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Abstract. This study focuses on the relationship between satellite-measured fraction of absorbed photosynthetically active radiation (fAPAR) and crop yield cereals in Europe. Different features of the relationship between annual yield and multiple time series of fAPAR, collected during different periods of the year, were investigated. The two key outcomes of the analysis are the identification of the period: (i) from March to October as the one having the highest positive correlation between fAPAR and yield and (ii) from February to May as the period characterised by most of the estimated negative correlation. While both periods align well with the commonly assumed dynamic of the growing season, spatial differences are also observed across Europe. On the one hand, the Mediterranean regions report the highest correlation values (r>0.8) and the longest continuous periods with positive statistically significant results (up to 7 months), covering most of the growing season. On the other hand, the central
European region is characterised by the most limited positive correlation
values, with only 2 months or less showing statistically significant
results. While marked differences in the overall capability to capture the
full dynamic of yield are observed across Europe, fAPAR anomalies seem
capable of discriminating low-yield years from the rest in most of the cases.

Abstract. An early warning system for drought events can provide valuable information
for decision makers dealing with water resources management and international
aid. However, predicting such extreme events is still a big challenge. In
this study, we compare two approaches for drought predictions based
on forecasted precipitation derived from the Ensemble extended forecast model (ENS) of the ECMWF, and on forecasted monthly occurrence anomalies of weather
regimes (MOAWRs), also derived from the ECMWF model. Results show that the MOAWRs approach outperforms the one based on forecasted
precipitation in winter in the north-eastern parts of the European continent,
where more than 65 % of droughts are detected 1 month in advance. The approach
based on forecasted precipitation achieves better performance in
predicting drought events in central and eastern Europe in both spring and
summer, when the local atmospheric forcing could be the key driver of the
precipitation. Sensitivity tests also reveal the challenges in predicting
small-scale droughts and drought onsets at longer lead times. Finally, the results show that the ENS model of the ECMWF successfully
represents most of the observed linkages between large-scale atmospheric
patterns, depicted by the weather regimes and drought events over Europe.

Seasonal climate forecasts could be an important planning tool for farmers, government and insurance companies that can lead to better and timely management of seasonal climate risks. However, climate seasonal forecasts are often under-used, because potential users are not well aware of the capabilities and limitations of these products. This study aims at assessing the merits and caveats of a statistical empirical method, the ensemble streamflow prediction system (ESP, an ensemble based on reordering historical data) and an operational dynamical forecast system, the European Centre for Medium-Range Weather Forecasts—System 4 (S4) in predicting summer drought in Europe. Droughts are defined using the Standardized Precipitation Evapotranspiration Index for the month of August integrated over 6 months. Both systems show useful and mostly comparable deterministic skill. We argue that this source of predictability is mostly attributable to the observed initial conditions. S4 shows only higher skill in terms of ability to probabilistically identify drought occurrence. Thus, currently, both approaches provide useful information and ESP represents a computationally fast alternative to dynamical prediction applications for drought prediction. ; We acknowledge the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, for making the data available on their website http://www.esrl.noaa.gov/psd/. This work was partially funded by the Projects IMPREX (EU–H2020 PE024400) and SPECS (FP7-ENV-2012-308378). Marco Turco was supported by the Spanish Juan de la Cierva Programme (IJCI-2015-26953). ; Peer Reviewed ; Postprint (published version)

Seasonal climate forecasts could be an important planning tool for farmers, government and insurance companies that can lead to better and timely management of seasonal climate risks. However, climate seasonal forecasts are often under-used, because potential users are not well aware of the capabilities and limitations of these products. This study aims at assessing the merits and caveats of a statistical empirical method, the ensemble streamflow prediction system (ESP, an ensemble based on reordering historical data) and an operational dynamical forecast system, the European Centre for Medium-Range Weather Forecasts—System 4 (S4) in predicting summer drought in Europe. Droughts are defined using the Standardized Precipitation Evapotranspiration Index for the month of August integrated over 6 months. Both systems show useful and mostly comparable deterministic skill. We argue that this source of predictability is mostly attributable to the observed initial conditions. S4 shows only higher skill in terms of ability to probabilistically identify drought occurrence. Thus, currently, both approaches provide useful information and ESP represents a computationally fast alternative to dynamical prediction applications for drought prediction. ; We acknowledge the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, for making the data available on their website http://www.esrl.noaa.gov/psd/. This work was partially funded by the Projects IMPREX (EU–H2020 PE024400) and SPECS (FP7-ENV-2012-308378). Marco Turco was supported by the Spanish Juan de la Cierva Programme (IJCI-2015-26953). ; Peer Reviewed ; Postprint (published version)

Abstract. Extreme-temperature anomalies such as heat and cold waves may have strong impacts on human activities and health. The heat waves in western Europe in 2003 and in Russia in 2010, or the cold wave in southeastern Europe in 2012, generated a considerable amount of economic loss and resulted in the death of several thousands of people. Providing an operational system to monitor extreme-temperature anomalies in Europe is thus of prime importance to help decision makers and emergency services to be responsive to an unfolding extreme event. In this study, the development and the validation of a monitoring system of extreme-temperature anomalies are presented. The first part of the study describes the methodology based on the persistence of events exceeding a percentile threshold. The method is applied to three different observational datasets, in order to assess the robustness and highlight uncertainties in the observations. The climatology of extreme events from the last 21 years is then analysed to highlight the spatial and temporal variability of the hazard, and discrepancies amongst the observational datasets are discussed. In the last part of the study, the products derived from this study are presented and discussed with respect to previous studies. The results highlight the accuracy of the developed index and the statistical robustness of the distribution used to calculate the return periods.

DROP is a global land dataset to monitor meteorological drought that gathers an ensemble of observation-based datasets providing near-real time estimates with associated uncertainty using a probabilistic approach. Accurate and timely drought information is essential to move from post-crisis to pre-impact drought-risk management. 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 input and output 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, mimicking weather/climate prediction studies, we develop DROP (DROught Probabilistic), a new global land gridded dataset, in which an ensemble of observations-based datasets is used to obtain the best near-real time estimate together with its associated uncertainty. This approach makes the most of the available information and brings it to the end-users. 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. ; M.T. has received funding from the European Union's Horizon 2020 Research And Innovation Programme under the Marie Skłodowska-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 Fundación Séneca—Regional Agency for Science and Technology of Murcia through the CLIMAX project (20642/JLI/18) and by the Plan Propio de Investigación of the University of Murcia (Grant UMU-2017-10604). M.G.D. acknowledges funding by the Spanish Ministry of Science, Innovation and Universities Ramón 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. ; Peer Reviewed ; Postprint (published version)

At the beginning of the Medieval Climate Anomaly, in the ninth and tenth century, the medieval eastern Roman empire, more usually known as Byzantium, was recovering from its early medieval crisis and experiencing favourable climatic conditions for the agricultural and demographic growth. Although in the Balkans and Anatolia such favourable climate conditions were prevalent during the eleventh century, parts of the imperial territories were facing significant challenges as a result of external political/military pressure. The apogee of medieval Byzantine socio-economic development, around AD 1150, coincides with a period of adverse climatic conditions for its economy, so it becomes obvious that the winter dryness and high climate variability at this time did not hinder Byzantine society and economy from achieving that level of expansion. Soon after this peak, towards the end of the twelfth century, the populations of the Byzantine world were experiencing unusual climatic conditions with marked dryness and cooler phases. The weakened Byzantine socio-political system must have contributed to the events leading to the fall of Constantinople in AD 1204 and the sack of the city. The final collapse of the Byzantine political control over western Anatolia took place half century later, thus contemporaneous with the strong cooling effect after a tropical volcanic eruption in AD 1257. We suggest that, regardless of a range of other influential factors, climate change was also an important contributing factor to the socio-economic changes that took place in Byzantium during the Medieval Climate Anomaly. Crucially, therefore, while the relatively sophisticated and complex Byzantine society was certainly influenced by climatic conditions, and while it nevertheless displayed a significant degree of resilience, external pressures as well as tensions within the Byzantine society more broadly contributed to an increasing vulnerability in respect of climate impacts. Our interdisciplinary analysis is based on all available sources ...

At the beginning of the Medieval Climate Anomaly, in the ninth and tenth century, the medieval eastern Roman empire, more usually known as Byzantium, was recovering from its early medieval crisis and experiencing favourable climatic conditions for the agricultural and demographic growth. Although in the Balkans and Anatolia such favourable climate conditions were prevalent during the eleventh century, parts of the imperial territories were facing significant challenges as a result of external political/military pressure. The apogee of medieval Byzantine socio-economic development, around AD 1150, coincides with a period of adverse climatic conditions for its economy, so it becomes obvious that the winter dryness and high climate variability at this time did not hinder Byzantine society and economy from achieving that level of expansion. Soon after this peak, towards the end of the twelfth century, the populations of the Byzantine world were experiencing unusual climatic conditions with marked dryness and cooler phases. The weakened Byzantine socio-political system must have contributed to the events leading to the fall of Constantinople in AD 1204 and the sack of the city. The final collapse of the Byzantine political control over western Anatolia took place half century later, thus contemporaneous with the strong cooling effect after a tropical volcanic eruption in AD 1257. We suggest that, regardless of a range of other influential factors, climate change was also an important contributing factor to the socio-economic changes that took place in Byzantium during the Medieval Climate Anomaly. Crucially, therefore, while the relatively sophisticated and complex Byzantine society was certainly influenced by climatic conditions, and while it nevertheless displayed a significant degree of resilience, external pressures as well as tensions within the Byzantine society more broadly contributed to an increasing vulnerability in respect of climate impacts. Our interdisciplinary analysis is based on all available sources of information on the climate and society of Byzantium, that is textual (documentary), archaeological, environmental, climate and climate model-based evidence about the nature and extent of climate variability in the eastern Mediterranean. The key challenge was, therefore, to assess the relative influence to be ascribed to climate variability and change on the one hand, and on the other to the anthropogenic factors in the evolution of Byzantine state and society (such as invasions, changes in international or regional market demand and patterns of production and consumption, etc.). The focus of this interdisciplinary

Drought and heat stress affect global wheat production and food security. Since these climate hazards are expected to increase in frequency and intensity due to anthropogenic climate change, there is a growing need for effective planning and adaptive actions at all timescales relevant to the stakeholders and users in this sector. This work aims at assessing the forecast quality in predicting the evolution of drought and heat stress by using user-relevant agro-climatic indices such as Standardized Precipitation Evapotranspiration Index (SPEI) and Heat Magnitude Day Index (HMDI) on a multi-annual timescale, as this time horizon coincides with the long-term strategic planning of stakeholders in the wheat sector. We present the probabilistic skill and reliability of initialized decadal forecast to predict these indices for the months preceding the wheat harvest on a global spatial scale. The results reveal the usefulness of the study in a climate services context while showing that decadal climate forecasts are skillful and reliable over several wheat harvesting regions. ; We would like to acknowledge financial support from the European Union's Horizon 2020 Research and Innovation programme (MED-GOLD; Grant No. 776467, EUCP; Grant No. 776613 and FOCUS-Africa; Grant No. 869575) and from the Ministerio de Economía y Competitividad (MINECO) as part of the project CLINSA (Grant No. CGL2017-85791-R). This study has also received support from C3S_34c (contract number: ECMWF/COPERNICUS/2019/C3S_34c_DWD) of the Copernicus Climate Change Service (C3S) operated by ECMWF. B.S.M. acknowledges additional financial support from the Marie Sklodowska-Curie fellowship (Grant No. 713673) and from a fellowship of 'la Caixa' Foundation (ID 100010434). The fellowship code is LCF/BQ/IN17/11620038. ; Peer Reviewed ; Postprint (published version)