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https://doi.org/10.5194/bg-2019-321
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-2019-321
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 17 Sep 2019

Submitted as: research article | 17 Sep 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Biogeosciences (BG).

Towards a global understanding of vegetation–climate dynamics at multiple time scales

Nora Linscheid1,*, Lina M. Estupinan-Suarez1,*, Alexander Brenning2,3, Nuno Carvalhais1,4, Felix Cremer2,5, Fabian Gans1, Anja Rammig6, Markus Reichstein1,3,7, Carlos A. Sierra1, and Miguel D. Mahecha1,7 Nora Linscheid et al.
  • 1Max Planck Institute for Biogeochemistry, Hans–Knoell–Str. 10, 07745 Jena, Germany
  • 2Department of Geography, Friedrich Schiller University Jena, Loebdergraben 32, 07743 Jena, Germany
  • 3Michael Stifel Center Jena for Data–Driven & Simulation Science, Ernst–Abbe–Platz 2, 07743 Jena, Germany
  • 4Departamento de Ciencias e Engenharia do Ambiente, DCEA, Faculdade de Ciencias e Tecnologia, FCT Universidade Nova de Lisboa, Caparica, Portugal
  • 5Institute for Data Science, German Aerospace Center DLR, 07745 Jena, Germany
  • 6TUM School of Life Sciences Weihenstephan, Technical University of Munich, Hans–Carl–von–Carlowitz–Platz 2, 85354 Freising, Germany
  • 7German Centre for Integrative Biodiversity Research (iDiv), Deutscher Platz 5e, 04103 Leipzig, Germany
  • *These authors contributed equally.

Abstract. Climate variables carry signatures of variability at multiple time scales. How these modes of variability are reflected in the state of the terrestrial biosphere is still not quantified, nor discussed at the global scale. Here, we set out to gain a global understanding of the relevance of different modes of variability in vegetation greenness and its co-variability with climate. We used > 30 years of remote sensing records of Normalized Difference Vegetation Index (NDVI) to characterize biosphere variability across time scales from sub-monthly oscillations to decadal trends using discrete Fourier decomposition. Climate data of air temperature (Tair) and precipitation (Prec) were used to characterize atmosphere-biosphere co-variability at each time scale.

Our results show that short-term (intra-annual) and longer-term (inter-annual and longer) modes of variability make regionally highly important contributions to NDVI variability: Short-term oscillations focus in the tropics where they shape 27 % of NDVI variability. Longer-term oscillations shape 9 % of NDVI variability, dominantly in semi-arid shrublands. Assessing dominant time scales of vegetation-climate co-variation, a natural surface classification emerges which captures patterns not represented by conventional classifications, especially in the tropics. Finally, we find that correlations between variables can differ and even invert signs across time scales. For southern Africa for example, correlation between NDVI and Tair is positive for the seasonal signal, but negative for short-term and longer-term oscillations, indicating that both short and long-term temperature anomalies can induce stress on vegetation dynamics. Such contrasting correlations between time scales exist for 15 % of vegetated area for NDVI with Tair, and 27 % with Prec, indicating global relevance of scale-specific climate sensitivities.

Our analysis provides a detailed picture of vegetation-climate co-variability globally, characterizing ecosystems by their intrinsic modes of temporal variability. We find that (i) correlations of NDVI with climate can differ between scales, (ii) non-dominant sub-signals in climate variables may dominate the biospheric response, and (iii) possible links may exist between short-term and longer-term scales. These heterogeneous ecosystem responses on different time scales may depend on climate zone and vegetation type, and are to date not well understood, nor always correspond to transitions in dominant vegetation types. These scale dependencies can be a benchmark for vegetation model evaluation and for comparing remote sensing products.

Nora Linscheid et al.
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Short summary
Vegetation typically responds to variation in temperature and rainfall within days. Yet seasonal changes in meteorological conditions, as well as decadal climate variability additionally shape the state of ecosystems. It remains unclear how vegetation responds to climate variability on these different time scales. We find that the vegetation response to climate variability depends on the time scale considered. This scale dependency should be considered for modeling land-atmosphere interactions.
Vegetation typically responds to variation in temperature and rainfall within days. Yet seasonal...
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