The squared cosine is a measure of how well the variable "fits" the axis. Thus, with your small data set, the squared cosines for the four axes for each variable will sum to 1 (i.e., all the variation has been explained in four axes). The first three variables fit axis 1 very well, but none of the others, the fourth variable fits axis 2.
The contribution is how much each variable contributes to each axis. Each column of values will sum to 100.
Thus, in your toy example, 97.9% of the variation in the first variable is "explained" by the first axis. Of the variation that the first axis explains, however, about 1/3 of the variation explained by that axis comes from your first three variables, and only a tiny fraction from var 4.
Thus, in your toy example, the first axis "explains" almost all the variation in the first three variables, and the second axis is showing the variation in the fourth variable.
Your can think about is as the cos2 shows how the the variation in a single variable is spread across the axes, whereas the contribution shows where the variation represented by each axis is coming from.
In CA, these two values are sometimes called the absolute and relative contributions. I also prefer the way Greenacre does this where everything is standardised to per mills (i.e., out of 1000) so we don't get these very small numbers.
Best wishes, Kris.