Outline

1. Improving Thermocline Feedbacks in Coupled Models via Inverse Modeling of the Entrainment Temperature (Te)Rong-Hua Zhang, Ragu Murtugudde, and Antonio J. Busalacchi Earth System Science Interdisciplinary Center, University of Maryland, College Park, USA

2. Outline • Introduction A SST anomaly model with an empirical Te parameterization scheme • Model descriptions OGCM: The Cane-Gent ocean model; An embedded SST anomaly model with empirical Te schem; An empirical wind stress anomaly model. The embedded coupled system • The uncoupled OGCM simulations • The coupled atmos-ocean simulations Standard coupling Embedded coupling • Summary

3. SST anomaly model Te: The Temperature of subsurface water entrained into the mixed layer

4. SST variabilityin the Tropical Pacific Ocean • Physical understanding Thermocline => SST variability Subsurface temp is a major source in the central & eastern Equ. Pacific • Parameterizations of physical processes: e.g., entrainment into the mixed layer (i.e., Layer OGCMs); Kv (e.g., KPP scheme in level OGCMs) • Ocean models(different complexity, vertical coordinates etc) • Great progressin SSTA simulations & predictions: ENSO forecast systems

5. Challenge in SSTA modeling • Systematic biases & errorsin SSTA simulation & prediction Ocean models: Coupled models: • Uncertainty in parameterization of entrainment & vertical mixing processes: Subsurface effect on SST associated with Te • Mean ocean climatology cold tongue; diffuse thermocline; warm subsurface temperature • Intermodel differences Intermediate model level OGCMs layer OGCMs

6. Some approaches to improving SSTA simulations & predictions 1. Continue to improve parameterizations: Great focus on We & Kv, (but Te equally important !!) 2. Different ocean & coupled models: Intermediate ocean model: Layer models: isopycnal coordinate, … Level models: z-coordinate (e.g., GFDL MOM) (Te depiction in different models !!) 3. Flux corrections in coupled models (better mean climatology!!) 4. MOS (model output statistics) corrections (get SSTAs first and then try to correct them regardless of reasons!!) 5. Ocean data assimilation

7. Our approach: Te + embedding • Anomaly approach for SST modeling => great advantages !! • Focusing on Te (vs. We & Kv): The role of Te in SSTA: both variability & error =>correcting major error source: dynamical & interactive ways (vs. MOS correcting) • An empirical Te parameterization: SL => Te => Subsurface effect on SST due to entrainment & vertical mixing • Embedding approach in the context of OGCMs: Taking advantage of a SST anomaly model with Te

8. SST anomaly model Te: The Temperature of subsurface water entrained into the mixed layer

9. Parameterizing the Temperature of water Entrained (Te) into the mixed layer • Inverse modeling of Te Historical data ( simulated & observed) + a SSTA model Obs. SST fields etc. => SST anomaly model => Te • Using sea level (SL) => Te: why ? (1) Well correlations: SL  thermocline  Te (2) SL simulated very well even with simple models (3) SL observations available from satellites • Statistical relationships based on history: SLTe Given SL from ocean model => Te => SST calculation

10. Hybrid Coupled Model at ESSIC/UMD • The Gent-Cane ocean model A sigma-layer, reduced-gravity OGCM with (1) A hybrid mixing scheme Chen, Rothstein & Busalacchi (1995) (2) Coupling to an advective atmos mixed layer model (Murtugudde, Seager & Busalacchi 1995) (3) Model specifications: Tropical Pacific domain: 25N-25S; 31-layers; Resolution: 1 deg in longitude and 0.5 deg in latitude • An empirical atmospheric wind stress anomaly model (SVD-based)

11. Data Sets and experiments • Fluxes(Wind stress; Heat & water fluxes): NCEP-NCAR reanalysis products; • SST (Reynolds et al.): • Te model constructionfrom NCEP-NCAR winds (1) Ocean model run: => mean fields; anomaly fields (e.g., sea level; currents etc.) => inverse modeling => Te fields (2) EOF-based, seasonally varying (12 models for each month)) • The OGCM simulations: cross validation NCEP-NCAR winds: 1963-1996; 1963-1979; 1980-1996 • The standard & embedded coupled simulations

15. Standard & embedded coupling Momentum, heat & water fluxes SST anomaly model us, vs, ws Te model OGCM (Gent-Cane model) SL

18. Summary Approach: Inverse modelling (obs. + model) of Te => optimized subsurface effect on SST & balanced heat budget for SST => improved thermocline feedback => better SST & El Nino simulation Applications: Intermediate model: straightforward OGCMs: embedding methods

19. Some Te-related publications • Zhang, R.-H., A. J. Busalacchi, and Raghuram G. Murtugudde, 2005: Improving SST anomaly simulations in a layer ocean model with an embedded entrainment temperature submodel, J. Climate, submitted. • Zhang, R.-H., S. E. Zebiak, R. Kleeman, and N. Keenlyside, 2005: Retrospective El Nino hindcast/forecast using an improved intermediate coupled model. Mon. Wea. Rev., in press. • Zhang, R.-H., and A. J. Busalacchi, 2005: Interdecadal changes in properties of El Nino in an intermediate coupled model, J. Climate, in press. • Zhang, R.-H., R. Kleeman, S. E. Zebiak, N. Keenlyside, and S. Raynaud, 2005: An Empirical Parameterization of Subsurface Entrainment Temperature for Improved SST Simulations in an Intermediate Ocean Model. J. Climate, 18, 350-371. • Zhang, R.-H., and S. E. Zebiak, 2004: An embedding method for improving interannual variability simulations in a hybrid coupled model of the tropical Pacific ocean-atmosphere system, J. Climate, 17, 2794-2812.

21. Summary • An effecive approach to improving SSTA simulations A SST anomaly model with an empirical Te parameterization • Embedding method: A SSTA anomaly model + a layer OGCM • Demonstrated improvement in SSTA and El Nino simulations in context of OGCMs various sensitivity experiments • Applications to other models and regions • Coupled prediction for ENSO

25. ESSIC Hybrid Coupled Model: Standard Coupling

26. ESSIC Hybrid Coupled Model: Embedded Coupling