Issues in Ocean-Atmosphere-Land-Ice CouplingIssues 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 MillerScripps Institution of Oceanography

Issues in Ocean-Atmosphere-Land-Ice CouplingIssues in Ocean-Atmosphere-Land-Ice Coupling

Experience

Dynamical OceanographyOcean TidesCoupled Ocean-Atmosphere ModelingDynamics of Pacific Decadal VariabilityOcean Data AssimilationOcean Ecosystem Response to Physical ForcingPredictability (Temporal and Spatial)

Issues in Ocean-Atmosphere CouplingIssues 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?

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

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

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?-

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 usingspecified SST as BC

Dynamics don’t necessarily beat statistics:

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

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 Scripps Coupled Ocean-Atmosphere Regional (SCOAR) Model

Tehuantepec

Papagayo

H. Seo, A. Miller, and J. Roads (J. Climate, 2007)

By Combining Knowledge of Oceans and Atmosphere,We can Better Understand Both

Stress divergence

Latent heatSST - 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?

RSMAtmosmodel: 16 km

ROMSOceanmodel: 7 km

California Coastal Region Coupled Modeling: Miller and Norris (NSF funding)

SCOAR runsSeo et al. (2007)

Madden Julian Oscillation (MJO) in theCommunity 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)

Adjoint Sensitivity Analysis the California Current SystemMoore et al. (JPO, 2009)

Data Assimilation “Fits” for April 2002 and 2003- Strong constraints over 30-day periods allowsdiagnosis of 4D physical processes that helpexplain 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)

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

Thanks!

Ocean Integration in Earth System Prediction Capability

Data Assimilation