scholarly journals Assessment and Improvement of Global Gridded Sea Surface Temperature Datasets in the Yellow Sea Using In Situ Ocean Buoy and Research Vessel Observations

2020 ◽  
Vol 12 (5) ◽  
pp. 759
Author(s):  
Kyungman Kwon ◽  
Byoung-Ju Choi ◽  
Sung-Dae Kim ◽  
Sang-Ho Lee ◽  
Kyung-Ae Park
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Yellow Sea ◽  
Bias Correction ◽  
Sea Surface ◽  
The Yellow Sea ◽  
In Situ Observation ◽  
Observation Data ◽  
Warm Bias

The sea surface temperature (SST) is essential data for the ocean and atmospheric prediction systems and climate change studies. Five global gridded sea surface temperature products were evaluated with independent in situ SST data of the Yellow Sea (YS) from 2010 to 2013 and the sources of SST error were identified. On average, SST from the gridded optimally interpolated level 4 (L4) datasets had a root mean square difference (RMSD) of less than 1 °C compared to the in situ observation data of the YS. However, the RMSD was relatively high (2.3 °C) in the shallow coastal region in June and July and this RMSD was mostly attributed to the large warm bias (>2 °C). The level 3 (L3) SST data were frequently missing in early summer because of frequent sea fog formation and a strong (>1.2 °C/12 km) spatial temperature gradient across the tidal mixing front in the eastern YS. The missing data were optimally interpolated from the SST observation in offshore warm water and warm biased SST climatology in the region. To fundamentally improve the accuracy of the L4 gridded SST data, it is necessary to increase the number of SST observation data in the tidally well mixed region. As an interim solution to the warm bias in the gridded SST datasets in the eastern YS, the SST climatology for the optimal interpolation can be improved based on long-term in situ observation data. To reduce the warm bias in the gridded SST products, two bias correction methods were suggested and compared. Bias correction methods using a simple analytical function and using climatological observation data reduced the RMSD by 19–29% and 37–49%, respectively, in June.

scholarly journals CMIP5-Based Projection of Decadal and Seasonal Sea Surface Temperature Variations in East China Shelf Seas

2021 ◽  
Vol 9 (4) ◽  
pp. 367
Author(s):  
Huiqiang Lu ◽  
Chuan Xie ◽  
Cuicui Zhang ◽  
Jingsheng Zhai
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Sea Surface ◽  
The Yellow Sea ◽  
East China ◽  
Cmip5 Models ◽  
Projection Data ◽  
Observation Data ◽  
Model Intercomparison ◽  
Shelf Seas

The East China Shelf Seas, comprising the Bohai Sea, the Yellow Sea, and the shelf region of East China Sea, play significant roles among the shelf seas of the Western North Pacific Ocean. The projection of sea surface temperature (SST) changes in these regions is a hot research topic in marine science. However, this is a very difficult task due to the lack of available long-term projection data. Recently, with the high development of simulation technology based on numerical models, the model intercomparison projects, e.g., Phase 5 of the Climate Model Intercomparison Project (CMIP5), have become important ways of understanding climate changes. CMIP5 provides multiple models that can be used to estimate SST changes by 2100 under different representative concentration pathways (RCPs). This paper developed a CMIP5-based SST investigation framework for the projection of decadal and seasonal variation of SST in East China Shelf Seas by 2100. Since the simulation results of CMIP5 models may have degrees of errors, this paper uses hydrological observation data from World Ocean Atlas 2018 (WOA18) for model validation and correction. This paper selects seven representative ones including ACCESS1.3, CCSM4, FIO-ESM, CESM1-CAM5, CMCC-CMS, NorESM1-ME, and Max Planck Institute Earth System Model of medium resolution (MPI-ESM-MR). The decadal and seasonal SST changes in the next 100 years (2030, 2060, 2090) are investigated by comparing with the present analysis in 2010. The experimental results demonstrate that SST will increase significantly by 2100: the decadal SST will increase by about 1.55 °C, while the seasonal SST will increase by 1.03–1.95 °C.

scholarly journals Optimal Configuration Method of Sampling Points Based on Variability of Sea Surface Temperature

2017 ◽  
Vol 2017 ◽  
pp. 1-10
Author(s):  
Chang Liu ◽  
Yuning Lei ◽  
Feng Gao ◽  
Meizhen Zhao
Keyword(s):  
Spatial Variability ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Sea Surface ◽  
Optimal Configuration ◽  
In Situ Observation ◽  
Distribution Method ◽  
Sampling Points ◽  
Compression Sensing

In situ observation is one of the most direct and efficient ways to understand the ocean, but it is usually limited in terms of spatial and temporal coverage. The determination of optimal sampling strategies that effectively utilize available resources to maximize the information content of the collected ocean data is becoming an open problem. The historical sea surface temperature (SST) dataset contains the spatial variability information of SST, and this prior knowledge can be used to optimize the configuration of sampling points. Here, a configuration method of sampling points based on the variability of SST is studied. Firstly, in order to get the spatial variability of SST in the ocean field to be sampled, the historical SST data of the field is analyzed. Then, K-means algorithm is used to cluster the subsampled fields to make the configuration of sampling points more suitable. Finally, to evaluate the sampling performance of the new configuration method of sampling points, the SST field is reconstructed by the method based on compression sensing algorithm. Results show that the proposed optimal configuration method of sampling points significantly outperforms the traditional random sampling points distribution method in terms of reconstruction accuracy. These results provide a new method for configuring sampling points of ocean in situ observation with limited resources.

scholarly journals Influence of Sea Surface Temperature on a Mesoscale Convective System producing Extreme Rainfall over the Yellow Sea

2021 ◽  
Author(s):  
Yunhee Kang ◽  
Jong-Hoon Jeong ◽  
Dong-In Lee
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Yellow Sea ◽  
Extreme Rainfall ◽  
Sea Surface ◽  
The Yellow Sea ◽  
Convective System ◽  
Mesoscale Convective ◽  
Upper Level ◽  
Convective Cells

AbstractAn extreme rainfall-producing linear mesoscale convective systems (MCSs) occurred over the Yellow Sea, Korea, on 13 August 2012, producing 430 mm of rainfall in less than 12 h, causing devastating flash floods and landslides. To understand the causative processes underlying this event, we examined the linear MCSs formation and development mechanisms using observations and cloud-resolving models. The organized linear MCSs produced extreme rainfall at Gunsan in a favorable large-scale environment. The synoptic environment showed the stationary surface front elongating from China to Korea; a southwesterly low-level jet transported the warm, moist air from low latitudes towards the front. In the upper-level synoptic environment, the trough and entrance regions of the upper-level jet were north of the heavy rainfall, promoting the development of convection. The extreme rainfall over the Gunsan area resulted from the back-building mode of the MCSs, in which new convective cells continued to pass over the same area while the entire convective system was nearly stationary. The sea surface temperature (SST) during the extreme rainfall events was abnormally 1°C higher than the 30-year climatological mean, and a local warm pool (>28.5°C) existed where the convective cells were continuously initiated. Cloud-resolving models simulated the low-level convergence, and the latent heat flux was large in the local warm SST field. These induced MCSs formation and development, contributing to a significant rainfall increase over the Yellow Sea. The terrain’s influence on the large rainfall amount in the area was also noted.

scholarly journals Sea Surface Temperature Daytime Climate Analyses Derived from Aerosol Bias-Corrected Satellite Data

2006 ◽  
Vol 19 (3) ◽  
pp. 410-428 ◽  
Author(s):  
Nicholas R. Nalli ◽  
Richard W. Reynolds
Keyword(s):  
High Resolution ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Satellite Data ◽  
Bias Correction ◽  
Tropical Rainfall Measuring Mission ◽  
Stratospheric Aerosol ◽  
Sea Surface ◽  
Diurnal Warming

Abstract This paper describes daytime sea surface temperature (SST) climate analyses derived from 16 years (1985–2000) of reprocessed Advanced Very High Resolution Radiometer (AVHRR) Pathfinder Atmospheres (PATMOS) multichannel radiometric data. Two satellite bias correction methods are employed: the first being an aerosol correction, the second being an in situ correction of satellite biases. The aerosol bias correction is derived from observed statistical relationships between the slant-path aerosol optical depth and AVHRR multichannel SST (MCSST) depressions for elevated levels of tropospheric and stratospheric aerosol. Weekly analyses of SST are produced on a 1° equal-angle grid using optimum interpolation (OI) methodology. Four separate OI analyses are derived based on 1) MCSST without satellite bias correction, 2) MCSST with aerosol satellite bias correction, 3) MCSST with in situ correction of satellite biases, and 4) MCSST with both aerosol and in situ corrections of satellite biases. These analyses are compared against the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager OI SST, along with the extended reconstruction SST in situ analysis product. The OI analysis 1 exhibits significant negative and positive biases. Analysis 2, derived exclusively from satellite data, reduces globally the negative bias associated with elevated atmospheric aerosol, and subsequently reveals pronounced variations in diurnal warming consistent with recently published works. Analyses 3 and 4, derived from in situ correction of satellite biases, alleviate biases (positive and negative) associated with both aerosol and diurnal warming, and also reduce the dispersion. The PATMOS OISST 1985–2000 daytime climate analyses presented here provide a high-resolution (1° weekly) empirical database for studying seasonal and interannual climate processes.

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