1. Issues in Ocean-Atmosphere-Land-Ice Coupling Ocean Integration in Earth System Prediction Capability Data Assimilation University of Maryland September 27, 2011 Ocean Integration in Earth System Prediction Capability Data Assimilation University of Maryland September 27, 2011 Art Miller Scripps Institution of Oceanography

2. Issues in Ocean-Atmosphere-Land-Ice Coupling Experience Dynamical Oceanography Ocean Tides Coupled Ocean-Atmosphere Modeling Dynamics of Pacific Decadal Variability Ocean Data Assimilation Ocean Ecosystem Response to Physical Forcing Predictability (Temporal and Spatial)

3. Issues in Ocean-Atmosphere Coupling Predictability Philosophy -Physical Basis for Prediction *True dynamic modes, waves, enhanced persistence? -Development of Modeling Capability *Simplicity vs. Complexity? -Quantification of Skill *Better than persistence or a statistical model? -Application in Real-Time *Who keeps it going?

4. The Atmosphere -Weather time scales *Tough to beat persistence of day-zero ocean forcing -Weekly to seasonal time scales *Madden Julian Oscillations (regional) *Tough to beat persistence of day-zero ocean forcing -Seasonal to interannual time scales *ENSO (regional and teleconnections) *Ocean coupling essential -Decadal timescales *Ocean, ice coupling essential but dynamics not clear -Centennial timescales *Deterministic greenhouse gas forcing

5. The Ocean -Tidal time scales *Forcing by SLP, winds, heat-flux has limited predictability *Internal tides propagate through changing stratification -Weekly to seasonal time scales *Mesoscale eddies difficult to initialize *Coupling to surface flux anomalies *Competition from wind-forced response and background -Seasonal to interannual time scales *Oceanic subsurface conditions difficult to initialize *High frequency wind influences -Decadal timescales *Initialization process unclear due to dynamical uncertainty -Centennial timescales *Ocean mixing and deep circulation concerns

6. Coupled Ocean-Atmosphere -Surface Flux Parameterization *Details of the atmospheric boundary layer *Details of upper ocean mixing -Physical processes *Mesoscale eddies drive flux anomalies via SST anomalies - ABL response clear, tropospheric response unclear - Eddy and frontal evolution sensitivities -Dynamical Testing *Coupled versus uncoupled runs to quantify feedbacks -Initialization and Use of Forecasts *Various initialization methods *Coupled model climate biases - Systematic error corrections? - Anomaly initialization? -

7. Just because it is “coupled” doesn’t mean it is better… Miller and Roads, 1990 A simplified coupled model of extended-range predictability. (Journal of Climate) “Improvement” in forecast skill when using a midlatitude coupled O-A model vs. Uncoupled atmosphere with persistent SST Improvement in “skill” when using specified SST as BC

8. Dynamics don’t necessarily beat statistics:

9. Some personal current research topics: Regional coupled ocean-atmosphere modeling - mesoscale SST affects on the atmosphere Global coupled ocean-atmosphere modeling - MJO in CCSM4 Ocean data assimilation and ROMS adjoint - ocean sensitivities to forcing - source of upwelling affecting fisheries

10. Wind (arrows) and Sea Surface Temperature (color) in the E. Tropical Pacific  Ocean affects atmosphere, atmosphere affects ocean  Intertropical Convergence Zone (ITCZ) and Eastern Pacific Warm Pool  Cross-equatorial trade winds  Gap Winds  Tropical Depressions and Hurricanes  Coastal Upwelling and Equatorial front  Tropical Instability Waves Scripps Coupled Ocean-Atmosphere Regional (SCOAR) Model Tehuantepec Papagayo H. Seo, A. Miller, and J. Roads (J. Climate, 2007)

11. By Combining Knowledge of Oceans and Atmosphere, We can Better Understand Both Stress divergence Latent heat SST - wind Stress curl Coupling of SST with Atmospheric Boundary Layer is observed and modeled in the CCS region over eddy scales How does this coupling affect statistics of ocean eddies, marine layer, and coastal climate? RSM Atmos model: 16 km ROMS Ocean model: 7 km California Coastal Region Coupled Modeling: Miller and Norris (NSF funding) SCOAR runs Seo et al. (2007)

12. Madden Julian Oscillation (MJO) in the Community Climate System Model (CCSM4.0) Aneesh Subramanian et al. (2011) Funded by ONR Composite MJO: OLR, 850mb winds (Model)Coherence spectra: OLR, Winds (Obs, Model)

13. Adjoint Sensitivity Analysis the California Current System Moore et al. (JPO, 2009)

14. Data Assimilation “Fits” for April 2002 and 2003 - Strong constraints over 30-day periods allows diagnosis of 4D physical processes that help explain the large disparity in sardine spawning Nearshore spawning, many eggs: El Nino Song et al., 2011 Offshore spawning, fewer eggs: La Nina Data includes: T-S (CalCOFI, Argo, CUFES), SLH (AVISO), SST (AVHRR)

15. Data Assimilation Model Fits: (2) Quantifying Upwelling Sources Adjoint tracer model (run backwards) for source waters (boxes) of surface ocean 2003 source waters in nearshore spawning area transported from more productive deep water in the central California Current Song et al., 2011 Orange indicates location of water 30 days before arriving in BOX

16. Thanks! Ocean Integration in Earth System Prediction Capability Data Assimilation