Assimilating Ocean Observation Data for ENSO Monitoring and Forecasting

El Nino–Southern Oscillation (ENSO) is one of the most influential fluctuations of the coupled atmosphere-ocean climate system in the Seasonal-to-Interannual (SI) time scale for global and local communities. Ocean data assimilation systems are generally adopted for monitoring ENSO because the variation of the heat content in the ocean interior of the equatorial Pacific is considered a good precursor of El Ninos, and essential for understanding the ENSO process. They are also utilized for the initialization of Coupled Atmosphere-OceanGeneral Circulation Models (CGCMs), along with atmosphere data assimilation systems in the SI forecasting systems of various operational centers. In this paper, we discuss the capacity of current operational ocean data assimilation systems adopted for ENSO monitoring and SI forecasting based on studies using the system of the Japan Meteorological Agency (JMA). It then demonstrates the benefits of assimilating ocean observation data through those systems in SI forecasts using the JMA seasonal and ENSO forecasting system. It also introduces the recent effort of JMA/Meteorological Research Institute (MRI) to resolve “coupled shock", which is one of the most crucial issues concerning the initialization with uncoupled ocean and atmosphere data assimilation systems in SI forecasting. This chapter is organized as follows. Section 2 summarizes the process of developing ocean data assimilation systems for up-to-date ENSOmonitoring and SI forecasting. In particular, it describes efforts to improve the Temperature-Salinity (T-S) balance in the assimilation results over the past decade. We then introduce the ocean data assimilation system used in the JMA seasonal and ENSO forecasting system as a state-of-the-art system in Section 3. Section 4 demonstrates the importance of assimilating ocean observation data through the ocean data assimilation system for ENSO and seasonal forecasting. Section 5 introduces the recent effort to resolve the coupled shock at JMA/MRI. This chapter is summarized in Section 6. Assimilating Ocean Observation Data for ENSO Monitoring and Forecasting

[1]  Sophie Ricci,et al.  Incorporating State-Dependent Temperature–Salinity Constraints in the Background Error Covariance of Variational Ocean Data Assimilation , 2005 .

[2]  Keith Haines,et al.  Salinity Adjustments in the Presence of Temperature Data Assimilation , 2002 .

[3]  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 .

[4]  uSiNG Godae SyStEmS.  Observing system evaluations using GODAE systems , 2009 .

[5]  Peter J. Webster,et al.  Monsoon and Enso: Selectively Interactive Systems , 1992 .

[6]  Y. Fujii,et al.  Three-dimensional analysis of temperature and salinity in the equatorial Pacific using a variational method with vertical coupled temperature-salinity empirical orthogonal function modes , 2003 .

[7]  Yign Noh,et al.  Simulations of temperature and turbulence structure of the oceanic boundary layer with the improved near-surface process , 1999 .

[8]  M. Kamachi,et al.  Estimation of the Equatorial Pacific Salinity Field Using Ocean Data Assimilation Systems , 2010 .

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

[10]  A. Rosati,et al.  System Design and Evaluation of Coupled Ensemble Data Assimilation for Global Oceanic Climate Studies , 2007 .

[11]  T. Motoi,et al.  Reevaluation of the Takano–Oonishi Scheme for Momentum Advection on Bottom Relief in Ocean Models , 1999 .

[12]  Timothy P. Boyer,et al.  World ocean database 2009 , 2006 .

[13]  R. Reynolds,et al.  The NCEP/NCAR 40-Year Reanalysis Project , 1996, Renewable Energy.

[14]  James A. Carton,et al.  A Simple Ocean Data Assimilation Analysis of the Global Upper Ocean 1950–95. Part I: Methodology , 2000 .

[15]  Randy Showstack,et al.  World Ocean Database , 2009 .

[16]  Lippincott Williams Wilkins,et al.  Part I , 1997, Neurology.

[17]  Peter R. Oke,et al.  Observational Requirements of GODAE Systems , 2010 .

[18]  Christian L. Keppenne,et al.  Ensemble Kalman filter assimilation of temperature and altimeter data with bias correction and application to seasonal prediction , 2005 .

[19]  Jiang Zhu,et al.  Roles of vertical correlations of background error and T‐S relations in estimation of temperature and salinity profiles from sea surface dynamic height , 2004 .

[20]  Antonio J. Busalacchi,et al.  The Tropical Ocean‐Global Atmosphere observing system: A decade of progress , 1998 .

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

[22]  Ming Ji,et al.  Use of TOPEX/Poseidon Sea Level Data for Ocean Analyses and ENSO Prediction: Some Early Results , 2000 .

[23]  Ming Ji,et al.  Retrospective Analysis of the Salinity Variability in the Western Tropical Pacific Ocean Using an Indirect Minimization Approach , 2000 .

[24]  T. Yasuda,et al.  Coupled Climate Simulation by Constraining Ocean Fields in a Coupled Model with Ocean Data , 2009 .

[25]  S. Belamari,et al.  On the Impact of Salinity Barrier Layer on the Pacific Ocean Mean State and ENSO , 2011 .

[26]  Bin Wang,et al.  Atmosphere-Warm Ocean Interaction and Its Impacts on Asian-Australian Monsoon Variation* , 2003 .

[27]  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 .

[28]  James A. Carton,et al.  A Simple Ocean Data Assimilation Analysis of the Global Upper Ocean 1950-95. Part II: Results , 2000 .

[29]  Jiang Zhu,et al.  Assimilation of Satellite Altimetry into a Western North Pacific Operational Model , 2001 .

[30]  Tong Lee,et al.  Ocean State Estimation for Climate Research , 2010 .

[31]  Satoshi Sugimoto,et al.  Objective analyses of sea‐surface temperature and marine meteorological variables for the 20th century using ICOADS and the Kobe Collection , 2005 .

[32]  Bin Wang,et al.  Choice of South Asian Summer Monsoon Indices , 1999 .

[33]  B. Liebmann,et al.  Description of a complete (interpolated) outgoing longwave radiation dataset , 1996 .

[34]  Yosuke Fujii,et al.  Application of Nonlinear Constraints in a Three-Dimensional Variational Ocean Analysis , 2005 .

[35]  M. Mcphaden,et al.  Observed correlation of surface salinity, temperature and barrier layer at the eastern edge of the western Pacific warm pool , 2006 .

[36]  S. Levitus,et al.  World ocean atlas 2009 , 2010 .

[37]  Tong Lee,et al.  Ocean Initialization for Seasonal Forecasts , 2009 .

[38]  David L. T. Anderson,et al.  Impact of initialization strategies and observations on seasonal forecast skill , 2009 .

[39]  Y. Fujii.  Preconditioned Optimizing Utility for Large-dimensional analyses (POpULar) , 2005 .

[40]  Keith Haines,et al.  Salinity Assimilation Using S(T): Covariance Relationships , 2006 .

[41]  S. Kobayashi,et al.  The JRA-25 Reanalysis , 2007 .

[42]  Arthur Vidard,et al.  A multivariate treatment of bias for sequential data assimilation: Application to the tropical oceans , 2007 .

[43]  Roger Lukas,et al.  The mixed layer of the western equatorial Pacific Ocean , 1991 .

[44]  Marina Tonani,et al.  Mediterranean Forecasting System: An improved assimilation scheme for sea‐level anomaly and its validation , 2005 .

[45]  T. Ose,et al.  Predictability of the Mean Location of Typhoon Formation in a Seasonal Prediction Experiment with a , 2010 .

[46]  N. S. Cooper.  The Effect of Salinity on Tropical Ocean Models , 1988 .

[47]  B. S. Gohil,et al.  An Overview of Global Observing Systems Relevant to GODAE , 2009 .

[48]  George L. Mellor,et al.  Continuous assimilation of Geosat altimeter data into a three-dimensional primitive equation Gulf Stream model , 1994 .

[49]  Yan Du,et al.  Indian Ocean Capacitor Effect on Indo–Western Pacific Climate during the Summer following El Niño , 2009 .

[50]  F. Molteni,et al.  INITIALIZATION FOR SEASONAL AND DECADAL FORECASTS , 2011 .

[51]  M. Mcphaden,et al.  Variability of surface layer hydrography in the tropical Pacific Ocean , 1997 .

[52]  D. Behringer,et al.  Impacts of Argo salinity in NCEP Global Ocean Data Assimilation System: The tropical Indian Ocean , 2008 .

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

[54]  J. Picaut,et al.  An Advective-Reflective Conceptual Model for the Oscillatory Nature of the ENSO , 1997 .

[55]  L. J. Mangum,et al.  TOGA-TAO: A Moored Array for Real-time Measurements in the Tropical Pacific Ocean , 1991 .

[56]  D. Roemmich,et al.  Fresh Equatorial Jets , 1994 .

[57]  Christophe Maes,et al.  Estimating the influence of salinity on sea level anomaly in the ocean , 1998 .

[58]  Ichiro Fukumori,et al.  A Partitioned Kalman Filter and Smoother , 2002 .

[59]  P. Delecluse,et al.  A modeling study of salinity variability and its effects in the tropical Pacific Ocean during the 1993–1999 period , 2002 .

[60]  John Derber,et al.  An Ocean Analysis System for Seasonal to Interannual Climate Studies , 1995 .

[61]  Lawrence L. Takacs,et al.  Data Assimilation Using Incremental Analysis Updates , 1996 .