Reinitializing Sea Surface Temperature in the Ensemble Intermediate Coupled Model for Improved Forecasts

The Ensemble Intermediate Coupled Model (EICM) is a model used for studying the El Niño-Southern Oscillation (ENSO) phenomenon in the Pacific Ocean, which is anomalies in the Sea Surface Temperature (SST) are observed. This research aims to implement Cressman to improve SST forecasts. The simulation considers two cases in this work: the control case and the Cressman initialized case. These cases are simulations using different inputs where the two inputs differ in terms of their resolution and data source. The Cressman method is used to initialize the model with an analysis product based on satellite data and in situ data such as ships, buoys, and Argo floats, with a resolution of 0.25 × 0.25 degrees. The results of this inclusion are the Cressman Initialized Ensemble Intermediate Coupled Model (CIEICM). Forecasting of the sea surface temperature anomalies was conducted using both the EICM and the CIEICM. The results show that the calculation of SST field from the CIEICM was more accurate than that from the EICM. The forecast using the CIEICM initialization with the higher-resolution satellite-based analysis at a 6-month lead time improved the root mean square deviation to 0.794 from 0.808 and the correlation coefficient to 0.630 from 0.611, compared the control model that was directly initialized with the low-resolution in-situ-based analysis.

[1]  Jiang Zhu,et al.  Ensemble hindcasts of ENSO events over the past 120 years using a large number of ensembles , 2009 .

[2]  Antonio J. Busalacchi,et al.  A TOGA Retrospective , 2010 .

[3]  Xinrong Wu,et al.  Idealized Experiments for Optimizing Model Parameters Using a 4D-Variational Method in an Intermediate Coupled Model of ENSO , 2018, Advances in Atmospheric Sciences.

[4]  S. M. Siadatmousavi,et al.  Capabilities of data assimilation in correcting sea surface temperature in the Persian Gulf , 2017 .

[5]  Jieshun Zhu,et al.  Improving ENSO prediction in a hybrid coupled model with an embedded entrainment temperature parameterisation , 2013 .

[6]  Elizabeth C. Kent,et al.  Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century , 2003 .

[7]  Claire E. Bulgin,et al.  Sea surface temperature datasets for climate applications from Phase 1 of the European Space Agency Climate Change Initiative (SST CCI) , 2014 .

[8]  Rong-Hua Zhang,et al.  The IOCAS intermediate coupled model (IOCAS ICM) and its real-time predictions of the 2015–2016 El Niño event , 2016 .

[9]  Jieshun Zhu,et al.  On the role of oceanic entrainment temperature ( T e ) in decadal changes of El Niño/Southern Oscillation , 2011 .

[10]  Venkatramani Balaji,et al.  Initialization of an ENSO Forecast System Using a Parallelized Ensemble Filter , 2005 .

[11]  Jiang Zhu,et al.  A successful real-time forecast of the 2010–11 La Niña event , 2013, Scientific Reports.

[12]  Assimilation of the satellite SST data in the 3D CEMBS model , 2015 .

[13]  Thomas M. Smith,et al.  Improved Global Sea Surface Temperature Analyses Using Optimum Interpolation , 1994 .

[14]  K. Wyrtki,et al.  El Niño—The Dynamic Response of the Equatorial Pacific Oceanto Atmospheric Forcing , 1975 .

[15]  Jong-Seong Kug,et al.  An El‐Nino Prediction System using an intermediate ocean and a statistical atmosphere , 2000 .

[16]  J. McCreary,et al.  A Model of Tropical Ocean-Atmosphere Interaction , 1983 .

[17]  Thomas M. Smith,et al.  Extended Reconstructed Sea Surface Temperature Version 4 (ERSST.v4): Part II. Parametric and Structural Uncertainty Estimations , 2015 .

[18]  Thomas M. Smith,et al.  Improvements to NOAA’s Historical Merged Land–Ocean Surface Temperature Analysis (1880–2006) , 2008 .

[19]  A comparison of ocean model data and satellite observations of features affecting the growth of the North Equatorial Counter Current during the strong 1997–1998 El Niño , 2020 .

[20]  M. Latif,et al.  Predicting the '97 El Niño event with a global climate model , 1998 .

[21]  Omar Lakkis,et al.  A Saint-Venant Model for Overland Flows with Precipitation and Recharge , 2016, Mathematical and Computational Applications.

[22]  Dake Chen COUPLED DATA ASSIMILATION FOR ENSO PREDICTION , 2010 .

[23]  G. P. Cressman AN OPERATIONAL OBJECTIVE ANALYSIS SYSTEM , 1959 .

[24]  M. Cane,et al.  A Model El Niñ–Southern Oscillation , 1987 .

[25]  J. McCreary A linear stratified ocean model of the equatorial undercurrent , 1981, Philosophical Transactions of the Royal Society of London. Series A, Mathematical and Physical Sciences.

[26]  W. Pichel,et al.  Deriving the operational nonlinear multichannel sea surface temperature algorithm coefficients for NOAA-15 AVHRR/3 , 2001 .

[27]  A. Moore,et al.  Influence of stochastic forcing on ENSO prediction , 2004 .

[28]  A. Busalacchi,et al.  The Roles of Atmospheric Stochastic Forcing (SF) and Oceanic Entrainment Temperature (Te) in Decadal Modulation of ENSO , 2008 .

[29]  Jiang Zhu,et al.  Improved ensemble-mean forecasting of ENSO events by a zero-mean stochastic error model of an intermediate coupled model , 2016, Climate Dynamics.

[30]  N. Keenlyside,et al.  Annual cycle of equatorial zonal currents in the Pacific , 2002 .

[31]  Jiang Zhu,et al.  Ensemble hindcasts of SST anomalies in the tropical Pacific using an intermediate coupled model , 2006 .

[32]  T. Barnett,et al.  ENSO and ENSO-related Predictability. Part I: Prediction of Equatorial Pacific Sea Surface Temperature with a Hybrid Coupled Ocean–Atmosphere Model , 1993 .

[33]  Xinrong Wu,et al.  Testing a four-dimensional variational data assimilation method using an improved intermediate coupled model for ENSO analysis and prediction , 2016, Advances in Atmospheric Sciences.

[34]  Xiaofeng Li,et al.  Validation of coastal sea and lake surface temperature measurements derived from NOAA/AVHRR data , 2001 .

[35]  Geir Evensen,et al.  The Ensemble Kalman Filter: theoretical formulation and practical implementation , 2003 .

[36]  Thomas M. Smith,et al.  Interdecadal Changes of 30-Yr SST Normals during 1871–2000 , 2003 .

[37]  A. E. Gill An Estimation of Sea-Level and Surface-Current Anomalies during the 1972 El Niño and Consequent Thermal Effects , 1983 .

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

[39]  U. Humphries,et al.  Application of Data Assimilation and the Relationship between ENSO and Precipitation , 2021, Mathematical and Computational Applications.

[40]  Mark A. Cane,et al.  Experimental forecasts of El Niño , 1986, Nature.

[41]  N. Keenlyside,et al.  A new intermediate coupled model for El Niño simulation and prediction , 2003 .

[42]  A. Barnston,et al.  Skill of Real-Time Seasonal ENSO Model Predictions During 2002–11: Is Our Capability Increasing? , 2012 .

[43]  Eric Freeman,et al.  The International Comprehensive Ocean-Atmosphere Data Set – Meeting Users Needs and Future Priorities , 2019, Front. Mar. Sci..

[44]  N. Keenlyside,et al.  An empirical parameterization of subsurface entrainment temperature for improved SST anomaly simulations in an intermediate ocean model , 2005 .

[45]  Dake Chen,et al.  An Improved Procedure for EI Ni�o Forecasting: Implications for Predictability , 1995, Science.

[46]  Ming Ji,et al.  Impact of Data Assimilation on Ocean Initialization and El Niño Prediction , 1997 .

[47]  Dejian Yang,et al.  Progress in ENSO prediction and predictability study , 2018, National Science Review.

[48]  J. Bjerknes ATMOSPHERIC TELECONNECTIONS FROM THE EQUATORIAL PACIFIC1 , 1969 .