Editorial: Impact of Deep Oceanic Processes on Circulation and Climate Variability: Examples From the Mediterranean Sea and the Global Ocean

Bue, Nadia Lo; Artale, V.; Schröeder, Katrin

doi:10.3389/fmars.2021.801479

Cited by 2 publications

(2 citation statements)

References 21 publications

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“…The subduction and spreading of surface water anomalies and the consequent warming of intermediate and deep water masses are already showing their feedback on ocean dynamics and the atmosphere (air-sea fluxes) and will continue to do so in the coming years, decades, or even centuries as these warming water masses spread or re-emerge (Artale et al, 2018;Lo Bue et al, 2021). Marine heatwaves have also become and will become more frequent (Frölicher et al, 2018;Darmaraki et al, 2019a;Darmaraki et al, 2019b;Oliver et al, 2021;Juza et al, 2022;Hamdeno and Alvera-Azcarate, 2023).…”

Section: Figurementioning

confidence: 99%

Heat content and temperature trends in the Mediterranean Sea as derived from Argo float data

Kubin,

Menna,

Mauri

et al. 2023

Front. Mar. Sci.

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The Mediterranean Sea is very sensitive to climatic changes due to its semi-enclosed nature and is therefore defined as one of the hotspots in future climate change projections. In this study, we use Argo float data to assess climatologies and trends in temperature and Ocean Heat Content (OHC) throughout the Mediterranean Sea and for specific sub-basins (e.g. Western and Eastern Mediterranean, Gulf of Lion, South Adriatic). The amount of the OHC, spatially averaged in bins of 1°x1° over the period from 2001 to 2020, increases from west to east in the Mediterranean Sea. Time series of temperature and OHC from 2005 to 2020, estimated in the surface and intermediate layers (5-700 m) and deeper layer (700-2000 m), reveal significant warming trends and an increase of OHC. The upper 700 m of the Mediterranean Sea show a temperature trend of 0.041 ± 0.012°C·yr-1, corresponding to an annual increase in OHC of 3.59 ± 1.02 W·m-2. The Western Mediterranean Sea (5-700 m) is warming fastest with an increase in temperature at a rate of 0.070 ± 0.015°C·yr-1, corresponding to a yearly increase in OHC of 5.72 ± 1.28 W·m-2. Mixing and convection events within convection sites and along boundary currents transport and disperse the temperature and OHC changes. Significant warming trends are evident in the deeper layers (700-2000 m) of the two deep convection sites in the Mediterranean Sea (Gulf of Lion, South Adriatic), with an exceptionally strong warming trend in the South Adriatic from 2013 to 2020 of 0.058 ± 0.005°C·yr-1, corresponding to a yearly increase in OHC of 9.43 ± 0.85 W·m-2. The warming of the different water masses will show its feedback on ocean dynamics and air-sea fluxes in the next years, decades, and even centuries as these warming waters spread or re-emerge. This will provide more energy to the atmosphere, resulting in more extreme weather events and will also stress ecosystems and accelerate the extinction of several marine species. This study contributes to a better understanding of climate change in the Mediterranean region, and should act as another wake-up call for policy makers and society.

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Section: Figurementioning

confidence: 99%

Heat content and temperature trends in the Mediterranean Sea as derived from Argo float data

Kubin,

Menna,

Mauri

et al. 2023

Front. Mar. Sci.

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“…Approximately 97% of all stored free water is found in the oceans, which make up 71% of the surface of the planet (Durack, 2015). Despite the key roles that oceans play in the Earth's energy budget, the complexity involved still poses some di culties in understanding all the relevant processes (Lo Bue et al, 2021). A crucial aspect of ocean expeditions in oceanography has been the study of ocean currents, which usually involves measuring relevant phenomena, particularly tides, temperature, salinity, and wind speed.…”

Section: Introductionmentioning

confidence: 99%

An approach for good modelling and forecasting of sea surface salinity in a coastal zone using machine learning LASSO regression models built with sparse satellite time-series datasets

Ajibola-James,

Okeke

2024

Preprint

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The risks of upstream seawater intrusion from coastal zones, particularly to the environment and people’s health,are gradually becoming serious issues thatrequire proactive environmental monitoring and good modellingapproaches. However, the temporal resolutions of relevant contemporary all-weather satellites that detect SSS are unable to support real-time applicationsthat can provide the required early warning information for mitigating such risks. Our current practical knowledge of the efficiency of machine learning (ML) least absolute shrinkage and selection operator (LASSO) regression modelsbuilt with relatively sparse all-weather satellite data for achieving relatively accurate predictor variable selection,collinearity detection,and high SSS prediction accuracy is still limited. In this paper, we utilized relatively sparse time series all-weather satellite datasets consisting of 6 potential predictor variables (PPVs), wind speed (WS), high wind speed (HWS), sea surface temperature (SST), absolute dynamic topography (ADT), sea level anomalies (SLAs) and precipitation (PRECIP) (January 2016-December 2020) to construct an ML LASSO model (using the forecastMLlibrary in R/R-studio) to predict SSS ona tropical coast (Nigerian coastal zone). We utilized the same datasets for building the L0-regularized regression (L0) model (using the L0Learnlibrary) to determine the relative importance of the PPVs for the ML time series forecasting of the SSS and to detect collinearity. The output was used to determinethe abilityof the LASSO model to determinethe relative importance of the PPVs for forecasting SSS and detecting collinearity. We determinedthe best combination of lookback (LB) and h-step-ahead (H) parametervalues for building a relatively accurate ML LASSO model with the datasets. We determinedand validatedthe relative importance of the PPVs for forecasting the monthly SSS using the LASSO model with the best combination of parametervalues. We predict and validate the monthly SSS values for January-December 2021 with a relatively accurate model. We show that the LB:24 and H:12 parametervalues,with an RMSE of 0.54437, are the best for building a relatively accurate LASSO model with such datasets. We show that the WS, HWS, and SLA are the most important PPVs for achieving relatively accurate SSS forecasts with the model. However, we show the limitations of such a LASSO model in achieving relatively accurate predictor variable selection and collinearity detection. We show practical solutions to such limitations by utilizing the L0 model to assist the LASSO model in achieving relatively high SSS prediction accuracy. Finally, we predict the monthly SSS values using the relatively accurate LASSO model and validate them with the observed SSS (January-December 2021) and obtain an RMSE of 0.7428 and a MAPE of 1.9039%. AMAPE value approximately5 times less than 10% implies a high SSS prediction accuracy that can be replicated to provide useful early warning information for mitigating such risks in any coastal zone. The results imply that the good practice for using such satellite datasets to build a relatively accurate ML LASSO model for forecasting should begin with rigorous supervised-automatic deletion of observation records with null values and outliers,followed by unbiased selection of appropriate parametervalues and important predictor variables and collinearity assessment.

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Editorial: Impact of Deep Oceanic Processes on Circulation and Climate Variability: Examples From the Mediterranean Sea and the Global Ocean

Cited by 2 publications

References 21 publications

Heat content and temperature trends in the Mediterranean Sea as derived from Argo float data

Heat content and temperature trends in the Mediterranean Sea as derived from Argo float data

An approach for good modelling and forecasting of sea surface salinity in a coastal zone using machine learning LASSO regression models built with sparse satellite time-series datasets

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