Consistent Predictability of the Ocean State Ocean

18 The Ocean State Ocean Model OSOM is an application of the Regional Ocean Mod19 eling System spanning the Rhode Island waterways, including Narragansett Bay, Mt. 20 Hope Bay, larger rivers, and the Block Island Shelf circulation from Long Island to 21 Nantucket. This paper discusses the physical aspects of the estuary (Narragansett 22 and Mount Hope Bays and larger rivers) to evaluate physical circulation predictabil23 ity. This estimate is intended to help decide if a forecast and prediction system is 24 warranted, to prepare for coupling with biogeochemistry and fisheries models with 25 widely disparate timescales, and to find the spin-up time needed to establish the cli26 matological circulation of the region. Perturbed initial condition ensemble simulations 27 are combined with metrics from information theory to quantify the predictability of the 28 OSOM forecast system–i.e., how long anomalies from different initial conditions per29 sist. The predictability timescale in this model agrees with readily estimable timescales 30 such as the freshwater flushing timescale evaluated using the total exchange flow (TEF) 31 framework, indicating that the estuarine dynamics rather than chaotic transport is the 32 dominant model behavior limiting predictions. The predictability of the OSOM is ∼7 33 to 40 days, varying with parameters, region, and season. 34 Plain Language Summary 35 A new model of waterways near Rhode Island is introduced and examined. The 36 model is intended for studying the physical circulation of this region and its ecosystem 37 changes. This study uses a variety of metrics to assess for how long a forecast with this 38 model might be useful (i.e., how long the model’s initial state determines its behavior) 39 and relatedly how long to run (or spin up) the model to have poorly known initial 40 conditions not affect the result systematically. 41

[1]  B. Fox‐Kemper,et al.  Internal vs Forced Variability Metrics for Geophysical Flows Using Information Theory , 2020 .

[2]  B. Meyssignac,et al.  Contributions of Atmospheric Forcing and Chaotic Ocean Variability to Regional Sea Level Trends Over 1993–2015 , 2018, Geophysical Research Letters.

[3]  Z. Liu,et al.  Potential predictability and forecast skill in ensemble climate forecast: a skill-persistence rule , 2018, Climate Dynamics.

[4]  Eric Greiner,et al.  The Mercator Ocean Global High‐Resolution Monitoring and Forecasting System , 2018, New Frontiers in Operational Oceanography.

[5]  Hernan G. Arango,et al.  A Coastal Ocean Forecast System for U.S. Mid-Atlantic Bight and Gulf of Maine , 2018, New Frontiers in Operational Oceanography.

[6]  J. Molines,et al.  Intrinsic and Atmospherically Forced Variability of the AMOC: Insights from a Large-Ensemble Ocean Hindcast , 2017 .

[7]  R. Hetland,et al.  Time scales in Galveston Bay: An unsteady estuary , 2016 .

[8]  J. Lerczak,et al.  A Comparison of Bulk Estuarine Turnover Timescales to Particle Tracking Timescales Using a Model of the Yaquina Bay Estuary , 2015, Estuaries and Coasts.

[9]  R. C. Beardsley,et al.  Northeast Coastal Ocean Forecast System (NECOFS): A Multi-scale Global-Regional-Estuarine FVCOM Model , 2014 .

[10]  P. Lionello,et al.  Storm Surge Ensemble Prediction for the City of Venice , 2014 .

[11]  Arun Kumar,et al.  Is There a Relationship between Potential and Actual Skill , 2014 .

[12]  Hernan G. Arango,et al.  The Regional Ocean Modeling System (ROMS) 4-dimensional variational data assimilation systems Part I - System overview and formulation , 2011 .

[13]  P. MacCready Calculating Estuarine Exchange Flow Using Isohaline Coordinates , 2011 .

[14]  Timothy DelSole,et al.  Model Fidelity versus Skill in Seasonal Forecasting , 2010 .

[15]  Candace A. Oviatt,et al.  Narragansett Bay Hypoxic Event Characteristics Based on Fixed-Site Monitoring Network Time Series: Intermittency, Geographic Distribution, Spatial Synchronicity, and Interannual Variability , 2009 .

[16]  Stephen Cusack,et al.  Improved Surface Temperature Prediction for the Coming Decade from a Global Climate Model , 2007, Science.

[17]  Janis C Kurtz,et al.  A Classification of U.S. Estuaries Based on Physical and Hydrologic Attributes , 2007, Environmental monitoring and assessment.

[18]  Andrew J. Majda,et al.  Quantifying predictability through information theory: small sample estimation in a non-Gaussian framework , 2005 .

[19]  Timothy DelSole,et al.  Predictability and Information Theory. Part I: Measures of Predictability , 2004 .

[20]  H. Burchard,et al.  A generic length-scale equation for geophysical turbulence models , 2003 .

[21]  E. F. Bradley,et al.  Bulk Parameterization of Air–Sea Fluxes: Updates and Verification for the COARE Algorithm , 2003 .

[22]  Andrew J. Majda,et al.  A mathematical framework for quantifying predictability through relative entropy , 2002 .

[23]  Nancy E. Monsen,et al.  A comment on the use of flushing time, residence time, and age as transport time scales , 2002 .

[24]  R. Kleeman Measuring Dynamical Prediction Utility Using Relative Entropy , 2002 .

[25]  John F. Mustard,et al.  The Use of Satellite Data to Quantify Thermal Effluent Impacts , 1999 .

[26]  S. Griffies,et al.  A Conceptual Framework for Predictability Studies , 1999 .

[27]  D. Chapman Numerical Treatment of Cross-Shelf Open Boundaries in a Barotropic Coastal Ocean Model , 1985 .

[28]  M. Pilson On the residence time of water in Narragansett Bay , 1985 .

[29]  J. Shukla,et al.  Dynamical predictability of monthly means , 1981 .

[30]  G. A. Barnard,et al.  Transmission of Information: A Statistical Theory of Communications. , 1961 .

[31]  D. Ullman Hydrodynamic Modeling of Narragansett Bay in Support of the EcoGEM Ecological Model , 2019 .

[32]  M. Hashemi,et al.  Numerical simulation of coastal erosion and its mitigation by living shoreline methods : A case study in southern Rhode Island , 2018 .

[33]  C. Wertman Circulation & Exchange Within Shelf & Estuarine Waters Driven by the Atmosphere, Tides and Buoyancy , 2018 .

[34]  Sang Joon Kim,et al.  A Mathematical Theory of Communication , 2006 .

[35]  Alexander F. Shchepetkin,et al.  The regional oceanic modeling system (ROMS): a split-explicit, free-surface, topography-following-coordinate oceanic model , 2005 .

[36]  D. Bergondo Examining the processes controlling water column variability in Narragansett Bay: Time series data and numerical modeling , 2004 .

[37]  M. Roulston,et al.  Evaluating Probabilistic Forecasts Using Information Theory , 2002 .

[38]  Alexander F. Shchepetkin,et al.  Open boundary conditions for long-term integration of regional oceanic models , 2001 .

[39]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .

[40]  G. North,et al.  Information Theory and Climate Prediction , 1990 .