Varved lake sediments are exceptional archives of
paleoclimatic information due to their precise chronological control and
annual resolution. However, quantitative paleoclimate reconstructions based
on the biogeochemical composition of biochemical varves are extremely rare,
mainly because the climate–proxy relationships are complex and obtaining
biogeochemical proxy data at very high (annual) resolution is difficult.
Recent developments in high-resolution hyperspectral imaging (HSI) of
sedimentary pigment biomarkers combined with micro X-ray fluorescence (µXRF) elemental mapping make it possible to measure the structure and
composition of varves at unprecedented resolution. This provides
opportunities to explore seasonal climate signals preserved in biochemical
varves and, thus, assess the potential for annual-resolution climate
reconstruction from biochemical varves. Here, we present a geochemical
dataset including HSI-inferred sedimentary pigments and µXRF-inferred
elements at very high spatial resolution (60 µm, i.e. >100
data points per varve year) in varved sediments of Lake Żabińskie,
Poland, over the period 1966–2019 CE. We compare these data with local
meteorological observations to explore and quantify how changing seasonal
meteorological conditions influenced sediment composition and varve
formation processes. Based on the dissimilarity of within-varve multivariate
geochemical time series, we classified varves into four types. Multivariate
analysis of variance shows that these four varve types were formed in years
with significantly different seasonal meteorological conditions. Generalized
additive models (GAMs) were used to infer seasonal climate conditions based
on sedimentary variables. Spring and summer (MAMJJA) temperatures were
predicted using Ti and total C (Radj2=0.55; cross-validated
root mean square error (CV-RMSE) = 0.7 ∘C, 14.4 %). Windy
days from March to December (mean daily wind speed >7 m s-1) were
predicted using mass accumulation rate (MAR) and Si (Radj2=0.48; CV-RMSE = 19.0 %). This study demonstrates that high-resolution
scanning techniques are promising tools to improve our understanding of
varve formation processes and climate–proxy relationships in biochemical
varves. This knowledge is the basis for quantitative high-resolution
paleoclimate reconstructions, and here we provide examples of calibration
and validation of annual-resolution seasonal weather inference from varve
biogeochemical data.
Introduction
Quantitative paleoclimatic reconstructions are essential for understanding
how the climate system functions (IPCC, 2013; Tierney et
al., 2020). Spatially distributed, high-resolution paleoclimatic records are
necessary to understand regional-scale paleoclimate variability and to
contextualize current climate change (Neukom
et al., 2019; PAGES2k Consortium, 2013). Varved lake sediments have long
been recognized as unique archives of climate (De Geer,
1908) because of their precise, annually resolved, age control, and because
of the wide variety of paleoenvironmental information preserved in varved
sediments (Zolitschka et al.,
2015). Numerous studies have identified close relationships between
instrumental meteorological records and data obtained from varved sediments,
in some cases at annual resolution, demonstrating the great potential of
varves for high-resolution paleoclimatic reconstructions. The majority of
these studies have related meteorological parameters with sedimentary
variables in clastic varves that reflect transport of minerogenic material
(Lapointe
et al., 2020; Francus et al., 2002; Trachsel et al., 2010; Elbert et al.,
2012). In contrast to clastic varves, biogenic and biochemical varves tend
to feature a greater variety of components and processes affecting the
sedimentary composition. Biogenic varves are composed of primarily
biological components (i.e. diatoms and other organic matter). Biochemical
varves may be considered a mixed varve type of biological remains and
chemical precipitates that are mainly biologically induced (i.e. calcite
precipitation induced by phytoplankton blooms; Zolitschka et al., 2015).
Microfossil assemblages have been commonly used for quantitative climate
reconstructions from biogenic varves, however these techniques face
challenges for sub-decadal-scale analyses (Telford,
2019). Interpretation of climate signals recorded in the sedimentary
properties of biochemical and biogenic varves has proven much more
challenging than in clastic varves due to more complex and often non-linear
interactions between climate forcing, ecological and hydrochemical response,
endogenic organic matter and mineral formation, and sedimentation (Zolitschka et al., 2015). An
extensive compilation of more than 1000 varve-related publications
(https://pastglobalchanges.org/science/end-aff/varves-wg/varve-related-publications
last access: 8 October 2021) includes only three studies
(Amann
et al., 2014; Swierczynski et al., 2012; Tian et al., 2011) that report
significant correlations between meteorological data and bulk geochemical
data from biogenic varves. Accordingly, climate reconstructions from
geochemical proxies of biogenic varves are extremely rare. Despite their
widespread occurrence in temperate zones, biogenic and biochemical varves
remain an under-utilized archive for high-resolution quantitative
paleoclimate reconstructions.
During recent decades, high-resolution sediment scanning techniques, such
as X-ray fluorescence (XRF) and reflectance spectroscopy, have been
increasingly used for environmental reconstructions, including quantitative
sub-decadal climate reconstructions from varved (Amann
et al., 2014; Trachsel et al., 2010; Lapointe et al., 2020) and non-varved
lacustrine sediments
(von
Gunten et al., 2012; Boldt et al., 2015). The speed and resolution of these
non-destructive scanning measurements (often 0.2–2 mm resolution) is
impossible to achieve with conventional biogeochemical methods that require
destructive sampling of sediment cores. In this study, we use cutting-edge
micro X-ray fluorescence (µXRF) imaging (to measure elements) and
hyperspectral imaging (HSI, sedimentary pigments) methods that improve upon
commonly used linescan techniques by producing two-dimensional images of
geochemical data at 60 µm resolution. This improvement is critically
important for sub-annual- and annual-resolution analyses because annual
layers can be delineated more precisely and consistently using images of
geochemical data. Additionally, 60 µm resolution yields an average of
∼100 data points per varve at our site, enabling detailed
investigation of (sub)seasonal-scale geochemical variability.
Here, these high-resolution imaging spectroscopy techniques were applied to
sediments of Lake Żabińskie to gain insights into climate–proxy
relationships in biochemical varves and to investigate how recent climatic
variability is recorded in varve composition. This site features varves with
excellent preservation, high sedimentation rates (6 mm yr-1 during
1966–2019), and complex varve structures showing substantial year-to-year
variations. We focus on a 54-year period (1966–2019) chosen because there
was no uncertainty in the varve count over this interval. This chronological
precision is required for an assessment of climate–proxy signals at seasonal
resolution and makes the Lake Żabińskie sediments uniquely
well-suited for this purpose. This study aims to answer the following
research questions: (1) how are seasonal weather conditions recorded in
biochemical varves, and (2) how can varve composition be used to reconstruct
seasonal meteorological conditions?
Materials and methodsSite description and core collection
Lake Żabińskie is a kettle-hole lake formed in the post-glacial
landscape of the Masurian Lakeland in Poland (54.1318∘ N,
21.9836∘ E; Fig. 1). The basin is small
(41.6 ha) and relatively deep (44.4 m), which promotes thermal
stratification. Limnological data from 2011–2013 show that complete mixing
of the water column occurs 0–2 times per year
(Bonk et al., 2015). The catchment geology is
mainly glacial till, sandy moraines, and fluvioglacial sands and gravels.
Anoxic and eutrophic conditions have led to good preservation of thick
biochemical varves. Bonk et al. (2015)
documented varve formation processes at Lake Żabińskie via
limnological monitoring, sediment trapping, and microscopic and geochemical
investigation of recent sediments. An annual cycle of sedimentation was
described with algal blooms and Si deposition in spring, followed by calcite
precipitation in spring and summer, then Fe and S enriched sediments in
fall, and finally organic and lithogenic detritus (enriched in K and Ti)
deposited in winter. Further investigations of the sedimentary record have
documented changes to the lake mixing regime, trophy, and catchment erosion,
with the most significant environmental changes occurring after major
deforestation and the development of agriculture in the catchment during the
17th century (Zander
et al., 2021a; Żarczyński et al., 2019; Hernández-Almeida et al.,
2017; Wacnik et al., 2016; Bonk et al., 2016).
Sediment cores used in this study were retrieved in 2012 (ZAB-12-1), and
2020 (ZAB-20-1) using an UWITEC gravity corer (∅90 mm). Resin-embedded
sediment slabs and thin sections were produced following the method of Żarczyński et al. (2018).
Core correlation was done on a varve-by-varve basis using images of the
cores, resin blocks, and thin sections.
(a) Orthophoto map of the Lake Żabińskie catchment
(imagery from Polish Head Office of Geodesy and Cartography). Dark green
colors represent forests; lighter colors represent cultivated areas. (b)
Bathymetric map of Lake Żabińskie. (c) Summary of monthly
meteorological data over the period 1966–2019.
Chronology
Varve counting was performed on scanned images of thin sections and these
counts were transferred to images of geochemical scanning data.
Identification of varve boundaries was facilitated by previous studies on
varve microfacies in Lake Żabińskie
(Żarczyński
et al., 2018; Bonk et al., 2015), as well as µXRF elemental
measurements. The beginning of the varve year was defined as the onset of
calcite precipitation as this point can be consistently identified for every
year in both scanning data and thin sections as a sharp rise in Ca counts
(see Fig. S1). Three researchers performed the varve count independently.
Varve thickness measurements were homogenized to account for differing
sedimentation rates in the two cores by correcting the counting interval of
each thin section to the actual depth covered by the same interval in the
2020 core. Dry bulk density was measured for each varve by carefully
sampling material from within a single varve into a 3 cm3 syringe
and then measuring the weight of the sample after drying. Mass accumulation
rates (MARs) were calculated by multiplying varve thickness and dry bulk
density for each varve.
The varve count was tested using fallout from the 1986 Chernobyl incident as
an independent time marker. Fallout was identified using 137Cs
activities measured using gamma ray spectrometry (Tylmann et al., 2016). Core
ZAB-12-1 was sampled for 137Cs activity in 3-year intervals. Individual
varves were sampled from core ZAB-20-1 for the years 2017, 2011, 1989–1984,
and 1966 to better resolve the 1986 Chernobyl peak and to confirm baseline
137Cs activities during years not affected by Chernobyl fallout.
Geochemical scanning measurements
µXRF measurements were conducted on resin-embedded sediment slabs
extracted from the 2012 and 2020 cores using a Bruker M4 Tornado. The
scanner was equipped with a Rh X-ray source with voltage and current set to
50 kv and 300 µA, respectively. Counts were measured along the
two-dimensional surface of the slabs with a measurement spot size of 20 µm and counting time of 20 ms/pixel. The measurement step (pixel size) was
set to 60 µm.
Hyperspectral imaging was performed on the fresh ZAB-20-1 core using a
Specim PFD-CL-65-V10E camera following methods described in Butz
et al. (2015) and using the same acquisition settings and calculations as in
Zander et al. (2021a). The scanning resolution (pixel size) was 60 µm. Relative absorption band depth (RABD) indices were used to
quantify the abundance of sedimentary pigments. RABD655-685max
represents total chloropigments (TChl), and RABD845 represents
bacteriopheopigments-a (Bphe). The RABD indices were calibrated to pigment
concentrations (µg gd.s.-1 d.s. = dry sediments) using the
calibration method described in Zander et al. (2021a). TChl is used as a
proxy for total algal productivity (Rein and Sirocko, 2002;
Leavitt and Hodgson, 2002), whereas Bphe is produced by anoxygenic
phototrophic purple sulfur bacteria and is a specific biomarker for anoxic
conditions overlapping with the photic zone (Butz et al., 2015;
Sinninghe Damsté and Schouten, 2006). An additional spectral index,
Rmean (mean reflectance/total brightness), was used as a proxy for calcite (Butz et al., 2017) to improve the
alignment of HSI and µXRF data.
CNS elemental analysis
Total carbon (TC), total inorganic carbon (TIC), total nitrogen (TN), and
total sulfur (TS) were quantified using a Vario El Cube elemental analyzer
(Elementar) equipped with a SoliTIC module and thermal conductivity detector
following methods described in Żarczyński et al. (2019).
Individual varves in ZAB-20-1 were sampled for these analyses. Total organic
carbon (TOC) was calculated by subtracting TIC from TC. To account for
post-depositional loss due to early diagenetic decay of organic matter, TOC
and TN values were corrected using formulas developed by Gälman et al. (2008). The estimates
of TOC lost were added to TC values. Table S1 reports data with and without
the correction applied. All subsequent analyses were done with and without
the correction; the results are robust and are weakly influenced by the
correction.
Data analysis
Downcore profiles of µXRF and HSI data were established by averaging
rows of pixels across 2 mm wide subsets of the rasterized geochemical data
(thus each data point used for analysis represents a
60×2000µm
area). Data analysis was conducted in R 4.0.2 (R Core Team, 2020).
The code and data used to generate plots and statistics reported in this
paper are available at 10.48350/156383 (Zander et al., 2021b). The
HSI pigment data (from the fresh ZAB-20-1 core) were aligned with the µXRF data (from resin-embedded slabs) using the location of varve boundaries
manually identified on images of the core and resin-embedded slabs. This
alignment was refined at the sub-varve scale using a dynamic time warping
algorithm (Tormene et al., 2008) to maximize the
correlation between Rmean (total reflectance; HSI) and Ca (µXRF) (Fig. S1). Settings were applied such that the data could not be shifted by more
than one year (typically less).
To classify varve types (Vtys), the package “distantia”
(Benito and Birks, 2020) was used to calculate the
dissimilarity measure psi (Ψ; Gordon and
Birks, 1974), which measures the dissimilarity between pairs of multivariate
time series (here the within-varve geochemical profiles). The ward.d2
hierarchical clustering algorithm was then used to identify groups of varves
with a similar sequence of geochemical data through the year. Prior to the
classification step, data were detrended, log-transformed, and normalized.
Raw data were used for further analyses. To determine if the years defined
by Vtys experienced differing seasonal meteorological conditions, a
multivariate analysis of variance (MANOVA) test was performed using seasonal
meteorological data from Kętrzyn, Poland, located 40 km west of the
study site. Meteorological data were retrieved from the National Research Institute of the Polish Institute of
Meteorology and Water Management using the open-data API (application programming interface) and climate 0.9.1 R package (Czernecki et al.,
2020). We considered a 15-month period from March to May the following year
to account for the uncertainty of assigning the varve boundary (spring
calcite precipitation) to a fixed point in the year. We focus on mean daily
temperature, the 90th percentile of daily precipitation, and the
90th percentile of daily mean wind speed based on the expectation that
days with intense precipitation or wind would have a stronger effect on the
sediments than mean seasonal values.
To further investigate relationships between seasonal meteorological
conditions and varve composition, a redundancy analysis (RDA) was performed
(“vegan” package, Oksanen et al., 2020). Meteorological
variables were used as explanatory variables, and annual mean geochemical
data from varve layers were response variables. Additionally, correlation
coefficients (Pearson's r) were calculated for monthly and seasonal
meteorological variables and mean annual proxy data. The significance of
correlations was assessed with p values that were corrected for
autocorrelation using the method of Bretherton et al. (1999) and were
corrected for multiple testing using the false discovery rate approach of Benjamini and Hochberg (1995).
Generalized additive models (GAMs) were used to reconstruct meteorological
parameters from sedimentary variables. The method is analogous to multiple
linear regression, but GAMs have the advantage of utilizing flexible
predictor functions that can account for non-linear relationships between
predictor and response variables (Wood, 2017). The target
meteorological parameters for reconstruction were selected based on the
results of the previous correlation analysis and analysis of variance, which
identified temperatures in spring and summer and windiness throughout
multiple seasons as the most important variables driving variability in
varve composition and structure. Predictor variables were selected based on
the strength of their linear relations. Multiple combinations of possible
predictor variables were evaluated and final models were selected based on
their predictive power, but also mechanistic process understanding and
plausibility. Models were fit using the restricted maximum likelihood (REML)
smoothness selection (Wood, 2011). Model skill was assessed
by the 10-fold cross-validated root mean square error of prediction
(CV-RMSE). Because this statistic can underestimate errors when data are
autocorrelated (Telford, 2019), a split-period approach
was also used to calculate RMSE, as well as additional calibration
statistics reduction of error (RE), and coefficient of efficiency (CE)
(Cook et al., 1994). In this approach,
models are trained using a subset of the data (calibration period) and then
their performance is tested on the excluded data (verification period).
Results and interpretationChronology
The varve count showed no age uncertainty to the depth of the 1966 CE varve
year. A shift in the character of the varves (less prominent calcite lamina,
more Fe) and poorer preservation led to uncertainty in the varve count in
sediments older than 1965. Correlation of varves between multiple cores from
different coring years provides further confidence in the varve count (Fig. S2). Average varve thickness in the period 1966–2019 was 6.0 mm. Fallout
from the 1986 Chernobyl event, recorded in 137Cs activities, peaks in
the varves of 1985 and 1986 (Fig. 2). The 137Cs
activities in these two varves are indistinguishable from each other within
the measurement uncertainty (∼8 Bq kg-1). The similar
activities in these two varves is explained by the fact that the accident
occurred in late April 1986 (roughly coincident with the varve boundary).
Additionally, some post-depositional diffusion of 137Cs is expected
(Klaminder et al., 2012). These
results provide independent validation of the accuracy of the varve count.
Results of CNS elemental analysis, mass accumulation rates, and
137Cs activities displaying the 1986 peak from the Chernobyl accident.
See Sect. 3.3 for an explanation of the Varve Types (Vty).
Geochemical results and interpretation
Varves in Lake Żabińskie are dominated by aquatic organic material
and endogenic calcite. Several long-term trends (1966–2019) can be observed
in the geochemical results (Fig. 2;
Fig. 3). TOC, Ca, and S show increasing trends toward
the present while MAR, Fe, Si, and Mn decrease. The major increase in Ca is
particularly notable, with much higher peaks after around
1992 indicating more calcite precipitation in the epilimnion. At the same
time, Mn-rich layers become less frequent after 1992, most likely indicating
less frequent seasonal mixing events. Mn layers are preserved during
seasonal mixing of the water column that oxygenates the hypolimnion.
Oxygenation of the bottom waters leads to precipitation of Mn that had
previously been reductively dissolved in the normally anoxic hypolimnion
(Scholtysik et al., 2020; Schaller and Wehrli, 1996). Less frequent mixing after 1992 is likely
also responsible for increasing S towards the top of the core due to better
preservation of (Fe) sulfides under reducing conditions (Eusterhues et al., 2005). TChl does not show a strong long-term trending but features significant year-to-year
variability. Together, increasing TOC towards the present, stable TChl and
decreasing Si are interpreted as a result of a shift towards cyanobacteria
and away from siliceous diatoms, as has previously been reported at Lake
Żabińskie (Amann
et al., 2014).
High-resolution geochemical data obtained from imaging
spectroscopy (µXRF = micro X-ray fluorescence, HSI = hyperspectral
imaging). (a) Composite sequence shown as both spatial maps and down-core
profiles. Varve boundaries are shown as horizontal lines. Varve types (Vtys)
are plotted on the left side (see Sect. 3.3 for an explanation of Vty).
µXRF data are from resin-embedded sediment slabs, data from
1966–1990 are from the 2012 core, and data from 1991–2019 are from the 2020 core.
HSI data are from the wet 2020 core. Red dashed lines indicate the
calibration range for pigment concentrations obtained using HSI. (b)
Close-ups of spatial distribution of elements and pigments in four varves.
Aligning and averaging all varve years on a fractional varve-year scale
enables visualization and description of a “canonical” varve year that
represents the typical sequence of geochemical variables through an annual
cycle (Fig. 4a). The “canonical” varve year begins
with calcite precipitation (as defined in our varve counting procedure),
which occurs in the midst of peak primary production (recorded by TChl).
Initial spring blooms of diatoms and other siliceous algae occur immediately
after ice breakup, typically in March or April. Calcite precipitation
follows after algal blooms uptake CO2 and temperatures rise (Bonk et al., 2015). Based on this
information, the varve year represents an annual cycle normally beginning in
April or May. Calcite precipitation continues throughout the summer with one
to three distinct calcite laminae preserved. P typically tracks closely with
Ca (Fig. 4a), suggesting co-precipitation of P with
carbonates. TChl declines from high values at the start of the varve year
and is low through late summer and fall, before rising during winter
deposition of fine organic detrital material and peaking during spring algal
blooms marking the end of the varve year. Si counts follow a similar
seasonal pattern as TChl, with major diatom blooms associated with the
largest peaks in both Si and TChl. However, silicate minerals are also a
major source of Si, and Si actually correlates better with K (r=0.36, p<0.01; Fig. S3) than TChl (r=0.24, p<0.01). Ti
follows an opposite pattern compared to Ca with lowest values in the
beginning of the varve year and maximal values near the end of the of varve
year. High values of Ti denote the ice-cover period because biogenic
production is minimal under ice cover; therefore, sedimentation is dominated
by slow settling of fine lithogenic detrital material
(Bonk et al., 2015). Bphe concentrations are
generally low, leading to more measurement noise than the other proxies.
Bphe shows a seasonal pattern with highest values occurring in late
summer and fall, when the anoxic boundary remains shallow, before cooling in the
epilimnion leads to mixing and lowering of the anoxic boundary
(Bonk et al., 2015). This terminal
stratification period likely features greater light penetration compared to
more productive times of spring and summer, ideal for growth of anoxygenic
phototrophic bacteria that require light penetration at the chemocline (Sinninghe Damsté and Schouten, 2006). Fe and
S show a less-pronounced seasonal pattern than other elements but tend to
reach highest values in fall and winter. Fe often tracks Ti over the course
of the year, indicating lithogenic detrital input is an important source of
Fe. Mn has the most variable seasonal pattern with large peaks occurring at
different points in the varve year; however, Mn peaks are generally absent
in late summer and early fall, and some years show no Mn-rich layers (likely
indicating a lack of deep mixing).
(a) Average annual sequence of key geochemical variables across a
varve year. Varve year begins in spring with calcite precipitation. (b)
Groups of annual time series determined from hierarchical clustering based
on the dissimilarity measure ψ applied to the annual time series. Data
plotted are means of varve types, with shaded regions representing 80 % of
the data for each group.
Classification of varve type
The results of the hierarchical clustering algorithm based on the
dissimilarity measure of multivariate time series (Figs. S4 and S5) show
distinct differences in the seasonal deposition of different elements and
pigments (Fig. 4b), which correspond to different
conditions in the lake and catchment.
Varve type 1 (Vty-1; n=7, Fig. 4b) occurs occasionally throughout the
record and is characterized by low lithogenic input (Ti), and generally
lower counts of redox sensitive elements Fe, S, and Mn, though Mn peaks are
occasionally present. This pattern indicates that, in the years with Vty-1,
the water column was strongly stratified and calm, with minimal erosional
input or sediment focusing bringing detrital lithogenic material to the lake
center. Additionally, elevated values of TChl and high Si near the end of
the varve year indicate high productivity at the onset of the following
spring prior to calcite deposition.
Vty-2 (n=12, Fig. 4b) occurs exclusively since 2004 and is the most
common varve type in the past two decades. This type is defined by the
highest Ca values in the first half of the varve year, a distinct rise in
Bphe, Fe, S, and Mn in the second half of the varve year, and relatively high
Ti values in winter. This pattern indicates years with a warm and productive
epilimnion in summer, leading to extensive anoxia in fall, promoting growth
of anoxygenic phototrophic bacteria and formation of iron sulfides. Mn peaks
in some years indicate mixing in spring or late fall.
Vty-3 (n=22, Fig. 4b) dominates during the period 1966–1987 and is
identified in only two years after 1987. This type is defined by generally
high values of Fe, Mn, and P, and low values of Ca and TChl. The pattern of
Fe is variable. High values of Mn and Fe throughout the varve year suggest that complete mixing
of the water column occurred more often during these years. Detectable
concentrations of Bphe in most years are evidence that strong thermal
stratification and anoxic conditions still occurred in summer and early fall.
Median values of TChl are low in Vty-3 years, though there are some years
with large TChl peaks (algal blooms). Oxygenation of the water column may
have limited TChl preservation (Leavitt and
Hodgson, 2002).
Vty-4 (n=13, Fig. 4b) occurs primarily from 1991–2003 and is
characterized by high Ca, TChl, and S values. Mn is variable with very high
peaks occurring in some years and no Mn in other years. Mn peaks tend to
occur either in summer or late fall and early winter. Fe and Ti peak in winter.
Bphe is again present mainly in fall. The geochemical pattern and
interpretation is similar to Vty-2, with the main differences being lower
TChl in the first half of the varve year (lower spring and summer
productivity) and higher S throughout the year. The very high S counts
likely indicate strongly reducing conditions in the hypolimnion and
sediments because under oxic conditions sedimentary S would be released as
sulfate into the overlying waters (Håkanson and Jansson,
1983).
Relationships between varve composition and meteorological conditions
A MANOVA test was applied to test the hypothesis that the four different
varve types (Vtys) were formed in years with different seasonal
meteorological conditions. The MANOVA test yields a significant result (p=0.001), allowing us to reject the null hypothesis. Specifically,
temperatures in spring (MAM), summer (JJA), and fall (SON) and windiness in
spring and
fall were significantly different (p<0.05) in the years
corresponding to the four varve types (Fig. 5). The
differences in the meteorological data are consistent with the geochemical
patterns defined by the varve types. Vty-2 shows a strong effect of weather
conditions with consistently warmer temperatures and less wind in these
years. The geochemical character of Vty-2 indicates productive,
well-stratified, and anoxic conditions with intensive calcite precipitation
(Fig. 4b). This suggests calcite precipitation is strongly influenced by
epilimnetic temperatures, consistent with research in other lakes (e.g. Stabel, 1986). Vty-3 is associated with cooler
temperatures and more wind (Fig. 5), which promoted more frequent lake
mixing and lowered lake productivity, as evidenced by high Mn, P, and Fe
preservation and low TChl in these varves (Fig. 4b). Varve types 1 and 4
show greater variability in the meteorological data (Fig. 5), with no
clearly interpretable effect of weather on their formation.
Boxplots of seasonal meteorological variables separate by varve
types (Vty). Red boxes and * symbols indicate meteorological variables with
significantly (p<0.05) different means for the four Vtys, based on
an analysis of variance test (* p<0.1, ** p<0.05, *** p<0.01).
Relationships between meteorological conditions and varve composition were
further explored using a redundancy analysis (RDA; Fig. S6) and correlation
analysis between annual mean values of sedimentary variables and
monthly and seasonal meteorological variables (Fig. 6;
Table 1). The results of the RDA indicate that 46.8 % of the variance in the response variables (sedimentary variables) is
shared with the explanatory variables (seasonal meteorology). The first RDA
axis explains 28 % of the variance and shows strong (opposing) effects of
temperature and wind on several sedimentary variables, particularly MAR, Ca,
and TC. Precipitation has very little shared variability with the
sedimentary data.
Correlation matrices of Pearson correlation coefficient (r) for
sedimentary variables (annual mean values) and (a) mean monthly temperature,
(b) 90th percentile of daily mean wind speeds for each month, (c)
90th percentile of daily precipitation for each month, and (d) seasonal
versions of the aforementioned meteorological variables. Significance of
correlations is identified by * symbols (* padj<0.1, **
padj<0.05, *** padj<0.01) where p values have
been adjusted for autocorrelation (Bretherton et al., 1999) and false
discovery rate (Benjamini and Hochberg, 1995).
Correlation matrix of Pearson correlation coefficients for selected
meteorological variables and data from varves (annual mean values of
geochemical data). Bold values indicate significant correlations (padj<0.05). The p values were corrected for autocorrelation
(Bretherton et al., 1999) and the false
discovery rate (Benjamini and Hochberg, 1995).
Temperature 90th percentile wind Wind daysMAMJJASONDJFAnnMAMJJAMAMJJASONDJFMar–DecCa0.580.450.310.290.580.61-0.32-0.24-0.36-0.21-0.50Fe-0.41-0.33-0.28-0.39-0.55-0.440.310.120.300.020.41Mn-0.08-0.17-0.15-0.26-0.28-0.140.460.090.00-0.020.26Si-0.44-0.46-0.30-0.01-0.36-0.530.480.450.490.400.62P-0.20-0.31-0.10-0.30-0.37-0.290.450.250.380.140.41S0.260.29-0.070.090.200.32-0.31-0.16-0.14-0.11-0.33K-0.57-0.35-0.320.11-0.30-0.550.280.360.500.410.45Ti-0.66-0.34-0.39-0.04-0.44-0.600.240.270.360.250.36Bphe0.030.120.13-0.110.010.090.06-0.30-0.080.07-0.05TChl0.200.280.250.200.340.28-0.310.00-0.22-0.07-0.24TOC0.400.350.29-0.180.190.44-0.41-0.33-0.36-0.44-0.55TIC0.460.510.270.470.660.56-0.42-0.29-0.38-0.14-0.44TN0.520.460.27-0.050.340.58-0.40-0.27-0.27-0.33-0.50TC0.590.590.390.170.570.69-0.58-0.44-0.51-0.42-0.70MAR-0.18-0.40-0.140.06-0.17-0.330.510.370.520.560.63
The monthly correlation analysis (Fig. 6) provides
insight into how relationships between sedimentary variables and weather
vary throughout the varve year. Generally, temperatures are positively
correlated to Ca, TC, TIC, and TN, and negatively correlated with lithogenic
elements (Ti, K). Spring and summer temperatures show the strongest
relationship with the composition of varve layers; in particular TC is well
correlated with MAMJJA temperatures (r=0.69, padj=0.004;
Table 1).
These correlations most probably reflect a combination of
temperature-related mechanisms whereby warmer temperatures increase the
duration of the growing season (shortened ice cover), increase algal growth
rates (Butterwick et
al., 2005), and lower the solubility of carbonates in the epilimnion (Plummer
and Busenberg, 1982). Calcium carbonate solubility is controlled directly by
water temperature and secondarily through CO2 uptake from algal
production (Stabel, 1986). An additional effect of
temperature on sedimentary carbon is that stronger thermal stratification
and more extensive and persistent anoxia can increase carbon preservation
during and after deposition (Bartosiewicz et al.,
2019). Negative correlations with Ti and K are likely driven by a dilution
effect, whereby warmer epilimnetic temperatures and longer growing seasons
lead to increased production of endogenous carbonate and organic matter.
This increase in endogenous material results in lower concentrations of
lithogenic components (Ti) when considering the mean composition of a varve.
The geochemical patterns of Vty-2 and Vty -3 illustrate this mechanism (Fig. 4b). In Vty-3, which formed during cooler years, high Ti values make up a
greater portion of the varve year compared to Vty-2 (warmer years).
Therefore, annual mean values of Ti tend to be lower in warmer years. Our
results do not show a significant correlation between TChl and spring
temperature, as was previously found by Amann
et al. (2014) at Lake Żabińskie over the period 1907–2008 CE (r=0.36, padj<0.05 for annual-resolution data). In our dataset
the correlation is positive but not statistically significant (r=0.20,
padj=0.30; Table 1), an example of how
climate–proxy relationships are not always stable in time
(Blass et al.,
2007).
Correlations between windiness and sedimentary variables show mostly an
opposing pattern to temperature. The variable with the most consistent
strong relationship to windiness is MAR (mass accumulation rate). These
positive correlations suggest a strong effect of sediment focusing due to
wind-driven turbulence in shallow parts of the lake, as has been observed in
other lakes with varved sediments (e.g. Nuhfer et al., 1993; Roeser et
al., 2021). The importance of sediment focusing at Lake Żabińskie was previously identified by higher-than-expected 210Pb and 137Cs inventories (Tylmann et al., 2016). Si also
shows significant correlations with windiness. Increased Si during windy
years can be attributed to a combination of three mechanisms: (1)
resuspension of lithogenic material (silicate minerals); (2) resuspension of
siliceous algae remains (Raubitschek et al.,
1999); (3) increased production of siliceous algae due to increased
availability of nutrients such as N, P, and Si in the epilimnion due to
mixing of the water column (Conley et al., 1993).
Wind-driven mixing also strongly affects redox conditions within the lake.
Positive correlations between wind and Mn, Fe, and P can be attributed to
deep mixing events driven by wind, particularly in spring (Naeher et al., 2013). Lowering
of the anoxic boundary results in (re)precipitation of Mn and Fe
(hydr)oxides and improves preservation of these elements in the sediments
through “geochemical focusing” (Scholtysik et al.,
2020; Schaller and Wehrli, 1996). This geochemical focusing is also an
important contribution to higher MARs during windier years (i.e. Vty-3; Fig. 4b). The importance of wind shear on Lake Żabińskie biogeochemical
cycling has previously been identified in the studies of the Holocene
sedimentary record of Lake Żabińskie
(Zander
et al., 2021a; Żarczyński et al., 2019; Hernández-Almeida et al.,
2014). A significant negative correlation between bacteriopheophytin and
July windiness could be explained by increased turbidity during windy
summers (and limited light at the chemocline) and/or a lowering of the
anoxic boundary below the photic zone; both effects would limit growth of
anoxygenic phototrophic bacteria. Notably weaker correlations between
sedimentary variables and February wind are attributed to the fact that ice
cover is consistently extensive throughout this month.
The correlation analysis (Fig. 6, Table 1) highlights proxies that are
potentially suitable for paleoclimate reconstructions and for which seasons
they are most sensitive. Accordingly, warm-season temperatures can be
reconstructed using a combination of Ti and TC (or other carbonate or organic
proxies), while wind can be reconstructed using MAR, Si, K, or P though there
is not a strong seasonal signal in the correlations. Bphe shows potential
for reconstructing winds specifically in summer; however, the Bphe data are
noisy due to low concentrations, making this signal weak and less
consistently reproducible compared to the other proxies (Fig. S7). Over the
Holocene, Bphe concentrations were closely linked to forest cover
surrounding the lake, which affects wind exposure (Zander et al., 2021a).
Temperature and wind reconstructions using generalized additive models
Based on the correlation analysis and investigation of relationships between
varve types and meteorological variables, we selected spring and summer
(MAMJJA) temperature and the number of windy days (mean daily wind speed
>7 m s-1) from March to December as the most suitable
targets for reconstruction. We chose the March to December timeframe for the
wind model because this is the typical ice-free period and therefore is
expected to be most susceptible to wind effects. The threshold for a wind
day was selected based on the 95th percentile of daily wind speeds
within a year to emphasize the importance of extreme wind events. We use the
95th percentile here rather than the 90th percentile used for
monthly and seasonal analysis because the March to December period is
longer, making higher percentiles less susceptible to outliers.
Generalized additive models (GAMs) were used to predict meteorological
conditions based on varve data at annual resolution. We found TC and Ti
provided significant predictive power for MAMJJA temperature and a GAM fit
to these variables explains 56.4 % of the deviance in MAMJJA temperatures
(R2adj=0.55; Fig. 7). Both predictor
variables are significant (p<0.05). Partial effect plots (Fig. S8)
show the relationships between temperature and predictor variables are
nearly linear. Residual analysis confirms the good fit of the model with no
trends or significant autocorrelation apparent. The model captures the
trends in temperature well; however, it appears to underestimate extreme
variations (particularly cold or
warm years). The 10-fold cross-validated root mean square error (CV-RMSE) is
0.69 ∘C (14.4 % of the range), and RMSE calculated based on a
split-period approach is similar (0.68–0.80 ∘C; Table S2). Models
generated using subsets of the data (before and after 1992) agree well with
the full model (Fig. S9), although year-to-year variability is significantly
dampened in the model fit to data from 1992–2019, and this model shows no
significant relationship with Ti. Still, the close agreement of these models
based on small samples (n=26) suggest that the climate–proxy
relationships are not drastically impacted by decadal-scale shifts in varve
type. Consistently positive values of RE (reduction of error) and CE
(coefficient of efficiency) are an indication that the calibration model has
predictive skill (Cook et al., 1994; Table S2). These calibration statistics provide evidence that this calibration may
be viable for paleoclimate reconstruction.
Results of generalized additive models used to predict spring and
summer temperature and number of windy days from March to December (daily
mean wind speeds >7 m s-1).
Wind days were successfully reconstructed using a model with predictors MAR
and Si. The years 1993 and 1994 were removed prior to fitting the model
because wind data were missing from these years. This model explains 49.6 % of the deviance in wind days (R2adj=0.48;
Fig. 7) and both MAR and Si are significant
predictors (p<0.05). Partial effect plots show
that the relationship between the predictor variables and wind days are
approximately linear (Fig. S10). Residual analysis shows no strong trends,
but the distribution appears slightly non-normal, suggesting the model fit
may be flawed. Wind speeds at Kętrzyn station show markedly higher
values from 1970–1985, and the model is able to capture this trend well;
however, very low wind speeds in 1966 and 1968 are not well captured by the
model. The model has a 10-fold CV-RMSE of 6.1 d (19.0 %), though this
error increases substantially when using a split-period method (22.9 %–48.0 %, Table S2). The results of the split-period validation show major
differences in model outputs depending on the training data, with the model
fit to data from 1992–2019 unable to capture the trends in the full dataset
(Fig. S9). The RE ranges from -0.28 to 0.61, depending on the selection of
the calibration period (Table S2), and the CE is consistently negative.
These results indicate that despite the good fit over the full calibration
period, the model has relatively weak predictive skill and the climate–proxy
relationships may be unstable. The short study period, and major shifts in
mean values between calibration and verification periods, could lead to
unrealistically pessimistic results from the split-period validation.
Additionally, wind speeds are more spatially variable than temperature; thus
it is possible that local variations between wind speeds at Lake
Żabińskie and the Kętrzyn station increase the model error.
Further research over longer periods is needed before this model can be
applied for paleoclimate reconstruction.
DiscussionChallenges for high-resolution climate reconstruction
Quantitative climate reconstructions from geochemical proxies of biogenic
varves are extremely scarce in the published literature (as shown by the
PAGES Varve Working Group compilation:
https://pastglobalchanges.org/science/end-aff/varves-wg/varve-related-publications) (last access: 8 October 2021). Our results show that novel scanning techniques with much higher
spatial (and temporal) resolution offer new possibilities. However, several
challenges concerning high-resolution climate reconstructions from biogenic
varves remain and are worthy of discussion. Measurements of sedimentary
variables include some degree of noise that generally becomes more
significant as resolution is increased (Blass et al., 2007). However, we find
good reproducibility of our data across different cores, suggesting that a
60×2000µm measurement area is large enough to produce a
robust signal (Fig. S7). Uncertainty in assigning temporal values to data
within a single varve remains a limitation because deposition rates are
highly variable throughout the year. Determining the season of deposition of
certain lamina is possible (i.e. the first calcite lamina is deposited in
April–June), but temporal uncertainty of a couple months or even more should
be expected. Another important challenge is covariance of target
meteorological variables. The climate variables used for reconstructions in
this study (MAMJJA temperature and March–December wind days) are correlated (r=-0.52, padj<0.05; Fig. S11), and both are significantly
correlated (with opposing signs) with multiple sedimentary variables
(Table 1). This covariance confounds interpretation
of the proxy data because changes in a single variable (for instance, TC)
may be influenced by both temperature and wind. Autocorrelation of both
proxy and meteorological time series also presents challenges when
evaluating the significance of correlations and model performance
(Telford, 2019). We account for autocorrelation by using
the adjusted-n method for significance tests
(Bretherton et al., 1999) and by using a
split-period approach to calculate RMSE in addition to the cross-validated
RMSE (Table S2). A longer calibration period would strengthen confidence in
the calibration model; however, age uncertainty would require smoothing or
aggregating data to lower temporal resolution, preventing annual-resolution
analysis. The short 54-year calibration period increases the possibility
that climate–proxy relationships identified here are not stable over longer
time periods (Blass
et al., 2007). This is especially problematic for sites that have
experienced major environmental changes (often human-induced) outside the
calibration period, as is known for Lake Żabińskie
(Hernández-Almeida et al., 2017).
Future work could investigate the climate–proxy relationships studied here
over longer timescales.
Non-climatic factors, such as human activity in the catchment, diagenetic
effects, internal variability in biogeochemical cycling, etc., also
influence varve structure and composition at a variety of timescales. The
proposed temperature reconstruction in this study assumes variations in TC
are driven largely by temperature (via the effects of temperature on algal
production and calcite precipitation). However, nutrient levels also likely
influence the carbon contents of the sediments (Fiskal et al., 2019). Diatom-based P
reconstructions show a slight increasing trend over the period 1966–2010;
however, phosphate fertilizer use in the region peaked in the 1970s and has
declined since then (Witak et al., 2017).
Sediment P also shows a decreasing trend over the study period (Fig. 3) and
a negative correlation with TC (Fig. S12). Additionally, agricultural
activities around Lake Żabińskie have decreased since 1950, leading
to afforestation of abandoned fields and reduced soil erosion in the
catchment (Wacnik et al., 2016). Based on
these trends, it appears more likely that increasing TC over the study
period is related to warming temperatures rather that nutrient input to the
lake. Recent reductions in soil erosion and afforestation around the lake
(which reduces wind exposure) likely also influence MAR, which could be one
reason why the wind reconstruction does not perform well based on the
split-period validation.
Potential for high-resolution climate reconstruction
Despite the aforementioned caveats, our results suggest that geochemical
variables, particularly from high-resolution scanning techniques, are a
promising tool for high-resolution quantitative climate reconstructions from
biochemical varves. The high-resolution data obtained from imaging
spectroscopy, thick varves at Lake Żabińskie, and annual certainty
of the varve count provide a unique opportunity to test relationships
between seasonal meteorological conditions and varve composition. We present
an innovative method to define varve types objectively using a multivariate
time series clustering approach based on sub-annual variations in
geochemistry. This technique may be considered a quantitative and
time-efficient alternative (or complement) to microfacies approaches to
varve classification (e.g.
Żarczyński et al., 2019). We demonstrate that varves with different
sub-annual geochemical time series formed during years with significant
differences in meteorological conditions (Fig. 5).
The results of this analysis inform our interpretations of how
meteorological variability is recorded in biochemical varves of Lake
Żabińskie.
High-resolution imaging spectroscopy techniques offer several important
advantages for sub-annual and annual-resolution analyses of varves. The 60 µm resolution enabled us to study the annual cycle of sedimentation in
great detail and observe significant differences in the year-to-year
sequence of geochemical parameters. Images of geochemical data provide
information about the spatial nature of geochemical variations that is not
available from linescan data (i.e. distinguishing nodules from continuous
layers). The high-resolution images make it possible to delineate varve
boundaries consistently and precisely. This is also important for annual-resolution analyses – small offsets in the location of varve boundaries can
lead to significant errors in annual values calculated from varve layers. In
this study, due to the multi-millimeter-thick varves it was possible to sample
annual layers for destructive analyses; however, at sites with thinner
varves non-destructive scanning techniques are essential for annual-resolution analyses.
The spring and summer temperature model shows strong potential for
paleoclimate reconstruction at this site and possibly other sites with
similar calcareous varves. The CV-RMSE of 0.69 ∘C is greater than
other published reconstructions based on biogeochemical proxies from lake
sediments (Amann
et al., 2014; von Gunten et al., 2012), but this is because our calibration
is at annual resolution, whereas the cited studies used smoothed sub-decadal
data. Our RMSE is lower than those typically reported from climate transfer
functions based on microfossil assemblages
(Heiri et al., 2003), which are
the typical approach for quantitative climate reconstruction from biogenic
varves. In contrast to microfossil approaches that require large samples and
time-consuming analyses, geochemical data are quickly measured at
high resolution using non-destructive techniques such as XRF and reflectance
spectroscopy. This makes it possible to efficiently apply these techniques
to more sites, over longer time periods, and at higher resolution. We
suggest that these spectroscopic techniques have untapped potential for
quantitative climate reconstructions from biochemical varves, particularly
at high (annual) resolution. The calibration model presented here should be
tested over longer timescales, but our results provide the foundation for
quantitative climate reconstruction from biochemical varves at Lake
Żabińskie, and other sites with similar varve-formation processes.
Conclusions
The results of this study demonstrate the potential of high-resolution
spectroscopy imaging techniques to enhance our understanding of sub-varve-scale sedimentary processes and the relation to seasonal climate variables.
The sequence of geochemical variables through the course of the varve year
was shown to be influenced by changing seasonal meteorological conditions.
Correlation analysis identified spring and summer (MAMJJA) temperatures, and
windiness during the ice-free period as meteorological variables with the
greatest potential for proxy–climate calibration from these sediments.
Generalized additive models were applied to reconstruct these two variables
at annual resolution over the study period (1966–2019). Total C and Ti were
used as predictors for MAMJJA temperature and yielded a reconstruction with
good prediction performance (CV-RMSE = 0.69 ∘C, 14.4 %).
Mass accumulation rate (MAR) and Si were used to predict March–December wind
days (CV-RMSE = 6.1 wind days, 19.0 %). Split-period validation
provides evidence that the temperature reconstruction model has predictive
skill and could be applied outside the calibration period, whereas the wind
reconstruction model warrants caution if applied beyond the calibration
period. Our results provide a rare example of quantitative climate
calibration based on bulk geochemical data from biochemical varves with
implications for paleoclimate reconstructions at other sites with calcareous
varves. The approach used in this study can be applied to other sites with
varved sediments to generate high-resolution paleoclimate reconstructions.
Ultra-high-resolution spectroscopy imaging techniques, such as those applied
in this study, show great potential for a variety of paleoenvironmental
reconstructions including archives beyond lake sediments such as
speleothems, fossils, tree ring cores, and many others.
Code and data availability
Data and code required to reproduce plots and statistics reported in this paper are available at the Bern Open Repository and Information System (BORIS; 10.48350/156383, Zander et al., 2021b).
The supplement related to this article is available online at: https://doi.org/10.5194/cp-17-2055-2021-supplement.
Author contributions
PDZ, MG, MZ, and WT designed the study. MG and WT provided funding, resources, and supervision. PDZ, MZ, and SR performed laboratory analyses and prepared figures. PZ did the data analysis and led the writing. All authors contributed to writing and editing.
Competing interests
The contact author has declared that neither they nor their co-authors have any competing interests.
Disclaimer
Publisher’s note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
Joanna Piłczyńska assisted with lab analyses.
Financial support
This research has been supported by the Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (grant no. 200021_172586) and the Narodowe Centrum Nauki (grant no. 2015/18/E/ST10/00325).
Review statement
This paper was edited by Pierre Francus and reviewed by Saija Saarni and Daniel Schillereff.
ReferencesAmann, B., Lobsiger, S., Fischer, D., Tylmann, W., Bonk, A., Filipiak, J.,
and Grosjean, M.: Spring temperature variability and eutrophication history
inferred from sedimentary pigments in the varved sediments of Lake
Żabińskie, north-eastern Poland, AD 1907–2008, Glob. Planet. Change,
123, 86–96, 10.1016/j.gloplacha.2014.10.008, 2014.Bartosiewicz, M., Przytulska, A., Lapierre, J., Laurion, I., Lehmann, M. F.,
and Maranger, R.: Hot tops, cold bottoms: Synergistic climate warming and
shielding effects increase carbon burial in lakes, Limnol. Oceanogr. Lett.,
4, 132–144, 10.1002/lol2.10117, 2019.Benito, B. M. and Birks, H. J. B.: distantia: an open-source toolset to
quantify dissimilarity between multivariate ecological time-series,
Ecography (Cop.)., 43, 660–667, 10.1111/ecog.04895, 2020.Benjamini, Y. and Hochberg, Y.: Controlling the False Discovery Rate: A
Practical and Powerful Approach to Multiple Testing, J. R. Stat. Soc. Ser.
B, 57, 289–300, 10.1111/j.2517-6161.1995.tb02031.x, 1995.Blass, A., Grosjean, M., Troxler, A., and Sturm, M.: How stable are
twentieth-century calibration models? A high-resolution summer temperature
reconstruction for the eastern Swiss Alps back to AD 1580 derived from
proglacial varved sediments, The Holocene, 17, 51–63,
10.1177/0959683607073278, 2007.Boldt, B. R., Kaufman, D. S., McKay, N. P., and Briner, J. P.: Holocene
summer temperature reconstruction from sedimentary chlorophyll content, with
treatment of age uncertainties, Kurupa Lake, Arctic Alaska, The Holocene, 25, 641–650,
10.1177/0959683614565929, 2015.Bonk, A., Tylmann, W., Amann, B., Enters, D., and Grosjean, M.: Modern
limnology and varve-formation processes in lake Żabińskie,
northeastern Poland: Comprehensive process studies as a key to understand
the sediment record, J. Limnol., 74, 358–370,
10.4081/jlimnol.2014.1117, 2015.Bonk, A., Kinder, M., Enters, D., Grosjean, M., Meyer-Jacob, C., and
Tylmann, W.: Sedimentological and geochemical responses of Lake
Żabińskie (north-eastern Poland) to erosion changes during the last
millennium, J. Paleolimnol., 56, 239–252,
10.1007/s10933-016-9910-6, 2016.Bretherton, C. S., Widmann, M., Dymnikov, V. P., Wallace, J. M., and
Bladé, I.: The effective number of spatial degrees of freedom of a
time-varying field, J. Clim., 12, 1990–2009,
10.1175/1520-0442(1999)012<1990:TENOSD>2.0.CO;2, 1999.Butterwick, C., Heaney, S. I., and Talling, J. F.: Diversity in the
influence of temperature on the growth rates of freshwater algae, and its
ecological relevance, Freshw. Biol., 50, 291–300,
10.1111/j.1365-2427.2004.01317.x, 2005.Butz, C., Grosjean, M., Fischer, D., Wunderle, S., Tylmann, W., and Rein,
B.: Hyperspectral imaging spectroscopy: a promising method for the
biogeochemical analysis of lake sediments, J. Appl. Remote Sens., 9, 096031,
10.1117/1.jrs.9.096031, 2015.Butz, C., Grosjean, M., Goslar, T., and Tylmann, W.: Hyperspectral imaging
of sedimentary bacterial pigments: a 1700-year history of meromixis from
varved Lake Jaczno, northeast Poland, J. Paleolimnol., 58, 57–72,
10.1007/s10933-017-9955-1, 2017.Conley, D. J., Schelske, C. L., and Stoermer, E. F.: Modification of the
biogeochemical cycle of silica with eutrophication, Mar. Ecol. Prog. Ser.,
101, 179–192, 10.3354/meps101179, 1993.Cook, E. R., Briffa, K. R., and Jones, P. D.: Spatial regression methods in
dendroclimatology: A review and comparison of two techniques, Int. J.
Climatol., 14, 379–402, 10.1002/joc.3370140404, 1994.Czernecki, B., Glogowski, A., and Nowosad, J.: Climate: An R Package to
Access Free In-Situ Meteorological and Hydrological Datasets For
Environmental Assessment, Sustainability, 12, 394 10.3390/su12010394, 2020.De Geer, G.: On late quaternary time and climate, Geol. Föreningen i
Stock, Förhandlingar, 30, 459–464,
10.1080/11035890809445600, 1908.Elbert, J., Grosjean, M., von Gunten, L., Urrutia, R., Fischer, D.,
Wartenburger, R., Ariztegui, D., Fujak, M., and Hamann, Y.: Quantitative
high-resolution winter (JJA) precipitation reconstruction from varved
sediments of Lago Plomo 47∘ S, Patagonian Andes, AD 1530–2002, The Holocene, 22,
465–474, 10.1177/0959683611425547, 2012.Eusterhues, K., Heinrichs, H., and Schneider, J.: Geochemical response on
redox fluctuations in Holocene lake sediments, Lake Steisslingen, Southern
Germany, Chem. Geol., 222, 1–22,
10.1016/j.chemgeo.2005.06.006, 2005.Fiskal, A., Deng, L., Michel, A., Eickenbusch, P., Han, X., Lagostina, L., Zhu, R., Sander, M., Schroth, M. H., Bernasconi, S. M., Dubois, N., and Lever, M. A.: Effects of eutrophication on sedimentary organic carbon cycling in five temperate lakes, Biogeosciences, 16, 3725–3746, 10.5194/bg-16-3725-2019, 2019.Francus, P., Bradley, R. S., Abbott, M. B., Patridge, W., and Keimig, F.:
Paleoclimate studies of minerogenic sediments using annually resolved
textural parameters, Geophys. Res. Lett., 29, 59-1–59-4,
10.1029/2002GL015082, 2002.Gälman, V., Rydberg, J., De-Luna, S. S., Bindler, R., and Renberg, I.:
Carbon and nitrogen loss rates during aging of lake sediment: Changes over
27 years studied in varved lake sediment, Limnol. Oceanogr., 53, 1076–1082,
10.4319/lo.2008.53.3.1076, 2008.Gordon, A. D. and Birks, H. J. B.: Numerical methods in Quaternary
palaeoecology: II. Comparison of pollen diagrams, New Phytol., 73, 221–249,
10.1111/j.1469-8137.1974.tb04621.x, 1974.Håkanson, L. and Jansson, M.: Principles of Lake Sedimentology, Springer-Verlag, Berlin, 73–75, 1983.Heiri, O., Birks, H. J. B., Brooks, S. J., Velle, G., and Willassen, E.:
Effects of within-lake variability of fossil assemblages on quantitative
chironomid-inferred temperature reconstruction, Palaeogeogr. Palaeoclimatol.
Palaeoecol., 199, 95–106, 10.1016/S0031-0182(03)00498-X,
2003.Hernández-Almeida, I., Grosjean, M., Tylmann, W., and Bonk, A.:
Chrysophyte cyst-inferred variability of warm season lake water chemistry
and climate in northern Poland: training set and downcore reconstruction, J.
Paleolimnol., 53, 123–138, 10.1007/s10933-014-9812-4, 2014.Hernández-Almeida, I., Grosjean, M., Gómez-Navarro, J. J.,
Larocque-Tobler, I., Bonk, A., Enters, D., Ustrzycka, A., Piotrowska, N.,
Przybylak, R., Wacnik, A., Witak, M., and Tylmann, W.: Resilience, rapid
transitions and regime shifts: Fingerprinting the responses of Lake
Żabińskie (NE Poland) to climate variability and human disturbance
since AD 1000, The Holocene, 27, 258–270, 10.1177/0959683616658529, 2017.IPCC: Climate Change 2013: The Physical Science Basis. Contribution of:
Working Group I to the Fifth Assessment Report of the Intergovernmental
Panel on Climate Change, edited by: Stocker, T. F., Qin, D., Plattner,
G.-K., Tignor, M., Allen, S. K., Boschung, J., Nauels, A., Xia, Y., Bex, V.,
and Midgley, P. M., Cambridge University Press, 1535 pp., 10.1017/CBO9781107415324, 2013.Klaminder, J., Appleby, P., Crook, P., and Renberg, I.: Post-deposition
diffusion of 137Cs in lake sediment: Implications for radiocaesium
dating, Sedimentology, 59, 2259–2267,
10.1111/j.1365-3091.2012.01343.x, 2012.Lapointe, F., Bradley, R. S., Francus, P., Balascio, N. L., Abbott, M. B.,
Stoner, J. S., St-Onge, G., de Coninck, A., and Labarre, T.: Annually
resolved Atlantic sea surface temperature variability over the past 2,900 y,
P. Natl. Acad. Sci. USA, 117, 27171–27178,
10.1073/pnas.2014166117, 2020.Leavitt, P. R. and Hodgson, D. A.: Sedimentary Pigments, in: Tracking
environmental change using lake sediments, Springer, Dordrecht, 295–325,
10.1007/0-306-47668-1, 15, 2002.Naeher, S., Gilli, A., North, R. P., Hamann, Y., and Schubert, C. J.:
Tracing bottom water oxygenation with sedimentary Mn/Fe ratios in Lake
Zurich, Switzerland, Chem. Geol., 352, 125–133,
10.1016/j.chemgeo.2013.06.006, 2013.Neukom, R., Steiger, N., Gómez-Navarro, J. J., Wang, J., and Werner, J.
P.: No evidence for globally coherent warm and cold periods over the
preindustrial Common Era, Nature, 571, 550–554,
10.1038/s41586-019-1401-2, 2019.Nuhfer, E. B., Anderson, R. Y., Bradbury, J. P., and Dean, W. E.: Modern
sedimentation in Elk Lake, Clearwater County, Minnesota, in: Elk Lake,
Minnesota: Evidence for Rapid Climate Change in the North-Central United
States, edited by: Bradbury, J. P. and Dean, W. E., Geol. S. Am. Spec. Pap., 276, Boulder, CO, 75–96, 10.1130/SPE276-p75,
1993.Oksanen, J., Blanchet, F. G., Friendly, M., Kindt, R., Legendre, P.,
McGlinn, D., Minchin, P. R., O'Hara, R. B., Simpson, G. L., Solymos, P.,
Stevens, M. H. H., Szoecs, E., and Wagner, H.: vegan: Community Ecology
Package, available at: https://cran.r-project.org/package=vegan, (last access 17 August 2021), 2020.PAGES2k Consortium: Continental-scale temperature variability during the
past two millennia, Nat. Geosci., 6, 339–346,
10.1038/ngeo1797, 2013.Plummer, L. N. and Busenberg, E.: The solubilities of calcite, aragonite and
vaterite in CO2-H2O solutions between 0 and 90 ∘C, and
an evaluation of the aqueous model for the system
CaCO3-CO2-H2O, Geochim. Cosmochim. Acta, 46, 1011–1040,
10.1016/0016-7037(82)90056-4, 1982.Raubitschek, S., Lücke, A., and Schleser, G. H.: Sedimentation patterns
of diatoms in Lake Holzmaar, Germany – (on the transfer of climate signals
to biogenic silica oxygen isotope proxies), J. Paleolimnol., 21, 437–448,
10.1023/A:1008022532458, 1999.R Core Team: R: A Language and Environment for Statistical Computing, available at:
https://www.r-project.org,(last access 18 May 2021), 2020.Rein, B. and Sirocko, F.: In-situ reflectance spectroscopy – Analysing
techniques for high-resolution pigment logging in sediment cores, Int. J.
Earth Sci., 91, 950–954, 10.1007/s00531-002-0264-0, 2002.Roeser, P., Dräger, N., Brykała, D., Ott, F., Pinkerneil, S.,
Gierszewski, P., Lindemann, C., Plessen, B., Brademann, B., Kaszubski, M.,
Fojutowski, M., Schwab, M. J., Słowiński, M., Błaszkiewicz, M., and
Brauer, A.: Advances in understanding calcite varve formation: new insights
from a dual lake monitoring approach in the southern Baltic lowlands, Boreas, 50, 419–440, 10.1111/BOR.12506, 2021.Schaller, T. and Wehrli, B.: Geochemical-focusing of manganese in lake
sediments – An indicator of deep-water oxygen conditions, Aquat.
Geochemistry, 2, 359–378, 10.1007/bf00115977, 1996.Scholtysik, G., Dellwig, O., Roeser, P., Arz, H. W., Casper, P., Herzog, C.,
Goldhammer, T., and Hupfer, M.: Geochemical focusing and sequestration of
manganese during eutrophication of Lake Stechlin (NE Germany),
Biogeochemistry, 151, 313–334, 10.1007/s10533-020-00729-9,
2020.Sinninghe Damsté, J. S. and Schouten, S.: Biological markers for anoxia in the photic zone of the water column, Handb. Environ. Chem., Vol. 2 React. Process., 2N, 127–163, 10.1007/698_2_005, 2006.Stabel, H. H.: Calcite precipitation in Lake Constance: Chemical
equilibrium, sedimentation, and nucleation by algae, Limnol. Oceanogr., 31,
1081–1094, 10.4319/lo.1986.31.5.1081, 1986.Swierczynski, T., Brauer, A., Lauterbach, S., Martín-Puertas, C.,
Dulski, P., von Grafenstein, U., and Rohr, C.: A 1600 yr seasonally resolved
record of decadal-scale flood variability from the Austrian Pre-Alps,
Geology, 40, 1047–1050, 10.1130/G33493.1, 2012.Telford, R. J.: Review and test of reproducibility of subdecadal resolution
palaeoenvironmental reconstructions from microfossil assemblages, Quat. Sci.
Rev., 222, 105893, 10.1016/j.quascirev.2019.105893, 2019.Tian, J., Nelson, D. M., and Hu, F. S.: How well do sediment indicators
record past climate? An evaluation using annually laminated sediments, J.
Paleolimnol., 45, 73–84, 10.1007/s10933-010-9481-x, 2011.Tierney, J. E., Poulsen, C. J., Montañez, I. P., Bhattacharya, T., Feng,
R., Ford, H. L., Hönisch, B., Inglis, G. N., Petersen, S. V., Sagoo, N.,
Tabor, C. R., Thirumalai, K., Zhu, J., Burls, N. J., Foster, G. L.,
Goddéris, Y., Huber, B. T., Ivany, L. C., Turner, S. K., Lunt, D. J.,
McElwain, J. C., Mills, B. J. W., Otto-Bliesner, B. L., Ridgwell, A., and
Zhang, Y. G.: Past climates inform our future, Science, 370, eaay3701,
10.1126/science.aay3701, 2020.Tormene, P., Giorgino, T., Quaglini, S., and Stefanelli, M.: Matching
Incomplete Time Series with Dynamic Time Warping: An Algorithm and an
Application to Post-Stroke Rehabilitation, Artif. Intell. Med., 45, 11–34,
10.1016/j.artmed.2008.11.007, 2008.Trachsel, M., Grosjean, M., Schnyder, D., Kamenik, C., and Rein, B.:
Scanning reflectance spectroscopy (380–730 nm): A novel method for
quantitative high-resolution climate reconstructions from minerogenic lake
sediments, J. Paleolimnol., 44, 979–994,
10.1007/s10933-010-9468-7, 2010.Tylmann, W., Bonk, A., Goslar, T., Wulf, S., and Grosjean, M.: Calibrating
210Pb dating results with varve chronology and independent
chronostratigraphic markers: Problems and implications, Quat. Geochronol.,
32, 1–10, 10.1016/j.quageo.2015.11.004, 2016.von Gunten, L., Grosjean, M., Kamenik, C., Fujak, M., and Urrutia, R.:
Calibrating biogeochemical and physical climate proxies from non-varved lake
sediments with meteorological data: Methods and case studies, J.
Paleolimnol., 47, 583–600, 10.1007/s10933-012-9582-9, 2012.Wacnik, A., Tylmann, W., Bonk, A., Goslar, T., Enters, D., Meyer-Jacob, C.,
and Grosjean, M.: Determining the responses of vegetation to natural
processes and human impacts in north-eastern Poland during the last
millennium: combined pollen, geochemical and historical data, Veg. Hist.
Archaeobot., 25, 479–498, 10.1007/s00334-016-0565-z, 2016.Witak, M., Hernández-Almeida, I., Grosjean, M., and Tylmann, W.:
Diatom-based reconstruction of trophic status changes recorded in varved
sediments of Lake Żabińskie (northeastern Poland), AD 1888–2010,
Oceanol. Hydrobiol. Stud., 46, 1–17, 10.1515/ohs-2017-0001,
2017.Wood, S. N.: Fast stable restricted maximum likelihood and marginal
likelihood estimation of semiparametric generalized linear models, J. R.
Stat. Soc., 73, 3–36, 10.1111/j.1467-9868.2010.00749.x,
2011.Wood, S. N.: Generalized additive models: an introduction with R, CRC press, Boca Raton, FL, 496 pp., ISBN 9781315370279,
10.1201/9781315370279, 2017.Zander, P. D., Żarczyński, M., Vogel, H., Tylmann, W., Wacnik, A.,
Sanchini, A., and Grosjean, M.: A high-resolution record of Holocene primary
productivity and water-column mixing from the varved sediments of Lake
Żabińskie, Poland, Sci. Total Environ., 755, 143713,
10.1016/j.scitotenv.2020.143713, 2021a.
Zander, P. D., Żarczyński, M., Tylmann, W., Rainford, S., and Grosjean, M.: Seasonal climate signals preserved in biochemical varves: insights from novel high-resolution sediment scanning techniques [Dataset and Code], Bern Open Repository and Information System, 10.48350/156383, 2021b.Żarczyński, M., Tylmann, W., and Goslar, T.: Multiple varve
chronologies for the last 2000 years from the sediments of Lake
Żabińskie (northeastern Poland) – Comparison of strategies for
varve counting and uncertainty estimations, Quat. Geochronol., 47, 107–119,
10.1016/j.quageo.2018.06.001, 2018.Żarczyński, M., Wacnik, A., and Tylmann, W.: Tracing lake mixing and
oxygenation regime using the Fe/Mn ratio in varved sediments: 2000 year-long
record of human-induced changes from Lake Żabińskie (NE Poland),
Sci. Total Environ., 657, 585–596,
10.1016/j.scitotenv.2018.12.078, 2019.Zolitschka, B., Francus, P., Ojala, A. E. K., and Schimmelmann, A.: Varves
in lake sediments – a review, Quaternary Sci. Rev., 117, 1–41,
10.1016/j.quascirev.2015.03.019, 2015.