Pattern scaling is used to quantify regional projections of future climate change under arbitrary emission scenarios. The resulting spatial patterns, typically of temperature and precipitation, are valuable for integrated assessment, climate mitigation, and regional impact studies. In this research we implement a linear regression method from Lynch et al. (2016) for a novel goal: to pattern scaling future ocean pH change. We explore ocean pH patterns generated from two predictors, global mean surface temperature change and global mean pH change, and compare the patterns to individual CMIP5 models as well as an ensemble mean of surface pH. Both predictors are equally skillful at generating patterns with a mean goodness of fit greater than 99%. We find that high (>50°) latitude pH is most sensitive to changes in both global mean temperature and global mean pH. In comparison, the eastern tropical Pacific upwelling region has the lowest sensitivity to both predictors. Coupling this pattern scaling with the explicit ocean-carbonate chemistry capabilities of Hector, a reduced-form climate carbon-cycle model, allows us to robustly emulate the marine carbonate system of much more complex earth system models. This capability will help fill gaps in scenario coverage, conduct studies on regional impacts, and investigate future changes in ocean acidification.