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Data on surface solar radiation are scarce in high‐latitude regions, and few studies have evaluated the performance of reanalysis products in estimating solar radiation in those regions. Here, an extensive solar radiation dataset is compiled from 98 stations across Alaska to evaluate 11 different surface solar radiation products (seven reanalysis and four observation‐derived). No product can capture all aspects of the ground‐based observations, and there is ample room for improvement; root mean square errors (RMSEs) of daily, monthly, and annual average comparisons of the products against observations are 38–65, 19–39, and 11–17 W⋅m⁻², respectively. ERA5, MERRA2, and ERA‐Interim performed the best in Alaska. Daily records from all products show large RMSEs of 60–108 W⋅m⁻²during May–July, equivalent to 30–55% of the observed solar radiation during this season. The sparseness of Alaskan observations, cloud cover, and algorithm issues may be potential sources of bias.

Land surface air temperature is an essential climate variable for understanding rapid global environmental changes. Sparse network coverage prior to the 1950s is a significant source of uncertainty in global climate change evaluations. Recognizing the importance of spatial coverage, more stations are continually being added to global climate networks. A challenge is how to best use the information introduced by the new station observations to enhance our understanding and assessment of global climate states and changes, particularly for times prior to the mid‐20th century. In this study, Data INterpolating Empirical Orthogonal Functions (DINEOF) were used to reconstruct mean monthly air temperatures from the Global Historical Climatology Network‐monthly (GHCNm version 4) over the land surface from 1880 through 2017. The final reconstructed air temperature dataset covers about 95% of the global land surface area, improving the spatial coverage by ~80% during 1880–1900 and by 10%–20% since the 1950s. Validation tests show that the mean absolute error of the reconstructed data is less than 0.82°C. Comparison with the Coupled Model Intercomparison Project Phase 5 (CMIP5) climate model output shows that the reconstructed dataset substantially reduces the bias in global datasets caused by sparse station coverage, particularly before the 1950s.

Abstract. Recent observations of near-surface soil temperatures over the circumpolarArctic show accelerated warming of permafrost-affected soils. Theavailability of a comprehensive near-surface permafrost and active layerdataset is critical to better understanding climate impacts and toconstraining permafrost thermal conditions and its spatial distribution inland system models. We compiled a soil temperature dataset from 72 monitoringstations in Alaska using data collected by the U.S. Geological Survey, theNational Park Service, and the University of Alaska Fairbanks permafrostmonitoring networks. The array of monitoring stations spans a large range oflatitudes from 60.9 to 71.3^∘N and elevations from near sea level to∼1300m, comprising tundra and boreal forest regions. This datasetconsists of monthly ground temperatures at depths up to 1m,volumetric soil water content, snow depth, and air temperature during1997–2016. These data have been quality controlled in collection andprocessing. Meanwhile, we implemented data harmonization evaluation for theprocessed dataset. The final product (PF-AK, v0.1) is available at the ArcticData Center (https://doi.org/10.18739/A2KG55).