Abstract
Abstract. Climate variables carry signatures of variability at multiple timescales. How these modes of variability are reflected in the state of the terrestrial biosphere is still not quantified or 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 covariability with climate. We used >30 years of remote sensing records of the normalized difference vegetation index (NDVI) to characterize biosphere variability across timescales from submonthly oscillations to decadal trends using discrete Fourier decomposition. Climate data of air temperature (Tair) and precipitation (Prec) were used to characterize atmosphere–biosphere covariability at each timescale. Our results show that short-term (intra-annual) and longer-term (interannual 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 semiarid shrublands. Assessing dominant timescales of vegetation–climate covariation, 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 timescales. 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 timescales exist for 15 % of vegetated areas 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 covariability globally, characterizing ecosystems by their intrinsic modes of temporal variability. We find that (i) correlations of NDVI with climate can differ between scales, (ii) nondominant subsignals 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 timescales may depend on climate zone and vegetation type, and they are to date not well understood and do not 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.
Highlights
Ecosystems and climate interact on multiple spatial and temporal scales
We found very similar results when repeating the time series decomposition and the dominant oscillation regime classification based on enhanced vegetation index (EVI) and normalized difference vegetation index (NDVI) from Moderate Resolution Imaging Spectroradiometer (MODIS) (Didan et al, 2019; Huete, 1997; Huete et al, 2002) for the years 2001–2015 (Fig. S6)
Comparing the three classifications among each other, we find that dominant temporal patterns in NDVI can be linked to certain land cover types such as shrubs and broadleaf forest: Sankey diagrams (Fig. 2b and c) display which proportion of land surface is commonly classified across different class combinations in the three data layers of the co-oscillation regime, GLC2000, and Köppen–Geiger for evergreen broadleaf forest (EBF, Fig. 2b) and areas dominated by longer-term NDVI (Fig. 2c)
Summary
Ecosystems and climate interact on multiple spatial and temporal scales. For example, the main driver of photosynthesis during the daily cycle typically is light availability, assuming no other resource limitation. Understanding the implications of such timescale dependencies of climate–vegetation interactions is challenging due to the variety of interwoven processes. These dependencies range from short-term climate extremes and biotic stress (e.g., insect outbreaks) to seasonal dynamics in climate-driven phenology and long-term dynamics that can again either reflect intrinsic ecosystem dynamics (e.g., vegetation successional dynamics) or climate-change- or landuse-induced process alterations. Investigating vegetation– climate dynamics globally across multiple timescales requires long-term observation on relevant vegetation dynamics and climate variables in combination with a method to separate ecosystem variability at different timescales
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