Multidecadal variability in Mediterranean Sea surface temperature and its sources

Author(s):  
Xiaoqin Yan ◽  
Youmin Tang
2011 ◽  
Vol 24 (16) ◽  
pp. 4385-4401 ◽  
Author(s):  
Salvatore Marullo ◽  
Vincenzo Artale ◽  
Rosalia Santoleri

Abstract Two sea surface temperature (SST) time series, the Extended Reconstructed SST version 3 (ERSST.v3) and the Hadley Centre Sea Ice and Sea Surface Temperature dataset (HadISST), are used to investigate SST multidecadal variability in the Mediterranean Sea and to explore possible connections with other regions of the global ocean. The consistency between these two time series and the original International Comprehensive Ocean–Atmosphere Dataset version 2.5 (ICOADS 2.5) over the Mediterranean Sea is investigated, evaluating differences from monthly to multidecadal scales. From annual to longer time scales, the two time series consistently describe the same trends and multidecadal oscillations and agree with Mediterranean ICOADS SSTs. At monthly time scales the two time series are less consistent with each other because of the evident annual cycle that characterizes their difference. The subsequent analysis of the Mediterranean annual SST time series, based on lagged-correlation analysis, multitaper method (MTM), and singular spectral analysis (SSA), revealed the presence of a significant oscillation with a period of about 70 yr, very close to that of the Atlantic multidecadal oscillation (AMO). An extension of the analysis to other World Ocean regions confirmed that the predominance of this multidecadal signal with respect to longer period trends is a unique feature of the Mediterranean and North Atlantic Ocean, where it reaches its maximum at subpolar latitudes. Signatures of multidecadal oscillations are also found in the global SST time series after removing centennial and longer-term components. The analysis also reveals that Mediterranean SST and North Atlantic indices are significantly correlated and coherent for periods longer than about 40 yr. For time scales in the range 40–55 yr the coherence between the Mediterranean and subpolar gyre temperatures is higher than the coherence between the Mediterranean SST and North Atlantic Oscillation (NAO) or AMO. Finally, the results of the analysis are discussed in the light of possible climate mechanisms that can couple the Mediterranean Sea with the North Atlantic and the Global Ocean.


2007 ◽  
Vol 152 (2) ◽  
pp. 351-361 ◽  
Author(s):  
Stefano Goffredo ◽  
Erik Caroselli ◽  
Elettra Pignotti ◽  
Guido Mattioli ◽  
Francesco Zaccanti

2021 ◽  
Author(s):  
Evangelos Moschos ◽  
Alexandre Stegner ◽  
Olivier Schwander ◽  
Patrick Gallinari

<p>Mesoscale eddies are oceanic vortices with radii of tens of kilometers, which live on for several months or even years. They carry large amounts of heat, salt, nutrients, and pollutants from their regions of formation to remote areas, making it important to detect and track them. Using satellite altimetric maps, mesoscale eddies have been detected via remote sensing with advancing performance over the last years <strong>[1]</strong>. However, the spatio-temporal interpolation between satellite track measurements, needed to produce these maps, induces a limit to the spatial resolution (1/12° in the Med Sea) and large amounts of uncertainty in non-measured areas.</p><p>Nevertheless, mesoscale oceanic eddies also have a visible signature on other satellite imagery such as Sea Surface Temperature (SST), portraying diverse patterns of coherent vortices, temperature gradients, and swirling filaments. Learning the regularities of such signatures defines a challenging pattern recognition task, due to their complex structure but also to the cloud coverage which can corrupt a large fraction of the image.</p><p>We introduce a novel Deep Learning approach to classify sea temperature eddy signatures <strong>[2]</strong>. We create a large dataset of SST patches from satellite imagery in the Mediterranean Sea, containing Anticyclonic, Cyclonic, or No Eddy signatures, based on altimetric eddy detections of the DYNED-Atlas <strong>[3]</strong>. Our trained Convolutional Neural Network (CNN) can differentiate between these signatures with an accuracy of more than 90%, robust to a high level of cloud coverage.</p><p>We furtherly evaluate the efficiency of our classifier on SST patches extracted from oceanographic numerical model outputs in the Mediterranean Sea. Our promising results suggest that the CNN could complement the detection, tracking, and prediction of the path of mesoscale oceanic eddies.</p><p><strong>[1]</strong> <em>Chelton, D. B., Schlax, M. G. and Samelson, R. M. (2011). Global observations of nonlinear mesoscale eddies. Progress in oceanography, 91(2),167-216.</em></p><p><strong>[2]</strong> <em>E. Moschos, A. Stegner, O. Schwander and P. Gallinari, "Classification of Eddy Sea Surface Temperature Signatures Under Cloud Coverage," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 13, pp. 3437-3447, 2020, doi: 10.1109/JSTARS.2020.3001830.</em></p><p><strong>[3]</strong> <em>https://www.lmd.polytechnique.fr/dyned/</em></p>


2020 ◽  
Vol 12 (1) ◽  
pp. 132 ◽  
Author(s):  
Andrea Pisano ◽  
Salvatore Marullo ◽  
Vincenzo Artale ◽  
Federico Falcini ◽  
Chunxue Yang ◽  
...  

Estimating long-term modifications of the sea surface temperature (SST) is crucial for evaluating the current state of the oceans and to correctly assess the impact of climate change at regional scales. In this work, we analyze SST variations within the Mediterranean Sea and the adjacent Northeastern Atlantic box (west of the Strait of Gibraltar) over the last 37 years, by using a satellite-based dataset from the Copernicus Marine Environment Monitoring Service (CMEMS). We found a mean warming trend of 0.041 ± 0.006 ∘ C/year over the whole Mediterranean Sea from 1982 to 2018. The trend has an uneven spatial pattern, with values increasing from 0.036 ± 0.006 ∘ C/year in the western basin to 0.048 ± 0.006 ∘ C/year in the Levantine–Aegean basin. The Northeastern Atlantic box and the Mediterranean show a similar trend until the late 1990s. Afterwards, the Mediterranean SST continues to increase, whereas the Northeastern Atlantic box shows no significant trend, until ~2015. The observed change in the Mediterranean Sea affects not only the mean trend but also the amplitude of the Mediterranean seasonal signal, with consistent relative increase and decrease of summer and winter mean values, respectively, over the period considered. The analysis of SST changes occurred during the “satellite era” is further complemented by reconstructions also based on direct in situ SST measurements, i.e., the Extended Reconstructed SST (ERSST) and the Hadley Centre Sea Ice and Sea Surface Temperature dataset (HadISST), which go back to the 19th century. The analysis of these longer time series, covering the last 165 years, indicates that the increasing Mediterranean trend, observed during the CMEMS operational period, is consistent with the Atlantic Multidecadal Oscillation (AMO), as it closely follows the last increasing period of AMO. This coincidence occurs at least until 2007, when the apparent onset of the decreasing phase of AMO is not seen in the Mediterranean SST evolution.


2017 ◽  
Vol 30 (18) ◽  
pp. 7317-7337 ◽  
Author(s):  
A. Bellucci ◽  
A. Mariotti ◽  
S. Gualdi

Abstract Results from a study inspecting the origins of multidecadal variability in the North Atlantic sea surface temperature (NASST) are presented. The authors target in particular the 1940–75 “warm-to-cold” transition, an event that is generally framed in the context of the longer-term Atlantic multidecadal variability (AMV) cycle, in turn associated with the Atlantic meridional overturning circulation (AMOC) internal variability. Here the authors examine the ability of uninitialized, historical integrations from the phase 5 of the Coupled Model Intercomparison Project (CMIP5) archive to retrospectively reproduce this specific episode of twentieth-century climatic history, under a hierarchy of forcing conditions. For this purpose, both standard and so-called historical Misc CMIP5 simulations of the historical climate (combining selected natural and anthropogenic forcings) are exploited. Based on this multimodel analysis, evidence is found for a significant influence of anthropogenic agents on multidecadal sea surface temperature (SST) fluctuations across the Atlantic sector, suggesting that anthropogenic aerosols and greenhouse gases might have played a key role in the 1940–75 North Atlantic cooling. However, the diagnosed forced response in CMIP5 models appears to be affected by a large uncertainty, with only a limited subset of models displaying significant skill in reproducing the mid-twentieth-century NASST cooling. Such uncertainty originates from the existence of well-defined behavioral clusters within the analyzed CMIP5 ensembles, with the bulk of the models splitting into two main clusters. Such a strong polarization calls for some caution when using a multimodel ensemble mean in climate model analyses, as averaging across fairly distinct model populations may result, through mutual cancellation, in a rather artificial description of the actual multimodel ensemble behavior. A potentially important role for both anthropogenic aerosols and greenhouse gases with regard to the observed North Atlantic multidecadal variability has clear implications for decadal predictability and predictions. The uncertainty associated with alternative aerosol and greenhouse gas emission scenarios should be duly accounted for in designing a common protocol for coordinated decadal forecast experiments.


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