Based on a second-order turbulence mixed
layer model, Dr. Tiejun LING, senior scientist of the National Marine Environmental
Forecasting Center, China (NMEFC), and his research team, have developed a new ocean mixed
The model is capable of reproducing
a more realistic sea surface temperature (SST) diurnal cycle than existing models.
Meanwhile, through a series of algorithms and parallel optimization, the computational
efficiency is greatly improved in the new mixed layer model, and thus has the
potential to be a considerably useful tool in studying the long-term diurnal variation
of SST. The findings of this research have recently been published in Advances in Atmospheric Sciences, and
the paper is featured on the front cover of the issue in which it appears
(Volume 35, Issue 12).
On the cover: The schematic
indicates the mechanism of the SST diurnal cycle in the ocean. Also depicted is
a typical buoy station, the high-frequency SST data from which are used to validate
the simulated SST data.
the ocean mixed layer model
Air–sea interaction is an important
process affecting climatic variation, and
SST is one of the most important parameters governing this interaction. As one of
the dominant scales of variation in SST, the
diurnal cycle, which varies globally by 2℃, has a significant impact on the evolution of weather and climate systems.
Typically, the diurnal variation of
SST is studied through observation or model
data. In terms of the former (i.e., observational data), the spatial coverage of drifting buoys and satellite-borne data are non-uniform
and discontinuous, making it difficult to study the
long-term variation of SST. The modeling approach, however, can help with this problem.
Having access to high-frequency SST data can benefit the study of warming thresholds,
peak values, and timings.
Compared with empirical models, an ocean mixed
can reproduce more realistic dynamic and thermal
processes in the upper ocean, and the computational
cost is lower than that of a climate system model.
Diurnal variation of sea surface
of the hourly SST dataset
The accuracy of the SST diurnal variation
greatly depends on the reliability of the
model data. In this study, comparisons with in
situ observational data showed that the new model performs well, with a mean bias of 0.07°C, and root-mean-square error and correlation coefficient of
0.37°C and 0.98, respectively. Similar results
were obtained from satellite data.
Comparison of SST time series
between in situ buoy data and the new
model (MLSST) from 1 January 2010 to 31 December 2010 for selected buoy stations:
(a) T5N165E (5°N, 165°E); (b) T0N180W (0°N, 180°W); (c) T8S125W (8°S, 125°W); (d)
46076r (59.498°N, 147.983°W); (e) 44009r (38.461°N, 74.703°W).
of the variation in the SST diurnal cycle
Dr. LING and his research team explored
the new dataset and revealed some of the climatic characteristics associated with
the variation in the SST diurnal cycle. The
characteristics coincided with those obtained from satellite data.
The 31-year climatology revealed that
the SST diurnal variation is small across most regions, with higher values apparent
in the eastern and western equatorial Pacific, northern Indian Ocean, western Central
America, northwestern Australia, and several
Significant seasonal variation of diurnal
SST was found to exist in all basins. In the Atlantic and Pacific basins, the seasonal
pattern is oriented north–south, following the variation of solar insolation; whereas,
in the Indian basin it is dominated by monsoonal variability, according to Dr. LING.
“At the interannual scale, the results
highlight the relationship between the diurnal and interannual variations of SST, and reveal that the diurnal warming in the
central equatorial Pacific could be a potential climatic indicator for ENSO prediction,”
Dr. LING says the team found that SST
diurnal variation has a significant impact on global climate change at both the
seasonal and interannual scale. More importantly,
the diurnal warming of SST in the central equatorial Pacific could be a potential
indicator for ENSO prediction.
“Besides, this dataset has good research
and application prospects. These long-term, high-resolution hourly SST data could be applied in studies of the long-term trend in regional and global SST diurnal
variation, as well as the relationship between SST diurnal variation and regional and
global climate events,” he adds.
Software copyright has been obtained
for the ocean mixed layer model, and is being applied operationally at the National Marine Environmental Forecasting Center,
The research team will maintain running
the ocean mixed layer model operationally, continually updating the data and using
them to study the long-term trends of SST
diurnal variation and the effects of SST diurnal variation on global and regional
Li, X., T. J. Ling, Y. F. Zhang, and
Q. Zhou, 2018: A 31-year global diurnal sea surface temperature dataset created
by an ocean mixed-layer model. Adv. Atmos.
Sci., 35(12), 1443–1454, https://doi.org/10.1007/s00376-018-8016-7.