Global covariation of carbon turnover times with climate in terrestrial ecosystems (original) (raw)

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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

Authors

  1. Nuno Carvalhais
  2. Matthias Forkel
  3. Myroslava Khomik
  4. Jessica Bellarby
  5. Martin Jung
  6. Mirco Migliavacca
  7. Mingquan Μu
  8. Sassan Saatchi
  9. Maurizio Santoro
  10. Martin Thurner
  11. Ulrich Weber
  12. Bernhard Ahrens
  13. Christian Beer
  14. Alessandro Cescatti
  15. James T. Randerson
  16. Markus Reichstein

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 toNuno Carvalhais.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

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 (_r_sp.), a non-parametric measure of association that does not rely on the assumption of normal distribution of residuals (c, d); and the partial correlation (_r_p, 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 MODIS[71](/articles/nature13731#ref-CR71 "DiMiceli, C. M. et al. Annual Global Automated MODIS Vegetation Continuous Fields (MOD44B) at 250 m Spatial Resolution for Data Years Beginning Day 65, 2000 - 2010, Collection 5 Percent Tree Cover http://glcf.umd.edu/data/vcf/

             (University of Maryland, 2011)").

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: HWSD[33](/articles/nature13731#ref-CR33 "FAO/IIASA/ISRIC/ISSCAS/JRC. Harmonized World Soil Database v 1. 2 http://webarchive.iiasa.ac.at/Research/LUC/External-World-soil-database/HTML/

             (2012)") (1 m depth), NCSCD[38](/articles/nature13731#ref-CR38 "Hugelius, G. et al. The Northern Circumpolar Soil Carbon Database: spatially distributed datasets of soil coverage and soil carbon storage in the northern permafrost regions. Earth Syst. Sci. Data 5, 3–13 (2013)"),[39](/articles/nature13731#ref-CR39 "Tarnocai, C. et al. Soil organic carbon pools in the northern circumpolar permafrost region. Glob. Biogeochem. Cycles 23, GB2023 (2009)") (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

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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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