CLARA-A3: The third edition of the AVHRR-based CM SAF climate data record on clouds, radiation and surface albedo covering the period 1979 to 2023

Abstract

Abstract. 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.

Global Cloudiness and Cloud Top Information from AVHRR in the 42-Year CLARA-A3 Climate Data Record Covering the Period 1979–2020

“…For example, access to cloud probabilities allows users to utilize cloud information in a stricter mode (i.e., in a clear conservative or cloud conservative mode) for applications that are very sensitive to cloud masking errors. This has enabled safer downstream processing of other CLARA-A3 products such as surface radiation budget components and surface albedo (as is explained in [12]). This detailed evaluation of the improved cloud detection sensitivity (CDS) parameter also has positive implications for other applications.…”

An Ensemble Learning Model Reveals Accelerated Reductions in Snow Depth Over Arctic Sea Ice Under High‐Emission Scenarios

“…CLARA‐A2 offers albedo data from January 1982 to June 2019, and ICDR CLARA‐A2 offers albedo data from January 2019 to the present. After the first product stops updating, the second product can be used as a substitute for the first product (Karlsson et al., 2021). Compared with the on‐site data, the albedo used had a relative accuracy of 3%–15% in ice‐covered regions (Karlsson et al., 2017b).…”

“…Therefore, these products could be used for CMIP6 model validation. (b) Surface albedo: The surface albedo data (Karlsson et al, 2017a;Riihelä et al, 2013) After the first product stops updating, the second product can be used as a substitute for the first product (Karlsson et al, 2021). Compared with the on-site data, the albedo used had a relative accuracy of 3%-15% in ice-covered regions (Karlsson et al, 2017b).…”

Evaluation of Downward Shortwave Radiation Products Over the Loess Plateau

“…Data 1) CLARA-A3. The CLARA-A3 data is latest edition of CLARA data record which provides cloud properties and surface radiation parameters derived from the AVHRR sensor onboard the series of polar orbiting NOAA satellites and METOP satellites [15]. It was published by the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Satellite Application Facility on Climate Monitoring (CM SAF) in 2023.…”

“…However, these products show high uncertainty due to the ways that they are generated. Satellite DSR products from different satellite platforms have different accuracies affected by observation methods, observation periods and retrieval methods [11]- [15]. Data sources and assimilation schemes for the reanalysis data are diverse and input datasets (such as clouds and aerosols) of the assimilation process and model tend to cause systematic errors in the reanalysis DSR products [16], [17].…”