The Paris Agreement promotes forest management as a pathway towards halting climate warming through the reduction of carbon dioxide (CO2) emissions1. However, the climate benefits from carbon sequestration through forest management may be reinforced, counteracted or even offset by concurrent management-induced changes in surface albedo, land-surface roughness, emissions of biogenic volatile organic compounds, transpiration and sensible heat flux2,3,4. Consequently, forest management could offset CO2 emissions without halting global temperature rise. It therefore remains to be confirmed whether commonly proposed sustainable European forest-management portfolios would comply with the Paris Agreement—that is, whether they can reduce the growth rate of atmospheric CO2, reduce the radiative imbalance at the top of the atmosphere, and neither increase the near-surface air temperature nor decrease precipitation by the end of the twenty-first century. Here we show that the portfolio made up of management systems that locally maximize the carbon sink through carbon sequestration, wood use and product and energy substitution reduces the growth rate of atmospheric CO2, but does not meet any of the other criteria. The portfolios that maximize the carbon sink or forest albedo pass only one—different in each case—criterion. Managing the European forests with the objective of reducing near-surface air temperature, on the other hand, will also reduce the atmospheric CO2 growth rate, thus meeting two of the four criteria. Trade-off are thus unavoidable when using European forests to meet climate objectives. Furthermore, our results demonstrate that if present-day forest cover is sustained, the additional climate benefits achieved through forest management would be modest and local, rather than global. On the basis of these findings, we argue that Europe should not rely on forest management to mitigate climate change. The modest climate effects from changes in forest management imply, however, that if adaptation to future climate were to require large-scale changes in species composition and silvicultural systems over Europe5,6, the forests could be adapted to climate change with neither positive nor negative  climate effects.

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Data availability

Figures 1, 2, Table 1, Extended Data Figs. 2, 3, Supplementary Fig. 1 and Extended Data Table 1, 2 are based on a simulation experiment whose output files (about 7.4 Tb) will be provided upon reasonable request. The data files that were used to set the boundary conditions of ORCHIDEE-CAN and LMDzORCAN (about 70 Gb) will be provided upon reasonable request.

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M.J.M., K.N., J.R., Y.-Y.C., J.O. and S.L. were funded through the European Research Council (ERC) starting grant 242564 and A.V. was funded through the Agence de l'Environnement et de la Maîtrise de l'Energie (ADEME). S.L. and G.M. were partly funded through an Amsterdam Academic Alliance (AAA) fellowship. S.L. is grateful for the mentorship of E.-D. Schulze, I. A. Janssens and P. Ciais. The ORCHIDEE and LMDZ project teams and the Centre de Calcul Recherche et Technologie (CCRT) provided the run environment that enabled the land–atmosphere simulations conducted in this study.

Reviewer information

Nature thanks T. Pugh and K. Zhao for their contribution to the peer review of this work.

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Author notes

    • Aude Valade

    Present address: Global Ecology Unit CREAF-UAB, Cerdanyola del Vallès, Spain

    • Yi-Ying Chen

    Present address: Research Center for Environmental Changes (RCEC), Academia Sinica, Taipei, Taiwan

    • James Ryder

    Present address: National Physical Laboratory, Teddington, London, UK

    • Juliane Otto

    Present address: Helmholtz-Zentrum Geesthacht (HZG), Climate Service Center Germany (GERICS), Hamburg, Germany

    • Kim Naudts

    Present address: Max Planck Institute for Meteorology, Hamburg, Germany


  1. Faculty of Science, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands

    • Sebastiaan Luyssaert
    •  & Guillaume Marie
  2. Laboratoire des Sciences du Climat et de l’Environnement (LSCE/IPSL), CEA-CNRS-UVSQ, Université Paris-Saclay, Gif-sur-Yvette, France

    • Sebastiaan Luyssaert
    • , Yi-Ying Chen
    • , James Ryder
    • , Juliane Otto
    • , Kim Naudts
    • , Anne Sofie Lansø
    •  & Matthew J. McGrath
  3. Institut Pierre Simon Laplace (IPSL), Paris, France

    • Aude Valade
    •  & Josefine Ghattas
  4. Department of Agroecology, Aarhus University, Tjele, Denmark

    • Sylvestre Njakou Djomo


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S.L. and M.J.M. designed the study. M.J.M., J.O., J.R., Y.-Y.C., K.N., A.V. and S.L. developed, parameterized and validated ORCHIDEE-CAN. G.M., M.J.M., J.G. and S.L. conducted the simulation experiment. S.N.D. developed the life-cycle analysis method. G.M., Y.-Y.C. and S.L. analysed the data. G.M., M.J.M., J.O., J.R., Y.-Y.C., K.N., A.V., A.S.L. and S.L. contributed to the interpretation of the results.

Competing interests

The authors declare no competing interests.

Corresponding author

Correspondence to Sebastiaan Luyssaert.

Extended data figures and tables

  1. Extended Data Fig. 1 Setup of the simulation experiments.

    The experiments are described in the section ‘Simulation experiment’ in Supplementary Information. Simulations with ORCHIDEE-CAN are shown in black and simulations with LMDzORCAN are shown in red. Blue boxes denote intermediate calculations using the simulation results (see Supplementary Information, ‘Spatially optimized management portfolios’ and ‘Equilibrium climate for the management portfolios’). The simulations shown in this figure correspond to runs with reduced air temperature (BBESTT2M), maximized surface albedo (BESTALBEDO), minimized surface albedo (BWORSTALBEDO), maximized carbon sink (BBESTLCA), minimized carbon sink (BWORSTLCA) and business as usual (BWAC). BWAC, BWAC-P1 and BWAC-P2 were used to calculate the minimal model noise.

  2. Extended Data Fig. 2 Drivers of the mean bimonthly air temperature changes for 0.5° latitudinal bands.

    The notation is as in Fig. 2 and the labels at the top denote months (D J, December and January; F M, February and March; A M, April and May; and so on). Although all the components contribute to the change of the air temperature, changes in emissivity always result in cooling and changes in shortwave incoming radiation always result in warming. Consequently, emissivity and incoming shortwave radiation cannot explain the seasonal variation in air temperature changes. The other components are positively correlated with air temperature in some months and negatively correlated in others, which rules them out as the main driver of air temperature changes and suggests that the net effect is the outcome of the interplay between the different components. Source data

  3. Extended Data Fig. 3 Relationship between changes in springtime air temperature and changes in the fractional cover of deciduous forest for 0.5° latitudinal bands over Europe.

    Locations where the tree species are maintained between 2010 and 2100 (that is, the difference Δ of the deciduous area is 0) could experience similar air temperature changes as neighbouring locations where one tree species is replaced by another, especially in Scandinavia, suggesting advection of heat and moisture. Nevertheless, at lower latitudes the spatial scale of this advection is limited to a few pixels (for example, Fig. 2a) corresponding to a range of 50–200 km. Furthermore, the temperature effect quickly saturates with the fractional cover change and shows a strong dependence on geographical location (see Supplementary Information). Whether this apparent geographical dependence is the outcome of climatic differences or of differences between northern and southern European deciduous species could not be established with the experimental setup used in this study. Source data

  4. Extended Data Table 1 Changes in surface area of European forests by 2100 for six different forest-management portfolios
  5. Extended Data Table 2 Biogeochemical and biophysical effects over Europe in 2100 for two forest-management portfolios

Supplementary information

  1. Supplementary Information

    This file contains Supplementary Methods, a Supplementary Discussion, Supplementary Figure 1, Supplementary Tables 1-3 and Supplementary References.

  2. Supplementary Data

    This file contains source data for Supplementary Figure 1.

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