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The observable universe contains density perturbations on scales larger thanany finite volume survey. Perturbations on scales larger than a survey canmeasure degrade its power to constrain cosmological parameters. The dependenceof survey observables such as the weak lensing power spectrum on theselong-wavelength modes results in super-sample covariance. Accuratelyforecasting parameter constraints for future surveys requires accuratelyaccounting for the super-sample effects. If super-sample covariance is in facta major component of the survey error budget, it may be necessary toinvestigate mitigation strategies that constrain the specific realization ofthe long-wavelength modes. We present a Fisher matrix based formalism forapproximating the magnitude of super-sample covariance and the effectiveness ofmitigation strategies for realistic survey geometries. We implement ourformalism in the public code SuperSCRAM: Super-Sample Covariance Reduction andMitigation. We illustrate SuperSCRAM with an example application, where themodes contributing to super-sample covariance in the WFIRST weak lensing surveyare constrained by the low-redshift galaxy number counts in the wider LSSTfootprint. We find that super-sample covariance increases the volume of theerror ellipsoid in 7D cosmological parameter space by a factor of 4.5 relativeto Gaussian statistical errors only, but our simple mitigation strategy morethan halves the contamination, to a factor of 2.0.

Forecasting Super-Sample Covariance in Future Weak Lensing Surveys with SuperSCRAM