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This paper presents the third edition of the CM SAF cLoud, Albedo and surface RAdiation dataset from AVHRR data, CLARA-A3. The content of earlier CLARA editions, namely cloud, surface albedo, and surface radiation products, has been extended with two additional surface albedo products (blue and white sky albedo), three additional surface radiation products (net shortwave and longwave radiation, surface radiation budget) and two top of atmosphere radiation budget products (reflected solar flux and outgoing longwave radiation). The record length is extended to 42 years (1979–2020) by also incorporating results from the first version of the Advanced Very High Resolution Radiometer (AVHRR) imager (AVHRR/1). A continuous extension of the climate data record (CDR) has also been implemented by processing an interim climate data record (ICDR) based on the same set of algorithms but with slightly changed ancillary input data. All products are briefly described together with validation results and inter-comparisons with currently existing similar CDRs. The extension of the product portfolio and the temporal coverage of the data record, together with product improvements, is expected to enlarge the potential of using CLARA-A3 for climate change studies and, in particular, studies of potential feedback effects between clouds, surface albedo and radiation. The CLARA-A3 data record is hosted by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Satellite Application Facility on Climate Monitoring (CM SAF) and is freely available at https://doi.org/10.5676/EUM_SAF_CM/CLARA_AVHRR/V003.

Tropical deep connective clouds (DCCs) have large top of atmosphere (ToA) cloud radiative effects (CREs) in both the shortwave (SW) and longwave (LW), which both have average magnitudes of greater than 100 Wm-2. Due to the opposite sign of the two components, the overall ToA CRE is generally assumed to average to approximately 0 Wm-2. Although there are a number of mechanisms that contribute to this balance, the fact that the daytime only SW CRE balances with the LW CRE indicates that the diurnal lifecycle of DCCs is a key component of this balance. Understanding how the diurnal cycle of DCCs influences their CRE is vital for understanding how any changes in their diurnal cycle of these clouds may influence the climate. A year-long dataset of retrieved cloud properties and derived broadband radiative fluxes has been produced by the ESA Cloud CCI project using temporally highly resolved satellite observations. Using a novel method, we are able to detect and track both isolated DCCs and large, mesoscale convective systems (MCSs) over their entire lifecycle. We explicitly retrieve the cloud properties and CREs of DCCs over Africa, and how these properties change over the lifecycle of approximately 100,000 observed clouds. We find that the mean anvil SW CRE greatly varies depending on the initiation time of day and the lifetime of the DCC, whereas the LW CRE is consistent throughout the diurnal cycle and varies primarily with cloud top temperature. As a result of our study we can confirm that the mean observed ToA CRE of all DCCs (integrated over area and lifetime) is indeed approximately 0 Wm-2, but very few DCCs individually have mean CREs near this value. Instead, we find that DCCs occurring during the daytime have a large cooling effect, and those at nighttime have a warming effect, resulting in a bimodal distribution. While MCSs make the largest contribution to the overall effect due to their large areas and lifetimes, because they tend to exist during both nighttime and daytime the overall magnitude of their ToA CREs tend to be smaller than those of isolated DCCs. As a result, factors which influence the diurnal cycle of deep convection – such as changes in CAPE generation or convective inhibition – may have a more important influence on the properties of isolated DCCs rather than larger MCSs.

The role of clouds in the Arctic radiation budget is not well understood. Ground-based and airborne measurements provide valuable data to test and improve our understanding. However, the ground-based measurements are intrinsically sparse, and the airborne observations are snapshots in time and space. Passive remote sensing measurements from satellite sensors offer high spatial coverage and an evolving time series, having lengths potentially of decades. However, detecting clouds by passive satellite remote sensing sensors is challenging over the Arctic because of the brightness of snow and ice in the ultraviolet and visible spectral regions, and because of the small brightness temperature contrast to the surface. Consequently, the quality of the resulting cloud data products needs to be assessed quantitatively. In this study, we validate the cloud data products retrieved from the Advanced Very High Resolution Radiometer (AVHRR) post meridiem (PM) data from the polar-orbiting NOAA-19 satellite and compare them with those derived from the ground-based instruments during the sunlit months. The AVHRR cloud data products by the European Space Agency’s (ESA) Cloud Climate Change Initiative (Cloud_CCI) project, which uses the observations in the visible and IR bands to determine cloud properties. The ground-based measurements from four high-latitude sites have been selected for this investigation: Hyytiälä (61.84° N, 24.29° E), North Slope of Alaska (NSA, 71.32° N, 156.61° W), Ny-Ålesund (Ny-Å, 78.92° N, 11.93° E), and Summit (72.59° N, 38.42° W). The Liquid Water Path (LWP) ground-based data are retrieved from microwave radiometers, while the Cloud Top Height (CTH) has been determined from the integrated lidar-radar measurements. The quality of the satellite products, Cloud Mask and Cloud Optical Depth (COD), have been assessed using data from NSA, whereas LWP and CTH have been investigated over Hyytiälä, NSA, Ny-Å, and Summit. The Cloud_CCI COD results for liquid water clouds are in better agreement with the NSA radiometer data than those for ice clouds. For liquid water clouds, the Cloud_CCI COD is underestimated roughly by 2.8 Optical Depth (OD) units. When ice clouds are included, the underestimation increases to about 4.6 OD units. The Cloud_CCI LWP is overestimated over Hyytiälä by ≈ 7 gm−2, over NSA by ≈ 16 gm−2, and over Ny-Å by ≈ 24 gm−2. Over Summit, CCI LWP is overestimated for values ≤ 20 gm−2 and underestimated for values > 20 gm−2. Overall the results of the CCI LWP retrievals are within the ground-based instrument uncertainties. For CTH retrievals, the Cloud_CCI product overestimates single-layer clouds. To understand the effects of multi layer clouds on the CTH retrievals, the statistics are compared between the single layer clouds and all types (single + multi layer). When the multi layer clouds are included (i.e., all types), the observed CTH overestimation become underestimations of about 360–420 m. The CTH results over Summit station showed the highest biases compared to the other three sites. To understand the scale-dependent differences between the satellite and ground-based data, the Bland-Altman method is applied. This method does not identify any scale-dependent differences for all the selected cloud parameters except for the retrievals over the Summit station. In summary, the Cloud_CCI cloud data products investigated agree reasonably well with those retrieved from ground-based measurements, made at the four high-latitude sites.

Global Climate is one chapter from the State of the Climate in 2019 annual report and is avail- able from https://doi.org/10.1175/BAMS-D-20-0104.1 Compiled by NOAA’s National Centers for Environmental Information, State of the Climate in 2019 is based on contributions from scien- tists from around the world. It provides a detailed update on global climate indicators, notable weather events, and other data collected by environmental monitoring stations and instru- ments located on land, water, ice, and in space. The full report is available from https://doi.org/10.1175/2020BAMSStateoftheClimate.1.

We present version 3 (V3) of the Cloud_cci Along-Track Scanning Radiometer (ATSR) and Advanced ATSR (AATSR) data set. The data set was created for the European Space Agency (ESA) Cloud_cci (Climate Change Initiative) programme. The cloud properties were retrieved from the second ATSR (ATSR-2) on board the second European Remote Sensing Satellite (ERS-2) spanning 1995–2003 and the AATSR on board Envisat, which spanned 2002–2012. The data are comprised of a comprehensive set of cloud properties: cloud top height, temperature, pressure, spectral albedo, cloud effective emissivity, effective radius, and optical thickness, alongside derived liquid and ice water path. Each retrieval is provided with its associated uncertainty. The cloud property retrievals are accompanied by high-resolution top- and bottom-of-atmosphere shortwave and longwave fluxes that have been derived from the retrieved cloud properties using a radiative transfer model. The fluxes were generated for all-sky and clear-sky conditions. V3 differs from the previous version 2 (V2) through development of the retrieval algorithm and attention to the consistency between the ATSR-2 and AATSR instruments. The cloud properties show improved accuracy in validation and better consistency between the two instruments, as demonstrated by a comparison of cloud mask and cloud height with co-located CALIPSO data. The cloud masking has improved significantly, particularly in its ability to detect clear pixels. The Kuiper Skill score has increased from 0.49 to 0.66. The cloud top height accuracy is relatively unchanged. The AATSR liquid water path was compared with the Multisensor Advanced Climatology of Liquid Water Path (MAC-LWP) in regions of stratocumulus cloud and shown to have very good agreement and improved consistency between ATSR-2 and AATSR instruments. The correlation with MAC-LWP increased from 0.4 to over 0.8 for these cloud regions. The flux products are compared with NASA Clouds and the Earth's Radiant Energy System (CERES) data, showing good agreement within the uncertainty. The new data set is well suited to a wide range of climate applications, such as comparison with climate models, investigation of trends in cloud properties, understanding aerosol–cloud interactions, and providing contextual information for co-located ATSR-2/AATSR surface temperature and aerosol products. The following new digital identifier has been issued for the Cloud_cci ATSR-2/AATSRv3 data set: https://doi.org/10.5676/DWD/ESA_Cloud_cci/ATSR2-AATSR/V003 (Poulsen et al., 2019).

Satellite-based cloud, radiation flux, and sea ice records covering 34 years are used 1) to investigate autumn cloud cover trends over the Arctic, 2) to assess its relation with declining sea ice using Granger causality (GC) analysis, and 3) to discuss the contribution of the cloud–sea ice (CSI) feedback to Arctic amplification. This paper provides strong evidence for a positive CSI feedback with the capability to contribute to autumnal Arctic amplification. Positive low-level cloud fractional cover (CFClow) trends over the Arctic ice pack are found in October and November (ON) with magnitudes of up to about +9.6% per decade locally. Statistically significant anticorrelations between sea ice concentration (SIC) and CFCloware observed in ON over melting zones, suggesting an association. The GC analysis indicated a causal two-way interaction between SIC and CFClow. Interpreting the resultingFstatistic and its spatial distribution as a relation strength proxy, the influence of SIC on CFClowis likely stronger than the reverse. ERA-Interim reanalysis data suggest that ON CFClowis impacted by sea ice melt through surface–atmosphere coupling via turbulent heat and moisture fluxes. Due to weak solar insolation in ON, net cloud radiative forcing (CRF) exerts a warming effect on the Arctic surface. Increasing CFClowinduces a large-scale surface warming trend reaching magnitudes of up to about +8.3 W m−2per decade locally. Sensitivities of total CRF to CFClowranges between +0.22 and +0.66 W m−2per percent CFClow. Increasing surface warming can cause a melt season lengthening and hinders formation of perennial ice.

Das CM SAF (EUMETSAT Satellite Application Facility on Climate Monitoring) produziert, archiviert und stellt unter https://www.cmsaf.eu langjährige satellitenbasierte Klimadatensätze von vielen GCOS Essential Climate Variables (ECVs, essentielle Klimavariablen) bereit, die inzwischen auch die komplette aktuelle WMO Klimareferenzperiode 1990-2020 abdecken und damit eine gute Grundlage für die Analyse von Klimavariabilität und Klimawandel liefern. Seit 1999 hat das CM SAF kontinuierlich eine nachhaltige Infrastruktur zur Erzeugung von Klimadatensätzen aufgebaut, mit der Zeitreihen in hoher Qualität in einer operationellen Umgebung erzeugt werden, die auch aktuelle wissenschaftliche Entwicklungen berücksichtigen. Der inhaltliche Fokus des CM SAF liegt auf ECVs, wie Wolken, Wasserdampf, Niederschlag, Landoberflächentemperatur oder der Strahlungskomponenten (langwellig/kurzwellig) am Erdboden und am Oberrand der Atmosphäre, die durch GCOS (Global Climate Observing System) definiert wurden und im Zusammenhang mit dem globalen Energie und Wasser Kreislauf stehen. Einerseits nutzt das CM SAF dazu polarumlaufende Satelliten mit einer globalen räumlichen Abdeckung. Andererseits werden vom CM SAF für Afrika und Europa, Klimadatensätze für Wolken und Strahlung basierend auf den zeitlich hochaufgelösten Messungen der METEOSAT-Instrumente erzeugt. Alle Daten des CM SAF werden kostenlos abgebeben, sind umfangreich dokumentiert und unabhängig extern begutachtet, um eine hohe Qualität zu gewährleisten. Dies wird unterstützt durch einen umfassenden Service für Kunden, indem beispielsweise Trainingsworkshops und andere Aktivitäten angeboten werden. Diese Präsentation wird einen Überblick über die aktuellen und geplanten Aktivitäten des CM SAF geben und soll interessierten Nutzern durch beispielhafte Anwendungen den Umgang mit CM SAF Produkten verdeutlichen. Zudem werden zukünftige mögliche Anwendungen der Datensätze aufgezeigt.

The thermodynamic phase transition of clouds is still not well understood, therefore, the partitioning of ice and liquid in mixed phase clouds is often misrepresented in numerical models. We use 12 years of cloud observations from the geostationary Spinning Enhanced Visible and InfraRed Imager over the Southern Ocean to detect clouds which contain both liquid and ice pixels at their tops and we retrieve microphysical and radiative properties in each cloud object. The results show that large cloud droplet effective radius coincides with high ice fraction and high ice optical thickness for cloud top temperatures higher than −8 °C. We also found that the density of ice pixel clusters increases with the cloud ice fraction, for ice fraction lower than 0.5, suggesting a multiplication of ice pockets in line with previous studies, particularly efficient for clouds with high perimeter fractal dimension. Plain Language Summary Clouds with coexisting liquid droplets and ice crystals are frequent but they are still not well understood and often misrepresented in numerical models. We analyze the temperature, the optical properties of clouds, and the size of droplets and ice crystals from 12 years of satellite observations over the Southern Ocean. We find that clouds with large droplets are more likely to undergo glaciation than clouds with small droplets and that at temperatures higher than −8 °C the glaciation is probably associated with a higher concentration of ice crystals. We also analyze how liquid and ice are spatially distributed within clouds and we highlight that multiple ice pockets are formed when clouds glaciate rather than spreading from one ice pocket to the entire cloud. A better understanding of clouds allows a better representation of their interaction with the environment and therefore their impact on the climate. Clouds precipitate more easily if they consist of ice crystals, potentially reducing the lifetime of clouds and impacting the radiation balance at the surface and top of the atmosphere. Also, the spatial distribution of ice and liquid in clouds impacts how ice crystals and liquid droplets interact with each other.

The improvement of our understanding of the spatiotemporal variability of cloud properties and their governing processes is of high importance, given the crucial role of clouds in the climate system. The availability of long-term and high-quality satellite observations together with mature remote sensing techniques has made feasible the creation of multi-decadal climate data records for this purpose.Various cloud classification techniques have been developed and applied in the past, each with distinct advantages and disadvantages, allowing studying clouds from different perspectives. One of these techniques is the creation of cloud regimes which provides information on the prevalence of simultaneously occurring cloud types over a region. This study uses the k-means clustering method, applied to 2-dimensional histograms of cloud top pressure and optical thickness, in order to derive and analyze cloud regimes over Europe during the last decade. Europe is selected for this work because it is an appropriate region for studying cloud regimes since the prevailing atmospheric circulation patterns and its diverse geomorphology, result in a mixture of diverse cloud types. In order to achieve that, the CLoud property dAtAset using Spinning Enhanced Visible and Infrared (SEVIRI) edition 2.1 (CLAAS-2.1) data record, which is produced by the EUMETSAT Satellite Application Facility on Climate Monitoring (CM SAF), is used as basis for the derivation of the cloud regimes. In particular, pixel-level Cloud Optical Thickness (COT) and Cloud Top Pressure (CTP) products of CLAAS-2.1, from 2004 to 2017, are used in order to compute 2D histograms on a 1°×1° spatial resolution. Then the k-means clustering algorithm is applied, treating each 2D COT-CTP histogram of each grid point and time step as an individual data point. Various sensitivity studies on the subsampling of the data and the selection of the cloud regimes were carried out, in order to test the robustness of the method and of the results.In contrast to the previous studies and taking advantage of the geostationary orbit of Meteosat Second Generation (MSG), on which SEVIRI is aboard, a better sampling of the diurnal cycle of clouds is thus included in the derivation process of cloud regimes. Furthermore, the annual cycle of the produced cloud regimes is examined. In addition, for each regime, the time step with its highest spatial frequency of occurrence is selected for a visual comparison with the corresponding RGB image. Finally, a comparison of the cloud regimes against the synoptic large scale weather pattern classification is investigated. The weather pattern classification consists of 29 typical defined patterns of the daily synoptic circulation and it is produced by the German Weather Service (DWD).

The Hindu Kush Himalaya (HKH), the worlds ‘water tower’, contains the largest volume of snow and ice outside of the polar ice sheets and is the headwater area of Asia’s largest rivers. Due to the complex topography and its great spatial extent the HKH is characterised by variable temperature and precipitation pattern and thus exhibits large heterogeneity in the presence of seasonal snow cover (SSC). Previous studies usually focused on regional studies of snow cover area percentage or the influence of snow melt on the local hydrological system. Here we present a systematic overview of spatio-temporal SSC variability of the entire HKH region on a climate relevant time scale (four decades).Our results are based on Advanced Very High Resolution (AVHRR) data, collected onboard the polar orbiting satellites NOAA-7 to -19, providing daily, global imagery at a spatial resolution of 5 km since 1982 up to today. This unique dataset is exceptionally valuable to derive pixel-based SSC information using a Normalised Difference Snow Cover (NDSI) approach including additional thresholds related to topography and land cover, and developed in the frame of ESA CCI+ snow. Calibrated and geocoded reflectance data and a consistent cloud mask, derived in the ESA CCI cloud project, are used. A temporal gap-filling was applied to mitigate the influence of clouds. Reference snow maps from high-resolution optical satellite data as well as in-situ station data were used to validate the time series.The dataset allows analysis of the state and trends of SSC at regional and sub-regional level. We thus investigated spatio-temporal evolution and long-term variability of SSC for the entire HKH as well as for 14 hydrological basins. We find large spatial difference in the amount of SSC depending on the regional elevation and precipitation characteristics. Furthermore, we investigate SSC phenology, which is directly linked to climate change and thus of high relevance for seasonal water storage and mountain streamflow. Our analysis indicates a significant decline in snow cover area percentage (SCA %) during warm and dry summer month and a decreasing tendency from high winter through spring to early summer. At the hydrological basin level, no significant long-term trend was detected, however, both western and central basins indicate a decrease in SCA % and generally the latest years are strongly negative. Moreover, we examine SCA % anomalies at the highest available temporal frequency (daily information) and reveal an overall shortening of the SSC occurrence and a general decrease of SSC extent in the HKH region.

One of the largest uncertainties in numerical weather prediction and climate models is the representation of mixed-phase clouds. With the aim of understanding how the supercooled liquid fraction (SLF) in clouds with temperature from -40$^\circ$C to 0$^\circ$C is related to temperature, geographical location, and cloud type, our analysis contains a comparison of four satellite-based datasets (one derived from active and three from passive satellite sensors), and focuses on SLF distribution near-globally, but also stratified by latitude and continental/maritime regions. Despite the warm bias in cloud top temperature of the passive sensor compared to the active sensor and the phase mismatch in collocated data, all datasets indicate, at the same height-level, an increase of SLF with cloud optical thickness, and generally larger SLF in the Southern Hemisphere than in the Northern Hemisphere (up to about 20\% difference), with the exception of continental low-level clouds, for which the opposite is true.

Knowledge about the snow cover distribution is of high importance for climate studies, weather forecast, hydrological investigations, irrigation or tourism, respectively. The Hindu Kush Himalayan (HKH) region covers almost 3.5 million km2 and extends over eight different countries. The region is known as ‘water tower’ as it contains the largest volume of ice and snow outside of the polar ice sheets and it is the source of Asia’s largest rivers. These rivers provide ecosystem services, the basis for livelihoods and most importantly living water for drinking, irrigation, energy production and industry for two billion people, a fourth of the world’s population, living in the mountains and downstream.The spatio-temporal variability of snow cover in the HKH is high and studies reported average snow-covered area percentage of 10–18%, with greater variability in winter (21–42%) than in summer (2–4%). However, no study systematically investigated snow cover metrics, such as snow cover area percentage (SCA), snow cover duration (SCD) or snow cover onset (SCOD) and melt-out day (SCMD), for the entire region so far. Here, we thus present unique in-sights of regional and sub-regional snow cover dynamics for the HKH based on almost four decades, an exceptionally long and in view of the climate modelling community valuable timeseries, of satellite data obtained within the ESA CCI+ Snow project.Our results are based on Advanced Very High Resolution Radiometer (AVHRR) data, collected onboard the polar orbiting satellites NOAA-7 to -19, providing daily, global imagery at a spatial resolution of 5 km. Calibrated and geocoded reflectance data and a consistent cloud mask pre-processed and provided by the ESA Cloud_cci project as global 0.05° composites are used. The retrieval of snow extent considers the high reflectance of snow in the visible spectra and the low reflectance values in the short-wave infrared expressed in the Normalized Difference Snow Index (NDSI). Additional thresholds related to topography and land cover are included to derive the fractional snow cover of every pixel. A temporal gap-filling was applied to mitigate the influence of clouds. Reference snow maps from high-resolution optical satellite data as well as in-situ station data were used to validate the time series.

Global data records of cloud properties are an important part for the analysis of the Earth's climate system and its variability. One of the few sources facilitating such records are the measurements of the satellite-based Advanced Very High Resolution Radiometer (AVHRR) sensor that provides spatially homogeneous and high resolved information in multiple spectral bands. This information can be used to retrieve global cloud properties covering multiple decades, as, for example, composed as part of the CM SAF Cloud, Albedo, Radiation data record based on AVHRR (CLARA) series.In this presentation we introduce the edition 2.1 (CLARA-A2.1) of this record series, which is the temporally extended version of CLARA-A2. This extension includes three and a half more years at the end of the data record, which now covers the time period January 1982 to June 2019 (37.5 years). CLARA-A2.1 includes a comprehensive set of cloud parameters: fractional cloud cover, cloud top products, cloud thermodynamic phase and cloud physical properties, such as cloud optical thickness, particle effective radius and cloud water path. Cloud products are available as daily and monthly averages and histograms (Level 3) on a regular 0.25°×0.25° global grid and as daily, global composite products (Level 2b) with a spatial resolution of 0.05°×0.05°. Time series analyses of the CLARA-A2.1 cloud products show the homogeneity and stability of the extension.In addition to the general characteristics of the CLARA-A2.1 record, we will summarize the results of the thorough evaluation efforts that were conducted by validation against reference observations (e.g. SYNOP, DARDAR, CALIOP) and by comparisons to similar well established data records (e.g. Patmos-X, ISCCP-H and MODIS C6.1). CLARA-A2.1 cloud products show generally a very good agreement with all the compared data sets and fulfil CM SAF's accuracy, precision and decadal stability requirements. As an additional aspect, we will touch upon the CLARA Interim Climate Data Record (ICDR) concept that will soon be used for extending CLARA-A2.1 in near-real-time mode.

Liquid cloud droplets freeze homogeneously at -40°C. For temperature between -40 and 0°C, clouds can be either liquid, ice, or mixed-phase. Several variables determine the cloud phase: droplet size, ice nuclei concentration, meteorological parameters, etc. But, parameters which trigger, enhance or inhibit the phase transition are still poorly understood and disagreements remain between theory and observations. The phase transition is nonetheless important to determine cloud effects on climate.In the present study, we analyse satellite observations from the geostationary passive instrument SEVIRI. We used the CLAAS-2 dataset to retrieve cloud top microphysical and optical properties from 2005 to 2015 over the Southern Ocean.Cloud objects that contain liquid and ice pixels are identified for cloud top temperatures within specific temperature ranges: between -30 and -20°C, between -20 and -8°C, and between -8 to 0°C. The distributions of different cloud properties for mixed-phase, liquid or ice clouds are compared. For example, preliminary results show that cloud ice fraction increases with the cloud droplet size for cloud top temperature between -8 and 0°C. Indeed, ice fractions greater than 0.8 are associated with a median cloud droplet effective radius of 7 micrometers whereas ice fractions less than 0.2 are associated with a median cloud droplet effective radius of 12 micrometers. We hypothesize that this result can be associated to a secondary ice production process (e.g., the Hallet-Mossop process is the splinter production associated with riming process for temperature between -8 and -3°C and it can increase the ice particle concentration by several orders of magnitude). In line with our results, the Hallet-Mossop process is more efficient with larger cloud droplets. The spatial distribution of liquid and ice pixels within the cloud objects is also studied to better understand the phase partitioning of mixed-phase clouds.

The solar radiation reaching the Earth’s surface determines our climate and is therefore important to be monitored as consistent and complete as possible. Even though surface reference measurements of surface solar radiation are available (e.g. from the Baseline Surface Radiation Network (BSRN)), their density remains low and large areas, like the oceans, remain poorly covered. To fill the gaps in space and time, satellite-based data records (like CLARA-A2 and SARAH-2.1 from the EUMETSAT Satellite Application Facility on Climate Monitoring (CM SAF)) or model-based reanalysis data records (like ERA-5) are used. They provide surface solar radiation data with regional and global coverage, which are needed to understand its distribution and variability from the regional to the global scale.Here we present a validation and analysis of monthly mean surface solar irradiance from multiple satellite-based and reanalysis data sets on the regional and global scale with reference to a data base of hundreds of surface measurements over land and ocean, collected from different sources (incl. BSRN, GEBA, WRDC, and buoy networks). This study provides new insights about the quality and uncertainty of available state-of-the-art satellite-based and reanalysis data records for climate studies. Regions of agreement as well as areas where the gridded data records exhibit larger differences are identified, providing important information on our current knowledge of the surface solar radiation climatology and possible improvements for future developments.

This paper presents the spatiotemporal variability of warm rain events over southern West Africa (SWA) during the summer monsoon season for the first time, using Spinning Enhanced Visible Infrared Radiometer (SEVIRI) observations on the Meteosat geostationary satellites. The delineation of warm rain events is based on the principle that precipitating low-level clouds are associated with either sufficient water content or large cloud droplet size. Capitalising on the ability of spaceborne radar to resolve vertical cloud structures and detect the presence of precipitation, the delineation is trained by collocated SEVIRI and CloudSat observations. The resulting 12-years of observations from SEVIRI are used to examine the spatial, diurnal, seasonal and interannual variability of warm rain events over SWA. Warm rain events predominate during the monsoon in August, with little interannual variability, and persist over orography in the morning and the coasts after midday, likely enhanced by orographic lifting and land-sea breeze effects. Warm clouds have a much higher probability of precipitation along the coastlines of Liberia and Nigeria compared to the central SWA coastline and further inland. Finally, when evaluating an 8-day yet high-spatial resolution model simulation, we find that warm rain frequencies from the simulation agree well with SEVIRI near the coast but simulated warm cloud cover and thus warm rain frequencies are too low over the Gulf of Guinea. The probability of precipitation of warm clouds is also too low inland. The newly developed climatology creates opportunities to further investigate the diurnal cycle of warm rain, study aerosol-cloud-precipitation interactions, and assess the role of warm rain in the water cycle across Africa and beyond.

We present version 3 (V3) of the Cloud_cci ATSR-2/AATSR dataset. The dataset was created for the European Space Agency (ESA) Cloud_cci (Climate Change Initiative) program. The cloud properties were retrieved from the second Along- Track Scanning Radiometer (ATSR-2) on board the second European Remote Sensing Satellite (ERS-2) spanning 1995–2003 and the Advanced ATSR (AATSR) on board Envisat, which spanned 2002–2012. The data comprises a comprehensive set of cloud properties: cloud top height, temperature, pressure, spectral albedo, cloud effective emissivity, effective radius and optical thickness alongside derived liquid and ice water path. Each retrieval is provided with its associated uncertainty. The cloud property retrievals are accompanied by high-resolution top and bottom-of-atmosphere short- and long-wave fluxes that have been derived from the retrieved cloud properties using a radiative transfer model. The fluxes were generated for all-sky and clear-sky conditions. V3 differs from the previous version 2 (V2) through development of the retrieval algorithm and attention to the consistency between the ATSR-2 and AATSR instruments. The cloud properties show improved accuracy in validation and better consistency between the two instruments, as demonstrated by a comparison of cloud mask and cloud height with collocated CALIPSO data. The cloud masking has improved significantly, particularly the ability to detect clear pixels The Kuiper Skill score has increased from .49 to .66. The cloud top height accuracy is relatively unchanged. The AATSR liquid water path was compared with the Multisensor Advanced Climatology of Liquid Water Path (MAC-LWP) in regions of stratocumulous cloud and shown to have very good agreement and improved consistency between ATSR-2 and AATSR instruments, the Correlation with MAC-LWP increase from .4 to over .8 for these cloud regions. The flux products are compared with NASA Clouds and the Earth’s Radiant Energy System (CERES) data, showing good agreement within the uncertainty. The new dataset is well suited to a wide range of climate applications, such as comparison with climate models, investigation of trends in cloud properties, understanding aerosol-cloud interactions, and providing contextual information for collocated ATSR-2/AATSR surface temperature and aerosol products. For the Cloud_cci ATSR-2/AATSRv3 dataset a new digital identifier has been issued: https://doi.org/10.5676/DWD/ESA_Cloud_cci/ATSR2-AATSR/V003 Poulsen et al. (2019).

The Coupled Model Intercomparison Project (CMIP) is now moving into its sixth phase and aims at a more routine evaluation of the models as soon as the model output is published to the Earth System Grid Federation (ESGF). To meet this goal the Earth System Model Evaluation Tool (ESMValTool), a community diagnostics and performance metrics tool for the systematic evaluation of Earth system models (ESMs) in CMIP, has been developed and a first version (1.0) released as open source software in 2015. Here, an enhanced version of the ESMValTool is presented that exploits a subset of Essential Climate Variables (ECVs) from the European Space Agency's Climate Change Initiative (ESA CCI) Phase 2 and this version is used to demonstrate the value of the data for model evaluation. This subset includes consistent, long-term time series of ECVs obtained from harmonized, reprocessed products from different satellite instruments for sea surface temperature, sea ice, cloud, soil moisture, land cover, aerosol, ozone, and greenhouse gases. The ESA CCI data allow 'extending the calculation of performance metrics as summary statistics for some variables and add an important alternative data set in other cases where observations are already available. The provision of uncertainty estimates on a per grid basis for the ESA CCI data sets is used in a new extended version of the Taylor diagram and provides important additional information for a more objective evaluation of the models. In our analysis we place a specific focus on the comparability of model and satellite data both in time and space. The ESA CCI data are well suited for an evaluation of results from global climate models across ESM compartments as well as an analysis of long-term trends, variability and change in the context of a changing climate. The enhanced version of the ESMValTool is released as open source software and ready to support routine model evaluation in CMIP6 and at individual modeling centers. (C) 2017 Elsevier Inc. All rights reserved.

Clouds play a central role in the Earth's atmosphere, and satellite observations are crucial for monitoring clouds and understanding their impact on the energy budget and water cycle. Within the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Satellite Application Facility on Climate Monitoring (CM SAF), a new cloud property data record was derived from geostationary Meteosat Spinning Enhanced Visible and Infrared Imager (SEVIRI) measurements for the time frame 2004–2015. The resulting CLAAS-2 (CLoud property dAtAset using SEVIRI, Edition 2) data record is publicly available via the CM SAF website (https://doi.org/10.5676/EUM_SAF_CM/CLAAS/V002). In this paper we present an extensive evaluation of the CLAAS-2 cloud products, which include cloud fractional coverage, thermodynamic phase, cloud top properties, liquid/ice cloud water path and corresponding optical thickness and particle effective radius. Data validation and comparisons were performed on both level 2 (native SEVIRI grid and repeat cycle) and level 3 (daily and monthly averages and histograms) with reference datasets derived from lidar, microwave and passive imager measurements. The evaluation results show very good overall agreement with matching spatial distributions and temporal variability and small biases attributed mainly to differences in sensor characteristics, retrieval approaches, spatial and temporal samplings and viewing geometries. No major discrepancies were found. Underpinned by the good evaluation results, CLAAS-2 demonstrates that it is fit for the envisaged applications, such as process studies of the diurnal cycle of clouds and the evaluation of regional climate models. The data record is planned to be extended and updated in the future.

The variability of convective cloud spans a wide range of temporal and spatial scales and is of fundamental importance for global weather and climate systems. Datasets from geostationary satellite instruments such as SEVIRI provide high time resolution observations across a large area. In this study we use data from SEVIRI to quantify the diurnal cycle of cloud top temperature within the instrument's field of view and discuss these results in relation to retrieval biases inferred from a comparison against cloud top temperatures from CALIOP. We evaluate SEVIRI cloud top temperatures from the new CLAAS-2 (CLoud property dAtAset using SEVIRI, Edition 2) dataset against cloud top temperatures from CALIOP. Results show a mean bias of +0.44 K with a standard deviation of 11.7 K, which is in agreement with previous validation studies. The analysis of the spatiotemporal distribution of these errors shows that the absolute retrieval biases vary from less than 5 K over the southeast Atlantic Ocean up to 30 K over central Africa at night. Night and daytime retrieval biases can also differ by up to 30 K in some areas, potentially contributing to biases in the estimated amplitude of the diurnal cycle. This illustrates the importance of considering spatial and diurnal variations in retrieval errors when using the CLAAS-2 dataset. Keeping these biases in mind, we quantify the seasonal, diurnal and spatial variation of cloud top temperature across SEVIRI's field of view using the CLAAS-2 dataset. By comparing the mean diurnal cycle of cloud top temperature with the retrieval bias we find that diurnal variations in the retrieval bias can be small, but are often of the same order of magnitude as the amplitude of the observed diurnal cycle, indicating that in some regions the diurnal cycle apparent in the observations may be a significantly impacted by diurnal variability in the accuracy of the retrieval. We show that the CLAAS-2 dataset can measure the diurnal cycle of cloud tops accurately in regions of stratiform cloud such as the southeast Atlantic Ocean and Europe, where cloud top temperature retrieval biases are small and exhibit limited spatial and temporal variability. Quantifying the diurnal cycle over the tropics and regions of desert is more difficult, as retrieval biases are larger and display significant diurnal variability. CLAAS-2 cloud top temperature data are found to be of limited skill in measuring the diurnal cycle accurately over desert regions. In tropical regions such as Central Africa, the diurnal cycle can be described by the CLAAS-2 data to some extent, although retrieval biases appear to reduce the amplitude of the real diurnal cycle of cloud top temperatures. This is the first study to relate the diurnal variations in SEVIRI retrieval bias to observed diurnal cycles in cloud top temperature. Our results may be of interest to those in the observation and modelling communities when using cloud top properties data from SEVIRI, particularly for studies considering the diurnal cycle of convection.

The second edition of the satellite-derived climate data record CLARA ("The CM SAF cLoud, Albedo and surface RAdiation dataset from AVHRR data" – second edition denoted CLARA-A2) is described. The data record covers the 34-year period from 1982 until 2015 and consists of cloud, surface albedo and surface radiation budget products derived from the AVHRR (Advanced Very High Resolution Radiometer) sensor carried by polar-orbiting, operational meteorological satellites. The data record is produced by the EUMETSAT Climate Monitoring Satellite Application Facility (CM SAF) project as part of the operational ground segment. Its upgraded content and methodology improvements since edition 1 are described in detail as well as some major validation results. Some of the main improvements of the data record come from a major effort in cleaning and homogenising the basic AVHRR level 1 radiance record and a systematic use of CALIPSO-CALIOP cloud information for development and validation purposes. Examples of applications studying decadal changes in Polar Summer surface albedo and cloud conditions, as well as global cloud redistribution patterns, are provided.

The microwave radiometers (MWRs) on board the European Remote Sensing Satellites 1 and 2 (ERS-1 and ERS-2) and Envisat provide a continuous time series of brightness temperature observations between 1991 and 2012. Here we report on a new total column water vapour (TCWV) and wet tropospheric correction (WTC) dataset that builds on this time series. We use a one-dimensional variational approach to derive TCWV from MWR observations and ERA-Interim background information. A particular focus of this study lies on the intercalibration of the three different instruments, which is performed using constraints on liquid water path (LWP) and TCWV. Comparing our MWR-derived time series of TCWV against TCWV derived from Global Navigation Satellite System (GNSS) we find that the MWR-derived TCWV time series is stable over time. However, observations potentially affected by precipitation show a degraded performance compared to precipitation-free observations in terms of the accuracy of retrieved TCWV. An analysis of WTC shows further that the retrieved WTC is superior to purely ERA-Interim-derived WTC for all satellites and for the entire time series. Even compared to the European Space Agency's (ESA) operational WTC retrievals, which incorporate in addition to MWR additional observational data, the here-described dataset shows improvements in particular for the mid-latitudes and for the two earlier satellites, ERS-1 and ERS-2. The dataset is publicly available under doi:10.5676/DWD_EMIR/V001 (Bennartz et al., 2016).

Cloud liquid water path (LWP) is one of the target atmospheric parameters retrieved remotely from ground-based and space-borne platforms using different observation methods and processing algorithms. Validation of LWP retrievals is a complicated task since a cloud cover is characterised by strong temporal and spatial variability while remote sensing methods have different temporal and spatial resolutions. An attempt has been made to compare and analyse the collocated LWP data delivered by two satellite instruments SEVIRI and AVHRR together with the data derived from microwave observations by the ground-based radiometer RPG-HATPRO. The geographical region of interest is the vicinity of St. Petersburg, Russia, where the RPG-HATPRO radiometer is operating. The study is focused on two problems. The first one is the so-called scale difference problem, which originates from dissimilar spatial resolutions of measurements. The second problem refers to the land–sea LWP gradient. The radiometric site is located 2.5 km from the coastline where the effects of the LWP gradient are pronounced. A good agreement of data obtained at the microwave radiometer location by all three instruments (HATPRO, SEVIRI, and AVHRR) during warm and cold seasons is demonstrated (the largest correlation coefficient 0.93 was detected for HATPRO and AVHRR datasets). The analysis showed no bias of the SEVIRI results with respect to HATPRO data and a large positive bias (0.013–0.017 kg m−2) of the AVHRR results for both warm and cold seasons. The analysis of LWP maps plotted on the basis of the SEVIRI and AVHRR measurements over land and water surfaces in the vicinity of St. Petersburg revealed the unexpectedly high LWP values delivered by AVHRR during the cold season over the Neva River bay and over the Saimaa Lake and the abnormal land–sea LWP gradient in these areas. For the detailed evaluation of atmospheric state and ice cover in the considered geographical regions during the periods of ground-based and satellite measurements, reanalysis data were used. It is shown that the most probable reason for the observed artefacts in the AVHRR measurements over water and ice surfaces is the coarse resolution of the land–sea and snow–ice masks used by the AVHRR retrieval algorithm. The influence of a cloud field inhomogeneity on the agreement between the satellite and the ground-based data is studied. For this purpose, the simple estimate of the LWP temporal variability is used as a measure of the spatial inhomogeneity. It has been demonstrated that both instruments are equally sensitive to the inhomogeneity of a cloud field despite the fact that they have different spatial resolutions.

We present version 3 of the Cloud_cci AVHRR-PM dataset which contains a comprehensive set of cloud and radiative flux properties on a global scale covering the period of 1982 to 2016. The properties were retrieved from Advanced Very High Resolution Radiometer (AVHRR) measurements recorded by the afternoon (post meridiem, PM) satellites of the National Oceanic and Atmospheric Administration (NOAA) Polar Operational Environmental Satellites (POES) missions. The cloud properties in version 3 are of improved quality compared with the precursor dataset version 2, providing better global quality scores for cloud detection, cloud phase and ice water path based on validation results against A-Train sensors. Furthermore, the parameter set was extended by a suite of broadband radiative flux properties. They were calculated by combining the retrieved cloud properties with thermodynamic profiles from reanalysis and surface properties. The flux properties comprise upwelling and downwelling, shortwave and longwave broadband fluxes at the surface (bottom-of-atmosphere - BOA) and top-of-atmosphere (TOA). All fluxes were determined at AVHRR pixel level for all-sky and clear-sky conditions, which will particularly facilitate the assessment of the cloud radiative effect at BOA and TOA in future studies. Validation of the BOA downwelling fluxes against the Baseline Surface Radiation Network (BSRN) show a very good agreement. This is supported by comparisons of multi-annual mean maps with NASA's Clouds and the Earth's Radiant Energy System (CERES) products for all fluxes at BOA and TOA. The Cloud_cci AVHRR-PM version 3 dataset allows for a large variety of climate applications that build on cloud properties, radiative flux properties and/or the link between them. For the presented Cloud_cci AVHRR-PMv3 dataset a Digital Object Identifier has been issued: https://doi.org/10.5676/DWD/ESA_Cloud_cci/AVHRR-PM/V003 (Stengel et al., 2019).

Retrievals of cloud properties from geostationary satellite sensors offer extensive spatial and temporal coverage and resolution. The high temporal resolution allows the observation of diurnally resolved cloud properties. However, retrievals are sensitive to varying illumination and viewing geometries, including cloud glory and cloud bow conditions, which can lead to irregularities in the diurnal data record. In this study, these conditions and their effects on liquid cloud optical thickness and effective radius retrievals are analyzed using the Cloud Physical Properties (CPP) algorithm. This analysis is based on the use of Spinning Enhanced Visible and Infrared Imager (SEVIRI) reflectances and products from Meteosat-8 and Meteosat-10, which are located over the Indian and Atlantic Ocean, respectively, and cover an extensive common area under different viewing angles. Comparisons of the retrievals from two full days, over ocean and land, and using different spectral combinations of visible and shortwave-infrared channels, are performed, to assess the importance of these factors in the retrieval process. The sensitivity of the cloud-bow- and cloud-glory-related irregularities to the width of the assumed droplet size distribution is analyzed by using different values of the effective variance of the size distribution. The results suggest for marine stratocumulus clouds an effective variance of around 0.05, which implies a narrower size distribution than typically assumed in satellite-based retrievals. For the case with continental clouds a broader size distribution (effective variance around 0.15) is obtained. This highlights the importance of appropriate size distribution assumptions and provides a way to improve the quality of cloud products in future climate data record releases.

Estimates of the direct radiative effect (DRE) from absorbing smoke aerosols over the southeast Atlantic Ocean (SAO) require simulation of the microphysical and optical properties of stratocumulus clouds as well as of the altitude and shortwave (SW) optical properties of biomass burning aerosols (BBAs). In this study, we take advantage of the large number of observations acquired during the ObseRvations of Aerosols above Clouds and their intEractionS (ORACLES-2016) and Layered Atlantic Smoke Interactions with Clouds (LASIC) projects during September 2016 and compare them with datasets from the ALADIN-Climate (Aire Limitée Adaptation dynamique Développement InterNational) regional model. The model provides a good representation of the liquid water path but the low cloud fraction is underestimated compared to satellite data. The modeled total-column smoke aerosol optical depth (AOD) and above-cloud AOD are consistent (∼0.7 over continental sources and ∼0.3 over the SAO at 550 nm) with the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2), Ozone Monitoring Instrument (OMI) or Moderate Resolution Imaging Spectroradiometer (MODIS) data. The simulations indicate smoke transport over the SAO occurs mainly between 2 and 4 km, consistent with surface and aircraft lidar observations. The BBA single scattering albedo is slightly overestimated compared to the Aerosol Robotic Network (AERONET) and more significantly when compared to Ascension Island surface observations. The difference could be due to the absence of internal mixing treatment in the ALADIN-Climate model. The SSA overestimate leads to an underestimation of the simulated SW radiative heating compared to ORACLES data. ALADIN-Climate simulates a positive (monthly mean) SW DRE of about +6 W m−2 over the SAO (20∘ S–10∘ N and 10∘ W–20∘ E) at the top of the atmosphere and in all-sky conditions. Over the continent, the presence of BBA is shown to significantly decrease the net surface SW flux, through direct and semi-direct effects, which is compensated by a decrease (monthly mean) in sensible heat fluxes (−25 W m−2) and surface land temperature (−1.5 ∘C) over Angola, Zambia and the Democratic Republic of the Congo, notably. The surface cooling and the lower tropospheric heating decrease the continental planetary boundary layer height by about ∼200 m.

The Cloud Climate Change Initiative (Cloud_cci) satellite simulator has been developed to enable comparisons between the Cloud_cci climate data record (CDR) and climate models. The Cloud_cci simulator is applied here to the EC-Earth global climate model as well as the Regional Atmospheric Climate Model (RACMO) regional climate model. We demonstrate the importance of using a satellite simulator that emulates the retrieval process underlying the CDR as opposed to taking the model output directly. The impact of not sampling the model at the local overpass time of the polar-orbiting satellites used to make the dataset was shown to be large, yielding up to 100 % error in liquid water path (LWP) simulations in certain regions. The simulator removes all clouds with optical thickness smaller than 0.2 to emulate the Cloud_cci CDR's lack of sensitivity to very thin clouds. This reduces total cloud fraction (TCF) globally by about 10 % for EC-Earth and by a few percent for RACMO over Europe. Globally, compared to the Cloud_cci CDR, EC-Earth is shown to be mostly in agreement on the distribution of clouds and their height, but it generally underestimates the high cloud fraction associated with tropical convection regions, and overestimates the occurrence and height of clouds over the Sahara and the Arabian subcontinent. In RACMO, TCF is higher than retrieved over the northern Atlantic Ocean but lower than retrieved over the European continent, where in addition the cloud top pressure (CTP) is underestimated. The results shown here demonstrate again that a simulator is needed to make meaningful comparisons between modeled and retrieved cloud properties. It is promising to see that for (nearly) all cloud properties the simulator improves the agreement of the model with the satellite data.

Diurnal moist convection is an important element of summer precipitation over Central Europe and the Alps. It is poorly represented in models using parameterized convection. In this study, we investigate the diurnal cycle of convection during 11 days in June 2007 using the COSMO model. The numerical simulations are compared with satellite measurements of GERB and SEVIRI, AVHRR satellite-based cloud properties and ground-based precipitation and temperature measurements. The simulations use horizontal resolutions of 12 km (convection-parameterizing model, CPM) and 2 km (convection-resolving model, CRM) and either a one-moment microphysics scheme (1M) or a two-moment microphysics scheme (2M).They are conducted for a computational domain that covers an extended Alpine area from Northern Italy to Northern Germany. The CPM with 1M exhibits a significant overestimation of high cloud cover. This results in a compensation effect in the top of the atmosphere energy budget due to an underestimation of outgoing longwave radiation (OLR) and an overestimation of reflected solar radiation (RSR). The CRM reduces high cloud cover and improves the OLR bias from a domain mean of −20.1 to −2.6 W/m2. When using 2M with ice sedimentation in the CRM, high cloud cover is further reduced. The stronger diurnal cycle of high cloud cover and associated convection over the Alps, compared to less mountainous regions, is well represented by the CRM but underestimated by the CPM. Despite substantial differences in high cloud cover, the use of a 2M has no significant impact on the diurnal cycle of precipitation. Furthermore, a negative mid-level cloud bias is found for all simulations., Meteorologische Zeitschrift, 25 (2), ISSN:0941-2948, ISSN:1610-1227

The Cloud_cci satellite simulator has been developed to enable comparisons between the Cloud_cci Climate Data Record (CDR) and climate models. The Cloud_cci simulator is applied here to the EC-Earth Global Climate Model as well as the RACMO Regional Climate Model. We demonstrate the importance of using a satellite simulator that emulates the retrieval process underlying the CDR as opposed to taking the model output directly. The impact of not sampling the model at the local overpass time of the polar-orbiting satellites used to make the dataset was shown to be large, yielding up to 100 % error in Liquid Water Path (LWP) simulations in certain regions. The simulator removes all clouds with optical thickness smaller than 0.2 to emulate the Cloud_cci CDR's lack of sensitivity to very thin clouds. This reduces Total Cloud Fraction (TCF) globally by about 10 % for EC-Earth and by a few percent for RACMO over Europe. Globally, compared to the Cloud_cci CDR, EC-Earth is shown to be mostly in agreement on the distribution of clouds and their height, but it generally underestimates the high cloud fraction associated with tropical convection regions, and overestimates the occurrence and height of clouds over the Sahara and the Arabian sub-continent. In RACMO, TCF is higher than retrieved over the northern Atlantic Ocean, but lower than retrieved over the European continent, where in addition the Cloud Top Pressure (CTP) is underestimated. The results shown here demonstrate again that a simulator is needed to make meaningful comparisons between modelled and retrieved cloud properties. It is promising to see that for (nearly) all cloud properties the simulator improves the agreement of the model with the satellite data.

We present the key features of the Community Cloud retrieval for CLimate (CC4CL) processing algorithm. We focus on the novel features of the framework: the optimal estimation approach in general, explicit uncertainty quantification through rigorous propagation of all known error sources into the final product, and the consistency of our long-term, multi-platform time-series provided at various resolutions, from 0.5° to 0.02°. By describing all key input data and processing steps, we aim to inform the user about important features of this new retrieval framework, and its potential applicability to climate studies. We provide an overview of the retrieved and derived output variables. These are analysed for four, partly very challenging, scenes collocated with CALIOP (Cloud-Aerosol lidar with Orthogonal Polarization) observations in the high-latitudes and over the Gulf of Guinea/West Africa. The results show that CC4CL provides very realistic estimates of cloud top height and cover for optically thick clouds but, where optically thin clouds overlap, returns a height between the two layers. CC4CL is a unique, coherent, multi-instrument cloud property retrieval framework applicable to passive sensor data of several EO missions. Through its flexibility, CC4CL offers the opportunity for combining a variety of historic and current EO missions into one data set, which, compared to single sensor retrievals, is improved in terms of accuracy and temporal sampling.

The Community Cloud retrieval for Climate (CC4CL) is a cloud property retrieval system for satellite-based multispectral imagers and is an important component of the Cloud Climate Change Initiative (Cloud_cci) project. In this paper we discuss the optimal estimation retrieval of cloud optical thickness, effective radius and cloud top pressure based on the Optimal Retrieval of Aerosol and Cloud (ORAC) algorithm. Key to this method is the forward model which, includes the clear-sky model, the liquid water and ice cloud models, the surface model including a bidirectional reflectance distribution function (BRDF), the "fast" radiative transfer solution (which includes a multiple scattering treatment) All of these components and their assumptions and limitations will be discussed in detail. The forward model provides the accuracy appropriate for our retrieval method. The errors are comparable to the instrument noise for cloud optical thicknesses greater than 10. At optical thicknesses less than 10 modelling errors become more significant. The retrieval method is then presented describing optimal estimation in general, the non-linear inversion method employed, measurement and a priori inputs, the propagation of input uncertainties and the calculation of subsidiary quantities that are derived from the retrieval results. An evaluation of the retrieval was performed using measurements simulated with noise levels appropriate for the MODIS instrument. Results show errors less than 10 % for cloud optical thicknesses greater than 10. Results for clouds of optical thicknesses less than 10 have errors ranging up to 20 %.

Aerosol and cloud properties over South China during the 10-year period 2006–2015 are analysed based on observations from passive and active satellite sensors and emission data. The results show a decrease in aerosol optical depth over the study area by about 20 % on average, accompanied by an increase in liquid cloud cover and cloud liquid water path (LWP) by 5 % and 13 %, respectively. Analysis of aerosol types and emissions suggests that the main driver for their reduction is a decrease in biomass burning aerosols. These changes occurred mainly in late autumn and early winter months and coincided with changes in cloud properties. For the latter, possible explanatory mechanisms were examined, including changes in circulation patterns and aerosol-cloud interactions. Further analysis of changes in aerosol vertical profiles demonstrates a consistency of the observed aerosol and cloud changes with the aerosol semi-direct effect, which depends on their relative heights. Based on this mechanism, less absorbing aerosols in the cloud layer would lead to an overall decrease in evaporation of cloud droplets, thus increasing cloud LWP and cover.

On-line coupled meteorology-chemistry models permit the description of the aerosol-radiation (ARI) and aerosol-cloud interactions (ACI). The effect of atmospheric aerosols remains uncertain in climate modeling. One of the reasons is their variability in time and space, which could modify cloud microphysics and impact cloud radiative properties and climate. The aim of this work is to assess the representation of the ARI+ACI interactions in regional-scale coupled models when simulating the climate-chemistry-cloud-radiation system. The evaluated simulations are run under the umbrella of the Air Quality Model Evaluation International Initiative (AQMEII) Phase 2 and include ARI+ACI interactions. The model simulations are evaluated against observational data from the European Space Agency (ESA) Cloud_cci project. Results show an underestimation (overestimation) of cloud fraction (CFR) over land (ocean) areas. Lower bias and mean absolute error (MAE) are found in the ensemble mean. Cloud optical depth (COD) and cloud liquid ice path (CIP) are generally underestimated over the whole domain. MAE was in line with the bias. Cloud liquid water path (CWP) bias is broadly overestimated. Temporal correlation points to a general positive correlation between models and satellite observations. Finally, regarding the spatial variability CFR has the best capacity to represent it, whereas COD, CIP and CWP indicate a lower capacity. The differences found can be attributed to differences in the microphysics schemes used, for instance, the number of ice hydrometeors has seen to be relevant as well as the prognostic/diagnostic treatment of the CWP.

Fungal spores as a prominent type of primary biological aerosol particles (PBAP) have been incorporated into the COSMO-ART (Consortium for Small-scale Modelling-Aerosols and Reactive Trace gases) regional atmospheric model. Two literature-based emission rates for fungal spores derived from fungal spore colony counts and chemical tracer measurements were used as a parameterization baseline for this study. A third, new emission parameterization for fluorescent biological aerosol particles (FBAP) was adapted to field measurements from four locations across Europe. FBAP concentrations can be regarded as a lower estimate of total PBAP concentrations. Size distributions of FBAP often show a distinct mode at approx. 3 μm, corresponding to a diameter range characteristic for many fungal spores. Previous studies for several locations have suggested that FBAP are in many cases dominated by fungal spores. Thus, we suggest that simulated FBAP and fungal spore concentrations obtained from the three different emission parameterizations can be compared to FBAP measurements. The comparison reveals that simulated fungal spore concentrations based on literature emission parameterizations are lower than measured FBAP concentrations. In agreement with the measurements, the model results show a diurnal cycle in simulated fungal spore concentrations, which may develop partially as a consequence of a varying boundary layer height between day and night. Temperature and specific humidity, together with leaf area index (LAI), were chosen to drive the new emission parameterization which is fitted to the FBAP observations. The new parameterization results in similar root mean square errors (RMSEs) and correlation coefficients compared to the FBAP observations as the previously existing fungal spore emission parameterizations, with some improvements in the bias. Using the new emission parameterization on a model domain covering western Europe, FBAP in the lowest model layer comprise a fraction of 15% of the total aerosol mass over land and reach average number concentrations of 26 L−1. The results confirm that fungal spores and biological particles may account for a major fraction of supermicron aerosol particle number and mass concentration over vegetated continental regions and should thus be explicitly considered in air quality and climate studies.

Cloud property retrievals from 3 decades of the Advanced Very High Resolution Radiometer (AVHRR) measurements provide a unique opportunity for a long-term analysis of clouds. In this study, the acc ...