The influence of fluctuating SST by satellite data in the Barents and Norwegian seas during periods of early ontogenesis NEA cod in 1998-2016 on its strength.

2021 ◽  
Author(s):  
George Vanyushin ◽  
Tatiana Bulatova
Keyword(s):  
Comparative Analysis ◽  
Solar Activity ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Satellite Data ◽  
Temperature Data ◽  
Early Ontogenesis ◽  
Sea Surface ◽  
Solar Cycles ◽  
Cycle 24

<p><strong>The influence of fluctuating SST by satellite data in the Barents and Norwegian seas during periods of early ontogenesis NEA cod in 1998-2016 on its strength.</strong></p><p><strong> </strong></p><p>G.P. Vanyushin and T.V. Bulatova</p><p> </p><p>Russian Federal Research Institute of Fisheries and Oceanography (VNIRO), Moscow, Russia</p><p>e-mail: [email protected]</p><p><strong> </strong></p><p>Abstract</p><p>The paper presents preliminary results of the analysis of the influence of fluctuating seasonal sea surface temperature in the Barents and Norwegian seas during early ontogenesis of the Northeast Atlantic (NEA) cod in the period 1998-2016 on its future strength of generations at age 3+ accordingly in 2001-2019. The temperature data for control zones of these seas (May-October) to 1998-2016 were obtained from the analysis of daily infrared information by the NOAA series of satellites and quasisynchronous temperature data "in situ" from ships, buoys and coastal stations. Data about the strength of NEA cod generations at age 3+ to 2001-2019 was taken from ICES reports. Real comparative analysis was conducted for following three-zones: 1 - Murman-Novaya Zemlya zone (69-76N 30-54E), 2 – North Cape zone (71-76N 17-30E), 3 – West-Spitsbergen zone (69-76N 11-17E). Direct comparative analysis of these indicators revealed very low relationship between them, so R(less) 0,1 for every zone and the whole period. That is why we tried to use the data about distribution of monthly solar activity during solar cycles 23-24 in considering years. The border between these solar cycles is 2008-2009. New comparative analysis of the same indicators separated by cycle 23 (1998-2008 solar activity) and by cycle 24 (2009-2016 solar activity) revealed rather opposite results. In first case (cycle 23) R was received for zone 1 +0,72, zone 2 +0,62 and for zone 3 +0,50, but for cycle 24 R was accordingly equal for zone 1 -0,60, zone 2 -0,66 zone 3 -0,38. So, the influence of seasonal temperature conditions in the Barents and Norwegian seas during 1998-2016 on the strength of new NEA cod generations at age 3+ to 2001-2019 changed its sign on border between 23 and 24 cycles of solar activity for considering years. Perhaps, obtained dependence between these indicators is fairly only for this period of time. For all that ought to note intensification of different character influence of solar activity these cycles towards east.</p><p> </p><p>Keywords: satellite monitoring, sea surface temperature (SST), the North-East Atlantic (NEA) cod generations, solar activity, comparative analysis.</p>

scholarly journals Atmosphere–ocean interactions in the Greenland Sea during solar cycles 23–24, 2002–2011

2015 ◽  
Vol 12 (1) ◽  
pp. 103-134
Author(s):  
P. E. Binns
Keyword(s):  
Temperature Field ◽  
Solar Activity ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
North Atlantic ◽  
Sea Surface ◽  
Temperature Fields ◽  
Solar Cycles ◽  
Greenland Sea ◽  
Surface Temperature Field

Abstract. Relationships between solar activity and climate in the North Atlantic region have long been reported and, more recently, mechanisms have been proposed to explain these. Normally such relationships are tested over decadal time scales. Here, daily sea surface temperature fields bridging the period of exceptionally low solar activity between solar cycles 23 and 24 have been analysed. The day-to-day variability of the fields has been measured and the fields have been classified, using cluster analysis. The main water masses are clearly expressed, together with detail of their interactions. Three features relate to the level of solar activity. First, there is a statistically significant difference in the day-to-day variability of the sea surface temperature field between the period of lowest solar activity and the remaining periods. Second, during the transition from summer to winter, there are systematic, inter-annual changes in the day-to-day variability of the sea surface temperature field. Third, the forms of the late summer temperature fields exhibit symmetry about the years of lowest solar activity. These features are attributable to variability in the passage of weather systems. The influence on North Atlantic surface climate of variations in the solar ultraviolet band acting through the stratosphere has been reported in a number of studies. This provides a credible mechanism for solar activity influencing sea surface temperatures in the Greenland Sea.

scholarly journals DINCAE 1.0: a convolutional neural network with error estimates to reconstruct sea surface temperature satellite observations

2020 ◽  
Vol 13 (3) ◽  
pp. 1609-1622 ◽  
Author(s):  
Alexander Barth ◽  
Aida Alvera-Azcárate ◽  
Matjaz Licer ◽  
Jean-Marie Beckers
Keyword(s):  
Neural Network ◽  
Missing Data ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Error Variance ◽  
Temperature Data ◽  
Satellite Observations ◽  
Sea Surface ◽  
Function Decomposition ◽  
Surface Temperature Data

Abstract. A method to reconstruct missing data in sea surface temperature data using a neural network is presented. Satellite observations working in the optical and infrared bands are affected by clouds, which obscure part of the ocean underneath. In this paper, a neural network with the structure of a convolutional auto-encoder is developed to reconstruct the missing data based on the available cloud-free pixels in satellite images. Contrary to standard image reconstruction with neural networks, this application requires a method to handle missing data (or data with variable accuracy) in the training phase. The present work shows a consistent approach which uses the satellite data and its expected error variance as input and provides the reconstructed field along with its expected error variance as output. The neural network is trained by maximizing the likelihood of the observed value. The approach, called DINCAE (Data INterpolating Convolutional Auto-Encoder), is applied to a 25-year time series of Advanced Very High Resolution Radiometer (AVHRR) sea surface temperature data and compared to DINEOF (Data INterpolating Empirical Orthogonal Functions), a commonly used method to reconstruct missing data based on an EOF (empirical orthogonal function) decomposition. The reconstruction error of both approaches is computed using cross-validation and in situ observations from the World Ocean Database. DINCAE results have lower error while showing higher variability than the DINEOF reconstruction.

scholarly journals Can sparse proxy data constrain the strength of the Atlantic meridional overturning circulation?

2014 ◽  
Vol 7 (1) ◽  
pp. 419-432 ◽  
Author(s):  
T. Kurahashi-Nakamura ◽  
M. Losch ◽  
A. Paul
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Atlantic Meridional Overturning Circulation ◽  
Temperature Data ◽  
Meridional Overturning Circulation ◽  
Sea Surface ◽  
Proxy Data ◽  
Overturning Circulation ◽  
Meridional Overturning ◽  
Surface Temperature Data

Abstract. In a feasibility study, the potential of proxy data for the temperature and salinity during the Last Glacial Maximum (LGM, about 19 000 to 23 000 years before present) in constraining the strength of the Atlantic meridional overturning circulation (AMOC) with a general ocean circulation model was explored. The proxy data were simulated by drawing data from four different model simulations at the ocean sediment core locations of the Multiproxy Approach for the Reconstruction of the Glacial Ocean surface (MARGO) project, and perturbing these data with realistic noise estimates. The results suggest that our method has the potential to provide estimates of the past strength of the AMOC even from sparse data, but in general, paleo-sea-surface temperature data without additional prior knowledge about the ocean state during the LGM is not adequate to constrain the model. On the one hand, additional data in the deep-ocean and salinity data are shown to be highly important in estimating the LGM circulation. On the other hand, increasing the amount of surface data alone does not appear to be enough for better estimates. Finally, better initial guesses to start the state estimation procedure would greatly improve the performance of the method. Indeed, with a sufficiently good first guess, just the sea-surface temperature data from the MARGO project promise to be sufficient for reliable estimates of the strength of the AMOC.

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