Evaluation of Regional-Seale Soil Moisture-Surface Flux Dynamics in Earth System Models Based On satellite Observations of Land Surface Temperature

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. Plain Language Summary We present a methodology to assess how land evapotranspiration (ET) responds to soil moisture (SM) in climate models across the world. Our method is based on looking at the observed versus modeled evolution of land surface temperature and the overlying air temperature during rain free periods, when SM decreases and limits the amount of land ET. We observed a good relationship between modeled ET and the modeled relative evolution of these temperatures during dry periods. Therefore, our method can be used to evaluate how realistic are modeled SM-ET relationships when soils are drying. Our results show that in general, models capture fairly well the influence of SM on ET in arid climate zones, while they perform less well in continental climate zones. Our method provides a unique new tool to improve aspects of weather and climate models important for simulating phenomena such as heat waves.This research was funded under the U.K. NERC e-stress project (NE/K015990/1). Additional support was provided by IMPALA (NE/M017230/1) and European Commission grant agreement 282673 (EMBRACE), and through U.K. NERC support of the National Centre for Earth Observation. Observational data used in the study are available from http://reverb.echo.nasa.gov/(MODIS), http://due.esrin.esa.int/(ERA-Interim), http://disc.sci.gsfc.nasa.gov (TRMM), http://www.cpc.ncep.noaa.gov/(CMORPH), and http://chrs.web.uci.edu/(PERSIANN).Model data are available from http://pcmdi9.llnl.gov/(CMIP5) and http://www.embrace-project.eu (EMBRACE). We gratefully acknowledge the providers of climate model data in the CMIP5 archive and EMBRACE partners, in particular, Gill Martin from Met Office (United Kingdom), Frederique Cheruy from LMD (France), and Klaus Wyser from SMHI (Sweden), for providing outputs from additional simulations. The code and aggregated observations are available from GitHub website (https://github.com/ppharris/dry_spell_rwr)

A spatiotemporal analysis of the relationship between near-surface air temperature and satellite land surface temperatures using 17 years of data from the ATSR series

The relationship between satellite land surface temperature (LST) and ground-based observations of 2m air temperature (T2m) is characterised in space and time using >17 years of data. The analysis uses a new monthly LST climate data record (CDR) based on the Along-Track Scanning Radiometer (ATSR) series, which has been produced within the European Space Agency GlobTemperature project (http://www.globtemperature.info/). Global LST-T2m differences are analysed with respect to location, land cover, vegetation fraction and elevation, all of which are found to be important influencing factors. LSTnight (~10 pm local solar time, clear-sky only) is found to be closely coupled with minimum T2m (Tmin, all-sky) and the two temperatures generally consistent to within ±5 °C (global median LSTnight- Tmin= 1.8 °C, interquartile range = 3.8 °C). The LSTday (~10 am local solar time, clear-sky only)-maximum T2m (Tmax, all-sky) variability is higher (global median LSTday- Tmax= -0.1°C, interquartile range = 8.1 °C) because LST is strongly influenced by insolation and surface regime. Correlations for both temperature pairs are typically >0.9 outside of the tropics. The monthly global and regional anomaly time series of LST and T2m – which are completely independent data sets - compare remarkably well. The correlation between the data sets is 0.9 for the globe with 90% of the CDR anomalies falling within the T2m 95% confidence limits. The results presented in this study present a justification for increasing use of satellite LST data in climate and weather science, both as an independent variable, and to augment T2m data acquired at meteorological stations

Retrieval Consistency between LST CCI Satellite Data Products over Europe and Africa

The assessment of satellite-derived land surface temperature (LST) data is essential to ensure their high quality for climate applications and research. This study intercompared seven LST products (i.e., ATSR_3, MODISA, MODIST, SLSTRA, SLSTRB, SEVIR2 and SEVIR4) of the European Space Agency’s (ESA) LST Climate Change Initiative (LST_cci) project, which are retrieved for polar and geostationary orbit satellites, and three operational LST products: NASA’s MODIS MOD11/MYD11 LST and ESA’s AATSR LST. All data were re-gridded on to a common spatial grid of 0.05° and matched for concurrent overpasses within 5 min. The matched data were analysed over Europe and Africa for monthly and seasonally aggregated median differences and studied for their dependence on land cover class and satellite viewing geometry. For most of the data sets, the results showed an overall agreement within ±2 K for median differences and robust standard deviation (RSD). A seasonal variation of median differences between polar and geostationary orbit sensor data was observed over Europe, which showed higher differences in summer and lower in winter. Over all land cover classes, NASA’s operational MODIS LST products were about 2 K colder than the LST_cci data sets. No seasonal differences were observed for the different land covers, but larger median differences between data sets were seen over bare soil land cover classes. Regarding the viewing geometry, an asymmetric increase of differences with respect to nadir view was observed for day-time data, which is mainly caused by shadow effects. For night-time data, these differences were symmetric and considerably smaller. Overall, despite the differences in the LST retrieval algorithms of the intercompared data sets, a good consistency between the LST_cci data sets was determined

Comprehensive In Situ Validation of Five Satellite Land Surface Temperature Data Sets over Multiple Stations and Years

Global land surface temperature (LST) data derived from satellite-based infrared radiance measurements are highly valuable for various applications in climate research. While in situ validation of satellite LST data sets is a challenging task, it is needed to obtain quantitative information on their accuracy. In the standardised approach to multi-sensor validation presented here for the first time, LST data sets obtained with state-of-the-art retrieval algorithms from several sensors (AATSR, GOES, MODIS, and SEVIRI) are matched spatially and temporally with multiple years of in situ data from globally distributed stations representing various land cover types in a consistent manner. Commonality of treatment is essential for the approach: all satellite data sets are projected to the same spatial grid, and transformed into a common harmonized format, thereby allowing comparison with in situ data to be undertaken with the same methodology and data processing. The large data base of standardised satellite LST provided by the European Space Agency’s GlobTemperature project makes previously difficult to perform LST studies and applications more feasible and easier to implement. The satellite data sets are validated over either three or ten years, depending on data availability. Average accuracies over the whole time span are generally within ±2.0 K during night, and within ± 4.0 K during day. Time series analyses over individual stations reveal seasonal cycles. They stem, depending on the station, from surface anisotropy, topography, or heterogeneous land cover. The results demonstrate the maturity of the LST products, but also highlight the need to carefully consider their temporal and spatial properties when using them for scientific purposes

Quantifying uncertainty in satellite-retrieved land surface temperature from cloud detection errors

Clouds remain one of the largest sources of uncertainty in remote sensing of surface temperature in the infrared, but this uncertainty has not generally been quantified. We present a new approach to do so, applied here to the Advanced Along-Track Scanning Radiometer (AATSR). We use an ensemble of cloud masks based on independent methodologies to investigate the magnitude of cloud detection uncertainties in area-average Land Surface Temperature (LST) retrieval. We find that at a grid resolution of 625 km^2 (commensurate with 0.25 degrees grid size at the tropics), cloud detection uncertainties are positively correlated with cloud-cover fraction in the cell, and are larger during the day than at night. Daytime cloud detection uncertainties range between 2.5 K for clear-sky fractions of 10-20 % and 1.03 K for clear-sky fractions of 90-100 %. Corresponding nighttime uncertainties are 1.6 K and 0.38 K respectively. Cloud detection uncertainty shows a weaker positive correlation with the number of biomes present within a grid cell, used as a measure of heterogeneity in the background against which the cloud detection must operate (eg. surface temperature, emissivity and reflectance). Uncertainty due to cloud detection errors is strongly dependent on the dominant land cover classification. We find cloud detection uncertainties of magnitude 1.95 K over permanent snow and ice, 1.2 K over open forest, 0.9-1 K over bare soils and 0.09 K over mosaic cropland, for a standardised clear-sky fraction of 74.2 %. As the uncertainties arising from cloud detection errors are of a significant magnitude for many surface types, and spatially heterogeneous where land classification varies rapidly, LST data producers are encouraged to quantify cloud-related uncertainties in gridded products

Global observational diagnosis of soil moisture control on the land surface energy balance

An understanding of where and how strongly the surface energy budget is constrained by soil moisture is hindered by a lack of large-scale observations, and this contributes to uncertainty in climate models. Here we present a new approach combining satellite observations of land surface temperature and rainfall.We derive a Relative Warming Rate (RWR) diagnostic, which is a measure of how rapidly the land warms relative to the overlying atmosphere during 10 day dry spells. In our dry spell composites, 73% of the land surface between 60°S and 60°N warms faster than the atmosphere, indicating water-stressed conditions, and increases in sensible heat. Higher RWRs are found for shorter vegetation and bare soil than for tall, deep-rooted vegetation, due to differences in aerodynamic and hydrological properties. We show how the variation of RWR with antecedent rainfall helps to identify different evaporative regimes in the major nonpolar climate zones

Evaluation of regional-scale soil moisture-surface flux dynamics in Earth system models based on satellite observations of land surface temperature

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

Land Surface Temperature Product Validation Best Practice Protocol Version 1.0 - October, 2017

The Global Climate Observing System (GCOS) has specified the need to systematically generate andvalidate Land Surface Temperature (LST) products. This document provides recommendations on goodpractices for the validation of LST products. Internationally accepted definitions of LST, emissivity andassociated quantities are provided to ensure the compatibility across products and reference data sets. Asurvey of current validation capabilities indicates that progress is being made in terms of up-scaling and insitu measurement methods, but there is insufficient standardization with respect to performing andreporting statistically robust comparisons.Four LST validation approaches are identified: (1) Ground-based validation, which involvescomparisons with LST obtained from ground-based radiance measurements; (2) Scene-based intercomparisonof current satellite LST products with a heritage LST products; (3) Radiance-based validation,which is based on radiative transfer calculations for known atmospheric profiles and land surface emissivity;(4) Time series comparisons, which are particularly useful for detecting problems that can occur during aninstrument's life, e.g. calibration drift or unrealistic outliers due to undetected clouds. Finally, the need foran open access facility for performing LST product validation as well as accessing reference LST datasets isidentified

Insights Into the Aerodynamic Versus Radiometric Surface Temperature Debate in Thermal-Based Evaporation Modeling

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