4.3 Article

Temperature variability in a shallow, tidally isolated coral reef lagoon

Journal

JOURNAL OF GEOPHYSICAL RESEARCH-OCEANS
Volume 115, Issue -, Pages -

Publisher

AMER GEOPHYSICAL UNION
DOI: 10.1029/2009JC006023

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Funding

  1. Australian Research Council [DP0771055, DP0557126]
  2. SIO
  3. Australian Research Council [DP0771055, DP0557126] Funding Source: Australian Research Council

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Temperature data collected in the shallow, tidally isolated reef flat/lagoon of Lady Elliot Island off Queensland, Australia, show marked variability under solar and tidal forcing. Sea level drops below the height of the protective lagoon rim for a few hours during low tide, effectively isolating the remaining water. Because the lagoon is shallow, its temperature change (from diurnal solar forcing and cooling) is amplified. We develop a simple analytical model to predict the time evolution of mean lagoon temperature, beginning with a well-mixed control volume. This approach highlights the asymmetric flood/ebb physics of tidally isolated lagoons. After discussing the response of this model, we compare it with results from two idealized numerical simulations that illustrate differing aspects of lagoon temperature variability under potential flow and prevailing current situations. The conceptual model captures the essence of lagoon temperature variability and underscores the importance of solar-lunar phasing. However, because of the well-mixed assumption, it cannot reproduce sudden temperature transitions associated with new incoming water masses. Observations show that a slowly progressing thermal wave inundates the lagoon on rising tides. This wave is similar to our potential flow simulation in that it is approximately radially symmetric. On the other hand, it appears to advectively replace resident lagoon water, similar to our prevailing current simulations. We attempt to account for this behavior with a simple frontal modification to our conceptual model. Results show that this frontal model is able to capture the sudden temperature transitions present in the data and offers improved predictive capabilities over the well-mixed model.

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