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We show that some statistical properties of forced two-dimensional turbulencehave an important sensitivity to the form of large-scale dissipation which isrequired to damp the inverse cascade. We consider three models of large-scaledissipation: linear "Ekman" drag, non-linear quadratic drag, and scaleselective hypo-drag that damps only low-wavenumber modes. In all cases, thestatistically steady vorticity field is dominated by almost axisymmetricvortices, and the probability density function of vorticity is non-Gaussian.However, in the case of linear and quadratic drag, we find that the velocitystatistics is close to Gaussian, with non-negligible contribution coming fromthe background turbulent flow. On the other hand, with hypo-drag, theprobability density function of velocity is non-Gaussian and is predominantlydetermined by the properties of the vortices. With hypo-drag, the relativepositions of the vortices and the exponential distribution of the vortexextremum are important factors responsible for the non-Gaussian velocitystatistics.

Non-universal velocity probability densities in two-dimensional turbulence: the effect of large-scale dissipation