Land Surface Temperature Anomalies Research Articles

A satellite view of the exceptionally warm summer of 2022 over Europe

Abstract. 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.

Association of land surface temperature anomalies from GOES/ABI, MSG/SEVIRI, and Himawari-8/AHI with land earthquakes between 2010 and 2021

Every year, earthquakes cause numerous casualties and billions of dollars in economic damages, and so far, it is impossible to predict them. Many researchers have studied the land surface temperature (LST) anomalies as promising potential earthquake precursors. In this work, more than 30,000 land earthquakes with a Magnitude (Mw) larger than 4 have been studied from 2010 to 2021. Global LST nighttime (0–6 am) data from Meteosat Second Generation (MSG), Himawari-8, and the Geostationary Operational Environmental Satellite (GOES) satellites have been used to evaluate the LST anomalies in the zones affected by earthquakes with spatial resolutions of 1, 2, and 4 km, respectively. The time series moving standard deviation (STD) and Interquartile (IQT) have been used to estimate the LST anomalies. The Confusion Matrix (CM), the receiver operating curve (ROC), and some other figures of merit are computed to assess the goodness of positive LST anomalies and detection thresholds as potential proxies of earthquake occurrence. A positive LST anomaly is typically observed a few days [1–7 d] before the earthquake’s occurrence, followed by a negative LST anomaly afterwards [1–3 d].

Study on the prediction method of grasshopper occurrence risk in Inner Mongolia based on the maximum entropy model during the growing period.

Grasshoppers represent a significant biological challenge in Inner Mongolia's grasslands, severely affecting the region's animal husbandry. Thus, dynamic monitoring of grasshopper infestation risk is crucial for sustainable livestock farming. This study employed the Maxent model,along with remote sensing data, to forecast Oedaleus decorus asiaticus occurrence during the growing season, using grasshopper suitability habitats as a base. The Maxent model's predictive accuracy was high, with an AUC of 0.966. The most influential environmental variables for grasshopper distribution were suitable habitat data (34.27%), the temperature-vegetation dryness index during the spawning period (18.81%), and various other meteorological and vegetation factors. The risk index model was applied to calculate the grasshopper distribution across different risk levels for the years 2019-2022. The data indicated that the level 1 risk area primarily spans central, eastern, and southwestern Inner Mongolia. By examining the variable weights, the primary drivers of risk level fluctuation from 2019 to 2022 were identified as accumulated precipitation and land surface temperature anomalies during the overwintering period. This study offers valuable insights for future O. decorus asiaticus monitoring in Inner Mongolia.

Effects of Climate Change and Urbanization on Vegetation Phenology in the Bucharest Metropolitan Area

Being an essential issue in global warming, the response of urban vegetation to climate change and urbanization has become an increasing concern at both the local and global levels. This study aims to investigate the effect of the urban environment on vegetation phenology for the Bucharest metropolitan area in Romania and to identify the potential climate drivers that influence key phenology in the urban environment. In this study, we comprehensively analyzed the response of urban vegetation phenology shifts due to climate variability and urbanization in the Bucharest metropolitan area from a spatiotemporal perspective during the 2002- 2022 period. Through synergy use of time series of the main climate variables, Air temperature -AT, land surface temperature (LST), and biophysical variables derived from MODIS Terra/Aqua satellite and in-situ data, this study developed a complex statistical and spatial regression analysis. Green space was measured with satellite-derived vegetation indicators Normalized Vegetation Index (NDVI), and Enhanced Vegetation Index (EVI), Net Primary Production (NPP) data, which captures the combined availability of urban parks, street trees, forest, and periurban agricultural areas. Leaf Area Index (LAI) and Photosynthetically active radiation (FPAR) indicators have been used to characterize the effects of meteorological parameters and urbanization impacts on vegetation phenology and their changes. The results show that the response of vegetation phenology to urbanization level and climate parameters variability has a distinct spatiotemporal difference across the urban/periurban gradient. The findings of this study show that the land surface temperature anomalies associated with urbanization-induced climate warming, especially during strong summer heat waves and under urban heat islands alter urban vegetation biophysical properties, directly impacting its phenology shifts. At the metropolitan scale, the urban thermal environment directly impacts vegetation phenology patterns. The quantitative findings of this study are of great importance for understanding the complex impacts of urbanization and climate changes on vegetation phenology and for developing models to predict vegetation phenological changes under future urbanization.

Detecting Geothermal Anomalies Using Multi-Temporal Thermal Infrared Remote Sensing Data in the Damxung–Yangbajain Basin, Qinghai–Tibet Plateau

Geothermal energy is an eco-friendly, renewable source of underground thermal energy that exists in the interior of the earth. By tapping into these formations, fluids can be channeled to heat the rock formations above, resulting in a significantly higher land surface temperature (LST). However, LST readings are influenced by various factors such as sun radiation, cyclical variations, and precipitation, which can mask the temperature anomalies caused by geothermal heat. To address these issues and highlight the LST anomalies caused by geothermal heat, this paper proposes a methodology to efficiently and quickly calculate the multi-temporal LST leveraging of the Google Earth Engine (GEE) in the Damxung–Yangbajain basin, Qinghai–Tibet Plateau. This method incorporates terrain correction, altitude correction, and multi-temporal series comparison to extract thermal anomaly signals. The existing geothermal manifestations are used as a benchmark to further refine the methodology. The results indicate that the annual mean winter LST is a sensitive indicator of geothermal anomaly signals. The annual mean winter LST between 2015 and 2020 varied from −14.7 °C to 26.7 °C, with an average of 8.6 °C in the study area. After altitude correction and water body removal, the annual mean winter LST varied from −22.1 °C to 23.3 °C, with an average of 6.2 °C. When combining the distribution of faults with the results of the annual mean winter LST, this study delineated the geothermal potential areas that are located predominantly around the fault zone at the southern foot of the Nyainqentanglha Mountains. Geothermal potential areas exhibited a higher LST, ranging from 12.6 °C to 23.3 °C. These potential areas extend to the northeast, and the thermal anomaly range reaches as high as 19.6%. The geothermal potential area makes up 8.2% of the entire study area. The results demonstrate that the approach successfully identified parts of known geothermal fields and indicates sweet spots for future research. This study highlights that utilizing the multi-temporal winter LST is an efficient and cost-effective method for prospecting geothermal resources in plateau environments.

Dry-to-Wet Soil Gradients Enhance Convection and Rainfall over Subtropical South America

Abstract Soil moisture–precipitation (SM–PPT) feedbacks at the mesoscale represent a major challenge for numerical weather prediction, especially for subtropical regions that exhibit large variability in surface SM. How does surface heterogeneity, specifically mesoscale gradients in SM and land surface temperature (LST), affect convective initiation (CI) over South America? Using satellite data, we track nascent, daytime convective clouds and quantify the underlying antecedent (morning) surface heterogeneity. We find that convection initiates preferentially on the dry side of strong SM/LST boundaries with spatial scales of tens of kilometers. The strongest alongwind gradients in LST anomalies at 30-km length scale underlying the CI location occur during weak background low-level wind (<2.5 m s−1), high convective available potential energy (>1500 J kg−1), and low convective inhibition (<250 J kg−1) over sparse vegetation. At 100-km scale, strong gradients occur at the CI location during convectively unfavorable conditions and strong background flow. The location of PPT is strongly sensitive to the strength of the background flow. The wind profile during weak background flow inhibits propagation of convection away from the dry regions leading to negative SM–PPT feedback whereas strong background flow is related to longer life cycle and rainfall hundreds of kilometers away from the CI location. Thus, the sign of the SM–PPT feedback is dependent on the background flow. This work presents the first observational evidence that CI over subtropical South America is associated with dry soil patches on the order of tens of kilometers. Convection-permitting numerical weather prediction models need to be examined for accurately capturing the effect of SM heterogeneity in initiating convection over such semiarid regions.

Delineating reservoir area using geologic remote sensing analysis, Jizan (Saudi Arabia): Implications for geothermal exploration

Jizan Province is considered one of the potential geothermal areas in Southwest Saudi Arabia. However, the prospective geothermal reservoir is still not delineated and the geothermal energy reserve is not estimated properly. Therefore, this study aims to delineate the geothermal prospect area using remote sensing-based analysis. Permeable areas, the high anomaly of Land Surface Temperature (LST), and the existence of granitic rock were the parameters used in the remote sensing analysis. Digital Elevation Models (DEM) SRTM and Landsat 8 images were used as the data source. Automatically- and manually-extracted structural lineaments supported by fractures/faults interpretation were integrated to generate a final map of the permeable areas defined in the study area (with three main permeable zones; Zones 1, 2 and 3). The average value of five LST maps and the Natural Different Building Index (NDBI) were used to investigate the high anomaly of LST. Final fuzzy logic integration of the defined permeable area, high LST anomaly, and the existence of granitic rock allowed a unique opportunity for the delineation of the prospective geothermal reservoir area. Remote sensing results were validated using the field-based studies performed in the same area. Three zones are suggested as prospective geothermal potential areas (Zones A, B, and C), where Zone A is the most favourable due to its estimated largest area and is interpreted to belong to the same geothermal system as Zone C. This study improves the understanding of the prospective geothermal reservoir area in Jizan based on the delineation of high permeable zones, and high anomaly LST. The spatial distribution of radiogenic elements in the rock units is also considered an indicator of the heat source of the geothermal system.

Changes in air pollution, land surface temperature, and urban heat islands during the COVID-19 lockdown in three Chinese urban agglomerations

COVID-19 has notably impacted the world economy and human activities. However, the strict urban lockdown policies implemented in various countries appear to have positively affected pollution and the thermal environment. In this study, Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) and aerosol optical depth (AOD) data were selected, combined with Sentinel-5P images and meteorological elements, to analyze the changes and associations among air pollution, LST, and urban heat islands (UHIs) in three urban agglomerations in mainland China during the COVID-19 lockdown. The results showed that during the COVID-19 lockdown period (February 2020), the levels of the AOD and atmospheric pollutants (fine particles (PM2.5), NO2, and CO) significantly decreased. Among them, PM2.5 and NO2 decreased the most in all urban agglomerations, by >14 %. Notably, the continued improvement in air pollution attributed to China's strict control policies could lead to overestimation of the enhanced air quality during the lockdown. The surface temperature in all three urban agglomerations increased by >1 °C during the lockdown, which was mainly due to climate factors, but we also showed that the lockdown constrained positive LST anomalies. The decrease in the nighttime urban heat island intensity (UHIInight) in the three urban agglomerations was greater than that in the daytime quantity by >25 %. The reduction in surface UHIs at night was mainly due to the reduced human activities and air pollutant emissions. Although strict restrictions on human activities positively affected air pollution and UHIs, these changes were quickly reverted when lockdown policies were relaxed. Moreover, small-scale lockdowns contributed little to environmental improvement. Our results have implications for assessing the environmental benefits of city-scale lockdowns.

First Results on the Systematic Search of Land Surface Temperature Anomalies as Earthquakes Precursors

Every year, earthquakes cause thousands of casualties and high economic losses. For example, in the time frame from 1998 to 2018, the total number of casualties due to earthquakes was larger than 846 thousand people, and the recorded economic losses were about USD 661 billion. At present, there are no earthquake precursors that can be used to trigger a warning. However, some studies have analyzed land surface temperature (LST) anomalies as a potential earthquake precursor. In this study, a large database of global LST data from the Geostationary Operational Environmental Satellite (GOES) and AQUA satellites during the whole year 2020 has been used to study the LST anomalies in the areas affected by earthquakes. A total of 1350 earthquakes with a magnitude larger than M4 were analyzed. Two methods widely used in the literature have been used to detect LST anomalies in the detrended LST time series: the interquartile (IQT) method and the standard deviation (STD). To the authors’ knowledge, it is the first time that the confusion matrix (CM), the receiver operating characteristic curve (ROC), and some other figures of merit (FoM) are used to assess and optimize the performance of the methods, and to select the optimum combination that could be used as a proxy for their occurrence. A positive anomaly was found a few days before the studied earthquakes, followed by the LST decrease after the event. Further studies over larger regions and more extended periods will be needed to consolidate these encouraging results.

The urban climate of the largest cities of the European Arctic

Recent studies have found intense and persistent positive temperature anomalies associated with urban areas in high northern latitudes. However, understanding the intensity and variability of urban heat islands (UHI) in these regions remains limited. Here we study the urban climate of the seven largest cities in the European Arctic. These cities with populations ranging from 50,000 to nearly 300,000 span four countries and three bioclimatic zones in northern Fennoscandia. They include coastal and inland cities—and have complex local geography associated with topography and water bodies. We analyze land surface temperature (LST) satellite remote-sensing data with the Moderate Resolution Imaging Spectroradiometer (MODIS) from 2000 to 2020. The aim is to assess the intensity and temporal (seasonal and interannual) variability and trends of LST anomalies to learn about surface UHI development over different time frames in the urban Arctic. We observed persistent urban temperature anomalies in every season in the range of 1–5 °C, with the highest values for the largest city Murmansk (Russia). There is a strong inverse relationship between surface UHI intensity and its temporal variability: the more substantial the surface UHI, the more stable it is and the lower its temporal variability. Murmansk and Tromsø (Norway) have the highest thermal anomalies showing relatively low temporal variability, whereas the surface UHI in Bodø (Norway) is low and very volatile. We found no general direction of surface UHI change in the long term, as statistically significant changes were identified for only 10 of 56 cases. The study also suggests that in addition to the size (population) of the city, the presence of compact and dense urban infrastructure causes strong and stable urban temperature anomalies and has a more significant impact than the geographical setting of the city.

Detecting geothermal anomalies using Landsat 8 thermal infrared remote sensing data in the Ruili Basin, Southwest China.

With the recent increase in global focus on green energy, the application of thermal infrared remote sensing data for the detection of geothermal anomalies has attracted wide attention as it can overcome the difficulty of using only ground surveying. This study aimed to highlight areas of geothermal anomalies with land surface temperature (LST) time series data in winter derived from thermal infrared remote sensing. To extract LST anomaly areas in the Ruili Basin for geothermal prospecting, nine types of data on the study area in winter during 2014 ~ 2021 from Landsat 8 were analyzed. Landsat 8 LST inversion data based on the mono-window algorithm (MWA) can be used to identify hot springs, volcanoes, and other heat-related phenomena. Superimposing LST anomalies for each cycle through drilling data, excluding the heat island effect, geothermal anomaly regions could be plotted. The results show that the accuracy of MWA LST varied within 2K, which is acceptable for geothermal energy and higher than those of the radiative transfer equation (RTE) algorithm and MODIS LST products. Three high-LST regions in the southeast of the study area were identified as geothermal anomaly areas (A, B, and C), and region B was further verified through a comprehensive field investigation of geothermal wells, supplemented by the temperature gradient (TG) method. The findings reveal that the distribution of geothermal anomaly areas and high-LST areas are highly consistent with the northeast trending fault structure; faults act as thermal channels and help in accurately detecting local LST anomalies. Overall, the infrared remote sensing method proved to be a valid technique for detecting LST anomalies. Considering the synergy between thermal infrared surface detection and subsurface exploration methods, the identification of known geothermal fields (B) and other possible areas (A and C) has significance in the upscaling of local geologic information to regional prospecting, thus providing a direction for future geothermal research.

An Approach to Characterizing the Spatial Pattern and Scale of Regional Heat Islands Over Urban Agglomerations

AbstractGreat difficulty in characterizing the spatial pattern and scale of regional surface urban heat islands (RSUHIs) over urban agglomerations is mainly attributed to the RSUHI extent exceeding urban boundaries and strong variations in the background climate state and land cover. A new method that successfully addresses these problems by applying the spatial similarity of the land surface temperature (LST) annual cycle in urban agglomerations is proposed to characterize the RSUHI spatial pattern and scale in the Yangtze River Delta of China. This method has good adaptability to complex terrain and climate variations and simultaneously identifies the RSUHI spatial pattern and scale. The RSUHI spatial extent averaged 3.7 × 104 km2 in 2003–2020, which is 1.5–2.0 times the urban extent, and had a fluctuating but increasing tendency. Rapid urbanization and the spatial pattern‐shifting LST anomalies were mainly responsible for the fluctuating RSUHI extent during 2003–2020.

Integration of multiple earthquakes precursors before large earthquakes: A case study of 25 April 2015 in Nepal

In this work, we analyze magnetic data, Land Surface Temperature (LST), Outgoing Longwave Radiation (OLR), Air Temperature (AT) and Relative Humidity (RH) around the epicenter before and after the large EQs occurred on April 25, 2015 in Nepal. Daytime and nighttime LST values were retrieved from Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Terra satellite. We used the magnetic data acquired by Swarm satellites in the top side ionosphere, and daily OLR data was obtained from the United States National Oceanic and Atmospheric Administration (NOAA) polar-orbiting satellites. Moreover, AT and RH values were provided from the nearest meteorological ground station to the epicenter located at Katmandu airport. As data analysis, short-term (15 days before and 5 days after the earthquakes) and medium-term (2 months before and 1 month after the earthquakes) evaluations were carried out. For short-term analysis, the results of the magnetic field in the topside ionosphere detected an interesting anomaly on April 12, 2015. Besides, nighttime LST anomaly was observed on April 13, 2015, 14 days before the earthquakes, which was also an anomalous day for AT and RH analysis. Moreover, in the OLR analysis, April 12, 2015 was anomalous day based on the time series of the epicentral pixel that coincides with the magnetic field anomaly. Concerning the medium-term nighttime LST analysis, 13th April was the day, which may be associated with the earthquakes. For the medium-term OLR analysis, April 12, 2015 was found to be related to the seismic activity based on the epicentral pixel. Besides, the RH anomaly on 13 April was the only mutual day with the anomalies of LST and OLR for the medium-term analysis. Based on the short-term and medium-term results, these two days, 12 and April 13, 2015, may be associated with the preparation for the earthquake occurrence. Overall, it is concluded that these methods could be effective and reliable in detecting anomalies that precede upcoming EQs with further investigations.

An Analysis of the Stability and Trends in the LST_cci Land Surface Temperature Datasets Over Europe

AbstractLong‐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.

Monitoring Heat Extremes across Central Europe Using Land Surface Temperature Data Records from SEVIRI/MSG

The frequency and intensity of extreme hot events have increased worldwide, particularly over the past couple of decades. Europe has been affected by unprecedented mega heatwaves, namely the events that struck Western Europe in 2003 and Eastern Europe in 2010. The year 2018 was also reported as an unusually hot year, with record-breaking temperatures in many parts of Europe during spring and summer, associated with severe and unusual wildfires and significant crop losses in central and northern Europe. We show the ability of Land Surface Temperature (LST), retrieved from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard Meteosat Second Generation (MSG) to monitor heat extremes, using the 2018 European event as a showcase. The monitoring approach relies on monthly anomalies performed as departures from the median and the monthly number of hot days (NHD), both computed for satellite LST derived from MSG and MODIS, and for 2 m air temperature (T2m) from ERA5 reanalysis, using as threshold the 90th percentiles. Results show strong monthly LST anomalies during the spring and summer of 2018 extending over central and north Europe. Over a vast region in Central and Northern Europe, LST reached the last 15 years high record. Moreover, those outstanding warm LSTs persisted for more than four months. Results obtained using MODIS LST and ERA5 T2m show similar patterns, which, although slightly less intense, corroborate the exceptionality of the heat extremes observed over central and northern Europe during 2018. The spatial pattern of the number of monthly record high anomalies over the MSG observations period clearly depicts the regions in Northern and Central Europe affected by the complex phenomena that occurred in 2018, which resulted from the combined effect of an extreme heatwave in spring and summer with extensive dry conditions. Therefore, the results highlighted the suitability of MSG LST to evaluate and monitor heat extremes alone or combined with dry and bright conditions and prompts the potential of other climate data records from geostationary satellites to characterize these climate extremes that could become the norm in the near future over central and northern Europe.

Characterizing natural variability in complex hydrological systems using passive microwave-based climate data records: a case study for the Okavango Delta

Abstract. The Okavango River system in southern Africa is known for its strong interannual variability of hydrological conditions. Here, we present how this is exposed in surface soil moisture, land surface temperature, and vegetation optical depth as derived from the Land Parameter Retrieval Model, using an inter-calibrated, long-term, multi-sensor passive microwave satellite data record (1998–2020). We also investigate how these interannual variations relate to state-of-the-art climate reanalysis data from ERA5-Land. We analysed both the upstream river catchment and the Okavango delta, supported by independent data records of discharge measurements, precipitation, and vegetation dynamics observed by optical satellites. The seasonal vegetation optical depth anomalies have a strong correspondence with the MODIS leaf area index (correlation catchment: 0.74, delta: 0.88). Land surface temperature anomalies derived from passive microwave observations match best with those of ERA5-Land (catchment: 0.88, delta: 0.81) as compared to MODIS nighttime land surface temperature (LST) (catchment: 0.70, delta: 0.65). Although surface soil moisture anomalies from passive microwave observations and ERA5-Land correlate reasonably well (catchment: 0.72, delta: 0.69), an in-depth evaluation over the delta uncovered situations where passive microwave satellites record strong fluctuations, while ERA5-Land does not. This is further analysed using information on inundated area, river discharge, and precipitation. The passive microwave soil moisture signal demonstrates a response to both the inundated area and precipitation. ERA5-Land however, which, by default, does not account for any lateral influx from rivers, only shows a response to the precipitation information that is used as forcing. This also causes the reanalysis model to miss record low land surface temperature values as it underestimates the latent heat flux in certain years. These findings demonstrate the complexity of this hydrological system and suggest that future land surface model generations should also include lateral land surface exchange. Also, our study highlights the importance of maintaining and improving climate data records of soil moisture, vegetation, and land surface temperature from passive microwave observations and other observation systems.

Can Satellite-Based Thermal Anomalies Be Indicative of Heatwaves? An Investigation for MODIS Land Surface Temperatures in the Mediterranean Region

In recent years, an exceptional number of record-shattering temperature extremes have been observed, resulting in significant societal and environmental impacts. The Mediterranean region is particularly thermally vulnerable, frequently suffering from intense and severe heatwaves. Using daily temperature observations from 58 weather stations (NOAA Global Historical Climatology Network daily database) in the Mediterranean area, past heatwave episodes were initially detected. A daily LST time series was developed using Land Surface Temperature (LST) products from Moderate Resolution Imaging Spectroradiometer (MODIS) (Terra & Aqua satellites) for a 19-year period (2002–2020) at the station locations. LST anomalies were identified using percentile-based indices. It was found that remotely sensed-based LST presents the potential for understanding and monitoring heatwave events, as surface thermal anomalies were generally indicative of heatwaves. Approximately 42% (39%) of heatwave days during daytime (nighttime) coincided with LST anomalies; conversely, 51% of daytime LST anomalies overlapped with the exact days of a heatwave (38% at night). Importantly, the degree of association was significantly higher for extremely hot days (up to an 80% match) and long-lasting heatwaves (up to an 85% match). Rising trends in frequency and duration were observed for both heatwaves and LST anomalies. The results advance the understanding of surface-atmosphere coupling during extreme temperature days and reflect the suitability of thermal remote sensing in heatwave preparedness strategies.

Climate Anomalies and Spillover of Bat-Borne Viral Diseases in the Asia-Pacific Region and the Arabian Peninsula.

Climate variability and anomalies are known drivers of the emergence and outbreaks of infectious diseases. In this study, we investigated the potential association between climate factors and anomalies, including El Niño Southern Oscillation (ENSO) and land surface temperature anomalies, as well as the emergence and spillover events of bat-borne viral diseases in humans and livestock in the Asia–Pacific region and the Arabian Peninsula. Our findings from time series analyses, logistic regression models, and structural equation modelling revealed that the spillover patterns of the Nipah virus in Bangladesh and the Hendra virus in Australia were differently impacted by climate variability and with different time lags. We also used event coincidence analysis to show that the emergence events of most bat-borne viral diseases in the Asia–Pacific region and the Arabian Peninsula were statistically associated with ENSO climate anomalies. Spillover patterns of the Nipah virus in Bangladesh and the Hendra virus in Australia were also significantly associated with these events, although the pattern and co-influence of other climate factors differed. Our results suggest that climate factors and anomalies may create opportunities for virus spillover from bats to livestock and humans. Ongoing climate change and the future intensification of El Niño events will therefore potentially increase the emergence and spillover of bat-borne viral diseases in the Asia–Pacific region and the Arabian Peninsula.

Satellite Observations of Fire Activity in Relation to Biophysical Forcing Effect of Land Surface Temperature in Mediterranean Climate

The present work is aimed at gaining more knowledge on the nature of the relation between land surface temperature (LST) as a biophysical parameter, which is related to the coupled effect of the energy and water cycles, and fire activity over Bulgaria, in the Eastern Mediterranean. In the ecosystems of this area, prolonged droughts and heat waves create preconditions in the land surface state that increase the frequency and intensity of landscape fires. The relationships between the spatial–temporal variability of LST and fire activity modulated by land cover types and Soil Moisture Availability (SMA) are quantified. Long-term (2007–2018) datasets derived from geostationary MSG satellite observations are used: LST retrieved by the LSASAF LST product; fire activity assessed by the LSASAF FRP-Pixel product. All fires in the period of July–September occur in days associated with positive LST anomalies. Exponential regression models fit the link between LST monthly means, LST positive anomalies, LST-T2 (as a first proxy of sensible heat exchange with atmosphere), and FRP fire characteristics (number of detections; released energy FRP, MW) at high correlations. The values of biophysical drivers, at which the maximum FRP (MW) might be expected at the corresponding probability level, are identified. Results suggest that the biophysical index LST is sensitive to the changes in the dynamics of vegetation fire occurrence and severity. Dependences are found for forest, shrubs, and cultivated LCs, which indicate that satellite IR retrievals of radiative temperature is a reliable source of information for vegetation dryness and fire activity.

The Dominant Modes of Spring Land Surface Temperature Over Western Eurasia and Their Possible Linkages With Large‐Scale Atmospheric Teleconnection Patterns

AbstractPrevious studies suggest that the spring land surface temperature (LST) anomaly over Western Eurasia (WEA; 20°N–70°N, 0–70°E) is a potential driver for Asian even global climate. However, the interannual variability of LST over WEA together with its causes has not received enough attention. Based on multi‐source observations and reanalysis data, three dominant modes of spring LST over WEA were identified, and the roles of atmospheric teleconnection patterns in LST anomalies via altering cloud and water vapor were investigated from the perspective of surface energy budget. The first empirical orthogonal function (EOF1) mode exhibits a homogeneous warming pattern that is closely related to a negatively in‐phase combination of the Scandinavian and East Atlantic/Western Russia (EA/WR) patterns. Under the control of a predominant anomalous anticyclone, the warming is the result of enhanced downward longwave radiation due to more water vapor in a warmer atmosphere associated with anomalous southerly and more incident solar radiation due to reduced cloud cover. The EOF2 mode, which displays a meridional dipole structure, is dominated by the North Atlantic Oscillation. Coupled with an anomalous cyclone and anticyclone over western Europe and around the Caspian Sea, respectively, the former LST anomalies are primarily caused by cloud‐shortwave radiation processes, whereas the latter are dominated by downward longwave radiation due to water vapor. The EOF3 mode, characterized by a zonal dipole pattern, is dominated by the water vapor greenhouse effect related to the Eurasian pattern. The results of the study may provide a valuable signal for climate prediction.