European Climate Assessment Research Articles

Which droughts are becoming more frequent? A copula entropy analysis on the return period of droughts in Europe

AbstractClimate change and global warming have increased the frequency of extreme events, particularly over the past decades. Changes in drought frequency are not yet understood, especially regarding which drought features are becoming more frequent (e.g., are longer or high-magnitude droughts becoming more frequent, or both?). In this study, we present a novel methodology to accurately and consistently estimate changes in return periods of different types of droughts. For this purpose, we implemented copula-entropy theory to assess drought frequencies and their changes. As an example of the application of the proposed method, we used data from the European Climate Assessment and Dataset (ECA&D), from which we selected 26 stations in Central Europe with over 100 years of data and low incidence of missing days ($$\le$$ ≤ 0.3%). The results of the new analysis indicate that there was a high variability in the frequency change patterns across Central Europe over the last century; however, most regions have experienced an increase in the frequency of medium-to-high intensity droughts. The use of the copula-entropy theory to assess drought frequency is a reliable method that allows systematic monitoring and easy updating of current knowledge, and is computationally efficient and statistically robust.

Köppen climates and Scheffer index as indicators of timber risk in Europe (1901–2020)

Timber heritage faces numerous risks, with the threat from insect and fungal attack particularly sensitive to climate. The Scheffer index estimates the deterioration of wood by combining average temperature and days of rain. We determined the Scheffer index from twenty sites in Europe across the 20th and early 21st century using meteorological observations, largely from the European Climate Assessment Dataset. Results showed that Scheffer indices across Europe loosely align with Köppen climate classes, which are also defined in terms of temperature and rainfall. However, within a given Köppen class, a wide range of Scheffer indices were found, thus representing very different threats to heritage. This suggests that specific heritage climate parameters, such as the Scheffer index, are needed. Trends in the Scheffer index provide a fairly robust guide to management strategy by identifying locations where the risk for timber is increasing. However, a simple index cannot necessarily describe the complex interactions between biological pests and climate; such complexity is better handled with well-constructed models. Nevertheless, the Scheffer index offers heritage managers an easily calculated estimate of the spatial distribution of risk to timber and the likely direction of future change.

EURADCLIM: the European climatological high-resolution gauge-adjusted radar precipitation dataset

Abstract. The European climatological high-resolution gauge-adjusted radar precipitation dataset, EURADCLIM, addresses the need for an accurate (sub)daily precipitation product covering 78 % of Europe at a high spatial resolution. A climatological dataset of 1 and 24 h precipitation accumulations on a 2 km grid is derived for the period 2013 through 2020. The starting point is the European Meteorological Network (EUMETNET) Operational Program on the Exchange of Weather Radar Information (OPERA) gridded radar dataset of 15 min instantaneous surface rain rates, which is based on data from, on average, 138 ground-based weather radars. First, methods are applied to further remove non-meteorological echoes from these composites by applying two statistical methods and a satellite-based cloud-type mask. Second, the radar composites are merged with the European Climate Assessment & Dataset (ECA&D) with potentially ∼ 7700 rain gauges from National Meteorological and Hydrological Services (NMHSs) in order to substantially improve its quality. Characteristics of the radar, rain gauge and satellite datasets are presented, as well as a detailed account of the applied algorithms. The clutter-removal algorithms are effective while removing few precipitation echoes. The usefulness of EURADCLIM for quantitative precipitation estimation (QPE) is confirmed by comparison against rain gauge accumulations employing scatter density plots, statistical metrics and a spatial verification. These show a strong improvement with respect to the original OPERA product. The potential of EURADCLIM to derive pan-European precipitation climatologies and to evaluate extreme precipitation events is shown. Specific attention is given to the remaining artifacts in and limitations of EURADCLIM. Finally, it is recommended to further improve EURADCLIM by applying algorithms to 3D instead of 2D radar data and by obtaining more rain gauge data for the radar–gauge merging. The EURADCLIM 1 and 24 h precipitation datasets are publicly available at https://doi.org/10.21944/7ypj-wn68 (Overeem et al., 2022a) and https://doi.org/10.21944/1a54-gg96 (Overeem et al., 2022b).

Spring phenology is advancing at a faster rate than arrival times of Common Starling

Climate change poses significant impact on most migratory species, altering their breeding and non-breeding distributions, migration distances, and phenology. The ability of a population to track and adjust to the changing environmental conditions is a vital prerequisite for its persistence. In this study, we describe changes in spring arrival phenology of a short-distance migrant, the Common Starling Sturnus vulgaris, in Northeastern Europe and their relation to changes in the onset of meteorological spring during the last century. Phenological data were obtained from an open access database Chronicles of Nature Calendar that held 4291 usable records of the 1st spring observation date of Starlings from 171 phenological network sites between 1922 and 2017. Air temperature data were downloaded from the European Climate Assessment and Dataset database. We found that spring arrival time of Starlings was positively correlated with the onset of spring on a local and broad scale. Spring arrival progression followed a northeast gradient at an average rate of 1.6 days per one degree of latitude (ca. 71 km day−1) and 0.4 days per one degree of longitude (152 km day−1). During the 96-year long period, Starlings on average advanced their spring arrival time by 4.7 days at a rate of 0.05 days per year. However, changes in the onset of spring occurred more rapidly with an average rate of change of 0.18 days per year over the study period. These findings imply broad-scale adjustments in migratory patterns of a short-distance migrant that result from climate change-induced shifts in spring phenology. However, adaptations in bird migration phenology are not occurring fast enough to track and compensate for the rapid increase in air temperature, thus disrupting the earlier matching between bird arrival and spring onset.

Observational evidence of intensified nocturnal urban heat island during heatwaves in European cities

A heatwave (HW) is a large-scale meteorological event characterised by persistent and extremely high-temperature condition. At the local scale, the urban heat island (UHI) is another thermal-related phenomenon defined as an urban area warmer than its surrounding regions due to different surfaces’ capabilities to absorb and store heat. However, the assessment about the effect produced on UHI by HW events is not homogeneous. Indeed, regarding the capability of HWs to influence the urban-rural temperature difference, several studies report different conclusions describing both an exacerbation and a reduction of UHI during HW events. In this context, the present study analyses in situ long records of temperature measurements (20 years) to provide observational shreds of evidence of UHI modification under HW conditions. We examine data from the European Climate Assessment & Dataset and World Meteorological Organization computing the UHI index (UHII) to quantify the UHI effect intensity in 37 European cities during the last 20 summers. The results show an UHII intensification for 28 of the 32 cities affected by positive UHI during extremely high temperatures at night, while substantial variations are not observed during the daytime. The time evolution of UHI during a HW highlights that a more significant and persistent urban-rural temperature gradient explains the UHI intensification. Finally, the relationship between the large and local-scale temperature phenomena reveals that continental high-temperature periods are often associated with prominent temperature differences between small-scale urban and rural environments, assessing the impact of large-scale features on thermal stress at the local scale.

Using Long-Term Historical Meteorological Data for Climate Change Analysis in the Carpathian Region

In this study, we consider the historical climatological time series available in the meteorological yearbooks of the Royal Hungarian Central Institute of Meteorology and Earth Magnetism, first published in 1871. Data quality improvement of historical data includes the homogenization process with outlier checks and data gap filling by applying the MASH software. We investigated 13 stations from the Carpathian Region having the most complete monthly temperature and precipitation time series for the period of 1871–1918 and 8 stations with fog observations (1886–1916). First, statistical tests were conducted to compare the main statistics of the historical datasets (1871–1918) with current data (1971–2020). The sources of the current data are the National Meteorological Administration of Romania and the European Climate Assessment & Dataset. The results show significant changes between the two periods. In the whole region, the mean temperature in the last five decades (1971–2020) was 0.77 °C higher than in 1871–1918. Changes in the frequency of foggy situations were also detected. On an annual scale, in the last 31-year period (1990–2020), the number of foggy days increased by 16.2 compared with 1886–1916. Even if some local trends can be detected in the historical periods (e.g., Cluj-Napoca), significant changes are much more characteristic in the recent period.

Is there added value in the EURO‐CORDEX hindcast temperature simulations? Assessing the added value using climate distributions in Europe

AbstractRegional climate simulations with high horizontal resolutions are becoming increasingly common and although model development has continually enhanced the representation of atmospheric phenomena, the model improvements are variable, region and time scale‐dependant. The high computational costs of increasingly smaller grid‐spacing underline the need for a robust assessment of the benefits or losses associated to the dynamical downscaling of coarser resolution models (reanalysis, global climate models or ~tenths km runs), that is, quantitative added value evaluation. In the current study, a probability density function (PDF) matching score is used to determine the distribution added value (DAV) of the EURO‐CORDEX maximum and minimum temperatures from the hindcast simulations at 0.44° and 0.11° resolutions. The gridded maximum and minimum temperatures from European Climate Assessment & Dataset (E‐Obs) were used as benchmarks for the matching scores and DAVs were determined for the 0.44° and 0.11° simulations individually against ERA‐Interim as well as against each other. Temperature added value against ERA‐Interim is difficult to find due to the assimilation of surface temperatures in the reanalysis. Nonetheless, there is positive added value in Tmax for half of the models at the yearly time scale, and the increase in resolution also implies positive DAV in half of the models, with DAVs between 0.1 and 3.8%. More importantly, the benefits of downscaling are significantly visible in the extreme end tail of Tmax, where all models have high added value at all resolutions and seasons except in summer (from ~1 to ~21%). In minimum temperature there is added value in winter and autumn at both resolutions. The Mediterranean and the British Isles are the regions where larger positive DAV values for both variables and resolutions are identified. In Tmax, added value is also associated to the increase in resolution (from 50 to 10 km) for half of the models in all regions. Scandinavia, the Alps, and eastern Europe are regions where negative added value is clearly linked with the highest mountain peaks for both variables.

Spatiotemporal Analysis of the Frost Regime in the Iberian Peninsula in the Context of Climate Change (1975–2018)

Climate change is having many effects in the agricultural sector, which are being studied worldwide. Undoubtedly, warmer winters and earlier springs produce changes in frost regimes and severity that will affect the sustainability of agricultural production in the area. The Mediterranean region and the Iberian Peninsula (IP) are among the areas where the greatest impact of climate change is expected. Daily data from 68 weather stations of the IP belonging to the European Climate Assessment and Dataset (1975–2018) were used to conduct a spatiotemporal study of the frost regime. The variables calculated include the probability of three frost types according to their severity, frost day, mean absolute minimum yearly temperature, first frost day, last frost day, and frost-free period. These variables were integrated into a geographic information system, which allowed the graphical visualization of their patterns using of geostatistical interpolation techniques (kriging). Changes in frost variables were investigated using the Mann–Kendall test and Sen’s slope estimator. A general reduction in the number of frosts per year is observed (values between −0.04- and −0.8-day frosts per year), as well as an increase in the mean absolute minimum temperature (values between 0.04 and 0.10 °C per year), with very high significant trends throughout the territory. The reduction in the number of frosts is more pronounced at a higher elevation. Frost dates vary greatly due to the orographic characteristics of the IP. The generalized trend is of a significant delay of the autumn frosts (values between 0.4 and 1.06 days/year), as well as early spring frosts (between −0.429 and −1.29 days/year), and as a consequence a longer frost-free period, all changes were much stronger than those found in other regions of the world. These effects of climate change must be mitigated by modifying species, varieties, and cultivation techniques to guarantee sustainable agriculture.

Evaluation of the Tourism Climate Index in the Canary Islands

In this study, we performed a diagnostic and evolutive analysis of the bioclimatology of the Canary Islands, an Atlantic archipelago where the climate itself is a main feature promoting tourism. Among all the tourist-climate indices described in the literature, we evaluated the most widely used, which is the Tourism Climate Index (TCI) proposed by Mieczkowski (1985). Monthly mean TCI time series were calculated using meteorological data from the Spanish State Meteorological Agency database and the European Climate Assessment and Dataset. Our results show TCI values greater than 50 during almost every month in the period 1950–2018, with mean values over the entire time series between 70 and 80. According to the TCI classification scheme, these values correspond to a very good thermal comfort along all of the period. Our results also point to spring as the season with the best TCI, with maximum values around 80 for this index in April—excellent according to the TCI classification. However, we did not find a correlation between inbound arrivals and the TCI index, which might point to a lack of information available to tourists. This opens an opportunity for policymakers and tour operators to better publicize the best seasons for holidays in the islands.

Comparative Analysis between Daily Extreme Temperature and Precipitation Values Derived from Observations and Gridded Datasets in North-Western Romania

Climate gridded datasets are highly needed and useful in conducting data analysis for research and practical purposes. They provide long-term information on various climatic variables for large areas worldwide, making them suitable for use at any spatial level. It is essential to assess the accuracy of gridded data by comparing the data to measured values, especially when they are used as input parameters for hydro-climatic models. From the multitude of databases available for North-western Romania, we selected three, particularly the European Climate Assessment and Dataset (E-OBS), the Romanian Climatic Dataset (ROCADA), and the Climate of the Carpathian Region (CARPATCLIM). In this paper, we analyse the extreme precipitation and temperature data derived from the above-mentioned datasets over a common 50-year period (1961–2010) and compare the data with raw values to estimate the degree of uncertainty for each set of data. The observation data, recorded at two meteorological stations located in a complex topography region, were compared to the output of the gridded datasets, by using descriptive statistics for the mean and extreme annual and seasonal temperature and precipitation data, and trend analyses. The main findings are: the high suitability of the ROCADA gridded database for climate trend analysis and of the E-OBS gridded database for extreme temperature and precipitation analysis.

High‐resolution gridded climate data for Europe based on bias‐corrected EURO‐CORDEX: The ECLIPS dataset

AbstractClimate is an important driver of many ecological and social processes; the availability of high‐resolution climate data is thus one of the key presumptions for knowledge‐based decisions. We created a new climate dataset for Europe referred to as ECLIPS (European CLimate Index ProjectionS), which contains gridded data for 80 annual, seasonal and monthly climate variables for two past (1961–1990 and 1991–2010) and five future (2011–2020, 2021–2140, 2041–2060, 2061–2080 and 2081–2100) periods. The future data are based on five regional climate models (RCMs) driven by two greenhouse gas concentration scenarios, RCP 4.5 and RCP 8.5. Two ECLIPS versions were developed: ECLIPS 1.1 with a spatial resolution of 0.11° × 0.11°, which is the resolution of the underlying RCMs, and ECLIPS 2.0 downscaled to the resolution of 30 arcsec employing the delta approach. Both ECLIPS versions were tested against independent station data from the European Climate Assessment (ECA) dataset. Correlations of the ECA and ECLIPS 1.1 data ranged from 0.63 to 0.78, depending on the tested variable. The correlations increased to 0.78–0.93 for ECLIPS 2.0, suggesting substantial improvement of the match with station data due to the downscaling. A large number of climate projections, periods and indices as well as the availability of these data at two different spatial resolutions can support diverse studies across a range of disciplines and thus extend our understanding of climate‐sensitive dynamics of many social and ecological systems.

The applicability of the Hess–Brezowsky synoptic classification to the description of climate elements in Europe

This study deals with the applicability of the Hess–Brezowsky synoptic classification to the description of surface climate elements, specifically minimum and maximum temperature and precipitation, in Europe. The suitability of the Hess–Brezowsky classification for this purpose in the European domain is analyzed using the two-sample Kolmogorov–Smirnov test. The test was performed on European Climate Assessment and Dataset data from 113 stations for the years 1961–2000. The suitability of the classification for describing climate elements at a given station was assessed according to the percentage of the circulation types, under which the probability distribution functions of these elements differed from the rest of the values. The classification is deemed the most suitable for describing climate elements in Germany and its neighboring countries and least suitable in the Mediterranean, southeastern and Eastern Europe. The classification is more applicable to describing minimum and maximum temperature than precipitation, and its overall synoptic-climatological applicability is better in winter than in summer.

Extreme Precipitation Changes in Europe from the Last Millennium to the End of the Twenty-First Century

AbstractIn this study, we aim to better understand the current and future projections of precipitation extremes in Europe in the context of climatic variability over a long-term period from the last millennium to the end of the twenty-first century. The daily gridded precipitation data from five global climate models (GCMs) of phase 5 of the Coupled Model Intercomparison Project (CMIP5) are chosen to investigate natural variability and precipitation extremes during the last millennium (850–1849), the historical (1850–2005) period, and two representative concentration pathway (RCP2.6 and RCP8.5) scenarios (2006–99). First, the seasonal and annual precipitation and extreme statistics from GCMs are evaluated using reconstruction and observation. Second, the spatial and temporal patterns of extreme precipitation from GCMs are investigated from the last millennium to the end of the twenty-first century. Meanwhile, the characteristic changes of extreme precipitation for the five regions of Europe are further analyzed. The results revealed the following: GCMs underestimate extreme precipitation and overestimate mean precipitation compared with the observations from European Climate Assessment and Dataset (ECA&D); the majority of Europe except southern Europe will most likely have large magnitude increases in the extreme precipitation and mean precipitation in the future under both RCP scenarios; there is no systematic change of precipitation extremes from the last millennium to the historical period from all GCMs; and larger magnitude increases are shown in 100- and 200-yr than in 5- and 10-yr return period precipitation from both RCP scenarios. In addition, short-duration extreme precipitation will most likely increase more than longer-duration extremes.

Building Long Homogeneous Temperature Series across Europe: A New Approach for the Blending of Neighboring Series

ABSTRACTLong and homogeneous series are a necessary requirement for reliable climate analysis. Relocation of measuring equipment from one station to another, such as from the city center to a rural area or a nearby airport, is one of the causes of discontinuities in these long series that may affect trend estimates. In this paper, an updated procedure for the composition of long series, by combining data from nearby stations, is introduced. It couples an evolution of the blending procedure already implemented within the European Climate Assessment and Dataset (ECA&D, which combines data from stations no more than 12.5 km apart from each other) with a duplicate removal, alongside the quantile matching homogenization procedure. The ECA&D contains approximately 3000 homogenized series for each temperature variable prior to the blending procedure, and approximately 820 of these are longer than 60 years; the process of blending increases the number of long series to more than 900. Three case studies illustrate the effects of the homogenization on single blended series, showing the effectiveness of separate adjustments on extreme and mean values (Geneva, Switzerland), on cases in which blending is complex (Rheinstetten, Germany), and on series that are completed by adding relevant portions of Global Telecommunications System synoptic data (Siauliai, Lithuania). A trend assessment on the whole European continent reveals the removal of negative and very large trends, demonstrating a stronger spatial consistency. The new blended and homogenized dataset will allow a more reliable use of temperature series for indices calculation and for the calculation of gridded datasets and it will be available online for users (https://www.ecad.eu).

Implementation of linear regression and neural network models for local climate change assessment and forecasting based on time series data

Climate change studies are based on the data processing of the following time series for number of Ukrainian cities: daily precipitation and air temperature from the site of the European Climate Assessment & Dataset (ECAD number of outputs is 1; number of neurons in the hidden layer is 5; activation function type is sigmoid with the given slope 1) provided the consistent forecast with the horizon of 120 months. Low values of the maximum and average relative errors of the neural network model were achieved on the training set (4.45·10-2 and 2.99·10-4, respectively) and on the test set (3.60·10-2 and 5.69·10-3, respectively). Similarly, for the time series of monthly CO2 emissions for the Kyiv city after December 1, 2013, the predictive neural network model [240 × 5 × 1] provided consistent forecast with the horizon of 60 months. In general, the time series of monthly CO2 emission values are characterized by much smaller values of the consistent forecast horizon in comparison with the time series of the average monthly temperature, at least when using predictive neural network models.

Rainfall erosivity and extreme precipitation in the Pannonian basin

Abstract In order to assess the rainfall erosivity in the Pannonian basin, several parameters which describe distribution, concentration and variability of precipitation were used, as well as 9 extreme precipitation indices. The precipitation data is obtained from the European Climate Assessment and Dataset project for the period 1961-2014, for 8 meteorological stations in northern Serbia, 5 in Hungary and 1 in eastern Croatia. The extreme values of precipitation were calculated following the indices developed by the ETCCDI. RclimDex software package was used for indices calculation. Based on statistical analysis and the calculated values, the results have been presented with Geographic Information System (GIS) to point out the most vulnerable parts of the Pannonian basin, with regard to pluvial erosion. This study presents the first result of combined rainfall erosivity and extreme precipitation indices for the investigated area. Results of PCI indicate presence of moderate precipitation concentration (mean value 11.6). Trend analysis of FI (mean value 22.7) and MFI (mean value 70.2) implies a shift from being largely in the low erosivity class, to being completely in the moderate erosivity class in the future, thus indicating an increase in rainfall erosivity for most of the investigated area (except in the northwestern parts). Furthermore, the observed precipitation extremes suggest that both the amount and the intensity of precipitation are increasing. The knowledge about the areas affected by strong soil erosion could lead to introducing effective measures in order to reduce it. Long term analysis of rainfall erosivity is a significant step concerning flood prevention, hazard mitigation, ecosystem services, land use change and agricultural production.

SeNorge_2018, daily precipitation, and temperature datasets over Norway

Abstract. seNorge_2018 is a collection of observational gridded datasets over Norway for daily total precipitation: daily mean, maximum, and minimum temperatures. The time period covers 1957 to 2017, and the data are presented over a high-resolution terrain-following grid with 1 km spacing in both meridional and zonal directions. The seNorge family of observational gridded datasets developed at the Norwegian Meteorological Institute (MET Norway) has a 20-year-long history and seNorge_2018 is its newest member, the first providing daily minimum and maximum temperatures. seNorge datasets are used for a wide range of applications in climatology, hydrology, and meteorology. The observational dataset is based on MET Norway's climate data, which have been integrated by the “European Climate Assessment and Dataset” database. Two distinct statistical interpolation methods have been developed, one for temperature and the other for precipitation. They are both based on a spatial scale-separation approach where, at first, the analysis (i.e., predictions) at larger spatial scales is estimated. Subsequently they are used to infer the small-scale details down to a spatial scale comparable to the local observation density. Mean, maximum, and minimum temperatures are interpolated separately; then physical consistency among them is enforced. For precipitation, in addition to observational data, the spatial interpolation makes use of information provided by a climate model. The analysis evaluation is based on cross-validation statistics and comparison with a previous seNorge version. The analysis quality is presented as a function of the local station density. We show that the occurrence of large errors in the analyses decays at an exponential rate with the increase in the station density. Temperature analyses over most of the domain are generally not affected by significant biases. However, during wintertime in data-sparse regions the analyzed minimum temperatures do have a bias between 2 ∘C and 3 ∘C. Minimum temperatures are more challenging to represent and large errors are more frequent than for maximum and mean temperatures. The precipitation analysis quality depends crucially on station density: the frequency of occurrence of large errors for intense precipitation is less than 5% in data-dense regions, while it is approximately 30 % in data-sparse regions. The open-access datasets are available for public download at daily total precipitation (https://doi.org/10.5281/zenodo.2082320, Lussana, 2018b); and daily mean (https://doi.org/10.5281/zenodo.2023997, Lussana, 2018c), maximum (https://doi.org/10.5281/zenodo.2559372, Lussana, 2018e), and minimum (https://doi.org/10.5281/zenodo.2559354, Lussana, 2018d) temperatures.

Study of Temperature Evolution’s Trend on the Black Sea Shore, at Constanta

It is widely recognized that temperature’s changes have an important impact on the environment, in particular on sea waters, plants, animals and human life. This article presents the results of the study of the temperature’s evolution trend in Constanta, the main city and harbour on the Romanian Black Sea shore. Have been used the maximum and minimum daily temperatures, collected from the European Climate Assessment & Dataset project website. The data span over 58 years, from 1961 to 2018, period long enough to consider the results significant. Change points detection is done for annual and seasonal data, followed by testing the trend existence for maximum and minimum annual and seasonal series. The results proved the existence of increasing trend for all the temperature series, fact that confirms the global warming effect in this part of the world.

Trends in temperature-related age-specific and sex-specific mortality from cardiovascular diseases in Spain: a national time-series analysis

Climate change driven by human activities has increased annual temperatures in Spain by around 1°C since 1980. However, little is known regarding the extent to which the association between temperature and mortality has changed among the most susceptible population groups as a result of the rapidly warming climate. We aimed to assess trends in temperature-related cardiovascular disease mortality in Spain by sex and age, and we investigated the association between climate warming and changes in the risk of mortality. We did a country-wide time-series analysis of 48 provinces in mainland Spain and the Balearic Islands between Jan 1, 1980, and Dec 31, 2016. We extracted daily cardiovascular disease mortality data disaggregated by sex, age, and province from the Spanish National Institute of Statistics database. We also extracted daily mean temperatures from the European Climate Assessment and Dataset project. We applied a quasi-Poisson regression model for each province, controlling for seasonal and long-term trends, to estimate the temporal changes in the province-specific temperature-mortality associations with distributed lag non-linear models. We did a multivariate random-effects meta-analysis to derive the best linear unbiased prediction of the temperature-mortality association and the minimum mortality temperature in each province. Heat-attributable and cold-attributable fractions of death were computed by separating the contributions from days with temperatures warmer and colder than the minimum mortality temperature, respectively. Between 1980 and 2016, 4 576 600 cardiovascular deaths were recorded. For warm temperatures, the increase in relative risk (RR) of death from cardiovascular diseases was higher for women than men and higher for older individuals (aged ≥90 years) than younger individuals (aged 60-74 years), whereas for cold temperatures, RRs were higher for men than women, with no clear pattern by age group. The heat-attributable fraction of cardiovascular deaths was higher for women in all age groups, and the cold-attributable fraction was larger in men. The heat-attributable fraction increased with age for both sexes, whereas the cold-attributable fraction increased with age for men and decreased for women. Overall minimum mortality temperature increased from 19·5°C between 1980 and 1994 to 20·2°C between 2002 and 2016, which is similar in magnitude to, and occurred in parallel with, the observed mean increase of 0·77°C that occurred in Spain between these two time periods. In general, between 1980 and 2016, the risk and attributable fraction of cardiovascular deaths due to warm and cold temperatures decreased for men and women across all age groups. For all the age groups combined, between 1980-94 and 2002-16, the heat-attributable fraction decreased by -42·06% (95% empirical CI -44·39 to -41·06) for men and -36·64% (-36·70 to -36·04) for women, whereas the cold-attributable fraction was reduced by -30·23% (-30·34 to -30·05) for men and -44·87% (-46·77 to -42·94) for women. In Spain, the observed warming of the climate has occurred in parallel with substantial adaptation to both high and low temperatures. The reduction in the RR and the attributable fraction associated with heat would be compatible with an adaptive response specifically addressing the negative consequences of climate change. Nevertheless, the simultaneous reduction in the RR and attributable fraction of cold temperatures also highlights the importance of more general factors such as socioeconomic development, increased life expectancy and quality, and improved health-care services in the country. None.

Monitoring Soil Moisture Drought over Northern High Latitudes from Space

Mapping drought from space using, e.g., surface soil moisture (SSM), has become viable in the last decade. However, state of the art SSM retrieval products suffer from very poor coverage over northern latitudes. In this study, we propose an innovative drought indicator with a wider spatial and temporal coverage than that obtained from satellite SSM retrievals. We evaluate passive microwave brightness temperature observations from the Soil Moisture and Ocean Salinity (SMOS) satellite as a surrogate drought metric, and introduce a Standardized Brightness Temperature Index (STBI). We compute the STBI by fitting a Gaussian distribution using monthly brightness temperature data from SMOS; the normal assumption is tested using the Shapior-Wilk test. Our results indicate that the assumption of normally distributed brightness temperature data is valid at the 0.05 significance level. The STBI is validated against drought indices from a land surface data assimilation system (LDAS-Monde), two satellite derived SSM indices, one from SMOS and one from the ESA CCI soil moisture project and a standardized precipitation index based on in situ data from the European Climate Assessment & Dataset (ECA&D) project. When comparing the temporal dynamics of the STBI to the LDAS-Monde drought index we find that it has equal correlation skill to that of the ESA CCI soil moisture product ( 0.71 ). However, in addition the STBI provides improved spatial coverage because no masking has been applied over regions with dense boreal forest. Finally, we evaluate the STBI in a case study of the 2018 Nordic drought. The STBI is found to provide improved spatial and temporal coverage when compared to the drought index created from satellite derived SSM over the Nordic region. Our results indicate that when compared to drought indices from precipitation data and a land data assimilation system, the STBI is qualitatively able to capture the 2018 drought onset, severity and spatial extent. We did see that the STBI was unable to detect the 2018 drought recovery for some areas in the Nordic countries. This false drought detection is likely linked to the recovery of vegetation after the drought, which causes an increase in the passive microwave brightness temperature, hence the STBI shows a dry anomaly instead of normal conditions, as seen for the other drought indices. We argue that the STBI could provide additional information for drought monitoring in regions where the SSM retrieval problem is not well defined. However, it then needs to be accompanied by a vegetation index to account for the recovery of the vegetation which could cause false drought detection.