Effects of Inclusion of Adjoint Sea Ice Rheology on Backward Sensitivity Evolution Examined Using an Adjoint Ocean–Sea Ice Model

As part of the ongoing development of an ocean data assimilation system for operational ocean monitoring and seasonal prediction, an adjoint sea ice model was developed that incorporates sea ice rheology, which was omitted from previously developed adjoint models to avoid model instability. The newly developed adjoint model was merged with the existing system to construct a global ocean–sea ice adjoint model. A series of sensitivity experiments, in which idealized initial values were given for the adjoint sea ice area fraction and thickness, were conducted, with particular attention to the differences between the cases with free-drift approximation in the adjoint sea ice model as in previous studies and with full sea ice dynamics including rheology. The internal stress effects represented in the adjoint rheology induced remarkable differences in the evolution of the initialized and generated adjoint variables, such as for the sea ice velocity by O(102) in magnitude, which highlighted the importance of the adjoint rheology in the central Arctic Ocean. In addition, sensitivities with respect to the nonprognostic variables associated with the sea ice dynamics were obtained only through the adjoint rheology. These results suggested a potential for providing an improved global atmosphere–ocean–sea ice state estimation through a four-dimensional variational approach with the adjoint sea ice model as developed in this study.

[1]  Harold Ritchie,et al.  Impact of a Two-Way Coupling between an Atmospheric and an Ocean-Ice Model over the Gulf of St. Lawrence , 2004 .

[2]  M. Holland,et al.  Influence of initial conditions and climate forcing on predicting Arctic sea ice , 2011 .

[3]  Malcolm Davidson,et al.  CryoSat‐2 estimates of Arctic sea ice thickness and volume , 2013 .

[4]  Seymour W. Laxon,et al.  Optimization of a Sea Ice Model Using Basinwide Observations of Arctic Sea Ice Thickness, Extent, and Velocity , 2006 .

[5]  Takemasa Miyoshi,et al.  A simpler formulation of forecast sensitivity to observations: application to ensemble Kalman filters , 2012 .

[6]  Ibrahim Hoteit,et al.  Treating strong adjoint sensitivities in tropical eddy‐permitting variational data assimilation , 2005 .

[7]  Michael Steele,et al.  The force balance of sea ice in a numerical model , 1997 .

[8]  Yosuke Fujii,et al.  A Framework for Interpreting Regularized State Estimation , 2014, 1511.04790.

[9]  Hideyuki Nakano,et al.  Simulating present climate of the global ocean–ice system using the Meteorological Research Institute Community Ocean Model (MRI.COM): simulation characteristics and variability in the Pacific sector , 2011 .

[10]  W. Hibler A Dynamic Thermodynamic Sea Ice Model , 1979 .

[11]  P. Heimbach,et al.  Coupled Sea Ice–Ocean-State Estimation in the Labrador Sea and Baffin Bay , 2013 .

[12]  Yosuke Fujii,et al.  Data assimilation of sea ice concentration into a global ocean–sea ice model with corrections for atmospheric forcing and ocean temperature fields , 2016, Journal of Oceanography.

[13]  Yosuke Fujii,et al.  Four-dimensional variational ocean reanalysis: a 30-year high-resolution dataset in the western North Pacific (FORA-WNP30) , 2017, Journal of Oceanography.

[14]  Armin Köhl,et al.  An adjoint method for the assimilation of statistical characteristics into eddy-resolving ocean models , 2002 .

[15]  Y. Ishikawa,et al.  State Estimation of the North Pacific Ocean by a Four-Dimensional Variational Data Assimilation Experiment , 2003 .

[16]  R. Colony,et al.  The thickness distribution of sea ice , 1975 .

[17]  Detlef Stammer,et al.  Adjoint-Based Estimation of Eddy-Induced Tracer Mixing Parameters in the Global Ocean , 2012 .

[18]  J. Walsh,et al.  On Modeling Seasonal and Interannual Fluctuations of Arctic Sea Ice , 1982 .

[19]  R. Kwok,et al.  Arctic ice-ocean simulation with optimized model parameters: Approach and assessment , 2011 .

[20]  Nick Rayner,et al.  EN4: Quality controlled ocean temperature and salinity profiles and monthly objective analyses with uncertainty estimates , 2013 .

[21]  Takashi T. Sakamoto,et al.  Impact of the Assimilation of Sea Ice Concentration Data on an Atmosphere-Ocean-Sea Ice Coupled Simulation of the Arctic Ocean Climate , 2011 .

[22]  Jérôme Vialard,et al.  Three- and Four-Dimensional Variational Assimilation with a General Circulation Model of the Tropical Pacific Ocean. Part I: Formulation, Internal Diagnostics, and Consistency Checks , 2003 .

[23]  Thomas Kaminski,et al.  Recipes for adjoint code construction , 1998, TOMS.

[24]  William D. Hibler,et al.  Ridging and strength in modeling the thickness distribution of Arctic sea ice , 1995 .

[25]  Yosuke Fujii,et al.  Intercomparison of the Arctic sea ice cover in global ocean–sea ice reanalyses from the ORA-IP project , 2017, Climate Dynamics.

[26]  George L. Mellor,et al.  An Ice-Ocean Coupled Model , 1989 .

[27]  Ross J. Murray,et al.  Explicit Generation of Orthogonal Grids for Ocean Models , 1996 .

[28]  Yosuke Fujii,et al.  Development of a Four-Dimensional Variational Assimilation System for Coastal Data Assimilation around Japan , 2015 .

[29]  E. Hunke,et al.  An Elastic–Viscous–Plastic Model for Sea Ice Dynamics , 1996 .

[30]  Stephen G. Yeager,et al.  The global climatology of an interannually varying air–sea flux data set , 2009 .

[31]  James K. Yungel,et al.  Seasonal forecasts of Arctic sea ice initialized with observations of ice thickness , 2012 .

[32]  The improvement made by a modified TLM in 4DVAR with a geophysical boundary layer model , 2002 .

[33]  Yosuke Fujii,et al.  Meteorological research institute multivariate ocean variational estimation (MOVE) system : Some early results , 2006 .

[34]  G. Danabasoglu,et al.  JRA-55 based surface dataset for driving ocean–sea-ice models (JRA55-do) , 2018, Ocean Modelling.

[35]  R. Colony,et al.  The Horizontal Coherency of the Motion of Summer Arctic Sea Ice , 1980 .

[36]  Frank Kauker,et al.  Adjoint analysis of the 2007 all time Arctic sea‐ice minimum , 2009 .

[37]  W. Lipscomb Remapping the thickness distribution in sea ice models , 2001 .

[38]  Axel Schweiger,et al.  Evaluation of Seven Different Atmospheric Reanalysis Products in the Arctic , 2014 .

[39]  C. Kobayashi,et al.  The JRA-55 Reanalysis: General Specifications and Basic Characteristics , 2015 .

[40]  Toru Miyama,et al.  Development of a four‐dimensional variational coupled data assimilation system for enhanced analysis and prediction of seasonal to interannual climate variations , 2008 .

[41]  D. Stammer,et al.  Sea ice assimilation into a coupled ocean–sea ice model using its adjoint , 2017 .

[42]  H. Goosse,et al.  An assessment of ten ocean reanalyses in the polar regions , 2019, Climate Dynamics.

[43]  T. Yasuda,et al.  Improved Analysis of Seasonal-Interannual Fields Using a Global Ocean Data Assimilation System , 2013 .