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Ocean Surface Structure Assimilation at NRL

From the Summer 2015 issue of the JCSDA Quarterly

Forecasting the ocean surface stratification throughout the mixed layer is critically important to fisheries management, anticipation of harmful algal blooms and hypoxic events, search and rescue, disaster response, and safety at sea. Ocean processes that control the surface mixed layer development can be discerned from satellite measurements. These measurements include satellite altimetry, which observes the ocean mesoscale conditions controlling the underlying stratification and rate of entrainment at the mixed layer base. Surface temperature observations from infrared and microwave sensors are linked to the thermal content within the mixed layer. The surface winds and waves-observed by scatterometers, passive microwave sensors, altimeters, and synthetic aperture radars-inject turbulent energy at the ocean surface that mixes downward and sustains the mixed layer. In addition, surface latent and sensible heat fluxes, along with incoming solar and outgoing longwave radiation, are critical controllers of the ocean surface structure properties. The distribution of mixed layer depth in Figure 1 is an example of the combined effects of these processes.

Surface fluxes of latent and sensible heat are typically computed in ocean models using bulk parameterizations (e.g., Fairall et al., 2003) that depend on surface air temperature, humidity, neutrally buoyant winds, and ocean surface temperature. The flux parameterizations are based on observations obtained with precise instrumentation near the ocean surface during science experiments. Because meteorological models approximate this domain within the planetary boundary layer, there are errors in the information from the models. Similarly, ocean models directly use surface shortwave and longwave radiative fluxes that have been calculated by the meteorological models, which are also subject to error. These can lead to long-term biases when the atmospheric forecasts contain systematic errors. Quantifying these errors is the motivation for turning to satellite observations from SSMIS, AMSU, AMSR2, ATMS, and other sensors to directly measure the near surface atmosphere and derive surface radiation estimates.


Mixed layer depth in the Gulf of Mexico

Figure 1. The mixed layer depth (m) in the northeastern Gulf of Mexico forecasted by a 1-km resolution model covering the full Gulf for April 1, 2015. Deep mixed layers are associated with anticyclones, such as the large red Loop Current eddy in the southern portion of the domain, and smaller submesoscale eddies in the northern domain. Frontogenesis driven by these eddies produces filaments of thinned mixed layer entrained within the eddy field.


At the Ocean Dynamics and Prediction Branch of the Naval Research Laboratory (NRL), we have developed the NRL Ocean Surface Flux System (NFLUX). NFLUX uses a range of satellite sensors to observe the surface air temperature, moisture, and wind speed. It blends these observations with atmospheric model forecast fields in 2DVar analyses, estimating bias corrections to be applied before the turbulent fluxes are calculated for the ocean model.

These sensors have not traditionally been used to retrieve the surface flux parameters, so new retrieval algorithms have been developed. The algorithms are tuned based on ship observations from the regular network of returned Ship of Opportunity Program (SOOP) information from cargo ships and research vessels. In the future, we plan to move from the initial simple retrieval approach to a more correct physical retrieval through a radiative transfer model. Comparisons of both the observed parameters and the calculated heat fluxes with in situ buoy and ship measurements show that the assimilation of the satellite retrievals reduces global bias and RMS error statistics from the original atmospheric forecasts of the Navy Global Environmental Model (NAVGEM).

Ocean models typically directly apply net surface longwave and shortwave radiative fluxes calculated by the atmospheric model, but these are similarly subject to systematic error. The NFLUX system uses NOAA Microwave Integrated Retrieval System (MIRS) profiles and other satellite-based information as inputs to the Rapid Radiative Transfer Model (Iacono et al., 2000) to similarly estimate bias corrections for the radiative fluxes.

Comparisons with in situ measurements made from research vessels using the Shipboard Automated Meteorological and Oceanographic System (SAMOS; Smith et al., 2001) show global bias and RMS error reductions from the original NAVGEM fluxes, consistent with sample comparisons with Cloud and Earth Radiant Energy System (CERES; Wielicki et al., 1996) monthly mean fluxes (Figure 2). It should also be noted that the NFLUX system works in near-real time, without the long (days to months) latency of similar flux products.

Time series of satellite-derived corrections to the atmospheric model inputs provide the time decorrelation scales of errors and enable us to map these errors over a range of space and time scales from the hindcast in order to forecast the uncertainty. Low-frequency energy in the errors (from mean, to seasonal, to weekly) is applicable in projecting the hindcast corrections forward into the daily ocean forecasts.


Mean shortwave radiation

Figure 2. The June 2014 one-month mean shortwave radiation (W/m2) from 3-hourly NFLUX analyses (upper left) and the CERES SYN1deg-Month Ed3A dataset (upper right). Differences between the NAVGEM and NFLUX (lower left) and NAVGEM and CERES (lower right) one-month means show the assimilation of the satellite-derived radiation fluxes can provide real time information on atmospheric model bias for ocean forecasts.


Current research efforts aim to connect the subsurface ocean observations from Argo floats, ships of opportunity, the Tropical Ocean Global Atmosphere-Tropical Ocean Atmosphere (TOGA-TAO) project, and other sources to the satellite fluxes through four-dimensional variational (4DVar) assimilation in the ocean. The 4DVar is capable of propagating observation innovation information in the subsurface to the ocean surface fluxes where it joins with the satellite retrieved fluxes.

The ocean 4DVar validation has been completed. The operational implementation of the satellite flux retrievals will begin in FY16 at the Naval Oceanographic Office. Finally, in developments such as the Earth System Prediction Capability, in which global coupled models provide flux information to one another, assimilation in coupled models through estimates of fluxes becomes a more natural process.

Clark Rowley, Charlie Barron, Gregg Jacobs, and Jackie May (NRL)

References:

Fairall, C.W., E.F. Bradley, J.E. Hare, A.A. Grachev, and J.B. Edson, 2003. Bulk parameterization of air-sea fluxes: Updates and verification for the COARE algorithm. J. Climate, 16, 571-591.

Iacono, M., E.J. Mlawer, S.A. Clough, J.-J. Morcrette, 2000. Impact of an improved longwave radiational model, RRTM, on the energy budget and thermodynamic properties of the NCAR community climate model, CCM3. J. Geophys. Res., 105, 14873-14890.

Smith, S.R., D.M. Legler, and K.V. Verzone, 2001. Quantifying uncertainties in NCEP reanalyses using high quality research vessel observations. J. Climate, 14, 4062-4072.

Wielicki, B.A., B.R. Barkstrom, E.F. Harrison, R.B. Lee III, G.L. Smith, and J.E. Cooper, 1996. Clouds and the Earth's Radiant Energy System (CERES): An Earth Observing System Experiment. Bull. Amer. Meteor. Soc., 77, 853-868.

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