The accuracy of land surface temperature (LST) observations is critical to many applications. Any observation of LST is subject to incomplete knowledge, so an accurate assessment of the uncertainty budget is critical. We present a comprehensive and consistent approach to determining an uncertainty budget for LST products. We apply this approach to the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument on-board the Aqua satellite. In order to generate the uncertainty model, a new implementation of the generalised split-window algorithm is applied, in which retrieval coefficients are categorised by viewing angle and water vapour. Validation of the LST against in situ data shows a mean absolute bias of 0.37 K for daytime and 0.73 K for nighttime. The average standard deviation per site is 1.53 K for daytime and 1.21 K for nighttime. Uncertainties from the implemented model are estimates in their own right and are also validated. We do this by comparing the standard deviation of the differences between the satellite and in situ LSTs, and the total uncertainties of the validation matchups. We show that the uncertainty model provides a good fit. Our approach offers a framework for quantifying uncertainties for LST that is equally applicable across different sensors and different retrieval approaches.

Day-to-day variations in surface air temperature affect society in many ways, but daily surface air temperature measurements are not available everywhere. Therefore, a global daily picture cannot be achieved with measurements made in situ alone and needs to incorporate estimates from satellite retrievals. This article presents the science developed in the EU Horizon 2020–funded EUSTACE project (2015–19, www.eustaceproject.org) to produce global and European multidecadal ensembles of daily analyses of surface air temperature complementary to those from dynamical reanalyses, integrating different ground-based and satellite-borne data types. Relationships between surface air temperature measurements and satellite-based estimates of surface skin temperature over all surfaces of Earth (land, ocean, ice, and lakes) are quantified. Information contained in the satellite retrievals then helps to estimate air temperature and create global fields in the past, using statistical models of how surface air temperature varies in a connected way from place to place; this needs efficient statistical analysis methods to cope with the considerable data volumes. Daily fields are presented as ensembles to enable propagation of uncertainties through applications. Estimated temperatures and their uncertainties are evaluated against independent measurements and other surface temperature datasets. Achievements in the EUSTACE project have also included fundamental preparatory work useful to others, for example, gathering user requirements, identifying inhomogeneities in daily surface air temperature measurement series from weather stations, carefully quantifying uncertainties in satellite skin and air temperature estimates, exploring the interaction between air temperature and lakes, developing statistical models relevant to non-Gaussian variables, and methods for efficient computation.

Despite an increase in heatwaves and rising air temperatures in the Arctic, little research has been conducted into the temperatures of proglacial lakes in the region. An assumption persists that they are cold and uniformly feature a temperature of 1 °C. This is important to test, given the rising air temperatures in the region (reported in this study) and potential to increase water temperatures, thus increasing subaqueous melting and the retreat of glacier termini from where they are in contact with lakes. Through analysis of ASTER surface temperature product data, we report warm (>4 °C) proglacial lake surface water temperatures (LSWT) for both ice-contact and non-ice-contact lakes, as well as substantial spatial heterogeneity. We present in situ validation data (from problematic maritime areas) and a workflow that facilitates the extraction of robust LSWT data from the high-resolution (90 m) ASTER surface temperature product (AST08). This enables spatial patterns to be analysed in conjunction with surrounding thermal influences, such as parent glaciers and topographies. This workflow can be utilised for the analysis of the LSWT data of other small lakes and crucially allows high spatial resolution study of how they have responded to changes in climate. Further study of the LSWT is essential in the Arctic given the amplification of climate change across the region.

Time series of global and regional mean surface air temperature (SAT) anomalies are a common metric used to estimate recent climate change. Various techniques can be used to create these time series from meteorological station data. The degree of difference arising from using five different techniques, based on existing temperature anomaly dataset techniques, to estimate Arctic SAT anomalies over land and sea ice was investigated using reanalysis data as a test bed. Techniques that interpolated anomalies were found to result in smaller errors than noninterpolating techniques relative to the reanalysis reference. Kriging techniques provided the smallest errors in estimates of Arctic anomalies, and simple kriging was often the best kriging method in this study, especially over sea ice. A linear interpolation technique had, on average, root-mean-square errors (RMSEs) up to 0.55 K larger than the two kriging techniques tested. Noninterpolating techniques provided the least representative anomaly estimates. Nonetheless, they serve as useful checks for confirming whether estimates from interpolating techniques are reasonable. The interaction of meteorological station coverage with estimation techniques between 1850 and 2011 was simulated using an ensemble dataset comprising repeated individual years (1979–2011). All techniques were found to have larger RMSEs for earlier station coverages. This supports calls for increased data sharing and data rescue, especially in sparsely observed regions such as the Arctic.

Surface temperature data sets for, or including, the Arctic have been derived from various thermal infrared sensors. However, a combined, all surface temperature data set for the Arctic has not been generated previously. Here we present the first combined land, ocean, and ice surface temperature data set for the Arctic produced from Along‐Track Scanning Radiometer ‐ 2 and the Advanced Along‐Track Scanning Radiometer satellite sensors: the Along‐Track Scanning Radiometer Arctic combined Surface Temperature data set. Separate products, produced independently for each sensor and containing quantified uncertainties, together cover the period August 1995 to April 2012. Product validation, utilizing a more extensive in situ database than previous studies, shows that Along‐Track Scanning Radiometer Arctic combined Surface Temperature surface temperatures generally agree with in situ data and are similar to previous validation of input surface temperature retrievals. Biases range from −1.74 to 0.23 K over open ocean, sea ice, snow over land, and the Greenland ice sheet with higher variability over snow/ice. However, there are noticeable outliers in the validation results, particularly over Arctic land in boreal summer for Along‐Track Scanning Radiometer ‐ 2, which are likely due to cloud contamination resulting from a climatologically static snow field being used for that sensor. This study suggests that the Along‐Track Scanning Radiometer Arctic combined Surface Temperature data set presented here is a useful tool for assessment of models in the Arctic. Further work would have clear benefits including improvements to snow cover and cloud clearing to achieve a fully consistently processed, climate quality combined surface temperature data set for the Arctic region.