The COST (European Cooperation in Science and Technology) Action ES0601: advances in homogenization methods of climate series: an integrated approach (HOME) has executed a blind intercomparison and validation study for monthly homogenization algorithms. Time series of monthly temperature and precipitation were evaluated because of their importance for climate studies and because they represent two important types of statistics (additive and multiplicative). The algorithms were validated against a realistic benchmark dataset. The benchmark contains real inhomogeneous data as well as simulated data with inserted inhomogeneities. Random independent break-type inhomogeneities with normally distributed breakpoint sizes were added to the simulated datasets. To approximate real world conditions, breaks were introduced that occur simultaneously in multiple station series within a simulated network of station data. The simulated time series also contained outliers, missing data periods and local station trends. Further, a stochastic nonlinear global (network-wide) trend was added. Participants provided 25 separate homogenized contributions as part of the blind study. After the deadline at which details of the imposed inhomogeneities were revealed, 22 additional solutions were submitted. These homogenized datasets were assessed by a number of performance metrics including (i) the centered root mean square error relative to the true homogeneous value at various averaging scales, (ii) the error in linear trend estimates and (iii) traditional contingency skill scores. The metrics were computed both using the individual station series as well as the network average regional series. The performance of the contributions depends significantly on the error metric considered. Contingency scores by themselves are not very informative. Although relative homogenization algorithms typically improve the homogeneity of temperature data, only the best ones improve precipitation data. Training the users on homogenization software was found to be very important. Moreover, state-of-the-art relative homogenization algorithms developed to work with an inhomogeneous reference are shown to perform best. The study showed that automatic algorithms can perform as well as manual ones.

Various environmental factors influence the outbreak and spread of epidemic or even pandemic events which, in turn, may cause feedbacks on the environment. The novel coronavirus disease (COVID-19) was declared a pandemic on 13 March 2020 and its rapid onset, spatial extent and complex consequences make it a once-in-a-century global disaster. Most countries responded by social distancing measures and severely diminished economic and other activities. Consequently, by the end of April 2020, the COVID-19 pandemic has led to numerous environmental impacts, both positive such as enhanced air and water quality in urban areas, and negative, such as shoreline pollution due to the disposal of sanitary consumables. This study presents an early overview of the observed and potential impacts of the COVID-19 on the environment. We argue that the effects of COVID-19 are determined mainly by anthropogenic factors which are becoming obvious as human activity diminishes across the planet, and the impacts on cities and public health will be continued in the coming years.

The aim of this paper is to analyse the differences in the long-term regimes of extreme precipitation and floods across the Alpine–Carpathian range using seasonality indices and atmospheric circulation patterns to understand the main flood-producing processes. This is supported by cluster analyses to identify areas of similar flood processes, both in terms of precipitation forcing and catchment processes. The results allow to isolate regions of similar flood generation processes including southerly versus westerly circulation patterns, effects of soil moisture seasonality due to evaporation and effects of soil moisture seasonality due to snow melt. In many regions of the Alpine–Carpathian range, there is a distinct shift in flood generating processes with flood magnitude as evidenced by a shift from summer to autumn floods. It is argued that the synoptic approach proposed here is valuable in both flood analysis and flood estimation.

The Carpathians are the longest mountain range in Europe and a geographic barrier between Central Europe, Eastern Europe, and the Balkans. To investigate the climate of the area, the CARPATCLIM project members collected, quality-checked, homogenized, harmonized, and interpolated daily data for 16 meteorological variables and many derived indicators related to the period 1961–2010. The principal outcome of the project is the Climate Atlas of the Carpathian Region, hosted on a dedicated website (www.carpatclim-eu.org) and made of high-resolution daily grids (0.1° × 0.1°) of all variables and indicators at different time steps. In this article, we analyze the spatial and temporal variability of 10 variables: minimum, mean, and maximum temperature, daily temperature range, precipitation, cloud cover, relative sunshine duration, relative humidity, surface air pressure, and wind speed at 2 m. For each variable, we present the gridded climatologies for the period 1961–2010 and discuss the linear trends both on an annual and seasonal basis. Temperature was found to increase in every season, in particular in the last three decades, confirming the trends occurring in Europe; wind speed decreased in every season; cloud cover and relative humidity decreased in spring, summer, and winter, and increased in autumn, while relative sunshine duration behaved in the opposite way; precipitation and surface air pressure showed no significant trend, though they increased slightly on an annual basis. We also discuss the correlation between the variables and we highlight that in the Carpathian Region positive and negative sunshine duration anomalies are highly correlated to the corresponding temperature anomalies during the global dimming (1960s and 1970s) and brightening (1990s and 2000s) periods.