key events and trends in the different proxy
339 profiles. Steppe communities were prevalent in the watershed of Lake Baikal during the
340 early Holocene but declined abruptly at c. 10 kyr BP, before gradually declining to very
341 low values at c. 6.1 kyr BP (Fig. 3d). Pollen from steppe vegetation remained a small but
342 persistent feature of the record for the remainder of the Holocene. Pinus sylvestris (Scots
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343 pine) was virtually absent, but became dominant (i.e. over 50% total land pollen; TLP) by
344 7.0 kyr BP (Fig. 3b). For the remainder of the record tree pollen was above 80% TLP.
345 The first principal component (PC1) of the pollen data explained 73.3 % of the total
346 variance of the dataset (significant by comparison to the broken stick model, Line &
347 Birks 1996) and was dominated by a gradient between cold adapted species such as
348 dwarf birch and the eurythermic Scots Pine (Fig. S1). In general, there was a significant
349 long term increasing trend in PC1 from the start of the Holocene to become more stable
350 during the late Holocene at lower values of k (Fig. 3c, 4g).
351
352 Total diatom cell fluxes (DCF) ranged from c. 0.04 to 2.03 million cells cm 2 yr 1 (Fig.
353 3i). Fluxes were especially significant before 10 kyr BP (Fig. 4e). A final significant
354 decline in DCF was observed at 7.5 kyr BP (Fig. 4e), with no further significant
355 variability for the remainder of the Holocene. In contrast, the fluxes of benthic diatom
356 cells showed more significant variability, particularly at higher frequencies (i.e. higher
357 values of k) for much of the Holocene (Fig. 4f). For example, whilst there were large
358 oscillations in benthic diatom fluxes before c. 10 kyr BP, we also observed significant
359 flux declines at c. 7.5 and 5.5 kyr BP (Fig. 3j, 4f). Mean benthic flux rates for the
360 complete Holocene was 56,000 cells cm 2 yr 1, or c. 10% of mean diatom cell fluxes,
361 highlighting the overall dominance of the planktonic contribution to diatom productivity
362 in this core.
363
364 TOC values were very low during the initial stages of the early Holocene (11.6 – 10.1 kyr
365 BP; mean 1.2%), followed by a significant increase in %TOC values at 10.0 kyr (Fig. 3e;
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366 Fig. 4a), reflecting a step like shift into increasingly higher Holocene values. In general,
367 three other major periods of change were identified by SiZer analysis: an increase in
368 %TOC at 6.8 kyr BP, and declines in %TOC at 4.1 kyr BP and 2.8 kyr BP (Fig. 4a),
369 reflecting local minima (Fig 3e). In BAIK13 7, TOC in the uppermost sediments
370 deposited during the past 50 years reached 2.5% (Roberts 2016), the highest values since
13 371 4.7 kyr BP, and some of highest values for the whole Holocene. Sedimentary δ CTOC and
372 C/N ratios were also highly variable and show similar patterns to %TOC. For example,
13 373 sedimentary δ CTOC ranges between –30.7 to –27.0‰ (mean –29.03 ‰), with high
374 frequency oscillations found throughout the record (Fig. 3g), and significant periods of
375 change around 9.4, 7.4, 4.1, 3.6, 2.8 and 2.4 kyr BP (Fig. 4c). C/N ratios fluctuate
376 between 9.9 and 13.8 (mean = 11.6) (Fig. 3f). Abrupt and significant declines are
377 observed at 7.8, 4.1 and 2.8 kyr BP (Figs. 3f, 4b).
378
379 Organic carbon mass accumulation rates were highest during the early Holocene (11.6 –
380 9.0 kyr BP) (Fig. 3h). The SiZer analysis revealed this was also a major period of
381 variability, particularly at higher frequencies (Fig. 4d). For example, peak values of 12.5
382 g C m 2 yr 1 were observed at 10.4 kyr BP before they declined rapidly to c. 4.8 g C m 2
383 yr 1 at 10.1 kyr BP. A further significant decline was observed between 9.5 – 9.3 kyr BP.
384 Between c. 4.5 – 4.0 kyr CMAR exhibited a significant decline from 7.9 g C m 2 yr 1 to
385 3.1 g C m 2 yr 1. For much of the late Holocene, CMAR remained low < 5 g m 2 yr 1 with
386 a distinct minimum at 2.8 kyr BP. Mean Holocene CMAR was 5.9 g C m 2 yr 1. During
387 the past 5 decades, mean CMAR in BAIK13 7 were only c. 3 g C m 2 yr 1 (Fig. 3h).
388
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389 Modelled PC1 (i.e. the cold adapted/ eurythermic gradient in the pollen data)
390 relationships with organic geochemistry highlight stronger responses during the early
391 Holocene (Fig. 5a d) than late Holocene (Fig. 5e h). Although the most significant
392 (positive) relationship was between %TOC and PC1 during the early Holocene (Fig. 5b),
393 when expressed as burial rates, the strength of the relationship between PC1 and C
394 declined and was negative (Fig. 5d). A significant negative relationship between PC1 and
13 395 δ CTOC was also observed (Fig. 5a), although these relationships were not significant
396 following a sequential Bonferroni correction. In contrast, the only significant relationship
397 found during the late Holocene was between PC1 and C/N values which was also
398 removed once a a sequential Bonferroni correction was applied (Fig. 5g). Given that the
399 sequential Bonferroni corrections can be overly conservative and make it difficult to
400 observe multiple significant relationships in noisy (e.g. ecological) data (Moran 2003),
401 we attempt to ascribe a physical basis to patterns of variability related to uncorrected
402 significant models in the discussion where possible.
403
404
405 Discussion 406
407 Overall concentrations of sedimentary organic carbon in Lake Baikal are low due to high
408 remineralisation rates in the water column (Müller et al. , 2005) and poor burial efficiency
409 (Maerki et al. , 2006; Sobek et al. , 2009, 2014). Burial efficiency is as poor in Lake
410 Baikal as it is in the oceans because of low sediment accumulation rates leading to very
411 high oxygen exposure times (between 10 to over 1000 years, Sobek et al. 2009).
412 Moreover, organic carbon is dominated by autochthonous production (phytoplankton
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413 contribute approximately 90% of organic matter in Lake Baikal, with less than 10%
414 delivered from the catchment (Votintsev et al. , 1975)) which makes it less resistant to
415 oxidation (Sobek et al . 2009). Recently buried organic carbon is also subject to
416 substantial post depositional degradation, and while this may impact the very recent
417 measurements from BAIK13 7 (discussed below) the impact on our older sediments of >
418 800 years will likely be very minor (Sobek et al . 2014). Previous multiple lake studies
419 are usually based on single cores taken from central, deep locations, regions that are also
420 subject to sediment focussing, which can result in carbon burial rates higher than
421 expected. While some studies have made corrections for sediment focussing (e.g.
422 Anderson et al . 2014; Heathcote et al. 2015) others have not (e.g. Dong et al. 2012). Crest
423 environments on isolated and inter basin highs (i.e. the Vydrino Shoulder and the
424 Academician Ridge), are not subject to sediment focussing, so no corrections were
425 needed in this study.
426
427 What are the factors influencing carbon dynamics during early Holocene warming and
428 how do they compare to the late Holocene?
429
430 Early Holocene
431 Orbital configurations during the early Holocene resulted in very strong seasonality in
432 central Asia (Bush 2005); summers were warm and wet, while intensely cold winters
433 contributed to low mean northern hemisphere temperatures (Marcott et al. , 2013; Wanner
434 et al. , 2014) (Fig. 3m). High early Holocene summer insolation (Fig. 3n) led to rapid
435 melting of mountain glaciers and permafrost in southern Siberia (Groisman et al. , 2013),
20 Page 21 of 71 Global Change Biology
436 and increased river flow into Lake Baikal (Mackay et al. , 2011), resulting in lake levels
437 rising by approximately 15 m (Urabe et al. , 2004). High CMAR during the early
438 Holocene (Fig. 3h) most likely represents allochthonous sources from melting
439 permafrost, during summer months of high fluvial input (Fig 6g); higher than average
440 C/N (Fig. 6d) and δ13 C (Fig. 6e) values at this time are also indicative of increased
441 allochthonous carbon to Lake Baikal sediments (Table 2).
442
443 PC1 generally reflects vegetation responses to insolation driven changes in climate over
444 the Holocene (Tarasov et al . 2007) (Fig. 3m). Forest expansion mirrors the early
13 445 Holocene decline in global CO 2 concentrations (Fig. 3k) and an increase in ice core δ C
446 (Fig. 3l) is indicative of the contribution made by expanding boreal forests to the global
447 increase in terrestrial biomass (Elsig et al. , 2009). Forest expansion will have led to
448 stabilization of catchment soils which likely accounts for the significant negative
449 relationship between PC1 and carbon burial rates after 9.6 kyr BP. Lower CMAR values
450 may also be linked to lower Selenga River discharge at this time (Fig. 6g) (Prokushkin et
451 al. 2011).
452
453 Late Holocene
454
455 Scots Pine is a eurythermic and drought resistant conifer, and its maximum expansion
456 between 7 – 4 kyr BP (Fig 3b) is linked to regional summer temperature maxima and
457 gradually increasing aridity in southern Siberia (Bush 2005; Tarasov et al. , 2007) caused
458 by surface albedo feedbacks amplifying the climate system (Ganopolski et al. , (1998).
21 Global Change Biology Page 22 of 71
13 459 δ CTOC values are lowest during this period, probably because pelagic diatoms dominate
460 primary production at this time, as well as a potential contribution of respired carbon
461 delivered to the lake from mature forest soils (Table 2). Increased CMAR at c. 5 – 4.5 kyr
462 BP is coincident with a small peak in modelled summer relative humidity (Bush, 2005),
463 and may be related to organic carbon from melting permafrost being delivered to the lake.
464
465 Declining late Holocene annual average air temperatures (Fig. 3m) are implicated in a
466 renewed phase of Siberian permafrost formation on previously thawed surfaces, leading
467 to characteristic two layered frozen structures (Anisimov et al., 2002). Renewed
468 permafrost formation was likely responsible for persistent low carbon burial rates after 4
469 kyr BP (Fig. 6f). Persistent low CMAR observed here is in contrast to (i) mean CMAR
470 for lakes in SW Greenland, which showed no difference between mid and late Holocene
471 periods (Anderson et al. , 2009), and (ii) to mean CMAR for Chinese lakes which peaked
472 between 3 – 1 kyr BP, linked to intensified human impact (Wang et al . 2015). These
473 comparisons highlight the importance of regional activities when trying to understand
474 delivery of allochthonous matter to lakes, although the potential influence of sediment
475 focussing was not considered in either study.
476
477 How do carbon dynamics respond to abrupt, sub Milankovitch scale events?
478
479 (i) Early Holocene abrupt events
480 When ice sheets were still an important feature of North American and Eurasian
481 landmasses, early Holocene climate was punctuated by pervasive millennial scale
22 Page 23 of 71 Global Change Biology
482 variability (e.g. Bond et al. , 1997, 2001; Fisher et al. , 2002; Mayewski et al. , 2004;
483 Wanner et al. , 2008; Wanner & Bütikofer, 2008). Variability was associated with strong
484 meltwater pulses flowing into the north Atlantic from melting Northern Hemisphere ice
485 sheets (e.g. Bond et al. , 1997; Carlson et al. , 2008). These pulses resulted in atmospheric
486 cooling (Rasmussen et al. , 2006) which influenced terrestrial, freshwater and marine
487 ecosystems worldwide through teleconnection processes (Björck et al. , 1997; Mayewski
488 et al ., 2004; Berner et al ., 2010; Smith et al. 2016). Modelling studies show that
489 reductions in Atlantic Meridional Overturning Circulation (AMOC) lead to northern
490 surface wind anomalies in central Asia (Zhang & Delworth, 2005). The potassium (K +)
491 record from the GISP2 ice core is a proxy for the strength of the Siberian High (SH). K +
492 records show that the SH was exceptionally intense at c. 10.8, 10.3, 9.2 and 8.2 kyr BP
493 (Fig. 6b) (Mayewski et al. , 1997), periods coincident with reductions in AMOC. In east
494 Asia, these events (together with changes in solar variability and ENSO) have been
495 implicated in periods of weak Asian summer monsoon, (e.g. D’Arrigo et al. , 2005;
496 Dykoski et al. , 2005; Wang et al. , 2005; Cai et al. , 2008; Chen et al. , 2015), and
497 widespread aridity e.g. on the Tibetan Plateau (Thompson et al. , 1997). Very little is
498 known as to how these events impacted ecosystems in southern Siberia. During such
499 events, a cooler northern hemisphere led to a strengthening of the Asian winter monsoon
500 (Sun et al . 2012). We hypothesize that a more intense Siberian High resulted in a halt to
501 the expansion of taiga forest and a reduction in active permafrost layers, and caused a
502 decline in pelagic productivity in the lake itself, linked to extended periods of ice and
503 snow cover (Mackay et al . 2005).
504
23 Global Change Biology Page 24 of 71
505 Our data show that although significant changes in vegetation were occurring along the
506 forest – steppe transition zone during the early Holocene (Fig. 4g), the direction of
507 change (i.e. expansion of taiga forest) was unaltered, despite abrupt climate change
508 events (Fig. 3a, c; Fig. 6d). However, a small increase in steppe – forest index at 10.3 kyr
509 BP (Fig. 3d), is concurrent with increases in steppe vegetation in the eastern Sayan
510 Mountain range to the west of Lake Baikal (Mackay et al. , 2012), and to the east of
511 Baikal from Lake Kotokel (Bezrukova et al. , 2010). We conclude therefore that
512 insolation driven changes driving taiga forest expansion were stronger than sub
513 Milankovitch forcings, although the latter did appear to result in temporary increases in
514 steppe vegetation. The K+ peak at 10.3 kyr BP (Fig. 6b) was coincident with a significant
515 decline in CMAR (Fig. 3h; 4d) likely linked to both less permafrost melting and reduced
516 river flow (less glacier melt) into the lake because of increased cold and aridity (Mackay
517 et al. , 2011; Fig. 6g). At this time total diatom fluxes were highly variable (DCF) (Fig. 3i,
518 Fig. 4e) with a significant increase in benthic diatom flux (Fig. 3j; Fig. 4f), in line with
519 impacts expected from changes in ice cover associated with a more intense Siberian
520 High. These simultaneous, significant changes in both Lake Baikal and its catchment
521 (Fig. 4, 6) highlights the importance of our analyses in unambiguously identifying the
522 impacts of sub Milankovitch forcings on ecosystems remote from oceanic influences.
523
524 Although the 8.2 kyr event is one of most studied cold events linked to freshening of the
525 North Atlantic, few, if any, high resolution records exist for its impact anywhere in
526 Siberia (see Fig. 1 in Morrill et al . 2013). In general, temperatures around the Europe and
527 the North Atlantic cooled by approximately 1 °C, especially during wintertime (Alley &
24 Page 25 of 71 Global Change Biology
528 Ágústsdóttir 2005; Rohling & Pälike 2005), while there is strong evidence of increased
529 aridity, especially in regions affected by the Asian monsoon (Morrill et al . 2013). A fall
18 530 in Vydrino δ Odiatom values are indicative of reduced Selenga River flow (Fig. 6g), in
531 line with increased aridity caused by a stronger Siberian High (Mackay et al. , 2011),
532 albeit a Siberian High not as strong as that which developed at 10.3 kyr BP (Fig. 6b).
533 Even though we are able to reconstruct carbon dynamics at a resolution comparable to
534 that required by Morrill et al . (2013) of under 50 years, any impact of increased cooling /
535 aridity on regional ecosystems was minimal (Fig. 4). There is a small increase in the flux
536 of benthic diatoms (Fig. 3j) but this is unlikely to be significant (Fig. 4f). Tentatively,
537 therefore, our proxy data suggest that the 8.2 kyr event resulted in a small, temporary
538 shift in the composition of primary producers in Lake Baikal, although overall carbon
539 burial to the bottom sediments remained largely unchanged. Changes in vegetation
540 composition in the southern Siberian catchment did not change either. That we observed
541 no significant change in any of our analyses, suggests that climatic impacts in southern
542 Siberia were not as strong as experienced in regions around the e.g. North Atlantic.
543 Perhaps this is due to greater wintertime than summertime impacts (Alley & Ágústsdóttir
544 2005), promoting aridity through a more prolonged Siberian High, but little change to
545 summertime impacts such diatom growth and permafrost melting.
546
547 (ii) Mid to late Holocene abrupt events
548 Unlike early and late Holocene periods, it is not clear what caused mid Holocene cold
549 events (Wanner et al. , 2014). Nevertheless, the most striking change in all our
550 geochemical indicators since the demise of northern hemisphere ice sheets, occurs
25 Global Change Biology Page 26 of 71
551 between 4.4 – 4.0 kyr BP (Fig. 3, 6). After this event, none of these indicators return to
552 earlier Holocene values (Fig. 3), suggesting that a step change occurred with respect to
553 carbon dynamics at the forest steppe ecotone in southern Siberia.
554
555 The shift in carbon dynamics is coeval with abrupt hydrological changes reconstructed
556 elsewhere in the world, linked to major shifts in large scale ocean atmosphere tropical
557 dynamics, including a weakening of the El Niño Southern Oscillation (ENSO)
558 (McGregor et al. , 2013; Dixit et al. , 2014), and a weakening of the Asian summer
559 monsoon (Dykoski et al. , 2005; Wang et al. , 2005; Berkelhammer et al. 2012). Increased
560 aridity has also been reconstructed in Western Europe (Smith et al . 2016), the Middle
561 East (e.g. Cullen et al . 2000; Arz et al . 2006; continental North America (Booth et al. ,
562 2005; Newby et al. , 2014), and in northern Africa (Gasse, 2000). Kilimanjaro ice cover
563 also declined at this time, and a 3cm thick dust layer at c. 4 kyr BP is indicative of
564 extremely dry conditions (Thompson et al. , 2002; Fig. 6j). Dust records from ice cores on
565 the Tibetan Plateau (Thompson et al. , 1997) and tropical South America (Thompson et
566 al . 2000) provide further evidence of widespread aridity at this time, (Fig. 6i, k). It is
567 likely therefore, that the 4.1 kyr BP event in the Lake Baikal watershed may be due to a
568 complex set of interactions between atmosphere and tropical ocean dynamics causing
569 aridity in southern Siberia. In contrast, changes in diatom fluxes (Fig. 3i, j) were well
570 within existing variability. Indeed, there were no significant changes observed in total
571 diatom cell fluxes for the past 6 kyr in Lake Baikal (Fig. 4e), which suggests that factors
572 that caused major fluxes in diatoms during the early Holocene had little influence during
573 the second half of the interglacial.
26 Page 27 of 71 Global Change Biology
574
575 Late Holocene cold events were caused by several “overlapping” factors (such as
576 volcanic eruptions and solar minima) against a backdrop of low NH summer insolation
577 (e.g. Wanner et al. , 2008; 2014) and amplified by centennial scale oceanic variability
578 (Renssen et al. , 2006). The event dated at c. 2.8 kyr BP is concurrent with a deep, abrupt
579 reduction in solar activity (Fig. 6a) (Grand Solar Minimum) which led to a decline in
580 surface water temperatures in the North Atlantic (Andersson et al. , 2003) and weaker
581 meridional overturning circulation (Hall et al ., 2004). A small increase in GISP2 K +
582 concentrations (Fig. 6b) indicates a strengthened Siberian High, concomitant with glacier
583 advances in central Asia (Mayewski et al. , 2004), a weaker Asian summer monsoon
584 (Dykoski et al. , 2005) and dust inferred aridity over the Tibetan plateau (Thompson et
18 585 al. , 1997) (Fig. 6i). In the Lake Baikal region, the low resolution of δ Odiatom values at
586 this time precludes robust interpretation of Selenga flow into Lake Baikal, except to say
587 that it was likely low ((Fig. 6g). SiZer analyses reveals highly significant changes in
588 carbon dynamics at this time (Fig. 4a d), likely linked to a cooler, more arid climate. The
13 589 increase in sedimentary δ CTOC values (Fig. 3c) is concomitant with a small increase in
590 benthic diatom fluxes, perhaps indicative of a relative shift in the balance between near
591 and off shore primary producers at this time.
592
593 How much carbon is stored in Lake Baikal sediments deposited during the Holocene?
594
595 Mean carbon burial rates for BAIK13 7 for the past 50 years are 2.70 g C m 2 yr 1, similar
596 to previous estimated rates in the south basin of 2.62 g C m 2 yr 1 (Müller et al . 2005) and
27 Global Change Biology Page 28 of 71
597 2.7 g C m 2 yr 1 (Alin & Johnson 2007). Because of very high oxygen exposure times and
598 the dominance of autochthonous sources (Sobek et al . 2009), these values are very much
599 at the lower end of burial rates for lakes in general (Alin & Johnson, 2007) and northern,
600 mid latitude (Heathcote et al . 2015) and culturally eutrophic (Anderson et al . 2014) lakes
601 in particular. Values are similar however, to long term mean rates for European
602 (Kortelainen et al ., 2004; Kastowski et al. , 2011), high latitude (Anderson et al. , 2009,
603 Chinese (Wang et al. , 2015) and other large oligotrophic lakes (Dean & Gorham, 1998;
604 Einsele et al. , 2001). The surface area of Lake Baikal covers 31,722 km 2 (de Batist et al .
605 2006). Upscaling to the rate of organic carbon burial across the whole lake suggests that
606 at least c. 8.56 x 10 5 Pg organic carbon are buried each year (similar to a previous
607 estimate by Alin & Johnson (2007; 8.47 10 5 Pg C yr 1) but higher than that estimated by
608 Einsele et al . (2001; 6.3 x 10 5 Pg C yr 1)). These rates suggest that 0.1% 0.3% of
609 estimated global annual storage of carbon into lake sediments (0.03 – 0.07 Pg C yr 1; Cole
610 et al . 2007) occurs in Lake Baikal alone. In Europe, lakes are estimated to cover 240,000
611 km 2, and sequester 1.25 Mt C yr 1 (Kastowski et al. 2011). Lake Baikal sequesters only
612 about 7% of this amount, despite its area alone approximating to 15% of the surface area
613 of all European lakes. That carbon burial rates in Lake Baikal are less than might be
614 expected, is almost certainly down to its low burial efficiency.
615
616 Burial rates calculated for Lake Baikal were mainly obtained from the bottom sediments
617 from the south basin. However, sedimentation is not continuous in these regions because
618 large turbidite systems converge on the basin floors (Colman et al . 2003). The majority of
619 palaeoenvironmental studies from Lake Baikal are undertaken in regions of continuous
28 Page 29 of 71 Global Change Biology
620 sedimentation such as inter basin or isolated highs, including the Academician Ridge and
621 the Vydrino Shoulder (Fig. 1). It is from these two regions where the best resolved
622 Holocene profiles, with available TOC data, can be found (e.g. Horiuchi et al . 2000;
13 623 Watanabe et al . 2009) (Fig. S4). A compilation of Holocene %TOC and δ CTOC records
624 reveals similarities across the length of the lake (Fig. S3; Fig. S4 a,b). These temporally
625 coherent observations indicate that regional scale drivers influenced carbon dynamics
626 throughout Lake Baikal (Table 2) (Fig. 5d). We therefore estimated organic carbon burial
627 budgets during early (11.7 – 10 kyr BP, mid (10 – 4 kyr BP) and late (4 – 1 kyr BP)
628 Holocene periods. Burial rates of organic carbon were consistently higher at Vydrino than
629 on the Academician Ridge, and mean burial rates were substantially higher during the
630 early Holocene than the middle or late periods in both regions (Table 3). Burial rates are
631 likely higher on the Vydrino Shoulder because although autochthonous sources of
632 organic carbon dominate both regions, burial efficiencies on the Academician Ridge are
633 very low due to extraordinarily high oxygen exposure times of over 1000 years; on
634 Vydrino oxygen exposure times are of the order of 10s of years (Sobek et al. 2009).
635 There is considerable variation in burial rates between the two regions, but higher CMAR
636 during the early Holocene highlights the importance of melting glaciers and permafrost
637 on carbon budgets for the whole lake, not just coastal regions of the south basin. Using
638 mean burial rates for early, mid and late Holocene periods, we estimate that 1.03 Pg
639 organic carbon have been buried in Lake Baikal sediments since the start of the
640 Holocene, and almost one quarter of this was deposited before 10 kyr BP. Interestingly if
641 we had just used annual rate of carbon burial for at BAIK13 7 (2.7 g C m 2 yr 1), the
642 estimated budget for buried carbon during the Holocene is similar at 1.00 Pg C. Global
29 Global Change Biology Page 30 of 71
643 carbon storage in lake sediments during the Holocene range from 428 Pg (Cole et al .
644 2007) to 820 Pg (Einsele et al . 2001). Large lakes (area > 10,000 km 2) account for only
645 27 Pg C stored during the Holocene (Cole et al . 2007), so the Lake Baikal contribution to
646 this figure is relatively minor (c. 4%). In comparison to Boreal lakes in general, Holocene
647 carbon storage in Baikal sediments is still only between 4 5% (Kortelainen et al . 2004).
648 Finally, we estimate that TOC buried in Lake Baikal sediments since its formation is
649 likely to be substantially lower than the 4,500 Pg given by Alin & Johnson (2007). They
650 assumed constant sedimentation rates based on 210 Pb dated cores from Edgington et al .
651 (1991) of 0.0595 cm yr 1. However, these rates are from upper most sediments, and rates
652 decline as sediments become more compacted. For the Holocene, we estimate average
653 sedimentation rates of 0.0163 cm yr 1, while for other regions in the lake, sedimentation
654 rates have been estimated to be about 0.030 cm yr 1 (Colman et al. 2003). Correcting for
655 slower sedimentation rates in more compacted sediments, the total amount of organic
656 carbon buried in Baikal sediments may well be in the order of only c. 2,200 Pg carbon.
657
658 Although on a global perspective, Holocene carbon stored in Lake Baikal is relatively
659 minor, that almost one quarter was deposited during the first few thousand years may
660 have had major implications for biodiversity and ecosystem functioning of the lake.
661 Large supplies of allochthonous carbon exported to lakes influences lake water properties
662 including light and heat penetration because of the optical properties of dissolved organic
663 matter (Solomon et al . 2015). For example, light extinction rates are faster, so resulting in
664 a decline in primary production. These processes may account for the decline in diatom
665 cell fluxes concomitant with rapid increases in CMAR (Fig. 3h, i). Work is on going to
30 Page 31 of 71 Global Change Biology
666 assess overall impact on diatom productivity – biodiversity relationships, and our
667 unpublished results indicate a major decline in diatom palaeoproductivity at this time.
668
669 High resolution, multiproxy, palaeolimnology has demonstrated that carbon dynamics at
670 the forest – steppe ecotone were highly variable during the Holocene. Allochthonous
671 delivery was highest during the early Holocene because high summer insolation and
672 increasing northern hemisphere temperatures caused rapid glacier retreat and melting
673 permafrost, releasing carbon with little forest to stabilize catchment soils. We estimate
674 the approximately one quarter of the Holocene carbon budget was sequestered during this
675 period, which may have had a profound effect on primary production and diversity of
676 large celled diatom species. Warm summers during the Early Holocene were vulnerable
677 to extended winter cooling associated with periods of increased intensity of the Siberian
678 High. These resulted in abrupt drops in organic carbon burial rates, concomitant with
679 hydrological changes in the catchment. That these changes occurred almost
680 simultaneously with changes elsewhere (e.g. decline in Asian summer monsoon (Dykoski
681 et al. , 2005) and increased aridity on the Tibetan Plateau (Thompson et al. , 1997))
682 highlight that carbon dynamics in central Asia, far from oceanic influences, were highly
683 responsive to changes in the global climate system during the early Holocene. Sustained
684 low diatom productivity and carbon burial after c. 3 kyr BP is concurrent with the
685 neoglacial, linked to pronounced cooling (Marcott et al. , 2013) and aridity caused by
686 vegetation and snow / ice albedo feedbacks in central Asia (e.g. Ganopolski et al. , 1998;
687 Renssen et al. , 2006), leading to permafrost refreezing again.
688
31 Global Change Biology Page 32 of 71
689 Substantial warming over the past 50 years has led to permafrost degradation in southern
690 Siberia (Törnqvist et al ., 2014) and ecological changes in Lake Baikal (Hampton et al .
691 2014). Yet if current rates of permafrost warming are comparable to those during the
692 early Holocene (Anisimov et al ., 2002, the influence on carbon dynamics to Lake Baikal
693 have yet to be realised. One reason for the discrepancy may be related to river discharge,
694 which increases DOC input into Boreal lakes Prokushkin et al ., (2011). During the early
695 Holocene, river discharge into Lake Baikal was much greater (Mackay et al . 2011)
696 because glaciers were melting, causing lake levels to rise substantially (Urabe et al .
697 2004), which in turn likely resulted in the very high carbon burial rates observed. In
698 recent decades, average runoff from Selenga River basin has declined, leading to
699 decreased sediment loads (Törnqvist et al ., 2014). Low mean Baikal carbon burial rates
700 during the past 50 years are in contrast to other studies where recent increases in CMAR
701 have been attributed to increased agriculture, e.g. China (Dong et al ., 2012) and Europe
702 (Anderson et al ., 2014) or global warming / increased deposition of reactive nitrogen e.g.
703 northern lakes in North America (Heathcote et al . 2015). In the near future, it is doubtful
704 whether nutrient enrichment or warming will result in increased carbon burial to Baikal
705 sediments. There is increasing evidence that nutrient enrichment of coastal waters in Lake
706 Baikal are starting to have an impact on nearshore communities (Timoshkin et al . 2016),
707 but there is as yet no evidence of nutrient enrichment in pelagic Lake Baikal (Izmest’eva
708 et al . 2016). And although regional warming and forest fires are predicted to increase in
709 the near future, driving the forest steppe ecotone northwards (Tchebakova et al . 2009),
710 southern Siberia is predicted to become more arid (Törnqvist et al. , 2014), leading to a
711 decline in Selenga River discharge. So despite further permafrost degradation, large
32 Page 33 of 71 Global Change Biology
712 quantities of released organic carbon may yet not find a route into Lake Baikal. Taken
713 together, our data provide new and important insights into how abrupt climate change
714 events can influence Holocene carbon dynamics in even very remote regions. However,
715 understanding future changes to carbon dynamics must take account of hydrological
716 variability as well as warming temperatures.
717
718
719 Acknowledgements. We wish to acknowledge the various agencies who helped to fund
720 this work, especially UK NERC (IP/635/0300; NE/J010227/1), the EU FPV programme
721 (EVK2 CT 2000 0057) and the Norwegian Research Council (IGNEX ref: 249894/F20).
722 We thank Dr Alexander Prokopenko for providing the TOC data from the Buguldieka
723 Saddle, used in Fig S4B. We thank UCL Geography Cartography Unit who helped
724 prepared the figures, David Adger and two anonymous reviews for very insightful
725 comments which have helped to improve the manuscript considerably.
726
33 Global Change Biology Page 34 of 71
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1248
Core code Type Lat. Long. Water Core Analyses
depth length
CON01 piston 51.5849 104.8548 675 m 10.45 m DBD; 605 3 diatoms 14 13 CON01 box 51.5835 104.8518 665 m 2.50 m C; δ CTOC ; 605 5 TOC; C/N; CMAR; pollen BAIK13 7 gravity 51.5683 104.5286 1080 m 0.47 m DBD; TOC; CMAR 1249 1250
57 Global Change Biology Page 58 of 71
1251 Table 2: Factors likely to influence organic geochemistry in Lake Baikal sediments away 1252 from Holocene mean values: %TOC = 1.8%; CN = 11.6; δ13 C values = –29.03 ‰ 1253 13 Factor TOC C/N δ CORG Increased planktonic Increase Decrease Decrease 1
diatoms
Relative increase in Increase Decrease No change 2
pelagic productivity
Relative increase in near Decrease Unknown Increase 3
shore productivity
Increased picoplankton Increase Decrease Unknown 4
Increased terrestrial input Increase Increase Decrease 5
from mature soils
Catchment DOM No change Increase Increase 6
Increased C 4 terrestrial NA NA NA
input 7
Increased atmospheric No change No change No change
8 pCO 2
Increased ice cover 9 Decrease Unknown No change
Gas hydrates 10 No change No change No change
1254
1255 1: at present, approximately 90% of organic matter in Lake Baikal is derived from
1256 phytoplankton, mainly diatoms during spring and autumn overturn; open water diatoms
1257 range between –28‰ to –35‰ (mean –29‰); 2. In pelagic Baikal, the HCO 3 pool is so
1258 large, no isotopic discrimination takes place (Yoshii et al . 1999); 3: flora in littoral
58 Page 59 of 71 Global Change Biology
1259 regions have higher δ13 C values; aquatic macrophytes range between –5‰ to –18‰ and
1260 benthic algae between –5‰ to –11‰ (mean –9‰) (Kiyashko et al. , 1998; Yoshii, 1999;
1261 Yoshii et al. , 1999); 4: As far as we can ascertain, very little research has specifically
1262 looked at C fractionation in picoplankton. However, Sakata et al. , (1997) suggest values
1263 of –22‰ to –30‰; 5: well developed soils result in an increase in 13 C depleted respired
1264 CO 2 (Hammarlund, 1992; Reuss et al ., 2010); 6: dissolved organic matter from
1265 catchment rivers has δ13 C value of –26‰ to –27‰ (Yoshioka et al. , 2002); 7. molecular
1266 isotopic stratigraphy of sedimentary long chain n alkanes did not detect any C 4 plants
1267 within its watershed during the late Quaternary (Brincat et al. , 2000); 8: according to
1268 Prokopenko et al. , (1999) increased Holocene atmospheric CO 2 concentrations resulted in
13 1269 a decline in δ CORG values, but there is no relationship between Holocene CO 2
13 1270 concentrations and δ CORG values (Fig 3); 9: biogenic silica inferred productivity is
1271 much lower during cold glacial periods with significantly extended ice cover (Mackay,
1272 2007) but because of low overall primary production under the ice and higher CO 2
1273 solubility in colder water, isotopic discrimination is not thought to be important in Lake
1274 Baikal (Watanabe et al. , 2004) ; 10 : A within lake process unique to Lake Baikal is the
1275 occurrence of sedimentary methane hydrates (Granin & Granina, 2002). Prokopenko &
1276 Williams (2004) suggested that the relatively negative Holocene TOC δ13 C values (in
1277 comparison to values for the late glacial of c. –24‰) may have been caused by deglacial
1278 methane emissions, with methane accumulating under winter ice (Prokopenko &
1279 Williams, 2005). However, teragrams of methane would need to be emitted, but only 10s
1280 of megagrams have actually been measured (Schmid et al. , 2007), making it unlikely that
1281 δ13 C depleted methane drives lower sedimentary δ13 C values.
59 Global Change Biology Page 60 of 71
1282
1283
1284 Table 3: Organic carbon burial rates determined for early, middle and late Holocene
1285 periods, based on 5 Holocene studies (see text for details and Fig 1 for locations).
1286
Early Holocene Middle Holocene Late Holocene OC
CMAR (g C m 2 yr 1) CMAR (g C m 2 yr 1) CMAR (g C m 2 yr 1)
CON01 605 5 8.97 6.21 3.84
Ver94.St16 (AR) 2.90 1.66 2.97
5GC (AR) 5.45 1.97 1.17
StPC (AR) 1.19 0.44 1.21
6GC (AR) 5.01 2.77 1.81
Mean (s.d.) 4.71 (2.94) 2.61 (2.18) 2.20 (2.17) 1287
1288
60 Page 61 of 71 Global Change Biology
1289 Figure Legends
1290 Figure 1 . Map of Lake Baikal and its catchment, with locations of the different cores
1291 mentioned or utilized in this study highlighted.
1292
1293 Figure 2. ‘Bacon’ Age depth model (Blaauw & Christen, 2011) for Vydrino box core
1294 (CON01 605 05) of radiocarbon AMS dates calibrated using IntCal13 radiocarbon
1295 calibration curve (Reimer et al. , 2013).
1296
1297 Figure 3. Multiproxy data determined for Holocene sediments from the Vydrino
1298 Shoulder, Lake Baikal. Vegetation (3a d) and organic geochemistry data (3e h) are from
1299 Vydrino Shoulder core CON01 605 5. Diatom data (3i j) are from Vydrino Shoulder core
1300 CON01 605 3. (a): % Arboreal Pollen; (b): Pinus sylvestris pollen (%PinSylv); (c):
1301 Pollen PC1 scores; (d): steppe – forest index; (e): total organic carbon (%TOC); (f): total
13 1302 organic carbon / total organic nitrogen ratios (C/N); (g): δ CTOC (‰); (h): carbon mass
1303 accumulation rates (CMAR; g C m 2 yr 1) in 100 yr bins; (i): diatom cell fluxes (DCF cm
1304 2 yr 1 x10 6) from CON01 605 3; (j): benthic diatom fluxes (filled silhouette) with x5
1305 exaggeration to see fluxes in detail (empty silhouette); (k): CO 2 data (p.p.m.v.) from
1306 Dome C ice core (Flückiger et al. , 2002); (l): δ13 C ice core records Dome C ice core
1307 (Elsig et al. , 2009); (m): mean northern hemisphere temperature stack records for 60°
1308 latitude bands (30° N – 90° N) (Marcott et al. , 2013); (n): July insolation 50° N (W m 2)
1309 (Berger & Loutre, 1991). The horizontal dotted line at 6.1 kyr BP marks significant
1310 change in PC1 identified by breakpoint analysis. Light blue zones denote abrupt reversal
1311 events at c. 10.3, 8.2, 4.1 and 2.8 kyr BP.
61 Global Change Biology Page 62 of 71
1312
1313 Figure 4. Individual SiZer plots from our GAM SiZer analyses. Grey areas are periods of
1314 non significant change, while blue and red periods show periods of significant decreasing
1315 / increasing change, respectively.
1316
1317 Figure 5. Modelled relationships between PC1 scores and organic geochemistry for early
1318 (5a d) and late (5e h) periods. Solid line indicates a significant relationship, p=0.05.
1319
1320 Figure 6. Multi archive data plotted alongside ‘deviations from mean’ values of organic
1321 geochemical records (6c f) from Vydrino Shoulder core CON01 605 5. (a): Sunspot
1322 numbers (Solanki et al. , 2004); (b): K + ion concentrations (ppb) from GISP2 D core
1323 (Mayewski et al. , 1997); (c): total organic carbon (%TOC); (d): total organic carbon /
13 1324 total organic nitrogen ratios (C/N); (e): δ CTOC (‰); (f): carbon mass accumulation rates
2 1 18 1325 (CMAR; g C m yr ) in 100 yr bins; (g): δ Odiatom record from Vydrino Shoulder piston
1326 core CON01 605 05 (Mackay et al. , 2011); (h): four stacked records of relative
1327 abundance of haematite stained grains (%HSG) in North Atlantic sediments (Bond et al. ,
1328 2001); (i): dust concentrations (x10 3 ml 1) from Qinghai Tibetan Guliya ice core
1329 (Thompson et al. , 1997); (j): 50 yr mean dust concentrations (ml 1) from Mount
1330 Kilimanjaro ice core NIF3 (Thompson et al. , 2002) plotted on a log scale; (k): 50 yr
1331 mean dust concentrations (ml 1) from Huascarán ice core, Peru (Thompson et al. , 2000)
1332 plotted on a log scale; (l): XRF Mn element density (cps) from Shaban Deep basin,
1333 northern Red Sea core GeoB 5836 2 (Arz et al. , 2006); (m): δ18 O (‰) of shallow water
1334 foraminifera Globigerinoides ruber from Shaban Deep basin, northern Red Sea core
62 Page 63 of 71 Global Change Biology
1335 GeoB 5836 2 (Arz et al. , 2006); (n): Dolomite (% wt) from Gulf of Oman sediment core
1336 M5 422 (Cullen et al. , 2000); (o): δ 18 O (‰) of ostracod Melanoides tuberculata from
1337 palaeolake Kotla Dahar, NW India (Dixit et al. , 2014); (p): δ 18 O (‰) record from
1338 Mawmluh Cave speleothem, NE India (Berkelhammer et al. , 2012); (q): δ18 O (‰) record
1339 from Dongge Cave speleothem, SE China (Dykoski et al. , 2005). Light blue zones denote
1340 cold reversal events at c. 10.8, 10.3, 8.2, 4.1 and 2.8 kyr BP.
1341
1342
63 Global Change Biology Page 64 of 71
1343 Supplementary Figure Legends
1344
1345 Figure S1. PCA biplot of pollen data. Codes used include Cyp = Cyperaceae; AlnFrut =
1346 Alnus fruticosa type; Tubul = Compositae Asteroideae; PinSylv = Pinus sylvestris type;
1347 PinSib = Pinus sibirica type; Betnana = Betula nana type; Betun = Betula
1348 undifferentiated. Full details given in (Demske et al. , 2005).
1349
1350 Figure S2. Breakpoint analysis of pollen PCA axis 1 data.
1351
1352 Figure S3. Compiled δ13 C data from Lake Baikal. A: Vydrino, this study; B: St.5GC
1353 from the Academician Ridge (Watanabe et al. , 2009); C: St.5PC from the Academician
1354 Ridge (Watanabe et al. , 2009); D: St.6GC from the Academician Ridge (Watanabe et al. ,
1355 2009); E: Ver94/St16 from the Academician Ridge (Horiuchi et al. 2000).
1356
1357 Figure S4A. Compiled TOC data from Lake Baikal plotted against a radiocarbon age
1358 scale. A: Vydrino, this study; B: St.5GC from the Academician Ridge (Watanabe et al. ,
1359 2009); C: St.5PC from the Academician Ridge (Watanabe et al. , 2009); D: St.6GC from
1360 the Academician Ridge (Watanabe et al. , 2009). E: Core Ver94.St.16 from the
1361 Academician Ridge (Horiuchi et al. , 2000);
1362
1363 Figure S4B. Compiled Holocene TOC data from Lake Baikal plotted against a depth
1364 scale. A: Core Ver93/2 GC24 from the Buguldieka Saddle, opposite the shallow waters
1365 of the Selenga Delta (Karabanov et al . 2004); B: Core BDP 93 2 from the Buguldieka
64 Page 65 of 71 Global Change Biology
1366 Saddle, opposite the shallow waters of the Selenga Delta (Prokopenko et al . 1999).
1367 Approximate date horizons are derived from the revised chronology presented by
1368 Prokopenko et al . (2007), but no suitable age depth model is available from which to plot
1369 these up on an age scale.
65 Global Change Biology Page 66 of 71
100°N 104°N 108°N 112°N
56°E Upper Angara Upper Angara
Lake Baikal Barguzin Lower Angara
52°E Irkutsk
Selenga
48°E Ulan Bator
0 200km
North Basin
St.6GC Ver94.St.16
St.5PC St.5GC Academician Ridge
Barguzin River
Central Basin BDP-93-2 Ver93/2-GC24 Buguldieka Saddle
Angara
Selenga River
South Basin N CON01-605 Vydrino Shoulder
0 100km Pagee 67 of 71 Global Changeacc.shape: Biology 1.5 mem.strength: 4 acc.mean: 50 4 mem.mean: 0.7 38 5cm sections 3 −70 0.010 2 1 Log of Objecti v −85 0
0 4000 8000 0.000 0 100 300 0.0 0.4 0.8 Iteration Acc. rate (yr/cm) Memory
12000 VydrinoK 10000 8000 6000 cal yr BP 4000 2000 0
0 50 100 150 Depth (cm) (m) (a) (e) (g) (c) (k) (i)
13 - 2 - 1 % Arboreal Pollen Pollen PC1 %TOC δ C (‰) DCF (cm y r ) CO2 p.p.m.v. 90-30°N T anomaly (°C) 25 45 65 85 -0.4 -0.2 0.0 0.2 1.0 1.4 1.8 2.2 2.6 3.0 -27 -28 -29 -30 -31 0 1000000 2000000 255 265 275 285 -1.0 0.0 1.0 2.0 1 2
3 2.8 4 4.1 5 k y r
B 6 P 7 8 8.2 9 10 10.3 Global ChangeBiology 11
0 20 40 60 80 10 0 16 32 48 9 10 11 12 13 14 1.5 4.5 7.5 10.5 13.5 0 1000000 2000000 -6.2 -6.4 -6.6 -6.8 450 460 470 480 490 500 -2 -1 -2 -1 % PinSylv steppe - forest index C/N CMAR (g C m y r ) Benthic flux (cm yr ) δ13C (‰ VPDB) Jul Insolation 50°N (h) (j) (n) (b) (d) (f) (l) Page 68of71 Page 69of71 (g) (e) (d) (b) (a) (c) (f) k k k k k k k 20 10 45 35 25 15 45 35 25 15 30 20 10 80 70 60 50 40 30 20 10 80 70 60 50 40 30 20 10 80 70 60 50 40 30 20 10 1000 1000 1000 1000 1000 1000 1000 2000 2000 2000 2000 2000 2000 2000 3000 3000 3000 3000 3000 3000 3000 4000 4000 4000 4000 4000 4000 4000 5000 5000 5000 5000 5000 5000 5000 Benthic CellFLux Age (calyrBP) Diatom CellFlux 6000 6000 6000 6000 6000 6000 6000 CMAR %T C/N PC1 δ OC 13 C 7000 7000 7000 7000 7000 7000 7000 8000 8000 8000 8000 8000 8000 8000 Global ChangeBiology 9000 9000 9000 9000 9000 9000 9000 10000 10000 10000 10000 10000 10000 10000 11000 11000 11000 11000 11000 11000 11000 Early Holocene Global Change Biology Page 70 of 71 (a) (b) (c) (d)
−3.6 p = 0.026 p < 0.001 p = 0.739 p = 0.043 2.60 −27.5
2.4 C 2.50 −4.0
13 CN TOC 2.0 δ −29.0 CMAR 2.40
1.6 −4.4 2.30 −30.5
−0.3 −0.1 0.1 −0.3 −0.1 0.1 −0.3 −0.1 0.1 −0.3 −0.1 0.1
PC1 PC1 PC1 PC1
Late Holocene (e) (f) (g) (h) p = 0.2 p = 0.372 p = 0.036 p = 0.089
−3.6 1.8 2.50 C 13 −28.5 CN TOC δ −4.0
CMAR 1.4 2.40 −30.0 −4.4 2.30 1.0
0.05 0.10 0.15 0.20 0.05 0.10 0.15 0.20 0.05 0.10 0.15 0.20 0.05 0.10 0.15 0.20
PC1 PC1 PC1 PC1 Page 71of (i) (q) (o) (m) (g) (c) (k) (e) (a)
sunspot number dev-%TOC dev- 1 3 C ‰ Baikal δ 1 8 Odiatom ‰ Tibet Dust (103 ml-1) Huascaran dust (ml-1) Red Sea δ 1 8 O ‰ NW India δ 1 8 O ‰ SE China δ 1 8 O ‰ -10 20 50 80 110 -0.8 -0.4 0.0 0.4 0.8 3 2 1 0 -1 -2 22.5 27.5 32.5 37.5 2000 1000 0 50000 1000 1 0 -1 -2 8 3 -3 -8 -6 -7 -8 -9 -10 500 1 1000 1500 2002 0 2500 3003 0 2.8 3500 4 4000 4.1 4500 5005 0 k
y 5500 r 6
B 6000
P 6500 7007 0 7500
8008 0 8.2 8500
9009 0 9500 10 10000 10.3 10500 11 11000 10.8 11500 Global ChangeBiology 0 8 16 24 32 -2 -1 0 1 2 3 -5 -2 2 5 8 20 15 10 5 0 100000 1000 24 20 16 12 8 4 12 10 7 5 2 0 -3 -4 -5 -6 -7 -8 GISP2 K+ (ppb) dev-CN dev-CMAR %HSG Kilimanjaro dust (ml-1) Red Sea [Mn] Oman Dolomite % NE India δ 1 8 O ‰ (d) (f) (b) (h) (n) (l) (p) (j)