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SARS-CoV-2 infection causes a pulmonary disease (COVID-19) which spread worldwide generating fear, anxiety, depression in the general population as well as among subjects affected by mental disorders. Little is known about which different psychopathological changes the pandemic caused among individuals affected by different psychiatric disorders, which represents the aim of the present study. Specific psychometric scales were administered at three time points: T0 as outbreak of pandemic, T1 as lockdown period, T2 as reopening. Descriptive analyses and linear regression models were performed. A total of 166 outpatients were included. Overall, psychometric scores showed a significant worsening at T1 with a mild improvement at T2. Only psychopathology in schizophrenia (SKZ) patients and obsessive-compulsive (OC) symptoms did not significantly improve at T2. Subjects affected by personality disorders (PDs) resulted to be more compromised in terms of general psychopathology than depressed and anxiety/OC ones, and showed more severe anxiety symptoms than SKZ patients. In conclusion, subjects affected by PDs require specific clinical attention during COVID-19 pandemic. Moreover, the worsening of SKZ and OC symptoms should be strictly monitored by clinicians, as these aspects did not improve with the end of lockdown measures. Further studies on larger samples are needed to confirm our results. ClinicalTrials.gov Identifier: NCT04694482.

In October 2017, hundreds of wildfires ravaged the forests of the north and centre of Portugal. The fires were fanned by strong winds as tropical storm Ophelia swept the Iberian coast, dragging up smoke (together with Saharan dust from north-western Africa) into higher western European latitudes. Here we analyse the long-range transport of particulate matter (PM10) and study associations between PM10 and short-term mortality in the Portuguese population exposed to PM10 due to the October 2017 wildfires, the worst fire sequence in the country over the last decades. We analysed space- and ground-level observations to track the smoke plume and dust trajectory over Portugal and Europe, and to access PM10 concentrations during the wildfires. The effects of PM10 on mortality were evaluated using satellite data for exposure and Poisson regression models. The smoke plume covered most western European countries (including Spain, France, Belgium and the Netherlands), and reached the United Kingdom, where the population was exposed in average to an additional PM10 level of 11.7 µg/m3 during seven smoky days (three with dust) in relation to the reference days (days without smoke or dust), revealing the impact of the wildfires on distant populations. In Portugal, the population was exposed in average to additional PM10 levels that varied from 16.2 to 120.6 µg/m3 in smoky days with dust and from 6.1 to 20.9 µg/m3 in dust-free smoky days. Results suggest that PM10 had a significant effect on the same day natural and cardiorespiratory mortalities during the month of October 2017. For every additional 10 µg/m3 of PM10, there was a 0.89% (95% confidence interval, CI, 0-1.77%) increase in the number of natural deaths and a 2.34% (95% CI, 0.99-3.66%) increase in the number of cardiorespiratory-related deaths. With rising temperatures and a higher frequency of storms due to climate change, PM from Iberian wildfires together with NW African dust will tend to be more often transported into Northern European countries, which may carry health threats to areas far from the ignition sites. SA was supported by the Portuguese Foundation for Science and Technology (FCT), Portugal [grant number SFRH/BPD/109382/2015]. MT has received funding from the Spanish Ministry of Science, Innovation and Universities, Spain, through the project PREDFIRE (RTI2018-099711-J-I00), which is co-financed with the European Regional Development Fund (ERDF/FEDER). SC was supported by FCT [grant number SFRH/BPD/100948/2014]. The authors acknowledge Project REPAIR-CGL2014-59677-R and ACEX-CGL2017-87921-R of the Spanish Ministry of the Economy and Competitiveness and the FEDER European program for their support to conduct this research. Further support was granted by projects: (i) POCI-01-0145-FEDER-006939 (LEPABE – UID/EQU/00511/2013) funded by the European Regional Development Fund (ERDF), through COMPETE2020 - Programa Operacional Competitividade e Internacionalização (POCI) and by national funds, through FCT - Fundação para a Ciência e a Tecnologia; (ii) NORTE-01-0145-FEDER-000005-LEPABE-2-ECO-INNOVATION, supported by North Portugal Regional Operational Programme (NORTE 2020), under the Portugal 2020 Partnership Agreement, through the ERDF. info:eu-repo/semantics/publishedVersion

11 Pags.- 1 Tabl.- 4 Figs. Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence., We present a long-term assessment of precipitation trends in Southwestern Europe (1850–2018) using data from multiple sources, including observations, gridded datasets and global climate model experiments. Contrary to previous investigations based on shorter records, we demonstrate, using new long-term, quality controlled precipitation series, the lack of statistically significant long-term decreasing trends in precipitation for the region. Rather, significant trends were mostly found for shorter periods, highlighting the prevalence of interdecadal and interannual variability at these time-scales. Global climate model outputs from three CMIP experiments are evaluated for periods concurrent with observations. Both the CMIP3 and CMIP5 ensembles show precipitation decline, with only CMIP6 showing agreement with long term trends in observations. However, for both CMIP3 and CMIP5 large interannual and internal variability among ensemble members makes it difficult to identify a trend that is statistically different from observations. Across both observations and models, our results make it difficult to associate any declining trends in precipitation in Southwestern Europe to anthropogenic forcing at this stage., This work was supported by the research projects CGL2017-82216-R, CGL2017-83866-C3-3-R and PCI2019-103631, financed by the Spanish Commission of Science and Technology and FEDER; CROSSDRO project financed by the AXIS (Assessment of Cross(X)—sectoral climate Impacts and pathways for Sustainable transformation), JPI-Climate co-funded call of the European Commission and INDECIS which is part of ERA4CS, an ERA-NET initiated by JPI Climate, and funded by FORMAS (SE), DLR (DE), BMWFW (AT), IFD (DK), MINECO (ES), ANR (FR) with co-funding by the European Union (Grant 690462). Dhais Peña-Angulo received a 'Juan de la Cierva' postdoctoral contract (FJCI-2017-33652 Spanish Ministry of Economy and Competitiveness, MEC). Conor Murphy was supported by the Irish Environmental Protection Agency (Grant Nos. 2019-CCRP-MS.60). Marco Turco has received funding from the Spanish Ministry of Science, Innovation and Universities through the project PREDFIRE (RTI2018-099711-J-I00), which is co-financed with the European Regional Development Fund (ERDF/FEDER).

Wheat is the main staple crop and an important commodity in the Mediterranean and the Middle East. These are among the few areas in the world where the climate is suitable for growing durum wheat but also are among the most rapidly warming ones, according to the available scenarios of climate projections. How much food security and market stability in the Mediterranean and the Middle East, both depending on wheat production and its interannual variability, are going to be compromised by global warming is an overarching question. To contribute in addressing it, we use a recently established indicator to quantify crop production climate resilience. We present a methodological framework allowing to compute the annual production resilience indicator from nonstationary time series. We apply this approach on the wheat production of the 10 most important producers in the Mediterranean and the Middle East. Our findings shows that if no adaptation will take place, wheat production reliability in the Mediterranean and the Middle East will be threatened by climate change already at 1.5 °C global warming. Average climaterelated wheat production losses will exceed the worst past event even if the 2 °C mitigation target is met. These results call for urgent action on adaptation to climate change and support further efforts for mitigation, fully consistently with the Paris Agreement recommendations. Fil: Zampieri, Matteo. Joint Research Centre; Italia Fil: Toreti, Andrea. Joint Research Centre; Italia Fil: Ceglar, Andrej. Joint Research Centre; Italia Fil: Naumann, Gustavo. Joint Research Centre; Italia. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Turco, Marco. Universidad de Murcia; España Fil: Tebaldi, Claudia. Joint Global Change Research Institute; Italia

Highlights • Links between the large-scale atmospheric circulation and crop yields in Europe are examined. • An approach has been developed to identify predictors and build empirical climate-crop yield models. • Large-scale atmospheric circulation explains up to 70% of inter-annual crop yield variability. • The seasonal crop yield prediction could benefit from integration of derived empirical models into probabilistic framework., Understanding the effects of climate variability and extremes on crop growth and development represents a necessary step to assess the resilience of agricultural systems to changing climate conditions. This study investigates the links between the large-scale atmospheric circulation and crop yields in Europe, providing the basis to develop seasonal crop yield forecasting and thus enabling a more effective and dynamic adaptation to climate variability and change. Four dominant modes of large-scale atmospheric variability have been used: North Atlantic Oscillation, Eastern Atlantic, Scandinavian and Eastern Atlantic-Western Russia patterns. Large-scale atmospheric circulation explains on average 43% of inter-annual winter wheat yield variability, ranging between 20% and 70% across countries. As for grain maize, the average explained variability is 38%, ranging between 20% and 58%. Spatially, the skill of the developed statistical models strongly depends on the large-scale atmospheric variability impact on weather at the regional level, especially during the most sensitive growth stages of flowering and grain filling. Our results also suggest that preceding atmospheric conditions might provide an important source of predictability especially for maize yields in south-eastern Europe. Since the seasonal predictability of large-scale atmospheric patterns is generally higher than the one of surface weather variables (e.g. precipitation) in Europe, seasonal crop yield prediction could benefit from the integration of derived statistical models exploiting the dynamical seasonal forecast of large-scale atmospheric circulation.

Quantitative estimate of observational uncertainty is an essential ingredient to correctly interpret changes in climatic and environmental variables such as wildfires. In this work we compare four state-of-the-art satellite fire products with the gridded, ground-based EFFIS dataset for Mediterranean Europe and analyse their statistical differences. The data are compared for spatial and temporal similarities at different aggregations to identify a spatial scale at which most of the observations provide equivalent results. The results of the analysis indicate that the datasets show high temporal correlation with each other (0.5/0.6) when aggregating the data at resolution of at least 1.0{\deg} or at NUTS3 level. However, burned area estimates vary widely between datasets. Filtering out satellite fires located on urban and crop land cover classes greatly improves the agreement with EFFIS data. Finally, in spite of the differences found in the area estimates, the spatial pattern is similar for all the datasets, with spatial correlation increasing as the resolution decreases. Also, the general reasonable agreement between satellite products builds confidence in using these datasets and in particular the most-recent developed dataset, FireCCI51, shows the best agreement with EFFIS overall. As a result, the main conclusion of the study is that users should carefully consider the limitations of the satellite fire estimates currently available, as their uncertainties cannot be neglected in the overall uncertainty estimate/cascade that should accompany global or regional change studies and that removing fires on human-dominated land areas is key to analyze forest fires estimation from satellite products., Comment: M.T. and E.T. have received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sk{\l}odowska-Curie grant agreement No. 740073 (CLIM4CROP project) and grant agreement No. 748750 (SPFireSD project), respectively. The work of A.P. has been supported by the European Union's Horizon 2020 ECOPOTENTIAL project (grant agreement No. 641762)

A record 500,000 hectares burned in Portugal during the extreme wildfire season of 2017, with more than 120 human lives lost. Here we analyse the climatic factors responsible for the burned area (BA) from June to October series in Portugal for the period 1980–2017. Superposed onto a substantially stationary trend on BA data, strong oscillations on shorter time scales were detected. Here we show that they are significantly affected by the compound effect of summer (June-July-August) drought and high temperature conditions during the fire season. Drought conditions were calculated using the Standardized Precipitation Evapotranspiration Index (SPEI), the Standardized Precipitation Index (SPI) and the Standardized Soil Moisture Index (SSI). Then the extent to which the burned area has diverged from climate-expected trends was assessed. Our results indicate that in the absence of other drivers, climate change would have led to higher BA values. In addition, the 2017 extreme fire season is well captured with the model forced with climate drivers only, suggesting that the extreme fire season of 2017 could be a prelude to future conditions and likewise events. Indeed, the expected further increase of drought and high temperature conditions in forthcoming decades, point at a potential increase of fire risk in this region. The climate-fire model developed in this study could be useful to develop more skilled seasonal predictions capable of anticipating potentially hazardous conditions. M. Turco has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 740073 (CLIM4CROP project). S. Augusto was supported by FCT-MCTES (SFRH/BPD/109382/2015). RMT was supported by national funds through Fundação para a Ciência e a Tecnologia, Portugal (FCT) under project FireCast (PCIF/GRF/0204/2017). SJ was supported by the Plan Propio de Investigación of the University of Murcia (Grant No. UMU-2017-10604). Further support was provided under projects: (i) CGL2014-59677-R and CGL2017-87921-R also partially funded by FEDER; (ii) POCI-01-0145-FEDER-006939 (LEPABE -UID/EQU/00511/2013) funded by the European Regional Development Fund (ERDF), through COMPETE2020 - Programa Operacional Competitividade e Internacionalização (POCI) and by national funds, through FCT - Fundação para a Ciência e a Tecnologia; (iii) NORTE-01-0145-FEDER-000005-LEPABE-2-ECO-NNOVATION, supported by North Portugal Regional Operational Programme (NORTE 2020), under the Portugal 2020 Partnership Agreement, through the ERDF; (iv) Investigador FCT contract IF/01101/2014 (Nuno Ratola); (v) the Fundación Séneca - Regional Agency for Science and Technology of Murcia through the CLIMAX project (20642/JLI/18). We acknowledge the E-OBS dataset from the EU-FP6 project UERRA (http://www.uerra.eu) and the Copernicus Climate Change Service, and the data providers in the ECA&D project (https://www.ecad.eu); the European Forest Fire Information System-EFFIS (http://effis.jrc.ec.europa.eu) of the European Commission Joint Research Centre for the fire data.

The forecast in areas of very complex topography, as for instance the Alpine region, is still a challenge even for the new generation of numerical weather prediction models which aim at reaching the km-scale. The problem is enhanced by a general lack of standard observations, which is even more evident over the southern side of the Alps. For this reason, it would be useful to increase the performance of the mathematical models by locally assimilating non-conventional data. Since in ARPA Piemonte there is the availability of a great number of non-GTS stations, it has been decided to assimilate the 2 m temperature, coming from this dataset, in the very-high resolution version of the COSMO model, which has a horizontal resolution of about 3 km, more similar to the average resolution of the thermometers. Four different weather situations have been considered, ranging from spring to winter, from cloudy to clear sky. The aim of the work is to investigate the effects of the assimilation of non-GTS data in order to create an operational very high-resolution analysis, but also to test the option of running in the future a very short-range forecast starting from these analyses (RUC or Rapid Update Cycle). The results, in terms of Root Mean Square Error, Mean Error and diurnal cycle of some surface variables such as 2 m temperature, 2 m relative humidity and 10 m wind intensity show a positive impact during the assimilation cycle which tends to dissipate a few hours after the end of it. Moreover, the 2 m temperature assimilation has a slightly positive or neutral impact on the vertical profiles of temperature, eventhough some calibration is needed for the precipitation field which is too much perturbed during the assimilation cycle, while it is unaffected in the forecast period. So the stability of the planetary boundary layer, on the one hand, has not been particularly improved by the new-data assimilation, but, on the other hand, it has not been destroyed. It has to be pointed out that a correct description of the planetary boundary layer, even only the lowest part of it, could be helpful to the forecasters and, in general, to the users, in order to deal with meteorological hazards such as snow (in particular snow/rain limit definition), or fog (description of temperature inversions).

To the authors' knowledge there are relatively few studies that try to answer this question: "Are humans able to add value to computer-generated forecasts and warnings?". Moreover, the answers are not always positive. In particular some postprocessing method is competitive or superior to human forecast. Within the alert system of ARPA Piemonte it is possible to study in an objective manner if the human forecaster is able to add value with respect to computer-generated forecasts. Every day the meteorology group of the Centro Funzionale of Regione Piemonte produces the HQPF (Human Quantitative Precipitation Forecast) in terms of an areal average and maximum value for each of the 13 warning areas, which have been created according to meteo-hydrological criteria. This allows the decision makers to produce an evaluation of the expected effects by comparing these HQPFs with predefined rainfall thresholds. Another important ingredient in this study is the very dense non-GTS (Global Telecommunication System) network of rain gauges available that makes possible a high resolution verification. In this work we compare the performances of the latest three years of QPF derived from the meteorological models COSMO-I7 (the Italian version of the COSMO Model, a mesoscale model developed in the framework of the COSMO Consortium) and IFS (the ECMWF global model) with the HQPF. In this analysis it is possible to introduce the hypothesis test developed by Hamill (1999), in which a confidence interval is calculated with the bootstrap method in order to establish the real difference between the skill scores of two competitive forecasts. It is important to underline that the conclusions refer to the analysis of the Piemonte operational alert system, so they cannot be directly taken as universally true. But we think that some of the main lessons that can be derived from this study could be useful for the meteorological community. In details, the main conclusions are the following: – despite the overall improvement in global scale and the fact that the resolution of the limited area models has increased considerably over recent years, the QPF produced by the meteorological models involved in this study has not improved enough to allow its direct use: the subjective HQPF continues to offer the best performance for the period +24 h/+48 h (i.e. the warning period in the Piemonte system); – in the forecast process, the step where humans have the largest added value with respect to mathematical models, is the communication. In fact the human characterization and communication of the forecast uncertainty to end users cannot be replaced by any computer code; – eventually, although there is no novelty in this study, we would like to show that the correct application of appropriated statistical techniques permits a better definition and quantification of the errors and, mostly important, allows a correct (unbiased) communication between forecasters and decision makers.

Summary Mast seeding is a crucial population process in many tree species, but its spatio-temporal patterns and drivers at the continental scale remain unknown . Using a large dataset (8000 masting observations across Europe for years 1950–2014) we analysed the spatial pattern of masting across the entire geographical range of European beech, how it is influenced by precipitation, temperature and drought, and the temporal and spatial stability of masting–weather correlations. Beech masting exhibited a general distance-dependent synchronicity and a pattern structured in three broad geographical groups consistent with continental climate regimes. Spearman's correlations and logistic regression revealed a general pattern of beech masting correlating negatively with temperature in the summer 2 yr before masting, and positively with summer temperature 1 yr before masting (i.e. 2T model). The temperature difference between the two previous summers (DeltaT model) was also a good predictor. Moving correlation analysis applied to the longest eight chronologies (74–114 yr) revealed stable correlations between temperature and masting, confirming consistency in weather cues across space and time. These results confirm widespread dependency of masting on temperature and lend robustness to the attempts to reconstruct and predict mast years using temperature data.

In this study we compare three gridded observed datasets of daily precipitation (EOBS, MAP and NWIOI) over the Great Alpine Region (GAR) and a subregion in northwest Italy (NWI) in order to better understand the past variability of daily climate extremes and to set up a basis for developing regional climate scenarios. The grids are first compared with respect to their temporal similarity by calculating the correlation and relative mean absolute error of the time series. They are then compared with respect to their spatial similarity to the climatology of the ETCCDI indices (characterizing total precipitation, dry and wet spells and extremes with short return periods). The results indicate first that most EOBS gridpoint series in northeastern Italy have to be shifted back by 1 day to have maximum overlap of the measurement period and, second, that both the temporal and spatial similarities of most indices are higher between the NWIOI and MAP than between MAP or the NWIOI and EOBS. These results suggest that, although there is generally good temporal agreement between the three datasets, EOBS should be treated with caution, especially for extreme indices over the GAR region, and it does not provide reliable climatology over the NWI region. The high agreement between MAP and NWIOI, on the other hand, builds confidence in using these datasets. Users should consider carefully the limitations of the gridded observations available: the uncertainties of the observed datasets cannot be neglected in the overall uncertainties cascade that characterizes climate change studies.

We analyse the recent evolution of fires in Catalonia (north-eastern Iberian Peninsula), a typical Mediterranean region. We examine a homogeneous series of forest fires in the period 1970–2010. During this period, more than 9000 fire events greater than 0.5 ha were recorded, and the total burned area was more than 400 kha. Our analysis shows that both the burned area and number of fire series display a decreasing trend. Superposed onto this general decrease, strong oscillations on shorter time scales are evident. After the large fires of 1986 and 1994, the increased effort in fire prevention and suppression could explain part of the decreasing trend. Although it is often stated that fires have increased in Mediterranean regions, the higher efficiency in fire detection could have led to spurious trends and misleading conclusions.

Forest fires are a serious environmental hazard in southern Europe. Quantitative assessment of recent trends in fire statistics is important for assessing the possible shifts induced by climate and other environmental/socioeconomic changes in this area. Here we analyse recent fire trends in Portugal, Spain, southern France, Italy and Greece, building on a homogenized fire database integrating official fire statistics provided by several national/EU agencies. During the period 1985-2011, the total annual burned area (BA) displayed a general decreasing trend, with the exception of Portugal, where a heterogeneous signal was found. Considering all countries globally, we found that BA decreased by about 3020 km(2) over the 27-year-long study period (i.e. about -66% of the mean historical value). These results are consistent with those obtained on longer time scales when data were available, also yielding predominantly negative trends in Spain and France (1974-2011) and a mixed trend in Portugal (1980-2011). Similar overall results were found for the annual number of fires (NF), which globally decreased by about 12600 in the study period (about -59%), except for Spain where, excluding the provinces along the Mediterranean coast, an upward trend was found for the longer period. We argue that the negative trends can be explained, at least in part, by an increased effort in fire management and prevention after the big fires of the 1980's, while positive trends may be related to recent socioeconomic transformations leading to more hazardous landscape configurations, as well as to the observed warming of recent decades. We stress the importance of fire data homogenization prior to analysis, in order to alleviate spurious effects associated with non-stationarities in the data due to temporal variations in fire detection efforts.

Most of the total burned area in Europe occurs in Mediterranean regions, with severe economic and environmental damage, life loss and an average of about 4500 km2 burned every year. A better understanding of the impacts on wildfires of environmental and socioeconomic changes is crucial to develop adequate measures of prevention, adaptation and mitigation in this area. Here we focus on the impact of droughts on fires in European Mediterranean regions (Portugal, Spain, the south of France, Italy, Greece). This goal will be achieved through three specific supporting objectives: (1) Understanding past changes in fires in this region (extending the study of [1]); (2) Comparing and analyzing different drought indices (e.g. SPI, SPEI and SSI; see [2, 3] for more details on those indices); (3) Modeling the interaction between drought and fires (following and extending the study of [4]). We develop relatively simple regression models that link the fire activity to the key climate drivers. These models could be used to estimate fire responses to different climate change projections and environmental and socioeconomic scenarios ([5]).

We analyse the observed climate-driven changes in summer wildfires and their future evolution in a typical Mediterranean environment (NE Spain). By analysing observed climate and fire data from 1970 to 2007, we estimate the response of fire number (NF) and burned area (BA) to climate trends, disentangling the drivers responsible for long-term and interannual changes by means of a parsimonious Multi Linear Regression model (MLR). In the last forty years, the observed NF trend was negative. Here we show that, if improvements in fire management were not taken into account, the warming climate forcing alone would have led to a positive trend in NF. On the other hand, for BA, higher fuel flammability is counterbalanced by the indirect climate effects on fuel structure (i.e. less favourable conditions for fine-fuel availability and fuel connectivity), leading to a slightly negative trend. Driving the fire model with A1B climate change scenarios based on a set of Regional Climate Models from the ENSEMBLES project indicates that increasing temperatures promote a positive trend in NF if no further improvements in fire management are introduced.

The realization of innovative transport services, require increasingly greater flexibility and inexpensiveness of the service. In many cases the solution is to realize a demand responsive transportation system, in which there are two main goals: minimize costs and maximize flexibility. In this work, we address a Demand Responsive Transport System capable of managing incoming transport demand using a solution based on an insertion heuristics to solve an On-Line dynamic DaRP instance. The solutions provided by the heuristics are simulated dynamically in a discrete events environment in which it is possible to reproduce the movement of the vehicles, the passengers’ arrival to the stops, the delays due to the traffic congestion and possible anomalies in the behavior of the passengers. Finally, at the end of the simulation, a set of performance indicators summarize the solution planned by the heuristics.

The aim of this paper is to quantitatively characterize the climatology of daily precipitation indices in Catalonia (northeastern Iberian Peninsula) from 1951 to 2003. This work has been performed analyzing a subset of the ETCCDI (Expert Team on Climate Change Detection and Indices) precipitation indices calculated from a new interpolated dataset of daily precipitation, namely SPAIN02, regular at 0.2° horizontal resolution (around 20 km) and from two high-quality stations: the Ebro and Fabra observatories. Using a jack-knife technique, we have found that the sampling error of the SPAIN02 regional averaged is relatively low. The trend analysis has been implemented using a Circular Block Bootstrap procedure applicable to non-normal distributions and autocorrelated series. A running trend analysis has been applied to analyze the trend persistence. No general trends at a regional scale are observed, considering the annual or the seasonal regional averaged series of all the indices for all the time windows considered. Only the consecutive dry days index (CDD) at annual scale shows a locally coherent spatial trend pattern; around 30% of the Catalonia area has experienced an increase of around 2–3 days decade−1. The Ebro and Fabra observatories show a similar CDD trend, mainly due to the summer contribution. Besides this, a significant decrease in total precipitation (around −10 mm decade−1) and in the index "highest precipitation amount in five-day period" (RX5DAY, around −5 mm decade−1), have been found in summer for the Ebro observatory.

MedECC has published the First Mediterranean Assessment Report (MAR1) on the current state and expected risks of climate and environmental change in the Mediterranean Basin. The Chapter 2 "Drivers of change" describes characteristics and evolution of human-induced and natural factors that cause changes in the Mediterranean Basin ecosystems and human systems. In order to cover most major risks for people and biodiversity, four broad domains of change drivers are considered: climate change and variability, pollution, land and sea use changes and non-indigenous species., Preferred citation: Cherif S, Doblas-Miranda E, Lionello P, Borrego C, Giorgi F, Iglesias A, Jebari S, Mahmoudi E, Moriondo M, Pringault O, Rilov G, Somot S, Tsikliras A, Vila M, Zittis G 2020 Drivers of change. In: Climate and Environmental Change in the Mediterranean Basin – Current Situation and Risks for the Future. First Mediterranean Assessment Report [Cramer W, Guiot J, Marini K (eds.)] Union for the Mediterranean, Plan Bleu, UNEP/MAP, Marseille, France, pp. 59-180, https://doi.org/10.5281/zenodo.7100601.

MedECC has prepared the First Mediterranean Assessment Report (MAR1) on the current state and expected risks of climate and environmental change in the Mediterranean Basin. The Chapter 4 is dedicated to the Marine ecosystems, Coastal ecosystems,Terrestrial and freshwater ecosystems.

Chapter 3.2: Resources – Food of the First Mediterranean Assessment Report (MAR1) published by MedECC in November 2020., Preferred citation: Mrabet R, Savé R, Toreti A, Caiola N, Chentouf M, Llasat MC, Mohamed AAA, Santeramo FG, Sanz-Cobena A, Tsikliras A 2020: Food. In: Climate and Environmental Change in the Mediterranean Basin – Current Situation and Risks for the Future. First Mediterranean Assessment Report [Cramer W, Guiot J, Marini K (eds.)] Union for the Mediterranean, Plan Bleu, UNEP/MAP, Marseille, France, pp. 237-264, doi:10.5281/zenodo.7101080.