Pollen-based paleoclimate reconstructions of Beloslav Lake (Northeastern Bulgaria) during the last 6000 years using modern analog technique (MAT)

The aim of the present study is to reconstruct the palaeoclimate variables in the Beloslav Lake Region (Northeastern Bulgaria) during the last 6000 years, based on the pollen analysis from lacustrine core Bel-1 and using modern analog technique (MAT). Pollen data was used for reconstructions of four parameters: average annual temperature, average temperature of the warm and cold half-year and average annual precipitation.

Seasonal climate signals preserved in biochemical varves: insights from novel high-resolution sediment scanning techniques

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.

Supplemental Material: Contrasting northern and southern European winter climate trends during the Last Interglacial

Paleoclimate reconstructions, pollen–climate calibration data, and cross-validation results.

Supplemental Material: Contrasting northern and southern European winter climate trends during the Last Interglacial

Paleoclimate reconstructions, pollen–climate calibration data, and cross-validation results.

Seasonal climate signals preserved in biochemical varves: insights from novel high-resolution sediment scanning techniques

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 uXRF-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 this 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) temperature were predicted using Ti and total C (R2adj = 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) were predicted using mass accumulation rate (MAR) and Si (R2adj = 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.

δ2Hn-alkane and δ18Osugar biomarker proxies from leaves and topsoils of the Bale Mountains, Ethiopia, and implications for paleoclimate reconstructions

The hydrogen isotopic composition of leaf wax–derived n-alkane (δ2Hn-alkane) and oxygen isotopic composition of hemicellulose–derived sugar (δ18Osugar) biomarkers are valuable proxies for paleoclimate reconstructions. Here, we present a calibration study along the Bale Mountains in Ethiopia to evaluate how accurately and precisely the isotopic composition of precipitation is imprinted in these biomarkers. n-Alkanes and sugars were extracted from the leaf and topsoil samples and compound–specific δ2Hn-alkane and δ18Osugar values were measured using a gas chromatograph–thermal conversion–isotope ratio mass spectrometer (GC–TC–IRMS). The weighted mean δ2Hn-alkane and δ18Osugar values range from − 186 to − 89‰ and from + 27 to + 46‰, respectively. Degradation and root inputs did not appear to alter the isotopic composition of the biomarkers in the soil samples analyzed. Yet, the δ2Hn-alkane values show a statistically significant species dependence and δ18Osugar yielded the same species–dependent trends. The reconstructed leaf water of Erica arborea and Erica trimera is 2H– and 18O–enriched by + 55 ± 5 and + 9 ± 1‰, respectively, compared to precipitation. By contrast, Festuca abyssinica reveals the most negative δ2Hn-alkane and least positive δ18Osugar values. This can be attributed to “signal–dampening” caused by basal grass leaf growth. The intermediate values for Alchemilla haumannii and Helichrysum splendidum can be likely explained with plant physiological differences or microclimatic conditions affecting relative humidity (RH) and thus RH–dependent leaf water isotope enrichment. While the actual RH values range from 69 to 82% (x̄ = 80 ± 3.4%), the reconstructed RH values based on a recently suggested coupled δ2Hn-alkane –δ18Osugar (paleo–) hygrometer approach yielded a mean of 78 ± 21%. Our findings corroborate (i) that vegetation changes, particularly in terms of grass versus non–grassy vegetation, need to be considered in paleoclimate studies based on δ2Hn-alkane and δ18Osugar records and (ii) that the coupled δ2Hn-alkane –δ18Osugar (paleo–) hygrometer approach holds great potential for deriving additional paleoclimatic information compared to single isotope approaches.

Modern pollen-vegetation-climate relationships and pollen productivity estimates for common East Siberian taxa

<p>East Siberia is an ideal area for investigating the relationships between modern pollen assemblages and vegetation under the extremely cold and dry climate conditions. These relationships are the basis of paleovegetation and paleoclimate reconstructions from fossil pollen records. Pollen productivity estimates (PPE) are required for reliable pollen-based quantitative vegetation reconstructions. Here, we present a new pollen dataset of 48 moss/soil and 24 lake surface sediment samples collected from Chukotka and Yakutia. Generally, tundra and taiga vegetation sites can be well distinguished in the surface pollen assemblages from East Siberia. Moss/soil and lake samples have mostly similar pollen assemblages but contents of some pollen taxa may vary significantly in different sample types. We classified drone images based on field survey to obtain high-resolute vegetation data. Pollen counts in moss/soil samples and vegetation data can? be used in the Extended R-Value (ERV) model to estimate the relevant source area of pollen (RSAP) and the PPEs of major plant taxa. The result of PPE calculation for most common taxa (Alnus, Betula, Cyperaceae, Ericaceae, Larix, Pinus and Salix) can be used to improve vegetation reconstructions.</p>