Abstract

Climate change is shifting the phenological cycles of plants1, thereby altering the functioning of ecosystems, which in turn induces feedbacks to the climate system2. In northern (north of 30° N) ecosystems, warmer springs lead generally to an earlier onset of the growing season3,4 and increased ecosystem productivity early in the season5. In situ6 and regional7,8,9 studies also provide evidence for lagged effects of spring warmth on plant productivity during the subsequent summer and autumn. However, our current understanding of these lagged effects, including their direction (beneficial or adverse) and geographic distribution, is still very limited. Here we analyse satellite, field-based and modelled data for the period 1982–2011 and show that there are widespread and contrasting lagged productivity responses to spring warmth across northern ecosystems. On the basis of the observational data, we find that roughly 15 per cent of the total study area of about 41 million square kilometres exhibits adverse lagged effects and that roughly 5 per cent of the total study area exhibits beneficial lagged effects. By contrast, current-generation terrestrial carbon-cycle models predict much lower areal fractions of adverse lagged effects (ranging from 1 to 14 per cent) and much higher areal fractions of beneficial lagged effects (ranging from 9 to 54 per cent). We find that elevation and seasonal precipitation patterns largely dictate the geographic pattern and direction of the lagged effects. Inadequate consideration in current models of the effects of the seasonal build-up of water stress on seasonal vegetation growth may therefore be able to explain the differences that we found between our observation-constrained estimates and the model-constrained estimates of lagged effects associated with spring warming. Overall, our results suggest that for many northern ecosystems the benefits of warmer springs on growing-season ecosystem productivity are effectively compensated for by the accumulation of seasonal water deficits, despite the fact that northern ecosystems are thought to be largely temperature- and radiation-limited10.

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Data availability

The satellite NDVI3g data that support the findings of this study were downloaded from http://ecocast.arc.nasa.gov/data/pub/gimms/3g.v0/. The satellite LAI3g data are available from R. B. Myneni (rmyneni@bu.edu) on reasonable request. The LUE-FPAR3g GPP data can be requested from W.K.S. (wksmith@email.arizona.edu) and the FluxNetG GPP data from M. Jung (mjung@bgc-jena.mpg.de). The TRENDYv6 data are available from S.S. (s.a.sitch@exeter.ac.uk) on reasonable request.

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Acknowledgements

M.O. is funded through an EU Marie Curie Integration grant to W.B. M.F. is funded through the TU Wien Wissenschaftspreis 2015, a personal science award to W. Dorigo. V.H.’s contribution is supported through funding from the Earth Systems and Climate Change Hub of the Australian Government’s National Environmental Science Program. H.T. is supported by the National Key R&D Program of China (2017YFA0604702) and the US National Science Foundation (NSF; 1210360, 1243232). A.D.R. is funded through the Macrosystems Biology Program of the NSF (EF-1702697). This work used eddy covariance data acquired and shared by the FLUXNET community, including the following networks: AmeriFlux, AfriFlux, AsiaFlux, CarboAfrica, CarboEuropeIP, CarboItaly, CarboMont, ChinaFlux, Fluxnet-Canada, GreenGrass, ICOS, KoFlux, LBA, NECC, OzFlux-TERN, TCOS-Siberia and USCCC. The ERA-Interim reanalysis data were provided by ECMWF and processed by LSCE. The FLUXNET eddy covariance data processing and harmonization were carried out by the European Fluxes Database Cluster, AmeriFlux Management Project and Fluxdata project of FLUXNET, with the support of the CDIAC and ICOS Ecosystem Thematic Center, and the OzFlux, ChinaFlux and AsiaFlux offices. We thank M. Jung for providing upscaled FLUXNET GPP data.

Reviewer information

Nature thanks N. Parazoo and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Author information

Affiliations

  1. Institute for Climate and Atmospheric Science, School of Earth and Environment, University of Leeds, Leeds, UK

    • Wolfgang Buermann
    •  & Michael O’Sullivan
  2. Institute of the Environment and Sustainability, University of California, Los Angeles, Los Angeles, CA, USA

    • Wolfgang Buermann
  3. Climate and Environmental Remote Sensing Group, Department for Geodesy and Geoinformation, TU Wien, Vienna, Austria

    • Matthias Forkel
  4. College of Life and Environmental Sciences, University of Exeter, Exeter, UK

    • Stephen Sitch
  5. College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, UK

    • Pierre Friedlingstein
  6. CSIRO Oceans and Atmosphere, Canberra, Australian Capital Territory, Australia

    • Vanessa Haverd
  7. Department of Atmospheric Sciences, University of Illinois, Urbana, IL, USA

    • Atul K. Jain
  8. Institute of Applied Energy, Tokyo, Japan

    • Etsushi Kato
  9. Forest Research Institute Baden-Württemberg, Freiburg, Germany

    • Markus Kautz
  10. Climate and Environmental Physics, Physics Institute, University of Bern, Bern, Switzerland

    • Sebastian Lienert
  11. Oeschger Centre for Climate Change Research, University of Bern, Bern, Switzerland

    • Sebastian Lienert
  12. National Center for Atmospheric Research, Climate and Global Dynamics, Terrestrial Sciences Section, Boulder, CO, USA

    • Danica Lombardozzi
  13. Max Planck Institute for Meteorology, Hamburg, Germany

    • Julia E. M. S. Nabel
  14. International Center for Climate and Global Change Research, School of Forestry and Wildlife Sciences, Auburn University, Auburn, AL, USA

    • Hanqin Tian
  15. Research Center for Eco-Environmental Sciences, State Key Laboratory of Urban and Regional Ecology, Chinese Academy of Sciences, Beijing, China

    • Hanqin Tian
  16. Met Office Hadley Centre, Exeter, UK

    • Andrew J. Wiltshire
  17. Laboratoire des Sciences du Climat et de l’Environnement, LSCE CEA-CNRS-UVSQ, Gif sur Yvette, France

    • Dan Zhu
  18. School of Natural Resources and the Environment, University of Arizona, Tucson, AZ, USA

    • William K. Smith
  19. School of Informatics, Computing and Cyber Systems, Northern Arizona University, Flagstaff, AZ, USA

    • Andrew D. Richardson
  20. Center for Ecosystem Science and Society, Northern Arizona University, Flagstaff, AZ, USA

    • Andrew D. Richardson

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Contributions

W.B., M.F. and A.D.R. designed the research. W.B., M.F. and M.O. carried out the analysis and W.B. wrote the manuscript with contributions from all authors. S.S., P.F., V.H., A.K.J., E.K., M.K., S.L., D.L., J.E.M.S.N., H.T., A.J.W. and D.Z. contributed to the TRENDY results. W.K.S. contributed to the LUE-FPAR3g results.

Competing interests

The authors declare no competing interests.

Corresponding author

Correspondence to Wolfgang Buermann.

Extended data figures and tables

  1. Extended Data Fig. 1 Comparison of lagged productivity responses based on satellite greenness observations and in situ estimates of carbon fluxes across selected FLUXNET sites.

    ac, Site-specific correlations between spring temperature (T) and spring (a), summer (b) or autumn (c) satellite NDVI (x axis) plotted over the corresponding site-specific correlations between spring temperature and spring (a), summer (b) or autumn (c) flux-tower GPP (y axis). In b and c, the relationships are based on partial correlations (pr) between spring temperature and subsequent summer (b) or autumn (c) NDVI or GPP, with covarying effects of summer temperature and precipitation (b) and autumn temperature and precipitation (c) removed. (Partial) correlations are shown for two estimates of GPP: GPP-N (based on night-time partitioning of net ecosystem exchange) and GPP-D (daytime partitioning). d, For this comparison, satellite NDVI time series at 8-km (native) spatial resolution have been extracted for the 16 FluxNet tower sites with at least 10-year data records. Forest types for the tower sites are: ENF, evergeen needleleaf forest; DBF, deciduous broadleaf forest; MF, mixed forest. e, Maps showing the approximate locations of the FLUXNET tower sites. FLUXNET data for this comparative analysis are from the FLUXNET2015 dataset (tier 1).

  2. Extended Data Fig. 2 Random-forest analysis to explain the partial correlation pattern between annual spring temperature and summer satellite greenness on hemispheric and regional scales.

    a, Ranked importance of a set of explanatory variables in a random-forest model for the whole northern ecosystem study region, encompassing all vegetated non-agricultural land north of 30° N (see Supplementary Information, section 2, for details on the explanatory variables used). The ranking is based on the highest increment in mean squared error (IncMSE) between the observed and random-forest-predicted correlation after permuting the relevant explanatory variable. bf, Individual conditional expectation lines of the random-forest-predicted partial correlation (pr) between spring temperature (T) and summer NDVI for the five most important explanatory variables. Lines and shaded bands reflect the mean (regional-average response) and the 5%–95% percentile range (grid-cell-level responses to environmental predictors) for the northern (north of 30° N, non-managed) study region (red) and for the focus regions (Siberia, blue; western USA, green) (see Supplementary Information, section 2).

  3. Extended Data Fig. 3 Spatial pattern of lagged productivity responses based on the individual carbon-cycle models included in TRENDYv6.

    All patterns are based on monthly GPP over the period 1982–2011, using outputs from the ten TRENDYv6 models included in the analysis (see Methods). The maps summarize the direction of statistically significant (P < 0.05) correlation between annual spring temperature and spring, summer or autumn GPP. For details on classification scenarios and contour labels, see Fig. 2. Areas with no robust link between spring temperature and spring GPP (dark grey) and areas that are cultivated or managed (light grey) are also shown.

  4. Extended Data Fig. 4 Spatial pattern of lagged productivity and vegetation growth responses estimated through satellite-based and modelling approaches.

    af, Summary of the direction of robust (P < 0.05) correlations between annual spring temperature and spring, summer or autumn satellite NDVI (a), satellite LAI (b), satellite upscaled GPP (FluxNetG; c), satellite-data-driven LUE-modelled GPP (LUE-FPAR3g; d), and multi-model mean GPP (e) and LAI (f) based on the ten TRENDYv6 models. For details on scenario classifications and contour labels see Fig. 2. Arrows (arrows with strikethroughs) linking panels highlight qualitative agreement (disagreement) between the lagged responses of productivity and vegetation growth based on the various approaches.

  5. Extended Data Fig. 5 Changes in regional climate, satellite greenness and plant carbon fluxes from observation-constrained and modelling approaches for warm- and cold-spring years.

    af, Monthly anomalies in regionally averaged maximum composited climate (a, d), NDVI (b, e) and GPP (c, f) for warm- and cold-spring years, for the focus regions (ac, western USA; df, Siberia). The anomalies are relative to the mean of the study period (1982–2011) and are based on maximum composites of monthly means of the seven warmest- and coldest-spring years within the study period. The observation-constrained GPP anomalies (c, f) stem from FluxNetG, which combined GPP estimates from flux towers with climate and satellite greenness in a machine-learning framework (see Methods). The boundaries between the climatological seasons are indicated by vertical grey dashed lines. Uncertainty bounds (shaded areas) reflect the spread in the respective monthly anomalies within the compositing period (±1 s.d., n = 7). On the basis of these anomalies, we estimate, for a warm-spring year (relative to mean conditions) in Siberia (area, 2.5 × 106 km2), annual GPP increases of 0.4 Pg C and 1.7 Pg C for FluxNetG and the TRENDYv6 ensemble, respectively, which corresponds to higher plant carbon uptake in the TRENDYv6 ensemble by a factor of roughly four (f). This is, to a large extent (about 64%), because of the overestimation of positive lagged effects in the TRENDYv6 models, but another important factor (36%) is the higher sensitivity of concurrent carbon uptake to spring warming in the TRENDYv6 models (compared to FluxNetG).

  6. Extended Data Table 1 Comparison of how specific processes relevant to this study are represented in the TRENDYv6 carbon-cycle models

Supplementary information

  1. Supplementary Information

    This file contains Supplementary Text Sections 1-3, Supplementary Table S1 and Supplementary Figures S1-S7.

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https://doi.org/10.1038/s41586-018-0555-7

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