Improving statistical projections of ocean dynamic sea-level change using pattern recognition techniques

Regional emulation tools based on statistical relationships, such as pattern scaling, provide a computationally inexpensive way of projecting oceandynamic sea-level change for a broad range of climate change scenarios. Such approaches usually require a careful selection of one or more predictorvariables of climate change so that the statistical model is properly optimized. Even when appropriate predictors have been selected, spatiotemporaloscillations driven by internal climate variability can be a large source of statistical model error. Using pattern recognition techniques thatexploit spatial covariance information can effectively reduce internal variability in simulations of ocean dynamic sea level, significantly reducingrandom errors in regional emulation tools. Here, we test two pattern recognition methods based on empirical orthogonal functions (EOFs), namelysignal-to-noise maximizing EOF pattern filtering and low-frequency component analysis, for their ability to reduce errors in pattern scaling ofocean dynamic sea-level change. We use the Max Planck Institute Grand Ensemble (MPI-GE) as a test bed for both methods, as it is a type ofinitial-condition large ensemble designed for an optimal characterization of the externally forced response. We show that the two methods testedhere more efficiently reduce errors than conventional approaches such as a simple ensemble average. For instance, filtering only two realizations bycharacterizing their common response to external forcing reduces the random error by almost 60 %, a reduction that is only achieved by averagingat least 12 realizations. We further investigate the applicability of both methods to single-realization modeling experiments, including four CMIP5simulations for comparison with previous regional emulation analyses. Pattern filtering leads to a varying degree of error reduction depending onthe model and scenario, ranging from more than 20 % to about 70 % reduction in global-mean root mean squared error compared with unfilteredsimulations. Our results highlight the relevance of pattern recognition methods as a tool to reduce errors in regional emulation tools of oceandynamic sea-level change, especially when one or only a few realizations are available. Removing internal variability prior to tuning regionalemulation tools can optimize the performance of the statistical model, leading to substantial differences in emulated dynamic sea level compared tounfiltered simulations.