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In recent years, the climate change research community has become highlyinterested in describing the anthropogenic influence on extreme weather events,commonly termed "event attribution." Limitations in the observational recordand in computational resources motivate the use of uncoupled,atmosphere/land-only climate models with prescribed ocean conditions run over ashort period, leading up to and including an event of interest. In thisapproach, large ensembles of high-resolution simulations can be generated underfactual observed conditions and counterfactual conditions that might have beenobserved in the absence of human interference; these can be used to estimatethe change in probability of the given event due to anthropogenic influence.However, using a prescribed ocean state ignores the possibility that estimatesof attributable risk might be a function of the ocean state. Thus, theuncertainty in attributable risk is likely underestimated, implying anover-confidence in anthropogenic influence.  In this work, we estimate the year-to-year variability in calculations of theanthropogenic contribution to extreme weather based on large ensembles ofatmospheric model simulations. Our results both quantify the magnitude ofyear-to-year variability and categorize the degree to which conclusions ofattributable risk are qualitatively affected. The methodology is illustrated byexploring extreme temperature and precipitation events for the northwest coastof South America and northern-central Siberia; we also provides results forregions around the globe. While it remains preferable to perform a fullmulti-year analysis, the results presented here can serve as an indication ofwhere and when attribution researchers should be concerned about the use ofatmosphere-only simulations.

Quantifying the effect of interannual ocean variability on the attribution of extreme climate events to human influence