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Verdy, A, Cornuelle B, Mazloff MR, Rudnick DL.  2017.  Estimation of the tropical Pacific Ocean state 2010-13. Journal of Atmospheric and Oceanic Technology. 34:1501-1517.   10.1175/jtech-d-16-0223.1   AbstractWebsite

A data-assimilating 1/38 regional dynamical ocean model is evaluated on its ability to synthesize components of the Tropical Pacific Ocean Observing System. The four-dimensional variational data assimilation (4DVAR) method adjusts initial conditions and atmospheric forcing for overlapping 4-month model runs, or hindcasts, that are then combined to give an ocean state estimate for the period 2010-13. Consistency within uncertainty with satellite SSH and Argo profiles is achieved. Comparison to independent observations from Tropical Atmosphere Ocean (TAO) moorings shows that for time scales shorter than 100 days, the state estimate improves estimates of TAO temperature relative to an optimally interpolated Argo product. The improvement is greater at time scales shorter than 20 days, although unpredicted variability in the TAO temperatures implies that TAO observations provide significant information in that band. Larger discrepancies between the state estimate and independent observations from Spray gliders deployed near the Galapagos, Palau, and Solomon Islands are attributed to insufficient model resolution to capture the dynamics in strong current regions and near coasts. The sea surface height forecast skill of the model is assessed. Model forecasts using climatological forcing and boundary conditions are more skillful than climatology out to 50 days compared to persistence, which is a more skillful forecast than climatology out to approximately 20 days. Hindcasts using reanalysis products for atmospheric forcing and open boundary conditions are more skillful than climatology for approximately 120 days or longer, with the exact time scale depending on the accuracy of the state estimate used for initializing and on the reanalysis forcing. Estimating the model representational error is a goal of these experiments.

Furue, R, Jia YL, McCreary JP, Schneider N, Richards KJ, Muller P, Cornuelle BD, Avellaneda NM, Stammer D, Liu CY, Kohl A.  2015.  Impacts of regional mixing on the temperature structure of the equatorial Pacific Ocean. Part 1: Vertically uniform vertical diffusion. Ocean Modelling. 91:91-111.   10.1016/j.ocemod.2014.10.002   AbstractWebsite

We investigate the sensitivity of numerical model solutions to regional changes in vertical diffusion. Specifically, we vary the background diffusion coefficient, kappa(b), within spatially distinct subregions of the tropical Pacific, assess the impacts of those changes, and diagnose the processes that account for them. Solutions respond to a diffusion anomaly, delta kappa(b), in three ways. Initially, there is a fast response (several months), due to the interaction of rapidly propagating, barotropic and gravity waves with eddies and other mesoscale features. It is followed by a local response (roughly one year), the initial growth and spatial pattern of which can be explained by one-dimensional (vertical) diffusion. At this stage, temperature and salinity anomalies are generated that are either associated with a change in density ("dynamical" anomalies) or without one ("spiciness" anomalies). In a final adjustment stage, the dynamical and spiciness anomalies spread to remote regions by radiation of Rossby and Kelvin waves and by advection, respectively. In near equilibrium solutions, dynamical anomalies are generally much larger in the latitude band of the forcing, but the impact of off equatorial forcing by delta kappa(b) on the equatorial temperature structure is still significant. Spiciness anomalies spread equator ward within the pycnocline, where they are carried to the equator as part of the subsurface branch of the Pacific Subtropical Cells, and spiciness also extends to the equator via western-boundary currents. Forcing near and at the equator generates strong dynamical anomalies, and sometimes additional spiciness anomalies, at pycnocline depths. The total response of the equatorial temperature structure to delta kappa(b) in various regions depends on the strength and spatial pattern of the generation of each signal within the forcing region as well as On the processes of its spreading to the equator.