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Global covariation of carbon turnover times with climate in terrestrial ecosystems

Nature volume 514, pages 213–217 (2014)Cite this article

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Abstract

The response of the terrestrial carbon cycle to climate change is among the largest uncertainties affecting future climate change projections1,2. The feedback between the terrestrial carbon cycle and climate is partly determined by changes in the turnover time of carbon in land ecosystems, which in turn is an ecosystem property that emerges from the interplay between climate, soil and vegetation type3,4,5,6. Here we present a global, spatially explicit and observation-based assessment of whole-ecosystem carbon turnover times that combines new estimates of vegetation and soil organic carbon stocks and fluxes. We find that the overall mean global carbon turnover time is  years (95 per cent confidence interval). On average, carbon resides in the vegetation and soil near the Equator for a shorter time than at latitudes north of 75° north (mean turnover times of 15 and 255 years, respectively). We identify a clear dependence of the turnover time on temperature, as expected from our present understanding of temperature controls on ecosystem dynamics. Surprisingly, our analysis also reveals a similarly strong association between turnover time and precipitation. Moreover, we find that the ecosystem carbon turnover times simulated by state-of-the-art coupled climate/carbon-cycle models vary widely and that numerical simulations, on average, tend to underestimate the global carbon turnover time by 36 per cent. The models show stronger spatial relationships with temperature than do observation-based estimates, but generally do not reproduce the strong relationships with precipitation and predict faster carbon turnover in many semi-arid regions. Our findings suggest that future climate/carbon-cycle feedbacks may depend more strongly on changes in the hydrological cycle than is expected at present and is considered in Earth system models.

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Figure 1: Global distributions of total ecosystem carbon, turnover times of carbon in terrestrial ecosystems and its spatial covariation with climate variables.
Figure 2: Latitudinal gradients of whole ecosystem turnover times of carbon and associations to temperature and precipitation from data and models.
Figure 3: Biases in whole-ecosystem carbon turnover times between models (τmod) and observation-derived (τobs) data ensembles.

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Acknowledgements

We would like to thank C. Jones for comments that improved the manuscript. We are grateful to A. Ito, D. Zaks and S. Del Grosso for sharing their NPP data sets with us. We thank S. Schott for figure editing. We acknowledge support by the European Union (FP7) through the projects GEOCARBON (283080), CARBONES (242316) and EMBRACE (283201) and an ERC starting grant QUASOM (ERC-2007-StG-208516).

Author information

Authors and Affiliations

  1. Max Planck Institute for Biogeochemistry, Hans Knöll Strasse 10, 07745 Jena, Germany,

    Nuno Carvalhais, Matthias Forkel, Myroslava Khomik, Martin Jung, Mirco Migliavacca, Martin Thurner, Ulrich Weber, Bernhard Ahrens, Christian Beer & Markus Reichstein

  2. Departamento de Ciências e Engenharia do Ambiente, DCEA, Faculdade de Ciências e Tecnologia, FCT, Universidade Nova de Lisboa, 2829-516 Caparica, Portugal,

    Nuno Carvalhais

  3. School of Geography and Earth Sciences, McMaster University, Hamilton, Ontario L8S 4K1, Canada,

    Myroslava Khomik

  4. Institute of Biological and Environmental Sciences, School of Biological Sciences, University of Aberdeen, Aberdeen AB24 3UU, UK,

    Jessica Bellarby

  5. Lancaster Environment Centre, Lancaster University, Bailrigg, Lancaster LA1 4YQ, UK,

    Jessica Bellarby

  6. Remote Sensing of Environmental Dynamics Lab, DISAT, University of Milano-Bicocca, Piazza della Scienza 1, 20126 Milan, Italy,

    Mirco Migliavacca

  7. Department of Earth System Science, University of California Irvine, Irvine, 92697, California, USA

    Mingquan Μu & James T. Randerson

  8. Jet Propulsion Laboratory, California Institute of Technology, Pasadena, 91109, California, USA

    Sassan Saatchi

  9. Gamma Remote Sensing, Worbstrasse 225, 3073 Gümligen, Switzerland,

    Maurizio Santoro

  10. Department of Applied Environmental Science and Bolin Centre for Climate Research, Stockholm University, Svante Arrhenius väg 8, 10691 Stockholm, Sweden,

    Christian Beer

  11. European Commission, Joint Research Centre, Institute for Environment and Sustainability, Climate Risk Management Unit, Via E. Fermi, 2749, I-21027 Ispra, Italy,

    Alessandro Cescatti

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  1. Nuno Carvalhais
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Contributions

N.C. and M.R. designed the study and are responsible for the integrity of the work as a whole. N.C., M.F. and M. Migliavacca performed analysis and calculations. N.C. and M.R. mainly wrote the manuscript. M.K. and J.B. contributed to interpreting and processing the soil databases. M.T., M.S. and S.S. contributed to the vegetation carbon stocks datasets and interpretation. M.J. contributed to the GPP datasets and interpretation. C.B., M. Mu, M.T. and U.W. contributed to data provision, analysis or data processing. A.C., B.A., M.F., M.J. and J.T.R. contributed to analysis design and interpretation. All authors discussed and commented on the manuscript.

Corresponding author

Correspondence to Nuno Carvalhais.

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Extended data figures and tables

Extended Data Figure 1 Relative uncertainties in total ecosystem carbon.

Relative uncertainties in total ecosystem carbon stemming from the different data sources considered, reported as the ratio between the interquartile range (difference between the 75th and 25th percentiles) of the different estimates and the mean. The colour scale is binned to the 98th percentile of the spatial distribution of uncertainty (140%). A significant spatial variability was observed in the total ecosystem carbon uncertainties. The highest uncertainties locally and regarding total stocks per biome were observed in tundra (∼38%), followed by tropical savannahs and grasslands (∼30%). Deserts and croplands also showed significant relative uncertainties (both 27%). Overall, we observe a global relative uncertainty of 21%. We note unknown sources of uncertainties related to total carbon stocks, which relate mostly the representativeness of mosses in northern latitudes69 and tropical peatlands in Southeast Asia, although we find a total soil stock of  PgC (95% CI) in this region (−11.5° < latitude < 10° and 90° < longitude < 155°), which borders the upper envelope of the estimates in ref. 70.

Extended Data Figure 2 Local spatial correlations between turnover times of carbon in terrestrial ecosystems and temperature, and precipitation.

Local spatial correlations between τ and temperature (tas; a, c, e), and τ and precipitation (pr; b, d, f) using the 5.5°-by-5.5° moving-window approach. We use two alternative approaches to the Pearson correlation (a, b): the Spearman rank correlation (rsp.), a non-parametric measure of association that does not rely on the assumption of normal distribution of residuals (c, d); and the partial correlation (rp, e, f), measuring the degree of association between τ and temperature or precipitation, setting precipitation or, respectively, temperature as controlling variables (e, f). On local scales, using partial correlations may result in lost correlation owing to a strong local covariation of temperature and precipitation. Although we see this loss, the associative patterns between τ and both climate variables are generally maintained across the approaches used to calculate the correlations.

Extended Data Figure 3 Strength of association between turnover times of carbon in terrestrial ecosystems and temperature, and precipitation, using different methods.

Strength of association between τ and temperature (tas) and precipitation (pr) for Pearson correlations (a), Spearman correlations (b) and partial correlations (c). Each of these maps (a–c) shows regions where the association of τ is stronger with precipitation (blue) or temperature (red). The fraction of land grid cells with stronger significant correlations to temperature and precipitation are indicated above (for tas) and below (for pr) the colour bar. The colour gradients reflect the respective absolute correlation values. Despite stronger correlations with either temperature or precipitation, these cannot be said to be completely independent from the variable with lower correlation strength. d, Results of a conditional independence test on rejecting the null hypothesis that τ is independent from pr or tas given tas or, respectively, pr (ref. 64), showing that in 53% of the land grid cells, the dependence of τ on temperature or precipitation is not lost when controlling for precipitation or, respectively, temperature.

Extended Data Figure 4 Maximum relative importance of temperature and precipitation in the explained variance of turnover times of carbon.

a, Maximum relative importance of temperature (tas) or precipitation (pr) in the explained variance of τ using the LMG method. b, Relative importance of temperature (tas) or precipitation (pr) in improving the residual sum of squares of local bivariate regressions of τ against tas and pr. c, Normalized slopes of the bivariate regression between τ and precipitation and temperature, using a stepwise regression approach. Also, here the slopes correlate significantly with the strength of the association between the two variables. The fraction of land grid cells with stronger significant correlations to temperature and precipitation are indicated above (for tas) and below (for pr) the colour bar.

Extended Data Figure 5 Moving-window correlation between turnover times of carbon in terrestrial ecosystems and vegetation, and soil carbon stocks.

Moving-window correlation between τ and vegetation stocks (a); and between τ and carbon in soils (b). In general, τ correlates negatively with vegetation (a), indicating shorter turnover times with a higher proportion of carbon in the vegetation. The majority of the patterns are consistent with the overall reduction of residence times in ecosystem carbon given allocation to vegetation pools (shorter lived by comparison with soil carbon pools). Conversely, the significance of soil carbon stocks in explaining the spatial variability of τ is pervasive (b). These results translate the trends in increasing τ with allocation of assimilated carbon to more persistent carbon pools.

Extended Data Figure 6 Pearson correlations between turnover times of carbon in terrestrial ecosystems and tree cover, also controlled for the variability in precipitation.

a, Pearson correlations between τ and tree cover. The prevalence of strong negative correlations suggests that the association could be mediated by precipitation variability. b, Controlling for precipitation still showed many of those negative correlation regions. These negative correlations are most clear in regions where tree cover is not so high or where spatial variability seems higher. c, Map of tree cover percentage from MODIS71.

Extended Data Figure 7 Latitudinal profiles of total soil organic carbon as simulated by CMIP5 models and from the observation-derived data ensembles.

Latitudinal profiles of total soil organic carbon as simulated by CMIP5 models and from data: HWSD33 (1 m depth), NCSCD38,39 (1 m depth) and this study (MPI, to full soil depth).

Extended Data Table 1 Estimates of total ecosystem carbon for the globe and discriminated per biome
Full size table
Extended Data Table 2 Estimates of total ecosystem carbon turnover times, stocks and fluxes of carbon for each of the CMIP5 models and correlations with data
Full size table

Supplementary information

Supplementary Information

This file contains Supplementary Text and Data 1-6, Supplementary References, Supplementary Tables 1-7 and Supplementary Figures 1-13. Please note that you can download the Supplementary Data, relating to this paper, at the following link: http://www.bgc-jena.mpg.de/geodb/BGI/tau.php (PDF 7449 kb)

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Carvalhais, N., Forkel, M., Khomik, M. et al. Global covariation of carbon turnover times with climate in terrestrial ecosystems. Nature 514, 213–217 (2014). https://doi.org/10.1038/nature13731

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