scholarly journals Systematic Differences in Bucket Sea Surface Temperatures Caused by Misclassification of Engine Room Intake Measurements

2020 ◽  
Vol 33 (18) ◽  
pp. 7735-7753
Author(s):  
Duo Chan ◽  
Peter Huybers

AbstractDifferences in sea surface temperature (SST) biases among groups of bucket measurements in the International Comprehensive Ocean–Atmosphere Dataset, version 3.0 (ICOADS3.0), were recently identified that introduce offsets of as much as 1°C and have first-order implications for regional temperature trends. In this study, the origin of these groupwise offsets is explored through covariation between offsets and diurnal cycle amplitudes. Examination of an extended bucket model leads to expectations for offsets and amplitudes to covary in either sign, whereas misclassified engine room intake (ERI) temperatures invariably lead to negative covariance on account of ERI measurements being warmer and having a smaller diurnal amplitude. An analysis of ICOADS3.0 SST measurements that are inferred to come from buckets indicates that offsets after the 1930s primarily result from the misclassification of ERI measurements in points of five lines of evidence. 1) Prior to when ERI measurements become available in the 1930s, offset–amplitude covariance is weak and generally positive, whereas covariance is stronger and generally negative subsequently. 2) The introduction of ERI measurements in the 1930s is accompanied by a wider range of offsets and diurnal amplitudes across groups, with 3) approximately 20% of estimated diurnal amplitudes being significantly smaller than buoy and drifter observations. 4) Regressions of offsets versus amplitudes intersect independently determined end-member values of ERI measurements. 5) Offset-amplitude slopes become less negative across all regions and seasons between 1960 and 1980, when ERI temperatures were independently determined to become less warmly biased. These results highlight the importance of accurately determining measurement procedures for bias corrections and reducing uncertainty in historical SST estimates.

2015 ◽  
Vol 300 ◽  
pp. 434-446 ◽  
Author(s):  
Carl L. Amos ◽  
S. Martino ◽  
T.F. Sutherland ◽  
T. Al Rashidi

2018 ◽  
Vol 45 (1) ◽  
pp. 363-371 ◽  
Author(s):  
G. Carella ◽  
J. J. Kennedy ◽  
D. I. Berry ◽  
S. Hirahara ◽  
C. J. Merchant ◽  
...  

2008 ◽  
Vol 21 (20) ◽  
pp. 5304-5317 ◽  
Author(s):  
Hye-Mi Kim ◽  
Carlos D. Hoyos ◽  
Peter J. Webster ◽  
In-Sik Kang

Abstract The influence of sea surface temperature (SST) on the simulation and predictability of the Madden–Julian oscillation (MJO) is examined using the Seoul National University atmospheric general circulation model (SNU AGCM). Forecast skill was examined using serial climate simulations spanning eight different winter seasons with 30-day forecasts commencing every 5 days, giving a total of 184 thirty-day simulations. The serial runs were repeated using prescribing observed SST with monthly, weekly, and daily temporal resolutions. The mean SST was the same for all cases so that differences between experiments result from the different temporal resolutions of the SST boundary forcing. It is shown that high temporal SST frequency acts to improve 1) the MJO activity of 200-hPa velocity potential field over the entire Asian monsoon region at all lead times; 2) the percentage of filtered variance of the two leading EOF modes that explain the eastward propagation of MJO; 3) the power of the wavenumber 1 eastward propagating mode; and 4) the forecast skill of MJO, maintaining it for longer periods. However, the MJO phase relationship between MJO convection and SST, as is often the case with many atmosphere-only models, although well simulated at the beginning of forecast period becomes distorted rapidly as the forecast lead time increases, even with the daily SST forcing case. Comparison of AGCM simulations with coupled GCM (CGCM) integrations shows that ocean–atmosphere coupling improves considerably the phase relationship between SST and convection. The CGCM results reinforce that the MJO is a coupled phenomenon and suggest strongly the need of the ocean–atmosphere coupled processes to extend predictability.


2020 ◽  
Vol 20 (2) ◽  
pp. 129-141
Author(s):  
Tran Anh Tuan ◽  
Vu Hai Dang ◽  
Pham Viet Hong ◽  
Do Ngoc Thuc ◽  
Nguyen Thuy Linh ◽  
...  

In this article, the sea surface temperature trends and the influence of ENSO on the southwest sea of Vietnam were analyzed using the continuous satellite-acquired data sequence of SST in the period of 2002–2018. GIS and average statistical methods were applied to calculate the average monthly and seasonal sea surface temperature, the seasonal sea surface temperature anomalies for each year and for the whole study period. Subsequently, the changing trends of sea surface temperature in the northeast and southwest monsoon seasons were estimated using linear regression analysis. Research results indicated that the sea surface temperature changed significantly throughout the calendar year, in which the maximum and minimum sea surface temperature are 31oC in May and 26oC in January respectively. Sea surface temperature trends range from 0oC/year to 0.05oC/year during the Northeast monsoon season and from 0.025oC/year to 0.055oC/year during the southwest monsoon season. Results based on the Oceanic Niño Index (ONI) analysis also show that the sea surface temperature in the study area and adjacent areas is strongly influenced and significantly fluctuates during El Niño and La Niña episodes.


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