Low-Cloud Fraction and
ECVs
Although the above section describes climatic shifts in some ECVs, it is
unclear how these variabilities are associated with ΔCF. Our PLSR models
evaluated previous results, indicating that ΔCF is strongly related to
changes in ECVs (Fig. 4 ). Overall, eight components were
required as optimum to explain the ΔCF variability (Fig. 4a ),
which was equal to the number of predicting variables. Using this number
of components, the PLSR models could predict 55 ± 0.67 % of the ΔCF
variance (Fig. 4b ). Such models provided a mean RMSE of 10.16 ±
0.07 ×10-4 CF year-1 (Fig.
4c ) with a relative RMSE close to 8.76 ± 0.06 %. The VIP of these
models also revealed that ΔVSWC, all surface temperature trends (i.e.,
average, min, max), and ΔPressure are highly important for predicting
ΔCF among ECVs (Fig. 3d ).