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Forty years of cloud observations are available globally from satellites, allowing derivation of climate data records (CDRs) for climate change studies. The aim of this study is to investigate how stable these cloud CDRs are and whether they qualify stability requirements recommended by the WMO’s Global Climate Observing System (GCOS). We also investigate robust trends in global total cloud amount (CA) and cloud top temperature (CTT) that are significant and common across all CDRs. The latest versions of four global cloud CDRs, namely CLARA-A3, ESA Cloud CCI, PATMOS-x, and ISCCP-HGM are analysed. This assessment finds that all three AVHRR-based cloud CDRs (i.e., CLARA-A3, ESA Cloud CCI and PATMOS-x) satisfy even the strictest GCOS stability requirements for CA and CTT when averaged globally. While CLARA-A3 is most stable in global averages when tested against MODIS-Aqua, PATMOS-x offers the most stable CDR spatially. While we find these results highly encouraging, there remain, however, large spatial differences in the stability of and across the CDRs. All four CDRs continue to agree on the statistically significant decrease in global cloud amount over the last four decades, although this decrease is now weaker compared to the previous assessments. This decreasing trend has been stabilizing or even reversing in the last two decades; the latter is seen also in MODIS-Aqua and CALIPSO GEWEX datasets. Statistically significant trends in CTT are observed in global averages in the AVHRR-based CDRs, but the spatial agreement in the sign and the magnitude of the trends is weaker compared to those in CA. We also present maps of Common Stability Coverage and Common Trend Coverage that could provide a valuable metric to carry out an ensemble-based analysis of the CDRs.

Observations from the 2014 Arctic Clouds in Summer Experiment indicate that, in summer, warm-air advection over melting sea-ice results in a strong surface melting feedback forced by a very strong surface-based temperature inversion and fog formation exerting additional heat flux on the surface. Here, we analyze this case further using a combination of reanalysis dataset and satellite products in a Lagrangian framework, thereby extending the view spatially from the local icebreaker observations into a Langrangian perspective. The results confirm that warm-air advection induces a positive net surface-energy-budget anomaly, exerting positive longwave radiation and turbulent heat flux on the surface. Additionally, as warm and moist air penetrates farther into the Arctic, cloud-top cooling and surface mixing eventually erode the surface inversion downstream. The initial surface inversion splits into two elevated inversions while the air columns below the elevated inversions transform into well-mixed layers.

Results from four global cloud climate data records (ISCCP-HGM, ESA Cloud CCI V3, CLARA-A2 and PATMOS-x) have been inter-compared in global time series plots, in global maps and in zonal region plots covering the period in common, 1984–2009. The investigated cloud parameters were total cloud fraction and cloud top pressure. Averaged seasonal cycles of cloud cover, as observed by the CALIPSO-CALIOP sensor over the 2007–2015 period, were also used as an additional independent and high-quality reference for the study of global cloud cover. All CDRs show good agreement on global cloud amounts (~65%) and also a weak negative trend (0.5–1.9% per decade) over the period of investigation. Deviations between the CDRs are seen especially over the southern mid-latitude region and over the poles. Particularly good results are shown by PATMOS-x and by ESA Cloud CCI V3 when compared to the CALIPSO-CALIOP reference. Results for cloud top pressure show large differences (~60 hPa) between ISCCP-HGM and the other CDRs for the global mean. The two CDR groups show also opposite signs in the trend over the period.

The transport and distribution of short-lived climate forcers in the Arctic are influenced by the prevailing atmospheric circulation patterns. Understanding the coupling between pollutant distribution and dominant atmospheric circulation types is therefore important, not least to understand the processes governing the local processing of pollutants in the Arctic, but also to test the fidelity of chemistry transport models to simulate the transport from the southerly latitudes. Here, we use a combination of satellite-based and reanalysis datasets spanning over 12 years (2007–2018) and investigate the concentrations of NO2, O3, CO and aerosols and their co-variability during eight different atmospheric circulation types in the spring season (March, April and May) over the Arctic. We carried out a self-organizing map analysis of mean sea level pressure to derive these circulation types. Although almost all pollutants investigated here show statistically significant sensitivity to the circulation types, NO2 exhibits the strongest sensitivity among them. The circulation types with low-pressure systems located over the northeast Atlantic show a clear enhancement of NO2 and aerosol optical depths (AODs) in the European Arctic. The O3 concentrations are, however, decreased. The free tropospheric CO is increased over the Arctic during such events. The circulation types with atmospheric blocking over Greenland and northern Scandinavia show the opposite signal in which the NO2 concentrations are decreased and AODs are smaller than the climatological values. The O3 concentrations are, however, increased, and the free tropospheric CO is decreased during such events. The study provides the most comprehensive assessment so far of the sensitivity of springtime pollutant distribution to the atmospheric circulation types in the Arctic and also provides an observational basis for the evaluation of chemistry transport models.

This paper investigates the quality of global cloud fraction and cloud-top height products provided by the third edition of the CM SAF cLoud, Albedo and surface RAdiation dataset from the AVHRR data (CLARA-A3) climate data record (CDR) produced by the EUMETSAT Climate Monitoring Satellite Application Facility (CM SAF). Compared with with CALIPSO–CALIOP cloud lidar data and six other cloud CDRs, including the predecessor CLARA-A2, CLARA-A3 has improved cloud detection, especially over ocean surfaces, and improved geographical variation and cloud detection efficiency. In addition, CLARA-A3 exhibits remarkable improvements in the accuracy of its global cloud-top height measurements. For example, in tropical regions, previous underestimations for high-level clouds are reduced by more than 2 km. By taking advantage of more realistic descriptions of global cloudiness, this study attempted to estimate trends in the observable fraction of low-level clouds, acknowledging their importance in producing a net climate cooling effect. The results were generally inconclusive in the tropics, mainly due to the interference of El Nino modes during the period under study. However, the analysis found small negative trends over oceanic surfaces outside the core tropical region. Further studies are needed to verify the significance of these results.

The World Meteorological Organization (WMO) recommends that the most recent 30-year period, i.e., 1991–2020, be used to compute the climate normals of geophysical variables. A unique aspect of this recent 30-year period is that the satellite-based observations of many different essential climate variables are available during this period, thus opening up new possibilities to provide a robust, global basis for the 30-year reference period in order to allow climate-monitoring and climate change studies. Here, using the satellite-based climate data record of cloud and radiation properties, CLARA-A3, for the month of January between 1981 and 2020, we illustrate the difference between the climate normal, as defined by guidelines from WMO on calculations of 30 yr climate normals, and climatology. It is shown that this difference is strongly dependent on the climate variable in question. We discuss the impacts of the nature and availability of satellite observations, variable definition, retrieval algorithm and programmatic configuration. It is shown that the satellite-based climate data records show enormous promise in providing a climate normal for the recent 30-year period (1991–2020) globally. We finally argue that the holistic perspectives from the global satellite community should be increasingly considered while formulating the future WMO guidelines on computing climate normals.

Two three-dimensional reanalysis datasets of atmospheric composition, the Copernicus Atmosphere Monitoring Service reanalysis (CAMSRA) and the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), are analyzed for the years 2003–2018 with respect to dust transport into the Arctic. The reanalyses agree on that the largest mass transport of dust into the Arctic occurs across western Russia during spring and early summer, but substantial transport events occasionally also occur across other geographical areas during all seasons. In many aspects, however, the reanalyses show considerable differences: the mass transport in MERRA-2 is substantially larger, more spread out, and occurs at higher altitudes than in CAMSRA, while the transport in CAMSRA is to a higher degree focused to well-defined events in space and time; the integrated mass transport of the 10 most intense 36-hour dust events in CAMSRA constitutes 6 % of the total integrated dust transport 2003–2018, whereas the corresponding value for MERRA-2 is only 1 %.Furthermore, we compare the reanalyses with surface measurements of dust in the Arctic and dust extinction retrievals from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) satellite data. This comparison indicates that CAMSRA underestimates the dust transport into the Arctic and that MERRA-2 likely overestimates it. The discrepancy between CAMSRA and MERRA-2 can partially be explained by the assimilation process where too little dust is assimilated in CAMSRA while in MERRA-2, the assimilation process increases the dust concentration in remote areas. Despite the profound differences between the reanalyses regarding dust transport into the Arctic, this study still brings new insights into the spatio-temporal distribution of the transport. We estimate the annual dust transport into the Arctic to be within the range 1.5–31 Tg, where the comparison with observations indicates that the lower end of the interval is less likely.

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.

Northern China was hit by a severe dust storm on 15 March 2021, covering a large area and bring devastating impact to a degree that was unprecedented in more than a decade. In the study, we carried out a day-and-night continuous monitoring to the path of the moving dust, using multi-spectral data from the Chinese FY-4A satellite combined with the Japanese Himawary-8 from visible to near-infrared, mid-infrared and far-infrared bands. We monitored the whole process of the dust weather from the occurrence, development, transportation and extinction. The HYSPLIT(Hybrid Single Particle Lagrangian Integrated Trajectory) backward tracking results showed the following two main sources of dust affecting Beijing during the north China dust storm: one is from western Mongolia; the other is from arid and semi-arid regions of northwest of China. Along with the dust storm, the upper air mass, mainly from Siberia, brought a significant decrease in temperature. The transport path of the dust shown by the HYSPLIT backward tracking is consistent with that revealed by the satellite monitoring. The dust weather, which originated in western Mongolia, developed into the “3.15 dust storm” in north China, lasting more than 40 h, with a transport distance of 3900 km, and caused severe decline in air quality in northern China, the Korean peninsula and other regions. It is the most severe dust weather in the past 20 years in east Asia.

This study evaluates the impact of atmospheric horizontal resolution on the representation of cloud radiative effects (CREs) in an ensemble of global climate model simulations following the protocols of the High Resolution Model Intercomparison Project (HighResMIP). We compare results from four European modelling centres, each of which provides data from “standard”- and “high”-resolution model configurations. Simulated radiative fluxes are compared with observation-based estimates derived from the Clouds and Earth's Radiant Energy System (CERES) dataset. Model CRE biases are evaluated using both conventional statistics (e.g. time and spatial averages) and after conditioning on the phase of two modes of internal climate variability, namely the El Niño–Southern Oscillation (ENSO) and the North Atlantic Oscillation (NAO). Simulated top-of-atmosphere (TOA) and surface CREs show large biases over the polar regions, particularly over regions where seasonal sea-ice variability is strongest. Increasing atmospheric resolution does not significantly improve these biases. The spatial structure of the cloud radiative response to ENSO and NAO variability is simulated reasonably well by all model configurations considered in this study. However, it is difficult to identify a systematic impact of atmospheric resolution on the associated CRE errors. Mean absolute CRE errors conditioned on the ENSO phase are relatively large (5–10 W m-2) and show differences between models. We suggest this is a consequence of differences in the parameterization of SW radiative transfer and the treatment of cloud optical properties rather than a result of differences in resolution. In contrast, mean absolute CRE errors conditioned on the NAO phase are generally smaller (0–2 W m-2) and more similar across models. Although the regional details of CRE biases show some sensitivity to atmospheric resolution within a particular model, it is difficult to identify patterns that hold across all models. This apparent insensitivity to increased atmospheric horizontal resolution indicates that physical parameterizations play a dominant role in determining the behaviour of cloud–radiation feedbacks. However, we note that these results are obtained from atmosphere-only simulations and the impact of changes in atmospheric resolution may be different in the presence of coupled climate feedbacks.