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Deciphering the multiple effects of climate warming on the temporal shift of leaf unfolding

Nature Climate Change volume 12, pages 193–199 (2022)Cite this article

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Abstract

Changes in winter and spring temperatures have been widely used to explain the diverse responses of spring phenology to climate change. However, few studies have quantified their respective effects. Using 386,320 in situ observations of leaf unfolding date (LUD) of six tree species in Europe, we show that accelerated spring thermal accumulation and changes in winter chilling explain, on average, 61% and 39%, respectively, of the advancement in LUD for the period 1951–2019. We find that winter warming may not have delayed bud dormancy release, but rather it has increased the thermal requirement in reaching leaf unfolding. This increase in thermal requirement and the decreased efficiency of spring warming for thermal accumulation partly explain the weakening response of leaf unfolding to warming. Our study stresses the need to better assess the antagonistic and heterogeneous effects of winter and spring warming on leaf phenology, which is key to projecting future vegetation–climate feedbacks.

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Fig. 1: Contribution of changes in winter chilling and spring forcing to changes in LUD of six tree species in Europe, 1951–2019.
Fig. 2: Differences between mean winter (December–February) and mean spring (March–May) temperatures and Top (°C), and relative changes in different chilling and forcing metrics from 1951–1979 to 1980–1999 and 2000–2019.
Fig. 3: Schematic plot showing the impacts of rising temperatures (+3 and +6 °C) on winter chilling, spring forcing and LUD.
Fig. 4: Coefficient of determination (R2) for the linear regression function between the temperature sensitivity of LUD and different metrics of winter chilling and spring forcing.
Fig. 5: Differences between mean forcing temperature and T50.

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Data availability

Phenology data are available from the PEP network (http://www.pep725.eu). Climate data can be downloaded from the E-OBS site: http://ensembles-eu.metoffice.com.

Code availability

The codes of the Unified model and the programme (SCE-UA algorithm) used for parameterization and data analysis can be found at https://github.com/hchzhang/UnifiedModel.git.

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Acknowledgements

H.Z. and P.R. thank the Lateral-CNP project (no. 34823748) supported by Fonds de la Recherche Scientifique and the European Union’s Horizon 2020 research and innovation programme under grant agreements nos. 776810 (VERIFY) and 101003536 (ESM2025—Earth System Models for the Future). W.Y. is funded by the CAS interdisciplinary team (no. JCTD-2020-05). We thank all members of the PEP725 network for collecting and providing phenological data.

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Authors and Affiliations

  1. Department Geoscience, Environment & Society-BGEOSYS, Université libre de Bruxelles, Brussels, Belgium

    Haicheng Zhang & Pierre Regnier

  2. CEFE, CEFE, Université de Montpellier, CNRS, EPHE, IRD, Montpellier, France

    Isabelle Chuine

  3. Laboratoire des Sciences du Climat et de l’Environnement, IPSL-LSCECEA/CNRS/UVSQ Saclay, Gif-sur-Yvette, France

    Philippe Ciais

  4. School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai, China

    Wenping Yuan

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Contributions

H.Z. and I.C. designed this study. H.Z. performed specific simulation and data analysis. H.Z., P.R. and I.C. wrote most of the manuscript, with substantial contributions from P.C. and W.Y. All authors reviewed and revised the manuscript.

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Correspondence to Haicheng Zhang.

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Nature Climate Change thanks Ailene Ettinger, Benjamin Marquis, Chaoyang Wu 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 Geographical locations of the 2944 phenological observation sites included in this study, and the temporal distribution of the phenological records for each tree species included in this study.

AH: Aesculus hippocastanum; AG: Alnus glutinosa; BP: Betula pendula; FS: Fagus sylvatica; FE: Fraxinus excelsior; QR: Quercus robur.

Extended Data Fig. 2 Changes in the leaf unfolding dates of six tree species in Europe from 1951 to 2019 and the potential contributions of changes in winter chilling and spring forcing to these changes.

ΔLUD is the change in leaf unfolding date. ΔDdf0 is the changes in date when dormancy is released. ΔDFr is the potential changes in the duration (day) of forcing stage caused by changes in spring forcing temperatures. ΔDTA0 is the potential changes in the duration (in day) of forcing accumulation stage caused by rising forcing requirement due to chilling deficiency. Violins with the black and red dots show the changes of each variable from 1951–1979 to 1980–1999 (black, ΔD1990s-1970s), and to 2000–2019 (red, ΔD2010s-1970s), respectively. In each violin plot, the black and red dots refer to the mean and median value, respectively. The balloon represents the probability density distribution of each value. Whiskers indicate the interquartile (thick vertical bars) and 95 % confidence intervals (thin vertical bars). AH denotes Aesculus hippocastanum; AG denotes Alnus glutinosa; BP is Betula pendula; FS is Fagus sylvatica; FE is Fraxinus excelsior; QR is Quercus robur. The asterisks (**) indicate the changes in LUD are significantly different from zero (p<0.05, based on one-sample t-test).

Extended Data Fig. 3 Changes in the leaf unfolding dates of six tree species in Europe from period 1951–1979 to 1980–1999 and to 2000–2019, and the potential contributions of changes in winter chilling and spring forcing to these changes.

ΔDdf0 is the changes in date when dormancy is released (1st column). ΔDTA0 is the potential changes in the duration (in day) of forcing accumulation stage caused by rising forcing requirement due to chilling deficiency (2nd column). ΔDFD is the potential changes in the duration (in day) of forcing stage caused by changes in spring forcing temperatures (3rd column). Grid cells with insignificant (p>0.05 based on one-sample t-test) change are showed in grey. Here we only show the average change of each metrics for all samples within each 0.25°×0.25° grid cell.

Extended Data Fig. 4 Changes in temperature in central Europe from period 1951–1979 to 1980–1999 and to 2000–2019.

ΔTwinter, ΔTspring, ΔMAT and ΔTpreseason denote the changes in mean winter (1st column), spring (2nd column), annual (3rd column) and preseason (4th column) temperature (°C), respectively. The winter season is defined as December-February, and spring is defined as March-May. The preseason for each species at each site is defined as the period from the beginning of chilling accumulation to leaf unfolding date (see Methods). Grid cells with insignificant (p>0.05 based on one-sample t-test) change are showed in grey. We only show the average change of each metrics for all samples within each 0.25°×0.25° grid cell.

Extended Data Fig. 5 Spatial distribution of the average temperature sensitivity of leaf unfolding date in central Europe during the period 1951–2019.

ST_MAT, ST_spring, ST_winter and ST_preseason denote the sensitivity (day °C−1) of leaf unfolding date (LUD) to mean annual temperature, mean spring temperature mean winter temperature and mean preseason temperature, respectively. In this figure, the winter season is defined as December-February, and spring is defined as March-May. The preseason is defined as the period from the start date of chilling accumulation (dc0 in Fig. 3) to the mean LUD. Here we only show the average temperature sensitivity for all samples within each 0.25°×0.25° grid cell. The temperature sensitivities of LUD are insignificant (p>0.05 based on one-sample t-test) at grid cells with grey. Values in the bracket following the title of each subplot are the mean±standard deviation of the temperature sensitivities in all grid cells.

Extended Data Fig. 6 Comparison between observed and predicted leaf unfolding dates.

LUD is leaf unfolding date. AH: Aesculus hippocastanum; AG: Alnus glutinosa; BP: Betula pendula; FS: Fagus sylvatica; FE: Fraxinus excelsior; QR: Quercus robur.

Extended Data Fig. 7 Variations of the optimized parameter values for each species at each site.

Explanations of the nine optimized parameters can be found in Supplementary Table S1 above. AH: Aesculus hippocastanum; AG: Alnus glutinosa; BP: Betula pendula; FS: Fagus sylvatica; FE: Fraxinus excelsior; QR: Quercus robur.

Extended Data Fig. 8 Relative contributions of changes in winter chilling and spring forcing to the changes in the leaf unfolding dates.

ΔLUD is the change in leaf unfolding date. ΔDdf0 is the changes in date when dormancy is released. ΔDFr is the potential changes in the duration (in day) of forcing stage caused by changes in spring forcing temperatures. ΔDTA0 is the potential changes in the duration (in day) of forcing accumulation stage caused by rising forcing requirement due to chilling deficiency. Violins with the black and red dots show the changes of each variable from 1951–1979 to 1980–1999 (black, ΔD1990s-1970s), and to 2000–2019 (red, ΔD2010s-1970s), respectively. The asterisks (**) indicate that the relative contribution of a specific factor to the shift of LUD from the reference period 1951–1979 to 1980–1999 is significantly (p<0.05 based on paired-samples t-test) different from that from the reference period to 2000–2019. In each violin plot, the balloon represents the probability density distribution of each gradient of R2. Whiskers indicate the interquartile (thick vertical bars) and 95 % confidence intervals (thin vertical bars). AH: Aesculus hippocastanum; AG: Alnus glutinosa; BP: Betula pendula; FS: Fagus sylvatica; FE: Fraxinus excelsior; QR: Quercus robur.

Extended Data Fig. 9 Time series of the sensitivity of leaf unfolding date to mean winter temperature, mean spring temperature and mean preseason temperature for each species across all observation sites.

The preseason for each species at each site is defined as the period from the start date of chilling accumulation (dc0 in Fig. 3 and Supplementary Fig. S2) to leaf unfolding date (LUD). In each subplot, the solid line denotes the mean LUD or temperature in each year across all sites. The dashed line is the linear regression line. Upper and lower borders of the shaded area are the 75% and 25% percentile, respectively. R2 and p are determining coefficient and significance of the regression function, respectively. The time series of temperature sensitivity was obtained by conducting a reduced major axis regression for each species at each site with a 15-year moving window from 1951 to 2019 (that is we calculated the temperature sensitivity for each continuous 15 years). AH: Aesculus hippocastanum; AG: Alnus glutinosa; BP: Betula pendula; FS: Fagus sylvatica; FE: Fraxinus excelsior; QR: Quercus robur.

Extended Data Fig. 10 Comparison of root mean square error (RMSE) and Akaike information criterion (AIC) of the simulated leaf unfolding dates from the default Unified model and the revised Unified model which accounts for the effect of photoperiod on leaf unfolding.

UM_default and UM_photo denote the default (without photoperiod) and revised (with photoperiod) Unified model, respectively. In each violin plot, the balloon represents the probability density distribution of each variable. Whiskers indicate the interquartile (thick vertical bars) and 95 % confidence intervals (thin vertical bars). ** denotes that the RMSEs and AICs from the default and revised Unified model are significantly different (p<0.01).

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Zhang, H., Chuine, I., Regnier, P. et al. Deciphering the multiple effects of climate warming on the temporal shift of leaf unfolding. Nat. Clim. Chang. 12, 193–199 (2022). https://doi.org/10.1038/s41558-021-01261-w

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