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

Abstract

<p><strong>Abstract.</strong> 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 (T<sub><i>air</i></sub>) and precipitation (Prec) were used to characterize atmosphere-biosphere co-variability at each time scale.</p> <p> 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 T<sub><i>air</i></sub> 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 T<sub><i>air</i></sub>, and 27 % with Prec, indicating global relevance of scale-specific climate sensitivities.</p> <p> 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.</p>

Characterizing multiscale effects of climatic factors on the temporal variation of vegetation in different climatic regions of China

“…However, the discrepancy of dominant factors at the seasonal scales among MEMD and MWC might be attributed to the shortage of vegetation-climate relations at 0.5-year scales for MEMD, where precipitation had a dominant effect on vegetation. MEMD can partition original series into limited temporal scales, and generally attributed dominant variance to the seasonal process in ecology, which was consistent with previous study finding that IMFs decomposed by EMD contained more variation in the seasonal cycle and less modulation in the interannual temporal scales for NDVI (Linscheid et al, 2020). In addition, other methods, such as multiple regression or local correlation methods, should be integrated into MEMD for yielding the combined scale-specific effect of climate on vegetation, or obtaining their localized multivariate relationships.…”

“…For cropland at the seasonal scale (≤ 1 years), precipitation also played a leading role in the major crop producing areas (THSNC1, WHSNC2, and SHCSC3) and TP6, and RH had the dominant effect on crop productivity in TGIM4. In TWDNC5 region, precipitation seemed to exert the dominant influence on crop growth only at the scales of 4-8 years, probably because irrigation activities in the area disorganized the crop-precipitation relations, and the interannual variation of precipitation is greater than its intra-annual variation (Linscheid et al, 2020), which resulted in the leading effect at the scales of 4-8 years.…”

“…Our results showed that the interaction effects of multiple climatic factors on vegetation dynamic were stronger than the effect of individual climatic variables on NDVI at scales of >8 years. Such long-term climate variation may occur in the form of periodic atmospheric fluctuations (Linscheid et al, 2020), which might result in the long-term variation of NDVI. The combined climatic controls on NDVI were evaluated by previous studies.…”

Spatial and temporal variations in global soil respiration and their relationships with climate and land cover

“…The independent effects of climate and land-cover changes on the spatial and temporal variations in R s were difficult to separate because these variables covaried at different scales (60). For instance, climate change is known to control R s because of its close link with environmental factors (3,10,12), such as soil temperature, soil moisture, and substrate quality, while land-cover change (e.g., grassland to woodland) can be expected to change some or all of these environmental determinants of R s (24,25).…”

Loss of Natural Habitats: High-Altitude Vegetation, Pantanal

“…Alterations in land use, such as the conversion of natural areas to provide food, fiber, water, and shelter for the population, are the primary drivers of native vegetation loss (Foley et al 2005). Climate change can also affect the pattern of distribution and growth of vegetation through precipitation, temperature, and radiation, which can alter available energy and water, both of which are essential for plant growth, impacting the process of carbon accumulation in the water cycle as well as the decomposition and conversion of organic carbon (Linscheid et al 2020;Tai et al 2020;Pan et al 2018).…”