Mediterranean Sea Surface Temperature Research Articles

Unveiling the Relationship Between Mediterranean Tropical‐Like Cyclones and Rising Sea Surface Temperature

AbstractLeveraging the Copernicus high‐resolution multi‐year Mediterranean Sea Surface Temperature (SST) dataset, 15 selected tropical‐like cyclones (TLCs) are analyzed with the objective of elucidating the anomalies of SST at the time of cyclogenesis and the connection between the change in SST during the cyclone lifetime and its characteristics. The long‐term SST increase associated with climate change is identified by comparing detrended and original anomalies. Detrending removes the effect of the intensification of SST anomaly over time, revealing that no significant anomalies generally emerge in the early stages of the TLC lifetimes. Conversely, winter events exhibit early‐stage positive SST anomalies. Also, high SST values were observed during the intensification of the most intense cyclones. A cold SST anomaly is left after the passage of the cyclones, due to the intense sea surface fluxes extracting heat from the sea.

Long-term recurrent exposure to excessive heat in the east Mediterranean and climate resilience development – a case study in Israel

This work studies long-term trends of observed meteorological parameters and of exposure to excessive heat over 74 years in Israel (1950–2023). We report an increasing trend of recurring exposure of the Israeli population to excessive heat during most of the summer noon hours, with the heat index often above the physiologically no-risk threshold. Specifically, since the beginning of the millennium, a significant increase in summertime decadal means of ambient noontime temperature (Ta), absolute humidity (AH), and heat index (HI) is evident relative to the 1950’s (Ta: 0.06 °C/year, AH: 0.06 g/m3year, HI: 0.09 °C/year). The experienced increase summertime thermal discomfort by the Israeli population results from the significant and synergistic increase in co-exposure to ambient temperature and humidity. Indeed, long-term satellite data (Landsat 1984–2021) of the east Mediterranean Sea Surface Temperature (SST) reveal a significant change (SST: 0.05 °C/year), which elucidates the corresponding increase in the absolute humidity. Leishmaniasis is a climate-related vector-borne infectious disease. However, the 1956–2017 leishmaniasis incidence rates in Israel do not correlate with the significant increase in the ambient temperature and heat index, representing development of climate resilience in terms of administrated prevention measures (namely, systematic adaptation) to this climate-related disease.

Storylines of projected summer warming in Iberia using atmospheric circulation, soil moisture and sea surface temperature as drivers of uncertainty

This study explores the uncertainty of future summer warming over Iberia using storylines constructed from climate model simulations of the Climate Model Intercomparison Project Phase 6. Unlike prior storyline approaches focusing on remote drivers and global teleconnections of atmospheric circulation, we use regional factors that directly influence summer temperatures: ridging activity, soil moisture and Mediterranean sea surface temperature. These drivers explain a substantial portion of the observed variability across climate models, with ridging activity and soil moisture showing the strongest influence on Iberian warming. Under a high radiative forcing scenario (SSP5–8.5), the storylines of Iberian warming based on these two drivers range between 7 and 9 °C for the end of the 21st century. The storyline leading to the largest warming is characterised by a drying out of the soil conditions and an increase in the anticyclonic activity over Iberia. We find similar conclusions for simple extreme heat indicators, though the approach struggles with more complex heatwave metrics. We also propose a novel modification of the storyline approach to increase the data sample of climate responses by using different time intervals throughout the 21st century. This modification would allow the application of more complex statistical models, the exploration of non-linear relationships and the identification of other drivers shaping the regional climate projections.

Deep learning for the super resolution of Mediterranean sea surface temperature fields

Abstract. Sea surface temperature (SST) is one of the essential variables of the Earth's climate system. Being at the air–sea interface, SST modulates heat fluxes in and out of the ocean, provides insight into several upper and interior ocean dynamical processes, and is a fundamental indicator of climate variability potentially impacting the health of marine ecosystems. Its accurate estimation and regular monitoring from space is therefore crucial. However, even if satellite infrared/microwave measurements provide much better coverage than what is achievable from in situ platforms, they cannot sense the sea surface under cloudy and rainy conditions. Large gaps are present even in merged multi-sensor satellite products, and different statistical strategies, mostly based on optimal interpolation (OI) algorithms, have thus been proposed to obtain gap-free (L4) images. These techniques, however, filter out the signals below the space–time decorrelation scales considered, significantly smoothing most of the small mesoscale and submesoscale features. Here, deep learning models, originally designed for single-image super resolution (SR), are applied to enhance the effective resolution of SST products and the accuracy of SST gradients. SR schemes include a set of computer vision techniques leveraging convolutional neural networks to retrieve high-resolution data from low-resolution images. A dilated convolutional multi-scale learning network, which includes an adaptive residual strategy and implements a channel attention mechanism, is used to reconstruct features in SST data at 1/100° spatial resolution starting from 1/16° data over the Mediterranean Sea. The application of this technique shows an improvement in the high-resolution reconstruction, capturing small-scale features and providing a root-mean-squared-difference improvement of 0.02 °C with respect to the L3 ground-truth data.

Assessment of the Decadal Prediction Skill of Sahel Rainfall in CMIP5 and CMIP6

Abstract Accurately predicting the decadal variations in Sahel rainfall has important implications for the lives and economy in the Sahel. Previous studies found that the decadal variations in sea surface temperature (SST) in the Atlantic, Mediterranean Sea, Indian Ocean, and Pacific contribute to those in Sahel rainfall. This study evaluates the decadal prediction skills of Sahel rainfall from all the available hindcasts contributing to phases 5 and 6 of the Coupled Model Intercomparison Project (CMIP5 and CMIP6), in comparison with the related uninitialized simulations. A majority of the prediction systems show high skill with regard to Sahel rainfall. The high skill may be partly attributed to external forcings, which are reflected in good performance of the respective uninitialized simulations. The decadal prediction skills of the key SST drivers and their relationships with the Sahel rainfall are also assessed. Both the hindcasts and the uninitialized simulations generally present high skill for the Atlantic multidecadal variability (AMV) and Mediterranean Sea SST indices and low skill for the Indian Ocean basin mode (IOBM) and interdecadal Pacific variability (IPV) indices. The relationship between the Sahel rainfall and the AMV or Mediterranean Sea SST index is reasonably captured by most prediction systems and their uninitialized simulations, while that between the Sahel rainfall and the IOBM or IPV index is captured by only a few systems and their uninitialized simulations. The high skill of the AMV and Mediterranean Sea SST indices as well as the reasonable representations of their relationships with the Sahel rainfall by both the hindcasts and uninitialized simulations probably plays an important role in predicting the Sahel rainfall successfully. Significance Statement Predicting Sahel rainfall on the decadal time scale is of great importance. This study provides a thorough evaluation of the decadal prediction skills of Sahel rainfall in the current decadal prediction systems participating in phases 5 and 6 of the Coupled Model Intercomparison Project (CMIP5 and CMIP6). A majority of the systems achieve high prediction skill of Sahel rainfall, which probably results from the high prediction skill of some key sea surface temperature (SST) drivers, especially in the Atlantic and Mediterranean Sea SST, and their relationships with Sahel rainfall. This study provides a reference for better understanding the predictability of Sahel rainfall.

A climate modeling study over Northern Africa and the Mediterranean basin with multi-physics ensemble and coupling to a regional ocean modeling system

We have developed a physics ensemble of Weather Research and Forecasting (WRF) model simulations for the Middle East, Mediterranean and North Africa (MEMNA) regions. These simulations use different configurations for the cumulus, microphysics, surface layer, planetary boundary layer, and land surface schemes and are forced by the Community Earth System Model (CESM) General Circulation Model for the historical period 1979–1993. We have also created a complementary ensemble in which the WRF model is fully-coupled to the Regional Ocean Modelling System (ROMS) that simulates the dynamics of the entire Mediterranean Sea. Analysis of our ensembles reveals that the simulated precipitation and near surface temperature (T2) fields in WRF are largely influenced by the cumulus and the land surface schemes during the summer and winter seasons, respectively. The coupling of Weather Research and Forecasting to Regional Ocean Modelling System yields Mediterranean sea surface temperatures that are directly correlated with T2 and have higher spatial resolution than the global model. Meanwhile no significant difference is found between the atmospheric fields from the coupled and uncoupled runs because the Community Earth System Model sea surface temperatures over the Mediterranean, that are used for surface forcing in the uncoupled runs, are already in close agreement with both Regional Ocean Modelling System and observations. We conclude that our high-resolution coupled atmosphere-ocean modelling system is capable of producing climate data of good quality, and we identify those combinations of physics schemes that result in an acceptable level of bias that facilitates their use in future studies.

Evolution of marine heatwaves in warming seas: the Mediterranean Sea case study

Anomalous warming of the upper ocean is increasingly being observed in the Mediterranean Sea. Extreme events, known as marine heatwaves (MHWs), can have a profound impact on marine ecosystems, and their correct detection and characterization are crucial to define future impact scenarios. Here, we analyze MHWs observed over the last 41 years (1982–2022) in the Mediterranean sea surface temperatures (SSTs). We show that the intensification in frequency, intensity, and duration of Mediterranean MHWs in recent years is mainly due to a shift in SST mean that occurred in the last two decades and largely reduced when analyzing detrended SST data. Detrending thus allows the use of a fixed climatology without overestimating MHW properties over time and distinguishes long-term warming (i.e., trend) from transient and abrupt SST changes. Analogous results are also found over a shorter temporal period, by analyzing 13 years (2007–2020) of in situ data collected at different depths (5 to 40 m) at Columbretes Islands. Additionally, the in situ analysis reveals that atmospheric summer heatwaves could affect a layer of 10 m in depth. Lastly, a catalogue of the major Mediterranean MHWs that have occurred since 1982 is presented. This catalogue evidences an exceptionally long-lasting and intense MHW, starting in May 2022 and persisting, at least, until the end of the year, resulting in the event with the highest cumulative intensity just after the well-known 2003 MHW event.

Subseasonal Prediction of Central European Summer Heatwaves with Linear and Random Forest Machine Learning Models

Abstract Heatwaves are extreme near-surface temperature events that can have substantial impacts on ecosystems and society. Early warning systems help to reduce these impacts by helping communities prepare for hazardous climate-related events. However, state-of-the-art prediction systems can often not make accurate forecasts of heatwaves more than two weeks in advance, which are required for advance warnings. We therefore investigate the potential of statistical and machine learning methods to understand and predict central European summer heatwaves on time scales of several weeks. As a first step, we identify the most important regional atmospheric and surface predictors based on previous studies and supported by a correlation analysis: 2-m air temperature, 500-hPa geopotential, precipitation, and soil moisture in central Europe, as well as Mediterranean and North Atlantic sea surface temperatures, and the North Atlantic jet stream. Based on these predictors, we apply machine learning methods to forecast two targets: summer temperature anomalies and the probability of heatwaves for 1–6 weeks lead time at weekly resolution. For each of these two target variables, we use both a linear and a random forest model. The performance of these statistical models decays with lead time, as expected, but outperforms persistence and climatology at all lead times. For lead times longer than two weeks, our machine learning models compete with the ensemble mean of the European Centre for Medium-Range Weather Forecast’s hindcast system. We thus show that machine learning can help improve subseasonal forecasts of summer temperature anomalies and heatwaves. Significance Statement Heatwaves (prolonged extremely warm temperatures) cause thousands of fatalities worldwide each year. These damaging events are becoming even more severe with climate change. This study aims to improve advance predictions of summer heatwaves in central Europe by using statistical and machine learning methods. Machine learning models are shown to compete with conventional physics-based models for forecasting heatwaves more than two weeks in advance. These early warnings can be used to activate effective and timely response plans targeting vulnerable communities and regions, thereby reducing the damage caused by heatwaves.

Study of the Decadal Predictability of Mediterranean Sea Surface Temperature Based on Observations

Abstract Sea surface temperature (SST) changes in the Mediterranean Sea have profound impacts on both the Mediterranean regions and remote areas. Previous studies show that the Mediterranean SST has significant decadal variability that is comparable with the Atlantic multidecadal variability (AMV). However, few studies have discussed the characteristics and sources of the decadal predictability of Mediterranean SST based on observations. Here for the first time we use observational datasets to reveal that the decadal predictability of Mediterranean SST is contributed by both external forcings and internal variability for both annual and seasonal means, except that the decadal predictability of the winter mean SST in the eastern Mediterranean is mostly contributed by only internal variability. Besides, the persistence of the Mediterranean SST is quite significant even in contrast with that in the subpolar North Atlantic, which is widely regarded to have the most predictable surface temperature on the decadal time scale. After the impacts of external forcings are removed, the average prediction time of internally generated Mediterranean SST variations is more than 10 years and closely associated with the multidecadal variability of the Mediterranean SST that is closely related to the accumulated North Atlantic Oscillation forcing. Significance Statement Decadal prediction of sea surface temperature (SST) in the Mediterranean Sea is very important due to its profound climate impacts. Understanding the spatial/seasonal characteristics and sources of the decadal predictability of the Mediterranean SST are crucial for skillful decadal prediction of the Mediterranean SST and related variations. This study for the first time shows that the decadal predictability of the Mediterranean SST is quite significant even in comparison with the AMV. The decadal predictability of the Mediterranean SST is both region and season dependent. The internally generated decadal predictability of the Mediterranean SST is most probably related to the local ocean dynamics.

Seasonal rainfall trends of a key Mediterranean area in relation to large-scale atmospheric circulation: How does current global change affect the rainfall regime?

Current global warming causes a change in atmospheric dynamics, with consequent variations in the rainfall regimes. Understanding the relationship between global climate patterns, global warming, and rainfall regimes is crucial for the creation of future scenarios and for the relative modification of water management. The aim of this study is to improve knowledge of the relationship between North Atlantic Oscillation (NAO), East Atlantic (EA), and Western Mediterranean Oscillation (WeMO) with the seasonal rainfalls in Tuscany, Italy. The study area occupies a strategic position since it lies in a transition zone between the wet area of northern Europe and the dry area of the northern coast of Africa. This research, based on a statistical correlation method and on linear models, is designed to understand the relationship between seasonal rainfalls and climate patterns. The results of this study demonstrate that the use of linear models can yield more information than traditional statistical correlations. The results show a decrease in rainfall in the warm period of the year, namely in the summer, when its expression is most visible. This phenomenon is ascribable to current global warming, which causes an increase in sea-surface temperatures. An increase in the Northern Atlantic Sea Surface Temperature and in the Mediterranean Sea Surface Temperature causes a reduction of the Iceland Low, with an extension of the Azores High. Moreover, an increase in the Genoa Gulf SST induces a weakening of the Genoa Gulf Low, one of the main cyclogenetic systems of the Mediterranean.

The feasibility of reconstructing hydroclimate over West Africa using tree-ring chronologies in the Mediterranean region

Dendrochronology in West Africa has not yet been developed despite encouraging reports suggesting the potential for long tree-ring reconstructions of hydroclimate in the tropics. This paper shows that even in the absence of local tree chronologies, it is possible to reconstruct the hydroclimate of a region using remote tree rings. We present the West Sub-Saharan Drought Atlas (WSDA), a new paleoclimatic reconstruction of West African hydroclimate based on tree-ring chronologies from the Mediterranean Region, made possible by the teleconnected climate relationship between the West African Monsoon and Mediterranean Sea surface temperatures. The WSDA is a one-half degree gridded reconstruction of summer Palmer Drought Severity indices from 1500 to 2018 CE, produced using ensemble point-by-point regression. Calibration and verification statistics of the WSDA indicate that it has significant skill over most of its domain. The three leading modes of hydroclimate variability in West Africa are accurately reproduced by the WSDA, demonstrating strong skill compared to regional instrumental precipitation and drought indices. The WSDA can be used to study the hydroclimate of West Africa outside the limit of the longest observed record and for integration and comparison with other proxy and archaeological data. It is also an essential first step toward developing and using local tree-ring chronologies to reconstruct West Africa’s hydroclimate.

Antalya İstasyonu II Deniz Seviyesi Değişiminin Değerlendirilmesi

Antalya, Turkey, coasts are very sensitive to external dynamics so there is a need to explain the sea level change process on the shores of Antalya more closely. In line with this purpose, the sea level change on the Antalya coast has been examined and evaluated monthly and annually with a statistical and analytical approach in this study. Accordingly, the correlations of some indexes such as Southern Oscillation (SOI), Arctic Oscillation (AO), Antarctic Oscillation (AAO), North Atlantic Oscillation (NAO), El Nino Southern Oscillation (NINO 3.4), and the parameters of arctic ice melt, Antalya air temperature, global air temperature, global ocean temperature, Mediterranean Sea surface temperature (SST) with the sea level change at Antalya Station II were examined to clarify the problem. The correlation study with the indexes revealed that there is no significant relationship between the indexes and the sea level change at Antalya Station II. Among the studied variables, the arctic ice melt, and the Antalya air temperature have relatively high correlation coefficients of 0.6959 and 0.6412 respectively in the monthly analysis. The Mediterranean SST seems to have the highest correlation with the sea level change with a correlation value of 0.93 in the annual analysis. Accordingly, analytical research was carried out to examine the correlation of the change of the sea level with the parameters that are thought to be the cause. Three effective parameters are identified and examined as the cause of sea level change observed at Antalya Station II, which are Mediterranean Sea temperature, ice melt, and other factors such as erosion. The result of this analytical study shows that the contribution to sea level change at Antalya Station II is mostly related to the temperature increase with a contribution of 82%, to ice melt with a contribution of 14%, and the other factors such as erosion with a contribution of 4%.

Skillful decadal prediction of unforced southern European summer temperature variations

We assess the capability of decadal prediction simulations from the Coupled Model Intercomparison Project phase 6 (CMIP6) archive to predict European summer temperature during the period 1970–2014. Using a multi-model ensemble average, we show that Southern European (SEU) summer temperatures are highly predictable for up to ten years in CMIP6. Much of this predictive skill, is related to the externally forced response: historical simulations explain about 90% of observed SEU summer temperature variance. Prediction skill for the unforced signal of SEU summer temperature is low: initialized model simulations explain less than 10% of observed variance after removing the externally forced response. An observed link between unforced SEU summer temperature and preceding spring Eastern North Atlantic—Mediterranean sea surface temperature (SST) motivates the application of a dynamical-statistical model to overcome the low summer temperature skill over Europe. This dynamical-statistical model uses dynamical spring SST predictions to predict European summer temperature, and significantly increases decadal prediction skill of unforced European summer temperature variations, showing significant prediction skill for unforced Southern European summer temperature 2–9 years ahead. As a result, dynamical-statistical models can benefit the decadal prediction of variables with initially limited skill beyond the forcing, such as summer temperature over Europe.

Planktic foraminiferal changes in the western Mediterranean Anthropocene

The increase in anthropogenic induced warming over the last two centuries is impacting marine environment. Planktic foraminifera are a globally distributed calcifying marine zooplankton responding sensitively to changes in sea surface temperatures and interacting with the food web structure. Here, we study two high resolution multicore records from two western Mediterranean Sea regions (Alboran and Balearic basins), areas highly affected by both natural climate change and anthropogenic warming. Cores cover the time interval from the Medieval Climate Anomaly to present. Reconstructed sea surface temperatures are in good agreement with other results, tracing temperature changes through the Common Era (CE) and show a clear warming emergence at about 1850 CE. Both cores show opposite abundance fluctuations of planktic foraminiferal species ( Globigerina bulloides , Globorotalia inflata and Globorotalia truncatulinoides), a common group of marine calcifying zooplankton. The relative abundance changes of Globorotalia truncatulinoides plus Globorotalia inflata describe the intensity of deep winter mixing in the Balearic basin. In the Alboran Sea, Globigerina bulloides and Globorotalia inflata instead respond to local upwelling dynamics. In the pre-industrial era, changes in planktic foraminiferal productivity and species composition can be explained mainly by the natural variability of the North Atlantic Oscillation, and, to a lesser extent, by the Atlantic Multidecadal Oscillation. In the industrial era, starting from about 1800 CE, this variability is affected by anthropogenic surface warming, leading to enhanced vertical stratification of the upper water column, and resulting in a decrease of surface productivity at both sites. We found that natural planktic foraminiferal population dynamics in the western Mediterranean is already altered by enhanced anthropogenic impact in the industrial era, suggesting that in this region natural cycles are being overprinted by human influences. • Western Mediterranean planktic foraminifera and sea surface temperature records. • Planktic foraminiferal changes explained by natural variability in pre-industrial times. • Anthropogenic warming depletes marine surface production.

An Integrated Traits Resilience Assessment of Mediterranean fisheries landings.

An increasing number of studies have been examining the functional configuration of biological communities or ecosystems using biological traits. Here, we investigated the temporal dynamics and resilience of the traits composition in Mediterranean fisheries landings over 31years (1985-2015). We transcribed the FAO Mediterranean landings dataset for 101 marine species into a dataset of 23 traits related to the life cycle, distribution, ecology and behaviour. Mediterranean mean Sea Surface Temperature (SST) was evaluated as a potential driver of the traits composition. Trait dynamics were evaluated both individually and holistically by developing an Integrated Traits Resilience Assessment (ITRA). ITRA is a variation of the Integrated Resilience Assessment (IRA), a method to infer resilience dynamics and build stability landscapes of complex natural systems. Changes in landings trait dynamics were documented both for individual traits and for the entire traits 'system', and a relevant regime shift was detected in the second half of the 1990s. The traits system switched to higher optimal temperature, more summer spawning, shorter life span, smaller maximum size, shallower optimal depth and planktivorous diet. This shift was found to be a lagged discontinuous response to sea warming, which gradually eroded the resilience of the original state of the traits system, leading it into a new basin of attraction. The inclusion of ecological/response traits (related to environmental preferences) in our analyses indicates potential mechanisms that explain the observed shift, while changes in functional/effect traits indicate potential impacts on ecosystem functioning. Our findings suggest that changes in the Mediterranean ecosystems are evidently larger than previously thought, with profound implications for the management of this highly impacted sea. ​.

Multidecadal Variability in Mediterranean Sea Surface Temperature and Its Sources

AbstractThe multidecadal variability in Mediterranean Sea surface temperature (MSMV) exerts important climate impacts on both the Mediterranean region and remote areas at hemispheric scales and has long been identified in previous studies. However, its key region and source are still unclear. For the first time, we show that the key region of the MSMV is in the eastern Mediterranean, where the MSMV can persist throughout the year. The MSMV in the central and western Mediterranean occurs mainly in summer. Comparison of the North Atlantic Oscillation (NAO) and Atlantic Multidecadal Variability (AMV) indices shows that the cumulative NAO index has a better consistency with the MSMV, suggesting that the MSMV most likely results from the cumulative effect of NAO atmospheric forcing on ocean circulation in the Mediterranean Sea. The stable lag relationship between the cumulative NAO index and the MSMV provides a natural indicator for the decadal prediction of the MSMV.

The heavy precipitation event of 14–15 October 2018 in the Aude catchment: a meteorological study based on operational numerical weather prediction systems and standard and personal observations

Abstract. The case of the heavy precipitation event on 14 and 15 October 2018 which has led to severe flash flooding in the Aude watershed in south-western France is studied from a meteorological point of view using deterministic and probabilistic numerical weather prediction systems, as well as a unique combination of observations from both standard and personal weather stations. This case features typical characteristics of Mediterranean heavy precipitation events such as its classic synoptic situation and its quasi-stationary convective precipitation that regenerates continuously, as well as some peculiarities such as the presence of a former hurricane and a pre-existing cold air mass close to the ground. Mediterranean Sea surface temperature and soil moisture anomalies are briefly reviewed, as they are known to play a role in this type of hydrometeorological events. A study of rainfall forecasts shows that the event had limited predictability, in particular given the small size of the watersheds involved. It is shown that the stationarity of precipitation, whose estimation benefits from data from personal stations, is linked to the presence near the ground of a trough and a strong potential virtual temperature gradient, the stationarity of both of which is highlighted by a combination of observations from standard and personal stations. The forecast that comes closest to the rainfall observations contains the warmest, wettest, and fastest low-level jet and also simulates near the ground a trough and a marked boundary between cold air in the west and warm air in the east, both of which are stationary.

Summer season sea surface temperature changes in the Aegean Sea based on 30years (1989-2019) of Landsat thermal infrared data.

In this research, sea surface temperature (SST) variability for the summer season in the Aegean Sea was analysed over a period of 30years by using the Landsat thermal infrared bands. A total of 88 Landsat 5, 7, and 8 thermal infrared satellite images from June, July, and August were used for each year from 1989 to 2019. To estimate SST from the thermal infrared band data, thermal infrared sensor at-sensor spectral radiance, and thermal infrared sensor top-of-atmosphere brightness temperatures were used. SST pixel values were extracted from thermal images for the 3-month summer season of each year. In order to validate the findings, regression analysis was performed between the Mediterranean Sea Ultra High Resolution SST L4 data and Landsat data for the 2008-2019 period. Regression constant R-squared values were found to be 0.9672 for June, 0.9550 for July, 0.9634 for August, and 0.9634 for all summer seasons. It was calculated that the minimum value of the average SST was 18.44 ± 2.87°C in 1992, and the maximum value of the average SST was 23.45 ± 0.70°C in 2018. According to the Landsat data, over the past 30years, the annual average SST changes were estimated to be 0.11°C, and the total changes of average SST were estimated to be 3.19 ± 1.26°C. As a result of the analysis and the validation, we can see that there is a rising trend in sea surface temperature in the Aegean Sea. However, it is difficult to determine whether this upward trend is related to global climate change.

A Warming Mediterranean: 38 Years of Increasing Sea Surface Temperature

The Mediterranean basin has been classified as a hot-spot for climate change. The Mediterranean Sea plays a fundamental regulatory role in the regional climate. We have analyzed the largest available and complete time series (1982–2019) of blended sea surface temperature (SST) data to study its seasonal cycle and look for a possible warming trend in the basin. From the analysis of the Mediterranean mean SST time series, a new temporal seasonal division is derived that differs from the one used in atmospheric climatology. Then, the SST time series were decomposed into their seasonal and trend components, and a consistent warming trend of 0.035 °C/year was obtained. The nature of this trend has been investigated, indicating a higher warming trend for both maximum and high/summer SST values than for the winter/colder ones. This reinforces the consistency of the SST increase since it is not only based on the presence of extreme values, but on a homogeneous basin global increase of high SST records as well. Although warming is found throughout the Mediterranean basin, the spatial variability found leads to the division of the basin into three distinct subareas regarding warming.

Ecology, distribution and expansion of a Mediterranean native invader, the fireworm Hermodice carunculata (Annelida)

Global change and the overall raise of seawater temperature are causing a poleward shift in species distribution, increasing the occurrence of warm-water biota in many ecosystems worldwide. In the Mediterranean, the ongoing sea warming is promoting native subtropical species to invader status. The bearded fireworm Hermodice carunculata (Amphinomida) is a thermophilic generalist predator spreading in the Central Mediterranean Sea. Although it is a typical inhabitant of Ionian coasts, several sightings have occurred at more northerly localities along the Tyrrhenian and Adriatic Seas, suggesting that density increases and range expansion are underway. These distributional records are in contrast with historical literature and museum collections, which provide poor sporadic information.The present study aimed at investigating the spatiotemporal changes that have occurred in H. carunculata abundances and distribution in the Mediterranean basins surrounding the Italian peninsula so far. We examined an exhaustive collection of existing information. A total of 857 reports of H. carunculata were collected from 368 localities. These data significantly expand knowledge on the life and feeding habit of H. carunculata. The integration of different data sources allowed the reconstruction of the shifts that have happened over the last 50 years, which are coherent with the consistent warming trend of Mediterranean Sea surface temperatures. Recent colonization events have occurred both along the Adriatic and Western Mediterranean coasts. The present findings provide unique insights into the expansion of H. carunculata, supporting future spreading throughout the Northern and Western Mediterranean. Tracking these changes is critical to assess potential impacts on coastal rocky-benthic communities, public health and commercial fisheries.