Validation and Analysis of Ocean Model Simulations using Satellite Ocean Color Fields

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Monday, 5 January 2015
Eric Bayler, NOAA/NESDIS, College Park, MD; and S. Nadiga, D. Behringer, and A. Mehra
Manuscript (1.3 MB)

Handout (466.5 kB)

Solar shortwave heating of the upper layers of the ocean is dependent on the wavelength of the incoming radiation and the optical properties of the water column; and correlates with chlorophyll concentration, which modulates the absorption of solar insolation in the upper ocean. Satellite ocean color measurements from the former Sea-viewing Wide Field-of-View (SeaWiFS) and the current Visible Infrared Imaging Radiometer Suite (VIIRS) missions provide the basis for examining the impact of differences in ocean chlorophyll on global ocean model simulations. National Oceanic and Atmospheric Administration's (NOAA) operational global variable-resolution Modular Ocean Model version 4 (MOM4) provides an efficient and robust platform for analyzing the impact of satellite chlorophyll data on ocean dynamics at greater than eddy-resolving spatial scales. The first simulation discussed here is a 20-year climatological model run to obtain the restoring fluxes necessary to detrend the simulations from erroneous trends and errors from various sources. This 20-year climatological run, employing the annual cycle of monthly-mean SeaWiFS chlorophyll-a values derived from the period 1998-2010, is forced with climatological Climate Forecast System Reanalysis (CFSR) fluxes and relaxed to observed climatological monthly sea surface temperature and salinity fields. The climatological mean restoring fluxes from this run are computed and added back on to the CFSR fluxes to obtain a new corrected forcing dataset, CFSR_CORR. Then, six different ocean model simulation cases (a-f) are examined, each spanning 2012-2014, of which the first three (a-c) are fully unconstrained simulations forced by CFSR_CORR with no surface relaxation, while the last three (d-f) are forced by uncorrected CFSR fluxes and constrained by relaxation to surface temperature and salinity fields. The notation “_C” is appended to the names of the constrained cases. The cases are: a) SW, using monthly climatological ocean color fields obtained from the SeaWiFS mission (1998-2010); b) VDLY, using the sequential daily VIIRS ocean color fields; c) VMON, using sequential monthly ocean color fields from VIIRS; and the corresponding constrained cases d) SW_C; e) VDLY_C; f) VMON_C, respectively. The first three runs are used to examine the ocean model's sensitivity and response to the choice of prescribed ocean color field. In particular, sea surface temperature (SST) response is critical, because changes in SST serve as feedback in coupled ocean-atmosphere models used for seasonal predictions. The last three runs are used to examine and validate the detrending technique used to remove secular trends in MOM4 simulations forced by CFSR. Through modification of density profiles, differential heating creates baroclinic pressure gradients, which, in turn, impact the three-dimensional circulation patterns of the upper ocean. Thus, we examine changes in upper-ocean heat content, mixed-layer depths, dynamic heights, and velocity in the top 300 m of the water column. Anomalous build-up of equatorial Pacific ocean heat content is an important variable for the recharge-discharge oscillator theory for the evolution of El Niño events. Here, we show that differences in the chlorophyll data inputs cause significant changes in the ocean heat content anomalies in the equatorial Pacific. Thus, for seasonal predictions, it is important that we study the impact that the choice of ocean color data has on the quality of ocean forecasts and, consequently, coupled forecasts. To assess robustness of results and conclusions, this analysis is repeated for the tropical Atlantic and Indian oceans. Finally, in addition to comparing the simulations, we conduct a preliminary validation study between the model simulations and independent (i.e, non-assimilated) observations, such as satellite sea-surface height (SSH) fields, vertical profiles of prognostic variables (e.g, temperature and salinity) from fixed-location buoys and moving ARGO buoys, and ocean heat content and mixed-layer depths from the NCEP Global Ocean Data Assimilation System (GODAS).