Climatological diurnal variability in sea surface temperature characterized from drifting buoy data
Open accessAbstract
Drifting buoy sea‐surface temperature (SST) records have been used to characterize the diurnal variability of ocean temperature at a depth of order 20 cm. We use measurements covering the period 1986–2012 from the International Comprehensive Ocean‐Atmosphere Data Set (ICOADS) version 2.5, which is a collection of marine surface observations that includes individual SST records from drifting buoys. Appropriately transformed, this dataset is well suited for estimation of the diurnal cycle, since many drifting buoys have high temporal coverage (many reports per day), and are globally distributed. For each drifter for each day, we compute the local‐time daily SST variation relative to the local‐time daily mean SST. Climatological estimates of subdaily SST variability are found by averaging across various strata of the data: in 10° latitudinal bands as well as globally; and stratified with respect to season, wind speed and cloud cover. A parameterization of the diurnal variability is fitted as a function of the variables used to stratify the data, and the coefficients for this fit are also provided with the data. Results are consistent with expectations based on the previous work: the diurnal temperature cycle peaks in early afternoon (circa 2 pm local time); there is an increase in amplitude and a decrease in seasonality towards the equator. Generally, the ocean at this depth cools on windy days and warms on calm days, so that a component of subdaily variability is the SST tendency on slower timescales. By not ‘closing’ the diurnal cycle when stratified by environmental conditions, this dataset differs from previously published diurnal‐cycle parameterizations. This thorough characterization of the SST diurnal cycle will assist in interpreting SST observations made at different local times of day for climatological purposes, and in testing and constraining models of the diurnal‐cycle and air‐sea interaction at high temporal resolution.
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The seasonal cycle in uncertainties reflects the greater impact of sampling uncertainty when sub-daily SST variability is increased in summer because of stronger diurnal cycle effects.
“This reflects the greater impact of sampling uncertainty in the observations when sub-daily SST variability is increased in summer because of stronger diurnal cycle effects70.”
From Tendencies, variability and persistence of sea surface temperature anomalies (2020) · cites this paper as [70]
The Results (section 1.2) report a larger summer diurnal SST cycle and greater variability around its average. The Analysis (section 1.1) explains that uncertainty in an estimated cycle depends on variability and sample size. The paper does not establish that a seasonal cycle in the citing document’s observational uncertainties is caused by a greater impact of sampling uncertainty. (LLM, conf=0.85)
Experimental — accuracy not yet measuredThe diurnal cycle in SST is typically in the peak-to-peak range of 0.1 K to 0.5 K.
“The diurnal cycle in sea surface temperature has been empirically characterised from sub-daily drifting buoy variability8 and by remote sensing9, and is typically in the peak-to-peak range of 0.1 K to 0.5 K.”
From Satellite-based time-series of sea-surface temperature since 1981 for climate applications (2019) · cites this paper as [8]
The Abstract says the authors used sub-daily drifting-buoy records to characterize the SST diurnal cycle, supporting that part of the citing sentence. The Results (§1.2) discuss how its amplitude varies and give a 0.1 K start-to-end difference for one seasonal example, but our copy does not state that the typical peak-to-peak range is 0.1–0.5 K. (LLM, conf=0.82)
Experimental — accuracy not yet measuredThe SST at 10.30 or 22.30 local mean solar time is a good approximation to the daily mean.
“The SST at 10.30 or 22.30 local mean solar time is a good approximation to the daily mean8.”
From Satellite-based time-series of sea-surface temperature since 1981 for climate applications (2019) · cites this paper as [8]
Section 1.2 (Results; page unavailable) reports that the arithmetic mean of SST anomalies at 10:30 a.m. and 10:30 p.m. closely agrees with the daily mean. It does not establish that the SST at either time individually is a good approximation to the daily mean. (LLM, conf=0.88)
Experimental — accuracy not yet measuredThere is a diurnal cycle in SST that has been empirically characterised using satellite observations and drifting buoys.
“There is a diurnal cycle in SST that has been empirically characterised using satellite observations9 and drifting buoys8.”
From Satellite-based time-series of sea-surface temperature since 1981 for climate applications (2019) · cites this paper as [8]
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References 19 · Cited by 39
Research works printed but not linked: 16
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- Authors
- Morak‐Bozzo, S., Merchant, C. J., Kent, E. C., Berry, D. I., & Carella, G.
- Affiliations
- University of Reading (GB)
- National Centre for Earth Observation (GB)
- National Oceanography Centre (GB)
- Corresponding author
- Morak‐Bozzo, S.
- Venue
- Geoscience Data Journal
- Publisher
- Wiley
- Published
- 2016-06
- Volume
- 3
- Issue
- 1
- Pages
- 20-28
- ISSN
- 2049-6060 (ISSN-L)
- OpenAlex
- W2475047746
- Cited by
- 39
- References
- 19
- Citations by year
- 2026: 3 · 2025: 6 · 2024: 4 · 2023: 3 · 2022: 6 · 2021: 1 · 2020: 6 · 2019: 6 · 2018: 2 · 2017: 2
- FWCI
- 2.70
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Geoscience Data Journal (publishedVersion, cc-by)
- CentAUR (University of Reading) (acceptedVersion)
- ePrints Soton (University of Southampton) (acceptedVersion)
- NERC Open Research Archive (Natural Environment Research Council) (acceptedVersion)
- Funders
- National Environment Research Council (NE/J02306X/2, NE/J020788/1)
- Topics
- Physical Sciences › Earth and Planetary Sciences › Oceanography › Oceanographic and Atmospheric Processes
- Physical Sciences › Environmental Science › Global and Planetary Change › Climate variability and models
- Physical Sciences › Earth and Planetary Sciences › Atmospheric Science › Meteorological Phenomena and Simulations
- Keywords
- sea surface temperature, diurnal variation, drifting buoys, ocean-atmosphere interaction, cloud cover, seasonal variation
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