02 Dec 2022
02 Dec 2022
Status: this preprint is open for discussion.

Improving Statistical Projections of Ocean Dynamic Sea-level Change Using Pattern Recognition Techniques

Víctor Malagón-Santos1, Aimée B. A. Slangen1, Tim H. J. Hermans1,2, Sönke Dangendorf3, Marta Marcos4, and Nicola Maher5,6,7 Víctor Malagón-Santos et al.
  • 1NIOZ Royal Netherlands Institute for Sea Research, Department of Estuarine & Delta Systems, P.O. Box 140, 4400 AC Yerseke, the Netherlands
  • 2University of Utrecht, Institute for Marine and Atmospheric research Utrecht (IMAU), Utrecht, The Netherlands
  • 3Department of River-Coastal Science and Engineering, Tulane University, New Orleans, USA
  • 4Mediterranean Institute for Advanced Studies (IMEDEA), Spanish National Research Council-University of Balearic Islands (CSIC-UIB), Esporles, Spain
  • 5Cooperative Institute for Research in Environmental Science, University of Colorado, Boulder, CO, USA
  • 6Department of Atmospheric and Oceanic Sciences, University of Colorado, Boulder, CO, USA
  • 7Max Planck Institute for Meteorology, Hamburg, Germany

Abstract. 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 model disagreement. 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 (EOF), namely signal-to-noise maximising EOF pattern filtering and low-frequency component analysis, for their ability to reduce errors in pattern scaling of ocean dynamic sea-level change. These two methods are applied to the initial-condition large ensemble MPI-GE, so that internal variability is optimally characterized while avoiding model biases. We show that pattern filtering provides an efficient way of reducing errors compared to other conventional approaches such as a simple ensemble average. For instance, filtering only two realizations by characterising their common response to external forcing reduces the random error by almost 60 %, a reduction level that is only achieved by averaging at least 12 realizations. We further investigate the applicability of both methods to single realization modelling experiments, including four CMIP5 simulations for comparison with previous regional emulation analyses. Pattern scaling 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 a few realizations are available. Removing internal variability prior to tuning regional emulation tools can optimize the performance of the statistical model and simplify the choice of suitable predictors.

Víctor Malagón-Santos et al.

Status: open (until 27 Jan 2023)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2022-1293', Anonymous Referee #1, 03 Jan 2023 reply
  • RC2: 'Comment on egusphere-2022-1293', Anonymous Referee #2, 05 Jan 2023 reply

Víctor Malagón-Santos et al.

Víctor Malagón-Santos et al.


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Short summary
Climate change will alter heat and freshwater fluxes and ocean circulation, driving local changes in sea level. This sea-level change component is known as ocean dynamic sea level (DSL), and it is usually projected using computationally expensive General Circulation Models. Statistical models are a cheaper alternative for projecting DSL, but may contain significant errors. Here, we partly remove those errors (driven by natural variability) by using pattern recognition techniques.