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 as 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 are some major validation results. Some of the main improvements to the data record come from a major effort in cleaning and homogenizing 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 Arctic summer surface albedo and cloud conditions are provided.

The primary goal of this paper is to present a model of snow surface albedo accounting for small-scale surface roughness effects. The model is based on photon recollision probability, and it can be combined with existing bulk volume albedo models, such as Two-streAm Radiative TransfEr in Snow (TARTES). The model is fed with in situ measurements of surface roughness from plate profile and laser scanner data, and it is evaluated by comparing the computed albedos with observations. It provides closer results to empirical values than volume-scattering-based albedo simulations alone. The impact of surface roughness on albedo increases with the progress of the melting season and is larger for larger solar zenith angles. In absolute terms, small-scale surface roughness can decrease the total albedo by up to about 0.1. As regards the bidirectional reflectance factor (BRF), it is found that surface roughness increases backward scattering especially for large solar zenith angle values.

The rapid warming of the Northern Hemisphere high latitudes and the observed changes in boreal forest areas affect the global surface albedo and climate. This study looks at the trends in the timing of the snow melt season as well as the albedo levels before and after the melt season in Northern Hemisphere land areas between 40°N and 80°N over the years 1982 to 2015. The analysis is based on optical satellite data from the Advanced Very High Resolution Radiometer (AVHRR). The results show that the changes in surface albedo already begin before the start of the melt season. These albedo changes are significant (the mean of absolute change is 4.4 albedo percentage units per 34 years). The largest absolute changes in pre-melt-season albedo are concentrated in areas of the boreal forest, while the pre-melt albedo of tundra remains unchanged. Trends in melt season timing are consistent over large areas. The mean of absolute change of start date of melt season is 11.2 days per 34 years, 10.6 days for end date of melt season and 14.8 days for length of melt season. The changes result in longer and shorter melt seasons, as well as changed timing of the melt, depending on the area. The albedo levels preceding the onset of melt and start of the melt season correlate with climatic parameters (air temperature, precipitation, wind speed). The changes in albedo are more closely linked to changes in vegetation, whereas the changes in melt season timing are linked to changes in climate.

A soil moisture estimation method was developed for Sentinel-1 synthetic aperture radar (SAR) ground range detected high resolution (GRDH) data to analyze moisture conditions in a gently undulating and heterogeneous subarctic area containing forests, wetlands, and open orographic tundra. In order to preserve the original 10-m pixel spacing, PIMSAR (pixel-based multitemporal nonlocal averaging) nonlocal mean filtering was applied. It was guided by multitemporal statistics of SAR images in the area. The gradient boosted trees (GBT) machine learning method was used for the soil moisture algorithm development. Discrete and continuous in situ soil moisture values were used for training and validation of the algorithm. For surface soil moisture, the root mean square error (RMSE) of the method was 6.5% and 8.8% for morning and evening images, respectively. The corresponding maximum errors were 34.1% and 33.8%. The pixelwise sensitivity to the training set and method choice was estimated as the variance of the soil moisture values derived using the algorithms for the three best methods with respect to the criteria: the smallest maximum error, the smallest RMSE value, and the highest coefficient of determination ( $R^{2}$ ) value. It was, on average, 6.3% with a standard deviation of 5.7%. Our approach successfully produced instantaneous high-resolution soil moisture estimates on daily basis for the subarctic landscape and can further be applied to various hydrological, biogeochemical, and management purposes.

Atmospheric effects, especially aerosols, are a significant source of uncertainty for optical remote sensing of surface parameters, such as albedo. Also to achieve a homogeneous surface albedo time series, the atmospheric correction has to be homogeneous. However, a global homogeneous aerosol optical depth (AOD) time series covering several decades did not previously exist. Therefore, we have constructed an AOD time series 1982–2014 using aerosol index (AI) data from the satellite measurements of the Total Ozone Mapping Spectrometer (TOMS) and the Ozone Monitoring Instrument (OMI), together with the Solar zenith angle and land use classification data. It is used as input for the Simplified Method for Atmospheric Correction (SMAC) algorithm when processing the surface albedo time series CLARA-A2 SAL (the Surface ALbedo from the Satellite Application Facility on Climate Monitoring project cLoud, Albedo and RAdiation data record, the second release). The surface reflectance simulations using the SMAC algorithm for different sets of satellite-based AOD data show that the aerosol-effect correction using the constructed TOMS/OMI based AOD data is comparable to using other satellite-based AOD data available for a shorter time range. Moreover, using the constructed TOMS/OMI based AOD as input for the atmospheric correction typically produces surface reflectance [-20]values closer to those obtained using in situ AOD values than when using other satellite-based AOD data.

This paper describes a new method for cloud-correcting observations of black-sky surface albedo derived using the Advanced Very High Resolution Radiometer (AVHRR). Cloud cover constitutes a major challenge for surface albedo estimation using AVHRR data for all possible conditions of cloud fraction and cloud type with any land cover type and solar zenith angle. This study shows how the new cloud probability (CP) data to be provided as part of edition A3 of the CLARA (CM SAF cLoud, Albedo and surface Radiation dataset from AVHRR data) record from the Satellite Application Facility on Climate Monitoring (CM SAF) project of EUMETSAT can be used instead of traditional binary cloud masking to derive cloud-free monthly mean surface albedo estimates. Cloudy broadband albedo distributions were simulated first for theoretical cloud distributions and then using global cloud probability (CP) data for 1 month. A weighted mean approach based on the CP values was shown to produce very-high-accuracy black-sky surface albedo estimates for simulated data. The 90 % quantile for the error was 1.1 % (in absolute albedo percentage) and that for the relative error was 2.2 %. AVHRR-based and in situ albedo distributions were in line with each other and the monthly mean values were also consistent. Comparison with binary cloud masking indicated that the developed method improves cloud contamination removal.