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As societal exposure to large fires increases, there is a growing concern about possible shifts in the fire regimes due to climate change. Understanding the response of fires to climatic variations is essential to adapt fire management systems and to design future prevention strategies. However, many aspects on this topic remain to be revealed.ONFIRE, a Spanish national research project supported by the State Investigation Agency and by Ministry of Ministry of Science and Innovation, aims to push the state-of-the-art knowledge of climate impacts on fires beyond its current limitations and applications. Moreover, this project is open to any contributions. Specifically, we encourage any researcher/fire agency to joint this initiative.In this contribution we show some preliminary results related to the main objectives of the ONFIRE project:The creation of a first-of-its-kind unified, open-access and user-friendly database comprised of all available burned area records from national inventories. A better understanding of past trends in fire series and their attribution to the anthropogenic component of climate change. An assessment of the spatio-temporal synchronicity of fire danger. The design and implementation of a public operational prototype system to perform global seasonal predictions of climate-driven fire risk for decision-making applications. AcknowledgmentsWe acknowledge funding through the project ONFIRE, grant PID2021-123193OB-I00, funded by MCIN/AEI/ 10.13039/501100011033. A.G. thanks the Ministerio de Ciencia, Innovación y Universidades of Spain for PhD contract FPU19/06536.

In light of the ongoing global climate change, a better understanding of global wildfire activity is the key to anticipate future impacts and minimize, as much as possible, their negative consequences on natural ecosystems and human economy. In this work we present a global-scale analysis of the inter-annual synchronicity of wildfires using two types of probabilistic networks, able to capture the underlying structures in the burned area data from two different approaches: correlation-based and bayesian networks. By studying their properties through centrality measures and community detection for the complex network, and inference from the Bayesian network, we seek to gain a better understanding of the interrelationships between fire activity in different regions of the planet, highlighting the most important teleconnection patterns. We expect this exploratory analysis to aid in the development and testing of plausible hypotheses about the underlying mechanisms supporting these relationships.

Seasonal forecasts of meteorological drought can help decision-making for weather-driven wildfires (Turco et al., 2018). However, one of the main drawbacks of drought prediction lies in the uncertainty of monitoring precipitation in near-real time. In this contribution we assess the predictability of the Standardized Precipitation Index (SPI) on a global scale, combining 11 datasets (DROP; Turco et al., 2020) as observed initial conditions with empirical and dynamic predictions of precipitation. The empirical predictions are based on the ensemble-based streamflow prediction system (ESP, an ensemble-based reordering of historical data) and the dynamics on the new generation seasonal prediction model developed by ECMWF (System 5; S5). Although both systems show comparable quality, S5 performs better at longer forecast timescales, especially over tropical regions.Subsequently, we investigate whether the S5 seasonal forecasts can predict area burned anomalies on a global scale. To do so, we link the seasonal climate predictions of S5 to an empirical climate-fire model, using standard regression techniques in the framework of generalised linear models. The seasonal climate predictions of S5 have shown high and significant performance (with a mean relative operating characteristic “ROC” area value of 0.87) over a large fraction of the burnable area (~47%).In summary, given that all data are publicy available in near real time, our results provide a basis for the development of a global probabilistic seasonal drought and burned area forecast product.ReferencesTurco, M., Jerez, S., Doblas-Reyes, F. J., AghaKouchak, A., Llasat, M. C., & Provenzale, A. (2018). Skilful forecasting of global fire activity using seasonal climate predictions. Nature Communications, 9(1), 1–9.Turco, M., Jerez, S., Donat, M. G., Toreti, A., Vicente-Serrano, S. M., & Doblas-Reyes, F. J. (2020). A global probabilistic dataset for monitoring meteorological droughts. Bulletin of the American Meteorological Society, 101(10), E1628–E1644.AcknowledgementsWe acknowledge funding through the project ONFIRE, grant PID2021-123193OB-I00,funded by MCIN/AEI/ 10.13039/501100011033.

This study assesses how the CMIP6 simulations capture the non-stationarity of the main source of winter climate variability in the Euro-Atlantic region, the North Atlantic Oscillation (NAO), observed in the recent past.For that purpose, we characterise the NAO long-term variability in climate reanalysis, analysing their features in several 30-year periods since 1851; and we evaluate whether CMIP6 historical simulations capture all the observed NAO “types”. Although the literature sometimes assumes that the NAO pattern is stationary, three groups of NAO pattern have been proved in the reanalyses depend on the location of their Action Centres (ACs): 1) the north AC locates over Iceland and the south AC in Azores, 2) the north AC locates over Southern Greenland and the south AC in the Western Mediterranean, and 3) the north AC locates over Northern Scandinavia and the south AC in the Azores.Our main finding is that the NAO long-term variability is not accurately captured by all CMIP6 models. In particular, the overestimation of the NAO group 3 is remarkable in most simulations. This NAO group mainly represents the last decades, which the literature has addressed with much interest for its exceptional features (e.g. NAO+ strengthening and northeastward shift of its north AC), and which has been generally associated with the anthropogenic warmer climate. We also found underestimation of NAO group 2.We have also found that each NAO group could be associated with precipitation anomalies in Europe. For example, the NAO group 3 implies drier(wet) conditions in the south(north). While group 2 implies the opposite pattern of anomalies. Therefore, we have reason to suggest that the lack of accuracy of models reproducing the non-stationarity of NAO may explain some of the bias in the expected changes of winter precipitation in Europe for future scenarios.

This contribution seeks to better understand recent changes in synchronous fire danger across Europe that can overwhelm fire suppression capacity. We analyze the spatio-temporal synchronicity of fire danger in Europe over the period 1979-2021 based on the Canadian Fire Weather Index, one of the most commonly used fire indices globally (FWI; Vitolo et al. 2020). The daily synchronicity index indicates the total area with a level of FWI above 50, that represents the extreme fire danger threshold as classified by the European Forest Fire Information System (EFFIS). The annual mean surface affected by synchronicity extreme fire danger increased by about 100000 km2 over the 42-year-long study period (i.e. 57% of the mean historical value). The expansion of synchronized fire potential can compromise fire management efforts.ReferencesVitolo, C., Di Giuseppe, F., Barnard, C., Coughlan, R., San-Miguel-Ayanz, J., Libertá, G., & Krzeminski, B. (2020). ERA5-based global meteorological wildfire danger maps. Scientific data, 7(1), 1-11.AcknowledgmentsWe acknowledge funding through the project ONFIRE, grant PID2021-123193OB-I00, funded by MCIN/AEI/ 10.13039/501100011033. A.G. thanks the Ministerio de Ciencia, Innovación y Universidades of Spain for PhD contract FPU19/06536.

Understanding the response of fire to climate variations is essential to adapt fire management systems under climate change. Although several studies have analysed the drivers of the average spatial variability of fire, the assessment of the temporal variability of fire in response to climate across the globe has proved challenging, largely due to complexity of the processes involved, the limitation of observation data and the compound effect of the multiple drivers, which usually cause non-linear effects.In this study, we analyse how much of the interannual variability in observed burned area (BA) is linked with temporal variations in climate at global scale. To solve this question, we use the burned area data of the FireCCI51. product for the period 2001-2019 at the global scale, and different climate metrics that are directly related to drought occurrence, including indices like the Fire Weather Index (FWI), the Standardized Precipitation Evapotranspiration Index (SPEI), and the Standardized Precipitation Index (SPI). Our study shows complex spatial patterns in the relationship between climate drivers and BA variability, highlighting where variations in FWI, SPI, SPEI or their interaction explain BA variability. While in some areas the interannual variability of burned area does not show a statistically significant influence of climate variability, over a substantial portion of the global burnable area (~60%) the BA variability can be explained by interannual variability of climate drivers. Globally, climate variability accounts for roughly two thirds (64%) of the observed temporal BA variability.

M.T. has received funding from the European Union’s Horizon 2020 Research And Innovation Programme under the Marie Sklodowska-Curie Grant Agreement 740073 (CLIM4CROP project) and from the Spanish Ministry of Science, Innovation and Universities through the project PREDFIRE (RTI2018-099711-J-I00), which is cofinanced with the European Regional Development Fund (ERDF/FEDER). S.J. was supported by the Spanish Ministry of Science, Innovation and Universities through the project EASE (RTI2018-100870-A-I00), the Fundacion Seneca—Regional Agency for Science and Technology of Murcia through the CLIMAX project (20642/JLI/18) and by the Plan Propio de Investigacion of the University of Murcia (Grant UMU-2017-10604). M.G.D. acknowledges funding by the Spanish Ministry of Science, Innovation and Universities Ramon y Cajal Grant Reference RYC-2017-22964. The authors thank the data providers listed in Table 1 for providing access to these datasets. Special thanks to Dr. Meng Zhao to provide the GRACE data and Dr. Hong Xuan Do for providing R scripts to read and process the GSIM data.

Solar and wind power curves typically exhibit inverted daily and annual cycles. However, their monthly anomalies show both positive and negative low correlation values across Europe, which compromises the effectiveness of their integration in the energy grid. This is because the well-known asymmetric response of the resources to the main large-scale teleconnection patterns vanishes and/or shows low synchronicity when the compound effect of these patterns is considered, as we show here. So we propose a step-wise method to help narrowing the monthly deviations of the total wind-plus-solar electricity production at the regional level from a given curve (here, the mean annual cycle of the total production), applied here across five continuous European regions but with straight application elsewhere and at other temporal scales. It detects the optimal shares of each power over previously identified sub-regions with homogeneous temporal variability of the monthly anomalies of the wind and solar capacity factors. Results show that, keeping the current total regional shares, just through a smart distribution of the power units, the standard deviation of the monthly anomalies of the total wind-plus-solar production is reduced up to 20% without loss in the mean capacity factor as compared to a base scenario with uniform distribution of the installations. This reduction grows above 50% if the total regional shares also came into the optimization game. Acknowledgments:This study was supported by the Spanish Ministry of Science, Innovation and Universities – Agencia Estatal de Investigación and the European Regional Development Fund through the project EASE (RTI2018-100870-A-I00), and by the Fundación Séneca – Agencia de Ciencia y Tecnología de la Región de Murcia through the project CLIMAX (20642/JLI/18).

Mediterranean ecosystems are prone to forest fires, as evidenced by several extreme fire seasons which struck in the last two decades, including both western (2003, 2005, 2017) and eastern (2007, 2018, 2021) Mediterranean sectors. These fire seasons had a massive impact on the economy and the environment, having also caused many human casualties, including 145 in Portugal 2017 and about 100 in Greece 2018. Moreover, it is now widely accepted that these outstanding fire seasons are often associated with unusually intense droughts and heatwaves (Turco et al., 2019; Ruffault et al, 2020). Additionally, there is strong evidence that the frequency of drought events in the Mediterranean basin has increased significantly in the last decades and is bound to increase further under different climate change scenarios (Tramblay et al., 2020).The relentless tendency for increasing summer temperatures in Europe in recent decades, when compared to the last 500 hundred years, also underlines that the increment in temperatures is extensive to central and Scandinavian countries (Sousa et al., 2020), where forest fires have become considerably more frequent. Recent assessments have emphasised the synergy between drought and extremely hot summers in the Mediterranean (Russo et al., 2020).In addition to this climate change scenarios point to a likely increase in the frequency of two specific heat-induced fire-weather types, precisely those that have been related to the largest wildfires observed in recent years (Ruffault et al., 2020). Heat-induced fire-weather types are characterized by compound dry and warm conditions occurring during summer heatwaves, either under moderate (heatwave type) or intense (hot drought type) drought. The frequency of heat-induced fire-weather is projected to increase by 14% by the end of the century (2071-2100) under the RCP4.5 scenario, and by 30% under the RCP8.5. In summary, these results consistently suggest that the frequency and extent of wildfires will increase throughout the Mediterranean Basin. Ruffault J., Curt T., Moron V., Trigo R.M., Mouillot F., Koutsias N., Pimont F., Martin-StPaul N., Barbero R., Dupuy J.-L., Russo A., Belhadj-Khedher C., (2020) Scientific Reports, 10, 13790, doi: 10.1038/s41598-020-70069-zRusso A., Gouveia C.M., Dutra E., Soares P.M.M., Trigo R.M. (2019) Environmental Research Letters, 14(1), 014011, doi: 10.1088/1748-9326/aaf09eSousa P., Barriopedro D., García-Herrera R., Ordoñez C., Soares P.MM, Trigo R.M. (2020) Communications Earth & Environment, 1, 48, doi: 10.1038/s43247-020-00048-9Turco M., Jerez S., Augusto S., Tarín-Carrasco P., Ratola N., Jimenez-Guerrero P., Trigo, R.M. (2019) Scientific Reports, 9, 1, doi: 10.1038/s41598-019-50281-2 This work was supported by national funds through FCT (Fundação para a Ciência e a Tecnologia, Portugal) under project FIRECAST (PCIF/GRF/0204/2017). M.T. is supported by the Spanish Ministry of Science, Innovation and Universities - Spanish State Research Agency and the European Regional Development Fund through the PREDFIRE projects (RTI2018-099711-J-I00, MCI/AEI/FEDER, EU) and the Ramón y Cajal grant (RYC2019-027115-I). S.J. thanks the Spanish Ministry of Science, Innovation and Universities - Agencia Estatal de Investigación and the European Regional Development Fund for the support received through the EASE project (RTI2018 100870 A I00).

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.

Here we present the first assessment of climate change impacts on the temporal variability of the joint production of wind and solar photovoltaic (PV) power across Europe. For that we adopted regional and continental perspectives (assuming a single European electricity grid), considered several temporal frequencies (from daily to annual), used state-of-the-art regional climate projections together with a climate-production model, and assumed a future massive deployment of wind and PV power installations. Results support that the spatio-temporal complementarity between the wind and solar resources helps to minimize the temporal variability of the combined production under both present (1971–2000) and future (2070–2099) climate conditions similarly. Thus the projected changes are overall negligible (well below ±5%). However, an additional assessment of theoretical upper/bottom bounds for these changes indicated significant potential increases in the stability of the joint production ranging from 5 to 25% across regions, 15% at the continental scale. This would be subordinated to the feasibility of reaching, with the future deployment strategies, individual wind and PV power production series with a perfect temporal anticorrelation. These results may encourage stakeholders to take holistically optimized decisions.

Biomass burning is one of the most critical factors impacting vegetation and atmospheric trends, with important societal implications, particularly when extreme weather conditions occur. Trends and factors of burned area (BA) have been analysed at regional and global scales, but little effort has been dedicated to study the interannual variability. This paper aimed to better understand factors explaining this variation, under the assumption that the more human control of fires the more frequently they occur, as burnings will be less dependent of weather cycles. Interannual variability of BA was estimated from the coefficient of variation of the annual BA (BA_CV) estimated from satellite data at 250 m, covering the period from 2001 to 2018. These data and the explanatory variables were resampled at 0.25-degree resolution for global analysis. Relations between this variable and explanatory factors, including human and climate drivers, were estimated using Random Forest (RF) and generalized additive models (GAM). BA_CV was negatively related to BA_Mean, implying that areas with higher average BA have lower variability as well. Interannual BA variability decreased when maximum temperature (TMAX) and actual and potential evapotranspiration (AET, PET) increased, cropland and livestock density increased and the human development index (HDI) values decreased. GAM models indicated interesting links with AET, PET and precipitation, with negative relation with BA_CV for the lower ranges and positive for the higher ones, the former indicating fuel limitations of fire activity, and the latter climate constrains. For the global RF model, TMAX, AET and HDI were the main drivers of interannual variability. As originally hypothesised, BA_CV was more dependent on human factors (HDI) in those areas with medium to large BA occurrence, particularly in tropical Africa and Central Asia, while climatic factors were more important in boreal regions, but also in the tropical regions of Australia and South America.

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).

Regional climate models (RCMs) are usually initialized and driven through the boundaries of their limited area domain by data provided by global models (GCMs). The mismatch between the low‐resolution GCM initial conditions and RCM's high resolution introduces physical inconsistencies between the various components of the RCM. These inconsistencies can be resolved by running the RCM during a period that is considered unreliable: the spin‐up period. There is no deterministic definition of the length that the spin‐up period should have. Here we try to provide general guidelines that can be used to the advantage of the community. We base our analysis on Weather Research and Forecasting (WRF) simulations over a Euro‐Cordex compliant domain and find that for 2‐m temperature and precipitation, rather short spin‐up periods (1 week) can be sufficient. Nevertheless, longer periods (6 months) are advisable, and start dates in non‐winter months should be pursued, as this ensures a more realistic representation of the snow cover. Thus, the issue is not only about the spin‐up length. As the soil subsystem evolves slowly and requires longer periods to reach equilibrium than the longest considered here (1 year), seasonality plays an important role in minimizing the impact of the unreliability of the soil initialization. Fortunately, except for goals where the deep soil‐atmosphere feedback are critical, the lack of equilibrium between them can be ignored, as it seems to have little effect on the simulation of the atmospheric variables most frequently used in RCM studies.

Accurate and timely information of evolving drought conditions is crucial to take early actions and alleviate their impacts. A number of drought datasets is already available. They cover the last three decades and provide data in near-real time (using different sources), but they are all "deterministic" (i.e. single realisation), and data partly differ between them. Here we first evaluate the quality of long-term and continuous climate data for timely meteorological drought monitoring considering the Standardized Precipitation Index. Then, by applying an ensemble approach, similarly to weather/climate prediction studies, we develop DROP (DROught Probabilistic; Turco et al. 2020), a new global land gridded dataset to monitor meteorological drought that gathers an ensemble of observation-based datasets providing near-real time estimates with associated uncertainty. This approach makes the most of the available information and brings it to the end-users. DROP, publicly available at https://drop.shinyapps.io/DROP/, is operationally updated every monthly and provides drought information in near-real time, i.e., up to the previous month. The high-quality and probabilistic information provided by DROP is useful for monitoring applications, and may help to develop global policy decisions on adaptation priorities in alleviating drought impacts, especially in countries where meteorological monitoring is still challenging. ReferencesTurco M, Jerez S, Donat M, Toreti M, Vicente-Serrano S M, Doblas-Reyes, F J. (2020). A global probabilistic dataset for monitoring meteorological droughts. Bulletin of the American Meteorological Society. Under review. AcknowledgmentsM.T. has received funding from the Spanish Ministry of Science, Innovation and Universities through the project PREDFIRE (RTI2018-099711-J-I00).

The increase in societal exposure and vulnerability to drought, call to move from post-crisis to pre-impact drought risk management. Accurate and timely information of evolving drought conditions is crucial to take early actions to avoid devastating long-term impacts. A previous study indicated that a statistical empirical method, the ensemble streamflow prediction system (ESP; an ensemble based on reordering historical data), represents a computationally fast alternative to dynamical prediction applications for drought prediction (Turco et al. 2017). Extending this work, here we present an assessment of the ability of the ESP method in predicting the drought of 2017 in Spain considering also the uncertainties coming from the observations. For this, four different datasets are used: that cover a period of 36 years (1981-2017) and with a spatial resolution of 0.25 x 0.25º based on observations of interpolated stations (E-OBS, AEMET), on reanalysis data (ERA5), and on combining stations and satellite data (CHIRPS). Meteorological droughts are defined using the Standardized Precipitation Index aggregated over the months April–September. All the datasets show a similar spatial pattern, with most of the domain suffering extreme drought conditions. In addition, the ESP system achieves reasonable skill in predicting this drought event 2 months in advance with, again, similar pattern among the different datasets. These results suggest the feasibility of the development of an operational early warning system, also considering that the data of CHIRPS and ERA5 are updated every month, i.e., that are available for near-real time applications. ReferencesTurco, M., et al. (2017). Summer drought predictability over Europe: empirical versus dynamical forecasts. Environmental Research Letters, 12(8), 084006. AcknowledgmentsThe authors acknowledge the ACEX project (CGL2017-87921-R) of the Ministerio de Economía y Competitividad of Spain. AHM thanks his predoctoral contract FPU18/00824 to the Ministerio de Ciencia, Innovación y Universidades of Spain. M.T. has received funding from the Spanish Ministry of Science, Innovation and Universities through the project PREDFIRE (RTI2018-099711-J-I00).

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

L’11 marzo 2020 l’Organizzazione Mondiale della Sanità (OMS) dichiara la malattia da nuovo coronavirus 2019 (COVID-19) una pandemia globale e la Lombardia risulta fin da subito la regione italiana più drammaticamente colpita. L’impatto della pandemia e del lockdown, in termini di conseguenze sulla salute mentale di pazienti, cittadini e operatori sanitari è ormai noto, ampiamente discusso e universalmente descritto, anche in luoghi e Paesi culturalmente e socialmente molto diversi tra di loro. Tutto ciò ha condotto a profondi cambiamenti nell’organizzazione dei Servizi ad opera dei Dipartimenti di Salute Mentale e Dipendenze (DSMD) e si è globalmente assistito, soprattutto in una fase iniziale, ad una contrazione delle prestazioni erogate (sia ospedaliere che territoriali) e ad un incremento degli interventi erogati in modalità telematica, con la definizione di procedure, linee guida e modalità operative alternative nei diversi Servizi afferenti ai DSMD. In questo lavoro vengono descritti alcuni esempi dei cambiamenti avvenuti all’interno del DSMD dell’ASST di Monza nel periodo marzo-maggio 2020. La ricaduta sul lungo periodo in termini psicopatologici e psicosociali di un evento di tale portata rimane complessa e, ad ora, solo in parte prevedibile in relazione alla sua evoluzione nel prossimo futuro: tale esperienza porrà nuove e fondamentali sfide ai Dipartimenti di Salute Mentale e, più in generale, ai Servizi sanitari, che si troveranno a ridefinire priorità e a rimodulare piani di intervento che consentano una rapida riorganizzazione in caso di nuove circostanze emergenziali ed il reperimento di risorse in grado di garantire interventi più rapidi, appropriati ed efficaci.

28 Pags.- 17 Figs.- 3 Tabls., We analysed long‐term variability and trends in meteorological droughts across Western Europe using the Standardized Precipitation Index (SPI). Precipitation data from 199 stations spanning the period 1851–2018 were employed, following homogenisation, to derive SPI‐3 and SPI‐12 series for each station, together with indices on drought duration and severity. Results reveal a general absence of statistically significant long‐term trends in the study domain, with the exception of significant trends at some stations, generally covering short periods. The largest decreasing trends in SPI‐3 (i.e., increasing drought conditions) were found for summer in the British and Irish Isles. In general, drought episodes experienced in the last two or three decades have precedents during the last 170·years, emphasizing the importance of long records for assessing change. The main characteristic of drought variability in Western Europe is its strong spatial diversity, with regions exhibiting a homogeneous temporal evolution. Notably, the temporal variability of drought in Western Europe is more dominant than long‐term trends. This suggests that long‐term drought trends cannot be confirmed in Western Europe using precipitation records alone. This study provides a long‐term regional assessment of drought variability in Western Europe, which can contribute to better understanding of regional climate change during the past two centuries., This work was supported by the research projects PCIN‐2015‐220 and CGL2017‐82216‐R financed by the Spanish Commission of Science and Technology and FEDER, IMDROFLOOD financed by the WaterWorks 2014 co‐funded call of the European Commission, CROSSDRO financed by the AXIS (Assessment of Cross[X] ‐ sectorial climate Impacts and pathways for Sustainable transformation) JPI‐Climate co‐funded call of the European Commission, 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), FCT (PT) with co‐funding by the European Union (Grant 690462), Irish Research Council COALESCE grant (COALESCE/2019/43). Dhais Peña‐Angulo received a ‘Juan de la Cierva’ postdoctoral contract (FJCI‐2017‐33652 Spanish Ministry of Economy and Competitiveness, MEC), Marco Turco has received funding from the Spanish Ministry of Science, Innovation and Universities through the project PREDFIRE (RTI2018‐099711‐J‐I00).

International audience; High-resolution meteorological data are necessary to understand and predict climate-driven impacts on the structure and function of terrestrial ecosystems. However, the spatial resolution of climate reanalysis data and climate model outputs is often too coarse for studies at local/landscape scales. Additionally, climate model projections usually contain important biases, requiring the application of statistical corrections. Here we present 'meteoland', an R package that integrates several tools to facilitate the estimation of daily weather over landscapes, both under current and future conditions. The package contains functions: (1) to interpolate daily weather including topographic effects; and (2) to correct the biases of a given weather series (e.g., climate model outputs). We illustrate and validate the functions of the package using weather station data from Catalonia (NE Spain), re-analysis data and climate model outputs for a specific county. We conclude with a discussion of current limitations and potential improvements of the package.

Background Oligoarticular juvenile idiopathic arthritis (oJIA) is the most frequent form of chronic arthritis in children; the clinical course is extremely variable. In this study we have characterized by flow cytometry synovial B and T cells subsets in patients with oJIA in order to identify any parameters that could predict a more aggressive course of disease. Methods B and T cells from synovial fluid (SF) of 39 patients with oJIA were characterized by flow cytometry. In 22 patients SF was analysed at the onset of the disease (GroupA), in 17 SF was analysed at articular relapse (Group B). All patients in Group A were followed up for at least for 2 years after SF analysis: 13 patients relapsed during the follow-up period. Results Comparison of SF from Group A and Group B demonstrated an activated phenotype in relapsed patients, with higher Switched Memory B cells (58.53 vs 36.07% of CD19+, p-value 0.004) and lower Naive B cells (8.53 vs 25.9 of CD19+, p-value 0.002) in Group B. Furthermore, patients from Group A who did not relapse showed lower percentages of synovial DNT (2.38 vs 1.50% of CD3+TCRalpha/beta+, p-value 0.025) and γδ T cells (19.1 vs 15.0% of CD3+ cells, p-value 0.004) at the onset, if compared with other Group A patients. Conclusions In oJIA relapse SF present an activated B phenotype. Patients at disease onset with DNTs

This contribution explores the evolution of the flood risk in the Metropolitan Area of Barcelona (MAB; Northeast Spain) from 1981 to 2015, and how it has been affected by changes in land use, population and precipitation. To complete this study, we analysed PRESSGAMA and INUNGAMA databases to look for all the information related to the floods and flash floods that have affected the chosen region. The ''Consorcio de Compensación de Seguros'', a state insurance company for extraordinary risks, provided data on economic damage. The extreme precipitation trend was analysed by the Fabra Observatory and El Prat-Airport Observatory, and daily precipitation data were provided by the State Meteorological Agency of Spain (AEMET) and the Meteorological Service of Catalonia (SMC). Population data were obtained from the Statistical Institute of Catalonia (IDESCAT). Changes in land use were estimated from the land use maps for Catalonia corresponding to 1956, 1993, 2000, 2005 and 2009. Prevention measures like rainwater tanks and improvements to the drainage system were also been considered. The specific case of Barcelona is presented, a city recognised by United Nations International Strategy for Disaster Reduction as a model city for urban resilience to floods. The evolution of flood events in the MAB does not show any significant trend for this period. We argue that the evolution in floods can be explained, at least in part, by the lack of trend in extreme precipitation indices, and also by the improvements in flood prevention measures.

Offline Land-Surface Model (LSM) simulations are useful for studying the continental hydrological cycle. Because of the nonlinearities in the models, the results are very sensitive to the quality of the meteorological forcing; thus, high-quality gridded datasets of screen-level meteorological variables are needed. Precipitation datasets are particularly difficult to produce due to the inherent spatial and temporal heterogeneity of that variable. They do, however, have a large impact on the simulations, and it is thus necessary to carefully evaluate their quality in great detail. This paper reports the quality of two high-resolution precipitation datasets for Spain at the daily time scale: the new SAFRAN-based dataset and Spain02. SAFRAN is a meteorological analysis system that was designed to force LSMs and has recently been extended to the entirety of Spain for a long period of time (1979/80-2013/14). Spain02 is a daily precipitation dataset for Spain and was created mainly to validate Regional Climate Models. In addition, ERA-Interim is included in the comparison to show the differences between local high-resolution and global low-resolution products. The study compares the different precipitation analyses with rain gauge data and assesses their temporal and spatial similarities to the observations. The validation of SAFRAN with independent data shows that this is a robust product. SAFRAN and Spain02 have very similar scores, although the later slightly surpasses the former. The scores are robust with altitude and throughout the year, save perhaps in summer, when a diminished skill is observed. As expected, SAFRAN and Spain02 perform better than ERA-Interim, which has difficulty capturing the effects of the relief on precipitation due to its low resolution. However, ERA-Interim reproduces spells remarkably well, in contrast to the low skill shown by the high-resolution products. The high-resolution gridded products overestimate the number of precipitation days, which is a problem that affects SAFRAN more than Spain02 and is likely caused by the interpolation method. Both SAFRAN and Spain02 underestimate high precipitation events, but SAFRAN does so more than Spain02. The overestimation of low precipitation events and the underestimation of intense episodes will probably have hydrological consequences once the data are used to force a land surface or hydrological model. We are grateful to the French National Centre for Meteorological Research (CNRM UMR3539, Météo-France CNRS) for allowing us to use the code of the SAFRAN analysis system for our studies, the Spanish State Meteorological Agency (AEMET) for sharing their very valuable observational data with us and the European Centre for Medium-Range Weather Forecasts (ECMWF) for making their ERA-Interim product openly available. This is a contribution to the FP7 eartH2Observe project (http://www.earth2observer.eu), which received funding from the European Union’s Seventh Programme for research, technological development and demonstration under grant agreement no. 603608. This work has been funded by the Spanish Economy and Competitiveness Ministry and the European Regional Development Fund through grant CGL2013-47261-R. This work has been supported by the Metropolitan Area of Barcelona Project (no. 308321; flood evolution in the metropolitan area of Barcelona from a holistic perspective: past, present and future) and the Spanish Project HOPE (CGL2014-52571-R) supported by the Ministry of Economy and Competitiveness. This work is a contribution to the HyMeX program (Hydrological cycle in the Mediterranean EXperiment; http://www.hymex.org).

On-going changes in drought, vegetation and wildfires in Israel provide a key example of possible future evolution in transition areas at the border between Mediterranean and arid climates. Here we present multiple lines of evidence suggesting that drought conditions in Israel, representing the eastern Mediterranean, have increased during the period 1980–2014. Drought conditions were calculated using the Standardized Precipitation Evapotranspiration Index (SPEI), the Standardized Precipitation Index (SPI) and the Standardized Soil Moisture Index (SSI). A 30-year series (1982–2011) of monthly Fraction of Photosynthetically Active Radiation absorbed by vegetation (FPAR) indicates generally positive trends in winter and spring and negative ones in summer and autumn, except in the transition zone between the southern Negev desert and the Mediterranean climate region, where a statistically significant negative trend in all seasons was found. Available ground observations suggest that fire activity has decreased during the period 1987–2011. Apparent year-to-year oscillations are superposed onto these long-term trends. We show that inter-annual variability of summer fires is related to antecedent wet conditions and to above normal vegetation conditions. These relationships suggest the summer fires in Israel are mainly limited by fuel availability rather than by fuel flammability. On the other hand, the year-to-year variations of spring and autumn fires are significantly related with drought indices. Thus, the increase of drought conditions together with climate projections for further warming and drying in this region, point at a potential increase of fire risk in the intermediate seasons.

In this study we assess the suitability of a recently introduced analog-based Model Output Statistics (MOS) downscaling method (referred to as MOS-Analog) for climate change studies and compare the results with a quantile mapping bias correction method. To this aim, we focus on Spain and consider daily precipitation output from an ensemble of Regional Climate Models provided by the ENSEMBLES project. The reanalysis-driven Regional Climate Model (RCM) data provide the historical data (with day-to-day correspondence with observations induced by the forcing boundary conditions) to conduct the analog search of the control (20C3M) and future (A1B) global climate model (GCM)-driven RCM values. First, we show that the MOS-Analog method outperforms the raw RCM output in the control 20C3M scenario (period 1971–2000) for all considered regions and precipitation indices, although for the worst-performing models the method is less effective. Second, we show that the MOS-Analog method broadly preserves the original RCM climate change signal for different future periods (2011–2040, 2041–2070, 2071–2100), except for those indices related to extreme precipitation. This could be explained by the limitation of the analog method to extrapolate unobserved precipitation records. These results suggest that the MOS-Analog is a spatially consistent alternative to standard bias correction methods, although the limitation for extreme values should be taken with caution in cases where this aspect is relevant for the problem.

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.

Special Issue: VALUE: Validating and Integrating Downscaling Methods for Climate Change Research: et al., VALUE is an open European collaboration to intercompare downscaling approaches for climate change research, focusing on different validation aspects (marginal, temporal, extremes, spatial, process-based, etc.). Here we describe the participating methods and first results from the first experiment, using “perfect” reanalysis (and reanalysis-driven regional climate model (RCM)) predictors to assess the intrinsic performance of the methods for downscaling precipitation and temperatures over a set of 86 stations representative of the main climatic regions in Europe. This study constitutes the largest and most comprehensive to date intercomparison of statistical downscaling methods, covering the three common downscaling approaches (perfect prognosis, model output statistics—including bias correction—and weather generators) with a total of over 50 downscaling methods representative of the most common techniques. Overall, most of the downscaling methods greatly improve (reanalysis or RCM) raw model biases and no approach or technique seems to be superior in general, because there is a large method-to-method variability. The main factors most influencing the results are the seasonal calibration of the methods (e.g., using a moving window) and their stochastic nature. The particular predictors used also play an important role in cases where the comparison was possible, both for the validation results and for the strength of the predictor–predictand link, indicating the local variability explained. However, the present study cannot give a conclusive assessment of the skill of the methods to simulate regional future climates, and further experiments will be soon performed in the framework of the EURO-CORDEX initiative (where VALUE activities have merged and follow on). Finally, research transparency and reproducibility has been a major concern and substantive steps have been taken. In particular, the necessary data to run the experiments are provided at http://www.value-cost.eu/data and data and validation results are available from the VALUE validation portal for further investigation: http://www.value-cost.eu/validationportal., This work has been performed in the framework of the VALUE COST Action ES1102, under FP7 programme J.M.G. and S.H. acknowledge partial funding from MULTI-SDM project (MINECO/FEDER, CGL2015-66583-R). B.H. and D.R. acknowledge COMPLEX project (FP7-ENV-2012, No: 308601). M.T. was supported by HOPE project (MINECO, CGL2014-52571-R). Participation of M.D., P.S. and R.H. was funded by the Ministry of Education, Youth, and Sports of the Czech Republic contracts LD12029, LD14043 and LD12059, respectively.

Summarization: Droughts can have strong environmental and socio-economic impacts in the Mediterranean region, in particular for countries relying on rain-fed agricultural production, but also in areas in which irrigation plays an important role and in which natural vegetation has been modified or is subject to water stress. The purpose of this review is to provide an assessment of the complexity of the drought phenomenon in the Mediterranean region and present various perspectives on drought in the present and under future climate change scenarios. The projections of various model experiments on future climate change scenarios strongly agree on an increased frequency and severity of droughts in the Mediterranean basin. Nevertheless, given the complexity of the phenomenon, with different types of droughts and complex interrelated impacts, significant future uncertainties remain. For example, uncertainties are stronger for hydrological droughts than meteorological droughts due to human influences and water withdrawal. Significant drought impacts are expected in the future, in particular for developing countries in the southern and eastern parts of the Mediterranean basin. To improve the resilience and adaptive capacities of societies and environments faced with drought, we aim to provide an overview of the key issues in research on climate change impacts on droughts, with a specific focus on the Mediterranean region, in order to: i) redefine more meaningful drought metrics tailored to the Mediterranean context, ii) better take into account vegetation and its feedback on droughts, iii) improve the modelling and forecasting of drought events through remote sensing and land surface models, and iv) promote a more integrated vision of droughts taking into account both water availability and water use. This overview reflects the complexity of the problem and the need to combine scientific research with adaptation solutions to deal with drought in the future. Presented on