Impact of Atmospheric and Model Physics Perturbations on a High‐Resolution Ensemble Data Assimilation System of the Red Sea

<p>The Ensemble Adjustment Kalman Filter of the Data Assimilation Research Testbed is implemented to assimilate observations of satellite sea surface temperature, altimeter sea surface height and in-situocean temperature and salinity profiles into an eddy-resolving 4km-Massachusetts Institute of Technology general circulation model (MITgcm) of the Red Sea. We investigate the impact of three different assimilation strategies (1) <em>Iexp</em>&#8211; inflates filter error covariance by 10%, (2) <em>IAexp</em>&#8211; adds ensemble of atmospheric forcing to Iexp, and (3) <em>IAPexp</em>&#8211; adds perturbed model physics toIAexp. The assimilation experiments are run for one year, starting from the same initial ensemble on 1<sup>st</sup>January, 2011 and the data are assimilated every three days.</p><p>Results demonstrate that the <em>Iexp</em> mainly improved the model outputs with respect to assimilation-free MITgcm run in the first few months, before showing signs of dynamical imbalances in the ocean estimates, particularly in the data-sparse subsurface layers. The <em>IAexp</em> yielded substantial improvements throughout the assimilation period with almost no signs of imbalances, including the subsurface layers. It further well preserved the model mesoscales features resulting in an improved forecasts for eddies, both in terms of intensity and location. Perturbing model physics in <em>IAPexp</em> slightly improved the forecast statistics. It further increased smoothness in the ocean forecasts and improved the placement of basin-scale eddies, but caused loss of some high-resolution features. Increasing hydrographic coverage helps recovering the losses and yields more improvements in <em>IAPexp</em> compared to <em>IAexp</em>. Switching off inflation in <em>IAexp</em> and <em>IAPexp</em> leads to further improvements, especially in the subsurface layers.</p>

[1]  Jean-Michel Brankart,et al.  Impact of uncertainties in the horizontal density gradient upon low resolution global ocean modelling , 2013 .

[2]  T. Miyoshi.  The Gaussian Approach to Adaptive Covariance Inflation and Its Implementation with the Local Ensemble Transform Kalman Filter , 2011 .

[3]  Ryan D. Torn,et al.  Performance Characteristics of a Pseudo-Operational Ensemble Kalman Filter , 2008 .

[4]  Amy S. Bower,et al.  The response of the Red Sea to a strong wind jet near the Tokar Gap in summer , 2013 .

[5]  J. Keller,et al.  Investigation of predictability during the extratropical transition of tropical cyclones using the THORPEX interactive grand global ensemble (TIGGE) , 2010 .

[6]  P. Gent,et al.  Isopycnal mixing in ocean circulation models , 1990 .

[7]  Lewis D. Grasso,et al.  The differentiation between grid spacing and resolution and their application to numerical modeling , 2000 .

[8]  Jiang Zhu,et al.  Initial ensemble generation and validation for ocean data assimilation using HYCOM in the Pacific , 2008 .

[9]  D. Behringer,et al.  Evaluation of the Global Ocean Data Assimilation System at INCOIS: The Tropical Indian Ocean , 2013 .

[10]  Fuqing Zhang,et al.  Tests of an Ensemble Kalman Filter for Mesoscale and Regional-Scale Data Assimilation. Part I: Perfect Model Experiments , 2006 .

[11]  L. Beal,et al.  Spreading of Red Sea overflow waters in the Indian Ocean , 2000 .

[12]  Ibrahim Hoteit,et al.  The eddy kinetic energy budget in the Red Sea , 2016 .

[13]  Thomas M. Smith,et al.  Daily High-Resolution-Blended Analyses for Sea Surface Temperature , 2007 .

[14]  Geir Evensen,et al.  Assimilation of ice concentration in a coupled ice–ocean model, using the Ensemble Kalman filter , 2003 .

[15]  William E. Johns,et al.  Observations of the summer Red Sea circulation , 2007 .

[16]  I. Hoteit,et al.  Impact of dynamical representational errors on an Indian Ocean ensemble data assimilation system , 2019, Quarterly Journal of the Royal Meteorological Society.

[17]  Peter R. Oke,et al.  TOPAZ4: an ocean-sea ice data assimilation system for the North Atlantic and Arctic , 2012 .

[18]  I. F. Stewart,et al.  The Red Sea: The Formation, Morphology, Oceanography and Environment of a Young Ocean Basin , 2015 .

[19]  Fuqing Zhang,et al.  Review of the Ensemble Kalman Filter for Atmospheric Data Assimilation , 2016 .

[20]  Ibrahim Hoteit,et al.  Impacts of warming on phytoplankton abundance and phenology in a typical tropical marine ecosystem , 2018, Scientific Reports.

[21]  Stephen M. Griffies,et al.  Biharmonic Friction with a Smagorinsky-Like Viscosity for Use in Large-Scale Eddy-Permitting Ocean Models , 2000 .

[22]  Philippe Gaspar,et al.  A simple eddy kinetic energy model for simulations of the oceanic vertical mixing: Tests at Station Papa and long-term upper ocean study site , 1990 .

[23]  Ross N. Bannister,et al.  A review of forecast error covariance statistics in atmospheric variational data assimilation. I: Characteristics and measurements of forecast error covariances , 2008 .

[24]  Ibrahim Hoteit,et al.  Regional ocean data assimilation. , 2015, Annual review of marine science.

[25]  M. Bell,et al.  Reducing ocean model imbalances in the equatorial region caused by data assimilation , 2017 .

[26]  Guillem Candille,et al.  A generic approach to explicit simulation of uncertainty in the NEMO ocean model , 2015 .

[27]  P. Vinayachandran,et al.  Westward movement of eddies into the Gulf of Aden from the Arabian Sea , 2007 .

[28]  Kristian Mogensen,et al.  The ECMWF operational ensemble reanalysis–analysis system for ocean and sea ice: a description of the system and assessment , 2019, Ocean Science.

[29]  M. Huddleston,et al.  Quality control of ocean temperature and salinity profiles — Historical and real-time data , 2007 .

[30]  C. Tanajura,et al.  An Investigation of Ocean Model Uncertainties Through Ensemble Forecast Experiments in the Southwest Atlantic Ocean , 2019, Journal of Geophysical Research: Oceans.

[31]  M. Balmaseda,et al.  Assimilation of altimeter data in the ECMWF ocean analysis system 3. , 2006 .

[32]  E. Kalnay,et al.  The pre-Argo ocean reanalyses may be seriously affected by the spatial coverage of moored buoys , 2017, Scientific Reports.

[33]  Jeffrey L. Anderson,et al.  A Monte Carlo Implementation of the Nonlinear Filtering Problem to Produce Ensemble Assimilations and Forecasts , 1999 .

[34]  William E. Johns,et al.  An Oceanic General Circulation Model (OGCM) investigation of the Red Sea circulation: 2. Three‐dimensional circulation in the Red Sea , 2003 .

[35]  J. Thepaut,et al.  The ERA‐Interim reanalysis: configuration and performance of the data assimilation system , 2011 .

[36]  Neill E. Bowler,et al.  The MOGREPS short‐range ensemble prediction system , 2008 .

[37]  Ibrahim Hoteit,et al.  Mitigating Observation Perturbation Sampling Errors in the Stochastic EnKF , 2015 .

[38]  G. Danabasoglu,et al.  An Ensemble Adjustment Kalman Filter for the CCSM4 Ocean Component , 2013 .

[39]  Ibrahim Hoteit,et al.  ENSO influence on the interannual variability of the Red Sea convergence zone and associated rainfall , 2018 .

[40]  Rong‐Hua Zhang,et al.  A Modified Vertical Mixing Parameterization for Its Improved Ocean and Coupled Simulations in the Tropical Pacific , 2019, Journal of Physical Oceanography.

[41]  J. McCreary,et al.  Impacts of regional mixing on the temperature structure of the equatorial Pacific Ocean. Part 2: Depth-dependent vertical diffusion , 2014 .

[42]  C. Donlon,et al.  The Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) system , 2012 .

[43]  R. Pacanowski,et al.  Parameterization of Vertical Mixing in Numerical Models of Tropical Oceans , 1981 .

[44]  Detlef Stammer,et al.  Variability of the Meridional Overturning in the North Atlantic from the 50-Year GECCO State Estimation , 2006 .

[45]  Patrick Heimbach,et al.  A MITgcm/DART ensemble analysis and prediction system with application to the Gulf of Mexico , 2013 .

[46]  Alexander Barth,et al.  A stochastic operational forecasting system of the Black Sea: Technique and validation , 2015 .

[47]  C. Wunsch,et al.  Estimated Global Hydrographic Variability , 2007 .

[48]  Jean-Michel Brankart,et al.  Comparative assimilation of Topex/Poseidon and ERS altimeter data and of TAO temperature data in the tropical Pacific ocean during 1994–1998, and the mean sea-surface height issue , 2003 .

[49]  Byoung-Ju Choi,et al.  Effect of model error representation in the Yellow and East China Sea modeling system based on the ensemble Kalman filter , 2016, Ocean Dynamics.

[50]  E. Kalnay,et al.  A Hybrid Global Ocean Data Assimilation System at NCEP , 2014 .

[51]  Gokhan Danabasoglu,et al.  Sensitivity of the global ocean circulation to parameterizations of mesoscale tracer transports , 1995 .

[52]  Ibrahim Hoteit,et al.  Eddies in the Red Sea: A statistical and dynamical study , 2014 .

[53]  David J. Stensrud,et al.  Surface Data Assimilation Using an Ensemble Kalman Filter Approach with Initial Condition and Model Physics Uncertainties , 2005 .

[54]  I. Hoteit,et al.  Sensitivity Studies of the Red Sea Eddies Using Adjoint Method , 2018, Journal of Geophysical Research: Oceans.

[55]  G. Mellor,et al.  Development of a turbulence closure model for geophysical fluid problems , 1982 .

[56]  Juanzhen Sun,et al.  Impacts of Initial Estimate and Observation Availability on Convective-Scale Data Assimilation with an Ensemble Kalman Filter , 2004 .

[57]  A. Bower,et al.  Formation and spreading of Red Sea Outflow Water in the Red Sea , 2015 .

[58]  Joaquín Tintoré,et al.  Vertical velocities at an ocean front , 2001 .

[59]  William R. Holland,et al.  The Role of Mesoscale Eddies in the General Circulation of the Ocean—Numerical Experiments Using a Wind-Driven Quasi-Geostrophic Model , 1978 .

[60]  J. Hansen,et al.  Implications of Stochastic and Deterministic Filters as Ensemble-Based Data Assimilation Methods in Varying Regimes of Error Growth , 2004 .

[61]  X. Deng,et al.  Model Error Representation in an Operational Ensemble Kalman Filter , 2009 .

[62]  Craig H. Bishop,et al.  The THORPEX Interactive Grand Global Ensemble , 2010 .

[63]  James Murphy,et al.  Perturbed parameter ensembles as a tool for sampling model uncertainties and making climate projections , 2011 .

[64]  Ibrahim Hoteit,et al.  Seasonal overturning circulation in the Red Sea: 1. Model validation and summer circulation , 2014 .

[65]  C. Tanajura,et al.  Assimilation of sea-level anomalies and Argo data into HYCOM and its impact on the 24 hour forecasts in the western tropical and South Atlantic , 2015 .

[66]  W. Large,et al.  Oceanic vertical mixing: a review and a model with a nonlocal boundary layer parameterization , 1994 .

[67]  Lakshmi Kantha,et al.  An oceanographic nowcast/forecast system for the Red Sea , 1997 .

[68]  Alicia R. Karspeck,et al.  An Ensemble Approach for the Estimation of Observational Error Illustrated for a Nominal 1° Global Ocean Model , 2016 .

[69]  R. Pielke.  Mesoscale Meteorological Modeling , 1984 .

[70]  I. Hoteit,et al.  Interannual Variability of the Exchange Flow Through the Strait of Bab‐al‐Mandeb , 2019, Journal of Geophysical Research: Oceans.

[71]  T. N. Palmer,et al.  Oceanic Stochastic Parameterizations in a Seasonal Forecast System , 2015, 1506.09181.

[72]  R. Bannister.  A review of forecast error covariance statistics in atmospheric variational data assimilation. II: Modelling the forecast error covariance statistics , 2008 .

[73]  C. Donlon,et al.  OSTIA : An operational, high resolution, real time, global sea surface temperature analysis system , 2007, OCEANS 2007 - Europe.

[74]  James C. McWilliams,et al.  Sensitivity to Surface Forcing and Boundary Layer Mixing in a Global Ocean Model: Annual-Mean Climatology , 1997 .

[75]  Peter R. Oke,et al.  Representation Error of Oceanic Observations for Data Assimilation , 2008 .

[76]  Neill E. Bowler,et al.  Inflation and localization tests in the development of an ensemble of 4D‐ensemble variational assimilations , 2017 .

[77]  Steven A. Orszag,et al.  Large Eddy Simulation of Complex Engineering and Geophysical Flows , 2010 .

[78]  L. A. Anderson,et al.  Physical and biological modeling in the Gulf Stream region:: I. Data assimilation methodology , 2000 .

[79]  Omar M. Knio,et al.  Ensemble data assimilation in the Red Sea: sensitivity to ensemble selection and atmospheric forcing , 2017, Ocean Dynamics.

[80]  Xiaodong Luo,et al.  Data Assimilation in Oceanography: Current Status and New Directions , 2018, New Frontiers in Operational Oceanography.

[81]  Peter R. Oke,et al.  Status and future of data assimilation in operational oceanography , 2015 .

[82]  J. Whitaker,et al.  Evaluating Methods to Account for System Errors in Ensemble Data Assimilation , 2012 .

[83]  Patrick Heimbach,et al.  An eddy-permitting, dynamically consistent adjoint-based assimilation system for the tropical Pacific: Hindcast experiments in 2000 , 2010 .

[84]  Dinh-Tuan Pham,et al.  A simplified reduced order Kalman filtering and application to altimetric data assimilation in Tropical Pacific , 2002 .

[85]  Richard J. Greatbatch,et al.  Challenges and Prospects in Ocean Circulation Models , 2019, Front. Mar. Sci..

[86]  I. Hoteit,et al.  Climatic features of the Red Sea from a regional assimilative model , 2017 .

[87]  P. Sakov,et al.  The impact of open boundary forcing on forecasting the East Australian Current using ensemble data assimilation , 2014 .

[88]  Jiang Zhu,et al.  Assimilating remote sensing and in situ observations into a coastal model of northern South China Sea using ensemble Kalman filter , 2011 .

[89]  Andrew J. Majda,et al.  Preventing Catastrophic Filter Divergence Using Adaptive Additive Inflation for Baroclinic Turbulence , 2017 .

[90]  Nicole L. Goebel,et al.  Impact of assimilating physical oceanographic data on modeled ecosystem dynamics in the California Current System , 2015 .

[91]  Jiping Xie,et al.  Ensemble optimal interpolation schemes for assimilating Argo profiles into a hybrid coordinate ocean model , 2010 .

[92]  P. Gent,et al.  Parameterizing eddy-induced tracer transports in ocean circulation models , 1995 .

[93]  S. Ricci,et al.  A multivariate balance operator for variational ocean data assimilation , 2005 .

[94]  M. Redi.  Oceanic Isopycnal Mixing by Coordinate Rotation , 1982 .

[95]  L. Perelman,et al.  A finite-volume, incompressible Navier Stokes model for studies of the ocean on parallel computers , 1997 .

[96]  C. E. Leith,et al.  Stochastic models of chaotic systems , 1995 .

[97]  Geir Evensen.  Spurious correlations, localization, and inflation , 2009 .

[98]  E. Kalnay,et al.  The local ensemble transform Kalman filter and the running-in-place algorithm applied to a global ocean general circulation model , 2013 .

[99]  Jeffrey L. Anderson.  Spatially and temporally varying adaptive covariance inflation for ensemble filters , 2009 .

[100]  Olwijn Leeuwenburgh,et al.  The impact of ensemble filter definition on the assimilation of temperature profiles in the tropical Pacific , 2005 .

[101]  A. Rosati,et al.  Modeling Global Ocean Biogeochemistry With Physical Data Assimilation: A Pragmatic Solution to the Equatorial Instability , 2018 .