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Subseasonal to seasonal forecasts are likely to improve from better sea surface temperature (SST) predictions, as SST is the bottom boundary condition for the marine atmosphere. We present research that extends the analysis and prediction of SST to include variability of upper ocean mixing to explore how the variability of the ocean mixed layer affects the intraseasonal statistics of SST and its covariance with tropical intraseasonal atmospheric variability. We present a conceptual framework to identify the contribution of fast (hourly to daily) co-variations in ocean mixed layer depth and atmospheric fluxes to seasonal to sub-seasonal sea surface temperature prediction. First, metrics from this framework will be analyzed from data collected throughout the tropical and subtropical oceans from moored platforms and profiling instruments to demonstrate how diurnal solar warming, fast wind gusts and rain showers, and daily variable clouds and winds rectify into longer timescale intraseasonal SST variability. We will then focus the pre-monsoon season in the Arabian Sea using observations of the upper ocean collected during the 2023 ASTRraL/EKAMSAT field program, highlighting the role of the diurnal warm layer variability on mean SST.
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A dataset of hourly sea surface temperature from drifting buoys
Abstract A dataset of sea surface temperature (SST) estimates is generated from the temperature observations of surface drifting buoys of NOAA’s Global Drifter Program. Estimates of SST at regular hourly time steps along drifter trajectories are obtained by fitting to observations a mathematical model representing simultaneously SST diurnal variability with three harmonics of the daily frequency, and SST low-frequency variability with a first degree polynomial. Subsequent estimates of non-diurnal SST, diurnal SST anomalies, and total SST as their sum, are provided with their respective standard uncertainties. This Lagrangian SST dataset has been developed to match the existing and on-going hourly dataset of position and velocity from the Global Drifter Program.
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- Award ID(s):
- 2126413
- PAR ID:
- 10451411
- Date Published:
- Journal Name:
- Scientific Data
- Volume:
- 9
- Issue:
- 1
- ISSN:
- 2052-4463
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1038/s41597-022-01670-2
