Leaf wax isotopes reveal enhanced humidity and earlier growing season during Dansgaard-Oeschger warming events in Europe

Leaf wax isotopes reveal enhanced humidity and earlier growing season during Dansgaard-Oeschger warming events in Europe | 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 Leaf wax isotopes reveal enhanced humidity and earlier growing season during Dansgaard-Oeschger warming events in Europe Paul Zander, Frank Sirocko, Xiaojing Du, Chijun Sun, Florian Rubach, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8339448/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract The study of past abrupt warming events provides knowledge of climate dynamics that are critical for future projections. During the last glacial period, Dansgaard-Oeschger (D/O) cycles caused rapid temperature increases across the North Atlantic at rates comparable to contemporary climate change. However, the hydroclimate response in Europe during these events remains poorly constrained due to scarce continuous paleohydrological records. Here, we present a continuous record of leaf wax hydrogen isotopes (δDwax) from a 60,000-year lake sediment record in Germany. δD wax is depleted during warm interstadials, contrasting with model simulations and published precipitation isotope proxies from central Europe. Using proxy system models combined with an isotope-enabled transient simulation (iTRACE), we demonstrate that this discrepancy arises from shifts in growing season timing and relative humidity, which modify the δD wax signal. Compared to cold stadials, warmer interstadials featured earlier growing season onset and increased relative humidity. These findings align with projections of intensified precipitation in this region under warming due to enhanced atmospheric moisture. Our results highlight how seasonality and humidity obscure precipitation δD signals in plant wax isotopes, demonstrating that incorporating these factors into proxy system models improves model-data comparisons and enables more robust paleoclimate reconstructions. Earth and environmental sciences/Climate sciences/Palaeoclimate Earth and environmental sciences/Climate sciences/Biogeochemistry Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Dansgaard-Oeschger cycles (D/O cycles) of the last glacial period featured possibly the most rapid rates of natural warming recorded in Earth’s history 1 . According to Greeland ice core records, temperatures increased by 5–15°C within several decades to 100 years 2 . The D/O cycles repeated on a millennial timescale in a seesaw pattern, with gradual cooling into stadial periods, and abrupt warming at the onset of warm interstadials 3 . The impacts of these events were greatest over the North Atlantic region but also extended across the globe 4 , 5 . The onset of interstadials can be considered imperfect analogs for modern and future warming and allow us to gain insights into the response of earth system components to rapid warming. For example, projections of precipitation and humidity in central Europe in future warming scenarios have substantial uncertainty. Models project an increase in winter and spring precipitation and drying trends during summer and fall in central Europe under the SSP3-7.0 scenario 6 . At the same time, extreme intensity precipitation events are projected to increase in all seasons, and summer droughts are likely to increase. Understanding the hydroclimate response during past periods of rapid warming could yield important insights that are relevant for future projections. The impact of D/O cycles in Europe is poorly constrained due to a scarcity of high-resolution, well-dated and continuous proxy records with a clear hydroclimate signal. Speleothems often preserve excellent paleoclimate records of hydroclimate; however, during the last glacial period, it was too cold for continuous speleothem in central and northern Europe 7 . Lake sediments can provide valuable continuous proxy records, however interpretation of hydrologic changes is often challenging. Pollen assemblages preserved in sediment cores suggest that warmer interstadial periods were likely wetter than cold stadial periods based on increased forest taxa during interstadials and a dominance of steppe taxa during stadials 8 , 9 , but uncertainties remain about the role of moisture versus changes in temperature and temperature seasonality in driving vegetation change. Moreover, pollen records with sufficient resolution to track D/O cycles are rare. Archives of precipitation isotopes are particularly valuable as paleoclimate proxies because they track a variety of atmospheric processes connected to hydroclimate and can be compared directly to isotope-enabled general circulation models 10 . However, continuous high-resolution δD or δ 18 O records from central and northern Europe spanning the last glacial period remain exceptionally rare, limiting our understanding of hydroclimate dynamics during abrupt climate changes. The hydrogen isotope ratio (δD) of terrestrial leaf waxes are strongly correlated with δD of mean annual precipitation 11 and have been widely used to reconstruct paleohydrology 12 – 14 . However, interpretation of δD wax records can be challenging due to multiple possible controls on the signal, particularly at temperate latitudes, and additional factors may modulate the δD wax signal from annual precipitation δD, such as changes in vegetation 12 , 14 , seasonality 15 , 16 , or humidity 17 , 18 . In this study, we present a new, 60,000 year record of leaf wax δD from lake sediments in western Germany. The chronology of these sediments is aligned to the Greenland NGRIP ice core record based on high-resolution organic carbon (C org ) data 19 , 20 providing a precise chronology necessary for investigating millennial-scale variability. The leaf wax samples were measured with sufficient (centennial-scale) resolution to investigate Dansgaard-Oeschger cycles during the past 60,000 years and assess the hydroclimate changes that occurred at these transitions. We assess the major controls on leaf wax δD during interstadial warming by comparing our proxy data with results of a transient isotope-enabled climate simulation, iTRACE 21 . Finally, we assess the evidence for hydroclimate changes during D/O events. Study site The Eifel Volcanic Field of western Germany features over 60 Pleistocene age maar crater basins. The region is characterized by a temperate oceanic climate (Cfb, in Köppen classification) with 800 mm of annual precipitation (Fig. 1 ). Precipitation is sourced dominantly from the North Atlantic, carried by westerly winds and falls evenly throughout the year. Vegetation outside of agricultural areas is primarily deciduous broad-leaved forests or mixed coniferous broadleaved forests. The Eifel Laminated Sediment Archive (ELSA) project has drilled and dated the sediments of numerous maar basins 8 , 19 , 22 . We analyzed sediment cores from three sites with stratigraphically correlated sediment records forming a continuous 60,000 year stack (Fig. 1 ): Auel (dry) Maar (cores AU3, AU4), Holzmaar (HM3, HM4), and Schalkenmehrener Maar (SMF1, SMF2). The chronology 19 is based on a combination of pollen stratigraphy, tephra, varve counts, radiocarbon ages, and tuning of high-resolution C org data with the NGRIP δ 18 O record 3 . Previous research at this site has documented significant environmental changes in response to D/O events with interstadials featuring higher lake productivity 19 , 20 , greater forest cover 8 , 9 , and modestly warmer summer temperatures 23 , while stadials were characterized by a steppe environment 8 with megafauna 9 , and long, harsh winters 23 , 24 . Results and discussion Sources of n-alkanes Significant changes in the distribution of n -alkanes occur throughout the record, correlated with changes in proxies for aquatic productivity. During intervals of high C org and chloropigment concentrations, short chain n -alkanes are more abundant, and the P aq (proportion aquatic 29 ) increases (Fig. 2 ). During the last glacial maximum, longer chain n -alkanes are relatively more abundant and the average chain length index (ACL) peaks, likely from dominantly grass vegetation at this time 9 . During early MIS-3 and the Holocene, the ACL is lower, indicating more tree-derived n -alkanes or more aquatic macrophytes 30 (Fig. 2 ). δ¹³C is positively correlated with the P aq for C23-C29 (Fig. S1 ); however, C31-δ¹³C is remarkably stable and does not covary with P aq . Because δ¹³C is much higher in submerged aquatic plants than terrestrial plants 30 – 32 , this relationship suggests that C29 and shorter chains have mixed terrestrial and aquatic sources with varying proportions over time, whereas the source of the C31 n-alkanes remained terrestrial throughout the record. Therefore, we focus our interpretation of δD on the C31 n -alkane and use δD wax to mean δD C31 in reference to our dataset. C31 is produced by most terrestrial vegetation, though it is more dominant in grasses than trees 33 . δD C31 is significantly correlated with δD of all other n -alkanes C23-C33 (Fig. S2, Fig. S3), confirming it is representative of the leaf wax δD signal at this site. δD variability 0–60 kyr b2k The multi-millennial-scale pattern shows higher δD wax during early MIS-3, a period when temperatures were relatively warm and the region was forested (Fig. 3 ). During Heinrich stadial 5 (H5), δD wax decreases by about 20–25‰ and remains generally low through the end of MIS-2, including the last glacial maximum (LGM). The variability of δD wax is lower during the glacial period, in particular from 40 − 15 kyr b2k (thousand years before 2000 CE). The deglacial period (15-11.7 kyr b2k) features increased variability, and a major rise into the onset of the Holocene. δD wax is generally high during the Holocene, with greater variability and some strong negative excursions during the late Holocene. The millennial-scale warming events recorded as Greenland Interstadials (GIs) show modest negative δD wax excursions in most instances. Figure 4 Fig. 4 : Impact of interstadials on n -alkane data. A) n -alkane δD from C23, C27, C29 and C31 chains over the period 35 − 27 kyr b2k. B) n -alkane δ¹³C. C) Proportion aquatic is (C23 + C25)/ (C23 + C25 + C29 + C31) 29 . D) NGRIP ice core δ 18 O 3 . E) Offset in n -alkane δD across stadial-interstadial transitions calculated as the mean δD of the interstadial period minus the mean of the 500 years before the onset of the interstadial.shows the mean value of δD wax during the GIs minus the mean of the 500 years preceding the event. Only stadial-interstadial transitions with at least two data points in both the interstadial and the preceding 500 years are plotted (except GI-1, where 2 samples from the previous 513 years were used as the baseline). GI-2, -11, and − 14 were excluded due to not having two samples in both the interstadial and preceding 500 year period. In 11 of the 12 rapid warming transitions that meet that criteria, δD wax decreases. GI-12 is the only interstadial that shows an increase in δD, which could be explained by it occurring immediately after (H5), which apparently caused a large depletion of precipitation isotopes. GI-14 also appears to have a positive δD anomaly, though only one data point was measured in the 1000 years preceding the event, so no strong statements can be made about the rapid warming transition. The negative excursions recorded in most GIs are even larger in C27 and C29 than C31, but this may be a result of increasing aquatic n -alkane sources; aquatic n -alkanes generally have more depleted δD than terrestrial in hydrologically open lakes 44 . Additionally, we attempted to isolate the hydroclimate signal in δD by accounting for effects related to global ice volume 45 , vegetation changes (and expected fractionation effects on δD 46 ), and temperature effects (see Fig. S4 for details). Trees generally have a smaller apparent fractionation than C3 grasses 12 , 46 , meaning more positive δD wax for the same moisture source, and there is a positive correlation between air temperature and δD precip in this region 28 , 47 . Because interstadials feature warmer temperatures and more trees, the magnitude of the negative excursion becomes larger in most interstadials after accounting for these effects. Interestingly, the response of δD wax to Heinrich stadials also tends to be negative (Fig. S5), with the Younger Dryas and four of five Heinrich stadials featuring depleted δD wax relative to the preceding 500 years. The effect is largest during H5 and becomes rather small during H4 to H1 with less than 5‰ change during each of those events. This suggests that the sensitivity of δD wax to major climate shifts or changes in North Atlantic source δD may be dependent on the climate state and/or ecosystem properties, with less sensitivity during glacial times when the ecosystem was mainly steppe vegetation 8 , 9 . Assessing the controls on δD through model-data comparisons We use the iTRACE transient isotope-enabled simulation 21 to investigate the H1 to GI-1 transition as a case study of a rapid warming event and its impact on δD precip and δD wax . iTRACE was run in CESM v1.3 and covers the last deglacial period from 20 kyr b2k to 11 kyr b2k. The simulation includes water isotope physical processes in the atmosphere, ocean and land surface, and simulates the deglaciation using a combination of forcings that include ice sheets and ocean bathymetry, solar insolation, greenhouse gases, and meltwater fluxes 21 . iTRACE ranks among the best-performing models for simulating millennial-scale hydroclimate responses 48 , and its simulated water isotopic variability agrees well with proxy records while revealing the physical processes driving these changes 21 . The iTRACE simulations show a strong signal of depleted δD precip and decreased precipitation over the North Atlantic and central Europe during H1 and opposite patterns during GI-1 (Fig. 5 ). The isotopic pattern is driven to a large extent by input of depleted meltwater to the North Atlantic during H1 49 . Because the North Atlantic is the dominant moisture source for western Europe 50 , changes in the composition of seawater in this region exert a strong control on δD precip . During GI-1, meltwater input ceased, and northward transport of warm, enriched water increased due to strengthening Atlantic Meridional Overturning Circulation (AMOC) 51 . These two factors (meltwater cessation and AMOC increase), combined with increased Northern Hemisphere temperatures, led to enriched δD precip in Europe during GI-1. The model results also show large changes in precipitation rate and humidity during the H1 to GI-1 transition with increased precipitation and more humid conditions in summer in the warm interstadial (Figs. 45 & 6 ). The comparison of modeled δD precip and our δD wax record shows a discrepancy in the response to the H1 to GI-1 transition (Fig. 6 ). Though δD wax varies during GI-1, including two very positive values during the first half of GI-1, the mean value is only 1‰ greater than during H1 (18.0-15.6 kyr BP). In contrast, iTRACE shows a 12‰ increase (Fig. 6 J). Other published archives of precipitation isotopes from this region such as ostracods in Ammersee, Germany 53 and Alpine speleothems 39 , 41 show a large 2–3‰ increase in δ 18 O at the onset of GI-1 (Fig. 3 ), which is in closer agreement with the model output (equivalent to 16–24‰ δD). Interestingly, δD wax from Bergsee, Germany shows a more muted response to the interstadial warming 54 , similar to the ELSA δD wax record. These results suggest that δD wax records from central Europe might be influenced to a large extent by factors other than δD of mean annual precipitation over the deglacial period. Given that both iTRACE model results and published central Europe δ 18 O records show a shift toward enriched precipitation isotopes during interstadials, the lack of a change, or even negative anomalies (Fig. 4 ), in δD wax suggest that processes that occur between precipitation and n -alkane biosynthesis are counteracting the increase in δD precip . We considered two plausible mechanisms that could drive negative δD wax anomalies in response to rapid warming. One is changes in the timing of the growing season. In many plant species, n -alkanes are synthesized during the early growing season, corresponding to the time of leaf flush, and record isotopic values of moisture during this time 15 , 16 , 56 , 57 . Warm interstadials feature an earlier onset of the growing season, which would lead to a more depleted plant moisture source due to the lower δD of winter and early spring precipitation compared to that of summer, as indicated by the seasonal δD precip cycle in both modern observations (Fig. 1 C) and the iTRACE simulation (Fig. 5 F). The second mechanism we considered is the effect of relative humidity (RH) on evaporative enrichment, which can occur within soils, or more importantly, within leaves. A compilation of leaf water and xylem δD measurements shows a significant correlation between RH and the offset between leaf water δD and xylem δD with a decrease of 0.7‰ in leaf water for a 1% increase RH 17 . We hypothesize that interstadials were more humid than the rest of the glacial period, which would decrease evaporative enrichment of leaf water leading to more depleted δD wax . Seeking to better determine the potential magnitude of these effects and whether accounting for them can reconcile the model-proxy mismatch, we tested three proxy system models (PSMs). PSM-1 forward models δD wax based on annual precipitation δD, PSM-2 uses early growing season (EGS) precipitation δD, and PSM-3 models δD wax using EGS precipitation δD and includes the effect of RH changes (see methods for details). The results of the three PSM scenarios (Fig. 6 ) show that considering changes in both humidity and growing season timing during the last deglaciation (PSM-3) provides the best match with the proxy data in terms of relative changes in δD wax response to H1 and GI-1 (Fig. 6 I and 6 J). The pronounced decrease in δD precip during H1 and the large increase during GI-1 are both substantially dampened when modeled as in PSM-3. Our results suggest that the markedly depleted δD precip in western Germany during H1, driven by meltwater input, is largely counteracted in the δD wax record by shifts in the seasonal timing of n -alkanes synthesis and changes in evaporative enrichment. Implications for δD proxy interpretation Due to strong correlations with δD precip 11 , 12 , 58 , δD wax is often interpreted as a sensitive recorder of δD precip . However, in certain regions and time periods factors that modify the δD precip signal become dominant, such as humidity (via evaporative enrichment of leaf water) 17 , 18 , 59 , seasonality 15 , 16 , 54 , 56 , 57 , and vegetation changes 12 . Differences of up to 20‰ are found between PSM-1 and PSM-3, demonstrating the important of considering changing seasonality and RH. Notably, the difference varies substantially over the deglacial period, meaning these non-precipitation effects are not constant in time or space. We observe a greater deviation between PSM-1 (δD wax ) and PSM-3 (δD wax_EGS_RH ) during cold and dry conditions, such as H1 and LGM. We did not incorporate vegetation effects on apparent fractionation in our PSMs because pollen is not well preserved during the LGM in the ELSA cores 8 making it difficult to quantitatively estimate vegetation effects over the time period considered for PSMs. Additionally, the RH effect contributes to different ε observed between different plant types making it difficult to disentangle the RH and vegetation effects 12 , 59 . However, changing vegetation has the potential to have a large effect on δD wax . In modern studies, C3 grasses have an apparent fractionation ε = -149‰, whereas for trees it is -121‰ 46 . Pollen in the ELSA cores, and other central European sites, show generally more forested conditions during interstadials, with a steppe environment common during stadials 8 , 9 , 60 , 61 . This shift toward increased trees during interstadials would be expected to increase δD wax due to biosynthetic fractionation effects, which is the opposite of the pattern we observe of depleted δD wax during interstadials (Fig. 4 ). C31 is dominantly sourced from grasses 33 , which may limit its sensitivity to vegetation changes compared to shorter chain lengths. RH and growing season effects during interstadials must have been large enough to overcome the enrichment expected due to afforestation to produce the negative anomalies observed in our record. Although we did not incorporate vegetation effects into the PSMs, an attempt to account for vegetation effects using the method of Konecky et al 46 . is shown in Fig. S4. Evaporative enrichment also impacts δD of leaf water. According to the Craig-Gordon model 17 , 62 – 64 ; the magnitude of evaporative enrichment depends on relative humidity, the isotopic composition of atmospheric vapor, and temperature. Although this effect is well-known, it is challenging to account for accurately in paleohydrological interpretations of δD wax and is often ignored. Some studies have used a dual-biomarker approach comparing aquatic and terrestrial δD biomarker measurements to constrain the effect of evaporative enrichment on terrestrial δD wax 55 , 65 . However, this approach assumes that lake water is not affected by evaporative enrichment and that the biological sources of δD aq and δD terr are consistent. Based on large changes observed in lake productivity proxies, δ¹³C, and Paq (Fig. 2 ), the latter assumption is likely invalid for our dataset, and thus we do not lean on this approach for our interpretation; however, we do present terr-aq δD in Fig. S6 to support our interpretation. This study builds on the existing WaxPSM 46 by incorporating relative humidity and seasonality effects to gain an understanding of the potential magnitude of these factors and how they varied over time at our study site. Our approach is guided by research on modern systems that shows that n -alkane δD records an early growing season signal 16 , 56 , 57 and that leaf water δD is enriched due to evaporative effects 17 , 18 . Continued research on modern and ancient systems may be able to refine this approach and further constrain how these factors contribute to apparent fractionation across time and space. The impact of a moving seasonal window is widespread in biological proxies 66 but has largely been overlooked in leaf wax palaeohydrological reconstructions, with some exceptions 15 , 54 , 67 . Our results show this effect can be very important in regions with a strong seasonal contrast in precipitation δD and should be considered explicitly in interpretation of δD wax . Paleoclimatic interpretation of ELSA δD wax record 0–60 ka Multiple factors contribute to the signal of our δD wax record over the past 60,000 years, with variable importance on different timescales. The multi-millennial-scale pattern shows generally enriched δD wax in early MIS-3 and the Holocene, and depleted values during glacial conditions from the onset of H5 at 50 kyr b2k until the deglaciation begins around 15 kyr b2k. During this interval δD wax is on average 18‰ lower than the Holocene. Precipitation isotope data from Koblenz and Trier indicate that δD precip increases by 1.8‰ for every °C of temperature change 28 (Fig. S7). Temperatures in the LGM are estimated to be 9–10°C lower than modern 68 , 69 , which is sufficient to explain the 18‰ depletion. However, growing season temperatures reconstructed from GDGTs suggest only ~ 3°C of cooling 23 , which would imply only a ~ 5‰ change in δD. Vegetation changes from forest to steppe taxa could play a large role in decreasing δD wax during the glacial period due to the more negative fractionation of grasses relative to trees. Based on pollen data 8 and estimates of ε of plant functional types 46 , we estimate that vegetation changes could cause 10–20‰ of depletion during the glacial period (Fig. S4). Therefore, we consider both temperature and vegetation changes to be important factors in driving depleted values from 50–15 kyr b2k. A slow ~ 8‰ enrichment of δD wax from 43–23 ky b2k, unrelated to temperature or vegetation, likely reflects a combination of: ~3‰ seawater enrichment from ice sheet growth (Fig. S4), delayed growing season onset, and decreased relative humidity. This interpretation is consistent with enhanced continentality over central Europe during the LGM, driven by sea level fall and expanded sea ice, which caused colder springs and increased aridity 70 – 73 . Millennial-scale events of the last glacial period feature unique dynamics affecting δD wax . Interestingly, both Heinrich stadials and GIs show a tendency towards negative anomalies in δD wax relative to the 500 years preceding each event (Fig. 4 & S6). Negative anomalies during Heinrich stadials can be explained by a combination of meltwater-driven depletion of North Atlantic seawater, colder temperatures causing depleted δD precip, and shifts toward steppe vegetation. The meltwater effect can cause approximately a 8–13‰ increase in δD precip in this region during a Heinrich event 49 , and is likely the largest factor in driving the signal during most Heinrich stadials. However, the response of δD wax appears to depend on the pre-existing vegetation state. During times when the landscape was forested, Heinrich stadials caused a shift towards steppe vegetation which amplifies that response of δD wax . For example, the largest depletion is observed during H5. This interval is characterized by the largest decline in tree pollen in the record (Fig. 3 ), and this vegetation shift likely led to a major decrease in δD wax due to the higher biosynthetic fractionation of grasses compared to trees 46 . Additionally, this period featured substantial cooling compared to preceding centuries 23 . In contrast, H2 is the only Heinrich stadial in which δD wax increases. Steppe conditions dominated before and during the event; therefore, biosynthetic fractionation did not vary significantly. The enrichment in δD wax during H2, despite meltwater input to the North Atlantic, could potentially be explained by a very delayed growing season onset and intense aridity. Peak loess sedimentation rates in the Rhine Valley corroborate arid conditions at this time 73 . The Younger Dryas (YD) has been intensively studied using compound specific isotopes at nearby Meerfelder Maar 55 , 65 and Gemündener Maar 74 . At Meerfelder Maar, δD wax decreased during the YD by 2.5–9‰ (Fig. S5). This is less than the δD precip change in iTRACE (decrease of 16‰). Rach et al. 65 and Hepp et al. 74 , use different dual-biomarker methods to estimate that RH decreased by roughly 10% during the Younger Dryas compared to GI-1. Based on the relationship between RH and leaf water 17 , a 10% drop in RH could lead to a 7‰ increase in δD wax , which would account for most of the gap between modeled δD precip and measured δD wax , with vegetation changes or growing season timing also weakening the depletion in δD wax relative to δD precip . The key aim of this study was to test whether past rapid warming events enhanced humidity in central Europe. Based on our model-proxy data comparisons, it is difficult to explain the consistent negative δD wax excursions during interstadials (Fig. 4 ) without invoking an increase in relative humidity. Table 1 summarizes the expected influence of the main factors affecting δD wax over stadial-interstadial transitions. Multiple lines of evidence suggest that precipitation in central Europe became relatively enriched during interstadial periods. Our case study of GI-1 using iTRACE demonstrates this enrichment relates largely to changes in North Atlantic seawater isotopic composition. This interstadial enrichment is found across multiple study sites, models and time periods. Alpine speleothem δ 18 O records track Greenland ice core δ 18 O stadial-interstadial variability during MIS-3 75 (Fig. 3 D) and MIS-5 76 , and similar features are observed in older periods 77 , 78 . A global compilation of speleothem records of D/O cycles showed a consistent enrichment pattern of precipitation isotopes in Europe and linked those changes to the North Atlantic 4 . In addition to iTRACE (iCESM), the COSMOS-wiso model also shows enrichment of precipitation during stadial-interstadial transitions 77 . The COSMOS-wiso simulation does not include prescribed meltwater forcing, but nonetheless the North Atlantic becomes relatively enriched during interstadial-like periods of AMOC intensification 77 , 79 . In summary, multiple lines of evidence confirm that precipitation isotopes become enriched in interstadials, which contrasts with our δD wax measurements, necessitating an interpretation of δD wax that includes factors beyond δD precip . PSM scenarios using iTRACE output provide estimates of the shift in δD wax that could be attributed to changing humidity or shifts in the timing of the growing season (Fig. 6 ). Given that precipitation was isotopically enriched, and that vegetation changes (shift from steppe to forest) would be expected to cause more enriched δD wax , both increased humidity and an earlier growing season are required to explain consistently depleted interstadial δD wax in our record. This implies increased relative humidity during the early growing season when n-alkanes are synthesized, supported by iTRACE simulations (Fig. 6 H). On average, δD wax was 3.4‰ lower following interstadial onset, which, is equivalent to a 5% RH increase, assuming that depletion is entirely due decreased leaf water enrichment 17 . However, this estimate is highly uncertain due to aforementioned confounding factors and likely underestimates the RH change because vegetation effects and North Atlantic moisture source composition were likely causing enrichment of δD wax, obscuring the RH effect. Paleoecological evidence from central and northern Europe strongly suggests that the timing of the growing season shifted earlier at the onset of the Bølling/Allerød (equivalent to GI-1) 80–82 . Additionally, model simulations of AMOC fluctuations indicate spring temperatures in Europe are particularly responsive to AMOC strength 72 . Our results provide independent evidence for earlier growing season onset during spring and highlight that changing seasonal dynamics were an important feature of millennial-scale variability during the last glacial period. Table 1 List of factors that influence δD wax over millennial timescales. Estimates of the approximate magnitude of each mechanism on δD wax over a transition from stadial to interstadial climate and the reasoning behind this estimate are provided. Mechanism Approximate effect on δD wax during GS to GI transition Evidence (source) Controls on precipitation δD North Atlantic δD 8–13‰ iTRACE simulation, Zhu et al., 2017 49 Condensation temperature 6–8‰ Warmer temperatures lead to enriched precipitation isotopes 47 Moisture source/transport ? iTRACE simulation indicates increased northerly moisture sources (depletion). Reduced sea ice in the proximal N Atlantic could shorten moisture paths (enrichment). Precipitation δD sum 12–20‰ iTRACE simulation, regional δ 18 O proxies 39 , 41 , 53 Terrestrial ecosystem factors Growing season timing -8‰ Seasonal pattern of δD precip , Tipple et al., 2013 15 Humidity effects on leaf water evaporation -4‰ Roden and Ehleringer, 1999 62 ; Cernusak et al., 2022 17 Biosynthetic fractionation 10–30‰ More trees and less grass decreases ε-bio 12 , 46 Our results also align with other biomarker isotope studies showing drier conditions in Germany during the Younger Dryas compared to GI-1 54,65,74 , but extends the leaf wax isotope record for central Europe to 60 kyr, enabling new paleohydrological interpretations of the glacial period and repeated rapid warming events. High humidity during interstadials is consistent with pollen data indicating a shift from steppe to partially forested environments during interstadials not only at the Eifel 8 , 9 , but also in other parts of central Europe 60 , 83 , 84 , which was likely driven by increased moisture availability. Interestingly, flood deposits are more common during colder times of the glacial period in Eifel lake sediments; however, this pattern may be driven by increased soil erosivity and hydrologic changes related to decreased vegetation 85 . Northwestern Iberian speleothem and pollen records also suggest wetter interstadial conditions 86 , 87 , and model simulations consistently show more precipitation during interstadial phases in central Europe 4 , 77 , 88 (Fig. 5 ). Several mechanisms could plausibly drive increased RH during interstadials. The Clausius–Clapeyron relation predicts that a warmer atmosphere holds more moisture 89 , However, for relative humidity (RH) to increase, the actual amount of moisture in the air must rise even faster, which requires an increased supply of water vapor. Increased moisture advection from the North Atlantic into western Europe can be expected during interstadials due to a reduction of sea ice 90 , 91 and increased evaporation rates over the North Atlantic. This mechanism is supported by model simulations of AMOC shutdown which find that when AMOC slows, cooler temperatures cause less evaporation over the North Atlantic, leading to less moisture advection into western Europe 92 . Interstadials represent an increase in AMOC strength 51 , 93 , and therefore, the opposite likely occurred during GIs, i.e., increased evaporation over the North Atlantic increased the moisture supply to western Europe. Atmospheric circulation changes could plausibly affect RH. However, in the iTRACE simulation, GI-1 features anomalously northerly winds (Fig. S8), which are not associated with humid conditions in Germany. Simulations of AMOC shutdown also suggest that dynamic processes did not substantially contribute to decreased precipitation in western Europe 92 . Another possibility is that increased vegetation during interstadials increased humidity locally via increased evapotranspiration and microclimate effects (shade and reduced wind near ground level) 94 . Disentangling these mechanisms requires further research, but the strongest driver of increased humidity during interstadials is likely the increased advection of North Atlantic moisture due to reduced sea ice and warmer atmospheric conditions, particularly in spring and early summer. Our results provide new palaeohydrological evidence for an increasingly humid atmosphere in central Europe in response to rapid climate warming. Methods Analysis of n-alkanes and stable isotopes Following freeze-drying, lipids were extracted from the sediments and separated into two fractions using an Accelerated Solvent Extractor 350 (ASE) following methods of Auderset et al., 2020 95 . The first fraction was extracted with n -hexane and contains the n -alkanes. An internal standard (hexatriacontane) was added to the extracts. The extracts were passed through silica columns with n -hexane to purify the n -alkanes. Samples with high organic content were passed through columns of copper (activated with 2N HCl) to remove sulfur. Concentrations of n -alkanes were determined via gas-chromatography (GC) analysis using an Agilent 7890B GC equipped with a VF-200 column and using flame ionization detection (FID). Measurements of stable isotopes of Hydrogen (δD) or carbon (δ 13 C) were conducted using a Thermo Trace 1310 GC with TriPlus RSH autosampler coupled to MAT 253 IRMS via GC IsoLink II and Conflo IV. Based on the GC-FID concentration data, the samples were diluted in n-hexane to achieve an injection amount of approximately 225–800 ng of C29 n-alkane, while aiming to keep injection amounts of all odd-chain n -alkanes from C23 to C33 within the range of 180–1000 ng. A total of 270 sediment samples were analyzed for δD, with 230 of these samples measured twice or more. Additionally, 80 samples were measured for δ13C, with each measurement duplicated. Large-volume (20 ul) injection was achieved using a Programmable Temperature Vaporization (PTV) inlet. δD and δ¹³C measurements were calibrated relative to international standards VSMOW for δD (Vienna Standard Mean Ocean Water) or VPDB for δ 13 C (Vienna Pee Dee Belemnite) via the use of reference standards obtained from Dr. Arndt Schimmelmann (Indiana University). A correction was applied for linearity based on the peak area. Uncertainty was estimated to be 4.0‰ for δD and 0.45‰ for δ¹³C based on the pooled standard deviation of replicated measurements 96 . Additional methodological details are provided in Zander et al. 34 . iTRACE simulation iTRACE is an isotope-enabled transient simulation of the last deglaciation (20-11ka 21 ), performed with the isotope-enabled Community Earth System Model version 1.3 (iCESM 97 ). iCESM1.3 couples the Community Atmosphere Model version 5.3 (CAM5.3), the Community Land Model version 4 (CLM4), the Parallel Ocean Program version 2 (POP2), and the Los Alamos Sea Ice Model, version 4 (CICE4). The model’s precipitation and water isotope simulations have been validated against modern observations 97 – 99 and it has been widely applied in paleoclimate and data–model comparison studies, such as the Asian Monsoon region 21 , the Indo-Pacific Warm Pool 100 – 103 and the North American monsoon region 104 , 105 . The transient simulation is driven by four additive forcing factors: continental ice sheets (ICE), orbital parameters (ORB), greenhouse gases (GHG), and meltwater fluxes (MWF). Continental ice sheet configuration follows the ICE-6G reconstruction 106 and is modified every 1000 year. Greenhouse gas concentrations (CO₂, CH₄, N₂O) are prescribed from ice-core records 107 – 109 . Meltwater fluxes are consistent with sea-level reconstructions and follow the scheme implemented in TRACE-21ka 110 . Further details of the forcings are provided in He et al. 21 Proxy system model calculations Data analysis was performed in R v 4.4.1 111 . Three proxy system models were considered to make more direct comparisons of iTRACE output and measured δD wax and to test the potential impacts of relative humidity and changing growing season timing on δD wax . PSM-1 uses a constant apparent fractionation factor ε = -120‰ based on the consensus value from the literature for terrestrial n -alkanes 12 , 46 : δD wax = δD precip + ε (1) where δD precip is mean annual precipitation δD from the iTRACE simulation. PSM-2 uses precipitation from the early growing season: δD wax = δD EGS + ε (2) Where δD EGS is early growing precipitation δD. We define the early growing season as the first 3 months of the year with mean monthly temperature greater than 5°C 52 , again based on iTRACE output. PSM-3 includes a correction for the effect of relative humidity on evaporative enrichment: δD wax = δD EGS + ε + k(EGS-RH) (3) where k = -0.7 based on the observed empirical relationship between humidity and leaf water enrichment 17 . EGS-RH is the mean relative humidity anomaly in iTRACE during the early growing season. We also considered an adjustment for changing vegetation based on pollen data 8 and ε values assigned to plant functional types in Konecky et al. 46 However, the poor resolution of pollen data during the deglacial period limited comparisons with iTRACE output. Nonetheless, the effect of vegetation could cause changes to ε of up to 20‰ (Fig. S4). Declarations Competing interests The authors have no competing interests to declare. Author contributions PDZ: conceptualization, formal analysis, investigation, methodology, project administration, funding acquisition, writing original draft, and visualization. FS: conceptualization, investigation, resources, funding acquisition, project administration, and review and editing. FR: methodology, investigation, and review and editing. XD: formal analysis, investigation, and review and editing. CS: formal analysis, visualization, investigation, and review and editing. GH: resources, funding acquisition, and project administration. AMG: conceptualization, methodology, resources, funding acquisition, project administration, and review and editing. Acknowledgements This research has been supported by funding from the University of Mainz, Germany, the Max Planck Institute for Chemistry, Germany, and the Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (grant no. P500PN_206731). A portion of this work was performed under the auspices of the US Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344; this is LLNL-JRNL-2014174. We thank Klaus Schwibus and Mareike Schmitt for technical assistance. Data availability n -alkane data used in this study will be archived at PANGAEA. iTRACE simulation results are available via the National Center for Atmospheric Research at https://doi.org/10.26024/b290-an76 . A data file of n -alkane measurements has been included for review purposes. References Dansgaard, W. et al. 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Supplementary Files alkanedataset20251205.csv Dataset 1 ELSAdDsupplementv3.docx Supplement to: Leaf wax isotopes reveal enhanced humidity and earlier growing season during Dansgaard-Oeschger warming events in Europe Cite Share Download PDF Status: Under Review 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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Zander","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4UlEQVRIiWNgGAWjYHACxgNg6gAD4wMGBgkgK4GwHpgWZgOStbBJQPgEtJiznzE4wPDHLo/veO+zqps7LOQZ2JMP4NVi2ZMD1MKTXCx55rjZ7dwzEoYNPM/wW2NwAKRFgjlxw400ttu5bRIJDBI5Bvi1nH8D1GJQn7jh/jO2YoiW/A/4tdwA2ZJwGGgLGxsz1Ba8OhgsZzwrOJBw4HjizDNpzNIgv7TxPMPvMHP+5I0PPvypTuw7fozxc+6OOnl+9uQH+B0GIhJgPMYGBgY2/M6CaoEDkJZRMApGwSgYBegAAJvGSaM3dmqjAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0001-7340-4768","institution":"Max Planck Institute for Chemistry","correspondingAuthor":true,"prefix":"","firstName":"Paul","middleName":"","lastName":"Zander","suffix":""},{"id":561128964,"identity":"ad4fe3a1-b858-4791-b3d9-870cd1e96e15","order_by":1,"name":"Frank Sirocko","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Frank","middleName":"","lastName":"Sirocko","suffix":""},{"id":561128965,"identity":"d516edda-bc81-4028-a3d5-0eb24ddcde42","order_by":2,"name":"Xiaojing Du","email":"","orcid":"https://orcid.org/0000-0001-9709-0732","institution":"George Mason University","correspondingAuthor":false,"prefix":"","firstName":"Xiaojing","middleName":"","lastName":"Du","suffix":""},{"id":561128966,"identity":"50220dd5-ec7e-4314-a2af-b984033fe630","order_by":3,"name":"Chijun Sun","email":"","orcid":"https://orcid.org/0000-0002-3668-346X","institution":"University of California Davis","correspondingAuthor":false,"prefix":"","firstName":"Chijun","middleName":"","lastName":"Sun","suffix":""},{"id":561128967,"identity":"721ff6b7-ba2c-47ae-ba86-91b65eb5855f","order_by":4,"name":"Florian Rubach","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Florian","middleName":"","lastName":"Rubach","suffix":""},{"id":561128968,"identity":"ab68ce80-be8d-4e79-a66e-fb7d790fceae","order_by":5,"name":"Gerald Haug","email":"","orcid":"","institution":"Max Planck Institute for Chemistry","correspondingAuthor":false,"prefix":"","firstName":"Gerald","middleName":"","lastName":"Haug","suffix":""},{"id":561128969,"identity":"32a7d6e3-1875-4f2d-8ce2-214a014ea004","order_by":6,"name":"Alfredo Martinez-Garcia","email":"","orcid":"https://orcid.org/0000-0002-7206-5079","institution":"Max Planck Institute for Chemistry","correspondingAuthor":false,"prefix":"","firstName":"Alfredo","middleName":"","lastName":"Martinez-Garcia","suffix":""}],"badges":[],"createdAt":"2025-12-11 18:35:48","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8339448/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8339448/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":98624503,"identity":"f7cc0dbe-576b-46e4-a30e-3820cadebd7e","added_by":"auto","created_at":"2025-12-19 17:08:28","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":2811481,"visible":true,"origin":"","legend":"","description":"","filename":"ELSAdDmanuscriptv3.docx","url":"https://assets-eu.researchsquare.com/files/rs-8339448/v1/a66199f78e8bb47f2ac13535.docx"},{"id":98626459,"identity":"3af28821-bfb9-4b5d-89e5-b1f8376b86bf","added_by":"auto","created_at":"2025-12-19 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17:08:26","extension":"png","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":91687,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8339448/v1/0be6ab8cb67caa4751993340.png"},{"id":98624910,"identity":"3fd71ac8-73e5-4f86-b169-5d5d5860a6b1","added_by":"auto","created_at":"2025-12-19 17:08:48","extension":"xml","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":203756,"visible":true,"origin":"","legend":"","description":"","filename":"COMMSENV2559800structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8339448/v1/035e18819ff3e445979cc9a0.xml"},{"id":98625163,"identity":"fcaf8ebd-e098-4556-a1b8-9084ead545eb","added_by":"auto","created_at":"2025-12-19 17:08:58","extension":"html","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":219239,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8339448/v1/35d10530e7b13d09a40a5316.html"},{"id":98625134,"identity":"e3447819-d201-49b4-9f23-7d2935df77ba","added_by":"auto","created_at":"2025-12-19 17:08:57","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":631311,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStudy site map and climate data\u003c/strong\u003e. A) Annual precipitation δD map with ELSA study area labeled (data from Online Isotopes in Precipitation Calculator\u003csup\u003e25,26\u003c/sup\u003e. B) Topographic map (data from \u003cem\u003ernaturalearthhires\u003c/em\u003e\u003csup\u003e27\u003c/sup\u003e\u003cem\u003e)\u003c/em\u003e of study site (AU is Auel Maar, SM is Schalkenmehrener Maar, HM is Holzmaar). C) Monthly precipitation amount (bars) and precipitation δD (black line) measured at Trier over the period 1978-2013 CE\u003csup\u003e28\u003c/sup\u003e. D) Monthly temperature at Trier (60 km south of Auel Maar), 1978-2013 CE\u003csup\u003e28\u003c/sup\u003e.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8339448/v1/38800fff160a4dec54feaf1a.png"},{"id":98626349,"identity":"183f0a8f-729b-49de-aa46-5a30a49b6baf","added_by":"auto","created_at":"2025-12-19 17:09:41","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":446328,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSedimentary organic carbon, and \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e-alkane indices and δ\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e13\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003eC\u003c/strong\u003e. A) C\u003csub\u003eorg\u003c/sub\u003e inferred from sediment reflectance\u003csup\u003e19\u003c/sup\u003e. \u0026nbsp;Brown labels on top of panel indicate the Greenland Interstadials (GI), blue labels indicate Heinrich stadials. YD = Younger Dryas. B) Summed concentration of odd-chain \u003cem\u003en\u003c/em\u003e-alkanes C23-C33. C) Average Chain Length, C25-C33. D) Proportion aquatic (P\u003csub\u003eaq\u003c/sub\u003e) (C23+C25)/ (C23+C25+C29+C31)\u003csup\u003e29\u003c/sup\u003e. E) δ\u003csup\u003e13\u003c/sup\u003eC of \u003cem\u003en\u003c/em\u003e-alkanes. Error bars represent the pooled standard deviation of replicate measurements\u003csup\u003e34\u003c/sup\u003e.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8339448/v1/677f9500138e965fa3e16dd3.png"},{"id":98625367,"identity":"8c5b951f-807f-46fb-a0fd-eccb360a27c1","added_by":"auto","created_at":"2025-12-19 17:09:04","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":311682,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eProxy data 0-60 ka\u003c/strong\u003e. A) \u003cem\u003en\u003c/em\u003e-alkane C31 δD from ELSA cores (this study). Gray lines indicate standard deviation of replicate measurements (4 ‰)\u003csup\u003e34\u003c/sup\u003e. B) Temperature of months above freezing reconstructed from branched glycerol dialkyl glycerol tetraethers from ELSA cores\u003csup\u003e23\u003c/sup\u003e. C) Pollen from ELSA cores\u003csup\u003e8\u003c/sup\u003e. D) Speleothem δ\u003csup\u003e18\u003c/sup\u003eO from central Europe. Data obtained from SISAL database\u003csup\u003e35\u003c/sup\u003e. BC = Bunker cave\u003csup\u003e36,37\u003c/sup\u003e, HM = Hölloch\u0026nbsp;im\u0026nbsp;Mahdtal\u003csup\u003e38,39\u003c/sup\u003e, KC = Kleegruben Cave\u003csup\u003e40\u003c/sup\u003e, SH = Sieben Hengste Cave\u003csup\u003e41\u003c/sup\u003e, SC = Spannagel Cave\u003csup\u003e42\u003c/sup\u003e. E) NGRIP ice core δ\u003csup\u003e18\u003c/sup\u003eO \u003csup\u003e3\u003c/sup\u003e. F) Incoming solar insolation for 50° N during summer (black line) and winter (dashed blue line)\u003csup\u003e43\u003c/sup\u003e. Vertical bars are described in Fig. 2 caption.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8339448/v1/facd6dcbd7904154f9acf293.png"},{"id":98625307,"identity":"938097d8-7124-4225-b508-c0de3456f2a5","added_by":"auto","created_at":"2025-12-19 17:09:02","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":183146,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eImpact of interstadials on \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e-alkane data.\u003c/strong\u003e A) \u003cem\u003en\u003c/em\u003e-alkane δD from C23, C27, C29 and C31 chains over the period 35-27 kyr b2k. B) \u003cem\u003en\u003c/em\u003e-alkane δ¹³C. C) Proportion aquatic is (C23+C25)/ (C23+C25+C29+C31)\u003csup\u003e29\u003c/sup\u003e. D) NGRIP ice core δ\u003csup\u003e18\u003c/sup\u003eO\u003csup\u003e3\u003c/sup\u003e. E) Offset in \u003cem\u003en\u003c/em\u003e-alkane δD across stadial-interstadial transitions calculated as the mean δD of the interstadial period minus the mean of the 500 years before the onset of the interstadial. For details on ice, temperature and vegetation effects, see Fig. S4.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8339448/v1/d9a465baac2e4c405c12e756.png"},{"id":98528000,"identity":"4c5462ee-b2f8-4de7-ac1b-6147d589648b","added_by":"auto","created_at":"2025-12-18 14:55:23","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":682756,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eiTRACE model results\u003c/strong\u003e. A) Precipitation rate anomalies for GI-1 (data from 14-13 kyr BP) versus H1; (data from 17-15 ka BP). B) Same as A but for H1 vs LGM (19-20 kyr BP). C) and D) Precipitation δD anomalies for same intervals as A and B. E) Monthly temperatures during key time intervals of the deglaciation (Hol = early Holocene). Lines indicate the mean value for that time interval, and shaded areas represent one standard deviation. The dashed line at 5 °C represents temperature threshold for onset of growing season\u003csup\u003e52\u003c/sup\u003e. Horizontal bars labeled EGS (H1) and EGS (GI-1) indicate the average early growing season for these two time periods. \u0026nbsp;F) Same as E), but monthly precipitation δD.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8339448/v1/16cf7ba8397db18a0c5e4396.png"},{"id":98625324,"identity":"aa13c4d2-58af-4f73-9be6-048d5b47a06f","added_by":"auto","created_at":"2025-12-19 17:09:02","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":304055,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eProxy data and iTRACE model output during the deglacial period. Left panel shows key proxy\u003c/strong\u003e water isotope records. Top right shows model output compared to δDwax measurements. Bottom right shows time-slice comparison of proxy data and three proxy system model scenarios.\u0026nbsp; A) NGRIP δ\u003csup\u003e18\u003c/sup\u003eO \u003csup\u003e3\u003c/sup\u003e. B) Ammersee, Germany Ostracod δ\u003csup\u003e18\u003c/sup\u003eO\u003csup\u003e53\u003c/sup\u003e. C) Speleothem δ\u003csup\u003e18\u003c/sup\u003eO from Hölloch im Mahdtal, Germany\u003csup\u003e39\u003c/sup\u003e and Sieben Hengste Cave, Switzerland\u003csup\u003e41\u003c/sup\u003e. D) Bergsee, Germany C31 \u003cem\u003en\u003c/em\u003e-alkane δD \u003csup\u003e55\u003c/sup\u003e. E) Meerfelder Maar, Germany C29 \u003cem\u003en\u003c/em\u003e-alkane δD\u003csup\u003e55\u003c/sup\u003e. F) ELSA n-alkane C31 δD (this study). G) Precipitation rate in iTRACE (annual). H) Relative humidity in iTRACE (black line is annual, blue line is the from the early growing season). I) Results of wax δD forward model scenarios compared to measured wax δD. J) Time slice comparison of dD\u003csub\u003ewax\u003c/sub\u003e and PSM scenarios comparing GI-1 (14.7-12.9 kyr b2k), H1 (18.0-15.6 kyr b2k), and LGM (20-18 kyr b2k).\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8339448/v1/89a0e36edfdb0e7218f2bf49.png"},{"id":98632082,"identity":"c49f87c1-a0af-4bf2-aa55-343c683eda09","added_by":"auto","created_at":"2025-12-19 17:21:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3409327,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8339448/v1/fad549bd-faef-43ab-b2ae-6deafb544f9e.pdf"},{"id":98527988,"identity":"96f6d421-1633-47ef-99e7-c10ba26524d7","added_by":"auto","created_at":"2025-12-18 14:55:23","extension":"csv","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":58769,"visible":true,"origin":"","legend":"Dataset 1","description":"","filename":"alkanedataset20251205.csv","url":"https://assets-eu.researchsquare.com/files/rs-8339448/v1/bb3ed7bb1ecc325d64efc424.csv"},{"id":98626582,"identity":"66da6e50-8161-41bb-9282-741a948c9ff9","added_by":"auto","created_at":"2025-12-19 17:09:49","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1746118,"visible":true,"origin":"","legend":"Supplement to: Leaf wax isotopes reveal enhanced humidity and earlier growing season during Dansgaard-Oeschger warming events in Europe","description":"","filename":"ELSAdDsupplementv3.docx","url":"https://assets-eu.researchsquare.com/files/rs-8339448/v1/66e5b3dcfcaadb623085a7b2.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Leaf wax isotopes reveal enhanced humidity and earlier growing season during Dansgaard-Oeschger warming events in Europe","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDansgaard-Oeschger cycles (D/O cycles) of the last glacial period featured possibly the most rapid rates of natural warming recorded in Earth\u0026rsquo;s history\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. According to Greeland ice core records, temperatures increased by 5\u0026ndash;15\u0026deg;C within several decades to 100 years\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. The D/O cycles repeated on a millennial timescale in a seesaw pattern, with gradual cooling into stadial periods, and abrupt warming at the onset of warm interstadials\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. The impacts of these events were greatest over the North Atlantic region but also extended across the globe\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. The onset of interstadials can be considered imperfect analogs for modern and future warming and allow us to gain insights into the response of earth system components to rapid warming.\u003c/p\u003e \u003cp\u003eFor example, projections of precipitation and humidity in central Europe in future warming scenarios have substantial uncertainty. Models project an increase in winter and spring precipitation and drying trends during summer and fall in central Europe under the SSP3-7.0 scenario\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. At the same time, extreme intensity precipitation events are projected to increase in all seasons, and summer droughts are likely to increase. Understanding the hydroclimate response during past periods of rapid warming could yield important insights that are relevant for future projections.\u003c/p\u003e \u003cp\u003eThe impact of D/O cycles in Europe is poorly constrained due to a scarcity of high-resolution, well-dated and continuous proxy records with a clear hydroclimate signal. Speleothems often preserve excellent paleoclimate records of hydroclimate; however, during the last glacial period, it was too cold for continuous speleothem in central and northern Europe\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Lake sediments can provide valuable continuous proxy records, however interpretation of hydrologic changes is often challenging. Pollen assemblages preserved in sediment cores suggest that warmer interstadial periods were likely wetter than cold stadial periods based on increased forest taxa during interstadials and a dominance of steppe taxa during stadials\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e, but uncertainties remain about the role of moisture versus changes in temperature and temperature seasonality in driving vegetation change. Moreover, pollen records with sufficient resolution to track D/O cycles are rare.\u003c/p\u003e \u003cp\u003eArchives of precipitation isotopes are particularly valuable as paleoclimate proxies because they track a variety of atmospheric processes connected to hydroclimate and can be compared directly to isotope-enabled general circulation models\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. However, continuous high-resolution δD or δ\u003csup\u003e18\u003c/sup\u003eO records from central and northern Europe spanning the last glacial period remain exceptionally rare, limiting our understanding of hydroclimate dynamics during abrupt climate changes. The hydrogen isotope ratio (δD) of terrestrial leaf waxes are strongly correlated with δD of mean annual precipitation\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e and have been widely used to reconstruct paleohydrology\u003csup\u003e\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. However, interpretation of δD\u003csub\u003ewax\u003c/sub\u003e records can be challenging due to multiple possible controls on the signal, particularly at temperate latitudes, and additional factors may modulate the δD\u003csub\u003ewax\u003c/sub\u003e signal from annual precipitation δD, such as changes in vegetation\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e, seasonality\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e, or humidity\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn this study, we present a new, 60,000 year record of leaf wax δD from lake sediments in western Germany. The chronology of these sediments is aligned to the Greenland NGRIP ice core record based on high-resolution organic carbon (C\u003csub\u003eorg\u003c/sub\u003e) data\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e providing a precise chronology necessary for investigating millennial-scale variability. The leaf wax samples were measured with sufficient (centennial-scale) resolution to investigate Dansgaard-Oeschger cycles during the past 60,000 years and assess the hydroclimate changes that occurred at these transitions. We assess the major controls on leaf wax δD during interstadial warming by comparing our proxy data with results of a transient isotope-enabled climate simulation, iTRACE\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Finally, we assess the evidence for hydroclimate changes during D/O events.\u003c/p\u003e\n\u003ch3\u003eStudy site\u003c/h3\u003e\n\u003cp\u003eThe Eifel Volcanic Field of western Germany features over 60 Pleistocene age maar crater basins. The region is characterized by a temperate oceanic climate (Cfb, in K\u0026ouml;ppen classification) with 800 mm of annual precipitation (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Precipitation is sourced dominantly from the North Atlantic, carried by westerly winds and falls evenly throughout the year. Vegetation outside of agricultural areas is primarily deciduous broad-leaved forests or mixed coniferous broadleaved forests. The Eifel Laminated Sediment Archive (ELSA) project has drilled and dated the sediments of numerous maar basins\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. We analyzed sediment cores from three sites with stratigraphically correlated sediment records forming a continuous 60,000 year stack (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e): Auel (dry) Maar (cores AU3, AU4), Holzmaar (HM3, HM4), and Schalkenmehrener Maar (SMF1, SMF2). The chronology\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e is based on a combination of pollen stratigraphy, tephra, varve counts, radiocarbon ages, and tuning of high-resolution C\u003csub\u003eorg\u003c/sub\u003e data with the NGRIP δ\u003csup\u003e18\u003c/sup\u003eO record\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Previous research at this site has documented significant environmental changes in response to D/O events with interstadials featuring higher lake productivity\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e, greater forest cover\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e, and modestly warmer summer temperatures\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e, while stadials were characterized by a steppe environment\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e with megafauna\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e, and long, harsh winters\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Results and discussion","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eSources of n-alkanes\u003c/h2\u003e \u003cp\u003eSignificant changes in the distribution of \u003cem\u003en\u003c/em\u003e-alkanes occur throughout the record, correlated with changes in proxies for aquatic productivity. During intervals of high C\u003csub\u003eorg\u003c/sub\u003e and chloropigment concentrations, short chain \u003cem\u003en\u003c/em\u003e-alkanes are more abundant, and the P\u003csub\u003eaq\u003c/sub\u003e (proportion aquatic\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e) increases (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). During the last glacial maximum, longer chain \u003cem\u003en\u003c/em\u003e-alkanes are relatively more abundant and the average chain length index (ACL) peaks, likely from dominantly grass vegetation at this time\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. During early MIS-3 and the Holocene, the ACL is lower, indicating more tree-derived \u003cem\u003en\u003c/em\u003e-alkanes or more aquatic macrophytes\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). δ\u0026sup1;\u0026sup3;C is positively correlated with the P\u003csub\u003eaq\u003c/sub\u003e for C23-C29 (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e); however, C31-δ\u0026sup1;\u0026sup3;C is remarkably stable and does not covary with P\u003csub\u003eaq\u003c/sub\u003e. Because δ\u0026sup1;\u0026sup3;C is much higher in submerged aquatic plants than terrestrial plants\u003csup\u003e\u003cspan additionalcitationids=\"CR31\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e, this relationship suggests that C29 and shorter chains have mixed terrestrial and aquatic sources with varying proportions over time, whereas the source of the C31 n-alkanes remained terrestrial throughout the record. Therefore, we focus our interpretation of δD on the C31 \u003cem\u003en\u003c/em\u003e-alkane and use δD\u003csub\u003ewax\u003c/sub\u003e to mean δD\u003csub\u003eC31\u003c/sub\u003e in reference to our dataset. C31 is produced by most terrestrial vegetation, though it is more dominant in grasses than trees\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. δD\u003csub\u003eC31\u003c/sub\u003e is significantly correlated with δD of all other \u003cem\u003en\u003c/em\u003e-alkanes C23-C33 (Fig. S2, Fig. S3), confirming it is representative of the leaf wax δD signal at this site.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eδD variability 0–60 kyr b2k\u003c/h3\u003e\n\u003cp\u003eThe multi-millennial-scale pattern shows higher δD\u003csub\u003ewax\u003c/sub\u003e during early MIS-3, a period when temperatures were relatively warm and the region was forested (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). During Heinrich stadial 5 (H5), δD\u003csub\u003ewax\u003c/sub\u003e decreases by about 20\u0026ndash;25\u0026permil; and remains generally low through the end of MIS-2, including the last glacial maximum (LGM). The variability of δD\u003csub\u003ewax\u003c/sub\u003e is lower during the glacial period, in particular from 40\u0026thinsp;\u0026minus;\u0026thinsp;15 kyr b2k (thousand years before 2000 CE). The deglacial period (15-11.7 kyr b2k) features increased variability, and a major rise into the onset of the Holocene. δD\u003csub\u003ewax\u003c/sub\u003e is generally high during the Holocene, with greater variability and some strong negative excursions during the late Holocene.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe millennial-scale warming events recorded as Greenland Interstadials (GIs) show modest negative δD\u003csub\u003ewax\u003c/sub\u003e excursions in most instances. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e: \u003cb\u003eImpact of interstadials on\u003c/b\u003e \u003cb\u003en\u003c/b\u003e\u003cb\u003e-alkane data.\u003c/b\u003e A) \u003cem\u003en\u003c/em\u003e-alkane δD from C23, C27, C29 and C31 chains over the period 35\u0026thinsp;\u0026minus;\u0026thinsp;27 kyr b2k. B) \u003cem\u003en\u003c/em\u003e-alkane δ\u0026sup1;\u0026sup3;C. C) Proportion aquatic is (C23\u0026thinsp;+\u0026thinsp;C25)/ (C23\u0026thinsp;+\u0026thinsp;C25\u0026thinsp;+\u0026thinsp;C29\u0026thinsp;+\u0026thinsp;C31)\u003csup\u003e29\u003c/sup\u003e. D) NGRIP ice core δ\u003csup\u003e18\u003c/sup\u003eO\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. E) Offset in \u003cem\u003en\u003c/em\u003e-alkane δD across stadial-interstadial transitions calculated as the mean δD of the interstadial period minus the mean of the 500 years before the onset of the interstadial.shows the mean value of δD\u003csub\u003ewax\u003c/sub\u003e during the GIs minus the mean of the 500 years preceding the event. Only stadial-interstadial transitions with at least two data points in both the interstadial and the preceding 500 years are plotted (except GI-1, where 2 samples from the previous 513 years were used as the baseline). GI-2, -11, and \u0026minus;\u0026thinsp;14 were excluded due to not having two samples in both the interstadial and preceding 500 year period. In 11 of the 12 rapid warming transitions that meet that criteria, δD\u003csub\u003ewax\u003c/sub\u003e decreases. GI-12 is the only interstadial that shows an increase in δD, which could be explained by it occurring immediately after (H5), which apparently caused a large depletion of precipitation isotopes. GI-14 also appears to have a positive δD anomaly, though only one data point was measured in the 1000 years preceding the event, so no strong statements can be made about the rapid warming transition. The negative excursions recorded in most GIs are even larger in C27 and C29 than C31, but this may be a result of increasing aquatic \u003cem\u003en\u003c/em\u003e-alkane sources; aquatic \u003cem\u003en\u003c/em\u003e-alkanes generally have more depleted δD than terrestrial in hydrologically open lakes\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. Additionally, we attempted to isolate the hydroclimate signal in δD by accounting for effects related to global ice volume\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e, vegetation changes (and expected fractionation effects on δD\u003csup\u003e46\u003c/sup\u003e), and temperature effects (see Fig. S4 for details). Trees generally have a smaller apparent fractionation than C3 grasses\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e\u003csub\u003e,\u003c/sub\u003e meaning more positive δD\u003csub\u003ewax\u003c/sub\u003e for the same moisture source, and there is a positive correlation between air temperature and δD\u003csub\u003eprecip\u003c/sub\u003e in this region\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. Because interstadials feature warmer temperatures and more trees, the magnitude of the negative excursion becomes larger in most interstadials after accounting for these effects.\u003c/p\u003e \u003cp\u003eInterestingly, the response of δD\u003csub\u003ewax\u003c/sub\u003e to Heinrich stadials also tends to be negative (Fig. S5), with the Younger Dryas and four of five Heinrich stadials featuring depleted δD\u003csub\u003ewax\u003c/sub\u003e relative to the preceding 500 years. The effect is largest during H5 and becomes rather small during H4 to H1 with less than 5\u0026permil; change during each of those events. This suggests that the sensitivity of δD\u003csub\u003ewax\u003c/sub\u003e to major climate shifts or changes in North Atlantic source δD may be dependent on the climate state and/or ecosystem properties, with less sensitivity during glacial times when the ecosystem was mainly steppe vegetation\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eAssessing the controls on δD through model-data comparisons\u003c/h3\u003e\n\u003cp\u003eWe use the iTRACE transient isotope-enabled simulation\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e to investigate the H1 to GI-1 transition as a case study of a rapid warming event and its impact on δD\u003csub\u003eprecip\u003c/sub\u003e and δD\u003csub\u003ewax\u003c/sub\u003e. iTRACE was run in CESM v1.3 and covers the last deglacial period from 20 kyr b2k to 11 kyr b2k. The simulation includes water isotope physical processes in the atmosphere, ocean and land surface, and simulates the deglaciation using a combination of forcings that include ice sheets and ocean bathymetry, solar insolation, greenhouse gases, and meltwater fluxes\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. iTRACE ranks among the best-performing models for simulating millennial-scale hydroclimate responses\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e, and its simulated water isotopic variability agrees well with proxy records while revealing the physical processes driving these changes\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe iTRACE simulations show a strong signal of depleted δD\u003csub\u003eprecip\u003c/sub\u003e and decreased precipitation over the North Atlantic and central Europe during H1 and opposite patterns during GI-1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The isotopic pattern is driven to a large extent by input of depleted meltwater to the North Atlantic during H1\u003csup\u003e49\u003c/sup\u003e. Because the North Atlantic is the dominant moisture source for western Europe\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e, changes in the composition of seawater in this region exert a strong control on δD\u003csub\u003eprecip\u003c/sub\u003e. During GI-1, meltwater input ceased, and northward transport of warm, enriched water increased due to strengthening Atlantic Meridional Overturning Circulation (AMOC)\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. These two factors (meltwater cessation and AMOC increase), combined with increased Northern Hemisphere temperatures, led to enriched δD\u003csub\u003eprecip\u003c/sub\u003e in Europe during GI-1. The model results also show large changes in precipitation rate and humidity during the H1 to GI-1 transition with increased precipitation and more humid conditions in summer in the warm interstadial (Figs.\u0026nbsp;45 \u0026amp; \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe comparison of modeled δD\u003csub\u003eprecip\u003c/sub\u003e and our δD\u003csub\u003ewax\u003c/sub\u003e record shows a discrepancy in the response to the H1 to GI-1 transition (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Though δD\u003csub\u003ewax\u003c/sub\u003e varies during GI-1, including two very positive values during the first half of GI-1, the mean value is only 1\u0026permil; greater than during H1 (18.0-15.6 kyr BP). In contrast, iTRACE shows a 12\u0026permil; increase (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eJ). Other published archives of precipitation isotopes from this region such as ostracods in Ammersee, Germany\u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e and Alpine speleothems\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e,\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e show a large 2\u0026ndash;3\u0026permil; increase in δ\u003csup\u003e18\u003c/sup\u003eO at the onset of GI-1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), which is in closer agreement with the model output (equivalent to 16\u0026ndash;24\u0026permil; δD). Interestingly, δD\u003csub\u003ewax\u003c/sub\u003e from Bergsee, Germany shows a more muted response to the interstadial warming\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e, similar to the ELSA δD\u003csub\u003ewax\u003c/sub\u003e record. These results suggest that δD\u003csub\u003ewax\u003c/sub\u003e records from central Europe might be influenced to a large extent by factors other than δD of mean annual precipitation over the deglacial period.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eGiven that both iTRACE model results and published central Europe δ\u003csup\u003e18\u003c/sup\u003eO records show a shift toward enriched precipitation isotopes during interstadials, the lack of a change, or even negative anomalies (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), in δD\u003csub\u003ewax\u003c/sub\u003e suggest that processes that occur between precipitation and \u003cem\u003en\u003c/em\u003e-alkane biosynthesis are counteracting the increase in δD\u003csub\u003eprecip\u003c/sub\u003e. We considered two plausible mechanisms that could drive negative δD\u003csub\u003ewax\u003c/sub\u003e anomalies in response to rapid warming. One is changes in the timing of the growing season. In many plant species, \u003cem\u003en\u003c/em\u003e-alkanes are synthesized during the early growing season, corresponding to the time of leaf flush, and record isotopic values of moisture during this time\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e,\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e. Warm interstadials feature an earlier onset of the growing season, which would lead to a more depleted plant moisture source due to the lower δD of winter and early spring precipitation compared to that of summer, as indicated by the seasonal δD\u003csub\u003eprecip\u003c/sub\u003e cycle in both modern observations (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC) and the iTRACE simulation (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF). The second mechanism we considered is the effect of relative humidity (RH) on evaporative enrichment, which can occur within soils, or more importantly, within leaves. A compilation of leaf water and xylem δD measurements shows a significant correlation between RH and the offset between leaf water δD and xylem δD with a decrease of 0.7\u0026permil; in leaf water for a 1% increase RH\u003csup\u003e17\u003c/sup\u003e. We hypothesize that interstadials were more humid than the rest of the glacial period, which would decrease evaporative enrichment of leaf water leading to more depleted δD\u003csub\u003ewax\u003c/sub\u003e. Seeking to better determine the potential magnitude of these effects and whether accounting for them can reconcile the model-proxy mismatch, we tested three proxy system models (PSMs). PSM-1 forward models δD\u003csub\u003ewax\u003c/sub\u003e based on annual precipitation δD, PSM-2 uses early growing season (EGS) precipitation δD, and PSM-3 models δD\u003csub\u003ewax\u003c/sub\u003e using EGS precipitation δD and includes the effect of RH changes (see methods for details).\u003c/p\u003e \u003cp\u003eThe results of the three PSM scenarios (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e) show that considering changes in both humidity and growing season timing during the last deglaciation (PSM-3) provides the best match with the proxy data in terms of relative changes in δD\u003csub\u003ewax\u003c/sub\u003e response to H1 and GI-1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eI and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eJ). The pronounced decrease in δD\u003csub\u003eprecip\u003c/sub\u003e during H1 and the large increase during GI-1 are both substantially dampened when modeled as in PSM-3. Our results suggest that the markedly depleted δD\u003csub\u003eprecip\u003c/sub\u003e in western Germany during H1, driven by meltwater input, is largely counteracted in the δD\u003csub\u003ewax\u003c/sub\u003e record by shifts in the seasonal timing of \u003cem\u003en\u003c/em\u003e-alkanes synthesis and changes in evaporative enrichment.\u003c/p\u003e\n\u003ch3\u003eImplications for δD proxy interpretation\u003c/h3\u003e\n\u003cp\u003eDue to strong correlations with δD\u003csub\u003eprecip\u003c/sub\u003e\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e, δD\u003csub\u003ewax\u003c/sub\u003e is often interpreted as a sensitive recorder of δD\u003csub\u003eprecip\u003c/sub\u003e. However, in certain regions and time periods factors that modify the δD\u003csub\u003eprecip\u003c/sub\u003e signal become dominant, such as humidity (via evaporative enrichment of leaf water)\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e, seasonality\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e,\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e,\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e, and vegetation changes\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Differences of up to 20\u0026permil; are found between PSM-1 and PSM-3, demonstrating the important of considering changing seasonality and RH. Notably, the difference varies substantially over the deglacial period, meaning these non-precipitation effects are not constant in time or space. We observe a greater deviation between PSM-1 (δD\u003csub\u003ewax\u003c/sub\u003e) and PSM-3 (δD\u003csub\u003ewax_EGS_RH\u003c/sub\u003e) during cold and dry conditions, such as H1 and LGM.\u003c/p\u003e \u003cp\u003eWe did not incorporate vegetation effects on apparent fractionation in our PSMs because pollen is not well preserved during the LGM in the ELSA cores\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e making it difficult to quantitatively estimate vegetation effects over the time period considered for PSMs. Additionally, the RH effect contributes to different ε observed between different plant types making it difficult to disentangle the RH and vegetation effects\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e. However, changing vegetation has the potential to have a large effect on δD\u003csub\u003ewax\u003c/sub\u003e. In modern studies, C3 grasses have an apparent fractionation ε = -149\u0026permil;, whereas for trees it is -121\u0026permil;\u003csup\u003e46\u003c/sup\u003e. Pollen in the ELSA cores, and other central European sites, show generally more forested conditions during interstadials, with a steppe environment common during stadials\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e,\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e. This shift toward increased trees during interstadials would be expected to increase δD\u003csub\u003ewax\u003c/sub\u003e due to biosynthetic fractionation effects, which is the opposite of the pattern we observe of depleted δD\u003csub\u003ewax\u003c/sub\u003e during interstadials (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). C31 is dominantly sourced from grasses\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e, which may limit its sensitivity to vegetation changes compared to shorter chain lengths. RH and growing season effects during interstadials must have been large enough to overcome the enrichment expected due to afforestation to produce the negative anomalies observed in our record. Although we did not incorporate vegetation effects into the PSMs, an attempt to account for vegetation effects using the method of Konecky et al\u003csup\u003e46\u003c/sup\u003e. is shown in Fig. S4.\u003c/p\u003e \u003cp\u003eEvaporative enrichment also impacts δD of leaf water. According to the Craig-Gordon model \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan additionalcitationids=\"CR63\" citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e; the magnitude of evaporative enrichment depends on relative humidity, the isotopic composition of atmospheric vapor, and temperature. Although this effect is well-known, it is challenging to account for accurately in paleohydrological interpretations of δD\u003csub\u003ewax\u003c/sub\u003e and is often ignored. Some studies have used a dual-biomarker approach comparing aquatic and terrestrial δD biomarker measurements to constrain the effect of evaporative enrichment on terrestrial δD\u003csub\u003ewax\u003c/sub\u003e\u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e,\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e. However, this approach assumes that lake water is not affected by evaporative enrichment and that the biological sources of δD\u003csub\u003eaq\u003c/sub\u003e and δD\u003csub\u003eterr\u003c/sub\u003e are consistent. Based on large changes observed in lake productivity proxies, δ\u0026sup1;\u0026sup3;C, and Paq (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), the latter assumption is likely invalid for our dataset, and thus we do not lean on this approach for our interpretation; however, we do present terr-aq δD in Fig. S6 to support our interpretation.\u003c/p\u003e \u003cp\u003eThis study builds on the existing WaxPSM\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e by incorporating relative humidity and seasonality effects to gain an understanding of the potential magnitude of these factors and how they varied over time at our study site. Our approach is guided by research on modern systems that shows that \u003cem\u003en\u003c/em\u003e-alkane δD records an early growing season signal\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e,\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e and that leaf water δD is enriched due to evaporative effects\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Continued research on modern and ancient systems may be able to refine this approach and further constrain how these factors contribute to apparent fractionation across time and space. The impact of a moving seasonal window is widespread in biological proxies\u003csup\u003e\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e but has largely been overlooked in leaf wax palaeohydrological reconstructions, with some exceptions\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e,\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e. Our results show this effect can be very important in regions with a strong seasonal contrast in precipitation δD and should be considered explicitly in interpretation of δD\u003csub\u003ewax\u003c/sub\u003e.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePaleoclimatic interpretation of ELSA δD\u003csub\u003ewax\u003c/sub\u003e record 0\u0026ndash;60 ka\u003c/h2\u003e \u003cp\u003eMultiple factors contribute to the signal of our δD\u003csub\u003ewax\u003c/sub\u003e record over the past 60,000 years, with variable importance on different timescales. The multi-millennial-scale pattern shows generally enriched δD\u003csub\u003ewax\u003c/sub\u003e in early MIS-3 and the Holocene, and depleted values during glacial conditions from the onset of H5 at 50 kyr b2k until the deglaciation begins around 15 kyr b2k. During this interval δD\u003csub\u003ewax\u003c/sub\u003e is on average 18\u0026permil; lower than the Holocene. Precipitation isotope data from Koblenz and Trier indicate that δD\u003csub\u003eprecip\u003c/sub\u003e increases by 1.8\u0026permil; for every \u0026deg;C of temperature change\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e (Fig. S7). Temperatures in the LGM are estimated to be 9\u0026ndash;10\u0026deg;C lower than modern\u003csup\u003e\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e,\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e, which is sufficient to explain the 18\u0026permil; depletion. However, growing season temperatures reconstructed from GDGTs suggest only\u0026thinsp;~\u0026thinsp;3\u0026deg;C of cooling\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e, which would imply only a\u0026thinsp;~\u0026thinsp;5\u0026permil; change in δD. Vegetation changes from forest to steppe taxa could play a large role in decreasing δD\u003csub\u003ewax\u003c/sub\u003e during the glacial period due to the more negative fractionation of grasses relative to trees. Based on pollen data\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e and estimates of ε of plant functional types\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e, we estimate that vegetation changes could cause 10\u0026ndash;20\u0026permil; of depletion during the glacial period (Fig. S4). Therefore, we consider both temperature and vegetation changes to be important factors in driving depleted values from 50\u0026ndash;15 kyr b2k. A slow\u0026thinsp;~\u0026thinsp;8\u0026permil; enrichment of δD\u003csub\u003ewax\u003c/sub\u003e from 43\u0026ndash;23 ky b2k, unrelated to temperature or vegetation, likely reflects a combination of: ~3\u0026permil; seawater enrichment from ice sheet growth (Fig. S4), delayed growing season onset, and decreased relative humidity. This interpretation is consistent with enhanced continentality over central Europe during the LGM, driven by sea level fall and expanded sea ice, which caused colder springs and increased aridity\u003csup\u003e\u003cspan additionalcitationids=\"CR71 CR72\" citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eMillennial-scale events of the last glacial period feature unique dynamics affecting δD\u003csub\u003ewax\u003c/sub\u003e. Interestingly, both Heinrich stadials and GIs show a tendency towards negative anomalies in δD\u003csub\u003ewax\u003c/sub\u003e relative to the 500 years preceding each event (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e \u0026amp; S6). Negative anomalies during Heinrich stadials can be explained by a combination of meltwater-driven depletion of North Atlantic seawater, colder temperatures causing depleted δD\u003csub\u003eprecip,\u003c/sub\u003e and shifts toward steppe vegetation. The meltwater effect can cause approximately a 8\u0026ndash;13\u0026permil; increase in δD\u003csub\u003eprecip\u003c/sub\u003e in this region during a Heinrich event\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e, and is likely the largest factor in driving the signal during most Heinrich stadials. However, the response of δD\u003csub\u003ewax\u003c/sub\u003e appears to depend on the pre-existing vegetation state. During times when the landscape was forested, Heinrich stadials caused a shift towards steppe vegetation which amplifies that response of δD\u003csub\u003ewax\u003c/sub\u003e. For example, the largest depletion is observed during H5. This interval is characterized by the largest decline in tree pollen in the record (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), and this vegetation shift likely led to a major decrease in δD\u003csub\u003ewax\u003c/sub\u003e due to the higher biosynthetic fractionation of grasses compared to trees\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. Additionally, this period featured substantial cooling compared to preceding centuries\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. In contrast, H2 is the only Heinrich stadial in which δD\u003csub\u003ewax\u003c/sub\u003e increases. Steppe conditions dominated before and during the event; therefore, biosynthetic fractionation did not vary significantly. The enrichment in δD\u003csub\u003ewax\u003c/sub\u003e during H2, despite meltwater input to the North Atlantic, could potentially be explained by a very delayed growing season onset and intense aridity. Peak loess sedimentation rates in the Rhine Valley corroborate arid conditions at this time\u003csup\u003e\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe Younger Dryas (YD) has been intensively studied using compound specific isotopes at nearby Meerfelder Maar\u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e,\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e and Gem\u0026uuml;ndener Maar\u003csup\u003e\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u003c/sup\u003e. At Meerfelder Maar, δD\u003csub\u003ewax\u003c/sub\u003e decreased during the YD by 2.5\u0026ndash;9\u0026permil; (Fig. S5). This is less than the δD\u003csub\u003eprecip\u003c/sub\u003e change in iTRACE (decrease of 16\u0026permil;). Rach et al.\u003csup\u003e65\u003c/sup\u003e and Hepp et al.\u003csup\u003e74\u003c/sup\u003e, use different dual-biomarker methods to estimate that RH decreased by roughly 10% during the Younger Dryas compared to GI-1. Based on the relationship between RH and leaf water\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e, a 10% drop in RH could lead to a 7\u0026permil; increase in δD\u003csub\u003ewax\u003c/sub\u003e, which would account for most of the gap between modeled δD\u003csub\u003eprecip\u003c/sub\u003e and measured δD\u003csub\u003ewax\u003c/sub\u003e, with vegetation changes or growing season timing also weakening the depletion in δD\u003csub\u003ewax\u003c/sub\u003e relative to δD\u003csub\u003eprecip\u003c/sub\u003e.\u003c/p\u003e \u003cp\u003eThe key aim of this study was to test whether past rapid warming events enhanced humidity in central Europe. Based on our model-proxy data comparisons, it is difficult to explain the consistent negative δD\u003csub\u003ewax\u003c/sub\u003e excursions during interstadials (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) without invoking an increase in relative humidity. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes the expected influence of the main factors affecting δD\u003csub\u003ewax\u003c/sub\u003e over stadial-interstadial transitions. Multiple lines of evidence suggest that precipitation in central Europe became relatively enriched during interstadial periods. Our case study of GI-1 using iTRACE demonstrates this enrichment relates largely to changes in North Atlantic seawater isotopic composition. This interstadial enrichment is found across multiple study sites, models and time periods. Alpine speleothem δ\u003csup\u003e18\u003c/sup\u003eO records track Greenland ice core δ\u003csup\u003e18\u003c/sup\u003eO stadial-interstadial variability during MIS-3\u003csup\u003e75\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD) and MIS-5\u003csup\u003e76\u003c/sup\u003e, and similar features are observed in older periods\u003csup\u003e\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e,\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e\u003c/sup\u003e. A global compilation of speleothem records of D/O cycles showed a consistent enrichment pattern of precipitation isotopes in Europe and linked those changes to the North Atlantic\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. In addition to iTRACE (iCESM), the COSMOS-wiso model also shows enrichment of precipitation during stadial-interstadial transitions\u003csup\u003e\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e\u003c/sup\u003e. The COSMOS-wiso simulation does not include prescribed meltwater forcing, but nonetheless the North Atlantic becomes relatively enriched during interstadial-like periods of AMOC intensification\u003csup\u003e\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e,\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e\u003c/sup\u003e. In summary, multiple lines of evidence confirm that precipitation isotopes become enriched in interstadials, which contrasts with our δD\u003csub\u003ewax\u003c/sub\u003e measurements, necessitating an interpretation of δD\u003csub\u003ewax\u003c/sub\u003e that includes factors beyond δD\u003csub\u003eprecip\u003c/sub\u003e.\u003c/p\u003e \u003cp\u003ePSM scenarios using iTRACE output provide estimates of the shift in δD\u003csub\u003ewax\u003c/sub\u003e that could be attributed to changing humidity or shifts in the timing of the growing season (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Given that precipitation was isotopically enriched, and that vegetation changes (shift from steppe to forest) would be expected to cause more enriched δD\u003csub\u003ewax\u003c/sub\u003e, both increased humidity and an earlier growing season are required to explain consistently depleted interstadial δD\u003csub\u003ewax\u003c/sub\u003e in our record. This implies increased relative humidity during the early growing season when n-alkanes are synthesized, supported by iTRACE simulations (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eH). On average, δD\u003csub\u003ewax\u003c/sub\u003e was 3.4\u0026permil; lower following interstadial onset, which, is equivalent to a 5% RH increase, assuming that depletion is entirely due decreased leaf water enrichment\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. However, this estimate is highly uncertain due to aforementioned confounding factors and likely underestimates the RH change because vegetation effects and North Atlantic moisture source composition were likely causing enrichment of δD\u003csub\u003ewax,\u003c/sub\u003e obscuring the RH effect.\u003c/p\u003e \u003cp\u003ePaleoecological evidence from central and northern Europe strongly suggests that the timing of the growing season shifted earlier at the onset of the B\u0026oslash;lling/Aller\u0026oslash;d (equivalent to GI-1)\u003csup\u003e80\u0026ndash;82\u003c/sup\u003e. Additionally, model simulations of AMOC fluctuations indicate spring temperatures in Europe are particularly responsive to AMOC strength \u003csup\u003e\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u003c/sup\u003e. Our results provide independent evidence for earlier growing season onset during spring and highlight that changing seasonal dynamics were an important feature of millennial-scale variability during the last glacial period.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eList of factors that influence δD\u003c/b\u003e\u003csub\u003e\u003cb\u003ewax\u003c/b\u003e\u003c/sub\u003e \u003cb\u003eover millennial timescales.\u003c/b\u003e Estimates of the approximate magnitude of each mechanism on δD\u003csub\u003ewax\u003c/sub\u003e over a transition from stadial to interstadial climate and the reasoning behind this estimate are provided.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMechanism\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eApproximate\u003c/p\u003e \u003cp\u003eeffect on δD\u003csub\u003ewax\u003c/sub\u003e during GS to GI transition\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eEvidence (source)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eControls on precipitation δD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c4\" namest=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorth Atlantic δD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u0026ndash;13\u0026permil;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eiTRACE simulation, Zhu et al., 2017\u003csup\u003e49\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCondensation temperature\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u0026ndash;8\u0026permil;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eWarmer temperatures lead to enriched precipitation isotopes\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMoisture source/transport\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eiTRACE simulation indicates increased northerly moisture sources (depletion). Reduced sea ice in the proximal N Atlantic could shorten moisture paths (enrichment).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrecipitation δD sum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e12\u0026ndash;20\u0026permil;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eiTRACE simulation, regional δ\u003csup\u003e18\u003c/sup\u003eO proxies\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e,\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e,\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTerrestrial ecosystem factors\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c4\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrowing season timing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-8\u0026permil;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eSeasonal pattern of δD\u003csub\u003eprecip\u003c/sub\u003e, Tipple et al., 2013\u003csup\u003e15\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHumidity effects on leaf water evaporation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-4\u0026permil;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eRoden and Ehleringer, 1999\u003csup\u003e62\u003c/sup\u003e; Cernusak et al., 2022\u003csup\u003e17\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBiosynthetic fractionation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u0026ndash;30\u0026permil;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eMore trees and less grass decreases ε-bio\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eOur results also align with other biomarker isotope studies showing drier conditions in Germany during the Younger Dryas compared to GI-1\u003csup\u003e54,65,74\u003c/sup\u003e, but extends the leaf wax isotope record for central Europe to 60 kyr, enabling new paleohydrological interpretations of the glacial period and repeated rapid warming events. High humidity during interstadials is consistent with pollen data indicating a shift from steppe to partially forested environments during interstadials not only at the Eifel\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e, but also in other parts of central Europe\u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e,\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e,\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e\u003c/sup\u003e, which was likely driven by increased moisture availability. Interestingly, flood deposits are more common during colder times of the glacial period in Eifel lake sediments; however, this pattern may be driven by increased soil erosivity and hydrologic changes related to decreased vegetation\u003csup\u003e\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e\u003c/sup\u003e. Northwestern Iberian speleothem and pollen records also suggest wetter interstadial conditions\u003csup\u003e\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e,\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e\u003c/sup\u003e, and model simulations consistently show more precipitation during interstadial phases in central Europe\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e,\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSeveral mechanisms could plausibly drive increased RH during interstadials. The Clausius\u0026ndash;Clapeyron relation predicts that a warmer atmosphere holds more moisture\u003csup\u003e\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e\u003c/sup\u003e, However, for relative humidity (RH) to increase, the actual amount of moisture in the air must rise even faster, which requires an increased supply of water vapor. Increased moisture advection from the North Atlantic into western Europe can be expected during interstadials due to a reduction of sea ice\u003csup\u003e\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e,\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e\u003c/sup\u003e and increased evaporation rates over the North Atlantic. This mechanism is supported by model simulations of AMOC shutdown which find that when AMOC slows, cooler temperatures cause less evaporation over the North Atlantic, leading to less moisture advection into western Europe\u003csup\u003e\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e\u003c/sup\u003e. Interstadials represent an increase in AMOC strength\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e,\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e\u003c/sup\u003e, and therefore, the opposite likely occurred during GIs, i.e., increased evaporation over the North Atlantic increased the moisture supply to western Europe. Atmospheric circulation changes could plausibly affect RH. However, in the iTRACE simulation, GI-1 features anomalously northerly winds (Fig. S8), which are not associated with humid conditions in Germany. Simulations of AMOC shutdown also suggest that dynamic processes did not substantially contribute to decreased precipitation in western Europe\u003csup\u003e\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e\u003c/sup\u003e. Another possibility is that increased vegetation during interstadials increased humidity locally via increased evapotranspiration and microclimate effects (shade and reduced wind near ground level)\u003csup\u003e\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e\u003c/sup\u003e. Disentangling these mechanisms requires further research, but the strongest driver of increased humidity during interstadials is likely the increased advection of North Atlantic moisture due to reduced sea ice and warmer atmospheric conditions, particularly in spring and early summer. Our results provide new palaeohydrological evidence for an increasingly humid atmosphere in central Europe in response to rapid climate warming.\u003c/p\u003e \u003c/div\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of n-alkanes and stable isotopes\u003c/h2\u003e \u003cp\u003eFollowing freeze-drying, lipids were extracted from the sediments and separated into two fractions using an Accelerated Solvent Extractor 350 (ASE) following methods of Auderset et al., 2020\u003csup\u003e95\u003c/sup\u003e. The first fraction was extracted with \u003cem\u003en\u003c/em\u003e-hexane and contains the \u003cem\u003en\u003c/em\u003e-alkanes. An internal standard (hexatriacontane) was added to the extracts. The extracts were passed through silica columns with \u003cem\u003en\u003c/em\u003e-hexane to purify the \u003cem\u003en\u003c/em\u003e-alkanes. Samples with high organic content were passed through columns of copper (activated with 2N HCl) to remove sulfur. Concentrations of \u003cem\u003en\u003c/em\u003e-alkanes were determined via gas-chromatography (GC) analysis using an Agilent 7890B GC equipped with a VF-200 column and using flame ionization detection (FID).\u003c/p\u003e \u003cp\u003eMeasurements of stable isotopes of Hydrogen (δD) or carbon (δ\u003csup\u003e13\u003c/sup\u003eC) were conducted using a Thermo Trace 1310 GC with TriPlus RSH autosampler coupled to MAT 253 IRMS via GC IsoLink II and Conflo IV. Based on the GC-FID concentration data, the samples were diluted in n-hexane to achieve an injection amount of approximately 225\u0026ndash;800 ng of C29 n-alkane, while aiming to keep injection amounts of all odd-chain \u003cem\u003en\u003c/em\u003e-alkanes from C23 to C33 within the range of 180\u0026ndash;1000 ng. A total of 270 sediment samples were analyzed for δD, with 230 of these samples measured twice or more. Additionally, 80 samples were measured for δ13C, with each measurement duplicated. Large-volume (20 ul) injection was achieved using a Programmable Temperature Vaporization (PTV) inlet. δD and δ\u0026sup1;\u0026sup3;C measurements were calibrated relative to international standards VSMOW for δD (Vienna Standard Mean Ocean Water) or VPDB for δ\u003csup\u003e13\u003c/sup\u003eC (Vienna Pee Dee Belemnite) via the use of reference standards obtained from Dr. Arndt Schimmelmann (Indiana University). A correction was applied for linearity based on the peak area. Uncertainty was estimated to be 4.0\u0026permil; for δD and 0.45\u0026permil; for δ\u0026sup1;\u0026sup3;C based on the pooled standard deviation of replicated measurements\u003csup\u003e\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e\u003c/sup\u003e. Additional methodological details are provided in Zander et al.\u003csup\u003e34\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eiTRACE simulation\u003c/h2\u003e \u003cp\u003eiTRACE is an isotope-enabled transient simulation of the last deglaciation (20-11ka\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e), performed with the isotope-enabled Community Earth System Model version 1.3 (iCESM\u003csup\u003e\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e\u003c/sup\u003e). iCESM1.3 couples the Community Atmosphere Model version 5.3 (CAM5.3), the Community Land Model version 4 (CLM4), the Parallel Ocean Program version 2 (POP2), and the Los Alamos Sea Ice Model, version 4 (CICE4). The model\u0026rsquo;s precipitation and water isotope simulations have been validated against modern observations\u003csup\u003e\u003cspan additionalcitationids=\"CR98\" citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e99\u003c/span\u003e\u003c/sup\u003e and it has been widely applied in paleoclimate and data\u0026ndash;model comparison studies, such as the Asian Monsoon region\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e, the Indo-Pacific Warm Pool\u003csup\u003e\u003cspan additionalcitationids=\"CR101 CR102\" citationid=\"CR100\" class=\"CitationRef\"\u003e100\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e103\u003c/span\u003e\u003c/sup\u003e and the North American monsoon region\u003csup\u003e\u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e104\u003c/span\u003e,\u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e105\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe transient simulation is driven by four additive forcing factors: continental ice sheets (ICE), orbital parameters (ORB), greenhouse gases (GHG), and meltwater fluxes (MWF). Continental ice sheet configuration follows the ICE-6G reconstruction\u003csup\u003e\u003cem\u003e\u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e106\u003c/span\u003e\u003c/em\u003e\u003c/sup\u003e and is modified every 1000\u0026nbsp;year. Greenhouse gas concentrations (CO₂, CH₄, N₂O) are prescribed from ice-core records\u003csup\u003e\u003cem\u003e\u003cspan additionalcitationids=\"CR108\" citationid=\"CR107\" class=\"CitationRef\"\u003e107\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e109\u003c/span\u003e\u003c/em\u003e\u003c/sup\u003e. Meltwater fluxes are consistent with sea-level reconstructions and follow the scheme implemented in TRACE-21ka\u003csup\u003e\u003cem\u003e\u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e110\u003c/span\u003e\u003c/em\u003e\u003c/sup\u003e. Further details of the forcings are provided in He et al.\u003csup\u003e\u003cem\u003e21\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eProxy system model calculations\u003c/h2\u003e \u003cp\u003eData analysis was performed in R v 4.4.1\u003csup\u003e111\u003c/sup\u003e. Three proxy system models were considered to make more direct comparisons of iTRACE output and measured δD\u003csub\u003ewax\u003c/sub\u003e and to test the potential impacts of relative humidity and changing growing season timing on δD\u003csub\u003ewax\u003c/sub\u003e. PSM-1 uses a constant apparent fractionation factor ε = -120\u0026permil; based on the consensus value from the literature for terrestrial \u003cem\u003en\u003c/em\u003e-alkanes \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e:\u003c/p\u003e \u003cp\u003eδD\u003csub\u003ewax\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;δD\u003csub\u003eprecip\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;ε (1)\u003c/p\u003e \u003cp\u003ewhere δD\u003csub\u003eprecip\u003c/sub\u003e is mean annual precipitation δD from the iTRACE simulation.\u003c/p\u003e \u003cp\u003ePSM-2 uses precipitation from the early growing season:\u003c/p\u003e \u003cp\u003eδD\u003csub\u003ewax\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;δD\u003csub\u003eEGS\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;ε (2)\u003c/p\u003e \u003cp\u003eWhere δD\u003csub\u003eEGS\u003c/sub\u003e is early growing precipitation δD. We define the early growing season as the first 3 months of the year with mean monthly temperature greater than 5\u0026deg;C\u003csup\u003e52\u003c/sup\u003e, again based on iTRACE output.\u003c/p\u003e \u003cp\u003ePSM-3 includes a correction for the effect of relative humidity on evaporative enrichment:\u003c/p\u003e \u003cp\u003eδD\u003csub\u003ewax\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;δD\u003csub\u003eEGS\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;ε\u0026thinsp;+\u0026thinsp;k(EGS-RH) (3)\u003c/p\u003e \u003cp\u003ewhere k = -0.7 based on the observed empirical relationship between humidity and leaf water enrichment\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. EGS-RH is the mean relative humidity anomaly in iTRACE during the early growing season.\u003c/p\u003e \u003cp\u003eWe also considered an adjustment for changing vegetation based on pollen data\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e and ε values assigned to plant functional types in Konecky et al.\u003csup\u003e46\u003c/sup\u003e However, the poor resolution of pollen data during the deglacial period limited comparisons with iTRACE output. Nonetheless, the effect of vegetation could cause changes to ε of up to 20\u0026permil; (Fig. S4).\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors have no competing interests to declare.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor contributions\u003c/h2\u003e \u003cp\u003ePDZ: conceptualization, formal analysis, investigation, methodology, project administration, funding acquisition, writing original draft, and visualization. FS: conceptualization, investigation, resources, funding acquisition, project administration, and review and editing. FR: methodology, investigation, and review and editing. XD: formal analysis, investigation, and review and editing. CS: formal analysis, visualization, investigation, and review and editing. GH: resources, funding acquisition, and project administration. AMG: conceptualization, methodology, resources, funding acquisition, project administration, and review and editing.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eThis research has been supported by funding from the University of Mainz, Germany, the Max Planck Institute for Chemistry, Germany, and the Schweizerischer Nationalfonds zur F\u0026ouml;rderung der Wissenschaftlichen Forschung (grant no. P500PN_206731). A portion of this work was performed under the auspices of the US Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344; this is LLNL-JRNL-2014174. We thank Klaus Schwibus and Mareike Schmitt for technical assistance.\u003c/p\u003e\u003ch2\u003eData availability\u003c/h2\u003e \u003cp\u003e \u003cem\u003en\u003c/em\u003e-alkane data used in this study will be archived at PANGAEA. iTRACE simulation results are available via the National Center for Atmospheric Research at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.26024/b290-an76\u003c/span\u003e\u003cspan address=\"10.26024/b290-an76\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eA data file of \u003cem\u003en\u003c/em\u003e-alkane measurements has been included for review purposes.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eDansgaard, W. \u003cem\u003eet al.\u003c/em\u003e Evidence for general instability of past climate from a 250-kyr ice-core record. \u003cem\u003eNature\u003c/em\u003e 364, 218\u0026ndash;220 (1993).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKindler, P. \u003cem\u003eet al.\u003c/em\u003e Temperature reconstruction from 10 to 120 kyr b2k from the NGRIP ice core. \u003cem\u003eClim. 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R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing (2024).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8339448/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8339448/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe study of past abrupt warming events provides knowledge of climate dynamics that are critical for future projections. During the last glacial period, Dansgaard-Oeschger (D/O) cycles caused rapid temperature increases across the North Atlantic at rates comparable to contemporary climate change. However, the hydroclimate response in Europe during these events remains poorly constrained due to scarce continuous paleohydrological records. Here, we present a continuous record of leaf wax hydrogen isotopes (δDwax) from a 60,000-year lake sediment record in Germany. δD\u003csub\u003ewax\u003c/sub\u003e is depleted during warm interstadials, contrasting with model simulations and published precipitation isotope proxies from central Europe. Using proxy system models combined with an isotope-enabled transient simulation (iTRACE), we demonstrate that this discrepancy arises from shifts in growing season timing and relative humidity, which modify the δD\u003csub\u003ewax\u003c/sub\u003e signal. Compared to cold stadials, warmer interstadials featured earlier growing season onset and increased relative humidity. These findings align with projections of intensified precipitation in this region under warming due to enhanced atmospheric moisture. Our results highlight how seasonality and humidity obscure precipitation δD signals in plant wax isotopes, demonstrating that incorporating these factors into proxy system models improves model-data comparisons and enables more robust paleoclimate reconstructions.\u003c/p\u003e","manuscriptTitle":"Leaf wax isotopes reveal enhanced humidity and earlier growing season during Dansgaard-Oeschger warming events in Europe","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-18 14:55:17","doi":"10.21203/rs.3.rs-8339448/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"communications-earth-and-environment","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"commsenv","sideBox":"Learn more about [Communications Earth and Environment](https://www.nature.com/commsenv/)","snPcode":"","submissionUrl":"","title":"Communications Earth \u0026 Environment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Communications Series","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"a384c467-522e-4382-be53-efea8f8df4b5","owner":[],"postedDate":"December 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":59742135,"name":"Earth and environmental sciences/Climate sciences/Palaeoclimate"},{"id":59742136,"name":"Earth and environmental sciences/Climate sciences/Biogeochemistry"}],"tags":[],"updatedAt":"2026-01-30T11:10:19+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-18 14:55:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8339448","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8339448","identity":"rs-8339448","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}