in Ecology

Enhanced effect of warming on the leaf-onset date of boreal deciduous broadleaf forest

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Abstract

The leaf-onset date is sensitive to climate warming. It is widely reported that the temperature sensitivity of the leaf-onset date (ST) of deciduous broadleaf forest (DBF) may decrease under dormancy-period warming. However, evidence of how boreal-DBF ST may generally change under dormancy-period warming is still lacking. Here, by analysing climate and satellite data, we find that, between 1982–1996 and 1998–2012, 74% of all 0.5° × 0.5° boreal-DBF-containing grid cells with a rise in boreal-DBF dormancy-period temperature exhibited an increase in boreal-DBF ST. We demonstrate that the observed general increase in boreal-DBF ST is largely attributable to a warming-related enhancement in dormancy-period chilling accumulation. Furthermore, we show that phenology models systematically underestimated the magnitude of the observed change in the mean boreal-DBF ST across all boreal-DBF-containing grid cells by a mean of 85%. This study has implications for improving phenology models and understanding the carbon cycle in boreal regions.

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Fig. 1: Change in the mean boreal-DBF ST across all boreal-DBF-containing grid cells between 1982–1996 and 1998–2012.
Fig. 2: Changes in boreal-DBF dormancy-period temperature and ST between 1982–1996 and 1998–2012 across all boreal-DBF-containing grid cells.
Fig. 3: Effect of dormancy-period warming on boreal-DBF ST.
Fig. 4: Satellite-observed and model-derived changes in mean boreal-DBF ST between 1982–1996 and 1998–2012.

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Data availability

The CRU-NCEP dataset is available at https://doi.org/10.5065/PZ8F-F017 (ref. 22). The MCD12Q1 product v6 is available at https://doi.org/10.5067/MODIS/MCD12Q1.006 (ref. 44). The GIMMS NDVI3g dataset v1 is available at https://data.tpdc.ac.cn/en/data/9775f2b4-7370-4e5e-a537-3482c9a83d88 (refs. 23,24). The HBEFRSPM dataset is available at https://doi.org/10.6073/pasta/0df24f471bd93d70aea30ffa0859a12e (ref. 50). The PWSHF dataset is available at https://doi.org/10.6073/pasta/bc5d2c15df4fa81aeadcd59ed7580c91 (ref. 52). The FLUXNET2015 dataset is available at https://fluxnet.fluxdata.org/data/fluxnet2015-dataset (ref. 53). The PhenoCam dataset v2 is available at https://doi.org/10.3334/ORNLDAAC/1674 (ref. 54). Data analysis was performed with MATLAB R2017b and IDL 8.4.

Code availability

The code for the ten phenology models is available via Zenodo at https://doi.org/10.5281/zenodo.15731368 (ref. 60).

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Acknowledgements

H.L. acknowledges support from grants funded by the National Key Research and Development Program of China (NKRDPC) (2024YFE0106700 and 2024YFF0729102). J.M.C. acknowledges support from a Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grant (RGPIN-2020-05163). T.F.K. acknowledges support from a National Aeronautics and Space Administration (NASA) Carbon Cycle Science Award (80NSSC21K1705) and from the Reducing Uncertainties in Biogeochemical Interactions through Synthesis and Computation (RUBISCO) Science Focus Area (SFA), which is sponsored by the Regional and Global Model Analysis (RGMA) Program in the Climate and Environmental Sciences Division (CESD) of the Office of Biological and Environmental Research (BER) in the U.S. Department of Energy (DOE) Office of Science. Q.L. acknowledges support from a Research Foundation – Flanders (FWO) Postdoctoral Fellowship (12ZK121N).

Author information

Authors and Affiliations

  1. Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Institute for Global Change Studies, Tsinghua University, Beijing, China

    Wenyu Li, Hui Lu, Han Wang & Peng Gong

  2. Institute for Ocean Engineering, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China

    Wenyu Li

  3. Department of Geography and Planning, University of Toronto, Toronto, Ontario, Canada

    Wenyu Li, Jing M. Chen, Zhou Zang & Jane Liu

  4. School of Civil Engineering and Water Resources, Qinghai University, Xining, China

    Hui Lu

  5. Key Laboratory of Water Ecology Remediation and Protection at Headwater Regions of Big Rivers, Ministry of Water Resources, Xining, China

    Hui Lu

  6. Key Laboratory for Humid Subtropical Eco-geographical Processes of the Ministry of Education, School of Geographical Sciences, Fujian Normal University, Fuzhou, China

    Jing M. Chen, Guofang Miao & Rong Wang

  7. Institute of Carbon Neutrality, Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing, China

    Shilong Piao

  8. State Key Laboratory of Tibetan Plateau Earth System, Environment and Resources, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, China

    Shilong Piao

  9. Department of Environmental Science, Policy, and Management, University of California, Berkeley, Berkeley, CA, USA

    Trevor F. Keenan & Congcong Li

  10. Climate and Ecosystem Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Trevor F. Keenan

  11. Plants and Ecosystems, Department of Biology, University of Antwerp, Antwerp, Belgium

    Qiang Liu

  12. School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing, China

    Qiang Liu

  13. State Key Laboratory of Climate System Prediction and Risk Management, Nanjing University of Information Science and Technology, Nanjing, China

    Qiang Liu

  14. School of Earth Sciences and Engineering, Hohai University, Nanjing, China

    Nan Xu

  15. School of Architecture and Urban Planning, Shenzhen University, Shenzhen, China

    Nan Xu

  16. Division of Environmental Health Sciences, School of Public Health, University of California, Berkeley, Berkeley, CA, USA

    Qu Cheng

  17. Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China

    Qu Cheng

  18. Department of Geography, California State University, Long Beach, Long Beach, CA, USA

    Wenjie Ji

  19. Laboratoire des Sciences du Climat et de l’Environnement, CEA–CNRS–UVSQ, Université Paris-Saclay, Gif-sur-Yvette, France

    Peng Zhu

  20. Department of Geography, The University of Hong Kong, Hong Kong, China

    Peng Zhu & Peng Gong

  21. Guangdong Provincial Key Laboratory of Urbanization and Geo-simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou, China

    Qinchuan Xin

  22. Department of Earth and Planetary Sciences, The University of Hong Kong, Hong Kong, China

    Peng Gong

Authors

  1. Wenyu Li
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  2. Hui Lu
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  3. Jing M. Chen
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  4. Shilong Piao
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  5. Trevor F. Keenan
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Contributions

W.L., H.L., S.P., J.M.C. and P.G. designed the study. W.L. performed the analyses with support from T.F.K., H.L., Q.L. and N.X. W.L., J.M.C. and H.L. drafted the manuscript. T.F.K., S.P. and P.G. substantially revised the paper. All authors contributed to the interpretation of the results and to the writing and revision of the paper.

Corresponding authors

Correspondence to
Hui Lu, Jing M. Chen or Peng Gong.

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Nature Climate Change thanks Daniel Buonaiuto and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Changes in mean boreal-DBF ST between 1982–1996 and 1998–2012.

(Delta {S}_{T}) indicates the change in boreal-DBF ST. The left green bar indicates the change between 1982–1996 and 1998–2012 in the mean boreal-DBF ST across all boreal-DBF-containing grid cells with a rise in boreal-DBF dormancy-period temperature ((Delta {DT}) (>) 0 °C) between 1982–1996 and 1998–2012. The right green bar indicates the change between 1982–1996 and 1998–2012 in the mean boreal-DBF ST across all boreal-DBF-containing grid cells with a fall in boreal-DBF dormancy-period temperature ((Delta {DT}) (<) 0 °C) between 1982–1996 and 1998–2012. Each of the black error bars indicates (pm) the standard error of the change ((n) (=) 2228 boreal-DBF-containing grid cells with a rise in boreal-DBF dormancy-period temperature for the left black error bar, and (n) (=) 623 boreal-DBF-containing grid cells with a fall in boreal-DBF dormancy-period temperature for the right black error bar). Each of the black asterisks indicates that the change is significant at the 0.01 level (two-sided t-test).

Extended Data Fig. 2 Frequency distribution of preseason length across all boreal-DBF-containing grid cells.

The gray histogram indicates the frequency distribution of preseason length.

Extended Data Fig. 3 Spatial distribution of the correlation (Pearson correlation) coefficient between boreal-DBF dormancy-period temperature and chilling accumulation (RT-C) for 1982–2012 across all boreal-DBF-containing grid cells for each of four chilling models.

The four chilling models are: the Dynamic Model (DM), the Utah Model (UM), the Chilling Hours Model 1 (CHM1), and the Chilling Hours Model 2 (CHM2). a, Spatial distribution of the correlation coefficient between boreal-DBF dormancy-period temperature and chilling accumulation for 1982–2012 across all boreal-DBF-containing grid cells for the DM. The map indicates the spatial distribution of the correlation coefficient between boreal-DBF dormancy-period temperature and chilling accumulation. The correlation coefficient between boreal-DBF dormancy-period temperature and chilling accumulation (=) (pm)0.36 and (pm)0.3 correspond to the 5% and 10% significance levels, respectively. The blue number just outside the blue parentheses indicates the percentage of all boreal-DBF-containing grid cells with a positive correlation coefficient between boreal-DBF dormancy-period temperature and chilling accumulation (({R}_{T-C}) (>) 0). The blue number inside the blue parentheses indicates the percentage of all boreal-DBF-containing grid cells with a significant positive correlation coefficient between boreal-DBF dormancy-period temperature and chilling accumulation ((p) (<) 0.1; two-sided t-test). The red number just outside the red parentheses indicates the percentage of all boreal-DBF-containing grid cells with a negative correlation coefficient between boreal-DBF dormancy-period temperature and chilling accumulation (({R}_{T-C}) (<) 0). The red number inside the red parentheses indicates the percentage of all boreal-DBF-containing grid cells with a significant negative correlation coefficient between boreal-DBF dormancy-period temperature and chilling accumulation ((p) (<) 0.1; two-sided t-test). b–d, Same as a, but for the UM, the CHM1, and the CHM2, respectively.

Extended Data Fig. 4 Spatial distribution of the change in boreal-DBF dormancy-period chilling accumulation (∆DCA) between 1982–1996 and 1998–2012 across all boreal-DBF-containing grid cells for each of four chilling models.

The four chilling models are: the Dynamic Model (DM), the Utah Model (UM), the Chilling Hours Model 1 (CHM1), and the Chilling Hours Model 2 (CHM2). a, Spatial distribution of the change in boreal-DBF dormancy-period chilling accumulation between 1982–1996 and 1998–2012 across all boreal-DBF-containing grid cells for the DM. The map indicates the spatial distribution of the change in boreal-DBF dormancy-period chilling accumulation. The black dots indicate boreal-DBF-containing grid cells with a significant change in boreal-DBF dormancy-period chilling accumulation ((p) (<) 0.1; two-sided t-test). The blue number just outside the blue parentheses indicates the percentage of all boreal-DBF-containing grid cells with an enhancement in boreal-DBF dormancy-period chilling accumulation ((Delta {DCA}) (>) 0). The blue number inside the blue parentheses indicates the percentage of all boreal-DBF-containing grid cells with a significant enhancement in boreal-DBF dormancy-period chilling accumulation ((p) (<) 0.1; two-sided t-test). The orange number just outside the orange parentheses indicates the percentage of all boreal-DBF-containing grid cells with a reduction in boreal-DBF dormancy-period chilling accumulation ((Delta {DCA}) (<) 0). The orange number inside the orange parentheses indicates the percentage of all boreal-DBF-containing grid cells with a significant reduction in boreal-DBF dormancy-period chilling accumulation ((p) (<) 0.1; two-sided t-test). b–d, Same as a, but for the UM, the CHM1, and the CHM2, respectively.

Extended Data Fig. 5 Spatial distribution of the change in boreal-DBF ST (∆ST) between the 15 years with the lowest boreal-DBF dormancy-period chilling accumulation and the 15 years with the highest boreal-DBF dormancy-period chilling accumulation from 1982–2012 (the 15 years with the highest boreal-DBF dormancy-period chilling accumulation − the 15 years with the lowest boreal-DBF dormancy-period chilling accumulation) across all boreal-DBF-containing grid cells for each of four chilling models.

The four chilling models are: the Dynamic Model (DM), the Utah Model (UM), the Chilling Hours Model 1 (CHM1), and the Chilling Hours Model 2 (CHM2). a, Spatial distribution of the change in boreal-DBF ST between the 15 years with the lowest boreal-DBF dormancy-period chilling accumulation and the 15 years with the highest boreal-DBF dormancy-period chilling accumulation from 1982–2012 across all boreal-DBF-containing grid cells for the DM. The map indicates the spatial distribution of the change in boreal-DBF ST. The black dots indicate boreal-DBF-containing grid cells with a significant change in boreal-DBF ST ((p) (<) 0.1; Chow test). The green number just outside the green parentheses indicates the percentage of all boreal-DBF-containing grid cells with an increase in boreal-DBF ST ((Delta {S}_{T}) (>) 0 days °C−1). The green number inside the green parentheses indicates the percentage of all boreal-DBF-containing grid cells with a significant increase in boreal-DBF ST ((p) (<) 0.1; Chow test). The pink number just outside the pink parentheses indicates the percentage of all boreal-DBF-containing grid cells with a decrease in boreal-DBF ST ((Delta {S}_{T}) (<) 0 days °C−1). The pink number inside the pink parentheses indicates the percentage of all boreal-DBF-containing grid cells with a significant decrease in boreal-DBF ST ((p) (<) 0.1; Chow test). b–d, Same as a, but for the UM, the CHM1, and the CHM2, respectively.

Extended Data Fig. 6 Spatial distribution of the parameter uncertainty in the model-derived change in boreal-DBF ST between 1982–1996 and 1998–2012 across all boreal-DBF-containing grid cells for each of 10 phenology models.

The 10 phenology models are: the Spring Warming Model 1 (SWM1), the Spring Warming Model 2 (SWM2), the Sequential Model 1 (SM1), the Sequential Model 2 (SM2), the Parallel Model 1 (PM1), the Parallel Model 2 (PM2), the Unified Model 1 (UM1), the Unified Model 2 (UM2), the Alternating Model (AM), and the DORMPHOT Model (DPM). a, Spatial distribution of the parameter uncertainty in the model-derived change in boreal-DBF ST between 1982–1996 and 1998–2012 across all boreal-DBF-containing grid cells for the SWM1. The map indicates the spatial distribution of the parameter uncertainty in the model-derived change in boreal-DBF ST. b–j, Same as a, but for the SWM2, the SM1, the SM2, the PM1, the PM2, the UM1, the UM2, the AM, and the DPM, respectively.

Extended Data Fig. 7 Spatial distribution of the model-derived change in boreal-DBF ST (∆ST) between 1982–1996 and 1998–2012 across all boreal-DBF-containing grid cells for each of 10 phenology models.

The 10 phenology models are: the Spring Warming Model 1 (SWM1), the Spring Warming Model 2 (SWM2), the Sequential Model 1 (SM1), the Sequential Model 2 (SM2), the Parallel Model 1 (PM1), the Parallel Model 2 (PM2), the Unified Model 1 (UM1), the Unified Model 2 (UM2), the Alternating Model (AM), and the DORMPHOT Model (DPM). a, Spatial distribution of the model-derived change in boreal-DBF ST between 1982–1996 and 1998–2012 across all boreal-DBF-containing grid cells for the SWM1. The map indicates the spatial distribution of the model-derived change in boreal-DBF ST. The black dots indicate boreal-DBF-containing grid cells with a significant model-derived change in boreal-DBF ST ((p) (<) 0.1; Chow test). The green number just outside the green parentheses indicates the percentage of all boreal-DBF-containing grid cells with a model-derived increase in boreal-DBF ST ((Delta {S}_{T}) (>) 0 days °C−1). The green number inside the green parentheses indicates the percentage of all boreal-DBF-containing grid cells with a significant model-derived increase in boreal-DBF ST ((p) (<) 0.1; Chow test). The pink number just outside the pink parentheses indicates the percentage of all boreal-DBF-containing grid cells with a model-derived decrease in boreal-DBF ST ((Delta {S}_{T}) (<) 0 days °C−1). The pink number inside the pink parentheses indicates the percentage of all boreal-DBF-containing grid cells with a significant model-derived decrease in boreal-DBF ST ((p) (<) 0.1; Chow test). b–j, Same as a, but for the SWM2, the SM1, the SM2, the PM1, the PM2, the UM1, the UM2, the AM, and the DPM, respectively.

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Li, W., Lu, H., Chen, J.M. et al. Enhanced effect of warming on the leaf-onset date of boreal deciduous broadleaf forest.
Nat. Clim. Chang. (2026). https://doi.org/10.1038/s41558-025-02528-2

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