Spatial Patterns of Vegetation Activity Related to ENSO in Northern South America

Abstract

Abstract Interannual variability of vegetation activity (i.e., photosynthesis) is strongly correlated with El Niño Southern Oscillation (ENSO). Globally, a reduction in carbon uptake by terrestrial ecosystems has been observed during the ENSO warm phase (El Niño) and the opposite during the cold phase (La Niña). However, this global perspective obscures the heterogeneous impacts of ENSO at regional scales. Particularly, ENSO has contrasting impacts on climate in northern South America (NSA) depending on the ENSO phase and geographical location, which in turn affect the activity of vegetation. Furthermore, changes of vegetation activity during multiple ENSO events are not well understood yet. In this study, we investigated the spatial and temporal differences in vegetation activity associated with ENSO variability and its three phases (El Niño, La Niña, Neutral) to identify hotspots of ENSO impacts in NSA, a region dominated by rainforest and savannas. To achieve this, we investigated time series of vegetation variables from 2001 to 2014 at moderate spatial resolution (0.0083textasciicircum$∘$). Data were aggregated through dimensionality reduction analysis (i.e., Global Principal Component Analysis). The leading principal component served as a proxy of vegetation activity (VAC). We calculated the cross-correlation between VAC and the multivariate ENSO index separately for each ENSO phase. Our results show that El Niño phase has a stronger impact on vegetation activity both in intensity and duration than La Niña phase. Moreover, seasonally dry ecoregions were more susceptible to El Niño impacts on vegetation activity. Understanding these differences is key for regional adaptation and differentiated management of ecosystems. , Plain Language Summary Precipitation and temperature are important climatic drivers of vegetation processes. In particular, El Niño Southern Oscillation (ENSO) events are related to changes in precipitation and temperature over large regions, which in turn affects the activity of the vegetation. In northern South America (NSA), these changes on climate are opposite during the same ENSO event depending on the geographical location. Moreover, local conditions and vegetation type can moderate or amplified changes in vegetation activity. Currently, the contrasting vegetation changes during the ENSO warm (El Niño) and cold (La Niña) phase are not well understood in NSA. Furthermore, it is unknown where the largest vegetation variability is occurring during ENSO phases. We combined different vegetation variables related to vegetation greenness and plant productivity over 14 years to estimate changes in vegetation activity related to photosynthesis. In this way, we assessed the variability of vegetation activity during El Niño and La Niña. We found that variability of vegetation activity is stronger and longer during El Niño than La Niña. In addition, vegetation in semi-arid ecoregions was more susceptible to El Niño. A better understating of how vegetation activity varies during different ENSO phases will improve regional conservation strategies under the increasing ENSO frequencies. , Key Points Interannual variability of vegetation activity in northern South America is related to changes in climate due to El Niño and La Niña events El Niño events have a stronger impact on vegetation activity both in intensity and duration than La Niña events Semi-arid ecoregions are the most vulnerable to El Niño, but further investigation is required to understand underlying processes

Publication
Journal of Geophysical Research: Biogeosciences
Miguel D. Mahecha
Miguel D. Mahecha
Professor for Earth System Data Science

Professor

Guido Kraemer
Guido Kraemer
Postdoctoral associate / Earth System Data Science

My research interests include the interactions between society and biosphere. I have been working on the extraction of the global dynamics of ecosystems and society. I have an interest in using machine learning and multivariate statistics to understand the behavior of complex systems.