RT Journal Article
T1 Potential SST drivers for Chlorophyll-a variability in the Alboran Sea: a source for seasonal predictability?
A1 López-Parages, Jorge
A1 Gómara Cardalliaguet, Íñigo
A1 Rodríguez Fonseca, María Belén
A1 García Lafuente, Jesús
AB This study investigates the link between large-scale variability modes of the sea surface temperature (SST) and the surface chlorophyll-a (Chl-a) concentration in spring along the northern flank of the Alboran Sea. To this aim, surface satellite-derived products of SST and Chl-a, together with atmospheric satellite variables, are used. Our results indicate that both the tropical North Atlantic and El Niño Southern Oscillation (ENSO) could trigger the development of anomalous distribution patterns of Chl-a in spring in northern Alboran. This anomalous feature of Chl-a is, in turn, associated with the alteration of the usual upwelling taking place in northern Alboran at that time of the year. The skill of the related SST signals, over the tropical North Atlantic and the tropical Pacific, as predictors of the aforementioned Chl-aresponse inAlboran,has also been assessed through a statistical prediction model with leave-one-out cross-validation. Our results confirm the predictive skill of ENSO to realistically estimate the coastal Chl-a concentration in spring in northern Alboran. In particular, during the El Niño/La Niña years, this Chl-a response can be robustly predicted with 4 months in advance. On the other hand, the tropical North Atlantic SSTs allow to significantly predict, up to 7 months in advance, the Chl-a concentration in spring offshore, in particular by the north of the Western andtheEastern Alboran gyres. The results presented here could contribute to develop a future seasonal forecasting tool of upwelling variability and living marine resources in northern Alboran.
PB Frontiers Media
SN 2296-7745
YR 2022
FD 2022-10-03
LK https://hdl.handle.net/20.500.14352/72934
UL https://hdl.handle.net/20.500.14352/72934
LA eng
NO © 2022 The Authors. The authors want to thank Pablo Duran-Rodrıguez for the integration of the statistical python code in a user-friendly tool, which has let us to perform, in an efficient way, the different simulations done in this paper. Thanks are also due to Irene Nadal for her contribution in designing Figure 1 and to Simone Sammartinofor useful discussions. Finally, we would like to thank the anonymous reviewers for their constructive suggestions and advice, which considerably improved the original manuscript.  JL-P was supported by a Postdoctoral Fellowship from the Research Own Plan of the University of Malaga (“Ayuda de Incorporacion de Doctores 2020”). Thanks are also given to the projects EU-H2020 TRIATLAS (No 817578) and CARMEN (PCI2021-122061-2B), the latter funded by both the Spanish Government (MCIN/AEI/10.13039/501100011033) and the European Union (NextGenerationEU/PRTR).
NO Postdoctoral Fellowship from the Research Own Plan of the University of Malaga (“Ayuda de Incorporacion de Doctores 2020”)
NO Spanish Government projects EU-H2020 TRIATLAS and CARMEN
NO European Union (NextGenerationEU/PRTR)
DS Docta Complutense
RD 9 may 2024