Vegetation anomalies caused by antecedent precipitation in most of the world

Abstract

Quantifying environmental controls on vegetation is critical to predict the net effect of climate change on global ecosystems and the subsequent feedback on climate. Following a non-linear Granger causality framework based on a random forest predictive model, we exploit the current wealth of multi-decadal satellite data records to uncover the main drivers of monthly vegetation variability at the global scale. Results indicate that water availability is the most dominant factor driving vegetation globally: about 61% of the vegetated surface was primarily water-limited during 1981-2010. This included semiarid climates but also transitional ecoregions. Intraannually, temperature controls Northern Hemisphere deciduous forests during the growing season, while antecedent precipitation largely dominates vegetation dynamics during the senescence period. The uncovered dependency of global vegetation on water availability is substantially larger than previously reported. This is owed to the ability of the framework to (1) disentangle the co-linearities between radiation/temperature and precipitation, and (2) quantify non-linear impacts of climate on vegetation. Our results reveal a prolonged effect of precipitation anomalies in dry regions: due to the long memory of soil moisture and the cumulative, nonlinear, response of vegetation, water-limited regions show sensitivity to the values of precipitation occurring three months earlier. Meanwhile, the impacts of temperature and radiation anomalies are more immediate and dissipate shortly, pointing to a higher resilience of vegetation to these anomalies. Despite being infrequent by definition, hydro-climatic extremes are responsible for up to 10% of the vegetation variability during the 1981-2010 period in certain areas, particularly in water-limited ecosystems. Our approach is a first step towards a quantitative comparison of the resistance and resilience signature of different ecosystems, and can be used to benchmark Earth system models in their representations of past vegetation sensitivity to changes in climate.

A non-linear Granger-causality framework to investigate climate–vegetation dynamics

“…As mentioned in Sect. 3.3, our experiments indicated that lags of more than 6 months do not add extra predictive power (not shown), even though the effect of anomalies in water availability on vegetation can extend for several months (Papagiannopoulou et al, 2017).…”

“…In those regions, more that 40 % of the variance of NDVI anomalies can be explained by antecedent climate variability. These results are further investigated by Papagiannopoulou et al (2017), who highlight the crucial role of water supply for the anomalies in vegetation greenness in these and other regions. On the other hand, the variance of NDVI explained in other areas, such as the Eurasian taiga, tropical rainforests, or China, is again below 10 %.…”

“…In those regions, 10-20 % of the variability in NDVI is explained by the occurrence of prolonged anomalies and/or extremes in climate, illustrating again the non-linear responses of vegetation. For more detailed results about lagged vegetation responses for specific climate drivers and the effect of climate extremes on vegetation, the reader is referred to Papagiannopoulou et al (2017).…”

“…Vegetation dynamics and the distribution of ecosystems are largely driven by the availability of light, temperature, and water; thus, they are mostly sensitive to climate conditions (Nemani et al, 2003;Seddon et al, 2016;Papagiannopoulou et al, 2017). Meanwhile, vegetation also plays a crucial role in the global climate system.…”

“…Plant life alters the characteristics of the atmosphere through the transfer of water vapour, exchange of carbon dioxide, partition of surface net radiation (e.g. albedo), and impacts on wind speed and direction (Nemani et al, 2003;McPherson et al, 2007;Bonan, 2008;Seddon et al, 2016;Papagiannopoulou et al, 2017). Because of the strong two-way relationship between terrestrial vegetation and climate variability, predictions of future climate can be improved through a better understanding of the vegetation response to past climate variability.…”