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 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 A Novel Method for Sea Surface Temperature Prediction Based on Deep Learning

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Xuan Yu ◽  
Suixiang Shi ◽  
Lingyu Xu ◽  
Yaya Liu ◽  
Qingsheng Miao ◽  
...  
Keyword(s):  
Time Series ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Root Mean Square ◽  
Spatial Information ◽  
Sea Surface ◽  
The Yellow Sea ◽  
Percentage Error ◽  
Good Prediction ◽  
Mean Square

Sea surface temperature (SST) forecasting is the task of predicting future values of a given sequence using historical SST data, which is beneficial for observing and studying hydroclimatic variability. Most previous studies ignore the spatial information in SST prediction and the forecasting models have limitations to process the large-scale SST data. A novel model of SST prediction integrated Deep Gated Recurrent Unit and Convolutional Neural Network (DGCnetwork) is proposed in this paper. The DGCnetwork has a compact structure and focuses on learning deep long-term dependencies in SST time series. Temporal information and spatial information are all included in our procedure. Differential Evolution algorithm is applied in order to configure DGCnetwork’s optimum architecture. Optimum Interpolation Sea Surface Temperature (OISST) data is selected to conduct experiments in this paper, which has good temporal homogeneity and feature resolution. The experiments demonstrate that the DGCnetwork significantly obtains excellent forecasting result, predicting SST by different lengths flexibly and accurately. On the East China Sea dataset and the Yellow Sea dataset, the accuracy of the prediction results is above 98% on the whole and all mean absolute error (MAE) values are lower than 0.33°C. Compared with the other models, root mean square error (RMSE), root mean square percentage error (RMSPE), and mean absolute percentage Error (MAPE) of the proposed approach reduce at least 0.1154, 0.2594, and 0.3938. The experiments of SST time series show that the DGCnetwork model maintains good prediction results, better performance, and stronger stability, which has reached the most advanced level internationally.

scholarly journals Assessment of a Coastal Offshore Wind Climate by Means of Mesoscale Model Simulations Considering High-Resolution Land Use and Sea Surface Temperature Data Sets

Atmosphere ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 379 ◽  
Author(s):  
Yuka Kikuchi ◽  
Masato Fukushima ◽  
Takeshi Ishihara
Keyword(s):  
Land Use ◽  
Wind Speed ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Mesoscale Model ◽  
Horizontal Resolution ◽  
Sea Surface ◽  
Offshore Wind ◽  
Observation Data ◽  
Wind Climate

In this study, offshore wind climate assessments are carried out by using mesoscale model Weather Research and Forecasting (WRF) and validated by measurement at a demonstration site located 3.1 km offshore of Choshi. An optimal nudging method is investigated by using offshore and meteorological observations. The land-use datasets are then created from a higher-resolution land-use data by using a maximum area sampling scheme according to the horizontal resolution of the mesoscale model. Finally, the sea surface temperature datasets are corrected by observation data. It is found that the relative error of annual wind speed is reduced from 7.3% to 2.2% and the correlation coefficient between predicted and measured wind speed is improved from 0.80 to 0.84 by considering the effects of land-use and sea surface temperature.

Sign in / Sign up

Export Citation Format

Share Document