Articles | Volume 10, issue 2
https://doi.org/10.5194/ascmo-10-69-2024
https://doi.org/10.5194/ascmo-10-69-2024
22 Jul 2024
 | 22 Jul 2024

Spatiotemporal methods for estimating subsurface ocean thermal response to tropical cyclones

Addison J. Hu, Mikael Kuusela, Ann B. Lee, Donata Giglio, and Kimberly M. Wood

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Cited articles

Argo: Argo float data and metadata from Global Data Assembly Centre (Argo GDAC), SEANOE [data set], https://doi.org/10.17882/42182, 2000. a
Argo Program: Implementation status, https://argo.ucsd.edu/about/status/ (last access: 10 December 2020), 2020. a
Balaguru, K., Chang, P., Saravanan, R., Leung, L. R., Xu, Z., Li, M., and Hsieh, J.-S.: Ocean barrier layers' effect on tropical cyclone intensification, P. Natl. Acad. Sci. USA, 109, 14343–14347, https://doi.org/10.1073/pnas.1201364109, 2012. a
Balaguru, K., Foltz, G. R., Leung, L. R., Asaro, E. D., Emanuel, K. A., Liu, H., and Zedler, S. E.: Dynamic Potential Intensity: An improved representation of the ocean's impact on tropical cyclones, Geophys. Res. Lett., 42, 6739–6746, https://doi.org/10.1002/2015GL064822, 2015. a
Bender, M. A. and Ginis, I.: Real-Case Simulations of Hurricane–Ocean Interaction Using A High-Resolution Coupled Model: Effects on Hurricane Intensity, Mon. Weather Rev., 128, 917–946, https://doi.org/10.1175/1520-0493(2000)128<0917:RCSOHO>2.0.CO;2, 2000. a, b, c
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We introduce a new statistical framework to estimate the change in subsurface ocean temperature following the passage of a tropical cyclone (TC). Our approach combines tools handling seasonal variations and spatial dependence in the data, culminating in a three-dimensional characterization of the interaction between TCs and the ocean. Our work allows us to obtain new scientific insights, and we expect it to be generally applicable to studying the impact of TCs on other ocean phenomena.