Description: The timing of the Monte Peron Landslide is revised to 2890 cal. BP based on a radiocarbon-dated sediment stratigraphy of Lago di Vedana. This age fosters the importance of hydroclimatic triggers in the light of accelerating global warming with a predicted increase of precipitation enhancing the regional predisposition to large landslides. Moreover, a layer enriched in allochthonous organic and minerogenic detritus dating to the same wet period is interpreted as response to a younger and yet unidentified mass wasting event in the catchment of Lago di Vedana. Rock debris of the Monte Peron Landslide impounded the Cordevole River valley and created a landslide-dammed lake. Around AD 1150, eutrophication of this lacustrine ecosystem started with intensified human occupation – a process that ended 150 years later, when the river was diverted back into its original bed. Most likely, this occurred due to artificial opening of the river dam. In consequence, Lago di Vedana was isolated from an open and minerogenic to an endorheic and carbonaceous lacustrine system. After a monastery was established nearby in AD 1457, a second eutrophication process was initiated due to intensified land use linked with deforestation. Only in the 18th and 19th century, deposition of organic matter decreased coinciding with climatic (Little Ice Age) and cultural changes. Conversational measures are the likely reasons for a trend towards less eutrophic conditions since AD 1950.

Description: The dataset represents pollen and sedimentary charcoal counting data as well as XRF and grain size data of the lacustrine sediment core recovered from Lake Maudit in Montagne d'Ambre (northern Madagascar, 1,250 m asl). From the center of the Lake Maudit, accessible from a peat bog, two parallel sediment cores (LM1A and LM1B) with lengths of 10.5 m and 10.75 m were recovered in 2017 using a Russian peat corer in June 2017 by Vincent Montade, Laurent Bremond and Sandratrinirainy Ranarilalatiana. For pollen and extraction, 0.5 cm3 subsamples at an interval varying between 8 and 48 cm on LM1B were treated with hydrochloric acid, hydrofluoric acid, acetolysis mixture and stored in glycerol. A minimum sum of 300 terrestrial pollen grains was counted for each subsample using a light microscope at 400x magnification. Pollen and fern-spore percentages were calculated on the terrestrial pollen sum. For charcoal-particle extraction, 1 cm3 of sediment was sampled every cm along the core LM1B and soaked in a 3% NaP2O4 solution plus bleach for several hours to deflocculate sediments and oxidize organic matter. Samples were sieved through a 160 μm mesh and charcoal particles were counted using a stereomicroscope at x40 magnification coupled to a digital camera. Semi-quantitative measurements of inorganic chemical elements were conducted using an ITRAX (CS-8) X-Ray fluorescence (XRF) core scanner with a molybdenum (Mo) tube at the Geomorphological–Sedimentological Laboratory of the Geomorphology and Polar Research (GEOPOLAR), University of Bremen. XRF scanning was conducted at 55 kV and 30 mA with 10 s of exposure time at 0.2 mm resolution from both sediment cores, LM1A and LM1B. These elements were normalized to the counts of incoherent radiation (“Mo inc”) derived from the XRF scanner, to account for lithological changes and sediment matrix effects. Using the XRF data, a Principal Component Analysis (PCA) was carried out, using the selected elements (Si, K, Ca, Ti, Mn, Fe, and Ni) as input variables. Prior to this multivariate statistic, the normalized elements were transformed using standard “z-transformation”. The PCA carried out with XRF data resulted in three main axes, with Axis 1 representing 61.5%, Axis 2 24.4%, and Axis 3 9.4% of the total data variance. For the grain size analysis ~1 cm3 subsamples at an interval of 5 cm were taken on LM1A and measured after destroying carbonates and organic matter according to standard protocols with HCl and H2O2. Measurements were carried out with a laser diffraction particle-size analyzer (LS 13320 Beckman Coulter) in seven cycles of 60 s each. The first reproducible signal was considered as reliable and final distribution data were calculated using the Fraunhofer optical model. Based on the lithological description (marker layers), the XRF-element patterns, and digital and radiographic images, LM1A and LM1B were parallelized and combined to a composite core (depth 1). In addition, several event-related deposits (deposited within only hours or maybe days) originating from the catchment have been identified. These events disturbed the normal sedimentation process and were removed to correct the master core depth (depth 2) before establishing the age-depth model. Between 0 and 208 cm the sediment core corresponds to peat sediment and below 208 cm to lacustrine sediment. Age-depth model have been only established on the lacustrine sediment section.

Description: The brGDGT abundances was microwave extracted from the sediment using (DCM):MeOH solvent twice. Then the polar and apolar part were separated and undergo High Performance Liquid Chromatography Mass Spectrometery (HPLC-APCI-MS, Agilent 1200) to quantify brGDGT concentration and fractional abundances.

Description: Paleoclimate reconstructions appear to be unavoidable steps in the future climate change understanding and especially the local calibration of the paleoclimate proxies. The Mongolian Plateau in particular, is a scarcely studied area. Here we present a latitudinal transect from the southern Siberian Baikal area to the Mongolian part of the Gobi desert: the New Mongolian-Siberian DataBase (NMSDB). The 49 surface samples presented in this dataset are from different types: moss polsters, surface soil samples and lacustrine top-cores. Two paleoclimate proxies have been carried here: pollen analysis and biomarkers (glycerol dialkyl glycerol tetraethers, GDGTs). The actual bioclimate parameters of each sample sites are derived from the ASTER data (NASA, 2014) for the elevation (m a.s.l.) and the WorldClim2.0 interpolated climate database (Fick et Hijmans, 2017) for the climate parameters. The latitude, longitude and elevation as well as the sample type and the proxy type for each site is given in this dataset. We also indicate the main vegetation type surrounding each surface samples.