This paper addresses a bias problem in the estimate of wavelet power spectra for atmospheric and oceanic datasets. For a time series comprised of sine waves with the same amplitude at different frequencies the conventionally adopted wavelet method does not produce a spectrum with identical peaks, in contrast to a Fourier analysis. The wavelet power spectrum in this definition, that is, the transform coefficient squared (to within a constant factor), is equivalent to the integration of energy (in physical space) over the influence period (time scale) the series spans. Thus, a physically consistent definition of energy for the wavelet power spectrum should be the transform coefficient squared divided by the scale it associates. Such adjusted wavelet power spectrum results in a substantial improvement in the spectral estimate, allowing for a comparison of the spectral peaks across scales. The improvement is validated with an artificial time series and a real coastal sea level record. Also examined is the previous example of the wavelet analysis of the Niño-3 SST data.

[1] Patterns of ocean current variability are examined on the West Florida Shelf by a neural network analysis based on the self-organizing map (SOM), using time series of moored velocity data that span the interval October 1998-September 2001. Three characteristic spatial patterns are extracted in a 3 Â 4 SOM array: spatially coherent southeastward and northwestward flow patterns with strong currents and a transition pattern of weak currents. On the synoptic weather timescale the variations of these patterns are coherent with the local winds. On the seasonal timescale the variations of the patterns are coherent with both the local winds and complementary sea surface temperature patterns. The currents are predominantly southeastward during fall-winter months (from October to March) and northwestward during summer months (from June to September). The spatial patterns extracted by the (nonlinear) SOM method are asymmetric, a feature that is not captured by the (linear) empirical orthogonal function method. Thus we find for the synoptic weather and longer timescales that (1) southeastward currents are generally stronger than northwestward currents, (2) the coastal jet axis is located further offshore for southeastward currents than for northwestward currents, and (3) the velocity vector rotations with depth are larger in shallower water when the currents are southeastward relative to when the currents are northwestward.

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The Self‐Organizing Map (SOM), an unsupervised learning neural network, is employed to extract patterns evinced by the Loop Current (LC) system and to identify regions of sea surface height (SSH) variability in the eastern Gulf of Mexico (GoM) from 23 years (1993–2015) of altimetry data. Spatial patterns are characterized as different LC extensions and different stages in the process of LC eddy shedding. The temporal evolutions and the frequency of occurrences of these patterns are obtained, and the typical trajectories of the LC system progression on the SOM grid are investigated. For an elongated, northwest‐extended, or west‐positioned LC, it is common for the LC anticyclonic eddy (LCE) to separate and propagate into the western GoM, while an initially separated LCE in close proximity to the west Florida continental slope often reattaches to the LC and develops into an elongated LC, or reduces intensity locally before moving westward as a smaller eddy. Regions of differing SSH variations are also identified using the joint SOM‐wavelet analysis. Along the general axis of the LC, SSH exhibits strong variability on time scales of 3 months to 2 years, also with energetic intraseasonal variations, which is consistent with the joint Empirical Orthogonal Function (EOF)‐wavelet analysis. In the more peripheral regions, the SSH has a dominant seasonal variation that also projects across the coastal ocean. The SOM, when applied to both space and time domains of the same data, provides a powerful tool for diagnosing ocean processes from such different perspectives.

The Deepwater Horizon oil spill was caused by a drilling rig explosion on 20 April 2010 that killed 11 people. It was the largest oil spill in U.S. history and presented an unprecedented threat to Gulf of Mexico marine resources. Although oil gushing to the surface diminished after the well was capped, on 15 July 2010, much remains to be known about the oil and the dispersants beneath the surface, including their trajectories and effects on marine life. A system for tracking the oil, both at the surface and at depth, was needed for mitigation efforts and ship survey guidance. Such a system was implemented immediately after the spill by marshaling numerical model and satellite remote sensing resources available from existing coastal ocean observing activities [e.g., Weisberg et al., 2009]. Analyzing this system's various strengths and weaknesses can help further improve similar systems designed for other emergency responses.

Assessment of direct and indirect impacts of oil and dispersants on the marine ecosystem in the northeastern Gulf of Mexico (NEGOM) from the Deepwater Horizon oil spill (April – July 2010) requires sustained observations over multiple years. Here, using satellite measurements, numerical circulation models, and other environmental data, we present some initial results on observed biological changes at the base of the food web. MODIS fluorescence line height (FLH, a proxy for phytoplankton biomass) shows two interesting anomalies. The first is statistically significant (>1 mg m−3 of chlorophyll‐a anomaly), in an area exceeding 11,000 km2 in the NEGOM during August 2010, about 3 weeks after the oil well was capped. FLH values in this area are higher (i.e., water is greener) than in any August since 2002, and higher than ever since 2002 in an area of ∼3,000 km2. Analyses of ocean circulation and other environmental data suggest that this anomaly may be attributed to the oil spill. The second is a spatially coherent FLH anomaly during December 2010 and January 2011, extending from Mobile Bay to the Florida Keys (mainly between 30 and 100‐m isobaths). This anomaly appears to have resulted from unusually strong upwelling and mixing events during late fall. Available data are insufficient to support or reject a hypothesis that the subsurface oil may have contributed to the enhanced biomass during December 2010 and January 2011.

Lagrangian particle trajectory models based on several altimetry-derived surface current products are used to hindcast the drifter trajectories observed in the eastern Gulf of Mexico during May to August 2010 (the Deepwater Horizon oil spill incident). The performances of the trajectory models are gauged in terms of Lagrangian separation distances (d) and a nondimensional skill score (s), respectively. A series of numerical experiments show that these altimetry-based trajectory models have about the same performance, with a certain improvement by adding surface wind Ekman components, especially over the shelf region. However, their hindcast skills are slightly better than those of the data assimilative numerical model output. After 3 days' simulation the altimetry-based trajectory models have mean d values of 75-83 and 34-42 km (s values of 0.49-0.51 and 0.35-0.43) in the Gulf of Mexico deep water area and on the West Florida Continental Shelf, respectively. These satellite altimetry data products are useful for providing essential information on ocean surface currents of use in water property transports, offshore oil and gas operations, hazardous spill mitigation, search and rescue, etc.

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