Consistent Predictability of the Ocean State Ocean Model Using Information Theory and Flushing Timescales

Abstract

The Ocean State Ocean Model (OSOM) is an application of the Regional Ocean Modeling System spanning the Rhode Island waterways, including Narragansett Bay, Mt. Hope Bay, larger rivers, and the Block Island Shelf circulation from Long Island to Nantucket. This study discusses the physical aspects of the estuary (Narragansett and Mount Hope Bays and larger rivers) to evaluate physical circulation predictability. This estimate is intended to help decide if a forecast and prediction system is warranted, to prepare for coupling with biogeochemistry and fisheries models with widely disparate timescales, and to find the spin‐up time needed to establish the climatological circulation of the region. Perturbed initial condition ensemble simulations are combined with metrics from information theory to quantify the predictability of the OSOM forecast system–i.e., how long anomalies from different initial conditions persist. The predictability timescale in this model agrees with readily estimable timescales such as the freshwater flushing timescale evaluated using the total exchange flow (TEF) framework, indicating that the estuarine dynamics rather than chaotic transport is the dominant model behavior limiting predictions. The predictability of the OSOM is ∼7–40 days, varying with parameters, region, and season.

Document Details

Document Type
Pub Defense Publication
Publication Date
Jul 01, 2021
Source ID
10.1029/2020jc016875

Entities

People

  • Aakash Sane
  • Baylor Fox-Kemper
  • Christopher Kincaid
  • David S. Ullman
  • Lewis Rothstein

Organizations

  • Brown University
  • National Science Foundation
  • Office of Naval Research
  • University of Rhode Island

Tags

Fields of Study

  • Environmental science

Readers

  • Coastal and Marine Engineering/Sediment Transport/Hydraulic Engineering
  • Ocean-Atmosphere Mesoscale Modeling, Data Assimilation, and Flux Boundary Layers
  • Theoretical Analysis.