Vegetation productivity in India is modulated by climate teleconnections
from the Pacific Ocean
Abstract
Vegetation productivity in India varies at intraseasonal to interannual
time scales, influenced by meteorological factors sensitive to
large-scale climate teleconnections. While the impact of global climate
variability on Indian monsoon and its extremes is well known, their
effects on Indian vegetation productivity are relatively less
understood. This study addresses this gap by decomposing dominant modes
of spatio-temporal variability of gross primary productivity (GPP) over
India and examining their dependence on climate teleconnections. We
found that El-Niño Southern Oscillation (ENSO) and Pacific Meridional
Mode (PMM) significantly impact GPP, especially in western and southern
peninsular India during the monsoon and post-monsoon seasons. However,
there is an east-west asymmetry in the PMM-GPP correlation. The western
region and southern peninsula are negatively correlated, while northeast
India positively correlates with PMM. Using wavelet decomposition, we
show that more than half of temporal variability in the GPP comprises
low-frequency components. These low-frequency signals primarily drive
the relationship between GPP and climate teleconnections. Next, we
identify the dominant spatial modes of low-frequency signals of GPP. We
tested the predictability of the principal components of GPP using
teleconnections and hydrometeorological variables. While most of the
predictive skill of GPP comes from its past (memory up to 5 months, R2
score of up to 0.5), adding teleconnection indices as predictors
improves the prediction skill at lead times (with an increase of 0.1-0.2
in R2 values). Our results underscore the utility of using
hydrometeorological and distant climate teleconnection in GPP prediction
for longer lead times.