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The low accuracy of satellite cloud fraction (CF) data over the Arctic seriously restricts the accurate assessment of the regional and global radiative energy balance under a changing climate. Previous studies have reported that no individual satellite CF product could satisfy the needs of accuracy and spatiotemporal coverage simultaneously for long-term applications over the Arctic. Merging multiple CF products with complementary properties can provide an effective way to produce a spatiotemporally complete CF data record with higher accuracy. This study proposed a spatiotemporal statistical data fusion framework based on cumulative distribution function (CDF) matching and the Bayesian maximum entropy (BME) method to produce a synthetic 1 ∘ × 1 ∘ CF dataset in the Arctic during 2000–2020. The CDF matching was employed to remove the systematic biases among multiple passive sensor datasets through the constraint of using CF from an active sensor. The BME method was employed to combine adjusted satellite CF products to produce a spatiotemporally complete and accurate CF product. The advantages of the presented fusing framework are that it not only uses the spatiotemporal autocorrelations but also explicitly incorporates the uncertainties of passive sensor products benchmarked with reference data, i.e., active sensor product and ground-based observations. The inconsistencies of Arctic CF between passive sensor products and the reference data were reduced by about 10 %–20 % after fusing, with particularly noticeable improvements in the vicinity of Greenland. Compared with ground-based observations, R2 increased by about 0.20–0.48, and the root mean square error (RMSE) and bias reductions averaged about 6.09 % and 4.04 % for land regions, respectively; these metrics for ocean regions were about 0.05–0.31, 2.85 %, and 3.15 %, respectively. Compared with active sensor data, R2 increased by nearly 0.16, and RMSE and bias declined by about 3.77 % and 4.31 %, respectively, in land; meanwhile, improvements in ocean regions were about 0.3 for R2 , 4.46 % for RMSE, and 3.92 % for bias. The results of the comparison with ERA5 and the Meteorological Research Institute – Atmospheric General Circulation model version 3.2S (MRI-AGCM3-2-S) climate model suggest an obvious improvement in the consistency between the satellite-observed CF and the reanalysis and model data after fusion. This serves as a promising indication that the fused CF results hold the potential to deliver reliable satellite observations for modeling and reanalysis data. Moreover, the fused product effectively supplements the temporal gaps of Advanced Very High Resolution Radiometer (AVHRR)-based products caused by satellite faults and the data missing from MODIS-based products prior to the launch of Aqua, and it extends the temporal range better than the active product; it addresses the spatial insufficiency of the active sensor data and the AVHRR-based products acquired at latitudes greater than 82.5 ∘ N. A continuous monthly 1 ∘ CF product covering the entire Arctic during 2000–2020 was generated and is freely available to the public at 10.5281/zenodo.7624605 (Liu and He, 2022). This is of great importance for reducing the uncertainty in the estimation of surface radiation parameters and thus helps researchers to better understand the Earth's energy imbalance. [ABSTRACT FROM AUTHOR]

Insufficient understanding of complex Arctic cloud properties introduced large errors in estimating radiant energy balance parameters at the regional and global scales. Comprehensive and reliable cloud information is necessary for improving the accuracy of flux inversion. This study evaluated daytime cloud fraction (CF) uncertainties from 16 available satellite products and estimated the spatiotemporal distributions of Arctic daytime CF during 2000–19. Our results show that the differences among multiple products had significant temporal and spatial heterogeneities. Temporally, the maximum and minimum interproduct discrepancies occurred in April and the summer months, respectively. Spatially, the largest uncertainties were seen over Greenland. Substantial inconsistency also occurred on the central and Pacific sides of the Arctic Ocean. The active satellite product tended to capture more clouds in these two regions. We found that the inconsistencies caused by sensor differences were smaller than those caused by algorithm differences; that is, for MODIS based CF products, the inconsistencies caused by different sensors and different algorithms are ±2% and ±5%, while for AVHRR-based products, these inconsistencies are ±6% and ±15%, respectively. The annual average daytime CF in sunlit months was 70.9% ± 2.93% and increased over the Arctic during study periods. These upward trends might cool the Arctic by approximately 0.05–0.5 W m−2 decade−1. In terms of the spatiotemporal distributions, the CF over the ocean is higher than that over the land, and the former increased significantly while the latter decreased; the CF trends of most products are positive in June and July but are opposite in other months. From this study, the findings based on multiple products would be more robust than that based on a single or few datasets. Significance Statement: This study aimed to comprehensively understand and obtain more robust general characteristics of the temporal and spatial distributions of Arctic daytime cloud fraction by comparing and analyzing the consistencies and discrepancies of multisource satellite products. It is important because the cloud fraction is a nonnegligible modulator of Earth's energy budget and climate change. Although the Arctic is the most climate-sensitive region, existing studies lack a comprehensive assessment of the cloud fraction over the entire Arctic. We analyzed 16 different cloud products and found that although the inconsistencies were inevitable, most products showed similar spatiotemporal distribution and trend distribution of daytime CF. This study provided a new idea for Arctic CF research under the existing conditions. [ABSTRACT FROM AUTHOR]

Quantifying and attributing the phenological changes in snow cover are essential for meteorological, hydrological, ecological, and societal implications. However, snow cover phenology changes have not been well documented. Evidence from multiple satellite and reanalysis data from 2001 to 2014 points out that the snow end date (De) advanced by 5.11 (±2.20) days in northern high latitudes (52-75°N) and was delayed by 3.28 (±2.59) days in northern mid-latitudes (32-52°N) at the 90% confidence level. Dominated by changes in De, snow duration days (Dd) was shorter in duration by 5.57 (±2.55) days in high latitudes and longer by 9.74 (±2.58) days in mid-latitudes. Changes in De during the spring season were consistent with the spatiotemporal pattern of land surface albedo change. Decreased land surface temperature combined with increased precipitation in mid-latitudes and significantly increased land surface temperature in high latitudes, impacted by recent Pacific surface cooling, Arctic amplification and strengthening westerlies, result in contrasting changes in the Northern Hemisphere snow cover phenology. Changes in the snow cover phenology led to contrasting anomalies of snow radiative forcing, which is dominated by De and accounts for 51% of the total shortwave flux anomalies at the top of the atmosphere. [ABSTRACT FROM AUTHOR]