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Future climate change impacts depend on temperatures not only through changesin their means but also through changes in their variability. Generalcirculation models (GCMs) predict changes in both means and variability;however, GCM output should not be used directly as simulations for impactsassessments because GCMs do not fully reproduce present-day temperaturedistributions. This paper addresses an ensuing need for simulations of futuretemperatures that combine both the observational record and GCM projections ofchanges in means and temporal covariances. Our perspective is that suchsimulations should be based on transforming observations to account for GCMprojected changes, in contrast to methods that transform GCM output to accountfor discrepancies with observations. Our methodology is designed for simulatingtransient (non-stationary) climates, which are evolving in response to changesin CO$_2$ concentrations (as is the Earth at present). This work builds onpreviously described methods for simulating equilibrium (stationary) climates.Since the proposed simulation relies on GCM projected changes in covariance, wedescribe a statistical model for the evolution of temporal covariances in a GCMunder future forcing scenarios, and apply this model to an ensemble of runsfrom one GCM, CCSM3. We find that, at least in CCSM3, changes in the localcovariance structure can be explained as a function of the regional mean changein temperature and the rate of change of warming. This feature means that thestatistical model can be used to emulate the evolving covariance structure ofGCM temperatures under scenarios for which the GCM has not been run. Whencombined with an emulator for mean temperature, our methodology can simulateevolving temperatures under such scenarios, in a way that accounts forprojections of changes while still retaining fidelity with the observationalrecord.

Temperatures in transient climates: improved methods for simulations with evolving temporal covariances