Observing Ocean–Atmosphere Interactions from an AutoNaut Uncrewed Surface Vessel.

Siddle, Elizabeth Adriana (2025) Observing Ocean–Atmosphere Interactions from an AutoNaut Uncrewed Surface Vessel. Doctoral thesis, University of East Anglia.

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Abstract

A novel Uncrewed Surface Vessel (Caravela USV) was successfully tested in its capacity as a platform to measure ocean and atmosphere bulk variables for calculating air-sea heat fluxes. These heat fluxes were then calculated using COARE 3.5 and used to investigate an ocean mixed layer heat budget in a 10 x 10 km square in the Northwestern Tropical Atlantic, as part of the EUREC4A campaign. An optimal estimate of net surface heat flux for the region was made using a combination of observed shortwave, latent and sensible heat fluxes from observed surface properties and ERA5 winds, and ERA5 longwave flux. Compared with this optimal estimate, ERA5 net surface heat flux was biased with 30Wm−2 excess heat loss to the atmosphere. I find that cloud cover representation, humidity and sea surface temperature were contributors to this bias. Closure of the ocean mixed layer heat budget was found to be better using surface heat fluxes alone (residual 0.06 °C) than when estimates of horizontal advection and entrainment were included (residual −0.18 °C) over the nine whole days spent in the study site, primarily due to uncertainties in the calculations of advection and entrainment. At hourly or daily scales, the effects of horizontal advection become more important to consider. Entrainment should not be neglected in this region at any time scale, as it was responsible for additional heat gain in to the mixed layer due to the presence of a temperature inversion in the barrier layer. The successful use of Caravela in this study demonstrates the effectiveness of USVs for surface flux measurement and heat budget closure. Better quantification of flux product biases at regional scales is evidently achievable with USV technology, with implications for improved climate modelling and weather prediction.

Item Type:	Thesis (Doctoral)
Faculty \ School:	Faculty of Science > School of Environmental Sciences
Depositing User:	Chris White
Date Deposited:	23 Dec 2025 09:16
Last Modified:	23 Dec 2025 09:16
URI:	https://ueaeprints.uea.ac.uk/id/eprint/101487
DOI:

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