Increased control of vegetation on global terrestrial energy fluxes

Abstract

Changes in vegetation structure are expected to influence the redistribution of heat and moisture; however, how variations in the leaf area index (LAI) affect this global energy partitioning is not yet quantified. Here, we estimate that a unit change in LAI leads to 3.66 ± 0.45 and −3.26 ± 0.41 W m−2 in latent (LE) and sensible (H) fluxes, respectively, over the 1982–2016 period. Analysis of an ensemble of data-driven products shows that these sensitivities increase by about 20% over the observational period, prominently in regions with a limited water supply, probably because of an increased transpiration/evaporation ratio. Global greening has caused a decrease in the Bowen ratio (B = H/LE) of −0.010 ± 0.002 per decade, which is attributable to the increased evaporative surface. Such a direct LAI effect on energy fluxes is largely modulated by plant functional types (PFTs) and background climate conditions. Land surface models (LSMs) misrepresent this vegetation control, possibly due to underestimation of the biophysical responses to changes in the water availability and poor representation of LAI dynamics.

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Fig. 1: Sensitivity of surface energy partitioning to LAI changes.
Fig. 2: Changes in surface energy partitioning associated with long-term trends in LAI.
Fig. 3: Comparison of LAI and climate effects on surface energy partitioning.
Fig. 4: Contribution of different PFTs to the LAI control on energy partitioning.
Fig. 5: Comparison of observational and LSM results.

Data availability

The observation-driven datasets analysed in this study are publicly available as referenced within the Article. Simulations from ten LSMs (CABLE-POP, CLASS-CTEM, CLM5.0, DLEM, ISAM, JSBACH, JULES, LPX-Bern, ORCHIDEE-CNP and VISIT) are available from the TRENDY dataset on request from S.S. All generated data are available from the corresponding author on request.

Code availability

The custom MATLAB (R2017b) code written to read and analyse data and generate figures is fully available on request from the corresponding author.

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Acknowledgements

The study was funded by the FP7 LUC4C project (grant number 603542). D.G.M. acknowledges funding from the European Research Council (ERC) under grant agreement 715254 (DRY–2–DRY). P.C. acknowledges support from European Research Council Synergy project SyG-2013-610028 IMBALANCE-P and ANR (reference ANR-16-CONV-0003 (CLAND)).

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Authors

Contributions

G.F. and A.C. conceived and designed the study; D.G.M. and B.M. provided GLEAM data; C.J. and Y.R. produced the archive of long-term BESS data and harmonized LAI datasets; K.Z. provided PLSH data; A.W., A.A., D.S.G., V.K.A., S.L., D.L., E.K., J.E.M.S.N., H.T., P.F. and S.S. ran the TRENDY v.7 simulations; R.A. harmonized LSM simulations; G.F. analysed the data, G.F. and A.C. interpreted the results and wrote the manuscript with contributions from all co-authors.

Corresponding author

Correspondence to Giovanni Forzieri.

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The authors declare no competing interests.

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Peer review information Nature Climate Change thanks Liang Chen and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Temporal variations of sensitivity of LE to LAI changes for single LAI and ET products and ensemble averages.

Temporal variations of sensitivities computed over a 13-year moving window for supply- and demand limited regions and the whole globe. Labels report the relative changes in sensitivities (Δrel, Methods) between the 1982–1999 period and the 2000–2016 period, ’*’ indicates the Student’s t-test significance with p-value ≤ 0.05. (ad, fi, kn) represent single combinations of LAI and ET products. (ps) represent ensemble results averaged across multiple LAI products. (e, j, o) represent ensemble results averaged across multiple ET products. (t) represents ensemble results across multiple LAI and ET products. In the ensemble configurations, values are expressed as ensemble average ± standard error. The spatial domains of supply- and demand-limited regions are shown in Supplementary Fig. 5.

Extended Data Fig. 2 Climate control on the vegetation-energy interplay.

(a, c, e) Sensitivity of latent heat \(\left(\frac{{\partial LE}}{{\partial LAI}}\right)\), sensible heat \(\left(\frac{{\partial H}}{{\partial LAI}}\right)\) and Bowen ratio \(\left(\frac{{\partial B}}{{\partial LAI}}\right)\) to LAI changes (on the y-axis) separately computed for the 1982–1999 and 2000–2016 periods and binned as a function of the aridity index (on the x-axis) (Eq. (3), Methods). Results of the Kolmogorov-Smirnov test is shown in label and reflects the significance level (pks) to reject the null hypothesis of dissimilar curves. (b, d, f) Temporal variations of sensitivities extrapolated from changes in precipitation and climate for moisture supply- and atmospheric demand-limited regions and the whole globe displayed with respect to the first year sensitivity (year 1982). Numbers at the bottom of the panel report the sensitivity values of the first year, while numbers on top report the coefficient of regression (R2) and the significance (Mann-Kendall test; pmk) of the fitting linear regression models. The spatial domains of supply- and demand-limited regions are shown in Supplementary Fig. 5.

Extended Data Fig. 3 Trends in vegetation and climate drivers.

Spatial patterns (a) and climate space (b) of long-term trend (1982–2016) in growing season averaged leaf area index (δLAI). (c, d), (e, f), (g, h) and (i, j) as (a, b) but for temperature (δT), precipitation (δP), short-wave incoming radiation (δSWIN) and the ratio between transpiration and total ET (δTr/ET). Values reflect the ensemble average of multiple products used in this study (e.g., GLASS v3, GIMMS3g v3 and TCDR v4 for δLAI). Areas in (a, c, e, g, i) labelled with black dots indicate trends that are statistically significant (Mann-Kendall test; p-value≤0.05). Values in (b, d, f, h, j) are binned as a function of climatological mean precipitation (P, on the x-axis) and air temperature (T, on the y-axis) and black dots show bins with average values statistically different from zero (Student’s t-test; p-value≤0.05).

Extended Data Fig. 4 Dynamics of global coverage of moisture supply- and atmospheric demand-limited regions.

(a) Temporal variations in global fraction of moisture supply- and atmospheric demand-limited zones, shown respectively on the left and right y-axis, based on the ensemble average of observation-driven products (Methods). (b) as (a) but for the ensemble average of LSM simulations.

Extended Data Fig. 5 Sensitivity of energy partitioning terms to multiple drivers.

(a, d, g, j) Sensitivity of latent heat to changes in LAI \(\left(\frac{{\partial LE}}{{\partial LAI}}\right)\), temperature \(\left(\frac{{\partial LE}}{{\partial T}}\right)\), precipitation \(\left(\frac{{\partial LE}}{{\partial P}}\right)\) and short-wave incoming radiation \(\left(\frac{{\partial LE}}{{\partial SW_{IN}}}\right)\) calculated for the 1982–2016 period as ensemble average of all observation-driven estimates. (b, e, h, k) and (c, f, i, l) as (a, d, g, j) but for sensible heat (H) and Bowen ratio (B). Areas labelled with black dots indicate estimates that are statistically significant (Student’s t-test; p-value ≤0.05).

Extended Data Fig. 6 Temporal variations of sensitivity of LE to LAI changes for single land surface models and ensemble averages.

Temporal variations of sensitivities computed over a 13-year moving window for supply- and demand-limited regions and the whole globe displayed with respect to the first year sensitivity (year 1989). Results refer to different scenarios: changes in CO2, climate and land use (S3, the most realistic scenario, left column); changes in CO2 only (S1, middle column) and changes in climate and land use only (S3-S1, right column). Black labels report the relative changes (Δrel, Methods) in global sensitivities between the 1982–1999 period and the 2000–2016 period, ‘*’ indicates the Student’s t-test significance with p-value≤0.05, while the number in brackets refer to the first year sensitivity value. Results are shown for each single land surface model (a1-a10, b1-b10, c1-c10) and for the ensemble average (a11, b11, c11). The spatial domains of supply- and demand-limited regions are shown in Supplementary Fig. 5.

Extended Data Fig. 7 Latitudinal profiles of sensitivity of LE to LAI, long-term trend in LAI, and the resulting greening effect on LE for each land surface model and the ensemble average.

Latitudinal profiles of sensitivity of latent heat to changes in LAI \(\left(\frac{{\partial LE}}{{\partial LAI}}\right)\) (left column), long-term trends in LAI (δLAI) (middle column) and associated changes in long-term trend in LE (δLELAI) (right column). Results are shown for each single land surface model (a1-a10, b1-b10, c1-c10) and for the ensemble average (a11, b11, c11). In the last row, results from observation-driven products are shown for comparison (red lines). Background colours in (c11) show areas of the globe subject to compensatory effects (grey), additive positive biases (orange) and additive negative biases (blue).

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Supplementary Figs. 1–7 and Tables 1 and 2.

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Forzieri, G., Miralles, D.G., Ciais, P. et al. Increased control of vegetation on global terrestrial energy fluxes. Nat. Clim. Chang. 10, 356–362 (2020). https://doi.org/10.1038/s41558-020-0717-0

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