Comparison of <scp>SST</scp> Diurnal Variation Models Over the Tropical Warm Pool Region

Zhang, Haifeng; Beggs, Helen; Merchant, Christopher J.; Wang, Xiaohua; Majewski, Leon; Kiss, Andrew E.; Rodríguez, José M.; Thorpe, Livia; Gentemann, Chelle; Brunke, Michael A.

doi:10.1029/2017jc013517

Cited by 16 publications

(10 citation statements)

References 49 publications

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“…There is a diurnal cycle in SST that has been empirically characterised using satellite observations 9 and drifting buoys 8 . The satellite observations are obtained at local times of day that change through the record (Fig.…”

Section: Methodsmentioning

confidence: 99%

“…3). The diurnal cycle in sea surface temperature has been empirically characterised from sub-daily drifting buoy variability 8 and by remote sensing 9 , and is typically in the peak-to-peak range of 0.1 K to 0.5 K. Under low-wind, strong-sun conditions, it can be ~5 K 10 . Different overpass times differentially sample SST, generating non-climatic signals if not adjusted for.…”

Section: Background and Summarymentioning

confidence: 99%

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Satellite-based time-series of sea-surface temperature since 1981 for climate applications

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A climate data record of global sea surface temperature (SST) spanning 1981–2016 has been developed from 4 × 1012 satellite measurements of thermal infra-red radiance. The spatial area represented by pixel SST estimates is between 1 km2 and 45 km2. The mean density of good-quality observations is 13 km−2 yr−1. SST uncertainty is evaluated per datum, the median uncertainty for pixel SSTs being 0.18 K. Multi-annual observational stability relative to drifting buoy measurements is within 0.003 K yr−1 of zero with high confidence, despite maximal independence from in situ SSTs over the latter two decades of the record. Data are provided at native resolution, gridded at 0.05° latitude-longitude resolution (individual sensors), and aggregated and gap-filled on a daily 0.05° grid. Skin SSTs, depth-adjusted SSTs de-aliased with respect to the diurnal cycle, and SST anomalies are provided. Target applications of the dataset include: climate and ocean model evaluation; quantification of marine change and variability (including marine heatwaves); climate and ocean-atmosphere processes; and specific applications in ocean ecology, oceanography and geophysics.

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Section: Methodsmentioning

confidence: 99%

Section: Background and Summarymentioning

confidence: 99%

Satellite-based time-series of sea-surface temperature since 1981 for climate applications

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“…The foundation of variables (with the subscript fnd) is thus computed by using the mean from 1 to 5 am in each day, for example, SST fnd =

\overset{SST ((), 1 - 5 am)}{true}

. The DV of variables (with the first letter d) is evaluated by computing the difference of variables to the foundation in the previous night; for example, dSST = SST( t ) − SST fnd will be used to discuss the DV of SST (e.g., Bernie et al, ; Seo et al, ; Zhang et al, ), where t is the time in the same day of SST fnd .…”

Section: Methods For Evaluating DVmentioning

confidence: 99%

Magnitude and Phase of Diurnal SST Variations in the ACCESS‐S1 Model During the Suppressed Phase of the MJOs

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Diurnal variations of sea surface temperature (DV SST) can affect the heat and moisture fluxes at the air-sea interface during the suppressed phase of the MJO, thereby conditioning a favorable atmosphere environment for the subsequent MJO active phase. In order to better understand the causes and impacts of DV SST, and the modeling requirements for faithfully simulating these processes, we explore the DV SST and their influences on the air-sea fluxes during the suppressed phases of three MJO events using a global coupled model, ACCESS-S1, which has a 1-m vertical resolution near the ocean surface and coupling frequency of 1 hr. Compared with the MTSAT-1R satellite SST measurements, ACCESS-S1 is able to simulate the magnitude of the DV SST reliably but has a phase delay in simulated peak SST of~3 hr. Imposing an instant vertical mixing in the upper 10 m in ACCESS-S1 would suppress the DV SST by >1°K and decrease the peak (>30 W m −2 ) and daily averaged latent heat flux. On the other hand, changing the atmosphere-ocean coupling frequency within several hours mostly affects the DV SST phase instead of magnitude. Extreme DV SST (>1°K) occurs only when ocean mixing layer depth = 1 m. It implies that vertical resolution of ≤1 m near the sea surface is important for the coupled models to simulate both the magnitude and phase of DV SST accurately and thereby the air-sea heat fluxes in future model forecast of the MJO. Key Points:• Using the vertical resolution of 1 m near the ocean surface affects DV SST more than using high coupling frequency, e.g., 1 hr • Global coupled model ACCESS-S1can predict the DV SST magnitude reliably but with the phase shift of 3 hr • Effects of DV SST magnitude on the air-sea heat fluxes are more significant than the DV SST phase Supporting Information:• Supporting Information S1

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“…Different models of diurnal variability have been proposed (e.g., Filipiak et al, 2010) and evaluated (e.g., Høyer, 2013, 2014;Karagali et al, 2017;While et al, 2017;Zhang et al, 2018). Diurnal SST variability has been quantified by satellite (Gentemann et al, 2008) and in situ (Morak-Bozzo et al, 2016) observations, yet it is not fully resolved by forced ocean and coupled ocean-atmosphere models.…”

Section: Better Practical Quantification Of Diurnal Variabilitymentioning

confidence: 99%

Observational Needs of Sea Surface Temperature

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Sea surface temperature (SST) is a fundamental physical variable for understanding, quantifying and predicting complex interactions between the ocean and the atmosphere. Such processes determine how heat from the sun is redistributed across the global oceans, directly impacting large-and small-scale weather and climate patterns. The provision of daily maps of global SST for operational systems, climate modeling and the broader scientific community is now a mature and sustained service coordinated by the Group for High Resolution Sea Surface Temperature (GHRSST) and the CEOS SST Virtual Constellation (CEOS SST-VC). Data streams are shared, indexed, processed, quality controlled, analyzed, and documented within a Regional/Global Task Sharing (R/GTS) framework, which is implemented internationally in a distributed manner. Products rely on a combination of low-Earth orbit infrared and microwave satellite imagery, geostationary orbit infrared satellite imagery, and in situ data from moored and drifting buoys, Argo floats, and a suite of independent, fully characterized and traceable in situ measurements for product validation (Fiducial Reference Measurements, FRM). Research and development continues to tackle problems such as instrument calibration, algorithm development, diurnal variability, derivation of high-quality skin and depth temperatures, and areas of specific interest such as the high latitudes and coastal areas. In this white paper, we review progress versus the challenges we set out 10 years ago in a previous paper, highlight remaining and new research and development challenges for the next 10 years

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Comparison of SST Diurnal Variation Models Over the Tropical Warm Pool Region

Cited by 16 publications

References 49 publications

Satellite-based time-series of sea-surface temperature since 1981 for climate applications

Satellite-based time-series of sea-surface temperature since 1981 for climate applications

Magnitude and Phase of Diurnal SST Variations in the ACCESS‐S1 Model During the Suppressed Phase of the MJOs

Observational Needs of Sea Surface Temperature

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