AbstractRainfall events are globally becoming less frequent but more intense under a changing climate, thereby shifting climatic conditions for terrestrial vegetation independent of annual rainfall totals1,2,3. However, it remains uncertain how changes in daily rainfall variability are affecting global vegetation photosynthesis and growth3,4,5,6,7,8,9,10,11,12,13,14,15,16,17. Here we use several satellite-based vegetation indices and field observations indicative of photosynthesis and growth, and find that global annual-scale vegetation indices are sensitive to the daily frequency and intensity of rainfall, independent of the total amount of rainfall per year. Specifically, we find that satellite-based vegetation indices are sensitive to daily rainfall variability across 42 per cent of the vegetated land surfaces. On average, the sensitivity of vegetation to daily rainfall variability is almost as large (95 per cent) as the sensitivity of vegetation to annual rainfall totals. Moreover, we find that wet-day frequency and intensity are projected to change with similar magnitudes and spatial extents as annual rainfall changes. Overall, our findings suggest that daily rainfall variability and its trends are affecting global vegetation photosynthesis, with potential implications for the carbon cycle and food security.
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Fig. 1: Sensitivity of vegetation function.Fig. 2: Example time series of dry savannah in Botswana.Fig. 3: Vegetation indices in years with less frequent, more intense wet days tend to increase in drier ecosystems and decrease in wetter ecosystems.Fig. 4: Daily rainfall variability trends are of similar absolute magnitude and spatial extent as shifts due to annual rainfall total, which consequently shifts annual mean vegetation function.
Data availability
The data used and created in the study are available in two repositories. The processed data inputs are available on Zenodo at https://doi.org/10.5281/zenodo.10947071 (ref. 97). The output data and reduced-size example input data are available on Zenodo at https://doi.org/10.5281/zenodo.13551521 (ref. 98). All datasets used in the study are freely available and were obtained as follows. The MODIS NDVI product can be obtained from https://modis.gsfc.nasa.gov/data/dataprod/mod13.php. AVHRR NDVI can be obtained from https://www.ncei.noaa.gov/data/land-normalized-difference-vegetation-index/access/. GOME-2 SIF can be downloaded from https://daac.ornl.gov/SIF-ESDR/guides/MetOpA_GOME2_SIF.html. OCO-2 SIF can be obtained from https://disc.gsfc.nasa.gov/datasets/OCO2_L2_Lite_SIF_10r/summary. The MT-DCA vegetation optical depth dataset retrieved from SMAP is freely available at https://doi.org/10.5281/zenodo.5579549. AIRS humidity and air temperature data are available at https://airs.jpl.nasa.gov/data/get-data/standard-data/. The MODIS land surface temperature product can obtained from https://lpdaac.usgs.gov/products/myd11c2v006/. MERRA-2 precipitation data can be accessed at https://gmao.gsfc.nasa.gov/reanalysis/MERRA-2/data_access/. CERES radiation can be accessed at https://asdc.larc.nasa.gov/project/CERES/CER_SYN1deg-Day_Terra-Aqua-MODIS_Edition4A. SMAP soil moisture can be obtained from https://nsidc.org/data/smap/data. GPM precipitation outputs are available at https://gpm.nasa.gov/data/directory. CPC precipitation data are available at https://psl.noaa.gov/data/gridded/data.cpc.globalprecip.html. REGEN precipitation data are available at https://thredds-x.ipsl.fr/thredds/catalog/FROGs/REGEN_ALL_V1-2019/catalog.html. FLUXNET gross primary production observations can be obtained from https://fluxnet.org. CMIP6 rainfall projections can be obtained from https://cds.climate.copernicus.eu.
Code availability
The code is available on Zenodo at https://doi.org/10.5281/zenodo.13551521 (ref. 98) to both create the figures and conduct the analysis. This repository includes the main analysis outputs and example input data. The full processed data inputs are available on Zenodo at https://doi.org/10.5281/zenodo.10947071 (ref. 97).
ReferencesPendergrass, A. G., Knutti, R., Lehner, F., Deser, C. & Sanderson, B. M. Precipitation variability increases in a warmer climate. Sci. Rep. 7, 17966 (2017).Article
ADS
PubMed
PubMed Central
Google Scholar
Pendergrass, A. G. & Knutti, R. The uneven nature of daily precipitation and its change. Geophys. Res. Lett. 45, 11,980–11,988 (2018).Article
Google Scholar
Feldman, A. F. et al. Plant responses to changing rainfall frequency and intensity. Nat. Rev. Earth Environ. https://doi.org/10.1038/s43017-024-00534-0 (2024).Article
Google Scholar
Thomey, M. L. et al. Effect of precipitation variability on net primary production and soil respiration in a Chihuahuan Desert grassland. Glob. Change Biol. 17, 1505–1515 (2011).Article
ADS
Google Scholar
Fay, P. A. et al. Relative effects of precipitation variability and warming on tallgrass prairie ecosystem function. Biogeosciences 8, 3053–3068 (2011).Article
ADS
Google Scholar
Liu, J. et al. Impact of temporal precipitation variability on ecosystem productivity. Wiley Interdiscip. Rev. Water 7, e1481 (2020).Article
Google Scholar
Sloat, L. L. et al. Increasing importance of precipitation variability on global livestock grazing lands. Nat. Clim. Change 8, 214–218 (2018).Article
ADS
Google Scholar
Ritter, F., Berkelhammer, M. & Garcia-Eidell, C. Distinct response of gross primary productivity in five terrestrial biomes to precipitation variability. Commun. Earth Environ. 1, 34 (2020).Article
ADS
Google Scholar
Guan, K. et al. Continental-scale impacts of intra-seasonal rainfall variability on simulated ecosystem responses in Africa. Biogeosciences 11, 6939–6954 (2014).Article
ADS
Google Scholar
Knapp, A. K. et al. Rainfall variability, carbon cycling, and plant species diversity in a mesic grassland. Science 298, 2202–2205 (2002).Article
ADS
PubMed
Google Scholar
Ross, I. et al. How do variations in the temporal distribution of rainfall events affect ecosystem fluxes in seasonally water-limited Northern Hemisphere shrublands and forests? Biogeosciences 9, 1007–1024 (2012).Article
ADS
Google Scholar
Su, J., Zhang, Y. & Xu, F. Divergent responses of grassland productivity and plant diversity to intra-annual precipitation variability across climate regions: a global synthesis. J. Ecol. 111, 1921–1934 (2023).Article
Google Scholar
Good, S. P. & Caylor, K. K. Climatological determinants of woody cover in Africa. Proc. Natl Acad. Sci. USA 108, 4902–4907 (2011).Article
ADS
PubMed
PubMed Central
Google Scholar
Zhang, F. et al. Precipitation temporal repackaging into fewer, larger storms delayed seasonal timing of peak photosynthesis in a semi‐arid grassland. Funct. Ecol. 36, 646–658 (2021).Article
Google Scholar
Xu, X., Medvigy, D. & Rodriguez-Iturbe, I. Relation between rainfall intensity and savanna tree abundance explained by water use strategies. Proc. Natl Acad. Sci USA. 112, 12992–12996 (2015).Article
ADS
PubMed
PubMed Central
Google Scholar
Case, M. F. & Staver, A. C. Soil texture mediates tree responses to rainfall intensity in African savannas. New Phytol. 219, 1363–1372 (2018).Article
PubMed
Google Scholar
Heisler-White, J. L., Blair, J. M., Kelly, E. F., Harmoney, K. & Knapp, A. K. Contingent productivity responses to more extreme rainfall regimes across a grassland biome. Glob. Change Biol. 15, 2894–2904 (2009).Article
ADS
Google Scholar
Jasechko, S. et al. Terrestrial water fluxes dominated by transpiration. Nature 496, 347–350 (2013).Article
ADS
PubMed
Google Scholar
Green, J. K. et al. Large influence of soil moisture on long-term terrestrial carbon uptake. Nature 565, 476–479 (2019).Article
ADS
PubMed
PubMed Central
Google Scholar
Rigden, A. J., Mueller, N. D., Holbrook, N. M., Pillai, N. & Huybers, P. Combined influence of soil moisture and atmospheric evaporative demand is important for accurately predicting US maize yields. Nat. Food 1, 127–133 (2020).Article
PubMed
Google Scholar
Wang, L. et al. Dryland productivity under a changing climate. Nat. Clim. Change 12, 981–994 (2022).Article
ADS
Google Scholar
Isbell, F. et al. High plant diversity is needed to maintain ecosystem services. Nature 477, 199–202 (2011).Article
ADS
PubMed
Google Scholar
Gherardi, L. A. & Sala, O. E. Effect of interannual precipitation variability on dryland productivity: a global synthesis. Glob. Change Biol. 25, 269–276 (2019).Article
ADS
Google Scholar
Nemani, R. R. et al. Climate-driven increases in global terrestrial net primary production from 1982 to 1999. Science 300, 1560–1563 (2003).Article
ADS
PubMed
Google Scholar
Maurer, G. E., Hallmark, A. J., Brown, R. F., Sala, O. E. & Collins, S. L. Sensitivity of primary production to precipitation across the United States. Ecol. Lett. 23, 527–536 (2020).Article
PubMed
Google Scholar
Sala, O. E., Parton, W. J., Joyce, L. A. & Lauenroth, W. K. Primary production of the central grassland region of the United States. Ecology 69, 40–45 (1988).Article
Google Scholar
Biederman, J. A. et al. CO2 exchange and evapotranspiration across dryland ecosystems of southwestern North America. Glob. Change Biol. 23, 4204–4221 (2017).Article
ADS
Google Scholar
Ukkola, A. M. et al. Annual precipitation explains variability in dryland vegetation greenness globally but not locally. Glob. Change Biol. 27, 4367–4380 (2021).Article
Google Scholar
Trugman, A. T., Medvigy, D., Mankin, J. S. & Anderegg, W. R. L. Soil moisture stress as a major driver of carbon cycle uncertainty. Geophys. Res. Lett. 45, 6495–6503 (2018).Article
ADS
Google Scholar
Denissen, J. M. C. et al. Widespread shift from ecosystem energy to water limitation with climate change. Nat. Clim. Change 12, 677–684 (2022).Article
ADS
Google Scholar
Zhu, Z. et al. Greening of the Earth and its drivers. Nat. Clim. Change 6, 791–795 (2016).Article
ADS
Google Scholar
Li, F. et al. Global water use efficiency saturation due to increased vapor pressure deficit. Science 381, 672–677 (2023).Article
ADS
PubMed
Google Scholar
Smith, W. K. et al. Large divergence of satellite and Earth system model estimates of global terrestrial CO2 fertilization. Nat. Clim. Change 6, 306–310 (2016).Article
ADS
Google Scholar
Trenberth, K. E. Changes in precipitation with climate change. Clim. Res. 47, 123–138 (2011).Article
Google Scholar
Lian, X., Zhao, W. & Gentine, P. Recent global decline in rainfall interception loss due to altered rainfall regimes. Nat. Commun. 13, 7642 (2022).Article
ADS
PubMed
PubMed Central
Google Scholar
Feldman, A. F., Short Gianotti, D. J., Trigo, I. F., Salvucci, G. D. & Entekhabi, D. Land–atmosphere drivers of landscape-scale plant water content loss. Geophys. Res. Lett. 47, e2020GL090331 (2020).Article
ADS
Google Scholar
Feldman, A. F. et al. Moisture pulse-reserve in the soil–plant continuum observed across biomes. Nat. Plants 4, 1026–1033 (2018).Article
PubMed
Google Scholar
Williams, C. A., Hanan, N., Scholes, R. J. & Kutsch, W. Complexity in water and carbon dioxide fluxes following rain pulses in an African savanna. Oecologia 161, 469–480 (2009).Article
ADS
PubMed
PubMed Central
Google Scholar
Humphrey, V. et al. Sensitivity of atmospheric CO2 growth rate to observed changes in terrestrial water storage. Nature 560, 628–631 (2018).Article
ADS
PubMed
Google Scholar
Sun, Y. et al. From remotely sensed solar-induced chlorophyll fluorescence to ecosystem structure, function, and service: Part I—Harnessing theory. Glob. Change Biol. 29, 2926–2952 (2023).Article
Google Scholar
Smith, W. K., Fox, A. M., MacBean, N., Moore, D. J. P. & Parazoo, N. C. Constraining estimates of terrestrial carbon uptake: new opportunities using long-term satellite observations and data assimilation. New Phytol. 225, 105–112 (2020).Article
PubMed
Google Scholar
Fatichi, S., Ivanov, V. Y. & Caporali, E. Investigating interannual variability of precipitation at the global scale: is there a connection with seasonality? J. Clim. 25, 5512–5523 (2012).Article
ADS
Google Scholar
Knapp, A. K. et al. Consequences of more extreme precipitation regimes for terrestrial ecosystems. Bioscience 58, 811–821 (2008).Article
Google Scholar
Green, J. K., Berry, J., Ciais, P., Zhang, Y. & Gentine, P. Amazon rainforest photosynthesis increases in response to atmospheric dryness. Sci. Adv. 6, eabb7232 (2020).Article
ADS
PubMed
PubMed Central
Google Scholar
Post, A. K. & Knapp, A. K. Plant growth and aboveground production respond differently to late-season deluges in a semi-arid grassland. Oecologia 191, 673–683 (2019).Article
ADS
PubMed
Google Scholar
Feldman, A. F., Chulakadabba, A., Short Gianotti, D. J. & Entekhabi, D. Landscape-scale plant water content and carbon flux behavior following moisture pulses: from dryland to mesic environments. Water Resour. Res. 57, e2020WR027592 (2021).Article
ADS
Google Scholar
Huxman, T. E. et al. Convergence across biomes to a common rain-use efficiency. Nature 429, 651–654 (2004).Article
ADS
PubMed
Google Scholar
Poulter, B. et al. Contribution of semi-arid ecosystems to interannual variability of the global carbon cycle. Nature 509, 600–603 (2014).Article
ADS
PubMed
Google Scholar
Ahlström, A. et al. The dominant role of semi-arid ecosystems in the trend and variability of the land CO2 sink. Science 348, 895–900 (2015).Article
ADS
PubMed
Google Scholar
Pendergrass, A. G. What precipitation is extreme? Science 360, 1072–1073 (2018).Article
ADS
PubMed
Google Scholar
Kannenberg, S. A., Bowling, D. R. & Anderegg, W. R. L. Hot moments in ecosystem fluxes: high GPP anomalies exert outsized influence on the carbon cycle and are differentially driven by moisture availability across biomes. Environ. Res. Lett. 15, 054004 (2020).Article
ADS
Google Scholar
Wainwright, C. M., Allan, R. P. & Black, E. Consistent trends in dry spell length in recent observations and future projections. Geophys. Res. Lett. 49, e2021GL097231 (2022).Piao, S. et al. Characteristics, drivers and feedbacks of global greening. Nat. Rev. Earth Environ. 1, 14–27 (2020).Article
ADS
Google Scholar
Higgins, S. I., Conradi, T. & Muhoko, E. Shifts in vegetation activity of terrestrial ecosystems attributable to climate trends. Nat. Geosci. 16, 147–153 (2023).Article
ADS
Google Scholar
Didan, K. MODIS/Terra Vegetation Indices 16-Day L3 Global 0.05 Deg CMG V061 EarthData https://doi.org/10.5067/MODIS/MOD13C1.061 (2021).Vermote, E. et al. NOAA Climate Data Record (CDR) of Normalized Difference Vegetation Index (NDVI), Version 4. AVH13C1 (NOAA National Centers for Environmental Information, 2014); https://doi.org/10.7289/V5PZ56R6.OCO-2-Science-Team, Gunson, M. & Eldering, A. OCO-2 Level 2 Bias-corrected Solar-induced Fluorescence and Other Select Fields from the IMAP-DOAS Algorithm Aggregated as Daily Files, Retrospective Processing V10r (Goddard Earth Sciences Data and Information Services Center, 2020).Joiner, J. et al. Global monitoring of terrestrial chlorophyll fluorescence from moderate-spectral-resolution near-infrared satellite measurements: methodology, simulations, and application to GOME-2. Atmos. Meas. Tech. 6, 2803–2823 (2013).Article
Google Scholar
Huffman, G., Stocker, E. F., Bolvin, D. T., Nelkin, E. J. & Tan, J. GPM IMERG Final Precipitation L3 Half Hourly 0.1 degree x 0.1 degree V06 (Goddard Earth Sciences Data and Information Services Center, 2019).Xie, P. et al. A gauge-based analysis of daily precipitation over East Asia. J. Hydrometeorol. 8, 607–626 (2007).Article
ADS
Google Scholar
Contractor, S. et al. Rainfall Estimates on a Gridded Network (REGEN)—a global land-based gridded dataset of daily precipitation from 1950 to 2016. Hydrol. Earth Syst. Sci. 24, 919–943 (2020).Article
ADS
Google Scholar
Roca, R. et al. FROGS: a daily 1° × 1° gridded precipitation database of rain gauge, satellite and reanalysis products. Earth Syst. Sci. Data 11, 1017–1035 (2019).Article
ADS
Google Scholar
Reichle, R. H. et al. Land surface precipitation in MERRA-2. J. Clim. 30, 1643–1664 (2017).Article
ADS
Google Scholar
Eyring, V. et al. Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev. 9, 1937–1958 (2016).Article
ADS
Google Scholar
Copernicus Climate Change Service Climate Data Store. CMIP6 climate projections. Climate Data Store https://doi.org/10.24381/cds.c866074c (2021).Joiner, J. et al. Estimation of terrestrial global gross primary production (GPP) with satellite data-driven models and eddy covariance flux data. Remote Sens. 10, 1346 (2018).Article
ADS
Google Scholar
NASA/LARC/SD/ASDC. CERES and GEO-Enhanced TOA, Within-Atmosphere and Surface Fluxes, Clouds and Aerosols Daily Terra-Aqua Edition4A [Data set]. EarthData https://doi.org/10.5067/Terra+Aqua/CERES/SYN1degDay_L3.004A (2017).Gelaro, R. et al. The Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2). J. Clim. 30, 5419–5454 (2017).Article
ADS
Google Scholar
Wan, Z., Hook, S. & Hulley, G. MYD11C2 MODIS/Aqua Land Surface Temperature/Emissivity 8-Day L3 Global 0.05 Deg CMG V006. EarthData https://doi.org/10.5067/MODIS/MYD11C2.006 (2015).O’Neill, P. E. et al. SMAP Enhanced L3 Radiometer Global Daily 9 km EASE-Grid Soil Moisture, Version 3 (NASA National Snow and Ice Data Center, 2019).Harmonized World Soil Database v2.0 (Food and Agriculture Organization of the United Nations, 2024); https://www.fao.org/soils-portal/data-hub/soil-maps-and-databases/harmonized-world-soil-database-v20/en/.Fan, Y., Miguez-Macho, G., Jobbágy, E. G., Jackson, R. B. & Otero-Casal, C. Hydrologic regulation of plant rooting depth. Proc. Natl Acad. Sci. USA 114, 10572–10577 (2017).Article
ADS
PubMed
PubMed Central
Google Scholar
Feldman, A. F., Konings, A., Piles, M. & Entekhabi, D. The Multi-Temporal Dual Channel Algorithm (MT-DCA) (Version 5) [Data set]. Zenodo https://doi.org/10.5281/zenodo.5619583 (2021).Kim, S. Ancillary Data Report: Landcover Classification JPL D-53057 (Jet Propulsion Laboratory, California Institute of Technology, 2013).Sala, O. E. & Lauenroth, W. K. Small rainfall events: an ecological role in semiarid regions. Oecologia 53, 301–304 (1982).Article
ADS
PubMed
Google Scholar
Giorgi, F., Raffaele, F. & Coppola, E. The response of precipitation characteristics to global warming from climate projections. Earth Syst. Dyn. 10, 73–89 (2019).Article
ADS
Google Scholar
Grömping, U. Estimators of relative importance in linear regression based on variance decomposition. Am. Stat. 61, 139–147 (2007).Article
MathSciNet
Google Scholar
Pastorello, G. et al. The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data. Sci. Data 7, 225 (2020).Article
PubMed
PubMed Central
Google Scholar
Ploton, P. et al. Spatial validation reveals poor predictive performance of large-scale ecological mapping models. Nat. Commun. 11, 4540 (2020).Article
ADS
PubMed
PubMed Central
Google Scholar
Lewińska, K. E. et al. Beyond “greening” and “browning”: trends in grassland ground cover fractions across Eurasia that account for spatial and temporal autocorrelation. Glob. Change Biol. 29, 4620–4637 (2023).Article
Google Scholar
Ludwig, M., Moreno-Martinez, A., Hölzel, N., Pebesma, E. & Meyer, H. Assessing and improving the transferability of current global spatial prediction models. Glob. Ecol. Biogeogr. 32, 356–368 (2023).Article
Google Scholar
James, G. M., Witten, D., Hastie, T. & Tibshirani, R. An Introduction to Statistical Learning: With Applications in R (Springer, 2014).Pedregosa, F. et al. Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).MathSciNet
Google Scholar
Brunsdon, C., Fotheringham, A. S. & Charlton, M. E. Geographically weighted regression: a method for exploring spatial nonstationarity. Geogr. Anal. 28, 281–298 (1996).Article
Google Scholar
Li, Y. et al. Widespread spring phenology effects on drought recovery of Northern Hemisphere ecosystems. Nat. Clim. Change 13, 182–188 (2023).Article
ADS
Google Scholar
Greene, W. H. Econometric Analysis (Prentice Hall, 2003).Griffin-Nolan, R. J., Slette, I. J. & Knapp, A. K. Deconstructing precipitation variability: rainfall event size and timing uniquely alter ecosystem dynamics. J. Ecol. https://doi.org/10.1080/10643389.2012.728825 (2021).Green, J. K. et al. Regionally strong feedbacks between the atmosphere and terrestrial biosphere. Nat. Geosci. 10, 410–414 (2017).Article
ADS
Google Scholar
Madani, N., Kimball, J. S., Jones, L. A., Parazoo, N. C. & Guan, K. Global analysis of bioclimatic controls on ecosystem productivity using satellite observations of solar-induced chlorophyll fluorescence. Remote Sens. 9, 530 (2017).Article
ADS
Google Scholar
Lundberg, S. M. & Lee, S.-I. A unified approach to interpreting model predictions. In 31st Conference on Neural Information Processing System (NeurIPS, 2017); https://papers.nips.cc/paper_files/paper/2017/hash/8a20a8621978632d76c43dfd28b67767-Abstract.html.Andrews, T. et al. On the effect of historical SST patterns on radiative feedback. J. Geophys. Res. Atmos. 127, e2022JD036675 (2022).Bueso, D. et al. Soil and vegetation water content identify the main terrestrial ecosystem changes. Natl. Sci. Rev. 10, nwad026 (2023).Ives, A. R. et al. Statistical inference for trends in spatiotemporal data. Remote Sens. Environ. 266, 112678 (2021).Article
Google Scholar
Cortés, J. et al. Where are global vegetation greening and browning trends significant? Geophys. Res. Lett. 48, 1–9 (2021).Article
Google Scholar
Cortés, J., Mahecha, M., Reichstein, M. & Brenning, A. Accounting for multiple testing in the analysis of spatio-temporal environmental data. Environ. Ecol. Stat. 27, 293–318 (2020).Article
Google Scholar
Keenan, T. F. et al. Increase in forest water-use efficiency as atmospheric carbon dioxide concentrations rise. Nature 499, 324–327 (2013).Article
ADS
PubMed
Google Scholar
Feldman, A. Feldman et al. Global one degree datasets. Zenodo https://doi.org/10.5281/zenodo.10947071 (2024).Feldman, A. et al. Feldman et al. 2024 “Large global scale vegetation sensitivity to daily rainfall variability”. Zenodo https://doi.org/10.5281/zenodo.13551521 (2024).Download referencesAcknowledgementsA.F.F. was supported by an appointment to the NASA Postdoctoral Program at the NASA Goddard Space Flight Center, administered by Oak Ridge Associated Universities under contract with NASA. A.F.F. was also partly supported by a NASA Terrestrial Ecology Program scoping study for dryland ecosystems. A.G.K. was supported by the Alfred P. Sloan Foundation and by NSF DEB 1942133. W.K.S. and B.P. acknowledge support from the NASA Carbon Cycle Science grant number 80NSSC23K0109. M.A. acknowledges Swiss National Science Foundation grant number 206603. L.W. acknowledges partial support from the US National Science Foundation (DEB-2307257 and DEB-2406931). USDA is an equal-opportunity employer and provider. We acknowledge the World Climate Research Programme, which, through its Working Group on Coupled Modelling, coordinated and promoted CMIP6. We thank the climate modelling groups for producing and making available their model output, the Earth System Grid Federation (ESGF) for archiving the data and providing access, and the multiple funding agencies that support CMIP6 and ESGF. This work used eddy covariance data acquired and shared by the FLUXNET community, including these networks: AmeriFlux, AfriFlux, AsiaFlux, CarboAfrica, CarboEuropeIP, CarboItaly, CarboMont, ChinaFlux, Fluxnet-Canada, GreenGrass, ICOS, KoFlux, LBA, NECC, OzFlux-TERN, TCOS-Siberia and USCCC. The FLUXNET eddy covariance data processing and harmonization was carried out by the ICOS Ecosystem Thematic Center, AmeriFlux Management Project and Fluxdata project of FLUXNET, with the support of CDIAC, and the OzFlux, ChinaFlux and AsiaFlux offices.Author informationAuthors and AffiliationsBiospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USAAndrew F. Feldman & Benjamin PoulterEarth System Science Interdisciplinary Center, University of Maryland, College Park, MD, USAAndrew F. FeldmanDepartment of Earth System Science, Stanford University, Stanford, CA, USAAlexandra G. KoningsDepartment of Earth and Environmental Engineering, Columbia University, New York, NY, USAPierre Gentine & Mitra CattryDepartment of Earth and Environmental Sciences, Indiana University Indianapolis, Indianapolis, IN, USALixin WangSchool of Natural Resources and the Environment, University of Arizona, Tucson, AZ, USAWilliam K. SmithAgricultural Research Service, US Department of Agriculture, Tucson, AZ, USAJoel A. BiedermanJet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USAAbhishek ChatterjeeAtmospheric Chemistry and Dynamics Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USAJoanna JoinerAuthorsAndrew F. FeldmanView author publicationsYou can also search for this author in
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PubMed Google ScholarContributionsA.F.F. conceived the study with input from B.P. A.F.F. conducted the analysis and wrote the initial paper. A.G.K., P.G., J.J., A.C. and B.P. provided guidance on the methods throughout the analysis. M.A., L.W., W.K.S. and J.A.B. provided guidance in part on methods and mainly on the interpretation of results. All authors contributed substantial revisions to the text and figures.Corresponding authorCorrespondence to
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Additional informationPublisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Extended data figures and tablesExtended Data Fig. 1 Across historical simulations and projections, rainfall is becoming less frequent, but more intense.Historical and projected rainfall trends of (a, b) wet day frequency, and (c, d) wet day intensity, and (e, f) annual rainfall total using CMIP6 historical simulations (1940–2020) and CMIP6 RCP8.5 models (2020–2099).Extended Data Fig. 2 Across observation-based rainfall datasets, rainfall is becoming less frequent, but more intense.Rainfall trends of (a, b) wet day frequency, and (c, d) wet day intensity, and (e, f) annual rainfall total using CPC gridded observations (1980–2020) and MERRA2 model reanalysis (1980–2020).Extended Data Fig. 3 Wet day frequency and annual rainfall amount have enough uncorrelated information to be included together and partitioned in a regression.(a) Variance inflation factor of wet day frequency and intensity. Higher values (especially much over 5) indicate multi-collinearity with annual rainfall mean and thus higher uncertainty partitioning effects between the variables. (b) Interannual coefficient of variation computed as the interannual standard deviation divided by interannual mean for each respective rainfall characteristic. Similar magnitudes between variables suggest variability of one variable is not dominating the regression.Extended Data Fig. 4 Mechanistic explanation of vegetation sensitivity to more intense, less frequent wet days across the global mean rainfall gradient (in Fig. 3).(a) Effect of soil, plant, and atmospheric factors on vegetation sensitivity to more intense, less frequent wet days. ** indicates significance (p < 0.05). Positive values suggest that increasing the respective driver promotes higher vegetation behavior in years with more intense, less frequent wet days. Computation of individual mechanistic factors is discussed in the Methods and their relationships with mean annual rainfall are shown in Fig. S15. Mean VPD, Soil Moisture, and Solar Radiation “Sensitivity” refers to the response of these climate variables to more intense, less frequent wet days (see text and Methods). (b) Variance explained of factors in (a).Extended Data Fig. 5 Empirically estimated vegetation trends due to daily rainfall variability trends.Spatial maps of empirically estimated vegetation trends due to trends in daily rainfall variability from (a) CPC, (b) MERRA2, (c) CMIP6 historical simulations, and (d) CMIP6 RCP8.5 projections.Supplementary informationSupplementary InformationThis file includes one notes section that covers several details about robustness of the results in the main text. Supplementary Figs. 1–20 provide robustness tests and context to Figs. 1, 3 and 4. Supplementary Tables 1 and 2 include details about the CMIP6 models and FLUXNET sites.Peer Review FileRights and permissionsSpringer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.Reprints and permissionsAbout this articleCite this articleFeldman, A.F., Konings, A.G., Gentine, P. et al. Large global-scale vegetation sensitivity to daily rainfall variability.
Nature (2024). https://doi.org/10.1038/s41586-024-08232-zDownload citationReceived: 18 September 2023Accepted: 16 October 2024Published: 11 December 2024DOI: https://doi.org/10.1038/s41586-024-08232-zShare this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy to clipboard
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