Surface Temperature In Regions Research Articles

Heat waves in Poland: The relations to atmospheric circulation and Arctic warming

AbstractFrom the beginning of the 21st century, the heat waves in Europe have become more frequent, severe and longer lasting, especially in the southern region. The longer duration of heat waves may be partly attributed to wave‐amplification, high‐amplitude waves in the mid‐latitudes, favouring the longer‐lasting blocking systems. The primary objective of the paper was to examine the long‐term changes of hot days and heat wave events and to identify the atmospheric circulation condition that prevails its occurrence. The other object was to investigate the correlations between hot days' occurrence in Poland and sea‐ice cover and sea surface temperature changes in the Arctic and its surroundings. Over the past few decades, Central and Southern Poland have experienced a considerable rise in maximum temperatures, which has led to an increase in the number of hot days. This increase is particularly prominent during the summer months (June, July and August) but also in May and September, which extends the hot season. Although heat waves have become more frequent and persistent since 2006, their mean intensity has remained unchanged. During all heat waves in Poland, the high‐pressure centre was located over the country or its close neighbourhood, mainly in the eastern sector but also in the south, southwest or over Scandinavia. The blocking systems were mainly related to the extended ridge of the Azores High, but also to strong blocking over the North Pole and Greenland reaching Scandinavia or anomalous high pressure in the East Arctic. The longest‐lasting heat waves were usually preceded by air masses advection from the Atlantic, but the highest potential temperature along the trajectories was observed for the southern direction of inflow. The growing number of hot days is statistically significantly related to the decreasing sea‐ice cover and increasing sea surface temperature in Arctic and Subarctic regions.

Impact of atmospheric forcing on SST biases in the LETKF-based ocean research analysis (LORA)

In the previous study, the authors have produced an eddy-resolving ocean ensemble analysis product called the local ensemble transform Kalman filter (LETKF)-based ocean research analysis (LORA) over the western North Pacific and Maritime Continent regions using an ocean data assimilation system driven by the Japanese operational atmospheric reanalysis dataset known as the JRA-55. However, the LORA includes warm biases in sea surface temperatures (SSTs) in coastal regions during the boreal winter. In this study, we perform sensitivity experiments with atmospheric forcing using an ocean forcing dataset known as the JRA55-do, which adjusts the JRA-55 to high-quality reference datasets to reduce biases and uncertainties. The results show that the nearshore warm SST biases are significantly improved by the JRA55-do. During the boreal autumn, the improvement comes from mainly two factors: (i) enhancement of surface cooling by latent heat releases caused by removing contamination of weak winds at the land grid cells, and (ii) weakening surface heating by downward shortwave radiation through the adjustment in the JRA55-do.During the boreal winter, enhanced cooling by the analysis increments suppresses the growth of the warm SST biases when the JRA55-do is used. However, if the JRA-55 dataset is used, the adaptive observation error inflation (AOEI) scheme acts negatively to keep the nearshore SST biases in winter. Based on the innovation statistics, the AOEI inflates the observation errors when the differences between the squared observation-minus-forecast innovations and the squared forecast ensemble spreads are larger than the prescribed observation error variance, and improves the accuracy in the open ocean, especially around the frontal regions. However, when substantial warm SST biases are formed in the previous season, AOEI's observation error inflation makes the analysis increments smaller and cannot suppress the warm biases.We also validate the analysis accuracy using various data such as sea surface height and horizontal velocities and find that the JRA55-do has significant advantages. Therefore, continuous maintenance and development of ocean forcing datasets are essential for ocean modeling and data assimilation.

The cooling effect of different scales of urban parks on land surface temperatures in cold regions

Green spaces are one of the elements that positively improve the urban microclimate conditions in the cities. This study aims to evaluate the cooling effect of urban parks of varying sizes in cold regions. Three urban parks with different land uses, small scale (size: 0.58 ha), medium scale (size: 1.50 ha), and large scale (size: 17.0 ha) were selected in the Elazığ province of Turkey. The impact of their land use on the Land Surface Temperature (LST) was investigated and the cooling effect of the parks was observed on internal and external buffer zones. The study was conducted during the sweltering hot summer period of July 2021, utilizing remote sensing and satellite imagery techniques. The results indicated that the mean LST difference of small, medium, and large scales were 2.4 °C, 4.3 °C and 5.7 °C lower than that found downtown 43.5 °C. The result indicated that the cooling effect of urban parks on their surroundings is directly proportional to their size. As the size of the park increased, the LST decreased and the LST difference between the parks and the city center increased. It was observed that LST values were found to decrease towards the park’s center from the park boundary and increased toward away from the park boundary. In small scale park, the average LST from the park border to the park center was 41.8 °C, 41.5 °C, 40.9 °C and 40.8 °C, respectively, with a total difference of 1.0 °C. In medium scale park, the LST was 39.7 °C, 39.2 °C, 38.9 °C and 38.4 °C, respectively, with a total difference of 1.3 °C. While, in large-scale parks, the average LST was defined as 38.8 °C, 37.6 °C, 36.0 °C and 35.0 °C, respectively, with a total difference of 3.8 °C. As the size of the park increased, this difference increased simultantly. The external buffer analysis shows that, from the park boundary to the outside of the park in all parks, the LST has a similar effect by a maximum of 1.0 °C. In cold regions, the green park has a greater cooling effect than in warm and dry regions. Urban parks, with their varying sizes and land use patterns, exert a significant influence on the microclimate of their surroundings.

Taking Titan’s Boreal Pole Temperature: Evidence for Evaporative Cooling in Ligeia Mare

From 2004 to 2017, the Cassini RADAR recorded the 2.2 cm thermal emission from Titan’s surface in its passive (radiometry) mode of operation. We use this data set to investigate the seasonal evolution of the effective temperature sensed by the microwave radiometer in two regions in the northern pole of the satellite: the sea Ligeia Mare, and its nearby solid terrains. We find that despite the arrival of summer at the end of the mission, the effective temperature of Ligeia Mare decreased by almost 1 K, while that of the solid region slowly increased until 2017 by 1.4 ± 0.3 K. These observations, as well as the lag in summer warming observed by Cassini’s Composite Infrared Spectrometer, can be explained by evaporative cooling in both the solid and liquid surfaces after the vernal equinox. It therefore supports the idea that the northern polar terrains are wet. Using an ocean circulation model, we show that the cooling of the sea surface should initiate convection in the sea’s interior, ultimately cooling the whole liquid column sensed by the Cassini radiometer and thus decreasing the temperature at depths even long after the evaporation period has ceased. Overall, this work highlights the key role of methane hydrology in controlling the surface and submarine temperatures in the boreal polar regions of Titan.

Improving Predictions of Tibetan Plateau Summer Precipitation Using a Sea Surface Temperature Analog-Based Correction Method

Boreal summer precipitation over the Tibetan Plateau (TP) is difficult to predict in current climate models and has become a challenging issue. To address this issue, a new analog-based correction method has been developed. Our analysis reveals a substantial correlation between the prediction errors of TP summer precipitation (TPSP) and previous February anomalies of sea surface temperature (SST) in the key regions of tropical oceans. Consequently, these SST anomalies can be selected as effective predictors for correcting prediction errors. With remote-sensing-based and observational datasets employed as benchmarks, the new method was validated using the rolling-independent validation method for the period 1992–2018. The results clearly demonstrate that the new SST analog-based correction method of dynamical models can evidently improve prediction skills of summer precipitation in most TP regions. In comparison to the original model predictions, the method exhibits higher skills in terms of temporal and spatial skill scores. This study offers a valuable tool for effectively improving the TPSP prediction in dynamical models.

Seasonal heat stress compromises testicular thermoregulation and semen quality of Dorper rams raised in a desert climate

Dorper rams are widely distributed throughout the world under different climatic conditions, however, little is known about their reproductive performance in desert regions. Ten Dorper rams were individually housed and exposed to thermoneutrality for 35 d in spring (23.6 ± 5.6 °C, mean ± SD) and outdoor heat stress (HS) for 35 d in summer (33.6 ± 2.0 °C) to evaluate the effect of seasonal HS on physiological responses, testicular biometry, and seminal quality under desert climatic conditions. Rectal temperature, respiration rate and coat surface temperatures in different body regions were measured every 7 d (0600, 1200, and 1800 h); also, testicular biometry was registered at 0600 h. Semen was collected via an artificial vagina 3 d after physiological variables were measured and seminal traits were evaluated. Rectal temperature, respiration rate and coat surface temperatures were higher (P < 0.01) at each hour of measurement in summer compared to spring. Overall, scrotal length and circumference, as well as testicular volume were higher (P < 0.01) in summer than in spring. Compared to spring conditions, summer HS caused lower (P ≤ 0.05) sperm concentration and viability combined with a higher percentage of sperm abnormalities without affecting ejaculate volume. Both mass and sperm motility were similar between seasons in the first two sampling weeks, and then decreased (P ≤ 0.03) due to summer HS. In conclusion, Dorper rams developed testicle hyperthermia and, consequently, showed poor semen quality due to the high environmental temperatures prevailing in desert regions.

Contribution of Landscape Transformation in the Development of Heat Islands and Sinks in Urban and Peri-Urban Regions in the Chota–Nagpur Plateau, India

Cities in the contemporary world are experiencing shifts in local climatic conditions that are primarily linked to rapid urban growth, carbon emissions, and environmental degradation. In the present study, land surface temperature (LST) in urban and peri-urban regions of the city of Ranchi was estimated and correlated with land use/land cover (LULC) using temporal LANDSAT satellite images from 2000 to 2014 on a bi-annual basis. The study exhibited a gradual increase in built-up land with total growth of 47.65 sq. km (135%) at the cost of vegetation (−6.8%), rock outcrops (−44.16%), and other LULCs (0.81%). Spatiotemporal LST variability exhibited an increase in area under high-temperature (&gt;38 °C) zones as compared to moderate (30 °C to 38 °C) and low-temperature (&lt;30 °C) zones. Although the LST distinctively escalated in all LULC in the month of May from 2000 to 2014, urban areas exhibited cooler conditions than their environs at a regional scale, except for 2006. A higher surface temperature was observed over bare rock (min./max.: 28–43 °C), fallow land (25–42 °C), and industrial built-up areas (31–43 °C), followed by vegetation (24–34 °C) and waterbodies (22–30 °C). The rapid land transformation to an impervious surface, degradation of vegetation coverage and its health, deforestation due to urbanization, the dumping of non-treated wastewater into reservoirs, and pollution altogether led to an increase in urban heat islands (UHI). The study demonstrated that the LST increased in urban regions but appeared cooler than those of its peri-urban counterparts because of the presence of barren land and rock outcrops. The existing tenancy act had an indirect impact on maintaining natural landscapes and controlling the LST within the Ranchi region. The study recommends nature-based solutions in terms of plantation and green roofing within the rising LST zones located in the urban core and in peripheral rock outcrops collectively to minimize the rising impact of LST in urban and peri-urban regions.

Glacier Surface Heatwaves Over the Tibetan Plateau

AbstractThe Tibetan Plateau (TP) has warmed at a rate twice the global average and presents unique warming patterns in surface temperature changes. However, key characteristics of glacier surface heatwave duration and intensity over the TP during the present extreme warming period are still unknown. In this study, we show that surface temperatures in glacial regions of the TP (0.37 ± 0.10°C per decade) have increased faster than those in non‐glacial areas (0.29 ± 0.05°C per decade) between 2001 and 2020. Moreover, the duration (5.3 ± 3.2 days per decade) and cumulative intensity (24.9 ± 16.3 days °C per decade) of glacier surface heatwaves have increased significantly during autumn. Our results demonstrate an elevation dependence to these key warming characteristics, which we also suggest are associated with extreme glacier mass loss. Here, we highlight potential threats to the sustainability of glacier water resources and increasing risk of glacier related hazards at the “roof of the world.”

AUTOMATED DETECTION OF CHILDHOOD OBESITY IN ABDOMINOPELVIC REGION USING THERMAL IMAGING BASED ON DEEP LEARNING TECHNIQUES

Childhood obesity is a preventable disorder which can reduce the risk of the comorbidities linked with an adult obesity. In order to improve the lifestyle of the obese children, early and accurate detection is required by using some non-invasive technique. Thermal imaging helps in evaluation of childhood obesity without injecting any form of harmful radiation in human body. The goal of this proposed research is to evaluate the body surface temperature in abdominopelvic and cervical regions and to evaluate which region is best for predicting childhood obesity using thermal imaging. Next, to customize the ResNet-18 and VGG-19 architecture using transfer learning approach and to obtain the best modified classifier and to study the classification accuracy between normal and obese children. The two-study region which was selected for this study was abdominopelvic and cervical region where the mean skin surface temperature was recorded. From the two selected body regions, abdominopelvic region has depicted highest temperature difference of 10.98% between normal and obese subjects. The proposed modified ResNet-18 model produced an overall accuracy of 94.2% than the modified VGG-19 model (86.5%) for the classification of obese and normal children. Thus, this study can be considered as a non-invasive and cost-effective way for pre-screening the obesity condition in children.

Short-term responses of phytoplankton size-fractionated structure and photosynthetic physiology to thermal effluent in a subtropical coastal bay

Elevated water temperature caused by the thermal discharge from power plants can exert multiple ecological impacts on the phytoplankton community in coastal ecosystems. Most recent studies have focused on the reshaping effects on the community structure; however, the short-term response of phytoplankton physiology to thermal discharge remains unclear. This study conducted research on the scope of thermal discharge from the nuclear power plant and the size-fractionated phytoplankton structure combined with photosynthetic physiology in Daya Bay, China. The thermal discharge significantly affected the surface temperature in the outlet regions, and the thermal plume mainly diffused along the northeast coast of the outfall site, resulting in a significant difference in the surface temperature between the inlet and outlet transects (p&amp;lt;0.05). Elevated surface temperatures decreased the total chlorophyll a concentrations by 33.19% at the outlet regions, with pico-phytoplankton decreasing the most. Chlorophyll a concentrations were higher at sites further away from the outlets, indicating that elevated water temperature might stimulate the rapid growth of phytoplankton, especially nano-phytoplankton which replaced pico-phytoplankton as the dominant group at stations away from the outlets. Significant negative correlations were observed between the photochemical quantum yield (Fv/Fm) and temperature (p&amp;lt;0.05), and the relative electron transport rate (rETR) and temperature (p&amp;lt;0.05). Phytoplankton showed a normal photosynthetic physiological state at most sites with a surface temperature&amp;lt;33°C but was severely affected at the outlet site with a 5°C rise, decreasing from ~0.5 on the inlet transect to 0.07. During the diurnal survey, the high temperatures near the outlet at midday had a compensatory effect on phytoplankton’s light suppression. The results indicated that the physiological state of phytoplankton was clearly influenced by the thermal discharge with diurnal variation, and different size-fractionated phytoplankton groups exhibited heterogeneous responses. The findings may provide further insights into the ecological impacts of thermal discharges as well as global warming in subtropical regions.

Changes in the ground surface temperature in permafrost regions along the Qinghai–Tibet engineering corridor from 1900 to 2014: A modified assessment of CMIP6

Numerous studies were published in the last two decades to evaluate and project the permafrost changes in its thermal state, mainly based on the soil temperature datasets from the Coupled Model Intercomparison Project (CMIP), and discuss the impacts of permafrost changes on regional hydrological, ecological and climatic systems and even carbon cycles. However, limited monitored soil temperature data are available to validate the CMIP outputs, resulting in the over-projection of future permafrost changes in CMIP3 and CMIP5. Moreover, future permafrost changes in CMIP6, particularly over the Qinghai–Tibet Plateau (QTP), where permafrost covers more than 40% of its territory, are still unknown. To address this gap, we evaluated and calibrated the monthly ground surface temperature (GST; 5 cm below the ground surface), which was often used as the upper boundary to simulate and project permafrost changes derived from 19 CMIP6 Earth System Models (ESMs) against in situ measurements over the QTP. We generated the monthly GST series from 1900 to 2014 for five sites based on the linear calibration models and validated them through the three other sites using the same calibration methods. Results showed that all of the ESMs could capture the dynamics of in situ GST with high correlations (r > 0.90). However, large errors were detected with a broad range of centred root-mean-square errors (1.14–4.98 °C). The Top 5 model ensembles (MME5) performed better than most individual ESMs and averaged multi-model ensembles (MME19). The calibrated GST performed better than the GST obtained from MME5. Both annual and seasonal GSTs exhibited warming trends with an average annual rate of 0.04 °C per decade in the annual GST. The average seasonal warming rate was highest in winter and spring and lowest in summer. This reconstructed GST data series could be used to simulate the long-term permafrost temperature over the QTP.

A Calculation Model for Ground Surface Temperature in High-Altitude Regions of the Qinghai-Tibet Plateau, China

As a major parameter in the energy balance of the ground surface, temperature represents the level of exchange of energy and moisture between the ground and air. The Qinghai-Tibet Plateau (QTP) has the permafrost region with the highest altitude and the largest area in low–middle latitude of the world. The variation in ground surface temperature has an impact on the existence and development of the permafrost. Therefore, the analysis of the ground surface temperature in the QTP is significant to reflect the energy exchange in permafrost regions. This paper collected solar radiation data and calculated the conversion coefficient from total solar radiation to long-wave radiation of the ground surface on different underlying surfaces. The ground surface temperature was inversely calculated and modified based on the reception of solar radiation on different underlying surfaces. A simplified calculation model of ground surface temperature was built to reflect the ground surface temperature on different underlying surfaces of the QTP. The calculation results were compared with MODIS and showed good fitness, providing a systematic and reliable method for calculating the ground surface temperature on the QTP. The above model plays a significant role in the estimation of soil moisture, ground surface energy and water balance.

Spatial and Temporal Variation of Land Surface Temperature and Its Spatially Heterogeneous Response in the Urban Agglomeration on the Northern Slopes of the Tianshan Mountains, Northwest China.

An in-depth study of the influence mechanism of oasis land surface temperature (LST) in arid regions is essential to promote the stable development of the ecological environment in dry areas. Based on MODIS, MYD11A2 long time series data from 2003 to 2020, the Mann–Kendall nonparametric test, the Sen slope, combined with the Hurst index, were used to analyze and predict the trend of LST changes in the urban agglomeration on the northern slopes of the Tianshan Mountains. This paper selected nine influencing factors of the slope, aspect, air temperature, normalized vegetation index (NDVI), precipitation (P), nighttime light index (NTL), patch density (PD), mean patch area (AREA_MN), and aggregation index (AI) to analyze the spatial heterogeneity of LST from global and local perspectives using the geodetector (GD) model and multi-scale geo-weighted regression (MGWR) model. The results showed that the average LSTs of the urban agglomeration on the northern slopes of the Tianshan Mountains in spring, summer, autumn, and winter were 31.53 °C, 47.29 °C, 22.38 °C, and −5.20 °C in the four seasons from 2003 to 2020, respectively. Except for autumn, the LST of all seasons showed an increasing trend, bare soil and grass land had a warming effect, and agricultural land had a cooling effect. The results of factor detection showed that air temperature, P, and NDVI were the dominant factors affecting the spatial variation of LST. The interaction detection results showed that the interaction between air temperature and NDVI was the most significant, and the two-factor interaction was more effective than the single-factor effect on LST. The MGWR model results showed that the effects of PD on LST were positively correlated, and the impact of AREA_MN and AI on LST were negatively correlated, indicating that the dense landscape of patches has a cooling effect on LST. Overall, this study provides information for managers to carry out more targeted ecological stability regulations in arid zone oases and facilitates the development of regulatory measures to maintain the cold island effect and improve the environment.

Combining machine learning and SMILEs to classify, better understand, and project changes in ENSO events

Abstract. The El Niño–Southern Oscillation (ENSO) occurs in three phases: neutral, warm (El Niño), and cool (La Niña). While classifying El Niño and La Niña is relatively straightforward, El Niño events can be broadly classified into two types: central Pacific (CP) and eastern Pacific (EP). Differentiating between CP and EP events is currently dependent on both the method and observational dataset used. In this study, we create a new classification scheme using supervised machine learning trained on 18 observational and re-analysis products. This builds on previous work by identifying classes of events using the temporal evolution of sea surface temperature in multiple regions across the tropical Pacific. By applying this new classifier to seven single model initial-condition large ensembles (SMILEs) we investigate both the internal variability and forced changes in each type of ENSO event, where events identified behave similarly to those observed. It is currently debated whether the observed increase in the frequency of CP events after the late 1970s is due to climate change. We found it to be within the range of internal variability in the SMILEs for trends after 1950, but not for the full observed period (1896 onwards). When considering future changes, we do not project a change in CP frequency or amplitude under a strong warming scenario (RCP8.5/SSP370) and we find model differences in EP El Niño and La Niña frequency and amplitude projections. Finally, we find that models show differences in projected precipitation and sea surface temperature (SST) pattern changes for each event type that do not seem to be linked to the Pacific mean state SST change, although the SST and precipitation changes in individual SMILEs are linked. Our work demonstrates the value of combining machine learning with climate models, and highlights the need to use SMILEs when evaluating ENSO in climate models because of the large spread of results found within a single model due to internal variability alone.

Camera fusion for real-time temperature monitoring of neonates using deep learning

The continuous monitoring of vital signs is a crucial aspect of medical care in neonatal intensive care units. Since cable-based sensors pose a potential risk for the immature skin of preterm infants, unobtrusive monitoring techniques using camera systems are increasingly investigated. The combination of deep learning–based algorithms and camera modalities such as RGB and infrared thermography can improve the development of cable-free methods for the extraction of vital parameters. In this study, a real-time approach for local extraction of temperatures on the body surface of neonates using a multi-modal clinical dataset was implemented. Therefore, a trained deep learning–based keypoint detector was used for body landmark prediction in RGB. Image registration was conducted to transfer the RGB points to the corresponding thermographic recordings. These landmarks were used to extract the body surface temperature in various regions to determine the central-peripheral temperature difference. A validation of the keypoint detector showed a mean average precision of 0.82. The registration resulted in mean absolute errors of 16.4 px (8.2 mm) for x and 22.4 px (11.2 mm) for y. The evaluation of the temperature extraction revealed a mean absolute error of 0.55 ^{circ }C. A final performance of 31 fps was observed on the NVIDIA Jetson Xavier NX module, which proves real-time capability on an embedded GPU system. As a result, the approach can perform real-time temperature extraction on a low-cost GPU module.Graphical abstract

The first evaluation of the FY-3D/MERSI-2 sensor’s thermal infrared capabilities for deriving land surface temperature in volcanic regions: a case study of Mount Etna

ABSTRACT In November 2017, the China Meteorological Administration launched a new polar orbiting satellite in its Fengyun (FY) series: FY-3D. With its main purpose being the collection of meteorological data, FY-3D featured a comprehensive payload that is equally exploitable by various Earth Science disciplines. One of its sensors, the MEdium Resolution Spectral Imager-2 (MERSI-2), provides visible and infrared imagery at spatial resolutions of 250 – 1000 m. These characteristics make MERSI-2 suitable for volcanological remote sensing and make it comparable to the National Aeronautics and Space Administration’s (NASA’s) Moderate Resolution Imaging Spectroradiometer (MODIS) sensors which themselves, have been widely used in volcanological applications. This paper evaluates the first clear and near-coincident MODIS – MERSI-2 images of Mount Etna (Italy) during an active volcanic phase in 2019 and in turn, provides the first assessment of MERSI-2’s utility in observing volcanic activity in the Thermal InfraRed (TIR). To ensure the comparability of both scenes, data from each were converted to Land Surface Temperature (LST) and comparisons were encouraging, with an r2 of 0.92, a mean temperature discrepancy of 0.26 K and a root mean squared error of 0.75 K. Having ascertained comparability, we focussed on the absolute temperatures detected at the eruption site, with the highest being 317.3 K and 328.1 K for MODIS and MERSI-2, respectively. The 20 minute gap between the acquisitions is the most likely the cause of this temperature discrepancy, suggesting variations in lava effusion rates and activity were occurring at Mount Etna over such timescales. This study confirms the applicability of MERSI-2 for observing volcanic activity and emphasises the significance of TIR volcanic monitoring and the importance that additional spaceborne platforms might have in reducing temporal gaps between image acquisitions. Given its unique characteristics, future studies should investigate the applicability of MERSI-2 in more varied volcanic settings.

Satellite Observed Sensitivity of Tropical Clouds and Moisture to Sea Surface Temperature on Various Time and Space Scales: 2. Focus on Marine Low Level Clouds

AbstractThis study examines how satellite observed relative humidity (RH) profiles, cloud covers, and cloud altitudes vary with sea surface temperature (SST) in low cloud regions on different temporal and spatial scales over the tropical oceans (30°N–30°S). We split low clouds into optically opaque and optically thin clouds and characterize for the first time their altitude variations with SST using space lidar observations. On the process scale, the median instantaneous observations at high spatial resolution show simultaneously decreasing low opaque cloud top altitude, decreasing low opaque cloud cover, increasing middle‐tropospheric RH, and decreasing lower‐tropospheric RH with SST. Collectively, these observational results suggest decreasing opaque marine boundary layer cloud cover and enhanced mixing between the middle and lower troposphere with SST on the process scale, consistent with previous Large‐Eddy Simulation studies. The covariations between opaque cloud cover and RH with SST are quantified on all time and space scales (from the process scale to the annual tropical mean scale). Meanwhile, on the process scale, low optically thin clouds, coexisting with opaque clouds, rise with SST (consistent with the literature mechanism), while the low optically thin cloud cover is insensitive to SST (not consistent with the literature mechanism). These results are then compared to a general circulation model, which captures the observed sign of change of opaque cloud cover and middle‐tropospheric RH with SST but fails to reproduce the observed vertical shrinkage of low opaque clouds with SST and the variations of low thin clouds with SST.

Impacts of afforestation on land surface temperature in different regions of China

To assess the impacts of afforestation on land surface temperature (LST) in China and provide a basis for future afforestation and warming mitigation policies, adjacent forest and open land are compared using ten years of satellite data. Most regions in China exhibit a diurnal asymmetry in the magnitude and sign of the effects of afforestation, with daytime cooling and nighttime warming. Afforestation has annual daily cooling effects in most regions of China because the magnitude of annual nighttime warming is generally weaker than that of the annual daytime cooling. We attributed the warming and cooling effects of afforestation to the changes of evapotranspiration (ET) and albedo. The annual average albedo of forest is smaller than that of open land, and the albedo differences between forest and open land vary spatially, with larger differences in places with snow cover. In the annual average, ET over forest is greater than that over open land, with the largest values in the southern of Yangtze River. The daytime differences in LST between forest and open land are driven by the cooling effects of ET and warming effects of albedo, and the cooling effects of ET dominate. The nighttime warming effects of afforestation is related with the release of the energy stored in the soil during daytime. Evergreen broadleaf forest and deciduous broadleaf forest are recommended as the choice of afforestation in south and in north of the Yangtze River respectively to decrease the LST if the afforestation program in China is to be expanded.

Attribution of local land surface temperature variations response to irrigation over the North China Plain

Irrigation substantially alters land surface temperature (LST) in different regions of the world. Studies have recently focused on quantifying irrigation-induced LST change based on remote sensing technology due to its high spatiotemporal resolution. However, the biophysical mechanisms of irrigation on LST remains poorly understood. Here we first investigated the impact of irrigation on LST during 2003–2012 over the North China Plain (NCP), which is one of the most intensively irrigated areas around the word. We then attributed the mechanisms underlying LST change between adjacent irrigated and non-irrigated croplands based on two surface energy balance-based methods: the Decomposed Temperature Metric (DTM) method and the intrinsic biophysical mechanism (IBM) method. The results indicate that at annual scale, irrigation produce an overall cooling effect over the NCP, with the mean observed LST change of −0.098 K, calculated LST change of −0.096 K for DTM method and −0.165 K for IBM method, respectively. Furthermore, the agreement between the annual observed and calculated LST difference indicate that DTM is a more robust method than IBM in quantifying irrigation-induced LST change over the NCP. The attribution method DTM reveals that components of albedo and emissivity has an average cooling effect of −0.012 K and −0.005 K, respectively, while incoming radiation lead to a weak warming effect of +0.01 K. The enhanced turbulent fluxes of latent heat flux dominate the cooling effect (−0.174 K on average), further offsets the sensible heat flux warming effect (+0.085 K). Another attribution method IBM demonstrates that the annual cooling effect of irrigation is mostly induced by changes in aerodynamic resistance (−0.175 K), whereas the biophysical contributions of albedo (−0.0005 K) and Bowen ratio (+0.001 K) have a negligible impact on LST. This study provides a useful reference for assessing local climate impact of irrigation when implementing environmental protection projects.

Eye Region Surface Temperature and Corticosterone Response to Acute Stress in a High-Arctic Seabird, the Little Auk.

Simple SummaryThermography, which is a method of measuring the heat emitted by various regions of the body, may be useful in detection of a heat increase in response to stress in vertebrates. Using this method, we studied how body surface temperature in eye-region (TEYE) changes in response to short-term stress (capturing and handling) in a wild-living, medium-sized polar seabird, the Little Auk. To this end, we measured TEYE in birds twice: first time—just after their capturing and initial handling and then second—after 30 min of keeping them in a bag. To control birds’ stress response, at the same time we made thermography, and we also collected blood samples from all the individuals, to establish the level of corticosterone (CORT, stress hormone). We found that both TEYE and CORT increased in response to the experimental procedure, although the strength of the TEYE and CORT increase were not related to each other. This indicates that thermography is a good tool for detection of initiation of birds’ reaction to a stress, which may be further useful in other studies, e.g., where there is a need to establish birds’ stress response non-invasively. Measuring changes in surface body temperature (specifically in eye-region) in vertebrates using infrared thermography is increasingly applied for detection of the stress reaction. Here we investigated the relationship between the eye-region temperature (TEYE; measured with infrared thermography), the corticosterone level in blood (CORT; stress indicator in birds), and some covariates (ambient temperature, humidity, and sex/body size) in a High-Arctic seabird, the Little Auk Alle alle. The birds responded to the capture-restrain protocol (blood sampling at the moment of capturing, and after 30 min of restrain) by a significant TEYE and CORT increase. However, the strength of the TEYE and CORT response to acute stress were not correlated. It confirms the results of a recent study on other species and all together indicates that infrared thermography is a useful, non-invasive measure of hypothalamic-pituitary-adrenal (HPA) axis reactivity under acute activation, but it might not be a suitable proxy for natural variation of circulating glucocorticoid levels.