Person: Gómara Cardalliaguet, Íñigo
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First Name
Íñigo
Last Name
Gómara Cardalliaguet
Affiliation
Universidad Complutense de Madrid
Faculty / Institute
Ciencias Físicas
Department
Física de la Tierra y Astrofísica
Area
Identifiers
5 results
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Item Reconstruction of erosivity density in northwest Italy since 1701(Hydrological Sciences Journal, 2021) Diodato, Nazzareno; Gómara Cardalliaguet, Íñigo; Baronetti, Alice; Fratianni, Simona; Bellocchi, GianniSocieties can be better prepared to face hydrological extremes (e.g. flash floods) by understanding the trends and variability of rainfall aggressiveness and its derivative, erosivity density (ED). Estimating extended time series of ED is, however, scientifically challenging because of the paucity of long-term high-resolution pluviometric observations. This research presents the longest ED time series reconstruction (1701–2019) in northwest Italy (Piedmont region) to date, which is analysed to identify damaging hydrological periods. With this aim, we developed a model consistent with a sample (1981–2015) of detailed novel Revised Universal Soil Loss Erosion-based high-resolution data and documentary hydrological extreme records. The modelled data show a noticeable rising trend in ED from 1897 onwards, together with an increase of extreme values for return periods of 10 and 50 years, consistent with the Clausius‐Clapeyron scaling of extreme rainfall. We also suggest the North Atlantic Oscillation and Atlantic Multidecadal Oscillation may be associated with rainfall extremes in Piedmont.Item Statistical-observational analysis of skillful oceanic predictors of heavy daily precipitation events in the Sahel(Atmosphere, 2020) Diakhaté, Moussa; Suárez Moreno, Roberto; Gómara Cardalliaguet, Íñigo; Mohino Harris, ElsaIn this paper, the sea surface temperature (SST) based statistical seasonal forecast model (S4CAST) is utilized to examine the spatial and temporal prediction skill of Sahel heavy and extreme daily precipitation events. As in previous studies, S4CAST points out the Mediterranean Sea and El Niño Southern Oscillation (ENSO) as the main drivers of Sahel heavy/extreme daily rainfall variability at interannual timescales (period 1982–2015). Overall, the Mediterranean Sea emerges as a seasonal short-term predictor of heavy daily rainfall (1 month in advance), while ENSO returns a longer forecast window (up to 3 months in advance). Regarding the spatial skill, the response of heavy daily rainfall to the Mediterranean SST forcing is significant over a widespread area of the Sahel. Contrastingly, with the ENSO forcing, the response is only significant over the southernmost Sahel area. These differences can be attributed to the distinct physical mechanisms mediating the analyzed SST-rainfall teleconnections. This paper provides fundamental elements to develop an operational statistical-seasonal forecasting system of Sahel heavy and extreme daily precipitation events.Item Skillful prediction of tropical Pacific fisheries provided by Atlantic Niños(Environmental research letters, 2021) Gómara Cardalliaguet, Íñigo; Rodríguez De Fonseca, María Belén; Mohino Harris, Elsa; Losada Doval, Teresa; Polo Sánchez, Irene; Coll, MartaTropical Pacific upwelling-dependent ecosystems are the most productive and variable worldwide, mainly due to the influence of El Niño Southern Oscillation (ENSO). ENSO can be forecasted seasons ahead thanks to assorted climate precursors (local-Pacific processes, pantropical interactions). However, due to observational data scarcity, little is known about the importance of these precursors for marine ecosystem prediction. Previous studies based on Earth System Model simulations forced by observed climate have shown that multiyear predictability of tropical Pacific marine primary productivity is possible. With recently released global marine ecosystem simulations forced by historical climate, full examination of tropical Pacific ecosystem predictability is now feasible. By complementing historical fishing records with marine ecosystem model data, we show herein that equatorial Atlantic sea surface temperatures (SSTs) constitute a valuable predictability source for tropical Pacific fisheries, which can be forecasted over large-scale areas up to three years in advance. A detailed physical-biological mechanism is proposed whereby equatorial Atlantic SSTs influence upwelling of nutrient-rich waters in the tropical Pacific, leading to a bottom-up propagation of the climate-related signal across the marine food web. Our results represent historical and near-future climate conditions and provide a useful springboard for implementing a marine ecosystem prediction system in the tropical Pacific.Item Influence of climate variability on the potential forage production of a mown permanent grassland in the French Massif Central(Agricultural and forest meteorology, 2020) Gómara Cardalliaguet, Íñigo; Bellocchi, Gianni; Martin, Raphaël; Rodríguez De Fonseca, María Belén; Ruiz Ramos, MargaritaClimate Services (CS) provide support to decision makers across socio-economic sectors. In the agricultural sector, one of the most important CS applications is to provide timely and accurate yield forecasts based on climate prediction. In this study, the Pasture Simulation model (PaSim) was used to simulate, for the period 1959–2015, the forage production of a mown grassland system (Laqueuille, Massif Central of France) under different management conditions, with meteorological inputs extracted from the SAFRAN atmospheric database. The aim was to generate purely climate-dependent timeseries of optimal forage production, a variable that was maximized by brighter and warmer weather conditions at the grassland. A long-term increase was observed in simulated forage yield, with the 1995–2015 average being 29% higher than the 1959–1979 average. Such increase seems consistent with observed rising trends in temperature and CO_(2), and multi-decadal changes in incident solar radiation. At interannual timescales, sea surface temperature anomalies of the Mediterranean (MED), Tropical North Atlantic (TNA), equatorial Pacific (El Niño Southern Oscillation) and the North Atlantic Oscillation (NAO) index were found robustly correlated with annual forage yield values. Relying only on climatic predictors, we developed a stepwise statistical multi-regression model with leave-one-out cross-validation. Under specific management conditions (e.g., three annual cuts) and from one to five months in advance, the generated model successfully provided a p-value < 0.01 in correlation (t-test), a root mean square error percentage (%RMSE) of 14.6% and a 71.43% hit rate predicting above/below average years in terms of forage yield collection. This is the first modeling study on the possible role of large-scale oceanic–atmospheric teleconnections in driving forage production in Europe. As such, it provides a useful springboard to implement a grassland seasonal forecasting system in this continent.Item Potential SST drivers for Chlorophyll-a variability in the Alboran Sea: a source for seasonal predictability?(Frontiers in Marine Science, 2022) López-Parages, Jorge; Gómara Cardalliaguet, Íñigo; Rodríguez De Fonseca, María Belén; García Lafuente, JesúsThis 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.