I am told that the positive semidefinite cone in $\mathbf{S}^2$ is
$$X = \begin{bmatrix} x & y \\ y & z \end{bmatrix} \in \mathbf{S}^2_+ \iff x \ge 0, \ \ \ z \ge 0, \ \ \ xz \ge y^2,$$
where $S^2_+$ is the set of symmetric $2 \times 2$ positive semidefinite matrices.
I suspect that the conditions $x \ge 0, z \ge 0, xz \ge y^2$ comes from the fact that the matrix is positive semidefinite, but can someone please make it clear why this is required for a positive semidefinite matrix?
This definition is the one I am using, but it doesn't say anything explicit about the values of the elements of the matrix.
Since $X$ is real symmetric and positive semi-definite, you can write it as
$$X = B^TB$$
(where B isn't unique -- $B^T$ could be for instance lower triangular as in Cholesky. But for any valid choice of $B$ the following arguments hold.)
Let $B= \begin{bmatrix} \mathbf b_1 \vert \mathbf b_2 \end{bmatrix}$. Then,
$$X = \begin{bmatrix} x & y \\ y & z \end{bmatrix} = \begin{bmatrix} \mathbf b_1^T \mathbf b_1 & \mathbf b_1^T \mathbf b_2 \\ \mathbf b_1^T \mathbf b_2 & \mathbf b_2^T \mathbf b_2 \end{bmatrix} = \begin{bmatrix} \big \Vert \mathbf b_1\big \Vert_2^2 & \mathbf b_1^T \mathbf b_2 \\ \mathbf b_1^T \mathbf b_2 & \big \Vert \mathbf b_2\big \Vert_2^2 \end{bmatrix}$$ By Cauchy-Schwarz, this implies
$$y^2 = (\mathbf b_1^T \mathbf b_2)^2 \leq \big \Vert \mathbf b_1\big \Vert_2^2 \cdot \big \Vert \mathbf b_2\big \Vert_2^2 = x \cdot z.$$