Climate reconstructions based on GDGT and pollen surface datasets from Mongolia and Baikal area: calibrations and applicability to extremely cold–dry environments over the Late Holocene

Abstract

Abstract. Our understanding of climate and vegetation changes throughout the Holocene is hampered by representativeness in sedimentary archives. Potential biases such as production and preservation of the markers are identified by comparing these proxies with modern environments. It is important to conduct multi-proxy studies and robust calibrations on each terrestrial biome. These calibrations use large databases dominated by forest samples. Therefore, including data from steppe and desert–steppe sites becomes necessary to better calibrate arid environments. The Mongolian Plateau, ranging from the Baikal area to the Gobi desert, is especially characterized by low annual precipitation and continental annual air temperature. The characterization of the climate system of this area is crucial for the understanding of Holocene monsoon oscillations. This study focuses on the calibration of proxy–climate relationships for pollen and glycerol dialkyl glycerol tetraethers (GDGTs) by comparing large Eurasian calibrations with a set of 49 new surface samples (moss polster, soil and mud from temporary dry ponds). These calibrations are then cross-validated by an independent dataset of top-core samples and applied to four Late Holocene paleosequences (two brGDGT and two pollen records) surrounding the Mongolian Plateau: in the Altai mountains, the Baikal area and the Qaidam basin, to test the accuracy of local and global calibrations. We show that (1) preserved pollen assemblages are clearly imprinted on the extremities of the ecosystem range but mitigated and unclear on the ecotones; (2) for both proxies, inferred relationships depend on the geographical range covered by the calibration database as well as on the nature of samples; (3) even if local calibrations suffer from reduced amplitude of climatic parameters due to local homogeneity, they better reflect actual climate than the global ones by reducing the limits for saturation impact; (4) a bias in climatic reconstructions is induced by the over-parameterization of the models by the addition of artificial correlation; and (5) paleoclimate values reconstructed here are consistent with Mongolia–China Late Holocene climate trends and validate the application of local calibrations for both pollen and GDGTs (closest fit to actual values and realistic paleoclimate amplitude). We encourage the application of this surface calibration method to reconstruct paleoclimate and especially consolidate our understanding of the Holocene climate and environment variations in arid central Asia.

Near-universal trends in brGDGT lipid distributions in nature

“…These discrepancies have led to independent modern calibrations for different sample types [e.g., soils and peats (36), lacustrine sediments (37) or SPM (38), bones (39), speleothems (40), and marine sediments (23)] and regions [e.g., East Africa (41) and China (42)] and have necessitated much effort to disentangle the allochthonous versus autochthonous sources of brGDGTs to sedimentary archives (2,23,(43)(44)(45)(46)(47)(48). Further complicating the matter, brGDGT distributions are affected by a wide array of other environmental parameters, including oxygen levels (26,27,29,(49)(50)(51)(52)(53)(54), salinity/electrical conductivity (55,56), seasonality (27,36,46,57), nutrient availability (58,59), and soil chemistry (28), which can mask or override relationships with temperature or pH. Last, an observed bias in brGDGT-derived temperatures toward warmer seasons has proven difficult to quantify, with various studies finding summer air temperature (60), the mean air temperature of months above freezing (MAF) (36,37,55), growing degree days above freezing (61), or other temperature indices to provide the strongest correlations in modern training sets.…”

Calibration and Application of Branched GDGTs to Tibetan Lake Sediments: The Influence of Temperature on the Fall of the Guge Kingdom in Western Tibet, China

“…We calculated the MAAT, Summer T, and MAF T bias of settling particles from traps (Dagze Co and Lake 578) to evaluate the performance of our calibrations (Figure 3g). Estimated biases of temperatures in Dagze Co and Lake 578 settling particles from traps revealed that bias ranges from −3.97°C to 12.65°C based on global lake calibrations (Martínez‐Sosa et al., 2021; Raberg et al., 2021), from −34.02°C to 27.86°C based on site‐specific calibrations (Feng et al., 2019; Harning et al., 2020; Zhao et al., 2021), and from −12.56°C to 32.81°C based on regional calibrations (Dang et al., 2018; Dugerdil et al., 2021; Russell et al., 2018; Stefanescu et al., 2021). Our new MBT′ 6Me ‐MAAT calibrations reconstruct temperatures for the sediment trap samples that are similar to annual temperatures recorded in the water column of Dagze Co, except at the chemocline (25 m), where reconstructed temperatures are warmer than observed temperatures (Figures 3g and 4d).…”

“…Numerous brGDGT‐MAAT calibrations have been developed for local (Dang et al., 2018; Dugerdil et al., 2021; Russell et al., 2018; Stefanescu et al., 2021), site‐specific (Feng et al., 2019; Harning et al., 2020; Zhao et al., 2021), and global (Martínez‐Sosa et al., 2021; Raberg et al., 2021) temperature reconstructions; however, to apply these calibrations to temperature reconstructions we also need to evaluate their accuracy at a given location. We tested previously published calibrations using our 29 core‐tops from the TP and settling particles from lake traps to investigate the accuracy of our new calibration.…”

“…We calculated the degrees of methylation of 5‐ and 6‐methyl brGDGTs (MBT′ 5Me and MBT′ 6Me ) from our new samples combined with previously published Chinese surface sediment samples (Dang et al., 2018; Table S1 in Supporting Information ) and regressed with measured temperatures to develop a new brGDGT‐temperature calibration (Figures 3a–3f). We compare applications using our new temperature calibrations with those from previously published calibrations from global lakes (Martínez‐Sosa et al., 2021; Raberg et al., 2021), and regional lake calibrations (Dang et al., 2018 [China]; Dugerdil et al., 2021 [Mongolian and Baikal]; Feng et al., 2019 [Southwestern China]; Harning et al., 2020 [Iceland]; Russell et al., 2018 [East African]; Stefanescu et al., 2021 [North America]; Zhao et al., 2021 [South Greenland]) to evaluate the performance of our calibration (Figures 3g and 3h).…”

“…We calculated the MAAT, Summer T, and MAF T bias of settling particles from traps (Dagze Co and Lake 578) to evaluate the performance of our calibrations (Figure 3g). Estimated biases of temperatures in Dagze Co and Lake 578 settling particles from traps revealed that bias ranges from −3.97°C to 12.65°C based on global lake calibrations (Martínez-Sosa et al, 2021;Raberg et al, 2021), from −34.02°C to 27.86°C based on site-specific calibrations (Feng et al, 2019;Harning et al, 2020;Zhao et al, 2021), and from −12.56°C to 32.81°C based on regional calibrations (Dang et al, 2018;Dugerdil et al, 2021;Russell et al, 2018;Stefanescu et al, 2021).…”