in Ecology

Greening masks stability loss in drylands

146 Views


Abstract

Drylands are already undergoing large-scale change due to climate change, yet understanding of this response remains limited by observational gaps, conflicting satellite records and substantial model uncertainty. Here, considering trends and interannual variability based on leaf area index (LAI), we show persistent (1982–2020) greening across global drylands and increasing interannual variability. Greening rates are higher than current dynamic global vegetation models and contradict reports of a recent greening slowdown. The interannual variability increase is spatially coherent, covering about 82% of global drylands, and is characterized by increasing LAI maxima and declining minima. Increasing rainfall sensitivity is the dominant driver of increasing variability with both inter- and intraannual rainfall variability acting as secondary drivers. The dynamic global vegetation models analysed cannot reproduce increasing variability and divergent LAI trajectories. Rising variability may indicate eminent state transitions that could intensify carbon cycle uncertainty and disrupt key dryland ecosystem services.

This is a preview of subscription content, access via your institution

Access options

Access through your institution

Buy this article

  • Purchase on SpringerLink
  • Instant access to the full article PDF.

USD 39.95

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Persistent greening yet increasing variability of annual growing season LAI across global drylands.
Fig. 2: Divergent trajectories of upper and lower bounds of the LAI distribution across global drylands.
Fig. 3: Comparison of observed and modelled trends in LAI versus LAICV.
Fig. 4: Potential drivers of increasing LAICV and comparison of spatial dominance patterns between observational multi-products average and TRENDY ensemble mean.

Similar content being viewed by others

Vegetation cover change as a growing driver of global leaf area index dynamics

Article
Open access
20 October 2025

Increasing sensitivity of dryland vegetation greenness to precipitation due to rising atmospheric CO2

Article
Open access
19 August 2022

Predictability of abrupt shifts in dryland ecosystem functioning

Article

03 January 2025

Explore related subjects

Discover the latest articles and news in related subjects.

  • Climate-change ecology

  • Ecological modelling

  • Ecosystem ecology

Data availability

All datasets used in this study are publicly available. The Global Inventory Modeling and Mapping Studies 4th Generation LAI dataset (GIMMS4g LAI) is available via Zenodo at https://doi.org/10.5281/zenodo.8281930 (ref. 79); the Global Mapping (GLOBMAP) LAI version 3 dataset is available via Zenodo at https://doi.org/10.5281/zenodo.4700264 (ref. 80); and the Seoul National University (SNU) LAI dataset is available at https://drive.google.com/drive/folders/1ltwcRgJ7IBEZ3mcc7S1eKzCuOon9mws5. Moderate Resolution Imaging Spectroradiometer (MODIS) version 6.1 LAI product (MOD15A2H) is available at https://www.earthdata.nasa.gov/data/catalog/lpcloud-mod15a2h-061. Eddy covariance GPP measurements were obtained from https://data.fluxnet.org/data/. Daily precipitation data were obtained from the CRU-JRA v2.5 forcing dataset at https://catalogue.ceda.ac.uk/uuid/43ce517d74624a5ebf6eec5330cd18d5. Monthly precipitation, temperature, vapour pressure deficit (VPD) and potential evapotranspiration (PET) data were obtained from the CRU TS v4.08 dataset at https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.08/. Annual land-cover data were obtained from the European Space Agency Climate Change Initiative (ESA CCI) Land Cover product v2.1.1 (1992–2020) at https://maps.elie.ucl.ac.be/CCI/viewer/download.php. The Global Land Cover 2000 dataset is available at https://forobs.jrc.ec.europa.eu/products/glc2000/glc2000.php. MODIS land-cover data (MCD12Q1 version 6.1) are available from NASA EARTHDATA at https://doi.org/10.5067/MODIS/MCD12Q1.061. Model-simulated LAI data were obtained from 13 DGVMs participating in the TRENDYv13 model intercomparison project. TRENDY model outputs are publicly available via the TRENDY project repository at https://mdosullivan.github.io/GCB/. AVHRR-LTDR Burned Area Grid product (version 1.1) from the ESA Fire Climate Change Initiative is available at https://data.ceda.ac.uk/neodc/esacci/fire/data/burned_area/AVHRR-LTDR/grid/v1.1/. The minimum dataset required to interpret, verify and reproduce the analyses presented in this study is publicly available via Zenodo at https://doi.org/10.5281/zenodo.21536423 (ref. 81).

Code availability

The code for the analyses is available via Zenodo at https://doi.org/10.5281/zenodo.21541318 (ref. 82).

References

  1. Wang, L. et al. Dryland productivity under a changing climate. Nat. Clim. Change 12, 981–994 (2022).

    Article 

    Google Scholar 

  2. Smith, W. K. et al. Remote sensing of dryland ecosystem structure and function: progress, challenges, and opportunities. Remote Sens. Environ. 233, 111401 (2019).

    Article 

    Google Scholar 

  3. Poulter, B. et al. Contribution of semi-arid ecosystems to interannual variability of the global carbon cycle. Nature 509, 600–603 (2014).

    Article 
    CAS 

    Google Scholar 

  4. Ahlström, A. et al. The dominant role of semi-arid ecosystems in the trend and variability of the land CO2 sink. Science 348, 895–899 (2015).

    Article 

    Google Scholar 

  5. 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 

    Google Scholar 

  6. Seneviratne, S. et al. Changes in climate extremes and their impacts on the natural physical environment. in Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation (eds Field, C. B. et al.) Ch. 3, 109–230 https://doi.org/10.7916/D8-6NBT-S431 (Columbia University, 2012).

  7. Gebrechorkos, S. H. et al. Warming accelerates global drought severity. Nature 642, 628–635 (2025).

    Article 
    CAS 

    Google Scholar 

  8. Pendergrass, A. G., Knutti, R., Lehner, F., Deser, C. & Sanderson, B. M. Precipitation variability increases in a warmer climate. Sci. Rep. 7, 17966 (2017).

    Article 

    Google Scholar 

  9. Zhang, Y. et al. Increasing sensitivity of dryland vegetation greenness to precipitation due to rising atmospheric CO2. Nat. Commun. 13, 4875 (2022).

    Article 
    CAS 

    Google Scholar 

  10. Fu, Z. et al. Critical soil moisture thresholds of plant water stress in terrestrial ecosystems. Sci. Adv. 8, eabq7827 (2022).

    Article 
    CAS 

    Google Scholar 

  11. Grossiord, C. et al. Plant responses to rising vapor pressure deficit. New Phytol. 226, 1550–1566 (2020).

    Article 

    Google Scholar 

  12. Novick, K. A. et al. The increasing importance of atmospheric demand for ecosystem water and carbon fluxes. Nat. Clim. Change 6, 1023–1027 (2016).

    Article 
    CAS 

    Google Scholar 

  13. Liang, M. et al. Unifying spatial scaling laws of biodiversity and ecosystem stability. Science 387, eadl2373 (2025).

    Article 
    CAS 

    Google Scholar 

  14. Donohue, I. et al. Navigating the complexity of ecological stability. Ecol. Lett. 19, 1172–1185 (2016).

    Article 

    Google Scholar 

  15. Odum, E. P., Finn, J. T. & Franz, E. H. Perturbation theory and the subsidy-stress gradient. Bioscience 29, 349–352 (1979).

    Article 

    Google Scholar 

  16. May, R. M. Will a large complex system be stable?. Nature 238, 413–414 (1972).

    Article 
    CAS 

    Google Scholar 

  17. Boulton, C. A., Lenton, T. M. & Boers, N. Pronounced loss of Amazon rainforest resilience since the early 2000s. Nat. Clim. Change 12, 271–278 (2022).

    Article 

    Google Scholar 

  18. Zhang, Y. et al. Warming and disturbances affect Arctic-boreal vegetation resilience across northwestern North America. Nat. Ecol. Evol. 8, 2265–2276 (2024).

    Article 

    Google Scholar 

  19. Chen, C. et al. China and India lead in greening of the world through land-use management. Nat. Sustain. 2, 122–129 (2019).

    Article 

    Google Scholar 

  20. Zhu, Z. et al. Greening of the Earth and its drivers. Nat. Clim. Change 6, 791–795 (2016).

    Article 
    CAS 

    Google Scholar 

  21. Piao, S. et al. Characteristics, drivers and feedbacks of global greening. Nat. Rev. Earth Environ. 1, 14–27 (2019).

    Article 

    Google Scholar 

  22. Zhang, Y., Song, C., Band, L. E., Sun, G. & Li, J. Reanalysis of global terrestrial vegetation trends from MODIS products: browning or greening?. Remote Sens. Environ. 191, 145–155 (2017).

    Article 

    Google Scholar 

  23. Tian, F. et al. Evaluating temporal consistency of long-term global NDVI datasets for trend analysis. Remote Sens. Environ. 163, 326–340 (2015).

    Article 

    Google Scholar 

  24. Wang, Z. et al. Large discrepancies of global greening: Indication of multi-source remote sensing data. Glob. Ecol. Conserv. 34, e02016 (2022).

    Google Scholar 

  25. Jiang, C. et al. Inconsistencies of interannual variability and trends in long-term satellite leaf area index products. Glob. Chang. Biol. 23, 4133–4146 (2017).

    Article 

    Google Scholar 

  26. Cao, S. et al. Spatiotemporally consistent global dataset of the GIMMS leaf area index (GIMMS LAI4g) from 1982 to 2020. Earth Syst. Sci. Data 15, 4877–4899 (2023).

    Article 

    Google Scholar 

  27. Jeong, S. et al. Persistent global greening over the last four decades using novel long-term vegetation index data with enhanced temporal consistency. Remote Sens. Environ. 311, 114282 (2024).

    Article 

    Google Scholar 

  28. Jeong, S. et al. Sustained global greening driven by continuous CO2 fertilization. Preprint at Research Square https://doi.org/10.21203/RS.3.RS-8621169/V1 (2026).

  29. Piao, S. et al. Interannual variation of terrestrial carbon cycle: issues and perspectives. Glob. Change Biol. 26, 300–318 (2020).

    Article 

    Google Scholar 

  30. Lin, S. et al. Underestimated interannual variability of terrestrial vegetation production by terrestrial ecosystem models. Glob. Biogeochem. Cycles 37, e2023GB007696 (2023).

    Article 
    CAS 

    Google Scholar 

  31. MacBean, N. et al. Dynamic global vegetation models underestimate net CO2 flux mean and inter-annual variability in dryland ecosystems. Environ. Res. Lett. 16, 094023 (2021).

    Article 
    CAS 

    Google Scholar 

  32. Fawcett, D. et al. Assessing model predictions of carbon dynamics in global drylands. Front. Environ. Sci. 10, 790200 (2022).

    Article 

    Google Scholar 

  33. Yang, R. et al. Divergent historical GPP trends among state-of-the-art multi-model simulations and satellite-based products. Earth Syst. Dyn. 13, 833–849 (2022).

    Article 

    Google Scholar 

  34. Liu, Y., Liu, R. & Chen, J. M. Retrospective retrieval of long-term consistent global leaf area index (1981-2011) from combined AVHRR and MODIS data. J. Geophys. Res. Biogeosci. 117, 4003 (2012).

    Article 

    Google Scholar 

  35. Sitch, S. et al. Trends and drivers of terrestrial sources and sinks of carbon dioxide: an overview of the TRENDY project. Glob. Biogeochem. Cycles 38, e2024GB008102 (2024).

    Article 
    CAS 

    Google Scholar 

  36. Yuan, W. et al. Increased atmospheric vapor pressure deficit reduces global vegetation growth. Sci. Adv. 5, eaax1396 (2019).

    Article 
    CAS 

    Google Scholar 

  37. Li, F. et al. Dryland dominance in the slowdown of global vegetation carbon uptake. Nat. Geosci. 19, 542–548 (2026).

    Article 
    CAS 

    Google Scholar 

  38. Tian, J. & Luo, X. Conflicting changes of vegetation greenness interannual variability on half of the global vegetated surface. Earth’s Future 12, e2023EF004119 (2024).

    Article 

    Google Scholar 

  39. Knapp, A. K. & Smith, M. D. Variation among biomes in temporal dynamics of aboveground primary production. Science 291, 481–484 (2001).

    Article 
    CAS 

    Google Scholar 

  40. Knapp, A. K., Ciais, P. & Smith, M. D. Reconciling inconsistencies in precipitation–productivity relationships: implications for climate change. New Phytol. 214, 41–47 (2017).

    Article 

    Google Scholar 

  41. Gutschick, V. P. & BassiriRad, H. Extreme events as shaping physiology, ecology, and evolution of plants: toward a unified definition and evaluation of their consequences. New Phytol. 160, 21–42 (2003).

    Article 

    Google Scholar 

  42. Haverd, V., Ahlström, A., Smith, B. & Canadell, J. G. Carbon cycle responses of semi-arid ecosystems to positive asymmetry in rainfall. Glob. Change Biol. 23, 793–800 (2017).

    Article 

    Google Scholar 

  43. Huxman, T. E. et al. Precipitation pulses and carbon fluxes in semiarid and arid ecosystems. Oecologia 141, 254–268 (2004).

    Article 

    Google Scholar 

  44. Sala, O. E., Gherardi, L. A., Reichmann, L., Jobbágy, E. & Peters, D. Legacies of precipitation fluctuations on primary production: theory and data synthesis. Philos. Trans. R. Soc. B 367, 3135–3144 (2012).

    Article 

    Google Scholar 

  45. Paschalis, A. et al. Rainfall manipulation experiments as simulated by terrestrial biosphere models: where do we stand?. Glob. Change Biol. 26, 3336–3355 (2020).

    Article 

    Google Scholar 

  46. Wu, D. et al. Asymmetric responses of primary productivity to altered precipitation simulated by ecosystem models across three long-term grassland sites. Biogeosciences 15, 3421–3437 (2018).

    Article 

    Google Scholar 

  47. Harp, R. D. & Horton, D. E. Observed changes in interannual precipitation variability in the United States. Geophys. Res. Lett. 50, e2023GL104533 (2023).

    Article 

    Google Scholar 

  48. Feldman, A. F. et al. Large global-scale vegetation sensitivity to daily rainfall variability. Nature 636, 380–384 (2024).

    Article 
    CAS 

    Google Scholar 

  49. Feldman, A. F. et al. Plant responses to changing rainfall frequency and intensity. Nat. Rev. Earth Environ. 5, 276–294 (2024).

    Article 

    Google Scholar 

  50. 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 

    Google Scholar 

  51. 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 

    Google Scholar 

  52. Hou, E. et al. Divergent responses of primary production to increasing precipitation variability in global drylands. Glob. Change Biol. 27, 5225–5237 (2021).

    Article 
    CAS 

    Google Scholar 

  53. Zhang, W. et al. Seasonal stabilization effects slowed the greening of the Northern Hemisphere over the last two decades. Nat. Commun. 16, 6287 (2025).

    Article 
    CAS 

    Google Scholar 

  54. Lian, X. et al. Diminishing carryover benefits of earlier spring vegetation growth. Nat. Ecol. Evol. 8, 218–228 (2024).

    Article 

    Google Scholar 

  55. Peñuelas, J. et al. Shifting from a fertilization-dominated to a warming-dominated period. Nat. Ecol. Evol. 1, 1438–1445 (2017).

    Article 

    Google Scholar 

  56. Zhang, Y., Keenan, T. F. & Zhou, S. Exacerbated drought impacts on global ecosystems due to structural overshoot. Nat. Ecol. Evol. 5, 1490–1498 (2021).

    Article 

    Google Scholar 

  57. Jump, A. S. et al. Structural overshoot of tree growth with climate variability and the global spectrum of drought-induced forest dieback. Glob. Change Biol. 23, 3742–3757 (2017).

    Article 

    Google Scholar 

  58. Tilman, D., Wedin, D. & Knops, J. Productivity and sustainability influenced by biodiversity in grassland ecosystems. Nature 379, 718–720 (1996).

    Article 
    CAS 

    Google Scholar 

  59. Veldhuis, M. P., Martinez-Garcia, R., Deblauwe, V. & Dakos, V. Remotely-sensed slowing down in spatially patterned dryland ecosystems. Ecography 2022, e06139 (2022).

    Article 

    Google Scholar 

  60. Lenton, T. M. et al. Remotely sensing potential climate change tipping points across scales. Nat. Commun. 15, 343 (2024).

    Article 
    CAS 

    Google Scholar 

  61. Van Passel, J. et al. Critical slowing down of the Amazon forest after increased drought occurrence. Proc. Natl Acad. Sci. USA 121, e2316924121 (2024).

    Article 

    Google Scholar 

  62. Li, Y. et al. Widespread spring phenology effects on drought recovery of Northern Hemisphere ecosystems. Nat. Clim. Change 13, 182–188 (2023).

    Article 

    Google Scholar 

  63. Archer, S. R. et al. Woody plant encroachment: causes and consequences. in Rangeland Ecosystems (ed Briske, D.) Ch. 2, 25–84 https://doi.org/10.1007/978-3-319-46709-2_2 (Springer Nature, 2017).

  64. Wieczorkowski, J. D., R Lehmann, C. E. & Jakub Wieczorkowski, C. D. Encroachment diminishes herbaceous plant diversity in grassy ecosystems worldwide. Glob. Change Biol. 28, 5532–5546 (2022).

    Article 
    CAS 

    Google Scholar 

  65. Teckentrup, L. et al. Assessing the representation of the Australian carbon cycle in global vegetation models. Biogeosciences 18, 5639–5668 (2021).

    Article 
    CAS 

    Google Scholar 

  66. Whitley, R. et al. Challenges and opportunities in land surface modelling of savanna ecosystems. Biogeosciences 14, 4711–4732 (2017).

    Article 

    Google Scholar 

  67. Pervin, R. et al. Importance of plant functional type, dynamic vegetation, and fire interactions for process-based modeling of gross carbon uptake across the drylands of western North America. Preprint at EGUsphere https://doi.org/10.5194/EGUSPHERE-2025-2841 (2025).

  68. Myneni, R. et al. MODIS/Terra Leaf Area Index/FPAR 8-Day L4 Global 500m SIN Grid V061. NASA Land Processes Distributed Active Archive Center https://doi.org/10.5067/MODIS/MOD15A2H.061 (2021).

  69. Scott, R. L., Jenerette, G. D., Potts, D. L. & Huxman, T. E. Effects of seasonal drought on net carbon dioxide exchange from a woody-plant-encroached semiarid grassland. J. Geophys. Res. Biogeosci. 114, G04004 (2009).

    Article 

    Google Scholar 

  70. Scott, R. L., Biederman, J. A., Hamerlynck, E. P. & Barron-Gafford, G. A. The carbon balance pivot point of southwestern U.S. semiarid ecosystems: insights from the 21st century drought. J. Geophys. Res. Biogeosci. 120, 2612–2624 (2015).

    Article 
    CAS 

    Google Scholar 

  71. Scott, R. L., Hamerlynck, E. P., Jenerette, G. D., Moran, M. S. & Barron-Gafford, G. A. Carbon dioxide exchange in a semidesert grassland through drought-induced vegetation change. J. Geophys. Res. Biogeosci. 115, G03026 (2010).

    Article 

    Google Scholar 

  72. CRU JRA v2.5: a forcings dataset of gridded land surface blend of Climatic Research Unit (CRU) and Japanese reanalysis (JRA) data; Jan.1901 – Dec.2023. CEDA Archive https://catalogue.ceda.ac.uk/uuid/43ce517d74624a5ebf6eec5330cd18d5/ (2024).

  73. Defourny, P. et al. Observed annual global land-use change from 1992 to 2020 three times more dynamic than reported by inventory-based statistics. In preparation (2023).

  74. Middleton, N. J. & Thomas, D. S. G. World Atlas of Desertification (United Nations Environment Programme; Edward Arnold, 1992).

  75. Harris, I., Osborn, T. J., Jones, P. & Lister, D. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset. Sci. Data 7, 109 (2020).

    Article 

    Google Scholar 

  76. Peano, D. et al. Global variability of simulated and observed vegetation growing season. J. Geophys. Res. Biogeosci. 124, 3569–3587 (2019).

    Article 

    Google Scholar 

  77. Sala, O. E. & Lauenroth, W. K. Small rainfall events: an ecological role in semiarid regions. Oecologia 53, 301–304 (1982).

    Article 
    CAS 

    Google Scholar 

  78. Bond, W. J., Woodward, F. I. & Midgley, G. F. The global distribution of ecosystems in a world without fire. New Phytol. 165, 525–538 (2005).

    Article 
    CAS 

    Google Scholar 

  79. Cao, S. et al. Spatiotemporally consistent global dataset of the GIMMS Leaf Area Index (GIMMS LAI4g) from 1982 to 2020 (V1.2). Zenodo https://doi.org/10.5281/zenodo.8281930 (2023).

  80. Liu, R., Liu, Y. & Chen, J. GLOBMAP global leaf area index since 1981. Zenodo https://doi.org/10.5281/zenodo.4700264 (2021).

  81. Zhang, W. et al. Minimal dataset for greening masks stability loss in drylands. Zenodo https://doi.org/10.5281/zenodo.21536423 (2026).

  82. Zhang, W. et al. Code for greening masks stability loss in drylands. Zenodo https://doi.org/10.5281/zenodo.21541318 (2026).

Download references

Acknowledgements

We would like to acknowledge the Global Carbon Project, which is responsible for the Global Carbon Budget, and we thank the land modelling groups for producing and making available their model outputs that were used in this analysis. Any use of trade, firm or product names is for descriptive purposes only and does not imply endorsement by the US Government. Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of our shareholders or sponsors. United States Department of Agriculture (USDA) is an equal-opportunity provider.

Funding

W.Z., W.K.S., B.P., S.C.R. and N.M. acknowledge funding for this research provided by the National Aeronautics and Space Administration (NASA) Carbon Cycle Science Program (grant 80NSSC23K0109). W.Z., R.L.S. and W.K.S acknowledge funding provided by the US Department of Agriculture (USDA; cooperative agreement 58–2022–2–001). Several authors also acknowledge funding provided by the NASA Terrestrial Ecology Program to scope a dryland field campaign (grant 80NSSC23K1420). N.M. acknowledges the support of the Natural Sciences and Engineering Research Council of Canada (NSERC) (funding reference numbers RGPIN/05249-2023 and DGECR/00302-2023). Cette recherche a été financée par le Conseil de recherches en sciences naturelles et en génie du Canada (CRSNG) (numéros de références RGPIN/05249-2023 et DGECR/00302-2023). N.M. acknowledges support from the Wolfe–Western Fellowship At-Large for Outstanding Newly Recruited Research Scholars Endowment Fund. Y.K. acknowledges support from the NASA Early Career Investigator Program (award number 80NSSC24K1562).

Author information

Authors and Affiliations

  1. School of Natural Resources and the Environment, University of Arizona, Tucson, AZ, USA

    Wen Zhang, David J. P. Moore, Yang Li & William K. Smith

  2. Department of Geography and Environment, Western University, London, Ontario, Canada

    Natasha MacBean

  3. Department of Biology, Western University, London, Ontario, Canada

    Natasha MacBean

  4. Biospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA

    Andrew F. Feldman

  5. Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, USA

    Andrew F. Feldman

  6. Woodwell Climate Research Center, Falmouth, MA, USA

    Christopher R. Schwalm

  7. Department of Biological Systems Engineering, Virginia Tech, Blacksburg, VA, USA

    Yanghui Kang

  8. Department of Environmental Science, University of Arizona, Tucson, AZ, USA

    Julia K. Green & Zexi Shen

  9. Spark Climate Solutions, Covina, CA, USA

    Benjamin Poulter

  10. Department of Environmental Studies, College of the Holy Cross, Worcester, MA, USA

    Mostafa Javadian

  11. Southwest Watershed Research Center, Agricultural Research Service, US Department of Agriculture, Tucson, AZ, USA

    Russell L. Scott

  12. Department of Landscape Architecture and Rural Systems Engineering, Seoul National University, Seoul, South Korea

    Youngryel Ryu

  13. NASA Ames Research Center, Moffett Field, Mountain View, CA, USA

    Sungchan Jeong

  14. U.S. Geological Survey (USGS), Southwest Biological Science Center, Moab, UT, USA

    Sasha C. Reed

Authors

  1. Wen Zhang
    View author publications

    Search author on:PubMed Google Scholar

  2. David J. P. Moore
    View author publications

    Search author on:PubMed Google Scholar

  3. Yang Li
    View author publications

    Search author on:PubMed Google Scholar

  4. Natasha MacBean
    View author publications

    Search author on:PubMed Google Scholar

  5. Andrew F. Feldman
    View author publications

    Search author on:PubMed Google Scholar

  6. Christopher R. Schwalm
    View author publications

    Search author on:PubMed Google Scholar

  7. Yanghui Kang
    View author publications

    Search author on:PubMed Google Scholar

  8. Julia K. Green
    View author publications

    Search author on:PubMed Google Scholar

  9. Benjamin Poulter
    View author publications

    Search author on:PubMed Google Scholar

  10. Mostafa Javadian
    View author publications

    Search author on:PubMed Google Scholar

  11. Russell L. Scott
    View author publications

    Search author on:PubMed Google Scholar

  12. Zexi Shen
    View author publications

    Search author on:PubMed Google Scholar

  13. Youngryel Ryu
    View author publications

    Search author on:PubMed Google Scholar

  14. Sungchan Jeong
    View author publications

    Search author on:PubMed Google Scholar

  15. Sasha C. Reed
    View author publications

    Search author on:PubMed Google Scholar

  16. William K. Smith
    View author publications

    Search author on:PubMed Google Scholar

Contributions

W.Z. and W.K.S. conceived the idea. W.Z., W.K.S and D.J.P.M. designed the study and participated in the early-stage discussion. W.Z. performed the analysis and wrote the first draft of the manuscript. D.J.P.M., Y.L., N.M., A.F.F., C.R.S., Y.K., J.K.G., B.P., M.J., R.L.S., Z.S., Y.R., S.J. and S.C.R. contributed to the interpretation of the results and revised the manuscript.

Corresponding authors

Correspondence to
Wen Zhang or William K. Smith.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Climate Change thanks Scott Collins, Xuanlong Ma and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Spatial patterns of long-term trends in annual growing season LAI (1982–2020) from three individual satellite products.

a, GIMMS4g; b, GLOBMAP; c, SNU. Colors show trend magnitude (% yr⁻1); black dots mark grid cells with significant trends (two-sided t-test, p < 0.05). Insets report the area fractions of significant greening (blue), significant browning (brown), and non-significant change (grey). Basemaps were generated with python3.12-mpltoolkits.basemap (version 2.0.0, https://matplotlib.org/basemap/stable/).

Extended Data Fig. 2 Decomposition of LAIcv Into standard deviation (std) and mean.

a, Time series of LAI std (left axis, purple) and mean (right axis, teal) based on multi satellite product average across global drylands using the 15-yr moving-window analysis (1982–2020). b, Joint map of trends in LAI std and mean. Slopes were estimated using linear regression applied to the global mean time series. Statistical significance was assessed using two-sided t-tests. Basemap in b was generated with python3.12-mpltoolkits.basemap (version 2.0.0, https://matplotlib.org/basemap/stable/).

Extended Data Fig. 3 Spatial patterns of long-term trends in LAIcv (1982–2020) across three individual satelite products using a 15-year moving window.

Trends in ({mathrm{LAI}}_{mathrm{cv}}) derived from (a) GIMMS4g, (b) GLOBMAP, and (c) SNU LAI. Colors indicate the magnitude of ({mathrm{LAI}}_{mathrm{cv}}) trends (% yr⁻1), and black dots denote grid cells with statistically significant trends (two-sided t-test, p < 0.05). Insets report the area fractions of significant increases in ({mathrm{LAI}}_{mathrm{cv}}) (purple), significant decreases in ({mathrm{LAI}}_{mathrm{cv}}) (green), and non-significant changes (grey). Basemaps were generated with python3.12-mpltoolkits.basemap (version 2.0.0, https://matplotlib.org/basemap/stable/).

Extended Data Fig. 4 Cross-product validation of increasing dryland LAI variability during the MODIS era (2001–2020).

(a) Temporal trends in dryland ({rm{LAI}}_{rm{CV}}) derived from four LAI products (MODIS, GIMMS4g, GLOBMAP, and SNU) during the MODIS era (2001–2020) using a 15-year moving window. Dashed lines indicate linear trends estimated by linear regression of the global mean time series. Statistical significance was assessed using two-sided t-tests; * denotes P<0.05, ** denotes P<0.01 and *** denotes P<0.001. The corresponding P values are 0.024 (MODIS), 1.753× 10⁻4 (GIMMS4g), 0.002 (GLOBMAP) and 3.537× 10⁻4 (SNU). (b–e) Spatial patterns of ({rm{LAI}}_{rm{CV}}) trends derived from (b) MODIS, (c) GIMMS4g, (d) GLOBMAP and (e) SNU during the overlapping period (2001–2020). Colors indicate the magnitude of ({mathrm{LAI}}_{mathrm{cv}}) trends (% yr⁻1), and black dots denote grid cells with statistically significant trends (two-sided t-test, p < 0.05). Basemaps in b–e were generated with python3.12-mpltoolkits.basemap (version 2.0.0, https://matplotlib.org/basemap/stable/).

Extended Data Fig. 5 Robustness of increasing dryland LAI variability across moving-window lengths using MODIS-era observations through 2024 (2001–2024).

a, c, e, Temporal trends in dryland ({rm{LAI}}_{rm{CV}}) derived from the MODIS LAI product using 5-years (a), 10-years (c), and 15-years (e) moving windows, respectively. Dashed lines indicate linear trends estimated by linear regression of the global mean time series. Statistical significance was assessed using two-sided t-tests; *** denotes P<0.001. The corresponding P values are 1.474 × 10⁻6 (5-years), 5.335× 10⁻6 (10-years), and 5.331 ×10⁻6 (15-years). b, d, f, Spatial maps illustrate the corresponding trends in ({rm{LAI}}_{rm{CV}}) for 5-years (b), 10-years (d) and 15-years (f) moving-window respectively. Colors indicate the magnitude of ({rm{LAI}}_{rm{cv}}) trends (% yr⁻1), and black dots denote grid cells with statistically significant trends (two-sided t-test, p < 0.05). Basemaps in b, d and f were generated with python3.12-mpltoolkits.basemap (version 2.0.0, https://matplotlib.org/basemap/stable/).

Extended Data Fig. 6 Partial correlations between LAICV and potential drivers.

Partial correlation between ({mathrm{LAI}}_{mathrm{CV}}) and (a) vegetation rainfall sensitivity (γ), (b) inter-annual rainfall variability (({rm{CV}}_{{rm{inter}}-{rm{rainfall}}})) and (c) intraannual rainfall variability ({rm{CV}}_{{rm{intra}}-{rm{rainfall}}})). Colours indicate correlation strength (blue = positive, red = negative). Only grid cells with significant correlations (two-sided t-test, P<0.05) are shown. Basemaps generated with python3.12-mpltoolkits.basemap (version 2.0.0, https://matplotlib.org/basemap/stable/).

Supplementary information

Supplementary Information (download PDF )

Supplementary Figs. 1–21 and Tables 1 and 2.

Reporting Summary (download PDF )

Peer Review File (download PDF )

Rights and permissions

Springer 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 permissions

About this article

Cite this article

Zhang, W., Moore, D.J.P., Li, Y. et al. Greening masks stability loss in drylands.
Nat. Clim. Chang. (2026). https://doi.org/10.1038/s41558-026-02733-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Version of record:

  • DOI: https://doi.org/10.1038/s41558-026-02733-7

Source: Ecology - nature.com

Multimodal fertility cues in chimpanzees: how body odours complement sexual swellings

Bacterial inoculation elicits changes to the coral epigenome