Large-scale variability in species richness is often argued to largely depend on climate [ 21, 22], but many competing explanations exist [ 15, 21, 23- 30].
PyC showed significant, large-scale variability among plots.
The large-scale variability of SSS can now be investigated with unprecedented space and time sampling.
Change in temperature over the period seems to be part of a large-scale variability that seems to be associated with climate variability.
Monthly mean time series were constructed for the CAPE and temperature at the 100-hPa level using daily values at 1200 hours GMT for revealing large-scale variability.
This study has shown that it is possible to separate large-scale variability created by topography and forest cover from plot-scale variability through specifically designed snow surveys.
The combination of elevation, aspect, and forest cover explained about 80 90% of the large-scale variability in snow accumulation for both years.
In this study, four simulations were performed with initial data interpolated from the 0.5° gridded ECMWF YOTC, with varying cloud microphysical settings (Table 1) to determine their impacts on the evolution of the large-scale variability and the initial disturbance.
This is a fairly large value, suggesting that both the annual peak in CAPE and the large-scale variability are strongly linked to decreases in the temperature at the 100-hPa level at Delhi.
Qualitative geological information is extremely rich in identifying large-scale variability patterns, which are identified by stochastic models only in data rich areas, and should be explicitly included in the calibration process.
The value of this coverage increases with time and provides the basis for placing recent observations into the context of long-term, large-scale variability and thus suggesting what the causes are likely to be.
