There are many ways that PCA can be explained, but here is one explanation.
The second graph is the scatterplot of PC1 and PC2. This is a plot of the observations on the new PC coordinate axes. You could think of it as a two-dimensional view of your original multidimensional space. It can tell you about relationships between the observations.
The third graph is the plot of the eigenvalues for the first two PCs. This can tell you about relationships between the variables.
The two together: suppose you see an outlying point in the upper right portion of the second graph. Look at the third graph and see which variables point in that direction. That outlying point is likely high in those variables. Similarly, any variables in the opposite direction would have low scores for the original variables.
Dan Obermiller