The Mediterranean Sea is considered a hot spot of global warming because it has been changing faster than the global ocean, creating a strong impact on the marine environment. Recent studies agree on the increase in the sea level, in the sea surface temperature, and in the sea surface salinity in the Mediterranean Sea over the last two decades. In this research, the possible interconnection between these and other parameters that contribute to the regulatory effect of the sea on the climate are identified and discussed. Spatio-temporal variability of four oceanographic and air–sea interaction parameters (sea-level, sea surface temperature, sea surface salinity, and freshwater flux) are estimated over the last 27 years by performing the empirical orthogonal function analysis. Climatic trends, and interannual and decadal variability of the different datasets are delineated and described in the whole Mediterranean and in its sub-basins. On the climatic scale, the Mediterranean and its sub-basins behave in a coherent way, showing the seal level, temperature, salinity, and freshwater flux rise. On the interannual scale, the temporal evolution of the sea level and sea surface temperature are highly correlated, whereas freshwater flux affects the variability of sea level, temperature, and the salinity field mainly in the Western and Central Mediterranean. The decadal signal associated with the Northern Ionian Gyre circulation reversals is clearly identified in three of the four parameters considered, with different intensities and geographical extents. This signal also affects the intermediate layer of the Eastern Mediterranean, from where it is advected to the other sub-basins. Decadal signal not associated with the Northern Ionian Gyre reversals is strongly related to the variability of main sub-basin scale local structures.

Atlantic Water (AW) and Levantine Intermediate Water (LIW) are important water masses that play a crucial role in the internal variability of the Mediterranean thermohaline circulation. To be more specific, their variability and interaction, along with other water masses that characterize the Mediterranean basin, such as the Western Mediterranean Deep Water (WMDW), contribute to modify the Mediterranean Outflow through the Strait of Gibraltar, and hence they may influence the stability of the global thermohaline circulation. This work aims to characterize AW and LIW in the Mediterranean Sea, taking advantage of the large observational dataset (freely available on https://argo.ucsd.edu, https://www.ocean-ops.org, last access: 17 January 2022; Wong et al., 2020) provided by Argo floats from 2001 to 2019. AW and LIW were identified using different diagnostic methods, highlighting the inter-basin variability and the strong zonal gradient that both denote the two water masses in this marginal sea. Their temporal variability was also investigated over the last 2 decades, providing a more robust view of AW and LIW characteristics, which have only been investigated using very short periods in previous studies due to a lack of data. A clear salinification and warming trend characterize AW and LIW over the last 2 decades (∼ 0.007 ± 0.140 and 0.006 ± 0.038 yr⁻¹; 0.026 ± 0.715 and 0.022 ± 0.232 ^∘C yr⁻¹, respectively). The salinity and temperature trends found at sub-basin scale are in good agreement with previous results. The strongest trends are found in the Adriatic basin in the properties of both AW and LIW.

Decadal climate predictions, obtained by constraining the initial condition of a dynamical model through a truthful estimate of the observed climate state, provide an accurate assessment of near-term climate change and are a useful tool to inform decision-makers on future climate-related risks. Here we present results from the CMIP6 (Coupled Model Intercomparison Project Phase 6) Decadal Climate Prediction Project (DCPP) decadal hindcasts produced with the operational CMCC (Euro-Mediterranean Center on Climate Change) decadal prediction system (DPS), based on the fully coupled CMCC-CM2-SR5 dynamical model. A 20-member suite of 10-year retrospective forecasts, initialized every year from 1960 to 2020, is performed using a full-field initialization strategy. The predictive skill for key variables is assessed and compared with the skill of an ensemble of non-initialized historical simulations so as to quantify the added value of the initialization. In particular, the CMCC DPS is able to skillfully reproduce past climate surface and subsurface temperature fluctuations over large parts of the globe. The North Atlantic Ocean is the region that benefits the most from initialization, with the largest skill enhancement occurring over the subpolar region compared to historical simulations. On the other hand, the predictive skill over the Pacific Ocean rapidly decays with forecast time, especially over the North Pacific. In terms of precipitation, the skill of the CMCC DPS is significantly higher than that of the historical simulations over a few specific regions, including the Sahel, northern Eurasia, and over western and central Europe. The Atlantic multidecadal variability is also skillfully predicted, and this likely contributes to the skill found over remote areas through downstream influence, circulation changes, and teleconnections. Considering the relatively small ensemble size, a remarkable prediction skill is also found for the North Atlantic Oscillation, with maximum correlations obtained in the 1–9 lead year range. Systematic errors also affect the forecast quality of the CMCC DPS, featuring a prominent cold bias over the Northern Hemisphere, which is not found in the historical runs, suggesting that, in some areas, the adopted full-field initialization strategy likely perturbs the equilibrium state of the model climate quite significantly. The encouraging results obtained in this study indicate that climate variability over land can be predictable over a multiyear range, and they demonstrate that the CMCC DPS is a valuable addition to the current generation of DPSs. This stresses the need to further explore the potential of the near-term predictions, further improving future decadal systems and initialization methods, with the aim to provide a reliable tool to inform decision-makers on how regional climate will evolve in the next decade.

The increasing availability of coarse-scale climate simulations and the need for ready-to-use high-resolution variables drive the climate community to the challenge of reducing computational resources and time for downscaling purposes. To this end, statistical downscaling is gaining interest as a potential strategy for integrating high-resolution climate information obtained through dynamical downscaling over limited years, providing a clear understanding of the gains and losses in combining dynamical and statistical downscaling. In this regard, several questions can be raised: (i) what is the performance of statistical downscaling, assuming dynamical downscaling as a reference over a shared time window; (ii) how much the performance of statistical downscaling is affected by changes in the number of years available for training; (iii) how does the climate normal considered for the training affect the predictions. This study addresses these issues by applying a statistical downscaling procedure based on the empirical quantile mapping bias adjustment, obtaining finer-resolution climate variables. This procedure was adopted in order to downscale temperature and precipitation from ERA5 climate reanalysis, having as reference both for training and validation, the respective variables obtained through the dynamical downscaling of ERA5 over Italy for about 30 years. The availability of such a long simulation allows us to define several long time windows, used to calibrate the statistical relationships and evaluate the performance of statistical downscaling versus dynamical downscaling over a shared blind prediction period, taking advantage of a set of spatial and temporal metrics. The study shows that (i) the statistical downscaling successfully represents mean values and extremes of temperature and precipitation; (ii) its performance remains satisfactory regardless of the number of years used as training; (iii) the shorter is the time window considered for the training, the higher is the sensitivity to changes in the time interval due to the inter-annual variability. Nevertheless, the performance deviations are somehow not so remarkable.

This study presents SD-EQM_GCMs_IT, a new high-resolution climate projection ensemble dataset over Italy, designed to support climate impact assessments and adaptation strategies. It is derived by statistically downscaling nine CMIP6 General Circulation Models (GCMs) using the Copernicus European Regional ReAnalysis (CERRA) as a training dataset. The Empirical Quantile Mapping (EQM) method ensures a realistic statistical distribution and enhances local climate characterization. The dataset provides daily values at 0.05° resolution from 1985 to 2100 for six Essential Climate Variables (ECVs), including temperature, humidity, wind speed, and precipitation. Two climate scenarios are considered: SSP1-2.6 and SSP3-7.0, for low and high challanges to mitigation and adaptation respectively. This dataset enhances the current climate information available for Italy by bridging the gap between existing CMIP6 global projections and the absence of an ensemble of regional climate models for the same scenarios. By offering high-resolution data, it equips policymakers, industries, and communities with refined climate insights to enhance resilience and adaptation efforts.