Small‐Scale Spatial Variations of Air‐Sea Heat, Moisture, and Buoyancy Fluxes in the Tropical Trade Winds

Abstract

Observations from two autonomous Wave Gliders and six Lagrangian Surface Wave Instrument Float with Tracking drifters in the northwestern tropical Atlantic during the January–February 2020 NOAA Atlantic Tradewind Ocean‐atmosphere Mesoscale Interaction Campaign (ATOMIC) are used to evaluate the spatial variability of bulk air‐sea heat, moisture, and buoyancy fluxes. Sea surface temperature (SST) gradients up to 0.7°C across 10–100 km frequently persisted for several days. SST gradients were a leading cause of systematic spatial air‐sea sensible heat flux gradients, as variations over 5 Wm−2 across under 20 km were observed. Wind speed gradients played no significant role and air temperature adjustments to SST gradients sometimes acted to reduce spatial flux gradients. Wind speed, air temperature, and air humidity caused high‐frequency spatial and temporal flux variations on both sides of SST gradients. A synthesis of observations demonstrated that fluxes were usually enhanced on the warm SST side of gradients compared to the cold SST side, with variations up to 10 Wm−2 in sensible heat and upward buoyancy fluxes and 50 Wm−2 in latent heat flux. Persistent SST gradients and high‐frequency air temperature variations each contributed up to 5 Wm−2 variability in sensible heat flux. Latent heat flux was instead mostly driven by air humidity variability. Atmospheric gradients may result from convective structures or high‐frequency turbulent fluctuations. Comparisons with 0.05°‐resolution daily satellite SST observations demonstrate that remote sensing observations or lower‐resolution models may not capture the small‐scale spatial ocean variability present in the Atlantic trade wind region.

Atmospheric Fronts Shaping the (Sub)Mesoscale SST‐Wind Coupling Over the Southern Ocean: Observational Case

“…Section 3.4 shows an impressive oceanic thermal imprint on the near-surface layer in the submesoscale regime, which is consistent with recent case studies in denoting the submesoscale air-sea coupling (Acquistapace et al, 2022;Gaube et al, 2019;Iyer et al, 2022;Shao et al, 2019). Compared to the case studies, the positive relationship observed between filter-derived SST perturbations and other variables such as wind, air temperature, and heat flux provides evidence for the existence of submesoscale air-sea coupling in a general context.…”

“…The mesoscale SST-wind coupling processes have been relatively extensively investigated in previous studies (see reviews by R. D. Small et al (2008); Seo et al (2023)), but the smallscale oceanic forcing (below the deformation radius) on the MABL processes is only beginning to be investigated (Czaja et al, 2019). Recently, both observations (Acquistapace et al, 2022;de Szoeke et al, 2021;Gaube et al, 2019;Iyer et al, 2022;Shao et al, 2019;Yang et al, 2024) and simulations (Conejero et al, 2024;Strobach et al, 2022;Sullivan & McWilliams, 2022;Sullivan et al, 2020;Wenegrat & Arthur, 2018) have shown that submesoscale SST variability can have direct impacts on the MABL, including surface wind speed, air-sea heat flux, and cloud formation (Acquistapace et al, 2022). The energetic submesoscale processes in the Southern Ocean also implied the strong submesoscale air-sea coupling (Siegelman et al, 2020;Su et al, 2018;Swart et al, 2023), but it remains largely unexplored, particularly from the observation perspective.…”

Statistics of Bubble Plumes Generated by Breaking Surface Waves

“…Figure 4a shows a schematic of a SWIFT track drifting through an intermittent field of saturated (with visible optical surface signature) and diffused (without visible optical surface signature) bubble clouds during a 512‐s burst of data along which echogram data are collected in a surface‐following reference frame. The buoy has a “wind slip” velocity relative to the surface water U slip ≈ 0.01 U 10 N that is caused by wind drag on the portion of the buoy above the surface (Iyer et al., 2022). Note that the example SWIFT track shown here is calculated with respect to the earth frame, so the example includes both the true surface current and the wind slip of the buoy (which combine together to make the observed drift velocity of the buoy, typically U drift ≈ 0.04 U 10 N ).…”

Ubiquitous Sea Surface Temperature Anomalies Increase Spatial Heterogeneity of Trade Wind Cloudiness on Daily Time Scale

“…Nevertheless, mesoscale to submesoscale SST spatial anomalies still exist ubiquitously in daily satellite SST analysis in the EUREC 4 A/ATOMIC area, albeit with a weak magnitude compared to the coastal North Brazil Current (NBC) rings (Fratantoni and Glickson 2002). In situ SST measurements from wave gliders in this wintertime ATOMIC campaign estimated SST variability up to 0.78C across an order of 10-100 km with a maximum gradient of (0.0478C km 21 ) (Iyer et al 2022a). Similar SST variability was also observed on board NOAA ship Ronald H. Brown in ATOMIC (Fig.…”