Abstract
The vapor pressure deficit reflects the difference between how much moisture the atmosphere could and actually does hold, a factor that fundamentally affects evapotranspiration, ecosystem functioning, and vegetation carbon uptake. Its spatial variability and long-term trends under natural versus human-influenced climate are poorly known despite being essential for predicting future effects on natural ecosystems and human societies such as crop yield, wildfires, and health. Here we combine regionally distinct reconstructions of pre-industrial summer vapor pressure deficit variability from Europe’s largest oxygen-isotope network of tree-ring cellulose with observational records and Earth system model simulations with and without human forcing included. We demonstrate that an intensification of atmospheric drying during the recent decades across different European target regions is unprecedented in a pre-industrial context and that it is attributed to human influence with more than 98% probability. The magnitude of this trend is largest in Western and Central Europe, the Alps and Pyrenees region, and the smallest in southern Fennoscandia. In view of the extreme drought and compound events of the recent years, further atmospheric drying poses an enhanced risk to vegetation, specifically in the densely populated areas of the European temperate lowlands.
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Data availability
All raw tree-ring δ18O chronologies used in this study together with the final summer VPD reconstructions can be downloaded at the NOAA National Centre for Environmental Information (NCEI): https://www.ncei.noaa.gov/access/paleo-search/study/38660.
The CMIP6 data used in this study are available at https://esgf-node.llnl.gov/search/cmip6/. Detailed inputs for the search query are as follows: source IDs are ACCESS-ESM1-5, CESM2, CMCC-ESM2, CNRM-ESM2-1, CanESM5, EC-Earth3-Veg, GFDL-ESM4, IPSL-CM6A-LR, MIROC-ES2L, MPI-ESM1-2-LR, MRI-ESM2-0, and UKESM1-0-LL; experiment IDs are piControl, historical and ssp245, also hist-nat and hist-noLu; variant label is r1i1p1f1 or the next lowest number if unavailable for some models; frequency is mon; and variables are tas and hurs.
Code availability
R-codes used for fuzzy cluster and principal component analysis are available at https://github.com/Treydte/code_NGeo23, and python-codes for site-based monthly and seasonal correlations with climate variables are available at https://github.com/andreaskessler/VPDSeasonalCorr.
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Acknowledgements
We acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for the Coupled Model Intercomparison Project (CMIP), and we thank the climate modeling groups for producing their model output and making it available. For the CMIP, the US Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of the software infrastructure in partnership with the Global Organization for Earth System Science Portals.
This study was funded by the EC projects ISONET EVK2-CT-2002-00147 and Millennium FP6-2004-GLOBAL-017008-2 and by the Swiss National Science Foundation (SNSF) projects iTREE CRSII3_136295 and TROXY 200021_175888. Other funding included ERC 724750 (AK), NASA’s Terrestrial Ecology programme (BP), Slovenian Research Agency P4-0107 (TL, PH), MIUR-PRIN 2002 2002075152 & MIUR-PRIN 2005 2005072877 (AS, LT), PALEOMEX-ISOMEX programme CNRS-INSU (VD), Fundació La Caixa through the Junior Leader Program LCF/BQ/LR18/11640004 (IDL), ERC 755865 & Academy of Finland 295319 and 343059 (KR-G), Czech Science Foundation CZ.02.1.01/0.0/0.0/16_019/0000797 (UB, JE), Spanish Ministry of Science and Technology REN2002-11476-E/CLI (EG), and ERC AdG 882727 (JE), UKRI EP/X025098/1 (NJL). L.L., R.P., and S.I.S. acknowledge support from the European Union’s Horizon 2020 Research and Innovation programme (grant agreement 821003 (4 C)). The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the opinions or policies of the funding agencies and supporting institutions.
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K.T., L.L., R.P., F.B., D.C.F., A. Kahmen, S.I.S. and N.J.L. designed the research; K.T., L.L., R.P., F.B., D.C.F., E.M.-S., B.P. and R.W. performed the research with input from A.G., A. Kahmen, A. Kessler, A.I.S. and S.I.S. K.T. wrote the paper. All listed authors from L.A.-H. to G.Y. were involved in data production and provided feedback on the manuscript.
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Extended data
Extended Data Fig. 1 Dependency of mean δ18O site values of the common period 1900-1994 on geographic location.
p-values below 0.05 indicate significant relationships.
Extended Data Fig. 2 Climate sensitivity of the site tree-ring δ18O chronologies in two independent time periods.
Periods cover 1920-1960 and 1961-2000; shorter for those records that end in 1994, 1996 and 1998, (see Extended Data Table 1). Colours indicate Pearson’s correlation coefficients calculated between each of the 45 site δ18O chronologies of the network and the closest gridpoints of VPD, maximum temperature (Tmax), precipitation sums and SPEI for each individual month from March of the year before to October of the actual year of tree-ring formation. For VPD and Tmax red colours indicate positive correlations, for PPT and SPEI red colours indicate negative correlations. Order of sites from North to South corresponds to the order in Extended Data Table 1.
Extended Data Fig. 3 Relationship between climate- δ18O correlations and geographic location for VPD, Tmax, precipitation (PPT) and SPEI in summer (June to August).
Each point indicates a correlation coefficient calculated between a site δ18O chronology and the closest gridpoint dataset, plotted as a function of latitude, longitude and elevation. Points above the dashed horizontal line are significant at P < 0.05. p-values in the legend indicate the significance of the slope of the linear regression line. Most p-values are above 0.05 which suggests independency of the correlation strength from geographical location. Note the inverse y-axis for PPT and SPEI.
Extended Data Fig. 4 Relationship between climate- d18O correlations and long-term means (period 1920-1994) of precipitation sums (PPT), SPEI, water balance (WAB), mean temperature (Tmean), maximum temperature (Tmax) and VPD.
Each point indicates a correlation coefficient calculated between a site δ18O chronology and the closest gridpoint climate dataset, plotted as a function of the nine selected climate long-term means (‘climatic regions’). R- and p-values in the legend indicate the significance of the relationship. Most p-values are above 0.05 which suggests independency of the correlation strength from the climatic region.
Extended Data Fig. 5 Contribution of the individual site chronologies to the common variance in the regional clusters of Northern Fennoscandia (NF), Southern Fennoscandia (SF), Western Europe (WE), Eastern Central Europe (ECE) and Alps & Pyrenees (AP).
Membership (%) the contribution to the common variance in a cluster in percentage. Mean rall is the mean inter-series correlation coefficient (that is, the common signal strength) of all chronologies contributing to the corresponding cluster, mean r>75 is the mean inter-series correlation of the chronologies with a membership >75% in a cluster (* P < 0.01, ** P < 0.001). Calculations are based on the period 1920-2000. Sites above a membership threshold of 75% (straight line) were used for the development of regional chronologies through nested principal component analysis. Site codes see Supplementary Table 1.
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Supplementary Results and Discussion, References, Tables 1–6 and Figs. 1–5.
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Treydte, K., Liu, L., Padrón, R.S. et al. Recent human-induced atmospheric drying across Europe unprecedented in the last 400 years. Nat. Geosci. 17, 58–65 (2024). https://doi.org/10.1038/s41561-023-01335-8
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DOI: https://doi.org/10.1038/s41561-023-01335-8
