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Global evaporation monitoring from Earth observation thermal infrared satellite missions is historically challenged due to the unavailability of any direct measurements of aerodynamic temperature. State-of-the-art one-source evaporation models use remotely sensed radiometric surface temperature as a substitute for the aerodynamic temperature and apply empirical corrections to accommodate for their inequality. This introduces substantial uncertainty in operational drought mapping over complex landscapes. By employing a non-parametric model, we show that evaporation can be directly retrieved from thermal satellite data without the need of any empirical correction. Independent evaluation of evaporation in a broad spectrum of biome and aridity yielded statistically significant results when compared with eddy covariance observations. While our simplified model provides a new perspective to advance spatio-temporal evaporation mapping from any thermal remote sensing mission, the direct retrieval of aerodynamic temperature also generates the highly required insight on the critical role of biophysical interactions in global evaporation research.

The Greenland Ice Sheet (GIS) is subject to amplified impacts of climate change and its monitoring is essential for understanding and improving scenarios of future climate conditions. Surface temperature over the GIS is an important variable, regulating processes related to the exchange of energy and water between the surface and the atmosphere. Few local observation sites exist; thus spaceborne platforms carrying thermal infrared instruments offer an alternative for surface temperature observations and are the basis for deriving ice surface temperature (IST) products. In this study several satellite IST products for the GIS were compared, and the first multi-sensor, gap-free (Level 4, L4) product was developed and validated for 2012. High-resolution Level 2 (L2) products from the European Space Agency (ESA) Land Surface Temperature Climate Change Initiative (LST_cci) project and the Arctic and Antarctic Ice Surface Temperatures from Thermal Infrared Satellite Sensors (AASTI) dataset were assessed using observations from the PROMICE (Programme for Monitoring of the Greenland Ice Sheet) stations and IceBridge flight campaigns. AASTI showed overall better performance compared to LST_cci data, which had superior spatial coverage and availability. Both datasets were utilised to construct a daily, gap-free L4 IST product using the optimal interpolation (OI) method. The resulting product performed satisfactorily when compared to surface temperature observations from PROMICE and IceBridge. Combining the advantages of satellite datasets, the L4 product allowed for the analysis of IST over the GIS during 2012, when a significant melt event occurred. Mean summer (June-August) IST was -5.5 +/- 4.5 degrees C, with an annual mean of -22.1 +/- 5.4 degrees C. Mean IST during the melt season (May-August) ranged from -15 to -1 degrees C, while almost the entire GIS experienced at least between 1 and 5 melt days when temperatures were -1 degrees C or higher. Finally, this study asses

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.

Taking the Temperature of the Earth: Steps towards Integrated Understanding of Variability and Change presents an integrated, collaborative approach to observing and understanding various surface temperatures from a whole-Earth perspective. The book describes the progress in improving the quality of surface temperatures across different domains of the Earth’s surface (air, land, sea, lakes and ice), assessing variability and long-term trends, and providing applications of surface temperature data to detect and better understand Earth system behavior. As cooperation is essential between scientific communities, whose focus on particular domains of Earth’s surface and on different components of the observing system help to accelerate scientific understanding and multiply the benefits for society, this book bridges the gap between domains.Includes sections on data validation and uncertainty, data availability and applicationsIntegrates remote sensing and in situ data sourcesPresents a whole earth perspective on surface temperature datasets, delving into all domains to build and understand relationships between the datasets

There is a lack of high‐quality global observations to evaluate soil drying impacts on surface fluxes in Earth system models (ESMs). Here we use a novel diagnostic based on the observed warming of the land relative to the atmosphere during dry spells (relative warming rate, RWR) to assess ESMs. The ESMs show that RWR is well correlated with changes in the partition of surface energy between sensible and latent heat across dry spells. Therefore, comparisons between observed and simulated RWR reveal where models are unable to capture a realistic soil moisture‐heat flux relationship. The results show that in general, models simulate dry spell ET dynamics well in arid zones while decreases in evaporative fraction appear excessive in some models in continental climate zones. Our approach can help guide land model development in aspects that are key in simulating extreme events like heat waves.