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We assess the performance of three different algorithms for estimating surface ocean currents from two linear array HF radar systems. The delay-and-sum beamforming algorithm, commonly used with beamforming systems, is compared with two direction-finding algorithms: Multiple Signal Classification (MUSIC) and direction finding using beamforming (Beamscan). A 7-month dataset from two HF radar sites (CSW and GTN) on Long Bay, South Carolina (United States), is used to compare the different methods. The comparison is carried out on three locations (midpoint along the baseline and two locations with in situ Eulerian current data available) representing different steering angles. Beamforming produces surface current data that show high correlation near the radar boresight (R2 ≥ 0.79). At partially sheltered locations far from the radar boresight directions (59° and 48° for radar sites CSW and GTN, respectively) there is no correlation for CSW (R2 = 0) and the correlation is reduced significantly for GTN (R2 = 0.29). Beamscan performs similarly near the radar boresight (R2 = 0.8 and 0.85 for CSW and GTN, respectively) but better than beamforming far from the radar boresight (R2 = 0.52 and 0.32 for CSW and GTN, respectively). MUSIC’s performance, after significant tuning, is similar near the boresight (R2 = 0.78 and 0.84 for CSW and GTN) while worse than Beamscan but better than beamforming far from the boresight (R2 = 0.42 and 0.27 for CSW and GTN, respectively). Comparisons at the midpoint (baseline comparison) show the largest performance difference between methods. Beamforming (R2 = 0.01) is the worst performer, followed by MUSIC (R2 = 0.37) while Beamscan (R2 = 0.76) performs best.

A hybrid, empirical radar wave inversion technique that treats swell and wind waves separately is presented and evaluated using a single 48-MHz radar unit and in situ wave measurements. This hybrid approach greatly reduces errors in radar wave inversion during swell seas. Our analysis suggests that, prior to the inversion, the second-order spectrum should be normalized using Barrick’s weighting function because this process removes harmonic and corner reflection peaks from the inversion and improves the results. In addition, the resulting calibration constants for the wind wave component are not wave-frequency dependent and are similar in magnitude to those found in previous studies using different operating-frequency radars. This result suggests radar frequency independence, although additional experimental verification is required. The swell component of the model presented here ignores the effect of swell’s propagation direction on the radar signal. Although this approach has several limitations and may only be useful near the coast (where swell propagates close to perpendicular to the coastline), the resulting wave inversion is accurate even when swell is propagating close to perpendicular to the radar beam direction. RMS differences relative to in situ wave height measurements range from 0.16 to 0.25 m as the radar beam angle increases from 22° to 56°.

Accurate estimates of Stokes drift are necessary to quantify Lagrangian transport and upper-ocean mixing. These can be estimated from directional wave spectra. Here, a methodology for the reconstruction of such spectra is developed using partitioned bulk wave parameters provided by global wave models. These reconstructed spectra agree well with global wave model–simulated full spectra. Regional wave model simulations with reconstructed spectra as open boundary conditions lead to more accurate estimates of bulk wave parameters in the coastal ocean. Furthermore, the reconstructed directional spectra can be used to improve high-frequency (HF) radar–derived surface Lagrangian current estimates. Stokes drift vertical profiles from complete directional spectra are more accurate, and therefore coupled ocean circulation and wave models should incorporate spectral estimates for wave–current interaction studies. Based on model simulations conducted here, it is recommended that regional wave modeling studies use partitioned rather than bulk wave parameter products from global wave simulations to reconstruct complete directional spectra for open boundary conditions. Finally, this study shows that inclusion of the peak spectral energy for each partition improves the ability to reconstruct more accurately directional spectra and surface Stokes drift. It is recommended that the global wave model hindcast/forecast include this additional bulk parameter.

The effects of alpine glaciers on the hydrology, physical features, and biogeochemistry of lakes have been investigated over contemporary time scales. However, the influence of these factors on algal communities over longer time scales remains unclear, yet is critical to paleolimnological interpretation of environmental change in alpine regions. We examined sedimentary algal pigments and fossil diatom assemblages in two proximal lakes with equivalent local climates, one glacier-fed and one snow-fed, in the central Rocky Mountains (USA) to determine how glacier meltwater has altered algal records over the last 3,000 years. Differences between the records of the two lakes intensified during the Medieval Climate Anomaly and the Little Ice Age, with the glacier-fed lake exhibiting an overall increase in fossil algal pigment concentrations and greater diatom assemblage turnover. Starting 1,000 years ago, the glacier-fed lake in this study showed evidence of nitrogen enrichment from glacier meltwater, as indicated by increasing relative abundances of Asterionella formosa and, to a lesser extent, Fragilaria crotonensis. Since the Little Ice Age, diatom species richness declined in the glacier-fed lake, and further decreased following the 1950s, while assemblage turnover increased. These results demonstrate that glaciers can strongly alter the algal sedimentary record and should be considered when interpreting high-elevation lake records.

Over the 20th century, surface water temperatures have increased in many lake ecosystems around the world, but long-term trends in the vertical thermal structure of lakes remain unclear, despite the strong control that thermal stratification exerts on the biological response of lakes to climate change. Here we used both neo- and paleoecological approaches to develop a fossil-based inference model for lake mixing depths and thereby refine understanding of lake thermal structure change. We focused on three common planktonic diatom taxa, the distributions of which previous research suggests might be affected by mixing depth. Comparative lake surveys and growth rate experiments revealed that these species respond to lake thermal structure when nitrogen is sufficient, with species optima ranging from shallower to deeper mixing depths. The diatom-based mixing depth model was applied to sedimentary diatom profiles extending back to 1750 AD in two lakes with moderate nitrate concentrations but differing climate settings. Thermal reconstructions were consistent with expected changes, with shallower mixing depths inferred for an alpine lake where treeline has advanced, and deeper mixing depths inferred for a boreal lake where wind strength has increased. The inference model developed here provides a new tool to expand and refine understanding of climate-induced changes in lake ecosystems.