Submitted as: model description paper 11 Mar 2021

Submitted as: model description paper | 11 Mar 2021

Review status: a revised version of this preprint was accepted for the journal GMD.

S2P3-R v2.0: computationally efficient modelling of shelf seas on regional to global scales

Paul R. Halloran1, Jennifer K. McWhorter1,2, Beatriz Arellano Nava1, Robert Marsh3, and William Skirving4,5 Paul R. Halloran et al.
  • 1College of Life and Environmental Sciences, University of Exeter, Exeter, UK
  • 2School of Biological Sciences, The University of Queensland, Brisbane, Queensland, Australia
  • 3University of Southampton, National Oceanography Centre, Southampton, UK
  • 4Coral Reef Watch, National Oceanic and Atmospheric Administration, College Park, MD, USA
  • 5ReefSense Pty Ltd, Cranbrook, Queensland, Australia

Abstract. The marine impacts of climate change on our societies will be largely felt through coastal waters and shelf seas. These impacts involve sectors as diverse as tourism, fisheries and energy production. Projections of future marine climate change come from global models. Modelling at the global scale is required to capture the feedbacks and large-scale transport of physical properties such as heat, which occur within the climate system, but global models currently cannot provide detail in the shelf-seas. Version 2 of the regional implementation of the Shelf Sea Physics and Primary Production (S2P3-R v2.0) model bridges the gap between global projections and local shelf-sea impacts. S2P3-R v2.0 is a highly simplified coastal shelf model, computationally efficient enough to be run across the shelf seas of the whole globe. Despite the simplified nature of the model, it can display regional skill comparable to state-of-the-art models, and at the scale of the global (excluding high-latitudes) shelf-seas can explain > 50 % of the interannual SST variability in ~60 % of grid cells, and > 80 % of interannual variability in ~20 % of grid cells. The model can be run at any resolution for which the input data can be supplied, without expert technical knowledge, and using a modest off-the-shelf computer. The accessibility of S2P3-R v2.0 places it within reach of an array of coastal managers and policy makers. S2P3-R v2.0 is set up to be driven directly with output from reanalysis products or daily atmospheric output from climate models such as those which contribute to the 6th phase of the Climate Model Intercomparison Project, making it a valuable tool for semi-dynamical downscaling of climate projections. The updates introduced into version 2.0 of this model are primarily focused around the ability to geographical relocate the model, model usability and speed, but also scientific improvements. The value of this model comes from its computational efficiency, which necessitates simplicity. This simplicity leads to several limitations, which are discussed in the context of evaluation at regional and global scales.

Paul R. Halloran et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2021-2', Anonymous Referee #1, 20 Apr 2021
  • RC2: 'Comment on gmd-2021-2', Anonymous Referee #2, 04 May 2021
  • AC1: 'Author response to reviewer comments on gmd-2021-2', Paul Halloran, 10 Jun 2021

Paul R. Halloran et al.

Data sets

S2P3v2.0 archived output from 65S-65N, 180W-180E model simulation Paul R. Halloran, Jennifer K. McWhorter, Beatriz Arellano Nava, Robert Marsh, and William Skirving

Model code and software

S2P3Rv2.0 Zenodo archive Paul R. Halloran, Jennifer K. McWhorter, Beatriz Arellano Nava, Robert Marsh, and William Skirving

Paul R. Halloran et al.


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Short summary
This paper describes the latest version of a simple model for simulating coastal oceanography in response to changes in weather and climate. The latest revision of this model makes scientific improvements, but focuses on improvements that allow the model to be run simply at large scales and for long periods of time to explore the implications of (for example) future climate change along large areas of coastline.