scholarly journals Surface water temperature, salinity, and density changes in the northeast Atlantic during the last 45,000 years: Heinrich events, deep water formation, and climatic rebounds

1995 ◽  
Vol 10 (3) ◽  
pp. 527-544 ◽  
Author(s):  
M. A. Maslin ◽  
N. J. Shackleton ◽  
U. Pflaumann
Keyword(s):  
Surface Water ◽  
Water Temperature ◽  
Deep Water ◽  
Surface Water Temperature ◽  
Deep Water Formation ◽  
Northeast Atlantic ◽  
Heinrich Events ◽  
Water Formation

Glacial northeast Atlantic surface water PCO2: Productivity and deep-water formation

1997 ◽  
Vol 144 (1-3) ◽  
pp. 177-190 ◽  
Author(s):  
M.A. Maslin ◽  
E. Thomas ◽  
N.J. Shackleton ◽  
M.A. Hall ◽  
D. Seidov
Keyword(s):  
Surface Water ◽  
Deep Water ◽  
Deep Water Formation ◽  
Northeast Atlantic ◽  
Water Formation

Southern ocean–sea-ice interaction: implications for climate and modelling

1990 ◽  
Vol 81 (4) ◽  
pp. 397-405 ◽  
Author(s):  
Douglas G. Martinson
Keyword(s):  
Surface Water ◽  
Sea Ice ◽  
Water Column ◽  
Deep Water ◽  
Stable Region ◽  
Deep Water Formation ◽  
Sea Ice Thickness ◽  
Water Column Stability ◽  
Water Formation ◽  
Ocean Region

ABSTRACTThe ocean/sea-ice interaction of the Antarctic open ocean region is described through a one-dimensional model. The model includes processes responsible for maintaining stability in this marginally stable region and reveals the importance of the various processes controlling deep water formation/ventilation and sea-ice thickness and their sensitivity to climate change. This information is used to estimate changes, as they impact water column stability, induced by glacial conditions. Increased stability is conducive to greater ice cover and less deep water formation/ventilation; decreased stability conducive to the opposite.Sensitivity studies show that the system is destabilised given: (1) shallowing of the pycnocline (induced by increased gyre vigor); (2) decrease in the ratio of heat to salt through the pycnocline (induced by introducing a colder and/or saltier deep water or by increasing the salinity of the surface water); (3) decreased pycnocline strength (induced by a fresher deep water or saltier surface water) and (4) increased atmospheric heat loss. Most of the assumed glacial conditions drive the system toward destabilisation, but the critical effect of changes in NADW characteristics depends strongly on the temperature and salinity of the replacement water. The importance of this deep water influence is evident today—as little as 3Wm−2 in the upper ocean heat balance or an additional 15 cm of ice growth is sufficient to overturn the water column in some regions.

scholarly journals Surface Water Temperature Predictions at a Mid-Latitude Reservoir under Long-Term Climate Change Impacts Using a Deep Neural Network Coupled with a Transfer Learning Approach

Water ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1109
Author(s):  
Nobuaki Kimura ◽  
Kei Ishida ◽  
Daichi Baba
Keyword(s):  
Climate Change ◽  
Surface Water ◽  
Water Temperature ◽  
Transfer Learning ◽  
Air Temperature ◽  
Climate Models ◽  
Temporal Variations ◽  
Training Data ◽  
Surface Water Temperature

Long-term climate change may strongly affect the aquatic environment in mid-latitude water resources. In particular, it can be demonstrated that temporal variations in surface water temperature in a reservoir have strong responses to air temperature. We adopted deep neural networks (DNNs) to understand the long-term relationships between air temperature and surface water temperature, because DNNs can easily deal with nonlinear data, including uncertainties, that are obtained in complicated climate and aquatic systems. In general, DNNs cannot appropriately predict unexperienced data (i.e., out-of-range training data), such as future water temperature. To improve this limitation, our idea is to introduce a transfer learning (TL) approach. The observed data were used to train a DNN-based model. Continuous data (i.e., air temperature) ranging over 150 years to pre-training to climate change, which were obtained from climate models and include a downscaling model, were used to predict past and future surface water temperatures in the reservoir. The results showed that the DNN-based model with the TL approach was able to approximately predict based on the difference between past and future air temperatures. The model suggested that the occurrences in the highest water temperature increased, and the occurrences in the lowest water temperature decreased in the future predictions.

Impact of climate warming on the surface water temperature of plateau lake

2021 ◽  
Author(s):  
Zongqi Peng ◽  
Jiaying Yang ◽  
Yi Luo ◽  
Kun Yang ◽  
Chunxue Shang
Keyword(s):  
Surface Water ◽  
Water Temperature ◽  
Climate Warming ◽  
Surface Water Temperature ◽  
Plateau Lake
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