The Satellite Application Facility on Climate Monitoring (CM-SAF) aims at the provision of satellite-derived geophysical parameter data sets suitable for climate monitoring. CM-SAF provides climatologies for Essential Climate Variables (ECV), as required by the Global Climate Observing System implementation plan in support of the UNFCCC. Several cloud parameters, surface albedo, radiation fluxes at the top of the atmosphere and at the surface as well as atmospheric temperature and humidity products form a sound basis for climate monitoring of the atmosphere. The products are categorized in monitoring data sets obtained in near real time and data sets based on carefully intercalibrated radiances. The CM-SAF products are derived from several instruments on-board operational satellites in geostationary and polar orbit as the Meteosat and NOAA satellites, respectively. The existing data sets will be continued using data from the instruments on-board the new joint NOAA/EUMETSAT Meteorological Operational Polar satellite. The products have mostly been validated against several ground-based data sets both in situ and remotely sensed. The accomplished accuracy for products derived in near real time is sufficient to monitor variability on diurnal and seasonal scales. The demands on accuracy increase the longer the considered time scale is. Thus, interannual variability or trends can only be assessed if the sensor data are corrected for jumps created by instrument changes on successive satellites and more subtle effects like instrument and orbit drift and also changes to the spectral response function of an instrument. Thus, a central goal of the recently started Continuous Development and Operations Phase of the CM-SAF (2007–2012) is to further improve all CM-SAF data products to a quality level that allows for studies of interannual variability.

A new satellite-derived climate dataset – denoted CLARA-A1 ("The CM SAF cLoud, Albedo and RAdiation dataset from AVHRR data") – is described. The dataset covers the 28 yr period from 1982 until 2009 and consists of cloud, surface albedo, and radiation budget products derived from the AVHRR (Advanced Very High Resolution Radiometer) sensor carried by polar-orbiting operational meteorological satellites. Its content, anticipated accuracies, limitations, and potential applications are described. The dataset is produced by the EUMETSAT Climate Monitoring Satellite Application Facility (CM SAF) project. The dataset has its strengths in the long duration, its foundation upon a homogenized AVHRR radiance data record, and in some unique features, e.g. the availability of 28 yr of summer surface albedo and cloudiness parameters over the polar regions. Quality characteristics are also well investigated and particularly useful results can be found over the tropics, mid to high latitudes and over nearly all oceanic areas. Being the first CM SAF dataset of its kind, an intensive evaluation of the quality of the datasets was performed and major findings with regard to merits and shortcomings of the datasets are reported. However, the CM SAF's long-term commitment to perform two additional reprocessing events within the time frame 2013–2018 will allow proper handling of limitations as well as upgrading the dataset with new features (e.g. uncertainty estimates) and extension of the temporal coverage.

This work presents a validation of three satellite-based radiation products over an extensive network of 313 pyranometers across Europe, from 2005 to 2015. The products used have been developed by the Satellite Application Facility on Climate Monitoring (CM SAF) and are one geostationary climate dataset (SARAH-JRC), one polar-orbiting climate dataset (CLARA-A2) and one geostationary operational product. Further, the ERA-Interim reanalysis is also included in the comparison. The main objective is to determine the quality level of the daily means of CM SAF datasets, identifying their limitations, as well as analyzing the different factors that can interfere in the adequate validation of the products.

This paper presents the third edition of The Satellite Application Facility on Climate Monitoring's (CM SAF) cloud, albedo, and surface radiation dataset from advanced very-high-resolution radiometer (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, and 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 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 intercomparisons 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) CM SAF and is freely available at https://doi.org/10.5676/EUM_SAF_CM/CLARA_AVHRR/V003 (Karlsson et al., 2023b).

Seasonal snow cover of the Northern Hemisphere (NH) greatly influences surface energy balance; hydrological cycle; and many human activities, such as tourism and agriculture. Monitoring snow cover at a continental scale is only possible from satellites or using reanalysis data. This study aims to analyze the time series of snow water equivalent (SWE), snow cover extent (SCE), and surface albedo in spring in ERA5 and ERA5-Land reanalysis data and to compare the time series with several satellite-based datasets. As reference data for the SWE intercomparison, we use bias-corrected SnowCCI v1 data for non-mountainous regions and the mean of Brown, MERRA-2, and Crocus v7 datasets for the mountainous regions. For surface albedo, we use the black-sky albedo datasets CLARA-A2 SAL, based on AVHRR data, and MCD43D51, based on MODIS data. Additionally, we use Rutgers and JAXA JASMES SCE products. Our study covers land areas north of 40^∘ N and the period between 1982 and 2018 (spring season from March to May). The analysis shows that both ERA5 and ERA5-Land overestimate total NH SWE by 150 % to 200 % compared to the SWE reference data. ERA5-Land shows larger overestimation, which is mostly due to very high SWE values over mountainous regions. The analysis revealed a discontinuity in ERA5 around the year 2004 since adding the Interactive Multisensor Snow and Ice Mapping System (IMS) from the year 2004 onwards considerably improves SWE estimates but makes the trends less reliable. The negative NH SWE trends in ERA5 range from −249 to −236 Gt per decade in spring, which is 2 to 3 times larger than the trends detected by the other datasets (ranging from −124 to −77 Gt per decade). SCE is accurately described in ERA5-Land, whereas ERA5 shows notably larger SCE than the satellite-based datasets. Albedo estimates are more consistent between the datasets, with a slight overestimation in ERA5 and ERA5-Land. The negative trends in SCE and albedo are strongest in May, when the albedo trend varies from −0.011 to −0.006 per decade depending on the dataset. The negative SCE trend detected by ERA5 in May ($-\mathrm{1.22}\times {\mathrm{10}}^{\mathrm{6}}$ km² per decade) is about twice as large as the trends detected by all other datasets (ranging from −0.66 to $-\mathrm{0.50}\times {\mathrm{10}}^{\mathrm{6}}$ km² per decade). The analysis also shows that there is a large spatial variability in the trends, which is consistent with other studies.