The global aerosol extinction from the CALIOP space lidar was used to compute aerosol optical depth (AOD) over a 9-year period (2007–2015) and partitioned between the boundary layer (BL) and the free troposphere (FT) using BL heights obtained from the ERA-Interim archive. The results show that the vertical distribution of AOD does not follow the diurnal cycle of the BL but remains similar between day and night highlighting the presence of a residual layer during night. The BL and FT contribute 69 and 31 %, respectively, to the global tropospheric AOD during daytime in line with observations obtained in Aire sur l'Adour (France) using the Light Optical Aerosol Counter (LOAC) instrument. The FT AOD contribution is larger in the tropics than at mid-latitudes which indicates that convective transport largely controls the vertical profile of aerosols. Over oceans, the FT AOD contribution is mainly governed by long-range transport of aerosols from emission sources located within neighboring continents. According to the CALIOP aerosol classification, dust and smoke particles are the main aerosol types transported into the FT. Overall, the study shows that the fraction of AOD in the FT – and thus potentially located above low-level clouds – is substantial and deserves more attention when evaluating the radiative effect of aerosols in climate models. More generally, the results have implications for processes determining the overall budgets, sources, sinks and transport of aerosol particles and their description in atmospheric models.

Can we build stable Climate Data Records (CDRs) spanning several satellite generations? This study outlines how the ClOud Fractional Cover dataset from METeosat First and Second Generation (COMET) of the EUMETSAT Satellite Application Facility on Climate Monitoring (CM SAF) was created for the 25-year period 1991–2015. Modern multi-spectral cloud detection algorithms cannot be used for historical Geostationary (GEO) sensors due to their limited spectral resolution. We document the innovation needed to create a retrieval algorithm from scratch to provide the required accuracy and stability over several decades. It builds on inter-calibrated radiances now available for historical GEO sensors. It uses spatio-temporal information and a robust clear-sky retrieval. The real strength of GEO observations—the diurnal cycle of reflectance and brightness temperature—is fully exploited instead of just accounting for single “imagery”. The commonly-used naive Bayesian classifier is extended with covariance information of cloud state and variability. The resulting cloud fractional cover CDR has a bias of 1% Mean Bias Error (MBE), a precision of 7% bias-corrected Root-Mean-Squared-Error (bcRMSE) for monthly means, and a decadal stability of 1%. Our experience can serve as motivation for CDR developers to explore novel concepts to exploit historical sensor data.

Large increase in stratospheric NO₂ content has been observed during the 2003–2004 Arctic winter. The first one, in early November 2003 is well documented and is due to a strong solar protons event. A second event occurred on January 22, 2004, leading to a large amount of NO₂ in the lower mesosphere. This second event can be analyzed using data from nighttime satellite measurements performed by the GOMOS and MIPAS instruments onboard Envisat, and by ground based column measurements performed by SAOZ. It seems that in‐situ production of NO₂ is located at an altitude of around 60 km associated with the precipitation of electrons with energy of a few hundred keV. These electrons originated from the high latitudes magnetosphere, and are associated with a solar coronal mass ejection. Therefore, a particular nighttime chemistry in the lower mesosphere is proposed to explain the measurements.

Surface solar radiation is fundamental for terrestrial life. It provides warmth to make our planet habitable, drives atmospheric circulation, the hydrological cycle and photosynthesis. Europe has experienced an increase in surface solar radiation, termed “brightening,” since the 1980s. This study investigates the causative factors behind this brightening. A novel algorithm from the EUMETSAT satellite application facility on climate monitoring (CM SAF) provides the unique opportunity to simulate surface solar radiation under various atmospheric conditions for clouds (clear‐sky or all‐sky), aerosol optical depth (time‐varying or climatological averages) and water vapor content (with or without its direct influence on surface solar radiation). Through a multiple linear regression approach, the study attributes brightening trends to changes in these atmospheric parameters. Analyzing 61 locations distributed across Europe from 1983 to 2020, aerosols emerge as key driver during 1983–2002, with Southern Europe and high elevations showing subdued effects (0%/decade–1%/decade) versus more pronounced impacts in Northern and Eastern Europe (2%/decade–6%/decade). Cloud effects exhibit spatial variability, inducing a negative effect on surface solar radiation (−3%/decade–−2%/decade) at most investigated locations in the same period. In the period 2001–2020, aerosol effects are much smaller, while cloud effects dominate the observed brightening (2%/decade–5%/decade). This study therefore finds a substantial decrease in the cloud radiative effect over Europe in the first two decades of the 21st century. Water vapor exerts negligible influence in both sub‐periods.

Grid datasets of sunshine duration at high spatial resolution and extending over many decades are required for quantitative applications in regional climatology and environmental change (e.g., modelling of droughts and snow/ice covers, evaluation of clouds in numerical models, mapping of solar energy potentials). We present a new gridded dataset of relative (and derived absolute) sunshine duration for Austria at a grid spacing of 1 km, extending back until 1961 at daily time resolution. Challenges in the dataset construction were consistency issues in the available station data, the scarcity of long time series, and the high variation of cloudiness in the study region. The challenges were addressed by special efforts to correct evident breaks in the station series and by adopting an analysis method, which combines station data with satellite data. The methodology merges the data sources non-contemporaneously, using statistical patterns distilled over a short period, which allowed involving satellite data even for the early part of the study period. The resulting fields contain plausible mesoscale structures, which could not be resolved by the station network alone. On average, the analyses explain 47% of the spatial variance in daily sunshine duration at the stations. Evaluation revealed a slight systematic underestimation (− 1.5%) and a mean absolute error of 9.2%. The average error is larger during winter, at high altitudes, and around the 1990s. The dataset exhibits a conditional bias, which can lead to considerable systematic errors (up to 15%) when calculating sunshine-related climate indices.