scholarly journals Subseafloor Temperature Variations Influenced by Variations in Bottom Water Temperature and Pressure: New High Resolution Observations and Implications

2019 ◽  
Vol 124 (1) ◽  
pp. 76-87 ◽  
Author(s):  
Earl E. Davis ◽  
Heinrich Villinger
Keyword(s):  
High Resolution ◽  
Water Temperature ◽  
Bottom Water ◽  
Bottom Water Temperature ◽  
Temperature Variations ◽  
Temperature And Pressure

scholarly journals Reconstructing bottom water temperatures from measurements of temperature and thermal diffusivity in marine sediments

Ocean Science ◽  
2015 ◽  
Vol 11 (4) ◽  
pp. 559-571
Author(s):  
F. Miesner ◽  
A. Lechleiter ◽  
C. Müller
Keyword(s):  
Thermal Properties ◽  
Water Temperature ◽  
North Sea ◽  
Bottom Water ◽  
Forward Model ◽  
Data Sets ◽  
Artificial Data ◽  
Bottom Water Temperature ◽  
Temperature Variations ◽  
The North

Abstract. Continuous monitoring of oceanic bottom water temperatures is a complicated task, even in relatively easy-to-access basins like the North or Baltic seas. Here, a method to determine annual bottom water temperature variations from inverse modeling of instantaneous measurements of temperatures and sediment thermal properties is presented. This concept is similar to climate reconstructions over several thousand years from deep borehole data. However, in contrast, the presented method aims at reconstructing the recent temperature history of the last year from sediment thermal properties and temperatures from only a few meters depth. For solving the heat equation, a commonly used forward model is introduced and analyzed: knowing the bottom water temperature variations for the preceding years and the thermal properties of the sediments, the forward model determines the sediment temperature field. The bottom water temperature variation is modeled as an annual cosine defined by the mean temperature, the amplitude and a phase shift. As the forward model operator is non-linear but low-dimensional, common inversion schemes such as the Newton algorithm can be utilized. The algorithms are tested for artificial data with different noise levels and for two measured data sets: from the North Sea and from the Davis Strait. Both algorithms used show stable and satisfying results with reconstruction errors in the same magnitude as the initial data error. In particular, the artificial data sets are reproduced with accuracy within the bounds of the artificial noise level. Furthermore, the results for the measured North Sea data show small variances and resemble the bottom water temperature variations recorded from a nearby monitoring site with relative errors smaller than 1 % in all parameters.

scholarly journals Reconstructing bottom water temperatures from measurements of temperature and thermal diffusivity in marine sediments

2014 ◽  
Vol 11 (5) ◽  
pp. 2391-2422
Author(s):  
F. Miesner ◽  
A. Lechleiter ◽  
C. Müller
Keyword(s):  
Steady State ◽  
Heat Flow ◽  
Water Temperature ◽  
Marine Sediments ◽  
Bottom Water ◽  
Measured Data ◽  
Temperature History ◽  
Artificial Data ◽  
Bottom Water Temperature ◽  
Steady State Heat

Abstract. Temperature fields in marine sediments are studied for various purposes. Often, the target of research is the steady state heat flow as a (possible) source of energy but there are also studies attempting to reconstruct bottom water temperature variations to understand more about climate history. The bottom water temperature propagates into the sediment to different depths, depending on the amplitude and period of the deviation. The steady state heat flow can only be determined when the bottom water temperature is constant while the bottom water temperature history can only be reconstructed when the deviation has an amplitude large enough or the measurements are taken in great depths. In this work, the aim is to reconstruct recent bottom water temperature history such as the last two years. To this end, measurements to depths of up to 6 m shall be adequate and amplitudes smaller than 1 K should be reconstructable. First, a commonly used forward model is introduced and analyzed: knowing the bottom water temperature deviation in the last years and the thermal properties of the sediments, the forward model gives the sediment temperature field. Next, an inversion operator and two common inversion schemes are introduced. The analysis of the inversion operator and both algorithms is kept short, but sources for further reading are given. The algorithms are then tested for artificial data with different noise levels and for two example data sets, one from the German North Sea and one from the Davis Strait. Both algorithms show good and stable results for artificial data. The achieved results for measured data have low variances and match to the observed oceanographic settings. Lastly, the desired and obtained accuracy are discussed. For artificial data, the presented method yields satisfying results. However, for measured data the interpretation of the results is more difficult as the exact form of the bottom water deviation is not known. Nevertheless, the presented inversion method seems rather promising due to its accuracy and stability for artificial data. Continuing to work on the development of more sophisticated models for the bottom water temperature, we hope to cover more different oceanographic settings in the future.

TEX 86 paleothermometer as an indication of bottom water temperature in the Yellow Sea

2015 ◽  
Vol 86 ◽  
pp. 19-31 ◽  
Author(s):  
Lei Xing ◽  
Julian P. Sachs ◽  
Wenxian Gao ◽  
Shuqing Tao ◽  
Xiaochen Zhao ◽  
...  
Keyword(s):  
Water Temperature ◽  
Yellow Sea ◽  
Bottom Water ◽  
The Yellow Sea ◽  
Bottom Water Temperature

Variance of Bottom Water Temperature at the Continental Margin of the Northern South China Sea

2021 ◽  
Vol 126 (2) ◽  
Author(s):  
Peng‐Qi Huang ◽  
Xian‐Rong Cen ◽  
Shuang‐Xi Guo ◽  
Yuan‐Zheng Lu ◽  
Sheng‐Qi Zhou ◽  
...  
Keyword(s):  
South China Sea ◽  
Water Temperature ◽  
Continental Margin ◽  
South China ◽  
Bottom Water ◽  
Northern South China Sea ◽  
Bottom Water Temperature ◽  
China Sea
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