Abstract
Rapid surges in vegetation growth—defined by thresholds in growth rate and duration—are critical yet understudied indicators of ecosystem responses to environmental change. Here, we investigate spatiotemporal patterns of such abrupt, short-lived flash flourishing events across the northern extratropical latitudes (NEL) from 2003 to 2022. We find more frequent occurrence of flash flourishing events at high latitudes (≥45° N), where their incidence is 1.6 times higher than at mid-latitudes. Moreover, there is an increasing tendency in frequency, duration, and intensity of flash flourishing events over the past two decades, alongside consistent rises in vegetation indices across onset, post-onset, and entire phases. Model simulations attribute these multiyear increases primarily to elevated atmospheric CO2, while temperature and radiation predominantly control phase-specific variability, with onset traits strongly predicting subsequent phenological responses. Together, these findings identify the patterns and drivers of NEL flash flourishing and highlight their large-scale impacts on ecosystem dynamics, offering critical insights for model improvement and the assessment of ecological shifts.
Similar content being viewed by others
A pronounced decline in northern vegetation resistance to flash droughts from 2001 to 2022
Global ecosystem responses to flash droughts are modulated by background climate and vegetation conditions
Flash droughts exacerbate global vegetation loss and delay recovery
Data availability
This study investigated rapid vegetation growth in NEL from 2003 to 2022 using five remote sensing datasets focused on solar-induced fluorescence (SIF) and vegetation indices (VIs) (Supplementary Table S1). The continuous SIF (CSIF) dataset (https://osf.io/8xqy6/), a globally harmonized spatiotemporal product generated through machine learning, was utilized. This dataset integrates raw SIF observations from the Orbiting Carbon Observatory 2 (OCO-2) with predictor variables derived from the MCD43C1 C6 reflectance product65. In addition to SIF, the PKU GIMMS NDVI dataset was employed, which combines data from the Advanced Very High Resolution Radiometer (AVHRR) and the Moderate-Resolution Imaging Spectroradiometer (MODIS)6 (Li et al. 66). To further investigate vegetation dynamics, Enhanced Vegetation Index (EVI) and kernel Normalized Difference Vegetation Index (kNDVI) data were sourced from the MODIS MOD13C1 version 6 product. EVI data were directly retrieved, while the kNDVI was calculated based on the MOD13C1v6 NDVI dataset50. Gap-filled leaf area index (LAI) data from the MODIS C6 product were also included in the analysis (http://globalchange.bnu.edu.cn/research/laiv6). Additionally, we analyzed simulated 19 gross primary productivity (GPP) and 12 LAI datasets generated by process-based ecosystem models within the TRENDY v12 framework (Supplementary Tables S2, S3). These models are built upon core biogeochemical and physiological principles, capturing key processes such as photosynthesis, respiration, carbon allocation in vegetation, and soil biogeochemistry (https://blogs.exeter.ac.uk/trendy/). They simulate the response of these processes to environmental drivers, including temperature, soil moisture, atmospheric CO₂ levels, and land-use and land-cover change (LULCC). The TRENDY models utilize atmosphere and CO₂ input fields derived from observational datasets28, which serve as the basis for model simulations and evaluations. Each model runs under four experimental scenarios: S0 represents a control case with static environmental forcing, S1 introduces time-varying CO₂ while keeping other factors unchanged, S2 further incorporates temporal atmospheric variations while maintaining constant LULCC, and S3 accounts for dynamic changes in CO₂, atmosphere, and LULCC. By analyzing the differences between S1–S0, S2–S1, and S3–S2, the distinct impacts of CO₂, atmosphere variability, and LULCC on vegetation flash flourishing growth can be isolated67. To validate the analysis, gross primary productivity (GPP) data from the FLUXNET data product, including FLUXNET2015 and AmeriFLUX datasets (https://fluxnet.org/data/regional-network-data/), were analyzed. Sites selected for this analysis met the following criteria: (i) located in NEL (>30°N), (ii) a minimum observation period of 10 years, and (iii) evidence of at least one instance of vegetation flash flourishing growth events. Based on these criteria, 44 sites were selected from the FLUXNET dataset, with detailed site information presented in Supplementary Table S4. Because the available site records extend from 2003 to 2021, we have defined this interval as the focal period for our FLUXNET GPP analysis. Environmental factors influencing vegetation flash flourishing growth were assessed using temperature (TMP), precipitation (PRE), and radiation (RAD) data from the CRU JRA v2.4 dataset (https://catalogue.ceda.ac.uk/uuid/aed8e269513f446fb1b5d2512bb387ad). Vapor Pressure Deficit (VPD) was calculated from ERA5 2 m TMP and 2 m dew point temperature data (https://cds.climate.copernicus.eu/apps/user-apps/app-c3s-daily-era5-statistic), following the method described by Bolton11. Soil moisture (SM) data were sourced from the surface soil moisture dataset of the Global Land Evaporation Amsterdam Model (GLEAMv4.1a)68. We used CRU JRA v2.4 temperature directly as the TMP predictor, while ERA5 temperature is only employed together with ERA5 dew point to derive a physically consistent VPD field. Thus, temperature and VPD are treated as distinct predictors derived from separate, internally consistent datasets, rather than mixing temperature from two sources within the same variable. Using the MODIS landcover dataset (MCD12C1v6, type 3), we categorized the vegetation in NEL into three types: grasslands, shrublands, and forests67 (comprising savannas, evergreen broadleaf forests, deciduous broadleaf forests, evergreen needleleaf forests, and deciduous needleleaf forests) (https://lpdaac.usgs.gov/products/mcd12c1v006/) (Supplementary Fig. S5). To ensure consistency across datasets, all satellite-based data were resampled to a spatial resolution of 1/12° × 1/12°, allowing for precise calculations of each flash growth event. Similarly, the TRENDY-simulated data were resampled to a coarser resolution of 1/2° × 1/2°. The study area in NEL (>30°N) was defined based on two criteria: NDVI values exceeding 0.125 and an irrigation coverage fraction of 10% or less (Data were collected around 2005; http://www.fao.org/aquastat/en/geospatial-information/global-maps-irrigated-areas/latest-version/). Additionally, cropland areas identified using the MCD12C1v6 dataset (land cover type 3) were excluded from the analysis to focus on non-agricultural ecosystems.
References
Hong, S. et al. Contrasting temperature effects on the velocity of early- versus late-stage vegetation green-up in the Northern Hemisphere. Glob. Chang. Biol. 28, 6961–6972 (2022).
Yao, J. et al. Impact of shifts in vegetation phenology on the carbon balance of a semiarid sagebrush ecosystem. Remote Sens. 14, 5924 (2022).
Boulton, C. A., Ritchie, P. D. L. & Lenton, T. M. Abrupt changes in Great Britain vegetation carbon projected under climate change. Glob. Chang. Biol. 26, 4436–4448 (2020).
Denham, S. O. et al. The rate of canopy development modulates the link between the timing of spring leaf emergence and summer moisture. J. Geophys. Res. Biogeosci. 128, e2022JG007217 (2023).
Lian, X. et al. Diminishing carryover benefits of earlier spring vegetation growth. Nat. Ecol. Evol. 8, 218–228 (2024).
Li, Y. et al. Widespread spring phenology effects on drought recovery of Northern Hemisphere ecosystems. Nat. Clim. Chang. 13, 182–188 (2023).
Kim, J. H., Sohn, S., Wang, Z. & Kim, Y. Nonuniform response of vegetation phenology to daytime and nighttime warming in urban areas. Commun. Earth Environ. 5, 308 (2024).
Wu, X. et al. Canopy structure regulates autumn phenology by mediating the microclimate in temperate forests. Nat. Clim. Chang. 14, 1299–1305 (2024).
Buitenwerf, R., Rose, L. & Higgins, S. I. Three decades of multi-dimensional change in global leaf phenology. Nat. Clim. Chang. 5, 364–368 (2015).
Shen, M. et al. Challenges in remote sensing of vegetation phenology. Innov. Geosci. 2, 100070 (2024).
Zeng, L., Wardlow, B. D., Xiang, D., Hu, S. & Li, D. A review of vegetation phenological metrics extraction using time-series, multispectral satellite data. Remote Sens. Environ. 237, 111511 (2020).
Park, H., Jeong, S. & Peñuelas, J. Accelerated rate of vegetation green-up related to warming at northern high latitudes. Glob. Chang. Biol. 26, 6190–6202 (2020).
Liu, Z. et al. Enhanced vegetation productivity driven primarily by rate not duration of carbon uptake. Nat. Clim. Chang. https://doi.org/10.1038/s41558-025-02311-3 (2025).
Chen, M. et al. Rapid growth of Moso bamboo (Phyllostachys edulis): cellular roadmaps, transcriptome dynamics, and environmental factors. Plant Cell 34, 3577–3610 (2022).
Jiang, N., Shen, M. & Yang, Z. Differential phenological responses to temperature among various stages of spring vegetation green-up. J. Plant Ecol. 17, rtae063 (2024).
Koolen, S. P. et al. The coexistence of trees, shrubs, and grasses creates a complex picture of land surface phenology in dry tropical ecosystems. Remote Sens. 17, 2883 (2025).
Toomey, M. et al. Greenness indices from digital cameras predict the timing and seasonal dynamics of canopy-scale photosynthesis. Ecol. Appl. 25, http://ameriflux.ornl.gov/ (2015).
Kay, B. D., Hajabbasi, M. A., Ying, J. & Tollenaar, M. Optimum versus non-limiting water contents for root growth, biomass accumulation, gas exchange and the rate of development of maize (Zea mays L.). Soil Tillage Res. 88, 42–54 (2006).
Wang, Y. et al. Increasing optimum temperature of vegetation activity over the past four decades. Earths Fut. 12, e2024EF004489 (2024).
Lian, X. et al. Summer soil drying exacerbated by earlier spring greening of northern vegetation. Sci. Adv. 6, eaax0255 (2020).
Zhang, Y., Keenan, T. F. & Zhou, S. Exacerbated drought impacts on global ecosystems due to structural overshoot. Nat. Ecol. Evol. 5, 1490–1498 (2021).
Chapin, F. S., Matson, P. A., Mooney, H. A. & Vitousek, P. M. Principles of terrestrial ecosystem ecology. New York, NY: Springer NewYork. (2002).
Mao, J. et al. Human-induced greening of the northern extratropical land surface. Nat. Clim. Chang. 6, 959–963 (2016).
Zhu, Z. et al. Greening of the Earth and its drivers. Nat. Clim. Chang. 6, 791–795 (2016).
Getu Engida, T., Nigussie, T. A., Aneseyee, A. B. & Barnabas, J. Land use/land cover change impact on hydrological process in the Upper Baro Basin, Ethiopia. Appl. Environ. Soil Sci. 2021, 6617541 (2021).
Winkler, K., Fuchs, R., Rounsevell, M. & Herold, M. Global land use changes are four times greater than previously estimated. Nat. Commun. 12, 2501 (2021).
Higgins, S. I., Conradi, T. & Muhoko, E. Shifts in vegetation activity of terrestrial ecosystems attributable to climate trends. Nat. Geosci. 16, 147–153 (2023).
Friedlingstein, P. et al. Global carbon budget 2022. Earth Syst. Sci. Data 14, 4811–4900 (2022).
Fleischer, K. & Terrer, C. Estimates of soil nutrient limitation on the CO2 fertilization effect for tropical vegetation. Glob. Chang. Biol. 28, 6366–6369 (2022).
Reich, P. B., Hobbie, S. E. & Lee, T. D. Plant growth enhancement by elevated CO2 eliminated by joint water and nitrogen limitation. Nat. Geosci. 7, 920–924 (2014).
Oguchi, R. et al. An intraspecific negative correlation between the repair capacity of photoinhibition of cold acclimated plants and the habitat temperature. Plant Cell Environ. https://doi.org/10.1111/pce.15270 (2024).
McDermid, S. S. et al. Disentangling the regional climate impacts of competing vegetation responses to elevated atmospheric CO2. J. Geophys. Res. Atmos. 126, e2020JD034108 (2021).
Solovchenko, A. E. & Merzlyak, M. N. Screening of visible and UV radiation as a photoprotective mechanism in plants. Russian J. Plant Physiol. 55, 719–737 (2008).
Rahmati, M. et al. Continuous increase in evaporative demand shortened the growing season of European ecosystems in the last decade. Commun. Earth Environ. 4, 236 (2023).
Jiasen, W. et al. Eco-stoichiometric characteristics of carbon, nitrogen and phosphorus in leaves and soil of camellia oleifera at different age. J. Southwest For. Univ. 39, 86–92 (2019).
Saini, K., Dwivedi, A. & Ranjan, A. High temperature restricts cell division and leaf size by coordination of PIF4 and TCP4 transcription factors. Plant Physiol. 190, 2380–2397 (2022).
Tan, Y. et al. The role of reactive oxygen species in plant response to radiation. Int. J. Mol. Sci. 24, https://doi.org/10.3390/ijms24043346 (2023).
Meng, F. et al. Consistent time allocation fraction to vegetation green-up versus senescence across northern ecosystems despite recent climate change. Sci. Adv. 10, https://www.science.org (2024).
Wang, L. et al. The widely increasing sensitivity of vegetation productivity to phenology in northern middle and high latitudes. Geophys. Res. Lett. 52, e2024GL113892 (2025).
Shi, S., Yang, P. & van der Tol, C. Spatial-temporal dynamics of land surface phenology over Africa for the period of 1982–2015. Heliyon 9, e16413 (2023).
Qiao, Y. et al. Accelerating effects of growing-season warming on tree seasonal activities are progressively disappearing. Curr. Biol. 33, 3625–3633.e3 (2023).
Liu, Z. et al. Increased early-season productivity drives earlier peak of vegetation photosynthesis across the Northern Hemisphere. Commun. Earth Environ. 6, 157 (2025).
Shi, S., Yang, P., Vrieling, A. & Tol, C. van der. Vegetation optimal temperature modulates global vegetation season onset shifts in response to warming climate. Commun. Earth Environ. 6, 203 (2025).
Xu, X., Riley, W. J., Koven, C. D. & Jia, G. Observed and simulated sensitivities of spring greenup to preseason climate in Northern Temperate and Boreal Regions. J. Geophys. Res. Biogeosci. 123, 60–78 (2018).
Ganguly, S., Friedl, M. A., Tan, B., Zhang, X. & Verma, M. Land surface phenology from MODIS: characterization of the collection 5 global land cover dynamics product. Remote Sens. Environ. 114, 1805–1816 (2010).
Zhang, X. et al. Monitoring vegetation phenology using MODIS. Remote Sens. Environ. 84, 471–475 (2003).
Piao, S. et al. Plant phenology and global climate change: current progresses and challenges. Global Change Biol. 25, 1922–1940 (2019).
Sun, Z. et al. Enhanced isoprene emission capacity and altered light responsiveness in aspen grown under elevated atmospheric CO2 concentration. Glob. Chang. Biol. 18, 3423–3440 (2012).
Richardson, A. D. et al. Terrestrial biosphere models need better representation of vegetation phenology: results from the North American Carbon Program Site Synthesis. Glob. Chang. Biol. 18, 566–584 (2012).
Mughal, N. et al. Adaptive roles of cytokinins in enhancing plant resilience and yield against environmental stressors. Chemosphere 364, 143189 (2024).
Cong, N. et al. Changes in satellite-derived spring vegetation green-up date and its linkage to climate in China from 1982 to 2010: a multimethod analysis. Glob. Chang. Biol. 19, 881–891 (2013).
Yuan, X. et al. Anthropogenic shift towards higher risk of flash drought over China. Nat. Commun. 10, 4661 (2019).
Jamal, P., Ali, M., Faraj, R. H., Ali, P. J. M. & Faraj, R. H. 1-6 Data Normalization and Standardization: A Technical Report. Machine Learning Technical Reports 1 https://docs.google.com/document/d/1x0A1nUz1WWtMCZb5oVzF0SVMY7a_58KQulqQVT8LaVA/edit# (2014).
Piao, S., Fang, J., Zhou, L., Ciais, P. & Zhu, B. Variations in satellite-derived phenology in China’s temperate vegetation. Glob. Chang. Biol. 12, 672–685 (2006).
Chen, A., Meng, F., Mao, J., Ricciuto, D. & Knapp, A. K. Photosynthesis phenology, as defined by solar-induced chlorophyll fluorescence, is overestimated by vegetation indices in the extratropical Northern Hemisphere. Agric. For. Meteorol. 323, 109027 (2022).
Ji, S. et al. Diverse responses of spring phenology to preseason drought and warming under different biomes in the North China Plain. Sci. Total Environ. 766, 144437 (2021).
Abdi, H. Partial least squares regression and projection on latent structure regression (PLS Regression). Wiley Interdiscip. Rev. Comput. Stat. 2, 97–106 (2010).
Rustam, F. et al. COVID-19 future forecasting using supervised machine learning models. IEEE Access 8, 101489–101499 (2020).
Zhang, Y. et al. Earth’s record-high greenness and its attributions in 2020. Remote Sens. Environ. 316, 114494 (2025).
Zhou, Z. H. & Feng, J. Deep forest. Natl. Sci. Rev. 6, 74–86 (2019).
Feng, Q. et al. Long-term gridded land evapotranspiration reconstruction using Deep Forest with high generalizability. Sci. Data 10, 908 (2023).
Lundberg, S. M., Allen, P. G. & Lee, S.-I. A Unified Approach to Interpreting Model Predictions. https://github.com/slundberg/shap (2017).
Rosseel, Y. Journal of Statistical Software Lavaan: An R Package for Structural Equation Modeling. http://www.jstatsoft.org/ (2017).
Liu, Y. et al. Drought legacies delay spring green-up in northern ecosystems. Nat. Clim. Chang. https://doi.org/10.1038/s41558-025-02273-6 (2025).
Zhang, Y., Joiner, J., Hamed Alemohammad, S., Zhou, S. & Gentine, P. A global spatially contiguous solar-induced fluorescence (CSIF) dataset using neural networks. Biogeosciences 15, 5779–5800 (2018).
Li, M. et al. Spatiotemporally consistent global dataset of the GIMMS Normalized Difference Vegetation Index (PKU GIMMS NDVI) from 1982 to 2022. Earth Syst. Sci. Data 15, 4181–4203 (2023).
Kong, X. et al. Exploring the environmental drivers of vegetation seasonality changes in the northern extratropical latitudes: a quantitative analysis. Environ. Res. Lett. 18, 094071 (2023).
Martens, B. et al. GLEAM v3: Satellite-based land evaporation and root-zone soil moisture. Geosci. Model Dev. 10, 1903–1925 (2017).
Acknowledgements
J.M. would like to thank Xiaoman Lu for helpful discussions. H.C. and X.K. were supported by the National Key Research and Development Program of China (2022YFF0801603). X.K. was supported by the Postgraduate Research and Practice Innovation Program of Jiangsu Province (KYCX24_1414). J.M., Y.W., and X.S. were supported by the Oak Ridge National Laboratory (ORNL) through the Reducing Uncertainties in Biogeochemical Interactions through Synthesis and Computing Scientific Focus Area and the Terrestrial Ecosystem Science Scientific Focus Area, funded by the Earth and Environmental Systems Sciences Division of the Biological and Environmental Research Office within the U.S. Department of Energy Office of Science. ORNL is managed by UT-Battelle, LLC, for the DOE under contract DE-AC05-00OR22725.
Author information
Authors and Affiliations
Contributions
J.M. conceived the study. X.K., J.M., H.C. wrote the manuscript. Z.Z., Y.H., Y.W., Y.Z., A.C., M.J., X.S., F.H. contributed to the framework of the manuscript and participated in the writing process. All authors have read and approved the final manuscript.
Corresponding authors
Ethics declarations
Competing interests
Author A.C. is an associate editor of the npj Climate and Atmospheric Science. A.C. was not involved in the journal’s review of, or decisions related to, this manuscript. This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains, and the publisher, by accepting the article for publication, acknowledges that the US government retains, a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan. The other authors declare no competing financial or non-financial interests.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary information
Supplementary information
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Reprints and permissions
About this article
Cite this article
Kong, X., Mao, J., Chen, H. et al. Flash flourishing of Northern Hemisphere vegetation and its drivers.
npj Clim Atmos Sci (2026). https://doi.org/10.1038/s41612-026-01346-3
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/s41612-026-01346-3
Source: Ecology - nature.com
