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This study provides a global analysis of the relationship between drought metrics obtained from several climatic, hydrologic and ecological variables in a climate change framework using CMIP6 model data. A comprehensive analysis of the evolution of drought severity on a global scale is carried out for the historical experiment (1850–2014) and for future simulations under a high emissions scenario (SSP5‐8.5). This study focuses on comparing trends in the magnitude and duration of drought events according to different standardized indices over the world land‐surface area. The spatial and temporal relationship between the different drought indices on a global scale was also evaluated. Overall, there is a fairly large consensus among models and drought metrics in pointing to drought increase in southern North America, Central America, the Amazon region, the Mediterranean, southern Africa and southern Australia. Our results show important spatial differences in drought projections, which are highly dependent on the drought metric employed. While a strong relationship between climatic indices was evident, climatic and ecological drought metrics showed less dependency over both space and time. Importantly, our study demonstrates uncertainties in future projections of drought trends and their interannual variability related to the relationship among indices, stressing the importance of coherent climatic, hydrological and plant physiological patterns when analyzing CMIP6 model simulations of droughts under a warming climate scenario. Plain Language Summary: Using climate change models, we perform a drought analysis in terms of climatic, hydrologic and ecological variables on a global scale, studying the projections under a high emission scenario. We analyze how drought events will evolve in the future with respect to their magnitude and duration, and if the different drought metrics agree in space and time. In general, models and metrics agree that there will be drought increase in southern North America, Central America, the Amazon region, the Mediterranean, southern Africa and southern Australia. However, results differ across the world and really depend on the metric used. We show that climatic indices are strongly connected with each other, but no so related to ecological ones. We also find that there are uncertainties in future projections of drought trends, highlighting that we should always take into account the spatial and temporal agreement between climatic, hydrological and plant physiological patterns when studying drought projections. Key Points: Important differences in drought projections as a function of drought metricsThe temporal relationship between the precipitation‐based climatic metrics is high worldwideA weak relationship is found between climatic and ecological drought indices [ABSTRACT FROM AUTHOR]

Trend analysis requires long-time series of observations, the continuity of which is not usually obtained since gaps emerge linked to problems with the device or external reasons. This paper explores the influence of dataset density on the parameters involved in linear trends of CO2 and CH4 half-hourly observations and daily ranges measured at a semi-rural site over a 6-year period. Half-hourly observation trends were 2.40 and 0.0085 ppm year−1 for CO2 and CH4 respectively, and a noticeable value of 1.98 ppm year−1 was obtained for the CO2 daily range, whereas the CH4 daily range remained steady. Random samples of variable numbers of observations were extracted from these time series. Robust statistics of location, spread, symmetry and concentration of observations were calculated and fitted with a third-degree polynomial expression for half-hourly observations and a linear equation for the daily range. In general, medians and interquartile ranges provided the best fits. Confidence intervals were also obtained. Around 350 and 760 half-hourly observations for CO2 and CH4 provided 95% of statistically significant correlations at the 0.1% level. Finally, daily evolution revealed the contrast between the two trace gases where CO2 increased with a ramp during the night that ended with a cliff whereas a sinusoidal evolution was associated with CH4. Moreover, the interquartile range presented a daily cycle for CO2 but not for CH4. [ABSTRACT FROM AUTHOR]

This paper is based on CO2 and CH4 semi-hourly mole fraction measurements obtained at the Low Atmosphere Research Centre (CIB) between 2010 and 2016 using a Picarro G1301 analyser. The main aims of the study were to examine the temporal variation of CO2 and CH4 by using six different kernel functions, and to study the suitability of these functions to the dataset. The method used for the current study was based on experimental contour plots of R2 values in order to simultaneously determine the bandwidths of kernel functions for the long-term and short-term. An Epanechnikov, a Gaussian, a biweight, a triangular, a tricubic and a rectangular kernel function were applied to extract the salient features of both the long-term (trend) and the short-term (seasonality). The average linear increase growth rates found were mainly attributed to the terrestrial biosphere cycle and changes in the atmospheric circulation regime. The seasonal cycle exhibited a cyclical variation, revealing summer minima for both gases, which may be explained by a biological minimum. Kernel analysis showed two nocturnal CO2 maxima, in spring and autumn, linked to an increase in rainfall. For CO2 daytime records, only the spring peak was detected. As regards CH4, the maximum was located in winter. The best fit for the trend was obtained by the biweight kernel. In contrast, the best adjustment for seasonality was achieved from the Gaussian and the triangular kernel. To sum up, optimal bandwidth selection is important when kernel regression functions are employed. Since no important differences were found between the kernels employed, those which involve least computational effort are recommended. [ABSTRACT FROM AUTHOR]

Air trajectories are useful tools to investigate the airflow and transport of substances released into the atmosphere. Web-based models are widely used to calculate trajectories reaching places that are being studied. This article considers 6 years of air trajectories as of October 2010 together with CO2 and CH4 concentrations. A bivariate smoothing function that employs the radial distance and direction of the trajectory to the measuring site was used to form trajectory groups from the minima of this function. Varied radial and angular windows were assayed to investigate the behaviour of the smoothing function. Curves associated with the number of minima were L-shaped and the windows selected corresponded to the ‘knee’ of the curves. Seven trajectory groups were considered to observe the response of the procedure against the radial distance and the direction. Seasonal evolution revealed the greatest radial extent for winter and the lowest for summer. Moreover, trajectories from the Atlantic Ocean were the most frequent. CO2 and CH4 concentrations were detrended using a linear function, and average trends were 2.34 and 0.0085 ppm year−1, respectively. Annual cycles of detrended concentrations were very soft and were linked to the site's ecosystem. CO2 presented one maximum in spring linked to substantial vegetation growth, and one minimum in summer, when vegetation dies and dispersion is maximum. CH4 maximum was observed in winter although the minimum was found in summer and attributed to oxidation with the hydroxyl radical in the troposphere and to dispersion in this season. Analysis of concentration trends for the groups proposed revealed the opposite behaviour of both gases in summer. Finally, maximum CO2 concentrations were marked by trajectories from North Africa affected by nearby cities, whereas minimum concentrations for both gases were noticeable for trajectories from the ocean in summer. [ABSTRACT FROM AUTHOR]