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Real-time Ocean Reanalyses Intercomparison: Ocean/Climate Monitoring Using Ensemble Ocean Reanalysis Products

This article discusses the challenges and opportunities in ocean reanalysis for monitoring climate variability and climate change. It explores the development of a coupled ocean-atmosphere data assimilation system, ecosystem forecast products for coastal regions, and the benefits of sustained ocean observing systems.

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Real-time Ocean Reanalyses Intercomparison: Ocean/Climate Monitoring Using Ensemble Ocean Reanalysis Products

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  1. Real-time Ocean ReanalysesIntercomparison: Ocean/Climate Monitoring Using Ensemble Ocean Reanalysis Products Y. Xue1, M. Balmaseda2, Y. Fujii3, G. Vecchi4, G. Vernieres5, O. Alves6, A. Kumar1, T. Lee7, D. Legler8 1 NCEP/NOAA, USA 2 ECMWF 3 JMA, Japan 4 GFDL/NOAA, USA 5 GSFC/NASA, USA 6 BOM, Australia 7 JPL/NASA, USA 8 CPO/NOAA, USA Climate Reanalysis Task Force, Telecon, June 25, 2014

  2. GODAE OceanView (GOV, 2009-2013) • following Global Ocean Data Assimilation Experiment (GODAE, 1997-2008) • Development of operational ocean analysis and forecasting systems • Utilization of operational ocean analysis and forecast products • Demonstration of value of sustained ocean observing systems

  3. Challenges and Opportunities for GODAE OceanView • To develop coupled ocean-atmosphere data assimilation system for improved weather and climate prediction • To develop ecosystem forecast products for coastal regions • To demonstrate benefits and optimize use of ocean observations • To improve ocean reanalyses for monitoring climate variability and climate change

  4. CLIVAR-GSOP/GODAE OceanView • Ocean Reanalysis Intercomparison • (ORA-IP, 2013-2014) • Reanalysis production is an on-going activity • New vintages are produced approximately every 5 years • Improved quality controlled observations (XBT corrections, Argo corrections and black lists) • Improved and extended forcing fluxes • Improved models and methods • We need to assess uncertainties among ocean reanalyses (through intercomparison and validation with independent data) • We need to facilitate the use of ocean reanalyses by other communities • We need to prepare for quasi-real time monitoring of the ocean Courtesy of M. Balmaseda

  5. See a summary at http://www.clivar.org/sites/default/files/Exchanges/Exchanges_64.pdf

  6. TAO/TRITON: Cornerstone of ENSO Observing System • Real time ENSO monitoring and prediction • Ocean initialization for seasonal predictions • Behringer et al. 1998; Alves et al. 2003; Balmaseda et al. 2007; Balmaseda and Anderson 2009; Stockdale et al. 2011; Xue et al. 2013 (for CFSv2) ENSO Diagnostic Discussion Yan Xue Climate Prediction Center

  7. Tropical Pacific Observing System (TPOS) 2020 Workshop (January 27-30, 2014, La Jolla, CA) • Highlight the impactsof the tropical Pacific observing system on information/services of societal relevance – El Nino monitoring and prediction • Evaluate existing and potential requirements for sustained observations of ocean variables in tropical Pacific Ocean – uncertainties in ocean estimation in tropical Pacific • Evaluate the adequacy of existing observing strategies • Recommend revisions and/or adjustments to enhance resilience, efficiency, integration. • Evaluate logistical requirements for implementation of the recommended Tropical Pacific Observing System. • Assess readiness of new technologies, their potential impact and feasibility in addressing requirements, and/or lowering costs per observation.

  8. Operational Ocean Reanalysis Argo Ocean Observing System Altimeter CTD Moorings XBT SST Ocean Model Data Assimilation System Operational Atmosphere Reanalysis Surface Fluxes Operational Ocean Reanalysis Ocean Monitoring Seasonal Forecast Model ENSO Prediction

  9. Operational Ocean Reanalyses Xue et al. 2012, J. Climate

  10. Real-Time Ocean Renalyses Intercomparison • Extend CLIVAR-GSOP/GODAE OceanView Ocean Reanalyses Intercomparison Project (ORA-IP) into real time • Assess uncertainties in temperature analysis of tropical Pacific in support of ENSO monitoring and prediction • Explore any connections between gaps in ocean observations and spreads among ensemble ORAs • Articulate needs for sustained ocean observing systems in support of TPOS2020 • Monitor consistency and discrepancy among ensemble ORAs in the global ocean (temperature, 300m heat content, depth of 20C isotherm) Yan Xue Climate Prediction Center

  11. http://www.cpc.ncep.noaa.gov/products/GODAS/multiora_body.htmlhttp://www.cpc.ncep.noaa.gov/products/GODAS/multiora_body.html

  12. The spread is large (>0.6C) between 140W-90W, and 120E-140E, where data distribution is poor.

  13. HC300 anomaly from GODAS has narrowest meridional extend. • HC300 anomaly from ECMWF has broadest meridional extend. • HC300 anomaly from BOM has cold bias in northwestern tropical Pacific.

  14. Jan Feb Mar Apr May

  15. Difference between GODAS and CFSR • Positive D20 anomaly in GODAS has much narrower meridional extend than CFSR between 130W-100W. • Positive D20 anomaly in GODAS is 3-9m lower than that in CFSR.

  16. Difference between GODAS and CFSR Zonal Wind Stress Anomaly Depth of 20C Isotherm Anomaly • Trade winds in R2 are much stronger than those in CFSR near 170W since 2010. • Consistent to the stronger trade winds in R2, D20 anomaly in GODAS is about 3-6m lower than that in CFSR east of 150W since 2010.

  17. Influences of ocean observations on spread among ocean reanalyses

  18. Summary • An ensemble of six operational ORAs has been collected to assess signal (ensemble mean) and uncertainties (ensemble spread) in upper ocean temperature analysis in the global ocean in near real-time • Extensive monitoring plots have been developed to assess uncertainties in temperature analysis of tropical Pacific in support of ENSO monitoring and prediction • We have explored connections between gaps in ocean observations and spread among ensemble ORAs • The spread of ensemble ORAs decreased abruptly in early 1990s when the TAO array was fully implemented. • The spread started to increase since 2010 and reached a peak value in 2013 when the TAO array return rate drop to 40%

  19. Thanks! Comments and Suggestions?

  20. Warm Water Volume Index AM 97 AM 14 AM 82 AM 06 AM 91 AM 09 AM 02 May 2002 (DJF NINO3.4=1.1) May 1991 (DJF NINO3.4=1.6) May 2006 (DJF NINO3.4=0.7) May 1997 (DJF NINO3.4=2.2) May 1982 (DJF NINO3.4=2.2) May 2009 (DJF NINO3.4=1.6) May 2014

  21. 300m Heat Content Anom. SST Anom. May 1991 (DJF NINO3.4=1.6) May 1982 (DJF NINO3.4=2.2) May 2002 (DJF NINO3.4=1.1) May 1997 (DJF NINO3.4=2.2) May 2006 (DJF NINO3.4=0.7) May 2014 May 2009 (DJF NINO3.4=1.6)

  22. 2014 1991

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