The present work aims to address the physical properties of different drought types under near-future climates in the Mediterranean. To do so, we use a multi-model mean of the bias-adjusted and downscaled product of five Earth System Models participating in the Coupled Model Intercomparison Project—phase6 (CMIP6), provided by Inter-Sectoral Impact Model Intercomparison Project (ISIMIP), under four shared socioeconomic pathways (SSP1–2.6, SSP2–4.5, SSP3–7.0, and SSP5–8.5) for the period 2021–2060, to estimate the Standardized Precipitation Evapotranspiration Index (SPEI) at 1-, 6-, and 12-month time scales, and address the meteorological, agricultural, and hydrological drought, respectively. Additionally, SPEI is calculated from the bias-adjusted CMIP6 historical simulations and the reanalysis ‘WFDE5’ for 1980–2014 as a historical and reference period. The comparison of the CMIP6 with WFDE5 reveals a consistently increasing tendency for drought occurrences in the Mediterranean, particularly for agricultural and hydrological drought time scales. Nonetheless, an overestimation in historical trend magnitude is shown by the CMIP6 with respect to WFDE5. The projection results depict drought frequencies ranging between 12 and 25% of the studied period 2021–2060, varying with regions and climate scenarios. The tendency to increase the drought frequency is more pronounced in the southern than northern Mediterranean countries. Drought severity is remarkable in the aggregated time scales; consequently, more pressure is foreseen in the food and water sectors. Drought seasonality reveals a higher tendency for drought occurrences in summer (autumn) months for the meteorological (agricultural) droughts. The driving factor(s) for drought occurrence strongly depends on regional climate characteristics.

This study focused on monitoring the water status of vegetation and soil by exploiting the synergy of optical and microwave satellite data with the aim of improving the knowledge of water cycle in cultivated lands in Egyptian Delta and Tunisian areas. Environmental analysis approaches based on optical and synthetic aperture radar data were carried out to set up the basis for future implementation of practical and cost-effective methods for sustainable water use in agriculture. Long-term behaviors of vegetation indices were thus analyzed between 2000 and 2018. By using SAR data from Sentinel-1, an Artificial Neural Network-based algorithm was implemented for estimating soil moisture and monthly maps for 2018 have been generated to be compared with information derived from optical indices. Moreover, a novel drought severity index was developed and applied to available data. The index was obtained by combining vegetation soil difference index, derived from optical data, and soil moisture content derived from SAR data. The proposed index was found capable of complementing optical and microwave sensitivity to drought-related parameters, although ground data are missing for correctly validating the results, by capturing drought patterns and their temporal evolution better than indices based only on microwave or optical data..

Regional coupled and Earth system models are fundamental numerical tools for climate investigations, downscaling of predictions and projections, process-oriented understanding of regional extreme events, and many more applications. Here we introduce a newly developed coupled regional modeling framework for the Mediterranean region, called MESMAR (Mediterranean Earth System model at ISMAR) version 1, which is composed of the Weather Research and Forecasting (WRF) atmospheric model, the NEMO oceanic model, and the hydrological discharge (HD) model, coupled via the OASIS coupler. The model is implemented at about $\mathrm{1}/\mathrm{12}$ ^∘ of horizontal resolution for the ocean and river routing, while roughly twice coarser for the atmosphere, and it is targeted to long-term investigations. We focus on the evaluation of skill score metrics from several sensitivity experiments devoted to (i) understanding the best vertical physics configuration for NEMO, (ii) identifying the impact of the interactive river runoff, and (iii) choosing the best-performing physics–microphysics suite for WRF in the regional coupled system. The modeling system has been developed for downscaling reanalyses and long-range predictions, as well as coupled data assimilation experiments. We then formulate and show the performance of the system when weakly coupled data assimilation is embedded in the system (variational assimilation in the ocean and spectral nudging in the atmosphere), in particular for the representation of extreme events like intense Mediterranean cyclones (i.e., medicanes). Finally, we outline plans for future extension of the modeling framework.

Atmospheric temperature is a key variable to detect and attribute climate change. Due to the relative sparseness of ground-based observations and heterogeneity of satellite data, global atmospheric reanalysis products are considered valuable datasets for studying and monitoring the climate, since these usually ensure spatially complete and continuous temporal coverage. Consequently, evaluating differences among the existing reanalyses is key to identifying inconsistencies. To this aim, the current study intercompares the climatological mean, variability, and linear trends for upper air temperature provided from four recent atmospheric reanalysis products (ERA5, ERA-Interim, MERRA-2, and JRA-55) The Reanalysis Multi-Model Ensemble-mean (RMME) is used as a comparator. Radiosonde observations are included for comparison on the regional scale (tropics). The results reveal that all evaluated reanalyses provide a consistent reproduction of the upper-air temperature profile. Temperature differences from RMME within ±0.25 K are found in both the troposphere and lower stratosphere, except for a few specific regions. Larger differences (>± 1.5 K) and discrepancies among the datasets are found in the upper stratosphere. Agreement between reanalyses increased after 1998. Differences in the temperature time series and seasonal cycle at the regional scales are smaller in the Northern Hemisphere mid-latitudes than in the tropics and Southern Hemisphere. A robust cooling trend in the lower stratosphere during the period of largest ozone depletion (1980-1997) and a warming trend in the troposphere for the period 1998-2018 are clearly found, at the global level, in all the datasets. Temperature trends and variability in the tropics are consistent in all reanalysis datasets with the homogenized radiosonde records from the lower to middle troposphere and in the lower stratosphere. However, large differences are found in the upper troposphere, tropical tropopause layer (TTL), and middle stratosphere. The well-known temperature variability in the lower stratosphere associated with Quasi-Biennial Oscillation (QBO) is captured in both reanalyses and observational datasets. Among the reanalyses, ERA5 shows the smallest temperature difference with respect to homogenized upper-air radiosoundings records.

Changes in the frequency of temperature extremes are often attributed to global warming. The recent availability of near‐surface temperature data records from reference networks, such as the U.S. Climate Reference Network (USCRN), enables the quantification of measurement uncertainties. Within an activity of the Copernicus Climate Change Service, the estimation of the measurement uncertainty has been provided for USCRN temperature data, using metadata made available by the National Oceanic and Atmospheric Administration (NOAA). In this paper, four climate extreme indices (Frost Days, Summer Days, Ice Days, Tropical Nights) and the related uncertainties are calculated for the period 2006–2020 from the USCRN data set and compared with traditional indices. Moreover, the asymmetric USCRN measurement uncertainties are propagated to estimate the uncertainties of climate indices. The comparison shows expanded uncertainties homogeneously distributed with the latitude and typically within 15 days per year for Frost Days and within 10 days for Ice Days, while smaller uncertainties are estimated for Summer Days and Tropical Nights, with values typically within six to seven days per year. Positive uncertainties are typically larger than negative ones for all the indices. The values of Frost and Ice Days with the related uncertainties for USCRN have also been compared with the corresponding values calculated from reanalyses data, showing differences typically within 60 days for median values, quite often smaller than USCRN and inconsistent within the related uncertainties, Overall, the results show that USCRN measurement uncertainties increase confidence in the estimation of climate extreme indices and decisions for adaptation.

In this paper, we explore different prognostic methods to account for skin sea surface temperature diurnal variations in a coupled ocean–atmosphere regional model of the Mediterranean Sea. Our aim is to characterise the sensitivity of the considered methods with respect to the underlying assumption of how the solar radiation shapes the warm layer of the ocean. All existing prognostic methods truncate solar transmission coefficient at a warm-layer reference depth that is constant in space and time; instead, we implement a new scheme where this latter is estimated from a chlorophyll dataset as the e-folding depth of solar transmission, which thus allows it to vary in space and time depending on seawater's transparency conditions. Comparison against satellite data shows that our new scheme, compared to the one already implemented within the ocean model, improves the spatially averaged diurnal signal, especially during winter, and the seasonally averaged one in spring and autumn, while showing a monthly basin-wide averaged bias smaller than 0.1 K year-round. In April, when most of the drifters' measurements are available, the new scheme mitigates the bias during nighttime, keeping it positive but smaller than 0.12 K during the rest of the monthly averaged day. The new scheme implemented within the ocean model improves the old one by about 0.1 K, particularly during June. All the methods considered here showed differences with respect to objectively analysed profiles confined between 0.5 K during winter and 1 K in summer for both the eastern and the western Mediterranean regions, especially over the uppermost 60 m. The new scheme reduces the RMSE on the top 15 m in the central Mediterranean for summertime months compared to the scheme already implemented within the ocean model. Overall, the surface net total heat flux shows that the use of a skin sea surface temperature (SST) parameterisation brings the budget about 1.5 W m⁻² closer to zero on an annual basis, despite all simulations showing an annual net heat loss from the ocean to the atmosphere. Our “chlorophyll-interactive” method proved to be an effective enhancement of existing methods, its strength relying on an improved physical consistency with the solar extinction implemented in the ocean component.