and a nonspatial nugget term ($U$). Comparing (\[ASL\]) and (\[joint2\]), we see a result of the nugget effect is that the $L^{\infty}$ norm (the maximum) in (\[joint2\]) is replaced with the $L^{1/\alpha}$ norm, and that (\[ASL\]) converges weakly to (\[joint2\]) as $\alpha$ goes to zero. Including a nugget aids in computation, as the likelihood becomes a simple product of univariate GEV densities. Including a nugget term also has advantages beyond computation. The GEVP has been criticized as unrealistically smooth \[@blanchet-2011\], and so a nugget may improve fit. Analogously, in the geostatistical literature for Gaussian data a nugget is not required, but is used routinely to account for small-scale phenomena that cannot be captured with a smooth spatial process \[@Cressie93 [@Banerjee03; @Handbook]\].

Simulation study {#ssim}
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In this section we conduct a simulation study to verify that the MCMC algorithm produces reliable results, to investigate sensitivity to knot selection, and to determine which parameters are the most difficult to estimate. Data and knots are placed on $m\times m$ regular grids covering $[l,u]\times[l,u]$, denoted ${\mathcal{S}}(m,l,u)$. For each simulation design, we generate data from the model described in Section \[sST\] at the $n=49$ locations ${\mathcal{S}}(7,0,6)$ and $T=10$ independent years. The GEV location parameter 