Abstract. 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.