Paper
18 December 2019 Influence of the uncertainty of the sea level data for the Pleistocene glacial cycles on the analysis of the subsea sediments thermal state
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Proceedings Volume 11208, 25th International Symposium on Atmospheric and Ocean Optics: Atmospheric Physics; 112086Q (2019) https://doi.org/10.1117/12.2539017
Event: XXV International Symposium, Atmospheric and Ocean Optics, Atmospheric Physics, 2019, Novosibirsk, Russian Federation
Abstract
The estimates of the uncertainty for the model simulated subsea permafrost characteristics relative to the uncertainty of paleoclimatic reconstructions of ocean level are obtained. This is done by using the model for thermophysical processes in the subsea sediments. This model is driven by four time series of temperature at the sediment top, TB, which is constructed for the last 400 kyr by using different combinations of the same reconstruction of the past surface air temperature but different sea level reconstructions. At each time instant t and each variable Y, the uncertainty metric is defined as a ratio ▵Y (t) / Ym(t), where ▵Y (t) is spread of the values of Y for different TB time series, and Ym(t) is the mean of Y over different realizations corresponding to different TB. The root-mean-square calculated value of thus defined metric for different time intervals is ≤ 50% for permafrost base depth with the exception of isolated time intervals and / or the deepest part of the shelf. This uncertainty is not symmetric with respect to the sign of the sea level uncertainty. In turn, uncertainty for the hydrate stability zone thickness is small for shallow shelf but becomes pronounced for intermediate and deep shelves. The most uncertainty is due to uncertainty of dates for oceanic regressions and transgressions.
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Valentina V. Malakhova and Alexey V. Eliseev "Influence of the uncertainty of the sea level data for the Pleistocene glacial cycles on the analysis of the subsea sediments thermal state", Proc. SPIE 11208, 25th International Symposium on Atmospheric and Ocean Optics: Atmospheric Physics, 112086Q (18 December 2019); https://doi.org/10.1117/12.2539017
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KEYWORDS
Process modeling

Methane

Atmospheric modeling

Atmospheric physics

Climate change

Climatology

Computational mathematics

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