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Improving statistical projections of ocean dynamic sea-level change using pattern recognition techniques
Malagon Santos, V.; Slangen, A.B.A.; Hermans, T.H.J.; Dangendorf, S.; Marcos, M.; Maher, N. (2023). Improving statistical projections of ocean dynamic sea-level change using pattern recognition techniques. Ocean Sci. 19(2): 499-515.

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In: Ocean Science. Copernicus: Göttingen. ISSN 1812-0784; e-ISSN 1812-0792, meer
Peer reviewed article  

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  • Malagon Santos, V., meer
  • Slangen, A.B.A., meer
  • Hermans, T.H.J., meer
  • Dangendorf, S.
  • Marcos, M.
  • Maher, N.

    Regional emulation tools based on statistical relationships, such as pattern scaling, provide a computationally inexpensive way of projecting ocean dynamic sea-level change for a broad range of climate change scenarios. Such approaches usually require a careful selection of one or more predictor variables of climate change so that the statistical model is properly optimized. Even when appropriate predictors have been selected, spatiotemporal oscillations driven by internal climate variability can be a large source of statistical model error. Using pattern recognition techniques that exploit spatial covariance information can effectively reduce internal variability in simulations of ocean dynamic sea level, significantly reducing random errors in regional emulation tools. Here, we test two pattern recognition methods based on empirical orthogonal functions (EOFs), namely signal-to-noise maximizing EOF pattern filtering and low-frequency component analysis, for their ability to reduce errors in pattern scaling of ocean 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 of initial-condition large ensemble designed for an optimal characterization of the externally forced response. We show that the two methods tested here more efficiently reduce errors than conventional approaches such as a simple ensemble average. For instance, filtering only two realizations by characterizing their common response to external forcing reduces the random error by almost 60 %, a reduction that is only achieved by averaging at least 12 realizations. We further investigate the applicability of both methods to single-realization modeling experiments, including four CMIP5 simulations for comparison with previous regional emulation analyses. Pattern filtering leads to a varying degree of error reduction depending on the model and scenario, ranging from more than 20 % to about 70 % reduction in global-mean root mean squared error compared with unfiltered simulations. Our results highlight the relevance of pattern recognition methods as a tool to reduce errors in regional emulation tools of ocean dynamic sea-level change, especially when one or only a few realizations are available. Removing internal variability prior to tuning regional emulation tools can optimize the performance of the statistical model, leading to substantial differences in emulated dynamic sea level compared to unfiltered simulations.

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