Dynamic environment but no temperature change since the late Paleogene at Lühe Basin (Yunnan, China)

Dynamic environment but no temperature change since the late Paleogene at Lühe Basin (Yunnan, China) | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Dynamic environment but no temperature change since the late Paleogene at Lühe Basin (Yunnan, China) Caitlyn Witkowski, Vittoria Lauretano, Alexander Farnsworth, Shufeng Li, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3857872/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The Eocene-Oligocene transition (EOT; ~34 million years ago) marks a critical shift from a greenhouse to an icehouse climate. Whereas temperatures derived from marine records show a consensus ~ 4°C cooling worldwide, there is an emerging picture that the terrestrial realm experienced a heterogenous response to rapid climate change. Here, we reconstruct an 8-million-year terrestrial temperature record across the EOT at a tectonically unresolved location at the margins of the Tibetan Plateau, Lühe Basin (Yunnan, China). Our multi-proxy organic geochemistry approach, complemented by sedimentological interpretations, shows that Lühe Basin was a dynamic fluvial environment that maintained relatively stable average temperatures from ~ 35 − 27 million years ago. These palaeotemperatures match our model-based estimates, as well as palaeobotany-based estimates at a nearby site; these stable palaeotemperature trends differ from the global marine cooling, supporting a heterogenous response of terrestrial sections. Furthermore, these palaeotemperature estimates match present-day values at this location, suggesting that this area has not undergone significant temperature change – and possibly no significant uplift – since the late Paleogene. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction The ‘Tibetan region’ (i.e., Himalaya–Tibet–Hengduan mountain area) is a major feature of our planet. Since the India-Eurasia continental collision during the early Paleogene, the Tibetan region has had a powerful and lasting impact on climate (e.g., Raymo and Ruddiman, 1992 ; France-Lanord and Derry, 1997 ; Farnsworth et al., 2019a ), the Asian monsoon systems (e.g., Huber and Goldner, 2012 ), and Asian biodiversity (e.g., Li et al., 2021 ). Today, the Tibetan region provides the headwaters for the ten largest rivers in Asia and consequently freshwater to nearly one-fourth of the global human population. It is also home to some of the richest biodiversity on Earth. Understanding the links between topography, climate, hydrology, and ecology in the broader region are thus key to the history of the region (Spicer et al., 2020 , and references therein), and fundamental for managing future natural resources, including biodiversity. However, the complex tectonic and climatic history of the Tibetan region remains unresolved. Following the India-Eurasia continental collision during the early Paleogene, globally compiled marine records document a congruent decline in carbon dioxide concentrations, growth of the Antarctic icesheet, and reorganisation of the global climate system during the late Paleogene, and mostly notably during the Eocene Oligocene Transition (EOT; e.g., Westerhold et al., 2020 ). However, the few existing terrestrial records demonstrate high heterogeneity in global change during this time, with some records suggesting no change over this period (Retallack, 2007 ; Sheldon et al., 2012; Kohn et al., 2015 ) while others suggest cooling (e.g., Zanazzi et al., 2007 ; Lauretano et al., 2021 ). Terrestrial records for the Tibetan region likewise show complex and heterogeneous changes in biodiversity (e.g., Li et al., 2021 ), which had downstream impacts on e.g., Yunnan, one of Asia’s biodiversity hotspots situated in southwestern China along the SE Tibetan margin (Li et al., 2020 ; Spicer et al., 2020 ). However, apart from these specific examples, the lack of other (well-dated) sections has hindered attempts to correlate these interior locations to the global Cenozoic climate trends extrapolated from marine records. Reconstructing the climatic history of sedimentary basins along the margin of Tibet is crucial to understand the connection between topographic relief and climate, their influence on the Asian monsoon system, and their link to global climate. Here, we reconstruct the climatic and environmental conditions during the late Paleogene in Yunnan, specifically from a key sedimentary repository located along the SE margin of the Tibetan Plateau known as Lühe Basin. We use organic geochemistry, sedimentology, and modelling, contextualised with palaeobotany, to develop a more robust and holistic understanding of this region. Although there have been recent efforts to better constrain late Paleogene climatic and environmental conditions throughout the Tibetan region using modelling and palaeobotanical tools (e.g., Su et al., 2019a ; 2020 ), few studies have used quantitative organic geochemical proxies. Because organic geochemical proxies can be measured continuously throughout a section, they more robustly demonstrate change over time as compared with the geologic-stage time slices used by models or with the individual and sporadic mega-fossil horizons used in palaeobotany; thus, the addition of organic geochemistry offers critical new perspectives at this location. 2. Results and Discussion 2.1 Vegetation reconstructions We determine vegetation using the distributions of n -alkanes derived from plant leaf waxes and/or mixed sources (Eglinton and Hamilton, 1967 ). Throughout the section, the n -alkane distribution has an odd-over-even preference (Fig. 3 , Fig. 4 d; Table S1), suggesting an origin from leaf waxes and a significant input of terrestrial plants (Eglinton and Hamilton, 1967 ). In most of these sediments, the apolar fractions are dominated by the C 29 n -alkane, followed by a high abundance of the C 27 and then C 31 n -alkanes (Figs. 2 a), such that the ACL ranges from 27.7 to 29.7 with a mean of 29.0 ± 0.3 σ (Fig. 6 a; Table S1). This ACL (Fig. 6 a) and the dominance of the C 29 n -alkane (Fig. 3 a, 4 a) across the section suggest that the vegetation at this site was likely dominated by woody gymnosperms and angiosperms (Bush and McInerney, 2013 ). Specifically, the ACL of 29.0 is more likely associated with deciduous rather than evergreen angiosperms, the latter tending to have slightly lower ACLs in modern vegetation (Bush and McInerney, 2013 ), although this is not definitive. The C 23 /(C 23 + C 31 ) n -alkane ratios show some fluctuations throughout the section, suggesting changes in vegetation type (Fig. 6 c; Table S1). For the whole section, the mean average is 0.4 ± 0.2 σ and the range is 0.0 to 0.9, spanning almost the entire theoretical range of 0.0 to 1.0. The C 23 /(C 23 + C 31 ) values 0.7 are considered indicative of high inputs from moss and macrophyte, particularly Sphagnum mosses due to their particularly high abundance of C 23 relative to C 31 n -alkanes (Bush and McInerney, 2013 ). There is greater fluctuation at the bottom of the section (0–73 m), where values rapidly change across the span of 0.1 to 0.9 values, indicating dynamic vegetation and environmental change. The upper part of the section (73–340 m) has generally lower C 23 /(C 23 + C 31 ) values, averaging 0.2 ± 0.1 σ with a range of 0.0 to 0.4, which indicates less frequent wetland-type deposition. However, these fluctuating values throughout the section that commonly exceed 0.1 indicate that the environment remained dynamic throughout. Further providing insights into vegetation type at this site, many sediments also contained diterpenoids and triterpenoids (Fig. 3 a; Table S2; Fig. S3, Fig. S4), compounds indicative of gymnosperms and angiosperms, respectively (Otto and Wilde, 2001 ; Diefendorf et al., 2012 ). Figure 3 a shows an example of a chromatogram with highly abundant terpenoids (presence, abundances, and their retention times), Figs. S3 and S4 shows the molecular structures of the terpenoids identified at this site, and Table S2 shows the relative abundance (i.e., absent, trace, present, or abundant) of each terpenoid at each sample depth. Seven diterpenoid biomarkers associated with gymnosperms were identified throughout the section (i.e., cadalene, norpimerane, 18-norabietane, 19-norabieta-8,11,13-triene, dehydroabietane, 10,18-bisnorabieta-5,7,9(10),11,13-pentaene, and simonellite), with notably high abundances from 6.7–8.2 m, 18.1–65 m, and 202.0-301.9 m. Trace amounts were found throughout most of the section (Table S2). In addition to their association with gymnosperms, the specific diterpenoid compounds identified at Lühe Basin are common among all major conifer groups and comprise a particularly high percentage of total diterpenoids in Pinaceae (Diefendorf et al., 2019 ). The possibility that these diterpenoids are conifer-derived is especially supported by the presence of simonellite, a compound found in conifer resin (Simoneit, 1986), that was consistently found in high abundances (often dominant compound) throughout the entire section. Several samples also contained the triterpenoids tetramethyl-octahydrochrysene and Des-A-lupane, compounds synthesized by nearly all angiosperms (Trendel et al., 1989 ), with notably high abundances from 18.1–46.5 m, 96.0-129.0 m, and 268.0-301.9 m. Trace amounts were found throughout most of the section (Table S2). The more frequent abundance of diterpenoids compared with triterpenoids in these sediments (Table S2) suggest that this environment was likely dominated by gymnosperms with some angiosperms, although it should be noted that taphonomic processes can skew plant preservation and associated biomarker distributions (Tang et al., 2020 ). Our biomarker-based vegetation reconstruction is consistent with the plant fossil assemblage recovered from the nearby Lühe town section (which likely corresponds to the 70 to 130 m interval of our coalmine section, see above). Based on high ACL values, the terpenoid interpretation, and variation in C 23 /(C 23 + C 31 ) ratios, our findings suggest variable vegetation inputs comprising mostly woody gymnosperms (likely conifers), some angiosperms, and brief periods of Sphagnum /macrophyte input (likely associated with localised peatland formation). Correspondingly, the palaeobotany analyses at the Lühe town section assign 38 floral genera to 26 angiosperms, 6 gymnosperms, and 4 ferns (Tang et al., 2020 ), which were dominated by Pinus (i.e., pine) from the family Pinaceae and Quercus (i.e., oak) from the family Fagaceae (Xu et al., 2008 ; Zhang et al., 2020). Palaeobotany analyses also provide evidence of tree stumps, fallen logs, and branches (Yi et al., 2003 ; Deng et al., 2022 ). Similar vegetation was described by the palynological assemblages from the Lühe town section, where evergreen oaks ( Quercus ) and alder ( Alnus ) were identified; palynomorphs were dominated by Quercoidites (43%), Titricolpites (13%), Pinuspollenites (7%), and Piceapollis (0–20%) (Tang et al., 2020 ). Palynological findings are not necessarily representative of in situ assemblages given that pollen may be blown/washed into the basin from the surrounding (and possibly higher elevation) areas; that said, they are indeed consistent with our biomarker-based assemblages in the Lühe Basin. Together, the results of the biomarker, palaeobotany, and palynology assemblages indicate a temperate forest with evergreen broadleaved taxa and conifers, and some deciduous broadleaved taxa and brief episodes of wetland formation. 2.2 Depositional environment reconstruction Here, we provide only a broad overview, integrating sedimentological and biomarker observations. A full and detailed interpretation of the sediment log from the coalmine section can be found in the Supplement (Table S3; Fig. S5). The measured ca. 340-m thick profile comprises alternations of organic-rich marls, mudstones, sandstones, and lignite deposits, representing various depositional environments typically found in a floodplain setting: active and abandoned channel deposits, proximal to distal overbank crevasse splay deposits, and sub-aerial soils to shallow pond swamps. The marked variation in sedimentary facies throughout the section suggests it documents a dynamic environment. Similarly, the TOC (%) ranges from 0.1 to 63.9% (Fig. 4 a), from organic-lean to essentially maximum (solely organic carbon) values, further indicating a highly variable depositional environment. This interpretation is also consistent with brGDGT-based pH values that vary between 3.0 and 7.7 pH (Fig. 4 b; described in Section 2.3 ) and with the dramatic changes in biomarker distributions. For example, P aq values span from 0.0 to 0.9 (representing terrestrial to aquatic-dominated OM, respectively), nearly the entire mathematical range of 0.0 to 1.0, with most values between 0.2 and 0.5 and a mean of 0.4 ± 0.2 σ (Fig. 6 b). P aq values that are 0.48 are common for submerged and floating macrophytes (Ficken et al., 2000 ). Because P aq sits in the middle of these two key ranges for much of the section, the depositional environment likely had input from both terrestrial and aquatic sources and was a wet terrestrial environment, like a floodplain, wetland, or shallow lacustrine environment. This wetness is further supported by the sedimentary succession (Fig. 6 ) and high abundance of Equisetum cf. pratense in this section (Zhang et al., 2007 ), a fern species that is indicative of wet terrestrial environments. Despite this variability throughout the section, there seems to be a marked difference between the lower (0–73 m) and upper part (73–340 m). The lower part of the section (0–73 m) alternates between swampy conditions and open flood basin conditions, characterized by fine-grained, typically muddy, sediments, as well as intervals with very high organic matter contents and subaerial exposed and oxidized rooting. This interpretation is supported by the frequent occurrence of high TOC (%) sediments (defined here as > 20%; Fig. 4 a), high P aq values, low reconstructed pH (see below), and high C 23 /(C 23 + C 31 ) n -alkane ratios, suggesting that this section was occasionally a peat-forming floodplain environment. The frequent occurrence of high TOC (%) is noted by seven samples in the much shorter 73 m lower section, as compared with only four in the following 250 m of the upper section. Likewise, P aq values in the lower section range from 0.1 to 0.9 with a mean of 0.4 ± 0.2 σ, whereas the upper section has significantly lower and less variable P aq values ranging from 0.0 to 0.4 with a mean of 0.2 ± 0.1 σ, again indicating a wetter lower section. Two outliers in the sediment intervals (26.7 and 58.5 m) are described in more detail in the Supplement. Based on all evidence, this lower 73-m interval appears to represent deposition in a relatively low energy fluvial and lacustrine (e.g., meander cut off) system, with fine-grained clay and silt deposition in flood plains, interbedded with lignites deposited in wet depositional environments, such as a flood basin, wetland/peatland, or shallow lake. The upper part of the section (73–340 m) is characterized by coarser-grained deposits representing small influxes of silt and fine sand, occasional rafted branches and leaves, distal crevasse splay features, and river transported wood fragments. At around 69 to 77 m, there is a gradual increase in the energy of the system, with increasingly coarse sediments. At 74.2 m, a major sand incursion brings in branches/logs, and crossbedding suggests lateral sand migration in a channel, although there is no evidence of basal erosion. At this point, the upper part of the section generally comprises higher energy fluvial environments, represented by e.g., log jams (at 74.2 m, 150 m, 187 m, 224 m, 250-271.5 m, 300.5-316.8 m, 334–336 m), inferred overbank incursion (at 85.8–97.9 m, 271.5-300.5, 315.1 m, 323.4-324.1 m, 331–340 m), and a thick sand body with crossbedding and leaf debris (at 101.3-105.5 m, 233.3–250 m, and 271.5-300.5 m). These are interspersed with some short intervals of swampy conditions, represented by iron horizons, sub-aerially exposed floodplain silts colonised by plants, and four organic-rich lignites. As opposed to the calm flood basin, wetland, or possible shallow lake in the lower part of the section, the upper part of the section suggests a much more dynamic environment, with deposition fluctuating between channel and floodplain. This interpretation supports the (albeit much lower resolution) overview of the Lühe coalmine section described in Wissink et al. ( 2016 ). The biomarker assemblages support this interpretation of the upper section representing a more dynamic environment, with deposition fluctuating between channel and floodplain. The P aq values are still quite variable, ranging from 0.0 to 0.4 with a mean of 0.2 ± 0.1 σ, but relatively lower and less variable compared with the lower section. These P aq values suggest a dynamic system dominated by (allochthonous) higher plant inputs over those from aquatic plants. High variability in pH further supports the interpretation of a dynamic upper section, where the lowest reconstructed pH (3.0 at 303.5 m) and highest reconstructed pH (7.7 at 311.0 m) values in the whole section occur nearly back-to-back, indicating a very rapid shift from a highly acidic (wetland) environment to a neutral pH setting. Taken together, our observations indicate a dynamic but evolving fluvial-lacustrine environment throughout the entire section, changing from a lower energy flood basin, wetland/peatland, and/or shallow lake to a higher energy floodplain and/or channel. We detect abundant terrestrial biomarkers (e.g., leaf waxes, terpenoids indicative of woody gymnosperms and angiosperms, and soil bacterial lipids), consistent with palaeobotanical and palynological evidence in the nearby Lühe town section (Tang et al., 2020 ). Together, this evidence indicate that the Lühe area was covered in deciduous and evergreen broad-leaved mixed forests. We also see evidence for this being a very wet environment, as indicated by e.g., variable P aq and C 23 /(C 23 + C 31 ) ratios. However, we do not see strong evidence for this being a deep lacustrine environment which could be indicated by e.g., abundant algal biomarkers and isoprenoidal GDGTs. Instead, we interpret this as a dynamic fluvial system – with the myriad of depositional environments that entails. 2.3 Climate reconstruction using brGDGT calibrations We reconstruct mean annual temperatures using branched glycerol dialkyl glycerol tetraethers (brGDGTs), membrane-spanning lipids likely synthesized by bacteria and widely used as paleothermometers (Sinninghe Damsté et al., 2000 ; Weijers et al., 2007 ). In the fifty-six samples analysed, thirty-eight yielded sufficient brGDGTs for temperature and pH reconstruction (Fig. 7 ; Table S1) from thermally immature sediments (see Supplemental Text). MBT’ 5me values range from 0.4 to 0.7 with a mean of 0.6 ± 0.1 σ; these values remain relatively stable throughout the section. Values in the lower section (0–73 m) and upper section (73–340 m) are nearly identical, respectively ranging from 0.4 to 0.7 with a mean of 0.6 ± 0.1 σ and ranging from 0.4 to 0.7 with a mean of 0.6 ± 0.1 σ. Over the section, CBT peat values range from − 2.1 to -0.2 with a mean of -1.0 ± 0.4 σ. Unlike MBT ’5me , CBT peat does yield differences between the lower and upper sections, respectively ranging from − 1.7 to -0.9 with a mean of -1.2 ± 0.3 σ and ranging from − 2.1 to -0.2 with a mean of -0.9 ± 0.4 σ. This variability in CBT peat (but not in MBT ’5me ) is due to four samples in the upper section (depths 273.5, 301.9, 311, and 331.5 m) that contain 6-methyl brGDGTs; these are the only samples that contain 6-methyl brGDGTs in this whole section. Our tests show no correlation of the 6-methyl brGDGTs with overall changes in depositional environment (e.g., against P aq values) but do show expected changes with pH (Wu et al., 2021 ; Wang et al., 2021). Indeed, the four 6-methyl brGDGT-containing samples have high pH values (respectively, 7.6, 6.3, 7.7, and 6.6 pH), as compared with the whole section which ranges from 3.0 to 7.7 pH with a mean of 5.5 ± 0.9 σ; the lower section from 3.8 to 5.9 pH with a mean of 5.1 ± 1.0 σ, and the upper section from 3.0 to 7.7 pH with a mean of 5.7 ± 1.0 σ. Previous work has proposed a wide variety of brGDGT calibrations for different depositional settings, including lacustrine, soil, and peat calibrations. Given the variability between these calibrations and the inferred behaviour of brGDGT in different settings, it is perhaps unsurprising that brGDGT indices exhibit large variability throughout the section. This makes it challenging to assign a calibration a priori , especially when brGDGTs are not always produced in the depositional setting in which they are found. Therefore, we initially apply three different brGDGT-temperature calibrations that reflect the depositional environmental variability within the Lühe Basin (Fig. 7 ). Most sediments (n = 46) are mudstones to sandstones and have a TOC (wt%) ranging between 0.1–23% with the majority < 3% (Fig. 4 ). Sandy sediments did not contain sufficient brGDGTs for analysis, but the brGDGTs in the other horizons (n = 33) could derive from either allochthonous input from surrounding mineral soils or in situ ‘lacustrine’ production. Thus, we initially apply the soil-calibrated mean annual air temperature (MAAT soil ; Naafs et al., 2017a ), the peat-calibrated MAAT (MAAT peat ; Naafs et al., 2017b ), and lake-calibrated MAAT for months above freezing (MAF lake ; Martínez-Sosa et al., 2021 ) (Fig. S1). We note that here our MAF values are likely equivalent to MAAT because this site appears to have no months below freezing, as indicated by the model results with three consecutive coldest month mean temperature (3CMMT) of 12.1°C (detailed in Section 2.4 ; Table 2 ) and palaeobotany-based Climate Leaf Analysis Multivariate Program coldest month mean temperature (CMMT) of 4.5°C (Table 3 ). The remaining sediments (n = 5) have TOC (%) ranging between 40–63%; these high TOC contents are most likely to have been deposited in a wetland setting and for those, we thus apply the peat-specific MAAT calibration (MAAT peat ; Naafs et al., 2017b ) (Fig. 7 ). Table 1. Climate model simulations (sim.) of the Asian regional impact with Priabonian (Eocene) to Chattian (Oligocene) boundary conditions are used to test the response of p CO ­2 and Tibetan topography configuration on temperature and precipitation. Grey indicates which parameters are included in the simulation. p CO 2 is represented as either a change from 4x to 2x pre-industrial p CO 2 or no change in p CO 2 . Tibetan topography is configured at different elevations to determine the impacts this may have one the broader climate system, here showing as: only a valley at 2.5 km, only a plateau at 4.5 km, or a change from 2.5 km valley to 4.5 km plateau. Topography shown in Fig. 5. Accompanying experiment results in Table S4. The response is shown on the right as change in mean annual air temperature (ΔMAAT, °C) and change in mean annual precipitation (ΔMAP, mm/yr). Sim. p CO 2 2.5 km 4.5 km 2.5 to 4.5 km ΔMAAT ΔMAP 1 4x to 2x Yes No No -6.0 150 2 4x to 2x No Yes No -6.0 198 3 4x to 2x No No Yes -6.2 182 4 No change Yes No No 1.4 167 5 No change No Yes No 1.7 173 6 No change No No Yes 1.2 199 Table 2 Climate model simulations for Priabonian to Chattian Lühe Basin. Conditions based on Getech model assumptions for the three target geological stages: Eocene Priabonian (37.71 to 33.90 Ma), Oligocene Rupelian (33.90 to 27.82 Ma), and Oligocene Chattian (27.82 to 23.03 Ma), including: paleo-rotations (Rot. Lat. = rotated latitude; Rot. Long. = rotated longitude); elevation (Elev., m); and p CO 2 assumed to have decreased from 4x to 2x pre-industrial values (1120 to 560 ppm). The resulting temperatures and precipitation are shown as: mean annual air temperature (MAAT, °C), three consecutive warmest-month mean temperatures (3WMMT, °C), three consecutive coldest-month mean temperatures (3CMMT, °C), and mean annual precipitation (MAP, mm/yr). Accompanying experiment results and boundary conditions are expanded in Table S5. Stage Rot. Lat. Rot. Long. Elev. (m) p CO 2 MAAT 3CMMT 3WMMT MAP Priabonian 28.5312 97.8944 2322 1120 (4x) 27.4 17.9 35.5 840 Rupelian 28.1658 97.5050 2578 560 (2x) 19.3 11.6 25.9 1040 Chattian 27.4190 97.2092 2805 560 (2x) 17.5 10.0 22.6 1220 Table 3 Climate Leaf Analysis Multivariate Program (CLAMP) climate estimates based on the Lühe town section leaf flora and analysed using the PhysgAsia2/Worldclim2 calibration . For more details on these metrics and how they are obtained see (Spicer et al., 2020 b). Row 1: Temperature-related parameters: mean annual air temperature (MAAT, °C); warmest month mean air temperature (WMMT, °C); coldest month mean air temperature (CMMT, °C); mean minimum temperature of the warmest month (MinT.W, °C); mean maximum temperature of the coldest month (MaxT.C, °C); thermicity i.e., a measure of cumulative heat (Therm). Row 2: Humidity and enthalpy-related parameters: relative humidity (RH, %); specific humidity (SH, g/kg); moist enthalpy (Enth, kJ/kg). Row 3: Vapour pressure deficit parameters: mean annual vapour pressure deficit (VPD.ann, hPa); mean winter vapour pressure deficit (VPD.win, hPa); mean spring vapour pressure deficit (VPD.spr, hPa); summer vapour pressure deficit (VPD.sum, hPa); autumn vapour pressure deficit (VPD.aut, hPa). Row 4: Precipitation and evapotranspiration-related parameters: precipitation during the three consecutive wettest months (3-Wet, cm); precipitation during the three consecutive driest months (3-Dry, cm); mean annual potential evapotranspiration (PET.ann, mm); mean monthly potential evapotranspiration during the warmest quarter (PET.wrm, mm); mean monthly potential evapotranspiration during the coldest quarter (PET.cld, mm). Row 5: Growth-related parameters: length of the growing season i.e., time when the mean temperature is > 10°C (LGS, months), growing degree days > 0°C (GDD0); growing degree days > 5°C (GDD5); growing season precipitation (GSP, cm); mean monthly growing season precipitation (MMGSP, cm). Temperature-related parameters MAAT (°C) WMMT (°C) CMMT (°C) MinT.W (°C) MaxT.C (°C) 15.9 ± 2.4 26.8 ± 2.9 4.6 ± 3.5 23 ± 2.9 10.4 ± 3.5 Humidity and enthalpy-related parameters RH (%) SH (g/kg) Enth (kJ/kg) Therm (°C) 65 ± 10 8.3 ± 1.8 321 ± 0.8 295 ± 75 Vapour pressure deficit parameters VPD.ann (hPa) VPD.win (hPa) VPD.spr (hPa) VPD.sum (hPa) VPD.aut (hPa) 6 ± 2.4 3.2 ± 1.5 4.7 ± 4 8.7 ± 3.5 7.4 ± 2 Precipitation and evapotranspiration-related parameters 3-Wet (cm) 3-Dry (cm) PET.ann (mm) PET.cld (mm) PET.wrm (mm) 111 ± 40 35 ± 10 1002 ± 166 27.5 ± 14 125 ± 24.5 Growth-related parameters LGS (month) GSP (cm) MMGSP (cm) GDD0 GDD5 9.8 ± 1.1 225 ± 64 24 ± 7 677 ± 118 735 ± 106 The overall record (regardless of calibration) exhibits a persistent degree of variability without a long-term trend. The trends for all three calibrations are virtually the same, but the absolute temperatures differ greatly: the MAAT soil estimates are ca. 9°C cooler than the MAF lake estimates (Fig. 7 ). MAAT soil values range from 1.9 to 14.4°C, with a mean of 8.5°C ± 3.5°C σ. MAF lake values range from 11.8 to 22.2°C, with a mean of 17.3°C ± 2.9°C σ. The five MAAT peat values range from 5.4 to 15.4°C, with a mean of 10.9°C ± 4.4°C σ, with values generally closer to those obtained using the soil calibration. The temperature trends throughout the section show variability, possibly due to mixing of in situ and allochthonous sources within the rapidly changing and dynamic depositional environment (Section 2.2 ). To test whether temperature is impacted by changes to the mixture of in situ versus allochthonous brGDGTs, we plot MBT’ 5me against P aq and MBT’ 5me against lithology; there are no apparent trends. This is possibly due to the preservation of brGDGTs in the low-energy clay and silt sediments (the sediments more likely to contain in situ signals) and lac of preservation of brGDGTs in the high-energy sand sediments (the sediments more likely to contain allochthonous signals). Thus, our brGDGT values likely reflect in situ production and high-fidelity temperature reconstructions. Given the results and discussion described in Section 2.2 (based on e.g., lithology, high P aq and C 23 /(C 23 + C 31 ) values), the depositional environment is clearly a dynamic fluvial system that is constantly wet.. With these independent pieces of evidence taken together this site represents lacustrine-type in situ production brGDGT and thus, the MAF lake calibration is to be the most appropriate choice for calibration. 2.4 Climate model results To contextualise the climatic and environmental changes occurring at our site, we employed a fully coupled atmosphere-ocean GCM with a range of perturbed Late Eocene and Oligocene boundary conditions. First, we tested the impacts on the broader Asian region (0°N-60°N, 60°E-120°E) from the Priabonian to Chattian, specifically the impacts that p CO 2 values and site topography have on temperature and precipitation (summary in Table 1 ; accompanying scenarios in Table S4). If we assume that p CO 2 decreased from 4x to 2x pre-industrial p CO 2 , we observe regional cooling by ca. 6°C, regardless of topographic boundary conditions inferred for the Tibetan Plateau i.e., with site elevations as either a constant valley at 2.5 km, a constant plateau at 4.5 km, or changing from a valley-to-plateau at 2.5 to 4.5 km (Table 1 , simulations 1–3; Fig. 5 ). For comparison, we evaluated the impact of not changing p CO 2 on regional temperature and precipitation. Assuming constant 4x pre-industrial p CO 2 from the Priabonian to Chattian, we observe slight regional warming by ca. 1.5°C, regardless of topographic boundary conditions inferred for the Tibetan Plateau (Table 1 , simulations 4–6). In all six model simulations in Table 1 , mean annual precipitation (MAP) increases 150–200 mm/yr from the Priabonian into the Chattian. MAP is seemingly neither influenced by p CO 2 values (e.g., from 4x to 2x pre-industrial p CO 2 or no change in p CO 2 ) nor by topographic configurations in the model conditions. This suggests that the modelled MAP was not impacted by local factors, but instead by global changes across this boundary e.g., the opening of ocean gateways (e.g., Drake Passage, creation of the Antarctic Circumpolar Current, and retreat of the Paratethys Sea), the expansion of the Antarctic icesheet, and broad reorganisation of the global climate system (Westerhold et al., 2020 ). Second, we tested the impacts on Lühe Basin region itself, given paleo-rotations for latitude/longitude and given elevations of the Lühe Basin in the Getech Plc. Model assumptions for the Priabonian (Eocene), Rupelian (Oligocene), and Chattian (Oligocene) (Table 2 ; accompanying experiment results in Table S5). It is worth noting that the model is very coarse and thus we cannot resolve the Lühe Basin location itself (only the region) nor represent accurate topography, as it is essentially a slab of constant elevation for the region. Given the globally assumed decrease from 4x to 2x pre-industrial p CO 2 across the EOT, we find that the Priabonian Lühe Basin region experienced MAAT of 27.4°C, with annual temperatures ranging from the three consecutive coldest months mean temperature (3CMMT) of 17.9°C to the three consecutive warmest months mean temperature (3WMMT) of 35.5°C. A Rupelian and Chattian Lühe Basin region experienced cooler MAATs of 19.3°C and 17.5°C respectively, ranging from 3CMMT of 11.6°C and 10.0°C and 3WMMT of 25.9°C and 22.6°C, respectively. This cooling in the model is unsurprising, given the assumption of decreasing p CO 2 from 4x to 2x pre-industrial values across the EOT, but is certainly notable in the context of the high temporal resolution of the data at this site which shows relatively constant averages across this time boundary. 2.5 The evolution of the Tibetan region and Eocene/Oligocene climate Our reconstructed MAATs across this section are consistent with a temperate climate. Based on the 40 Ar/ 39 Ar dating of feldspars within volcanic ashes exposed at 58 m, which provide ages of 33.32 ± 0.36 Ma (sample lvb11) and 34.38 ± 0.74 Ma (sample lv5 8.0), our Lühe coalmine section may or may not capture the Eocene-Oligocene transition (EOT) that occurred at 33.9 Ma (Fig. 2 ). We do not find evidence of significant cooling within the first 60 m of the section, the most likely part of our section to have captured the EOT. The lack of cooling could indicate that the Lühe Basin does not span the EOT. Alternatively (and likely, given our age model), our reconstruction may show that temperature in the Lühe Basin remained relatively stable across the EOT; in this case, the Lühe Basin did not experience the cooling observed in marine sections (e.g., Westerhold et al., 2020 ) but instead reflects the heterogenous expression previously observed in terrestrial sections (e.g., Zanazzi et al., 2007 ; Hren et al., 2013 ; Sheldon et al., 2016 ; Lauretano et al., 2021 ). If the Lühe Basin coalmine section does not include the EOT, to err on the side of caution, then our brGDGT-based records support the ‘quasi-static’ climate that has gained traction across palaeobotany and paleosol records (Retallack, 2007 ; Sheldon et al., 2012; Kohn et al., 2015 ). It should be noted that a relatively muted cooling of < 1–2°C might be difficult to detect in our proxy records, which are better suited for greater temperature oscillations (Naafs et al., 2017b ). That said, the lack of significant temperature change over ca. 8 Myr in the Lühe Basin coalmine section is notable (Fig. 7 ). The results from this section may represent one more important puzzle piece in the terrestrial expression of the EOT (e.g., Pound and Salzmann, 2017 ) and the possible influence of local factors on this response. If the Lühe Basin coalmine section does include the EOT, our biomarker data suggests that the Lühe Basin maintained relatively stable temperatures across the EOT whereas our model suggests ~ 6°C cooling from the Eocene Priabonian into the Oligocene Rupelian (assuming a decline in p CO 2 from 4x to 2x pre-industrial levels). This difference is likely due to spatial resolution of the model (which relies on large homogenous grids) as compared with the biomarker data (which records details on the specific/basin scale); the model cannot capture the complex topography that may influence the climate at this site. Our results do not conform to the marine record shift in temperature but ultimately support the emerging picture that terrestrial temperature records have a widely heterogenous response across the EOT (e.g., Pound and Salzmann, 2017 ), as evidenced from both qualitative and quantitative proxies (e.g., palaeobotanical, palynological, geochemical). Vegetation records provide the most extensive global dataset of changes across the EOT and generally show a variety of responses, partly influenced by local/regional factors and changes in precipitation (Pound and Salzmann, 2017 ). Palaeobotany assemblages from Argentina indicate a ‘quasi-static’ climate across the EOT (Kohn et al., 2015 ), whereas assemblages from North America suggest protracted cooling from the early into the middle Oligocene (Retallack et al., 2004 ). A palynological record from a lignite sequence in SE Australia also suggests a (qualitative) cooling across the EOT; in the same coeval facies, the organic geochemical brGDGT-based record shows 2.4°C cooling (Lauretano et al., 2021 ). Terrestrial geochemical records likewise depict a range of responses. Paleosol records from North America, Argentina, and Spain suggest that temperatures remained unvaried during this time (Retallack, 2007 ; Sheldon et al., 2012; Kohn et al., 2015 ), whereas another paleosol record from North America suggests ca. 2–3°C cooling (Retallack, 2007 ). Geochemical records from the clumped isotopic composition of freshwater gastropod shells from the UK indicate a more intense 4–6°C cooling from the late Eocene to the early Oligocene (Hren et al., 2013 ). Similarly, the stable hydrogen isotopic composition from volcanic glass suggests that a 5°C cooling occurred in Argentina (Colwyn and Hren, 2019 ). Differing still, the oxygen isotopic composition of fossil teeth in North America suggests a dramatic ca. 8°C temperature drop across the transition (Zanazzi et al., 2007 ). The terrestrial signal during this time certainly shows an unresolved heterogenous response, possibly due to localised factors such as albedo (e.g., based on soil type), vegetation type and associated impacts (e.g., transpiration and canopy cover (Fritts et al., 1961)), localised nutrient and carbon cycling, and detailed differences in topography (e.g., even as detailed as whether the site represents the north or south slope of a valley). Regardless of the inclusion/exclusion of the EOT, the coalmine section at Lühe basin certainly spans the Rupelian into the Chattian, where our biomarker-, palaeobotany-, and model-based temperature estimates support stable long-term temperatures in the Oligocene (Fig. 7 ). Our brGDGT MAF lake mean of 17.3°C ± 2.9 σ from (possibly) the Priabonian through the Rupelian and into the Chattian are not dissimilar to our model-based MAATs of ~ 19.3°C ± 0.5 σ for the Rupelian and ~ 17.5°C ± 0.5 σ for the Chattian (Table 2 ) nor dissimilar to the palaeobotanical-based MAATs of 14.5–15.5°C (bioclimatic analysis; Tang et al., 2020 ) and 16°C ± 2.4 calibration uncertainty(Climate Leaf Analysis Multivariate Program (CLAMP); Table 3 ) from the nearby Lühe town section from the Rupelian. The modelling and palaeobotanical results demonstrate a modest annual range of temperatures (e.g., model 3CMMT of 12.0°C and 3WMMT of 24.0°C, CLAMP CMMT of 4.5°C to WMMT of 26.9°C), with likely infrequent winter frosting and warm summers. This would suggest a warm temperate climate rather than fully subtropical climate, with taxa that have frost sensitive leaves that are prone to winter deciduousness. Importantly, these four different methods for estimating palaeotemperatures match the present-day MAAT for this site, which ranges from 15–20°C depending on the exact elevation in this region with dynamic topography. The lack of temperature change from the Oligocene to today suggests that this location has been at its present-day elevation since at least the early Oligocene, supporting the hypothesis that local uplift had already taken place by this time (Spicer et al., 2020 ; Wei et al., 2022 ). This proposal has recently been grounded in supporting data by Wu et al. ( 2022 ) who likewise suggest that Lühe Basin town section had reached its present elevation by the early Oligocene. Similarly, He et al. (2022) suggest that eastern Tibet reached its present elevation by the end of the Eocene. The modelling results are critical in further supporting this hypothesis. Topographic features e.g., a central Tibetan valley of 2.5 km, plateau of 4.5 km, or change from a 2.5 km valley to 4.5 km plateau (Table 1 ; Fig. 5 ) have virtually no impact on the larger climate of this region. An alternative explanation may be that there are complicating factors that impact temperature for the biomarker and palaeobotany proxies e.g., cloud coverage. However, this seems less likely given the proxy agreement with the model results throughout the Oligocene when we would expect more dynamism in the environment, e.g., precipitation. This coupled atmosphere-ocean general circulation paleoclimate model includes paleo-rotations (latitude/longitude), changes in globally dynamic temperatures (e.g., SST changes from the South China Sea), and changes in cloud coverage and precipitation. In addition, there is supporting evidence from moist enthalpy from CLAMP and oxygen isotopes from carbonate nodules that topography of the eastern margin of Tibet was established immediately prior to and during the basin development (e.g., He et al., 2022). However, we do see dramatic changes in the depositional environment throughout the Lühe coalmine section, particularly the changes in hydrology and overall energy of the system. In addition to the sedimentological and organic geochemistry evidence discussed in Section 2.3 , precipitation changes are confirmed by the model and palaeobotany results. Based on the nearby (Rupelian) Lühe town section, CLAMP-based precipitation (during the growing season) suggests averages of 2250 mm ± 640 σ, with the three consecutive wettest months (3-WET) around 1110 mm ± 400 σ and three consecutive driest months (3-DRY) around 340 mm ± 98 σ (Table 3 ). The overall precipitation may be overestimated in CLAMP, particularly for the dry months in warm climates because water is not a limiting growth factor for plants growing near aquatic depositional sites (Spicer et al., 2011 ). Indeed, the model suggests the mean annual precipitation values lower than CLAMP, going from 850 mm/yr in the Priabonian to 1040 mm/yr in the Rupelian to 1220 mm/yr in the Chattian. This increase in precipitation is worth noting, given the observed change in depositional environment energy at this site across this boundary. Multiple lines of data-based evidence show that there was no significant change in climate at this site during this time, which points to other reasons for the dynamic depositional environmental changes: topographic changes upstream from the Lühe Basin, precipitation changes, lateral channel migration, or a combination of these. A combination seems likely. The complex tectonic changes that have been explored for sites farther upstream in Tibet likely contribute to the change we see in this section (e.g., Su et al., 2019a ; 2019b ; 2020 ; Spicer et al., 2020 ), which likely impacted precipitation. The increased fluvial influence then led to lateral channel migration and/or an increase in river size/energy spilling across the basin due to a change in catchment size or the amount of precipitation. Ultimately, untangling topographic changes, precipitation changes, lateral channel migration, or a combination cannot be determined from one site. Determining whether there are synchronous regional upstream changes in catchment characteristics would require a multi-site comparison through the entire depositional succession across the catchment. Such a multi-site comparison would be a vital contribution to understanding the co-evolving climate and tectonics at this time and we recommend this multi-site approach for future research. 3. Conclusions We reconstructed paleoclimatic and paleoenvironmental conditions at the Lühe Basin coalmine section in central Yunnan, China, on the SE margin of Tibet, in the late Paleogene using organic geochemical tools, sedimentology, and climate modelling. Our (primarily) plant- and bacteria-derived biomarkers indicate that this site represented a dynamic environment, likely a floodplain, with occasional submerged peat/swamp deposits and occasional high energy riverine input that increased in frequency over time. The abundance of terrestrial biomarkers, which indicate woody gymnosperms (likely conifers) and angiosperms, is consistent with previous palaeobotany reconstructions of this area as covered by deciduous and evergreen broad-leaved forests. Temperatures reconstructed based on brGDGTs indicate variable values (ca. 12–22°C) but the overall average of 17°C across the section is consistent with model-based temperature estimates of 19°C and palaeobotany-based proxies from the nearby Lühe town section (bioclimatic analyses estimates of 15°C and CLAMP of 16°C). The temperature obtained from multiple independent lines of evidence, as well as additional evidence from the modelling experiments, shows a lack of cooling across the EOT, providing supporting evidence that the terrestrial response to rapid climate change is heterogeneous. Furthermore, these paleotemperature records are similar to present day temperature values, indicating that this site has likely been at its current elevation since (at least) the early Oligocene, supporting recent studies that suggest Eocene uplift of the region. 4. Methods More detailed materials and methodology can be found in Supplementary Material. Age. 40 Ar/ 39 Ar dating of feldspars within volcanic ashes exposed at 58 m in the section provides ages of 33.32 ± 0.36 Ma (sample lvb11) and 34.38 ± 0.74 Ma (sample lv5 8.0). Magnetostratigraphic interpretation of the Lühe coalmine section (Fig. 2 ) suggests that the section spans magnetochrons C15n to C9n (ca. 35 − 27 Ma, Gradstein, 2012, updated for Speijer et al., 2020 ), matching the geologic timescale updates conducted by Xu et al. (2023), who resampled the lower 180 m to provide a robust high-resolution Sr, Rb, Rb/Sr, and Ti data and cyclostratigraphy interpretations. This yields an average sedimentation rate of ca. 48 cm/kyr, consistent with the rates in other basins around the Tibetan Plateau (Li et al., 2020 ). Sediments. The Lühe coalmine section succession comprises alternations of organic-rich marls, mudstones, sandstones, and lignite (i.e., immature fossilised peat) deposits (Fig. 1 ; Fig. 2 ; Table S3; Fig. S5). The lower part of the section (0–73 m) comprises small grain sizes, ranging from clay to silt, and the upper part of the section (73–340 m) generally comprises larger grain sizes from sands to gravels, interspersed with some brief intervals of organic-rich silts. A thick coal interval (ca. 4 m) at ca. 50 m from the base of the coalmine contains 11 volcanic ash layers, some of which were used for the dating described above. Thermal maturity of sediments. Given that high thermal maturity can affect the fidelity of GDGT-based temperature reconstructions (Schouten et al., 2004 ; Schouten et al., 2013 ), we assess the thermal maturity. We calculated the carbon preference index (CPI; Bray and Evans, 1961 ; Eglinton and Hamilton, 1967 ) using (Σodd (C 21 “-“C 33 ) + Σodd (C 23 “-“C 35 )) / (2 × Σeven (C 22 “-“C 34 )) to avoid overestimation of the odd-over-even preference (Marzi et al., 1993 ). The CPI ranges from 2.0 to 11.3 with a mean of 5.6 ± 1.9 standard deviation (σ), suggesting that these sediments are immature (Fig. 4 d). Indices for vegetation and environmental reconstructions. The average chain length (ACL) of n -alkanes can be indicative of the dominant source vegetation and was calculated as ACL = Σ(C n × n ) / Σ(C n ) (Eglinton and Hamilton, 1967 ), using odd n -alkane chain-lengths from C 27 through C 35 (Ficken et al., 2000 ; Bush and McInerney, 2013 ). The P -aqueous ratio ( P aq ) calculated as P aq = (C 23 + C 25 )/(C 23 + C 25 + C 29 +C 31 ) (Ficken et al., 2000 ) and the C 23 /(C 23 + C 31 ) index (Nott et al., 2000 ) were both used to indicate wetland conditions. brGDGT indices for MAAT and pH. The GDGTs in our section could have been produced in peats or a shallow lake environment based on our environmental reconstructions (Sect. 2.4 ); we therefore used both peat and lake calibrations. The peat-calibrated MAAT is described as MAAT peat (°C) = 52.18 x MBT ’5me – 23.05 (Naafs et al., 2017) and lake-calibrated MAAT for months above freezing as MAF lake (°C) = [MBT’ 5me – 0.075] / 0.030 (Martínez-Sosa et al., 2021 ). pH was calculated using CBT peat = log [(Ib + IIa’ + IIb + IIb’ + IIIa’) / (Ia + IIa + IIIa)], where pH = 8.07 + 2.49 x CBT peat . Climate model simulations. We used HadCM3BL-M2.1aD (Valdes et al., 2017 ), a fully coupled ocean-atmosphere and dynamic vegetation General Circulation Model with latitude by longitude spatial grid (ca. 300 km), nineteen vertical levels in the atmosphere and twenty vertical levels in the ocean. Model boundary conditions (topography, bathymetry, and ice sheet configurations at low (3.75° x 2.5°) and scaled to the high model resolution (0.5° x 0.5°)) for each geologic stage, Priabonian (ca. 37.71 to 33.90 Ma), Rupelian (ca. 33.90 to 27.82 Ma), and Chattian (ca. 27.82 to 23.03 Ma), are provided by Getech Plc. Stage-specific solar luminosity was calculated using the methods of Gough ( 1981 ). p CO 2 values were 1120 ppm for the Priabonian and 560 ppm for the Rupelian and Chattian (Foster et al., 2017 ; Witkowski et al., 2018 ). Topographic changes were based on hypotheses posed by Spicer et al., 2020 (and references therein), either as a) a constant valley at 2.5 km elevation, b) a constant plateau at 4.5 km elevation, or c) a change from a valley at 2.5 km to a plateau at 4.5 km (Fig. 5 ). Each experiment was run for 12,422 model years to allow the surface and deep ocean to reach equilibrium and to achieve a state with no net energy imbalance at the top of the atmosphere. Climate means were calculated from the last 100-years of each simulation. Time-varying latitude and longitude plate paleo-rotations are provided for the Lühe Basin for each stage to allow for accurate comparison within the model. The paleo-coordinates (21.1°N) for Lühe were calculated using the Getech plate model. Declarations Data availability All data is provided with this manuscript. Author contributions RAS, TS, ZKZ, SFL, PJV, and RDP planned and funded the field campaign. CRW, VL, and JPM conducted the organic geochemistry analyses. AF conducted the model experiments. SHL and HT conducted the palaeobotany interpretations. CRW and VL interpreted the data and wrote the manuscript with contributions from all authors. Competing interests The authors declare that they have no conflict of interest. Acknowledgements and Financial support This research was carried out with funding from the joint UK-China Project administered by the Natural Science Foundation of China Project (No. 41661134049, 42072024, 41772026) and the UK Natural Environment Research Council (NERC; NE/P013805/1). We also thank the NERC for partial funding of the National Environmental Isotope Facility (NEIF; No. NE/V003917/1) that enabled HPLC-MS capabilities. 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Supplementary Files WitkowskiCommun.EarthEnvironSupplementTableS1S5alldata.xlsx Table S1-S5 SupplementalMaterial.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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1","display":"","copyAsset":false,"role":"figure","size":564631,"visible":true,"origin":"","legend":"\u003cp\u003eLocation and overview of the Lühe coal mine section. (a) Location map (25°10′N, 101°22′E) in Yunnan, (b) Yunnan’s location within China, and (c) Photograph of Lühe Basin coalmine section. Yellow line indicates the sampling log of this study, red indicates the section logged by Li et al. (2020), and label for the nearby Lühe town section is located 2.6 km away.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-3857872/v1/9348c6b67cb292aac624153c.png"},{"id":50324107,"identity":"0d5ad155-8827-4084-bc84-3a6dce48fd9b","added_by":"auto","created_at":"2024-01-29 18:38:40","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":283334,"visible":true,"origin":"","legend":"\u003cp\u003eGeologic age and simplified section log for Lühe Basin. (a) Age in millions of years (Ma) alongside the geological timescale by series and stage, and updated geomagnetic polarity timescales 2020 (Speijer et al., 2020). (b) Geomagnetic polarity for Lühe Basin reported in Li et al. (2020) and associated Chrons. The grey lines connect the global magnetostratigraphy with this basin. (c) Simplified section log for Lühe coalmine section, with grain sizes clay (cl), silt (s), sand (sd), and gravel (g), colours represent organics (black=organic rich, lignite-like, grey=low-to-mid organics, yellow=very low organic content, red=iron band). Star marks \u003csup\u003e40\u003c/sup\u003eAr/\u003csup\u003e39\u003c/sup\u003eAr dating. Detailed sediment log can be found in Fig. S5 and Table S3.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-3857872/v1/008f158a910983ee0485affc.png"},{"id":50323898,"identity":"ede3be21-2866-4a09-9a37-2574005e44f4","added_by":"auto","created_at":"2024-01-29 18:30:40","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":238087,"visible":true,"origin":"","legend":"\u003cp\u003eTotal ion chromatograms of the apolar fraction. (a) Depth from base 268.0 m with high content of terpenoids and \u003cem\u003en\u003c/em\u003e-alkanes exemplary of the section, especially the C\u003csub\u003e29\u003c/sub\u003e \u003cem\u003en\u003c/em\u003e-alkane dominance. (b) Depth from base 58.5 m exemplary of the two outliers with C\u003csub\u003e23\u003c/sub\u003e and C\u003csub\u003e25\u003c/sub\u003e \u003cem\u003en\u003c/em\u003e-alkane dominance. Numbers represent: 1. Cadalene, 2. Norpimerane, 3. 18-norbietane, 4. 18-norabieta-8,11,13-triene, 5. Dehydroabietane, 6. 10,18-Bisnorabieta-5,7,9(10),11,13-pentaene, 7. Naphtalene, 8. Simonellite, 9. TetramethyI-octahydrochrysene. Gold boxes zoom in on m/z 191 i.e., hopanes used for the thermal maturity index.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-3857872/v1/39c5c5963e62cf11cfdfa51d.png"},{"id":50323899,"identity":"e8dfc0dc-22fa-470c-b115-2eed8e15c2f8","added_by":"auto","created_at":"2024-01-29 18:30:40","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":308379,"visible":true,"origin":"","legend":"\u003cp\u003eIndicators for thermal maturity. (a) Percentage of total organic carbon (TOC) shown with green triangles. Note: scale is condensed above 10 %. Gray dotted line shows high TOC samples. (b) pH shown with blue squares. (c) Hopane isomerisation index of ββ/(ββ+αβ+βα) for C\u003csub\u003e29\u003c/sub\u003e (lavender circles) and C\u003csub\u003e31\u003c/sub\u003e (red diamonds) hopane. Red dotted line showing 2-pt moving average. Gray dotted line shows the threshold for (general) mature versus immature values. (d) Carbon preference index shown in yellow squares. Yellow dotted line showing 2-pt moving average. Gray dotted line shows the threshold for (general) mature versus immature values.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-3857872/v1/732a8b1753c0c657053ff791.png"},{"id":50324252,"identity":"208af86c-c9f4-42dd-bada-29f9fe46e966","added_by":"auto","created_at":"2024-01-29 18:46:40","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":226921,"visible":true,"origin":"","legend":"\u003cp\u003eOrographic height (m) in Lühe Basin region. (a) Priabonian valley at 2.5 km and (b) Priabonian plateau at 4.5 km. These two topographies are used in the experiments described in Table 1. Star marks the Lühe Basin.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-3857872/v1/d632b14150f8985e57934e11.png"},{"id":50323904,"identity":"9ede7623-afb7-404b-91ab-b0c20ddb07d5","added_by":"auto","created_at":"2024-01-29 18:30:40","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":304726,"visible":true,"origin":"","legend":"\u003cp\u003eIndicators for depositional environment. (a) Average chain length (ACL), an indication of organic matter sources, shown in lime squares. (b) \u003cem\u003eP\u003c/em\u003e-aqueous ratio (\u003cem\u003eP\u003c/em\u003e\u003csub\u003eaq\u003c/sub\u003e), an indication of wetness, shown in blue circles. Gray dotted lines indicate interpretation for terrestrial plants (below 0.23) and submerged and floating macrophytes (above 0.48). (c) C\u003csub\u003e23\u003c/sub\u003e/(C\u003csub\u003e23\u003c/sub\u003e+C\u003csub\u003e31\u003c/sub\u003e) \u003cem\u003en\u003c/em\u003e-alkane ratio, an indication of ecology, shown in green triangles. Gray dotted lines indicate woody angiosperms (below 0.1) and \u003cem\u003eSphagnum\u003c/em\u003e mosses (above 0.7).\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-3857872/v1/72be2c1fa7de8ec104ba72e6.png"},{"id":50323905,"identity":"2fb1a96f-4c86-4323-88f7-7d4cd66bc65c","added_by":"auto","created_at":"2024-01-29 18:30:40","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":263092,"visible":true,"origin":"","legend":"\u003cp\u003ebrGDGT reconstruction of temperature. Temperature from calibrations based on possible depositional environments: mean temperature for month above freezing using a lacustrine calibration (MAF\u003csub\u003elake\u003c/sub\u003e in lavender circles; Martínez-Sosa et al., 2021) and MAAT using a peat calibration for samples with total organic matter \u0026gt;30 % (MAAT\u003csub\u003epeat\u003c/sub\u003e in black squares; Naafs et al., 2017). Our model-based temperature is shown in gold, displayed by geologic stage level for the Priabonian, Rupelian, and Chattian. For comparison, mean annual air temperature (MAAT) estimates from bioclimatic analyses (BA) light green and Climate Leaf Analysis Multivariate Program (CLAMP) in dark green from the nearby Lühe town section, which represent a single temperature averaged that (should) correspond with ca. 70-130 m in our coalmine section. Light blue shading shows the range for modern day temperatures at this site.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-3857872/v1/554c39ddac8c9ef3dc08884a.png"},{"id":67321150,"identity":"cea92d41-852b-4ad7-95d7-3576ea0af690","added_by":"auto","created_at":"2024-10-23 15:39:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3220168,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3857872/v1/6565503b-45fc-4a67-a711-d17aa7c37a44.pdf"},{"id":50323900,"identity":"d8a34d15-985e-4f33-bdfa-68cb692cf3a1","added_by":"auto","created_at":"2024-01-29 18:30:40","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":505645,"visible":true,"origin":"","legend":"Table S1-S5","description":"","filename":"WitkowskiCommun.EarthEnvironSupplementTableS1S5alldata.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3857872/v1/1a33f472b897e06d74e286c8.xlsx"},{"id":50324305,"identity":"990faa47-a00e-469b-8e7f-8a565db85878","added_by":"auto","created_at":"2024-01-29 18:54:40","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":829082,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-3857872/v1/d42af6ebda10322b2daf0275.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Dynamic environment but no temperature change since the late Paleogene at Lühe Basin (Yunnan, China)","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe \u0026lsquo;Tibetan region\u0026rsquo; (i.e., Himalaya\u0026ndash;Tibet\u0026ndash;Hengduan mountain area) is a major feature of our planet. Since the India-Eurasia continental collision during the early Paleogene, the Tibetan region has had a powerful and lasting impact on climate (e.g., Raymo and Ruddiman, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e1992\u003c/span\u003e; France-Lanord and Derry, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Farnsworth et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019a\u003c/span\u003e), the Asian monsoon systems (e.g., Huber and Goldner, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), and Asian biodiversity (e.g., Li et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Today, the Tibetan region provides the headwaters for the ten largest rivers in Asia and consequently freshwater to nearly one-fourth of the global human population. It is also home to some of the richest biodiversity on Earth. Understanding the links between topography, climate, hydrology, and ecology in the broader region are thus key to the history of the region (Spicer et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, and references therein), and fundamental for managing future natural resources, including biodiversity.\u003c/p\u003e \u003cp\u003eHowever, the complex tectonic and climatic history of the Tibetan region remains unresolved. Following the India-Eurasia continental collision during the early Paleogene, globally compiled marine records document a congruent decline in carbon dioxide concentrations, growth of the Antarctic icesheet, and reorganisation of the global climate system during the late Paleogene, and mostly notably during the Eocene Oligocene Transition (EOT; e.g., Westerhold et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, the few existing terrestrial records demonstrate high heterogeneity in global change during this time, with some records suggesting no change over this period (Retallack, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Sheldon et al., 2012; Kohn et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) while others suggest cooling (e.g., Zanazzi et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Lauretano et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Terrestrial records for the Tibetan region likewise show complex and heterogeneous changes in biodiversity (e.g., Li et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), which had downstream impacts on e.g., Yunnan, one of Asia\u0026rsquo;s biodiversity hotspots situated in southwestern China along the SE Tibetan margin (Li et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Spicer et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, apart from these specific examples, the lack of other (well-dated) sections has hindered attempts to correlate these interior locations to the global Cenozoic climate trends extrapolated from marine records. Reconstructing the climatic history of sedimentary basins along the margin of Tibet is crucial to understand the connection between topographic relief and climate, their influence on the Asian monsoon system, and their link to global climate.\u003c/p\u003e \u003cp\u003eHere, we reconstruct the climatic and environmental conditions during the late Paleogene in Yunnan, specifically from a key sedimentary repository located along the SE margin of the Tibetan Plateau known as L\u0026uuml;he Basin. We use organic geochemistry, sedimentology, and modelling, contextualised with palaeobotany, to develop a more robust and holistic understanding of this region. Although there have been recent efforts to better constrain late Paleogene climatic and environmental conditions throughout the Tibetan region using modelling and palaeobotanical tools (e.g., Su et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2019a\u003c/span\u003e; \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), few studies have used quantitative organic geochemical proxies. Because organic geochemical proxies can be measured continuously throughout a section, they more robustly demonstrate change over time as compared with the geologic-stage time slices used by models or with the individual and sporadic mega-fossil horizons used in palaeobotany; thus, the addition of organic geochemistry offers critical new perspectives at this location.\u003c/p\u003e"},{"header":"2. Results and Discussion","content":"\u003cdiv id=\"Sec3\"\u003e\n \u003ch2\u003e2.1 Vegetation reconstructions\u003c/h2\u003e\n \u003cp\u003eWe determine vegetation using the distributions of \u003cem\u003en\u003c/em\u003e-alkanes derived from plant leaf waxes and/or mixed sources (Eglinton and Hamilton, \u003cspan\u003e1967\u003c/span\u003e). Throughout the section, the \u003cem\u003en\u003c/em\u003e-alkane distribution has an odd-over-even preference (Fig.\u0026nbsp;\u003cspan\u003e3\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan\u003e4\u003c/span\u003ed; Table S1), suggesting an origin from leaf waxes and a significant input of terrestrial plants (Eglinton and Hamilton, \u003cspan\u003e1967\u003c/span\u003e). In most of these sediments, the apolar fractions are dominated by the C\u003csub\u003e29\u003c/sub\u003e \u003cem\u003en\u003c/em\u003e-alkane, followed by a high abundance of the C\u003csub\u003e27\u003c/sub\u003e and then C\u003csub\u003e31\u003c/sub\u003e \u003cem\u003en\u003c/em\u003e-alkanes (Figs.\u0026nbsp;\u003cspan\u003e2\u003c/span\u003ea), such that the ACL ranges from 27.7 to 29.7 with a mean of 29.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3 \u0026sigma; (Fig.\u0026nbsp;\u003cspan\u003e6\u003c/span\u003ea; Table S1). This ACL (Fig.\u0026nbsp;\u003cspan\u003e6\u003c/span\u003ea) and the dominance of the C\u003csub\u003e29\u003c/sub\u003e \u003cem\u003en\u003c/em\u003e-alkane (Fig.\u0026nbsp;\u003cspan\u003e3\u003c/span\u003ea, \u003cspan\u003e4\u003c/span\u003ea) across the section suggest that the vegetation at this site was likely dominated by woody gymnosperms and angiosperms (Bush and McInerney, \u003cspan\u003e2013\u003c/span\u003e). Specifically, the ACL of 29.0 is more likely associated with deciduous rather than evergreen angiosperms, the latter tending to have slightly lower ACLs in modern vegetation (Bush and McInerney, \u003cspan\u003e2013\u003c/span\u003e), although this is not definitive.\u003c/p\u003e\n \u003cp\u003eThe C\u003csub\u003e23\u003c/sub\u003e/(C\u003csub\u003e23\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;C\u003csub\u003e31\u003c/sub\u003e) \u003cem\u003en\u003c/em\u003e-alkane ratios show some fluctuations throughout the section, suggesting changes in vegetation type (Fig.\u0026nbsp;\u003cspan\u003e6\u003c/span\u003ec; Table S1). For the whole section, the mean average is 0.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2 \u0026sigma; and the range is 0.0 to 0.9, spanning almost the entire theoretical range of 0.0 to 1.0. The C\u003csub\u003e23\u003c/sub\u003e/(C\u003csub\u003e23\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;C\u003csub\u003e31\u003c/sub\u003e) values\u0026thinsp;\u0026lt;\u0026thinsp;0.1 are considered indicative of low inputs from moss and macrophyte and values\u0026thinsp;\u0026gt;\u0026thinsp;0.7 are considered indicative of high inputs from moss and macrophyte, particularly \u003cem\u003eSphagnum\u003c/em\u003e mosses due to their particularly high abundance of C\u003csub\u003e23\u003c/sub\u003e relative to C\u003csub\u003e31\u003c/sub\u003e \u003cem\u003en\u003c/em\u003e-alkanes (Bush and McInerney, \u003cspan\u003e2013\u003c/span\u003e). There is greater fluctuation at the bottom of the section (0\u0026ndash;73 m), where values rapidly change across the span of 0.1 to 0.9 values, indicating dynamic vegetation and environmental change. The upper part of the section (73\u0026ndash;340 m) has generally lower C\u003csub\u003e23\u003c/sub\u003e/(C\u003csub\u003e23\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;C\u003csub\u003e31\u003c/sub\u003e) values, averaging 0.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1 \u0026sigma; with a range of 0.0 to 0.4, which indicates less frequent wetland-type deposition. However, these fluctuating values throughout the section that commonly exceed 0.1 indicate that the environment remained dynamic throughout.\u003c/p\u003e\n \u003cp\u003eFurther providing insights into vegetation type at this site, many sediments also contained diterpenoids and triterpenoids (Fig.\u0026nbsp;\u003cspan\u003e3\u003c/span\u003ea; Table S2; Fig. S3, Fig. S4), compounds indicative of gymnosperms and angiosperms, respectively (Otto and Wilde, \u003cspan\u003e2001\u003c/span\u003e; Diefendorf et al., \u003cspan\u003e2012\u003c/span\u003e). Figure\u0026nbsp;\u003cspan\u003e3\u003c/span\u003ea shows an example of a chromatogram with highly abundant terpenoids (presence, abundances, and their retention times), Figs. S3 and S4 shows the molecular structures of the terpenoids identified at this site, and Table S2 shows the relative abundance (i.e., absent, trace, present, or abundant) of each terpenoid at each sample depth. Seven diterpenoid biomarkers associated with gymnosperms were identified throughout the section (i.e., cadalene, norpimerane, 18-norabietane, 19-norabieta-8,11,13-triene, dehydroabietane, 10,18-bisnorabieta-5,7,9(10),11,13-pentaene, and simonellite), with notably high abundances from 6.7\u0026ndash;8.2 m, 18.1\u0026ndash;65 m, and 202.0-301.9 m. Trace amounts were found throughout most of the section (Table S2). In addition to their association with gymnosperms, the specific diterpenoid compounds identified at L\u0026uuml;he Basin are common among all major conifer groups and comprise a particularly high percentage of total diterpenoids in Pinaceae (Diefendorf et al., \u003cspan\u003e2019\u003c/span\u003e). The possibility that these diterpenoids are conifer-derived is especially supported by the presence of simonellite, a compound found in conifer resin (Simoneit, 1986), that was consistently found in high abundances (often dominant compound) throughout the entire section. Several samples also contained the triterpenoids tetramethyl-octahydrochrysene and Des-A-lupane, compounds synthesized by nearly all angiosperms (Trendel et al., \u003cspan\u003e1989\u003c/span\u003e), with notably high abundances from 18.1\u0026ndash;46.5 m, 96.0-129.0 m, and 268.0-301.9 m. Trace amounts were found throughout most of the section (Table S2). The more frequent abundance of diterpenoids compared with triterpenoids in these sediments (Table S2) suggest that this environment was likely dominated by gymnosperms with some angiosperms, although it should be noted that taphonomic processes can skew plant preservation and associated biomarker distributions (Tang et al., \u003cspan\u003e2020\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eOur biomarker-based vegetation reconstruction is consistent with the plant fossil assemblage recovered from the nearby L\u0026uuml;he town section (which likely corresponds to the 70 to 130 m interval of our coalmine section, see above). Based on high ACL values, the terpenoid interpretation, and variation in C\u003csub\u003e23\u003c/sub\u003e/(C\u003csub\u003e23\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;C\u003csub\u003e31\u003c/sub\u003e) ratios, our findings suggest variable vegetation inputs comprising mostly woody gymnosperms (likely conifers), some angiosperms, and brief periods of \u003cem\u003eSphagnum\u003c/em\u003e/macrophyte input (likely associated with localised peatland formation). Correspondingly, the palaeobotany analyses at the L\u0026uuml;he town section assign 38 floral genera to 26 angiosperms, 6 gymnosperms, and 4 ferns (Tang et al., \u003cspan\u003e2020\u003c/span\u003e), which were dominated by \u003cem\u003ePinus\u003c/em\u003e (i.e., pine) from the family Pinaceae and \u003cem\u003eQuercus\u003c/em\u003e (i.e., oak) from the family Fagaceae (Xu et al., \u003cspan\u003e2008\u003c/span\u003e; Zhang et al., 2020). Palaeobotany analyses also provide evidence of tree stumps, fallen logs, and branches (Yi et al., \u003cspan\u003e2003\u003c/span\u003e; Deng et al., \u003cspan\u003e2022\u003c/span\u003e). Similar vegetation was described by the palynological assemblages from the L\u0026uuml;he town section, where evergreen oaks (\u003cem\u003eQuercus\u003c/em\u003e) and alder (\u003cem\u003eAlnus\u003c/em\u003e) were identified; palynomorphs were dominated by \u003cem\u003eQuercoidites\u003c/em\u003e (43%), \u003cem\u003eTitricolpites\u003c/em\u003e (13%), \u003cem\u003ePinuspollenites\u003c/em\u003e (7%), and \u003cem\u003ePiceapollis\u003c/em\u003e (0\u0026ndash;20%) (Tang et al., \u003cspan\u003e2020\u003c/span\u003e). Palynological findings are not necessarily representative of \u003cem\u003ein situ\u003c/em\u003e assemblages given that pollen may be blown/washed into the basin from the surrounding (and possibly higher elevation) areas; that said, they are indeed consistent with our biomarker-based assemblages in the L\u0026uuml;he Basin.\u003c/p\u003e\n \u003cp\u003eTogether, the results of the biomarker, palaeobotany, and palynology assemblages indicate a temperate forest with evergreen broadleaved taxa and conifers, and some deciduous broadleaved taxa and brief episodes of wetland formation.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\"\u003e\n \u003ch2\u003e2.2 Depositional environment reconstruction\u003c/h2\u003e\n \u003cp\u003eHere, we provide only a broad overview, integrating sedimentological and biomarker observations. A full and detailed interpretation of the sediment log from the coalmine section can be found in the Supplement (Table S3; Fig. S5). The measured ca. 340-m thick profile comprises alternations of organic-rich marls, mudstones, sandstones, and lignite deposits, representing various depositional environments typically found in a floodplain setting: active and abandoned channel deposits, proximal to distal overbank crevasse splay deposits, and sub-aerial soils to shallow pond swamps.\u003c/p\u003e\n \u003cp\u003eThe marked variation in sedimentary facies throughout the section suggests it documents a dynamic environment. Similarly, the TOC (%) ranges from 0.1 to 63.9% (Fig.\u0026nbsp;\u003cspan\u003e4\u003c/span\u003ea), from organic-lean to essentially maximum (solely organic carbon) values, further indicating a highly variable depositional environment. This interpretation is also consistent with brGDGT-based pH values that vary between 3.0 and 7.7 pH (Fig.\u0026nbsp;\u003cspan\u003e4\u003c/span\u003eb; described in Section \u003cspan\u003e2.3\u003c/span\u003e) and with the dramatic changes in biomarker distributions. For example, \u003cem\u003eP\u003c/em\u003e\u003csub\u003eaq\u003c/sub\u003e values span from 0.0 to 0.9 (representing terrestrial to aquatic-dominated OM, respectively), nearly the entire mathematical range of 0.0 to 1.0, with most values between 0.2 and 0.5 and a mean of 0.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2 \u0026sigma; (Fig.\u0026nbsp;\u003cspan\u003e6\u003c/span\u003eb). \u003cem\u003eP\u003c/em\u003e\u003csub\u003eaq\u003c/sub\u003e values that are \u0026lt;\u0026thinsp;0.23 are considered indicative of terrestrial plant waxes, whereas values that are \u0026gt;\u0026thinsp;0.48 are common for submerged and floating macrophytes (Ficken et al., \u003cspan\u003e2000\u003c/span\u003e). Because \u003cem\u003eP\u003c/em\u003e\u003csub\u003eaq\u003c/sub\u003e sits in the middle of these two key ranges for much of the section, the depositional environment likely had input from both terrestrial and aquatic sources and was a wet terrestrial environment, like a floodplain, wetland, or shallow lacustrine environment. This wetness is further supported by the sedimentary succession (Fig.\u0026nbsp;\u003cspan\u003e6\u003c/span\u003e) and high abundance of \u003cem\u003eEquisetum\u003c/em\u003e cf. \u003cem\u003epratense\u003c/em\u003e in this section (Zhang et al., \u003cspan\u003e2007\u003c/span\u003e), a fern species that is indicative of wet terrestrial environments.\u003c/p\u003e\n \u003cp\u003eDespite this variability throughout the section, there seems to be a marked difference between the lower (0\u0026ndash;73 m) and upper part (73\u0026ndash;340 m). The lower part of the section (0\u0026ndash;73 m) alternates between swampy conditions and open flood basin conditions, characterized by fine-grained, typically muddy, sediments, as well as intervals with very high organic matter contents and subaerial exposed and oxidized rooting. This interpretation is supported by the frequent occurrence of high TOC (%) sediments (defined here as \u0026gt;\u0026thinsp;20%; Fig.\u0026nbsp;\u003cspan\u003e4\u003c/span\u003ea), high \u003cem\u003eP\u003c/em\u003e\u003csub\u003eaq\u003c/sub\u003e values, low reconstructed pH (see below), and high C\u003csub\u003e23\u003c/sub\u003e/(C\u003csub\u003e23\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;C\u003csub\u003e31\u003c/sub\u003e) \u003cem\u003en\u003c/em\u003e-alkane ratios, suggesting that this section was occasionally a peat-forming floodplain environment. The frequent occurrence of high TOC (%) is noted by seven samples in the much shorter 73 m lower section, as compared with only four in the following 250 m of the upper section. Likewise, \u003cem\u003eP\u003c/em\u003e\u003csub\u003eaq\u003c/sub\u003e values in the lower section range from 0.1 to 0.9 with a mean of 0.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2 \u0026sigma;, whereas the upper section has significantly lower and less variable \u003cem\u003eP\u003c/em\u003e\u003csub\u003eaq\u003c/sub\u003e values ranging from 0.0 to 0.4 with a mean of 0.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1 \u0026sigma;, again indicating a wetter lower section. Two outliers in the sediment intervals (26.7 and 58.5 m) are described in more detail in the Supplement. Based on all evidence, this lower 73-m interval appears to represent deposition in a relatively low energy fluvial and lacustrine (e.g., meander cut off) system, with fine-grained clay and silt deposition in flood plains, interbedded with lignites deposited in wet depositional environments, such as a flood basin, wetland/peatland, or shallow lake.\u003c/p\u003e\n \u003cp\u003eThe upper part of the section (73\u0026ndash;340 m) is characterized by coarser-grained deposits representing small influxes of silt and fine sand, occasional rafted branches and leaves, distal crevasse splay features, and river transported wood fragments. At around 69 to 77 m, there is a gradual increase in the energy of the system, with increasingly coarse sediments. At 74.2 m, a major sand incursion brings in branches/logs, and crossbedding suggests lateral sand migration in a channel, although there is no evidence of basal erosion. At this point, the upper part of the section generally comprises higher energy fluvial environments, represented by e.g., log jams (at 74.2 m, 150 m, 187 m, 224 m, 250-271.5 m, 300.5-316.8 m, 334\u0026ndash;336 m), inferred overbank incursion (at 85.8\u0026ndash;97.9 m, 271.5-300.5, 315.1 m, 323.4-324.1 m, 331\u0026ndash;340 m), and a thick sand body with crossbedding and leaf debris (at 101.3-105.5 m, 233.3\u0026ndash;250 m, and 271.5-300.5 m). These are interspersed with some short intervals of swampy conditions, represented by iron horizons, sub-aerially exposed floodplain silts colonised by plants, and four organic-rich lignites. As opposed to the calm flood basin, wetland, or possible shallow lake in the lower part of the section, the upper part of the section suggests a much more dynamic environment, with deposition fluctuating between channel and floodplain. This interpretation supports the (albeit much lower resolution) overview of the L\u0026uuml;he coalmine section described in Wissink et al. (\u003cspan\u003e2016\u003c/span\u003e). The biomarker assemblages support this interpretation of the upper section representing a more dynamic environment, with deposition fluctuating between channel and floodplain. The \u003cem\u003eP\u003c/em\u003e\u003csub\u003eaq\u003c/sub\u003e values are still quite variable, ranging from 0.0 to 0.4 with a mean of 0.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1 \u0026sigma;, but relatively lower and less variable compared with the lower section. These \u003cem\u003eP\u003c/em\u003e\u003csub\u003eaq\u003c/sub\u003e values suggest a dynamic system dominated by (allochthonous) higher plant inputs over those from aquatic plants. High variability in pH further supports the interpretation of a dynamic upper section, where the lowest reconstructed pH (3.0 at 303.5 m) and highest reconstructed pH (7.7 at 311.0 m) values in the whole section occur nearly back-to-back, indicating a very rapid shift from a highly acidic (wetland) environment to a neutral pH setting.\u003c/p\u003e\n \u003cp\u003eTaken together, our observations indicate a dynamic but evolving fluvial-lacustrine environment throughout the entire section, changing from a lower energy flood basin, wetland/peatland, and/or shallow lake to a higher energy floodplain and/or channel. We detect abundant terrestrial biomarkers (e.g., leaf waxes, terpenoids indicative of woody gymnosperms and angiosperms, and soil bacterial lipids), consistent with palaeobotanical and palynological evidence in the nearby L\u0026uuml;he town section (Tang et al., \u003cspan\u003e2020\u003c/span\u003e). Together, this evidence indicate that the L\u0026uuml;he area was covered in deciduous and evergreen broad-leaved mixed forests. We also see evidence for this being a very wet environment, as indicated by e.g., variable \u003cem\u003eP\u003c/em\u003e\u003csub\u003eaq\u003c/sub\u003e and C\u003csub\u003e23\u003c/sub\u003e/(C\u003csub\u003e23\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;C\u003csub\u003e31\u003c/sub\u003e) ratios. However, we do not see strong evidence for this being a deep lacustrine environment which could be indicated by e.g., abundant algal biomarkers and isoprenoidal GDGTs. Instead, we interpret this as a dynamic fluvial system \u0026ndash; with the myriad of depositional environments that entails.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\"\u003e\n \u003ch2\u003e2.3 Climate reconstruction using brGDGT calibrations\u003c/h2\u003e\n \u003cp\u003eWe reconstruct mean annual temperatures using branched glycerol dialkyl glycerol tetraethers (brGDGTs), membrane-spanning lipids likely synthesized by bacteria and widely used as paleothermometers (Sinninghe Damst\u0026eacute; et al., \u003cspan\u003e2000\u003c/span\u003e; Weijers et al., \u003cspan\u003e2007\u003c/span\u003e). In the fifty-six samples analysed, thirty-eight yielded sufficient brGDGTs for temperature and pH reconstruction (Fig.\u0026nbsp;\u003cspan\u003e7\u003c/span\u003e; Table S1) from thermally immature sediments (see Supplemental Text). MBT\u0026rsquo;\u003csub\u003e5me\u003c/sub\u003e values range from 0.4 to 0.7 with a mean of 0.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1 \u0026sigma;; these values remain relatively stable throughout the section. Values in the lower section (0\u0026ndash;73 m) and upper section (73\u0026ndash;340 m) are nearly identical, respectively ranging from 0.4 to 0.7 with a mean of 0.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1 \u0026sigma; and ranging from 0.4 to 0.7 with a mean of 0.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1 \u0026sigma;. Over the section, CBT\u003csub\u003epeat\u003c/sub\u003e values range from \u0026minus;\u0026thinsp;2.1 to -0.2 with a mean of -1.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4 \u0026sigma;. Unlike MBT\u003csub\u003e\u0026rsquo;5me\u003c/sub\u003e, CBT\u003csub\u003epeat\u003c/sub\u003e does yield differences between the lower and upper sections, respectively ranging from \u0026minus;\u0026thinsp;1.7 to -0.9 with a mean of -1.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3 \u0026sigma; and ranging from \u0026minus;\u0026thinsp;2.1 to -0.2 with a mean of -0.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4 \u0026sigma;. This variability in CBT\u003csub\u003epeat\u003c/sub\u003e (but not in MBT\u003csub\u003e\u0026rsquo;5me\u003c/sub\u003e) is due to four samples in the upper section (depths 273.5, 301.9, 311, and 331.5 m) that contain 6-methyl brGDGTs; these are the only samples that contain 6-methyl brGDGTs in this whole section. Our tests show no correlation of the 6-methyl brGDGTs with overall changes in depositional environment (e.g., against \u003cem\u003eP\u003c/em\u003e\u003csub\u003eaq\u003c/sub\u003e values) but do show expected changes with pH (Wu et al., \u003cspan\u003e2021\u003c/span\u003e; Wang et al., 2021). Indeed, the four 6-methyl brGDGT-containing samples have high pH values (respectively, 7.6, 6.3, 7.7, and 6.6 pH), as compared with the whole section which ranges from 3.0 to 7.7 pH with a mean of 5.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9 \u0026sigma;; the lower section from 3.8 to 5.9 pH with a mean of 5.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0 \u0026sigma;, and the upper section from 3.0 to 7.7 pH with a mean of 5.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0 \u0026sigma;.\u003c/p\u003e\n \u003cp\u003ePrevious work has proposed a wide variety of brGDGT calibrations for different depositional settings, including lacustrine, soil, and peat calibrations. Given the variability between these calibrations and the inferred behaviour of brGDGT in different settings, it is perhaps unsurprising that brGDGT indices exhibit large variability throughout the section. This makes it challenging to assign a calibration \u003cem\u003ea priori\u003c/em\u003e, especially when brGDGTs are not always produced in the depositional setting in which they are found. Therefore, we initially apply three different brGDGT-temperature calibrations that reflect the depositional environmental variability within the L\u0026uuml;he Basin (Fig.\u0026nbsp;\u003cspan\u003e7\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eMost sediments (n\u0026thinsp;=\u0026thinsp;46) are mudstones to sandstones and have a TOC (wt%) ranging between 0.1\u0026ndash;23% with the majority\u0026thinsp;\u0026lt;\u0026thinsp;3% (Fig.\u0026nbsp;\u003cspan\u003e4\u003c/span\u003e). Sandy sediments did not contain sufficient brGDGTs for analysis, but the brGDGTs in the other horizons (n\u0026thinsp;=\u0026thinsp;33) could derive from either allochthonous input from surrounding mineral soils or \u003cem\u003ein situ\u003c/em\u003e \u0026lsquo;lacustrine\u0026rsquo; production. Thus, we initially apply the soil-calibrated mean annual air temperature (MAAT\u003csub\u003esoil\u003c/sub\u003e; Naafs et al., \u003cspan\u003e2017a\u003c/span\u003e), the peat-calibrated MAAT (MAAT\u003csub\u003epeat\u003c/sub\u003e; Naafs et al., \u003cspan\u003e2017b\u003c/span\u003e), and lake-calibrated MAAT for months above freezing (MAF\u003csub\u003elake\u003c/sub\u003e; Mart\u0026iacute;nez-Sosa et al., \u003cspan\u003e2021\u003c/span\u003e) (Fig. S1). We note that here our MAF values are likely equivalent to MAAT because this site appears to have no months below freezing, as indicated by the model results with three consecutive coldest month mean temperature (3CMMT) of 12.1\u0026deg;C (detailed in Section \u003cspan\u003e2.4\u003c/span\u003e; Table\u0026nbsp;\u003cspan\u003e2\u003c/span\u003e) and palaeobotany-based Climate Leaf Analysis Multivariate Program coldest month mean temperature (CMMT) of 4.5\u0026deg;C (Table\u0026nbsp;\u003cspan\u003e3\u003c/span\u003e). The remaining sediments (n\u0026thinsp;=\u0026thinsp;5) have TOC (%) ranging between 40\u0026ndash;63%; these high TOC contents are most likely to have been deposited in a wetland setting and for those, we thus apply the peat-specific MAAT calibration (MAAT\u003csub\u003epeat\u003c/sub\u003e; Naafs et al., \u003cspan\u003e2017b\u003c/span\u003e) (Fig.\u0026nbsp;\u003cspan\u003e7\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eTable 1. Climate model simulations (sim.) of the Asian regional impact\u0026nbsp;with Priabonian (Eocene) to Chattian (Oligocene) boundary conditions are used to test the response of \u003cem\u003ep\u003c/em\u003eCO\u003csub\u003e\u0026shy;2\u003c/sub\u003e and Tibetan topography configuration on temperature and precipitation. Grey indicates which parameters are included in the simulation. \u003cem\u003ep\u003c/em\u003eCO\u003csub\u003e2\u003c/sub\u003e is represented as either a change from 4x to 2x pre-industrial \u003cem\u003ep\u003c/em\u003eCO\u003csub\u003e2\u003c/sub\u003e or no change in \u003cem\u003ep\u003c/em\u003eCO\u003csub\u003e2\u003c/sub\u003e. Tibetan topography is configured at different elevations to determine the impacts this may have one the broader climate system, here showing as: only a valley at 2.5 km, only a plateau at 4.5 km, or a change from 2.5 km valley to 4.5 km plateau. Topography shown in Fig. 5. Accompanying experiment results in Table S4. The response is shown on the right as change in mean annual air temperature (\u0026Delta;MAAT, \u0026deg;C) and change in mean annual precipitation (\u0026Delta;MAP, mm/yr).\u003c/p\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"515\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSim.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003eCO\u003csub\u003e2\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.116279069767442%\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.5 km\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.116279069767442%\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.5 km\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.767441860465116%\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.5 to 4.5 km\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.565891472868216%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026Delta;MAAT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.434108527131784%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026Delta;MAP\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e4x to 2x\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.116279069767442%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.116279069767442%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.767441860465116%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.565891472868216%\"\u003e\n \u003cp\u003e-6.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.434108527131784%\"\u003e\n \u003cp\u003e150\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e4x to 2x\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.116279069767442%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.116279069767442%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.767441860465116%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.565891472868216%\"\u003e\n \u003cp\u003e-6.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.434108527131784%\"\u003e\n \u003cp\u003e198\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003e4x to 2x\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.116279069767442%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.116279069767442%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.767441860465116%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.565891472868216%\"\u003e\n \u003cp\u003e-6.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.434108527131784%\"\u003e\n \u003cp\u003e182\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003eNo change\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.116279069767442%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.116279069767442%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.767441860465116%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.565891472868216%\"\u003e\n \u003cp\u003e1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.434108527131784%\"\u003e\n \u003cp\u003e167\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003eNo change\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.116279069767442%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.116279069767442%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.767441860465116%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.565891472868216%\"\u003e\n \u003cp\u003e1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.434108527131784%\"\u003e\n \u003cp\u003e173\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\"\u003e\n \u003cp\u003eNo change\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.116279069767442%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.116279069767442%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.767441860465116%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.565891472868216%\"\u003e\n \u003cp\u003e1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.434108527131784%\"\u003e\n \u003cp\u003e199\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 2\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003e\u003cstrong\u003eClimate model simulations for Priabonian to Chattian L\u0026uuml;he Basin.\u003c/strong\u003e Conditions based on Getech model assumptions for the three target geological stages: Eocene Priabonian (37.71 to 33.90 Ma), Oligocene Rupelian (33.90 to 27.82 Ma), and Oligocene Chattian (27.82 to 23.03 Ma), including: paleo-rotations (Rot. Lat. = rotated latitude; Rot. Long. = rotated longitude); elevation (Elev., m); and \u003cem\u003ep\u003c/em\u003eCO\u003csub\u003e2\u003c/sub\u003e assumed to have decreased from 4x to 2x pre-industrial values (1120 to 560 ppm). The resulting temperatures and precipitation are shown as: mean annual air temperature (MAAT, \u0026deg;C), three consecutive warmest-month mean temperatures (3WMMT, \u0026deg;C), three consecutive coldest-month mean temperatures (3CMMT, \u0026deg;C), and mean annual precipitation (MAP, mm/yr). Accompanying experiment results and boundary conditions are expanded in Table S5.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStage\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRot. Lat.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRot. Long.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eElev. (m)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003eCO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMAAT\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e3CMMT\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e3WMMT\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMAP\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePriabonian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28.5312\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e97.8944\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2322\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1120 (4x)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e840\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRupelian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28.1658\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e97.5050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2578\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e560 (2x)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1040\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChattian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27.4190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e97.2092\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2805\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e560 (2x)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1220\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 3\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003e\u003cstrong\u003eClimate Leaf Analysis Multivariate Program (CLAMP) climate estimates based on the L\u0026uuml;he town section leaf flora and analysed using the PhysgAsia2/Worldclim2 calibration\u003c/strong\u003e. For more details on these metrics and how they are obtained see (Spicer et al., \u003cspan\u003e2020\u003c/span\u003eb). Row 1: Temperature-related parameters: mean annual air temperature (MAAT, \u0026deg;C); warmest month mean air temperature (WMMT, \u0026deg;C); coldest month mean air temperature (CMMT, \u0026deg;C); mean minimum temperature of the warmest month (MinT.W, \u0026deg;C); mean maximum temperature of the coldest month (MaxT.C, \u0026deg;C); thermicity i.e., a measure of cumulative heat (Therm). Row 2: Humidity and enthalpy-related parameters: relative humidity (RH, %); specific humidity (SH, g/kg); moist enthalpy (Enth, kJ/kg). Row 3: Vapour pressure deficit parameters: mean annual vapour pressure deficit (VPD.ann, hPa); mean winter vapour pressure deficit (VPD.win, hPa); mean spring vapour pressure deficit (VPD.spr, hPa); summer vapour pressure deficit (VPD.sum, hPa); autumn vapour pressure deficit (VPD.aut, hPa). Row 4: Precipitation and evapotranspiration-related parameters: precipitation during the three consecutive wettest months (3-Wet, cm); precipitation during the three consecutive driest months (3-Dry, cm); mean annual potential evapotranspiration (PET.ann, mm); mean monthly potential evapotranspiration during the warmest quarter (PET.wrm, mm); mean monthly potential evapotranspiration during the coldest quarter (PET.cld, mm). Row 5: Growth-related parameters: length of the growing season i.e., time when the mean temperature is \u0026gt;\u0026thinsp;10\u0026deg;C (LGS, months), growing degree days\u0026thinsp;\u0026gt;\u0026thinsp;0\u0026deg;C (GDD0); growing degree days\u0026thinsp;\u0026gt;\u0026thinsp;5\u0026deg;C (GDD5); growing season precipitation (GSP, cm); mean monthly growing season precipitation (MMGSP, cm).\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003eTemperature-related parameters\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMAAT (\u0026deg;C)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eWMMT (\u0026deg;C)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCMMT (\u0026deg;C)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eMinT.W (\u0026deg;C)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMaxT.C (\u0026deg;C)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.9\u0026thinsp;\u0026plusmn;\u0026thinsp;2.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.8\u0026thinsp;\u0026plusmn;\u0026thinsp;2.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.6\u0026thinsp;\u0026plusmn;\u0026thinsp;3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e23\u0026thinsp;\u0026plusmn;\u0026thinsp;2.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.4\u0026thinsp;\u0026plusmn;\u0026thinsp;3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eHumidity and enthalpy-related parameters\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRH (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSH (g/kg)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEnth (kJ/kg)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTherm (\u0026deg;C)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65\u0026thinsp;\u0026plusmn;\u0026thinsp;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e321\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e295\u0026thinsp;\u0026plusmn;\u0026thinsp;75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003eVapour pressure deficit parameters\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eVPD.ann (hPa)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eVPD.win (hPa)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eVPD.spr (hPa)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eVPD.sum (hPa)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eVPD.aut (hPa)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u0026thinsp;\u0026plusmn;\u0026thinsp;2.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.7\u0026thinsp;\u0026plusmn;\u0026thinsp;4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e8.7\u0026thinsp;\u0026plusmn;\u0026thinsp;3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.4\u0026thinsp;\u0026plusmn;\u0026thinsp;2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrecipitation and evapotranspiration-related parameters\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e3-Wet (cm)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e3-Dry (cm)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePET.ann (mm)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003ePET.cld (mm)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePET.wrm (mm)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e111\u0026thinsp;\u0026plusmn;\u0026thinsp;40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35\u0026thinsp;\u0026plusmn;\u0026thinsp;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1002\u0026thinsp;\u0026plusmn;\u0026thinsp;166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e27.5\u0026thinsp;\u0026plusmn;\u0026thinsp;14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e125\u0026thinsp;\u0026plusmn;\u0026thinsp;24.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003eGrowth-related parameters\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLGS (month)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGSP (cm)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMMGSP (cm)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eGDD0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGDD5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e225\u0026thinsp;\u0026plusmn;\u0026thinsp;64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24\u0026thinsp;\u0026plusmn;\u0026thinsp;7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e677\u0026thinsp;\u0026plusmn;\u0026thinsp;118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e735\u0026thinsp;\u0026plusmn;\u0026thinsp;106\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eThe overall record (regardless of calibration) exhibits a persistent degree of variability without a long-term trend. The trends for all three calibrations are virtually the same, but the absolute temperatures differ greatly: the MAAT\u003csub\u003esoil\u003c/sub\u003e estimates are ca. 9\u0026deg;C cooler than the MAF\u003csub\u003elake\u003c/sub\u003e estimates (Fig.\u0026nbsp;\u003cspan\u003e7\u003c/span\u003e). MAAT\u003csub\u003esoil\u003c/sub\u003e values range from 1.9 to 14.4\u0026deg;C, with a mean of 8.5\u0026deg;C\u0026thinsp;\u0026plusmn;\u0026thinsp;3.5\u0026deg;C \u0026sigma;. MAF\u003csub\u003elake\u003c/sub\u003e values range from 11.8 to 22.2\u0026deg;C, with a mean of 17.3\u0026deg;C\u0026thinsp;\u0026plusmn;\u0026thinsp;2.9\u0026deg;C \u0026sigma;. The five MAAT\u003csub\u003epeat\u003c/sub\u003e values range from 5.4 to 15.4\u0026deg;C, with a mean of 10.9\u0026deg;C\u0026thinsp;\u0026plusmn;\u0026thinsp;4.4\u0026deg;C \u0026sigma;, with values generally closer to those obtained using the soil calibration. The temperature trends throughout the section show variability, possibly due to mixing of \u003cem\u003ein situ\u003c/em\u003e and allochthonous sources within the rapidly changing and dynamic depositional environment (Section \u003cspan\u003e2.2\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eTo test whether temperature is impacted by changes to the mixture of \u003cem\u003ein situ\u003c/em\u003e versus allochthonous brGDGTs, we plot MBT\u0026rsquo;\u003csub\u003e5me\u003c/sub\u003e against P\u003csub\u003e\u003cem\u003eaq\u003c/em\u003e\u003c/sub\u003e and MBT\u0026rsquo;\u003csub\u003e5me\u003c/sub\u003e against lithology; there are no apparent trends. This is possibly due to the preservation of brGDGTs in the low-energy clay and silt sediments (the sediments more likely to contain \u003cem\u003ein situ\u003c/em\u003e signals) and lac of preservation of brGDGTs in the high-energy sand sediments (the sediments more likely to contain allochthonous signals). Thus, our brGDGT values likely reflect \u003cem\u003ein situ\u003c/em\u003e production and high-fidelity temperature reconstructions. Given the results and discussion described in Section \u003cspan\u003e2.2\u003c/span\u003e (based on e.g., lithology, high \u003cem\u003eP\u003c/em\u003e\u003csub\u003eaq\u003c/sub\u003e and C\u003csub\u003e23\u003c/sub\u003e/(C\u003csub\u003e23\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;C\u003csub\u003e31\u003c/sub\u003e) values), the depositional environment is clearly a dynamic fluvial system that is constantly wet.. With these independent pieces of evidence taken together this site represents lacustrine-type \u003cem\u003ein situ\u003c/em\u003e production brGDGT and thus, the MAF\u003csub\u003elake\u003c/sub\u003e calibration is to be the most appropriate choice for calibration.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003e2.4 Climate model results\u003c/h3\u003e\n\u003cp\u003eTo contextualise the climatic and environmental changes occurring at our site, we employed a fully coupled atmosphere-ocean GCM with a range of perturbed Late Eocene and Oligocene boundary conditions.\u003c/p\u003e\n\u003cp\u003eFirst, we tested the impacts on the broader Asian region (0\u0026deg;N-60\u0026deg;N, 60\u0026deg;E-120\u0026deg;E) from the Priabonian to Chattian, specifically the impacts that \u003cem\u003ep\u003c/em\u003eCO\u003csub\u003e2\u003c/sub\u003e values and site topography have on temperature and precipitation (summary in Table \u003cspan\u003e1\u003c/span\u003e; accompanying scenarios in Table S4). If we assume that \u003cem\u003ep\u003c/em\u003eCO\u003csub\u003e2\u003c/sub\u003e decreased from 4x to 2x pre-industrial \u003cem\u003ep\u003c/em\u003eCO\u003csub\u003e2\u003c/sub\u003e, we observe regional cooling by ca. 6\u0026deg;C, regardless of topographic boundary conditions inferred for the Tibetan Plateau i.e., with site elevations as either a constant valley at 2.5 km, a constant plateau at 4.5 km, or changing from a valley-to-plateau at 2.5 to 4.5 km (Table \u003cspan\u003e1\u003c/span\u003e, simulations 1\u0026ndash;3; Fig. \u003cspan\u003e5\u003c/span\u003e). For comparison, we evaluated the impact of \u003cem\u003enot\u003c/em\u003e changing \u003cem\u003ep\u003c/em\u003eCO\u003csub\u003e2\u003c/sub\u003e on regional temperature and precipitation. Assuming constant 4x pre-industrial \u003cem\u003ep\u003c/em\u003eCO\u003csub\u003e2\u003c/sub\u003e from the Priabonian to Chattian, we observe slight regional warming by ca. 1.5\u0026deg;C, regardless of topographic boundary conditions inferred for the Tibetan Plateau (Table \u003cspan\u003e1\u003c/span\u003e, simulations 4\u0026ndash;6). In all six model simulations in Table \u003cspan\u003e1\u003c/span\u003e, mean annual precipitation (MAP) increases 150\u0026ndash;200 mm/yr from the Priabonian into the Chattian. MAP is seemingly neither influenced by \u003cem\u003ep\u003c/em\u003eCO\u003csub\u003e2\u003c/sub\u003e values (e.g., from 4x to 2x pre-industrial \u003cem\u003ep\u003c/em\u003eCO\u003csub\u003e2\u003c/sub\u003e or no change in \u003cem\u003ep\u003c/em\u003eCO\u003csub\u003e2\u003c/sub\u003e) nor by topographic configurations in the model conditions. This suggests that the modelled MAP was not impacted by local factors, but instead by global changes across this boundary e.g., the opening of ocean gateways (e.g., Drake Passage, creation of the Antarctic Circumpolar Current, and retreat of the Paratethys Sea), the expansion of the Antarctic icesheet, and broad reorganisation of the global climate system (Westerhold et al., \u003cspan\u003e2020\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eSecond, we tested the impacts on L\u0026uuml;he Basin region itself, given paleo-rotations for latitude/longitude and given elevations of the L\u0026uuml;he Basin in the Getech Plc. Model assumptions for the Priabonian (Eocene), Rupelian (Oligocene), and Chattian (Oligocene) (Table \u003cspan\u003e2\u003c/span\u003e; accompanying experiment results in Table S5). It is worth noting that the model is very coarse and thus we cannot resolve the L\u0026uuml;he Basin location itself (only the region) nor represent accurate topography, as it is essentially a slab of constant elevation for the region. Given the globally assumed decrease from 4x to 2x pre-industrial \u003cem\u003ep\u003c/em\u003eCO\u003csub\u003e2\u003c/sub\u003e across the EOT, we find that the Priabonian L\u0026uuml;he Basin region experienced MAAT of 27.4\u0026deg;C, with annual temperatures ranging from the three consecutive coldest months mean temperature (3CMMT) of 17.9\u0026deg;C to the three consecutive warmest months mean temperature (3WMMT) of 35.5\u0026deg;C. A Rupelian and Chattian L\u0026uuml;he Basin region experienced cooler MAATs of 19.3\u0026deg;C and 17.5\u0026deg;C respectively, ranging from 3CMMT of 11.6\u0026deg;C and 10.0\u0026deg;C and 3WMMT of 25.9\u0026deg;C and 22.6\u0026deg;C, respectively. This cooling in the model is unsurprising, given the assumption of decreasing \u003cem\u003ep\u003c/em\u003eCO\u003csub\u003e2\u003c/sub\u003e from 4x to 2x pre-industrial values across the EOT, but is certainly notable in the context of the high temporal resolution of the data at this site which shows relatively constant averages across this time boundary.\u003c/p\u003e\n\u003cdiv id=\"Sec7\"\u003e\n \u003ch2\u003e2.5 The evolution of the Tibetan region and Eocene/Oligocene climate\u003c/h2\u003e\n \u003cp\u003eOur reconstructed MAATs across this section are consistent with a temperate climate. Based on the \u003csup\u003e40\u003c/sup\u003eAr/\u003csup\u003e39\u003c/sup\u003eAr dating of feldspars within volcanic ashes exposed at 58 m, which provide ages of 33.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.36 Ma (sample lvb11) and 34.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.74 Ma (sample lv5 8.0), our L\u0026uuml;he coalmine section may or may not capture the Eocene-Oligocene transition (EOT) that occurred at 33.9 Ma (Fig.\u0026nbsp;\u003cspan\u003e2\u003c/span\u003e). We do not find evidence of significant cooling within the first 60 m of the section, the most likely part of our section to have captured the EOT. The lack of cooling could indicate that the L\u0026uuml;he Basin does not span the EOT. Alternatively (and likely, given our age model), our reconstruction may show that temperature in the L\u0026uuml;he Basin remained relatively stable across the EOT; in this case, the L\u0026uuml;he Basin did not experience the cooling observed in marine sections (e.g., Westerhold et al., \u003cspan\u003e2020\u003c/span\u003e) but instead reflects the heterogenous expression previously observed in terrestrial sections (e.g., Zanazzi et al., \u003cspan\u003e2007\u003c/span\u003e; Hren et al., \u003cspan\u003e2013\u003c/span\u003e; Sheldon et al., \u003cspan\u003e2016\u003c/span\u003e; Lauretano et al., \u003cspan\u003e2021\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eIf the L\u0026uuml;he Basin coalmine section \u003cem\u003edoes not\u003c/em\u003e include the EOT, to err on the side of caution, then our brGDGT-based records support the \u0026lsquo;quasi-static\u0026rsquo; climate that has gained traction across palaeobotany and paleosol records (Retallack, \u003cspan\u003e2007\u003c/span\u003e; Sheldon et al., 2012; Kohn et al., \u003cspan\u003e2015\u003c/span\u003e). It should be noted that a relatively muted cooling of \u0026lt;\u0026thinsp;1\u0026ndash;2\u0026deg;C might be difficult to detect in our proxy records, which are better suited for greater temperature oscillations (Naafs et al., \u003cspan\u003e2017b\u003c/span\u003e). That said, the lack of significant temperature change over ca. 8 Myr in the L\u0026uuml;he Basin coalmine section is notable (Fig.\u0026nbsp;\u003cspan\u003e7\u003c/span\u003e). The results from this section may represent one more important puzzle piece in the terrestrial expression of the EOT (e.g., Pound and Salzmann, \u003cspan\u003e2017\u003c/span\u003e) and the possible influence of local factors on this response.\u003c/p\u003e\n \u003cp\u003eIf the L\u0026uuml;he Basin coalmine section \u003cem\u003edoes\u003c/em\u003e include the EOT, our biomarker data suggests that the L\u0026uuml;he Basin maintained relatively stable temperatures across the EOT whereas our model suggests\u0026thinsp;~\u0026thinsp;6\u0026deg;C cooling from the Eocene Priabonian into the Oligocene Rupelian (assuming a decline in \u003cem\u003ep\u003c/em\u003eCO\u003csub\u003e2\u003c/sub\u003e from 4x to 2x pre-industrial levels). This difference is likely due to spatial resolution of the model (which relies on large homogenous grids) as compared with the biomarker data (which records details on the specific/basin scale); the model cannot capture the complex topography that may influence the climate at this site.\u003c/p\u003e\n \u003cp\u003eOur results do not conform to the marine record shift in temperature but ultimately support the emerging picture that terrestrial temperature records have a widely heterogenous response across the EOT (e.g., Pound and Salzmann, \u003cspan\u003e2017\u003c/span\u003e), as evidenced from both qualitative and quantitative proxies (e.g., palaeobotanical, palynological, geochemical). Vegetation records provide the most extensive global dataset of changes across the EOT and generally show a variety of responses, partly influenced by local/regional factors and changes in precipitation (Pound and Salzmann, \u003cspan\u003e2017\u003c/span\u003e). Palaeobotany assemblages from Argentina indicate a \u0026lsquo;quasi-static\u0026rsquo; climate across the EOT (Kohn et al., \u003cspan\u003e2015\u003c/span\u003e), whereas assemblages from North America suggest protracted cooling from the early into the middle Oligocene (Retallack et al., \u003cspan\u003e2004\u003c/span\u003e). A palynological record from a lignite sequence in SE Australia also suggests a (qualitative) cooling across the EOT; in the same coeval facies, the organic geochemical brGDGT-based record shows 2.4\u0026deg;C cooling (Lauretano et al., \u003cspan\u003e2021\u003c/span\u003e). Terrestrial geochemical records likewise depict a range of responses. Paleosol records from North America, Argentina, and Spain suggest that temperatures remained unvaried during this time (Retallack, \u003cspan\u003e2007\u003c/span\u003e; Sheldon et al., 2012; Kohn et al., \u003cspan\u003e2015\u003c/span\u003e), whereas another paleosol record from North America suggests ca. 2\u0026ndash;3\u0026deg;C cooling (Retallack, \u003cspan\u003e2007\u003c/span\u003e). Geochemical records from the clumped isotopic composition of freshwater gastropod shells from the UK indicate a more intense 4\u0026ndash;6\u0026deg;C cooling from the late Eocene to the early Oligocene (Hren et al., \u003cspan\u003e2013\u003c/span\u003e). Similarly, the stable hydrogen isotopic composition from volcanic glass suggests that a 5\u0026deg;C cooling occurred in Argentina (Colwyn and Hren, \u003cspan\u003e2019\u003c/span\u003e). Differing still, the oxygen isotopic composition of fossil teeth in North America suggests a dramatic ca. 8\u0026deg;C temperature drop across the transition (Zanazzi et al., \u003cspan\u003e2007\u003c/span\u003e). The terrestrial signal during this time certainly shows an unresolved heterogenous response, possibly due to localised factors such as albedo (e.g., based on soil type), vegetation type and associated impacts (e.g., transpiration and canopy cover (Fritts et al., 1961)), localised nutrient and carbon cycling, and detailed differences in topography (e.g., even as detailed as whether the site represents the north or south slope of a valley).\u003c/p\u003e\n \u003cp\u003eRegardless of the inclusion/exclusion of the EOT, the coalmine section at L\u0026uuml;he basin certainly spans the Rupelian into the Chattian, where our biomarker-, palaeobotany-, and model-based temperature estimates support stable long-term temperatures in the Oligocene (Fig.\u0026nbsp;\u003cspan\u003e7\u003c/span\u003e). Our brGDGT MAF\u003csub\u003elake\u003c/sub\u003e mean of 17.3\u0026deg;C\u0026thinsp;\u0026plusmn;\u0026thinsp;2.9 \u0026sigma; from (possibly) the Priabonian through the Rupelian and into the Chattian are not dissimilar to our model-based MAATs of ~\u0026thinsp;19.3\u0026deg;C\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5 \u0026sigma; for the Rupelian and ~\u0026thinsp;17.5\u0026deg;C\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5 \u0026sigma; for the Chattian (Table\u0026nbsp;\u003cspan\u003e2\u003c/span\u003e) nor dissimilar to the palaeobotanical-based MAATs of 14.5\u0026ndash;15.5\u0026deg;C (bioclimatic analysis; Tang et al., \u003cspan\u003e2020\u003c/span\u003e) and 16\u0026deg;C\u0026thinsp;\u0026plusmn;\u0026thinsp;2.4 calibration uncertainty(Climate Leaf Analysis Multivariate Program (CLAMP); Table\u0026nbsp;\u003cspan\u003e3\u003c/span\u003e) from the nearby L\u0026uuml;he town section from the Rupelian. The modelling and palaeobotanical results demonstrate a modest annual range of temperatures (e.g., model 3CMMT of 12.0\u0026deg;C and 3WMMT of 24.0\u0026deg;C, CLAMP CMMT of 4.5\u0026deg;C to WMMT of 26.9\u0026deg;C), with likely infrequent winter frosting and warm summers. This would suggest a warm temperate climate rather than fully subtropical climate, with taxa that have frost sensitive leaves that are prone to winter deciduousness.\u003c/p\u003e\n \u003cp\u003eImportantly, these four different methods for estimating palaeotemperatures match the present-day MAAT for this site, which ranges from 15\u0026ndash;20\u0026deg;C depending on the exact elevation in this region with dynamic topography. The lack of temperature change from the Oligocene to today suggests that this location has been at its present-day elevation since at least the early Oligocene, supporting the hypothesis that local uplift had already taken place by this time (Spicer et al., \u003cspan\u003e2020\u003c/span\u003e; Wei et al., \u003cspan\u003e2022\u003c/span\u003e). This proposal has recently been grounded in supporting data by Wu et al. (\u003cspan\u003e2022\u003c/span\u003e) who likewise suggest that L\u0026uuml;he Basin town section had reached its present elevation by the early Oligocene. Similarly, He et al. (2022) suggest that eastern Tibet reached its present elevation by the end of the Eocene. The modelling results are critical in further supporting this hypothesis. Topographic features e.g., a central Tibetan valley of 2.5 km, plateau of 4.5 km, or change from a 2.5 km valley to 4.5 km plateau (Table\u0026nbsp;\u003cspan\u003e1\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan\u003e5\u003c/span\u003e) have virtually no impact on the larger climate of this region. An alternative explanation may be that there are complicating factors that impact temperature for the biomarker and palaeobotany proxies e.g., cloud coverage. However, this seems less likely given the proxy agreement with the model results throughout the Oligocene when we would expect more dynamism in the environment, e.g., precipitation. This coupled atmosphere-ocean general circulation paleoclimate model includes paleo-rotations (latitude/longitude), changes in globally dynamic temperatures (e.g., SST changes from the South China Sea), and changes in cloud coverage and precipitation. In addition, there is supporting evidence from moist enthalpy from CLAMP and oxygen isotopes from carbonate nodules that topography of the eastern margin of Tibet was established immediately prior to and during the basin development (e.g., He et al., 2022).\u003c/p\u003e\n \u003cp\u003eHowever, we do see dramatic changes in the depositional environment throughout the L\u0026uuml;he coalmine section, particularly the changes in hydrology and overall energy of the system. In addition to the sedimentological and organic geochemistry evidence discussed in Section \u003cspan\u003e2.3\u003c/span\u003e, precipitation changes are confirmed by the model and palaeobotany results. Based on the nearby (Rupelian) L\u0026uuml;he town section, CLAMP-based precipitation (during the growing season) suggests averages of 2250 mm\u0026thinsp;\u0026plusmn;\u0026thinsp;640 \u0026sigma;, with the three consecutive wettest months (3-WET) around 1110 mm\u0026thinsp;\u0026plusmn;\u0026thinsp;400 \u0026sigma; and three consecutive driest months (3-DRY) around 340 mm\u0026thinsp;\u0026plusmn;\u0026thinsp;98 \u0026sigma; (Table\u0026nbsp;\u003cspan\u003e3\u003c/span\u003e). The overall precipitation may be overestimated in CLAMP, particularly for the dry months in warm climates because water is not a limiting growth factor for plants growing near aquatic depositional sites (Spicer et al., \u003cspan\u003e2011\u003c/span\u003e). Indeed, the model suggests the mean annual precipitation values lower than CLAMP, going from 850 mm/yr in the Priabonian to 1040 mm/yr in the Rupelian to 1220 mm/yr in the Chattian. This increase in precipitation is worth noting, given the observed change in depositional environment energy at this site across this boundary.\u003c/p\u003e\n \u003cp\u003eMultiple lines of data-based evidence show that there was no significant change in climate at this site during this time, which points to other reasons for the dynamic depositional environmental changes: topographic changes upstream from the L\u0026uuml;he Basin, precipitation changes, lateral channel migration, or a combination of these. A combination seems likely. The complex tectonic changes that have been explored for sites farther upstream in Tibet likely contribute to the change we see in this section (e.g., Su et al., \u003cspan\u003e2019a\u003c/span\u003e; \u003cspan\u003e2019b\u003c/span\u003e; \u003cspan\u003e2020\u003c/span\u003e; Spicer et al., \u003cspan\u003e2020\u003c/span\u003e), which likely impacted precipitation. The increased fluvial influence then led to lateral channel migration and/or an increase in river size/energy spilling across the basin due to a change in catchment size or the amount of precipitation. Ultimately, untangling topographic changes, precipitation changes, lateral channel migration, or a combination cannot be determined from one site. Determining whether there are synchronous regional upstream changes in catchment characteristics would require a multi-site comparison through the entire depositional succession across the catchment. Such a multi-site comparison would be a vital contribution to understanding the co-evolving climate and tectonics at this time and we recommend this multi-site approach for future research.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Conclusions","content":"\u003cp\u003eWe reconstructed paleoclimatic and paleoenvironmental conditions at the L\u0026uuml;he Basin coalmine section in central Yunnan, China, on the SE margin of Tibet, in the late Paleogene using organic geochemical tools, sedimentology, and climate modelling. Our (primarily) plant- and bacteria-derived biomarkers indicate that this site represented a dynamic environment, likely a floodplain, with occasional submerged peat/swamp deposits and occasional high energy riverine input that increased in frequency over time. The abundance of terrestrial biomarkers, which indicate woody gymnosperms (likely conifers) and angiosperms, is consistent with previous palaeobotany reconstructions of this area as covered by deciduous and evergreen broad-leaved forests. Temperatures reconstructed based on brGDGTs indicate variable values (ca. 12\u0026ndash;22\u0026deg;C) but the overall average of 17\u0026deg;C across the section is consistent with model-based temperature estimates of 19\u0026deg;C and palaeobotany-based proxies from the nearby L\u0026uuml;he town section (bioclimatic analyses estimates of 15\u0026deg;C and CLAMP of 16\u0026deg;C). The temperature obtained from multiple independent lines of evidence, as well as additional evidence from the modelling experiments, shows a lack of cooling across the EOT, providing supporting evidence that the terrestrial response to rapid climate change is heterogeneous. Furthermore, these paleotemperature records are similar to present day temperature values, indicating that this site has likely been at its current elevation since (at least) the early Oligocene, supporting recent studies that suggest Eocene uplift of the region.\u003c/p\u003e"},{"header":"4. Methods","content":"\u003cp\u003eMore detailed materials and methodology can be found in Supplementary Material.\u003c/p\u003e \u003cp\u003e \u003cb\u003eAge.\u003c/b\u003e \u003csup\u003e40\u003c/sup\u003eAr/\u003csup\u003e39\u003c/sup\u003eAr dating of feldspars within volcanic ashes exposed at 58 m in the section provides ages of 33.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.36 Ma (sample lvb11) and 34.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.74 Ma (sample lv5 8.0). Magnetostratigraphic interpretation of the L\u0026uuml;he coalmine section (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) suggests that the section spans magnetochrons C15n to C9n (ca. 35\u0026thinsp;\u0026minus;\u0026thinsp;27 Ma, Gradstein, 2012, updated for Speijer et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), matching the geologic timescale updates conducted by Xu et al. (2023), who resampled the lower 180 m to provide a robust high-resolution Sr, Rb, Rb/Sr, and Ti data and cyclostratigraphy interpretations. This yields an average sedimentation rate of ca. 48 cm/kyr, consistent with the rates in other basins around the Tibetan Plateau (Li et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eSediments.\u003c/b\u003e The L\u0026uuml;he coalmine section succession comprises alternations of organic-rich marls, mudstones, sandstones, and lignite (i.e., immature fossilised peat) deposits (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; Table S3; Fig. S5). The lower part of the section (0\u0026ndash;73 m) comprises small grain sizes, ranging from clay to silt, and the upper part of the section (73\u0026ndash;340 m) generally comprises larger grain sizes from sands to gravels, interspersed with some brief intervals of organic-rich silts. A thick coal interval (ca. 4 m) at ca. 50 m from the base of the coalmine contains 11 volcanic ash layers, some of which were used for the dating described above.\u003c/p\u003e \u003cp\u003e \u003cb\u003eThermal maturity of sediments.\u003c/b\u003e Given that high thermal maturity can affect the fidelity of GDGT-based temperature reconstructions (Schouten et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Schouten et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), we assess the thermal maturity. We calculated the carbon preference index (CPI; Bray and Evans, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1961\u003c/span\u003e; Eglinton and Hamilton, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1967\u003c/span\u003e) using (Σodd (C\u003csub\u003e21\u003c/sub\u003e\u0026ldquo;-\u0026ldquo;C\u003csub\u003e33\u003c/sub\u003e) + Σodd (C\u003csub\u003e23\u003c/sub\u003e\u0026ldquo;-\u0026ldquo;C\u003csub\u003e35\u003c/sub\u003e)) / (2\u0026thinsp;\u0026times;\u0026thinsp;Σeven (C\u003csub\u003e22\u003c/sub\u003e\u0026ldquo;-\u0026ldquo;C\u003csub\u003e34\u003c/sub\u003e)) to avoid overestimation of the odd-over-even preference (Marzi et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1993\u003c/span\u003e). The CPI ranges from 2.0 to 11.3 with a mean of 5.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9 standard deviation (σ), suggesting that these sediments are immature (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed).\u003c/p\u003e \u003cp\u003e \u003cb\u003eIndices for vegetation and environmental reconstructions.\u003c/b\u003e The average chain length (ACL) of \u003cem\u003en\u003c/em\u003e-alkanes can be indicative of the dominant source vegetation and was calculated as ACL\u0026thinsp;=\u0026thinsp;Σ(C\u003csub\u003e\u003cem\u003en\u003c/em\u003e\u003c/sub\u003e \u0026times; \u003cem\u003en\u003c/em\u003e) / Σ(C\u003csub\u003e\u003cem\u003en\u003c/em\u003e\u003c/sub\u003e) (Eglinton and Hamilton, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1967\u003c/span\u003e), using odd \u003cem\u003en\u003c/em\u003e-alkane chain-lengths from C\u003csub\u003e27\u003c/sub\u003e through C\u003csub\u003e35\u003c/sub\u003e (Ficken et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Bush and McInerney, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The \u003cem\u003eP\u003c/em\u003e-aqueous ratio (\u003cem\u003eP\u003c/em\u003e\u003csub\u003eaq\u003c/sub\u003e) calculated as \u003cem\u003eP\u003c/em\u003e\u003csub\u003eaq\u003c/sub\u003e = (C\u003csub\u003e23\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;C\u003csub\u003e25\u003c/sub\u003e)/(C\u003csub\u003e23\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;C\u003csub\u003e25\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;C\u003csub\u003e29\u003c/sub\u003e+C\u003csub\u003e31\u003c/sub\u003e) (Ficken et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) and the C\u003csub\u003e23\u003c/sub\u003e/(C\u003csub\u003e23\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;C\u003csub\u003e31\u003c/sub\u003e) index (Nott et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) were both used to indicate wetland conditions.\u003c/p\u003e \u003cp\u003e \u003cb\u003ebrGDGT indices for MAAT and pH.\u003c/b\u003e The GDGTs in our section could have been produced in peats or a shallow lake environment based on our environmental reconstructions (Sect. \u003cspan refid=\"Sec6\" class=\"InternalRef\"\u003e2.4\u003c/span\u003e); we therefore used both peat and lake calibrations. The peat-calibrated MAAT is described as MAAT\u003csub\u003epeat\u003c/sub\u003e (\u0026deg;C)\u0026thinsp;=\u0026thinsp;52.18 x MBT\u003csub\u003e\u0026rsquo;5me\u003c/sub\u003e \u0026ndash; 23.05 (Naafs et al., 2017) and lake-calibrated MAAT for months above freezing as MAF\u003csub\u003elake\u003c/sub\u003e (\u0026deg;C) = [MBT\u0026rsquo;\u003csub\u003e5me\u003c/sub\u003e \u0026ndash; 0.075] / 0.030 (Mart\u0026iacute;nez-Sosa et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). pH was calculated using CBT\u003csub\u003epeat\u003c/sub\u003e = log [(Ib\u0026thinsp;+\u0026thinsp;IIa\u0026rsquo; + IIb\u0026thinsp;+\u0026thinsp;IIb\u0026rsquo; + IIIa\u0026rsquo;) / (Ia\u0026thinsp;+\u0026thinsp;IIa\u0026thinsp;+\u0026thinsp;IIIa)], where pH\u0026thinsp;=\u0026thinsp;8.07\u0026thinsp;+\u0026thinsp;2.49 x CBT\u003csub\u003epeat\u003c/sub\u003e.\u003c/p\u003e \u003cp\u003e \u003cb\u003eClimate model simulations.\u003c/b\u003e We used HadCM3BL-M2.1aD (Valdes et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), a fully coupled ocean-atmosphere and dynamic vegetation General Circulation Model with latitude by longitude spatial grid (ca. 300 km), nineteen vertical levels in the atmosphere and twenty vertical levels in the ocean. Model boundary conditions (topography, bathymetry, and ice sheet configurations at low (3.75\u0026deg; x 2.5\u0026deg;) and scaled to the high model resolution (0.5\u0026deg; x 0.5\u0026deg;)) for each geologic stage, Priabonian (ca. 37.71 to 33.90 Ma), Rupelian (ca. 33.90 to 27.82 Ma), and Chattian (ca. 27.82 to 23.03 Ma), are provided by Getech Plc. Stage-specific solar luminosity was calculated using the methods of Gough (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1981\u003c/span\u003e). \u003cem\u003ep\u003c/em\u003eCO\u003csub\u003e2\u003c/sub\u003e values were 1120 ppm for the Priabonian and 560 ppm for the Rupelian and Chattian (Foster et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Witkowski et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Topographic changes were based on hypotheses posed by Spicer et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2020\u003c/span\u003e (and references therein), either as a) a constant valley at 2.5 km elevation, b) a constant plateau at 4.5 km elevation, or c) a change from a valley at 2.5 km to a plateau at 4.5 km (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Each experiment was run for 12,422 model years to allow the surface and deep ocean to reach equilibrium and to achieve a state with no net energy imbalance at the top of the atmosphere. Climate means were calculated from the last 100-years of each simulation. Time-varying latitude and longitude plate paleo-rotations are provided for the L\u0026uuml;he Basin for each stage to allow for accurate comparison within the model. The paleo-coordinates (21.1\u0026deg;N) for L\u0026uuml;he were calculated using the Getech plate model.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eData availability\u003c/p\u003e\n\u003cp\u003eAll data is provided with this manuscript.\u003c/p\u003e\n\u003cp\u003eAuthor contributions\u003c/p\u003e\n\u003cp\u003eRAS, TS, ZKZ, SFL, PJV, and RDP planned and funded the field campaign. CRW, VL, and JPM conducted the organic geochemistry analyses. AF conducted the model experiments. SHL and HT conducted the palaeobotany interpretations. CRW and VL interpreted the data and wrote the manuscript with contributions from all authors.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflict of interest.\u003c/p\u003e\n\u003cp\u003eAcknowledgements and Financial support\u003c/p\u003e\n\u003cp\u003eThis research was carried out with funding from the joint UK-China Project administered by the Natural Science Foundation of China Project (No. 41661134049, 42072024, 41772026) and the UK Natural Environment Research Council (NERC; NE/P013805/1). We also thank the NERC for partial funding of the National Environmental Isotope Facility (NEIF; No. NE/V003917/1) that enabled HPLC-MS capabilities. We thank the European Research Council under the European Union\u0026rsquo;s Seventh Framework Programme (FP/2007-2013) and European Research Council Grant Agreement (No. 340923) that enabled GC-MS capabilities. CRW was funded by a Royal Society Dorothy Hodgkin Fellowship (DHF\\R1\\21014), RAS was funded by an XTBG Visiting Scholarship, and BDAN was funded by a Royal Society Tata University Research Fellowship. We thank F. Sgouridis at the University of Bristol for technical assistance.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAplin, R.T. and Cambie, R.C.: Taxonomic distribution of some diterpene hydrocarbons, New Zealand Journal of Science (Wellington) 7, 258\u0026ndash;260, 1964\u003c/li\u003e\n\u003cli\u003eBray, E.E. and Evans, E.D.: Distribution of n-paraffins as a clue to recognition of source beds, Geochimica et Cosmochimica Acta, 22, 2\u0026ndash;15, 1961.\u003c/li\u003e\n\u003cli\u003eBush, R.R. and McInerney, F.A.: Leaf wax \u003cem\u003en\u003c/em\u003e-alkane distributions in and across modern plants: Implications for paleoecology and chemotaxonomy, Geochimica et Cosmochimica Acta, 117, 161\u0026ndash;179, 2013.\u003c/li\u003e\n\u003cli\u003eClymo, R.S.: \u003cem\u003eSphagnum\u003c/em\u003e-dominated peat bog: a naturally acid ecosystem, Philosophical Transactions of the Royal Society of London, 305, 487\u0026ndash;499, 1984.\u003c/li\u003e\n\u003cli\u003eColwyn, D.A. and Hren, M.T.: An abrupt decrease in Southern Hemisphere terrestrial temperature during the Eocene\u0026ndash;Oligocene transition, Earth and Planetary Science Letters, 512, 227\u0026ndash;235. https://doi.org/10.1016/J.EPSL.2019.01.052, 2019.\u003c/li\u003e\n\u003cli\u003eDe Jonge, C., Hopmans, E.C., Zell, C.I., Kim, J.-H., Schouten, and S., Sinninghe Damst\u0026eacute;, J.S.: Occurrence and abundance of 6-methyl branched glycerol dialkyl glycerol tetraethers in soils: Implications for palaeoclimate reconstruction, Geochimica et Cosmochimica Acta, 141, 97\u0026ndash;112. https://doi.org/10.1016/j.gca.2014.06.013, 2014.\u003c/li\u003e\n\u003cli\u003eDeng, W., De Franceschi, D., Xu, X., Del Rio, C., Low, S.L., Zhou, Z.-K., Spicer, R.A., Ren, L, Yang, R.-Q., Tian, Y., Wu, M., Yang, J., Liang, S., Wappler, T., and Su, T.: Taxodium-like wood fossil from the Oligocene southwestern China: Its arthropod borings and their implications for biogeography and paleoenvironment, Review of Palaeobotany and Palynology, 302, 104669, 2022.\u003c/li\u003e\n\u003cli\u003eDiefendorf, A.F., Freeman, K.F., and Wing, S.L.: Distribution and carbon isotope patterns of diterpenoids and triterpenoids in modern temperate C3 trees and their geochemical significance, Geochimica et Cosmochimica Acta, 85, 342\u0026ndash;356, 2012.\u003c/li\u003e\n\u003cli\u003eDiefendorf, A.F., Leslie, A.B., and Wing, S.L.: A phylogenetic analysis of conifer diterpenoids and their carbon isotopes for chemotaxonomic applications, Organic Geochemistry, 127, 50\u0026ndash;58, 2019.\u003c/li\u003e\n\u003cli\u003eEglinton, G. and Hamilton, R.J.: Leaf Epicuticular Waxes. 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Sen, and Xu, J.-X.: Late Miocene woods of Taxodiaceae from Yunnan, China, Acta Botanica Sinica 53, 1689\u0026ndash;1699, https://doi.org/10.1017/CBO9781107415324.004, 2003.\u003c/li\u003e\n\u003cli\u003eZanazzi, A., Kohn, M.J., Macfadden, B.J., and Terry, D.O.: Large temperature drop across the Eocene\u0026ndash;Oligocene transition in central North America, Nature, 445, 639\u0026ndash;642. https://doi.org/10.1038/nature05551, 2007.\u003c/li\u003e\n\u003cli\u003eZell, C., Kim, J.-H., Moreira-Turcq, P., Abril, G., Hopmans, E.C., Bonnet, M.-P., Sobrinho, R.L., and Sinninghe Damst\u0026eacute;, J.S.: Disentangling the origins of branched tetraether lipids and crenarchaeol in the lower Amazon River: Implications for GDGT-based proxies, Limnology and Oceanography, 58, 343\u0026ndash;353, 2013.\u003c/li\u003e\n\u003cli\u003eZhang, Y., Ferguson, D.K., Ablaev, A.G., Wang, Y., Li, C., Xie, L.: \u003cem\u003eEquisetum \u003c/em\u003ecf. \u003cem\u003epratense\u003c/em\u003e (Equisetaceae) from the Miocene of Yunnan in Southwestern China and Its Paleoecological Implications, International Journal of Plant Science, 168, 351\u0026ndash;359, 2007.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-3857872/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3857872/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe Eocene-Oligocene transition (EOT; ~34\u0026nbsp;million years ago) marks a critical shift from a greenhouse to an icehouse climate. Whereas temperatures derived from marine records show a consensus\u0026thinsp;~\u0026thinsp;4\u0026deg;C cooling worldwide, there is an emerging picture that the terrestrial realm experienced a heterogenous response to rapid climate change. Here, we reconstruct an 8-million-year terrestrial temperature record across the EOT at a tectonically unresolved location at the margins of the Tibetan Plateau, L\u0026uuml;he Basin (Yunnan, China). Our multi-proxy organic geochemistry approach, complemented by sedimentological interpretations, shows that L\u0026uuml;he Basin was a dynamic fluvial environment that maintained relatively stable average temperatures from ~\u0026thinsp;35\u0026thinsp;\u0026minus;\u0026thinsp;27\u0026nbsp;million years ago. These palaeotemperatures match our model-based estimates, as well as palaeobotany-based estimates at a nearby site; these stable palaeotemperature trends differ from the global marine cooling, supporting a heterogenous response of terrestrial sections. Furthermore, these palaeotemperature estimates match present-day values at this location, suggesting that this area has not undergone significant temperature change \u0026ndash; and possibly no significant uplift \u0026ndash; since the late Paleogene.\u003c/p\u003e","manuscriptTitle":"Dynamic environment but no temperature change since the late Paleogene at Lühe Basin (Yunnan, China)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-29 18:30:35","doi":"10.21203/rs.3.rs-3857872/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bc140c2e-24bb-431a-90a7-afd53444e63f","owner":[],"postedDate":"January 29th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-10-23T15:31:28+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-29 18:30:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3857872","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3857872","identity":"rs-3857872","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}