Search

Summer heatwaves are becoming increasingly dangerous over Europe, and their close monitoring is essential for human activities. Typically, they are monitored using the 2 m temperature from meteorological weather stations or reanalysis datasets. In this study, the 2022 extremely warm summer over Europe is analysed using satellite land surface temperature (LST), specifically the LSA SAF (Land Surface Analysis Satellite Application Facility) all-sky LST product (available from 2004 onwards). Since climate applications of LST are still poorly explored, heatwave diagnostics derived from satellite observations are compared with those derived using ERA5/ERA5-Land reanalysis data. Results highlight the exceptionality of 2022 in different metrics such as the mean LST anomaly, area under extreme heat conditions, number of hot days and heatwave magnitude index. In all metrics, 2022 ranked first when compared with the remaining years. Compared to 2018 (next in all rankings), 2022 exceeded its LST anomaly by 0.7 °C and each pixel had on average 7 more hot days. Satellite LST complements reanalysis diagnostics, as higher LST anomalies occur over areas under severe drought, indicating a higher control and amplification of the heatwave by surface processes and vegetation stress. These cross-cutting diagnostics increase the confidence across satellite data records and reanalyses, fostering their usage in climate applications. [ABSTRACT FROM AUTHOR]

Anthropogenic heat release (AHR) is the release of heat generated by anthropogenic energy consumption. The global mean flux of AHR is 0.03 W m−2, while AHR is geographically concentrated and fundamentally correlates with economic activity; furthermore, AHR can reach a level sufficient for impacting regional even large-scale climate. In this study, the impacts of AHR on the summer European heatwaves (EHWs) are examined by using the Community Earth System Model version 1 (CESM1). The results show that in Europe, AHR increases the summer mean 2-m temperature by 0.26 °C and the surface minimum and maximum temperatures by 0.14 °C and 0.41 °C, respectively. AHR exacerbates the extreme high temperatures in the summer in Europe, increasing EHW days by 1–2 days in central and eastern Europe in the summer annually from 1992 to 2013. AHR strengthens the surface wind that flows from the ocean to the land in Europe by increasing the land surface temperatures. AHR decreases the lower-troposphere stability (LTS) and reduces the low-cloud amounts in Europe, which leads to more solar shortwave radiation reaching the surface. AHR affects water vapor and the surface energy balance in Europe, which impacts on European summer heatwaves further. AHR acts as a non-negligible factor for summer extreme high temperature in Europe and a potential factor impacting EHW days. [ABSTRACT FROM AUTHOR]

The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) is a scientific mission that collects high spatio‐temporal resolution (∼70 m, 1–5 days average revisit time) thermal images since its launch on 29 June 2018. As a predecessor of future missions, one of the main objectives of ECOSTRESS is to retrieve and understand the spatio‐temporal variations in terrestrial evapotranspiration (ET) and its responses to soil water availability and atmospheric aridity. In the European ECOSTRESS Hub (EEH), by taking advantage of land surface temperature (LST) retrievals, we generated ECOSTRESS ET products over Europe and Africa using three models with different structures and parameterization schemes, namely Surface Energy Balance System (SEBS) and Two Source Energy Balance (TSEB) parametric models, as well as the non‐parametric Surface Temperature Initiated Closure (STIC) model. A comprehensive evaluation of the EEH ET products was conducted with respect to flux measurements from 19 eddy covariance sites in Europe over six different biomes with diverse aridity levels. Results revealed comparable performances of STIC and SEBS (RMSE of ∼70 W m−2). However, the relatively complex TSEB model produced a higher RMSE of ∼90 W m−2. Comparison between STIC ET estimates and the operational ECOSTRESS ET product from NASA PT‐JPL model showed a larger RMSE (around 50 W m−2 higher) for the PT‐JPL ET estimates. Substantial overestimation (>80 W m−2) in PT‐JPL ET estimates was evident over shrublands and savannas, presumably due to weak constraint of LST in the model. Overall, the EEH supports ET retrieval for the future high‐resolution thermal missions. Key Points: Evapotranspiration products over Europe and Africa were generated using three models (Surface Temperature Initiated Closure, Surface Energy Balance System, and Two Source Energy Balance) in the European ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) HubComparison at 19 eddy covariance sites revealed noteworthy model divergence with increasing aridity and vegetation sparsenessA substantial overestimation of the official NASA ECOSTRESS PT‐JPL evapotranspiration product was found under high water limitations [ABSTRACT FROM AUTHOR]

Substantial reductions in human and economic activities such as road traffic for several months in 2020 were one of the consequences of the Coronavirus pandemic. This unprecedented change in urban metabolism also affected temperature and air pollutants. This study investigates the effects of the first COVID-19 lockdown across 43 cities in Europe. It determines the influence of anthropogenic activities on nitrogen dioxide (NO2), ozone (O3), and particulate matter (PM2.5), as well as on land surface temperature (LST) and the surface urban heat island intensity (SUHII) using satellite, modeled, and mobility data. Our findings show that there are great temporal and spatial differences and distinct patterns between the cities regarding the magnitude of change in the variables under study. In general, the results indicate a substantial decrease in NO2 concentrations in most of the studied cities compared with the reference period of 2015–2019. However, reductions could not be attributed to mobility changes such as less traffic at transit stations, contrary to the results of previous studies. O3 levels increased during the first lockdown in accordance with the decreasing NO2 concentrations. The PM pattern was inconsistent over time and space. Similar to the NO2 results, no relation to the altered mobility behavior was found. No clear signal could be detected for LST and the SUHII, likely due to dominating meteorological influences. [ABSTRACT FROM AUTHOR]

Heatwaves and droughts are becoming more common and severe in Europe, causing changes in tree phenology, disrupting the sequestration of carbon and causing tree mortality on a continental scale. The responses of leaf shedding to heatwaves and droughts remain uncertain, although temperate deciduous forests may shed their leaves if exposed to extreme heat and water stress. Little information, however, is available about the extent and recurrence of early leaf shedding induced by drought, likely because it occurs in small forest patches and can be discriminated only during a few weeks. We used highly spatiotemporal Sentinel‐2 data as evidence of widespread drought‐induced early leaf shedding in Europe during 2017–2021. We estimated the timing of leaf shedding from NDVI time series and a threshold‐based method that extracts the end of the growing season. Then, we evaluated the heatwave and drought impacts at the end of season by analysing the z‐score of Landsat‐7 and ‐8 land surface temperature and the ERA5‐Land air temperature and aridity index. The 10‐m resolution Sentinel‐2 data identified early leaf shedding not detected by the low‐resolution (250 m) MODIS sensor. Early leaf shedding was observed across Europe during the entire study period and its occurrence was linked to preceding anomalously high temperatures and arid conditions. Our results also indicated that mean summer NDVI decreased significantly in the years following early leaf shedding, suggesting a legacy decline in vegetation productivity. Our study demonstrates that decametric satellite data can be used to monitor the responses of forests to extreme climate events at the canopy level and indicates that early leaf shedding associated with heatwaves and droughts is more widespread and frequent across the continent than previously thought. [ABSTRACT FROM AUTHOR]

Land Surface Temperature (LST) and Surface Net Radiation (SNR) are vital inputs for many land surface and hydrological models. However, current remote sensing datasets of these variables come mostly at coarse resolutions. Although high-resolution LST and SNR retrievals are available, they have large gaps due to cloud-cover that hinder their use as input in models. Here, we present a downscaled and continuous daily LST and SNR product across Europe for 2018-2019. The LST product is based on all-sky LST retrievals from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) onboard the geostationary Meteosat Second Generation (MSG) satellite, and clear-sky LST retrievals from the Sea and Land Surface Temperature Radiometer (SLSTR) onboard the polar-orbiting Sentinel 3 satellites. The product combines the medium spatial (approx. 5-7 km) but high temporal (30 minute) resolution, gap-free data from MSG, with the low temporal (2-3 days) but high spatial (1 km) resolution of the Sentinel 3 LST retrievals. The resulting 1 km and daily LST dataset is based on an hourly merging of both datasets through bias-correction and Kalman Filter assimilation. Longwave outgoing radiation is computed from the merged LST product in combination with MSG-based emissivity data. Shortwave outgoing radiation is computed from the incoming shortwave radiation from MSG and downscaled albedos using 1 km PROBA-V data. MSG incoming shortwave and longwave radiation and the outgoing radiation components at 1 km spatial resolution are used together to compute the final daily SNR dataset in a consistent manner. Validation results indicate an improvement of the root mean squared error by ca. 8% with a substantial increase in spatial detail compared to the original MSG product. The resulting pan-European LST and SNR dataset can be used for hydrological modelling and as input to models dedicated to estimating evaporation and surface turbulent heat fluxes and will be regularly updated in the future. [ABSTRACT FROM AUTHOR]

Long‐term satellite land surface temperature (LST) data are desirable to augment 2m air temperatures (T2m) measured in situ and as an independent measure of surface temperature change. However, previous studies show variable agreement between LST and T2m time series. The objective of this study is to assess the stability and trends in six new LST data sets from the European Space Agency's Climate Change Initiative for LST (LST_cci). LST anomalies are compared with homogenized station T2m anomalies over Europe, which verifies all six data sets are well coupled (LST vs T2m anomaly correlations and slopes: 0.6–0.9). The temporal stability of the LST_cci data is assessed through a comparison with the T2m anomaly time series. Only the LST_cci data sets for the MODerate resolution Imaging Spectroradiometer (MODIS) onboard Aqua and the Advanced Along‐Track Scanning Radiometer (AATSR) appear stable; the MODIS/Terra, ATSR‐2, and multisensor InfraRed and MicroWave data sets show non‐climatic discontinuities associated with changes in sensor and/or drift over time. For MODIS/Aqua (2002–2018), significant trends in LST of 0.64–0.66 K/decade compare well with the equivalent T2m trends of 0.52–0.59 K/decade. The LST and T2m trends for AATSR (2002–2012) are found to be statistically insignificant, likely due to the comparatively short study period and specific years available for analysis. No evidence is found to suggest that trends calculated using cloud‐free InfraRed observations are affected by clear‐sky bias. This study suggests that satellite LST data can be used to assess warming trends over land and for other climate applications if the required homogeneity is assured. Plain Language Summary: Long‐term changes in surface temperature due to climate change are usually studied using near‐surface air temperatures (T2m) from weather stations. However, these data are not available everywhere and changes in T2m are poorly understood in some regions (e.g., parts of Africa). This study assesses the potential for satellite‐observed land surface temperatures (LST) to help fill these gaps. The study finds that LST and T2m provide very similar information on surface temperatures and therefore LST can provide useful information where T2m data are unavailable. However, some of the LST data sets are found to be "unstable" and suffer from jumps and changes over time that result from for example, changes in satellite instrument or a change in the orbit of the satellite platform. These unstable LST data sets cannot be used to measure changes in surface temperature. However, the study finds that for "stable" LST data sets, which do not suffer from these non‐climatic jumps and changes, the changes in LST over time are very similar to, or even the same as, the changes in T2m. Therefore, this work suggests that satellite LST data could be used to measure changes in surface temperature where there are not enough stations to make these measurements using T2m. Key Points: The stability of six satellite land surface temperature data sets is assessed using homogenized station air temperatures over EuropeOnly two data sets are stable; the other four show non‐climatic discontinuities associated with a change in sensor and/or drift over timeSignificant trends in land surface temperature of ∼0.65 K/decade over Europe are found for one of the stable data sets over 2002–2018 [ABSTRACT FROM AUTHOR]

The land surface is a potential source of climate predictability over the Northern Hemisphere midlatitudes but has received less attention than sea surface temperature in this regard. This study quantified the degree to which realistic land initialization contributes to interannual climate predictability over Europe based on a coupled climate system model named FGOALS-g2. The potential predictability provided by the initialization, which incorporates the soil moisture and soil temperature of a land surface reanalysis product into the coupled model with a dimension-reduced projection four-dimensional variational data assimilation (DRP-4DVar)-based weakly coupled data assimilation (WCDA) system, was analyzed first. The effective predictability (i.e., prediction skill) of the hindcasts by FGOALS-g2 with realistic and well-balanced initial conditions from the initialization were then evaluated. Results show an enhanced interannual prediction skill for summer surface air temperature and precipitation in the hindcast over Europe, demonstrating the potential benefit from realistic land initialization. This study highlights the significant contributions of land surface to interannual predictability of summer climate over Europe. [ABSTRACT FROM AUTHOR]

How summertime temperature variability will change with warming has important implications for climate adaptation and mitigation. CMIP5 simulations indicate a compound risk of extreme hot temperatures in western Europe from both warming and increasing temperature variance. CMIP6 simulations, however, indicate only a moderate increase in temperature variance that does not covary with warming. To explore this intergenerational discrepancy in CMIP results, we decompose changes in monthly temperature variance into those arising from changes in sensitivity to forcing and changes in forcing variance. Across models, sensitivity increases with local warming in both CMIP5 and CMIP6 at an average rate of 5.7 ([3.7, 7.9]; 95% c.i.) × 10−3°C per W m−2 per °C warming. We use a simple model of moist surface energetics to explain increased sensitivity as a consequence of greater atmospheric demand (∼70%) and drier soil (∼40%) that is partially offset by the Planck feedback (∼−10%). Conversely, forcing variance is stable in CMIP5 but decreases with warming in CMIP6 at an average rate of −21 ([−28, −15]; 95% c.i.) W2 m−4 per °C warming. We examine scaling relationships with mean cloud fraction and find that mean forcing variance decreases with decreasing cloud fraction at twice the rate in CMIP6 than CMIP5. The stability of CMIP6 temperature variance is, thus, a consequence of offsetting changes in sensitivity and forcing variance. Further work to determine which models and generations of CMIP simulations better represent changes in cloud radiative forcing is important for assessing risks associated with increased temperature variance. Plain Language Summary: CMIP5 models show that, in the Northern Hemisphere midlatitudes, summertime temperature variability increases as the surface warms, indicating a compound risk of extreme hot months that have important implications for climate adaptation and mitigation. CMIP6 models, however, show only a moderate increase in temperature variability that is unrelated to warming. To understand this intergenerational discrepancy in CMIP results, we develop a framework to decompose changes in temperature variability into contributions from changes in the variability of external forcing and changes in the sensitivity of temperature to that forcing. We find that both CMIP5 and CMIP6 models show consistent increases in sensitivity as the surface warms, which we demonstrate to arise mainly from warming and drying using a simple diagnostic model. Changes in forcing variability, however, differ between CMIP5 and CMIP6. Whereas, forcing variability is stable in CMIP5, it decreases substantially with warming in CMIP6 and offsets the effect of sensitivity growth. Hence, although midlatitude land surface tends to become more sensitive in all models, whether temperature variability will increase with warming remains uncertain and relies on how forcing variability changes. Key Points: Summer temperature variance increases with warming in Northern Hemisphere midlatitudes in CMIP5 but not in CMIP6We develop a decomposition framework and a simple model to explain differences in variance changes across modelsTemperature sensitivity increases in both CMIP5 and CMIP6 but is offset by lower forcing variance in CMIP6 [ABSTRACT FROM AUTHOR]

Urban trees influence temperatures in cities. However, their effectiveness at mitigating urban heat in different climatic contexts and in comparison to treeless urban green spaces has not yet been sufficiently explored. Here, we use high-resolution satellite land surface temperatures (LSTs) and land-cover data from 293 European cities to infer the potential of urban trees to reduce LSTs. We show that urban trees exhibit lower temperatures than urban fabric across most European cities in summer and during hot extremes. Compared to continuous urban fabric, LSTs observed for urban trees are on average 0-4 K lower in Southern European regions and 8-12 K lower in Central Europe. Treeless urban green spaces are overall less effective in reducing LSTs, and their cooling effect is approximately 2-4 times lower than the cooling induced by urban trees. By revealing continental-scale patterns in the effect of trees and treeless green spaces on urban LST our results highlight the importance of considering and further investigating the climate-dependent effectiveness of heat mitigation measures in cities. Urban trees influence temperatures in cities. The authors here investigate in spatio-temporal variations in their cooling effect and find 8-12 K decreased temperatures for tree-rich urban areas in Central Europe during hot summers, and up to 4 K for Southern Europe, respectively. [ABSTRACT FROM AUTHOR]

In the period 1960–2010, the land surface air temperature (SAT) warmed more rapidly over some regions relative to the global mean. Using a set of time-slice experiments, we highlight how different physical processes shape the regional pattern of SAT warming. The results indicate an essential role of anthropogenic forcing in regional SAT changes from the 1970s to 2000s, and show that both surface–atmosphere interactions and large-scale atmospheric circulation changes can shape regional responses to forcing. Single forcing experiments show that an increase in greenhouse gases can lead to regional changes in land surface warming in winter (DJF) due to snow-albedo feedbacks, and in summer (JJA) due to soil-moisture and cloud feedbacks. Changes in anthropogenic aerosol and precursor (AA) emissions induce large spatial variations in SAT, characterized by warming over western Europe, Eurasia, and Alaska. In western Europe, SAT warming is stronger in JJA than in DJF due to substantial increases in clear sky shortwave radiation over Europe, associated with decreases in local AA emissions since the 1980s. In Alaska, the amplified SAT warming in DJF is due to increased downward longwave radiation, which is related to increased water vapor and cloud cover. In this case, although the model was able to capture the regional pattern of SAT change, and the associated local processes, it did not simulate all processes and anomalies correctly. For the Alaskan warming, the model is seen to achieve the correct regional response in the context of a wider North Pacific anomaly that is not consistent with observations. This demonstrates the importance of model evaluation that goes beyond the target variable in detection and attribution studies. [ABSTRACT FROM AUTHOR]

Weather extremes and extreme climate events, like late spring frosts, are expected to increase in frequency and duration during the next decades. Although spring phenology of European beech is well adapted to escape freeze damages on longer time scales, the effects of occasional late spring frosts (LSF) are among the main climatic damages to these forests to such an extent that they limit beech distribution and elevation range, especially at its southern margin. The aim of this work was to evaluate the short-term effects of two consecutive LSF events occurred in 2016 and 2017 in Italy on the beech forest vegetation activity. Remotely sensed land surface temperature (LST) data were used to detect the pixels where LSF occurred, while enhanced vegetation index (EVI) data were used to quantify LSF effects by computing a spring vegetation activity anomaly index (sAI). In 2016 and 2017, the LSF covered, respectively, about 29% and 32% of the total Italian beech-dominated area. The two LSF widely differed in their spatial patterns and their effects. In 2016, the pixels belonging to the sAI classes with the highest spring anomalies were also those where prolonged LSF occur, while, in 2017, the pixels belonging to the highest sAI classes were those that underwent the shorter (but probably more intense) LSF events. Under scenarios of increased frequency risk of repeated LSF, the proposed methodology may represent an automatic and low-cost tool both for monitoring and predicting European beech growth patterns. [ABSTRACT FROM AUTHOR]

Ecoregionalization is the process by which a territory is classified in similar areas according to specific environmental and climatic factors. The climate and the environment strongly influence the presence and distribution of vectors responsible for significant human and animal diseases worldwide. In this paper, we developed a map of the eco-climatic regions of Italy adopting a data-driven spatial clustering approach using recent and detailed spatial data on climatic and environmental factors. We selected seven variables, relevant for a broad set of human and animal vector-borne diseases (VBDs): standard deviation of altitude, mean daytime land surface temperature, mean amplitude and peak timing of the annual cycle of land surface temperature, mean and amplitude of the annual cycle of greenness value, and daily mean amount of rainfall. Principal Component Analysis followed by multivariate geographic clustering using the k-medoids technique were used to group the pixels with similar characteristics into different ecoregions, and at different spatial resolutions (250 m, 1 km and 2 km). We showed that the spatial structure of ecoregions is generally maintained at different spatial resolutions and we compared the resulting ecoregion maps with two datasets related to Bluetongue vectors and West Nile Disease (WND) outbreaks in Italy. The known characteristics of Culicoides imicola habitat were well captured by 2/22 specific ecoregions (at 250 m resolution). Culicoides obsoletus/scoticus occupy all sampled ecoregions, according to its known widespread distribution across the peninsula. WND outbreak locations strongly cluster in 4/22 ecoregions, dominated by human influenced landscape, with intense cultivations and complex irrigation network. This approach could be a supportive tool in case of VBDs, defining pixel-based areas that are conducive environment for VBD spread, indicating where surveillance and prevention measures could be prioritized in Italy. Also, ecoregions suitable to specific VBDs vectors could inform entomological surveillance strategies. [ABSTRACT FROM AUTHOR]

This study analyzes inter- and intra-zonal seasonal variability of surface urban heat islands (SUHIs) within the methodological framework of local climate zones (LCZs) in three central European cities (Prague, Brno and Novi Sad). These cities differ in urban area and structure as well as in topography and hinterland land-cover features. LCZs were delineated on the basis of a GIS-based classification method. Land surface temperature (LST) was derived from LANDSAT-8 scenes in the period 2013–2018. The first step was to detect seasonal SUHI intensity differences for built LCZ types and LST for land-cover types of LCZ. The results revealed the highest differences in summer and spring, and lowest in winter. The highest SUHI intensity values occur in densely built-up and industrial zones, and the lowest in sparsely-built city outskirts. The coolest LCZs based on LST were dense trees and water areas in spring and summer. The second step aimed to analyze the effects of vegetation on SUHI formation. Hence, 11 land cover subclasses (from dense trees to bush/scrub) were defined in order to research intra-zonal seasonal LST variability. The height and density of vegetation have substantial effects on intra-zonal variability of LST in land-cover types of LCZ, whereas differences between forest subclasses were relatively low. Finally, the character of the vegetation had a substantial influence on intra-zonal LCZ variability of LST and SUHI formation. Further research in this field could contribute to better understanding of micro- and mezzo-climate-scale patterns, as well as better adaptation to climate change in urban areas. Image 1 • Statistically significant seasonal SUHI variability between LCZs. • Highest SUHI differences between built and land cover classes occurred in summer. • Regional climate and city surrounding affect SUHI during winter, spring and autumn. • Statistically significant SUHI variability within LCZ land cover classes (A−C). • Density and tree/scrub pattern, together with land cover, affect seasonal SUHI. [ABSTRACT FROM AUTHOR]

The climate change and the proceeding urbanization create future health challenges. Consequently, more people around the globe will be impaired by extreme weather events, such as heat waves. This study investigates the causes for the emergence of surface urban heat islands and its change during heat waves in 70 European cities. A newly created climate class indicator, a set of meaningful landscape metrics, and two population-related parameters were applied to describe the Surface Urban Heat Island Magnitude (SUHIM) – the mean temperature increase within the urban heat island compared to its surrounding, as well as the Heat Magnitude (HM) – the extra heat load added to the average summer SUHIM during heat waves. We evaluated the relevance of varying urban parameters within linear models. The exemplary European-wide heat wave in July 2006 was chosen and compared to the average summer conditions using MODIS land surface temperature with an improved spatial resolution of 250 m. The results revealed that the initial size of the urban heat island had significant influence on SUHIM. For the explanation of HM the size of the heat island, the regional climate and the share of central urban green spaces showed to be critical. Interestingly, cities of cooler climates and cities with higher shares of urban green spaces were more affected by additional heat during heat waves. Accordingly, cooler northern European cities seem to be more vulnerable to heat waves, whereas southern European cities appear to be better adapted. Within the ascertained population and climate clusters more detailed explanations were found. Our findings improve the understanding of the urban heat island effect across European cities and its behavior under heat waves. Also, they provide some indications for urban planners on case-specific adaptation strategies to adverse urban heat caused by heat waves. [ABSTRACT FROM AUTHOR]

Extreme weather events can have large impacts on society and, in many regions, are expected to change in frequency and intensity with climate change. Owing to the relatively short observational record, climate models are useful tools as they allow for generation of a larger sample of extreme events, to attribute recent events to anthropogenic climate change, and to project changes of such events into the future. The modelling system known as weather@home, consisting of a global climate model (GCM) with a nested regional climate model (RCM) and driven by sea surface temperatures, allows to generate very large ensemble with the help of volunteer distributed computing. This is a key tool to understanding many aspects of extreme events. Here, a new version of weather@home system (weather@home 2) with a higher resolution RCM over Europe is documented and a broad validation of the climate is performed. The new model includes a more recent land-surface scheme in both GCM and RCM, where subgrid scale land surface heterogeneity is newly represented using tiles, and an increase in RCM resolution from 50 km to 25 km. The GCM performs similarly to the previous version, with some improvements in the representation of mean climate. The European RCM biases are overall reduced, in particular the warm and dry bias over eastern Europe, but large biases remain. The model is shown to represent main classes of regional extreme events reasonably well and shows a good sensitivity to its drivers. In particular, given the improvements in this version of the weather@home system, it is likely that more reliable statements can be made with regards to impact statements, especially at more localized scales. [ABSTRACT FROM AUTHOR]

This research reveals new insights into the weather drivers of interannual variation in land surface phenology (LSP) across the entire European forest, while at the same time establishes a new conceptual framework for predictive modelling of LSP. Specifically, the random-forest (RF) method, a multivariate, spatially non-stationary and nonlinear machine learning approach, was introduced for phenological modelling across very large areas and across multiple years simultaneously: the typical case for satellite-observed LSP. The RF model was fitted to the relation between LSP interannual variation and numerous climate predictor variables computed at biologically relevant rather than humanimposed temporal scales. In addition, the legacy effect of an advanced or delayed spring on autumn phenology was explored. The RF models explained 81 and 62%of the variance in the spring and autumn LSP interannual variation, with relative errors of 10 and 20%, respectively: a level of precision that has until now been unobtainable at the continental scale. Multivariate linear regression models explained only 36 and 25%, respectively. It also allowed identification of the main drivers of the interannual variation in LSP through its estimation of variable importance. This research, thus, shows an alternative to the hitherto applied linear regression approaches for modelling LSP and paves the way for further scientific investigation based on machine learning methods. [ABSTRACT FROM AUTHOR]

Soil moisture plays a fundamental role in regulating the summertime surface energy balance across Europe. Understanding the spatial and temporal behavior in soil moisture and its control on evapotranspiration (ET) is critically important and influences heat wave events. Global climate models (GCMs) exhibit a broad range of land responses to soil moisture in regions that lie between wet and dry soil regimes. In situ observations of soil moisture and evaporation are limited in space, and given the spatial heterogeneity of the landscape, are unrepresentative of the GCM gridbox scale. On the other hand, satelliteborne observations of land surface temperature (LST) can provide important information at the larger scale. As a key component of the surface energy balance, LST is used to provide an indirect measure of surface drying across the landscape. To isolate soil moisture constraints on evaporation, time series of clear-sky LST are analyzed during dry spells lasting at least 10 days from March to October. Averaged over thousands of dry spell events across Europe, and accounting for atmospheric temperature variations, regional surface warming of between 0.5 and 0.8 K is observed over the first 10 days of a dry spell. Land surface temperatures are found to be sensitive to antecedent rainfall; stronger dry spell warming rates are observed following relatively wet months, indicative of soil moisture memory effects on the monthly time scale. Furthermore, clear differences in surface warming rate are found between cropland and forest, consistent with contrasting hydrological and aerodynamic properties. [ABSTRACT FROM AUTHOR]

The responses of crop functioning to changing climate and atmospheric CO2 concentration ([CO2]) could have large effects on food production, and impact carbon, water and energy fluxes, causing feedbacks to climate. To simulate the responses of temperate crops to changing climate and [CO2], accounting for the specific phenology of crops mediated by management practice, we present here the development of a process-oriented terrestrial biogeochemical model named ORCHIDEE-CROP (v0), which integrates a generic crop phenology and harvest module and a very simple parameterization of nitrogen fertilization, into the land surface model (LSM) ORCHIDEEv196, in order to simulate biophysical and biochemical interactions in croplands, as well as plant productivity and harvested yield. The model is applicable for a range of temperate crops, but it is tested here for maize and winter wheat, with the phenological parameterizations of two European varieties originating from the STICS agronomical model. We evaluate the ORCHIDEE-CROP (v0) model against eddy covariance and biometric measurements at 7 winter wheat and maize sites in Europe. The specific ecosystem variables used in the evaluation are CO2 fluxes (NEE), latent heat and sensible heat fluxes. Additional measurements of leaf area index (LAI), aboveground biomass and yield are used as well. Evaluation results reveal that ORCHIDEE-CROP (v0) reproduces the observed timing of crop development stages and the amplitude of pertaining LAI changes in contrast to ORCHIDEEv196 in which by default crops have the same phenology than grass. A near-halving of the root mean square error of LAI from 2.38 ± 0.77 to 1.08 ± 0.34 m² m-2 is obtained between ORCHIDEEv196 and ORCHIDEE-CROP (v0) across the 7 study sites. Improved crop phenology and carbon allocation lead to a general good match between modelled and observed aboveground biomass (with a normalized root mean squared error (NRMSE) of 11.0-54.2%), crop yield, as well as of the daily carbon and energy fluxes with NRMSE of ~9.0-20.1 and ~9.4-22.3 % for NEE, and sensible and latent heat fluxes, respectively. The model data mistfit for energy fluxes are within uncertainties of the measurements, which themselves show an incomplete energy balance closure within the range 80.6-86.3 %. The remaining discrepancies between modelled and observed LAI and other variables at specific sites are partly attributable to unrealistic representation of management events. In addition, ORCHIDEE-CROP (v0) is shown to have the ability to capture the spatial gradients of carbon and energy-related variables, such as gross primary productivity, NEE, sensible heat fluxes and latent heat fluxes, across the sites in Europe, an important requirement for future spatially explicit simulations. Further improvement of the model with an explicit parameterization of nutrition dynamics and of management, is expected to improve its predictive ability to simulate croplands in an Earth System Model. [ABSTRACT FROM AUTHOR]

European forest use for fuel, timber and food dates back to pre-Roman times. Century-scale ecological processes and their legacy effects require accounting for forest management when studying today's forest carbon sink. Forest management reconstructions that are used to drive land surface models are one way to quantify the impact of both historical and today's large scale application of forest management on today's forest-related carbon sink and surface climate. In this study we reconstruct European forest management from 1600 to 2010 making use of diverse approaches, data sources and assumptions. Between 1600 and 1828, a demand-supply approach was used in which wood supply was reconstructed based on estimates of historical annual wood increment and land cover reconstructions. For the same period demand estimates accounted for the fuelwood needed in households, wood used in food processing, charcoal used in metal smelting and salt production, timber for construction and population estimates. Comparing estimated demand and supply resulted in a spatially explicit reconstruction of the share of forests under coppice, high stand management and forest left unmanaged. For the reconstruction between 1829 and 2010 a supply-driven back-casting method was used. The method used age reconstructions from the years 1950 to 2010 as its starting point. Our reconstruction reproduces the most important changes in forest management between 1600 and 2010: (1) an increase of 593 000 km² in conifers at the expense of deciduous forest (decreasing by 538 000 km²), (2) a 612 000 km2 decrease in unmanaged forest, (3) a 152 000 km² decrease in coppice management, (4) a 818 000 km2 increase in high stand management, and (5) the rise and fall of litter raking which at its peak in 1853 removed 50 Tg dry litter per year. [ABSTRACT FROM AUTHOR]

Abstract: The remote sensing measurement of land surface temperature from satellites provides a monitoring of this magnitude on a continuous and regular basis, which is a critical factor in many research fields such as weather forecasting, detection of forest fires or climate change studies, for instance. The main problem of measuring temperature from space is the need to correct for the effects of the atmosphere and the surface emissivity. In this work an automatic procedure based on the Vegetation Cover Method, combined with the GLOBCOVER land surface type classification, is proposed. The algorithm combines this land cover classification with remote sensing information on the vegetation cover fraction to obtain land surface emissivity maps for AATSR split-window bands. The emissivity estimates have been compared with ground measurements in two validation cases in the area of rice fields of Valencia, Spain, and they have also been compared to the classification-based emissivity product provided by MODIS (MOD11_L2). The results show that the error in emissivity of the proposed methodology is of the order of ±0.01 for most of the land surface classes considered, which will contribute to improve the operational land surface temperature measurements provided by the AATSR instrument. [Copyright &y& Elsevier]

Migratory birds are particularly exposed to habitat changes in their breeding and non-breeding grounds. Remote sensing technologies offer an excellent opportunity to monitor species' habitats from space at unprecedented spatiotemporal scales. We analyzed if remotely sensed ecosystem functioning attributes (EFAs) adequately predict the spatiotemporal variation of the Woodcock's (Scolopax rusticola) relative abundance in southwest Europe, during autumn migration and wintering periods. We used data gathered from Woodcock monitoring through citizen science (N = 355,654 hunting trips) between 2009 and 2018. We computed a comprehensive set of EFAs on a weekly basis from three MODIS satellite products: enhanced vegetation index (EVI), tasseled cap transformation (TCT), and land surface temperature (LST). We developed generalized linear mixed models to explore the predictive power of EFAs on Woodcock's abundance during the non-breeding season. Results showed that Woodcock abundance is correlated with spatiotemporal dynamics in primary productivity (measured through the EVI), water cycle dynamics (wetness component of TCT), and surface energy balance (LST) in both periods. Our findings underline the potential of combining citizen science and remote sensing data to monitor migratory birds throughout their life cycles—an issue of critical importance to ensure adequate habitat management in the non-breeding areas. [ABSTRACT FROM AUTHOR]

• Imposition of lockdown has reduced aerosols and land surface temperature by 1–2 °C over Europe and North America. • Reduced LST during the lockdown was corroborated with the negative anomaly of air temperature at selected ground stations. • A large drop in LST was found in urban areas (by ∼1–2 °C) compared to rural landscapes. • The lockdown has temporary implications in minimizing the UHI effect over selected cities. The outbreak of SARS CoV-2 (COVID-19) has posed a serious threat to human beings, society, and economic activities all over the world. Worldwide rigorous containment measures for limiting the spread of the virus have several beneficial environmental implications due to decreased anthropogenic emissions and air pollutants, which provide a unique opportunity to understand and quantify the human impact on atmospheric environment. In the present study, the associated changes in Land Surface Temperature (LST), aerosol, and atmospheric water vapor content were investigated over highly COVID-19 impacted areas, namely, Europe and North America. The key findings revealed a large-scale negative standardized LST anomaly during nighttime across Europe (–0.11 °C to –2.6 °C), USA (–0.70 °C) and Canada (–0.27 °C) in March–May of the pandemic year 2020 compared to the mean of 2015–2019, which can be partly ascribed to the lockdown effect. The reduced LST was corroborated with the negative anomaly of air temperature measured at meteorological stations (i.e. –0.46 °C to –0.96 °C). A larger decrease in nighttime LST was also seen in urban areas (by ∼1–2 °C) compared to rural landscapes, which suggests a weakness of the urban heat island effect during the lockdown period due to large decrease in absorbing aerosols and air pollutants. On the contrary, daytime LST increased over most parts of Europe due to less attenuation of solar radiation by atmospheric aerosols. Synoptic meteorological variability and several surface-related factors may mask these changes and significantly affect the variations in LST, aerosols and water vapor content. The changes in LST may be a temporary phenomenon during the lockdown but provides an excellent opportunity to investigate the effects of various forcing controlling factors in urban microclimate and a strong evidence base for potential environmental benefits through urban planning and policy implementation. [ABSTRACT FROM AUTHOR]

Land Surface Temperature (LST) is an important parameter for tracing the impact of changing climatic conditions on our environment. Describing the interface between long- and shortwave radiation fluxes, as well as between turbulent heat fluxes and the ground heat flux, LST plays a crucial role in the global heat balance. Satellite-derived LST is an indispensable tool for monitoring these changes consistently over large areas and for long time periods. Data from the AVHRR (Advanced Very High-Resolution Radiometer) sensors have been available since the early 1980s. In the TIMELINE project, LST is derived for the entire operating period of AVHRR sensors over Europe at a 1 km spatial resolution. In this study, we present the validation results for the TIMELINE AVHRR daytime LST. The validation approach consists of an assessment of the temporal consistency of the AVHRR LST time series, an inter-comparison between AVHRR LST and in situ LST, and a comparison of the AVHRR LST product with concurrent MODIS (Moderate Resolution Imaging Spectroradiometer) LST. The results indicate the successful derivation of stable LST time series from multi-decadal AVHRR data. The validation results were investigated regarding different LST, TCWV and VA, as well as land cover classes. The comparisons between the TIMELINE LST product and the reference datasets show seasonal and land cover-related patterns. The LST level was found to be the most determinative factor of the error. On average, an absolute deviation of the AVHRR LST by 1.83 K from in situ LST, as well as a difference of 2.34 K from the MODIS product, was observed. [ABSTRACT FROM AUTHOR]

Unsustainable development paths have reached critical levels in Europe. In recent years, in cities, urbanization has been contributing to the intensification of urban heat islands. To analyze the development of surface urban heat islands (SUHI) in Europe in the last few years, the present study combines the land surface temperature (LST) from MODIS with the urban classes of the CORINE land cover data within 617 functional urban areas (FUAs). Urban and industrial uses have significantly higher LST than green urban areas across all years (about 4 to 6 °C), as do agricultural areas within cities. Besides land cover, location also influences LST differences. While, e.g., Bolzano (Italy) shows particularly large LST differences (>6 °C) between the core and the commuting zone, this effect is hardly visible in Porto (Portugal) and Madrid (Spain) (<2.5 °C). Cities of moderate climates show increasing differences between a city and its commuting zones with rising LST (r = 0.68), i.e., less cooling effects at night. [ABSTRACT FROM AUTHOR]

A one-dimensional simulation model that simulates daily mean soil temperature on a daily time-step basis, named AGRISOTES (AGRIcultural SOil TEmperature Simulation), is described. It considers ground coverage by biomass or a snow layer and accounts for the freeze/thaw effect of soil water. The model is designed for use on agricultural land with limited (and mostly easily available) input data, for estimating soil temperature spatial patterns, for single sites (as a stand-alone version), or in context with agrometeorological and agronomic models. The calibration and validation of the model are carried out on measured soil temperatures in experimental fields and other measurement sites with various climates, agricultural land uses and soil conditions in Europe. The model validation shows good results, but they are determined strongly by the quality and representativeness of the measured or estimated input parameters to which the model is most sensitive, particularly soil cover dynamics (biomass and snow cover), soil pore volume, soil texture and water content over the soil column. [ABSTRACT FROM AUTHOR]

• FLS occurrence can be accurately modeled based on satellite data using machine learning. • model skill is highest in winter high pressure situations. • atmospheric conditions are the most important determinants of FLS presence. • surface characteristics have little influence on FLS presence The formation and development of fog and low stratus clouds (FLS) depend on meteorological and land surface conditions and their interactions with each other. While analyses of temporal and spatial patterns of FLS in Europe exist, the interactions between FLS determinants underlying them have not been studied explicitly and quantitatively at a continental scale yet. In this study, a state-of-the-art machine learning technique is applied to model FLS occurrence over continental Europe, using meteorological and land surface parameters from geostationary satellite and reanalysis data. Spatially explicit model units are created to test for spatial and seasonal differences in model performance and FLS sensitivities to changes in predictors, and effects of different data preprocessing procedures are evaluated. The statistical models show good performance in predicting FLS occurrence during validation, with R2 > 0.9 especially in winter high pressure situations.The predictive skill of the models seems to be dependent on data availability, data preprocessing, time period, and geographic characteristics. It is shown that atmospheric proxies are more important determinants of FLS presence than surface characteristics, in particular mean sea level pressure, near-surface wind speed and evapotranspiration are crucial, together with FLS occurrence on the previous day. Higher wind speeds, higher land surface temperatures and higher evapotranspiration tend to be negatively related to FLS. Spatial patterns of feature importance show the dominant influence of mean sea level pressure on FLS occurrence throughout the central European domain. When only high pressure situations are considered, wind speed (in the western study region) and evapotranspiration (in the eastern study region) gain importance, highlighting the influence of moisture advection on FLS occurrence in the western parts of the central European domain. This study shows that FLS occurrence can be accurately modeled using machine learning techniques in large spatial domains based on meteorological and land surface predictors. The statistical models used in this study provide a novel analysis tool for investigating empirical relationships in the FLS – land surface system and possibly infer processes. [ABSTRACT FROM AUTHOR]

Climate change and human land use are thought to play a dominant role in the dynamics of European central-latitude forests in the Holocene. A wide range of mathematical and statistical models have been used to study the effects of these variables on forest dynamics, including physiologically-based simulations and phenomenological community models. However, for statistical analysis of pollen count data, compositional data analysis is particularly well suited, because pollen counts give only relative information. We studied the effects of changes in human land use and temperature on European central-latitude forest dynamics at 7 sites over most of the last 10 ka , using a stochastic model for compositional dynamics of pollen count data. Our approach has a natural ecological interpretation in terms of relative proportional population growth rates, and does not require information on pollen production, dispersal, or deposition. We showed that the relative proportional population growth rates of Fagus and Picea were positively affected by intensified human land use, and that those of Tilia and Ulmus were negatively affected. Also, the relative proportional population growth rate of Fagus was negatively affected by increases in temperature above about 18 ∘ C. Overall, the effects of temperature on the rate of change of forest composition were more important than those of human land use. Although there were aspects of dynamics, such as short-term oscillations, that our model did not capture, our approach is broadly applicable and founded on ecological principles, and gave results consistent with current thinking. • Climate change and human land use are drivers of Holocene forest dynamics. • We applied compositional stochastic differential equations to pollen counts. • Human land use favoured Fagus and Picea over Tilia and Ulmus. • Temperature had larger effects than human land use on rate of change. [ABSTRACT FROM AUTHOR]

There is considerable demand for satellite observations that can support spatiotemporally continuous mapping of land surface temperature (LST) because of its strong relationships with many surface processes. However, the frequent occurrence of cloud cover induces a large blank area in current thermal infrared-based LST products. To effectively fill this blank area, a new method for reconstructing the cloud-covered LSTs of Terra Moderate Resolution Imaging Spectroradiometer (MODIS) daytime observations is described using random forest (RF) regression approach. The high temporal resolution of the Meteosat Second Generation (MSG) LST product assisted in identifying the temporal variations in cloud cover. The cumulative downward shortwave radiation flux (DSSF) was estimated as the solar radiation factor for each MODIS pixel based on the MSG DSSF product to represent the impact from cloud cover on incident solar radiation. The RF approach was used to fit an LST linking model based on the datasets collected from clear-sky pixels that depicted the complicated relationship between LST and the predictor variables, including the surface vegetation index (the normalized difference vegetation index and the enhanced vegetation index), normalized difference water index, solar radiation factor, surface albedo, surface elevation, surface slope, and latitude. The fitted model was then used to reconstruct the LSTs of cloud-covered pixels. The proposed method was applied to the Terra/MODIS daytime LST product for four days in 2015, spanning different seasons in southwestern Europe. A visual inspection indicated that the reconstructed LSTs thoroughly captured the distribution of surface temperature associated with surface vegetation cover, solar radiation, and topography. The reconstructed LSTs showed similar spatial pattern according to the comparison with clear-sky LSTs from temporally adjacent days. In addition, evaluations against Global Land Data Assimilation System (GLDAS) NOAH 0.25° 3-h LST data and reference LST data derived based on in-situ air temperature measurements showed that the reconstructed LSTs presented a stable and reliable performance. The coefficients of determination derived with the GLDAS LST data were all above 0.59 on the four examined days. These results indicate that the proposed method has a strong potential for reconstructing LSTs under cloud-covered conditions and can also accurately depict the spatial patterns of LST. • A new LST reconstruction method for cloud-covered pixels is proposed. • The reconstructed LSTs well captured the distribution of surface temperature. • The evaluations with GLDAS and air temperature data suggest its good performance. • The proposed method shows good potential for reconstructing cloud-covered LSTs. [ABSTRACT FROM AUTHOR]

Background: In the Netherlands, Aedes albopictus has been found each year since 2010 during routine exotic mosquito species surveillance at companies that import used tires. We developed habitat suitability models to investigate the potential risk of establishment and spread of this invasive species at these locations., Methods: We used two methodologies: first, a species distribution model based on the maximum entropy modelling approach (MaxEnt) taking into consideration updated occurrence data of the species in Europe, and secondly, a spatial logic conditional model based on the temperature requirements of the species and using land surface temperature data (LST model)., Results: Suitability assessment obtained with the MaxEnt model at European level accurately reflect the current distribution of the species and these results also depict moderately low values in parts of the Netherlands, Belgium, Denmark, the British islands and southern parts of Scandinavia. Winter temperature was the variable that contributed most to the performance of the model (47.3%). The results of the LST model showed that: (i) coastal areas are suitable for overwintering of eggs; (ii) large areas in the northern part of the country have a low suitability for adult survival; and (iii) the entire country is suitable for successful completion of the life-cycle if the species is introduced after the winter months. Results of the LST model revealed that temperatures in 2012 and 2014 did not limit the overwintering of eggs or survival of adults at the locations where the species was found. By contrast, for the years 2010, 2011 and 2013, overwintering of eggs at these locations is considered unlikely., Conclusions: Results using two modelling methodologies show differences in predicted habitat suitability values. Based on the results of both models, the climatic conditions could hamper the successful overwintering of eggs of Ae. albopictus and their survival as adults in many areas of the country. However, during warm years with mild winters, many areas of the Netherlands offer climatic conditions suitable for developing populations. Regular updates of the models, using updated occurrence and climatic data, are recommended to study the areas at risk.

Water and energy are recognized as the most influential climatic vegetation growth-limiting factors. These factors are usually measured from ground meteorological stations. However, since both vary in space, time, and scale, they can be assessed by satellite-derived biophysical indicators. Energy, represented by land surface temperature (LST), is assumed to resemble air temperature; and water availability, related to precipitation, is represented by the normalized difference vegetation index (NDVI). It is hypothesized that positive correlations between LST and NDVI indicate energy-limited conditions, while negative correlations indicate water-limited conditions. The current project aimed to quantify the spatial and seasonal (spring and summer) distributions of LST–NDVI relations over Europe, using long-term (2000–2017) MODIS images. Overlaying the LST–NDVI relations on the European biome map revealed that relations between LST and NDVI were highly diverse among the various biomes and throughout the entire study period (March–August). During the spring season (March–May), 80% of the European domain, across all biomes, showed the dominance of significant positive relations. However, during the summer season (June–August), most of the biomes—except the northern ones—turned to negative correlation. This study demonstrates that the drought/vegetation/stress spectral indices, based on the prevalent hypothesis of an inverse LST–NDVI correlation, are spatially and temporally dependent. These negative correlations are not valid in regions where energy is the limiting factor (e.g., in the drier regions in the southern and eastern extents of the domain) or during specific periods of the year (e.g., the spring season). Consequently, it is essential to re-examine this assumption and restrict applications of such an approach only to areas and periods in which negative correlations are observed. Predicted climate change will lead to an increase in temperature in the coming decades (i.e., increased LST), as well as a complex pattern of precipitation changes (i.e., changes of NDVI). Thus shifts in plant species locations are expected to cause a redistribution of biomes. [ABSTRACT FROM AUTHOR]

Monitoring soil moisture at the Earth'surface is of great importance for drought early warnings. Spaceborne remote sensing is a keystone in monitoring at continental scale, as satellites can make observations of locations which are scarcely monitored by ground-based techniques. In recent years, several soil moisture products for continental scale monitoring became available from the main space agencies around the world. Making use of sensors aboard polar satellites sampling in the microwave spectrum, soil moisture can be measured and mapped globally every few days at a spatial resolution as fine as 25 km. However, complementarity of satellite observations is a crucial issue to improve the quality of the estimations provided. In this context, measurements within the visible and infrared from geostationary satellites provide information on the surface from a totally different perspective. In this study, we design a new retrieval algorithm for daily soil moisture monitoring based only on the land surface temperature observations derived from the METEOSAT second generation geostationary satellites. Soil moisture has been retrieved from the retrieval algorithm for an eight years period over Europe and Africa at the SEVIRI sensor spatial resolution (3 km at the sub-satellite point). The results, only available for clear sky and partly cloudy conditions, are for the first time extensively evaluated against in-situ observations provided by the International Soil Moisture Network and FLUXNET at sites across Europe and Africa. The soil moisture retrievals have approximately the same accuracy as the soil moisture products derived from microwave sensors, with the most accurate estimations for semi-arid regions of Europe and Africa, and a progressive degradation of the accuracy towards northern latitudes of Europe. Although some possible improvements can be expected by a better use of other products derived from SEVIRI, the new approach developped and assessed here is a valuable alternative to microwave sensors to monitor daily soil moisture at the resolution of few kilometers over entire continents and could reveal a good complementarity to an improved monitoring system, as the algorithm can produce surface soil moisture with less than 1 day delay over clear sky and non-steady cloudy conditions (over 10% of the time). [ABSTRACT FROM AUTHOR]

Forest management (FM) can help to mitigate climate change by altering biogeochemical and biophysical processes at the earth surface. However, the biophysical climate effects of FM are not yet sufficiently understood and decision-makers lack scientific evidence when designing sustainable FM strategies. To enhance our understanding of the biophysical climate effect of FM, we used two LST datasets over Europe (MODIS, LSA SAF) derived from satellite remote sensing observations. We assessed the link between spatial patterns of LST and spatial patterns of forest structure and forest type, which can be related to different FM strategies. Our results show that the two management strategies of increasing tree cover density and broadleaf tree fraction provide local cooling during the day in summer. During nights and in winter both FM strategies mainly cause warming. During the day an increased broadleaf tree fraction seems to provide cooling in the afternoon, while an increased tree cover density provides largest cooling at around noon. In mediterranean forests, the summer cooling caused by an increased tree cover density is more than two times higher than the one caused by an increased broadleaf tree fraction. In the boreal forest both strategies provide a cooling of similar magnitude. In boreal and alpine forests an increase in tree cover density and broadleaved tree fraction leads to an increase in yearly mean temperatures. However, for all regions we observe a reduction in maximum temperatures in summer and potentially during heatwaves. [ABSTRACT FROM AUTHOR]

Extreme hot events are increasing in frequency and intensity worldwide, whit special emphasis on the high number of events which occurred during the last decade. Europe was particularly affected by unprecedented mega heat waves during the last decades, namely the events that have struck Western Europe in 2003 and Eastern Europe in 2010. Despite their intense impacts, heatwaves are usually of short duration, lasting in the order of a few days to a few weeks. Therefore, monitoring and assessment strategies of extreme hot events is imperative to further investigate their link with atmospheric conditions, especially within the framework of future warming scenarios and risk assessment.The year of 2018 was reported as an unusually hot year, with record breaking temperatures in many parts of Europe during spring and summer, which were associated to severe and unusual wildfires and significant crop losses in central and northern Europe.In this study we analyse the temperature extremes over Europe and their vegetation impacts during 2018 using Land Surface Temperature (LST) and Fraction Vegetation Cover (FVC) retrieved from SEVIRI/Meteosat available through LSA-SAF (http://lsa-saf.eumetsat.int). LST monthly means and anomalies were computed, considering the diurnal cycle of reprocessed Land Surface Temperature (LST), for the period between 2004-2015 with the LST CDR and for 2016 to present with the operational LST dataset. Monthly means and anomalies were also computed using FVC. ECMWF ERA-Interim skin temperature was also used for the period from 1980 to 2018 and monthly means and anomalies were computed for the extended period and for a sub-period simultaneous with LST dataset.The results exhibit strong spatial patterns of monthly LST anomalies during spring and summer of 2018 over central and north Europe. Over a particularly large region, breaking LST records with respect to last 15 years, persist for more than 4 months. ECMWF ERA-Interim skin temperatures corroborate the exceptional heat extremes observed over central and northern Europe during 2018, highlighting the ability and usefulness of the LST-CDR for climate variability and extremes assessment.Acknowledgements: This study was performed within the framework of the LSA-SAF, co-funded by EUMETSAT. This work was partially supported by national funds through FCT (Fundação para a Ciência e a Tecnologia, Portugal) under project IMPECAF (PTDC/CTA-CLI/28902/2017). Ana Russo thanks FCT for grants SFRH/BPD/99757/2014. [ABSTRACT FROM AUTHOR]