Surface Temperature Research Articles

Air and Surface Temperatures Differently Drive Terrestrial Carbon and Water Cycles in the High Latitudes

AbstractHigh‐latitude vegetation experience different temperatures than the ambient air temperature. While lacking a regional plant temperature product, we drove the dynamic ecosystem model, LPJ‐GUESS, with widely used ERA5‐land surface temperature (Tsurf, at radiative equilibrium) and air temperature to understand ecosystem process responses to these two temperatures. We show that tundra plants' growth is stimulated by warmer Tsurf in the summer, but in the boreal forests, colder Tsurf in the non‐summer months constrains leaf development and enzyme activity the following growing season. Tsurf drives higher productivity of tundra plant individuals but leads to less productive individuals in the boreal forest, although with compensatory changes (almost 68%) in vegetation structure. We demonstrate the importance of forcing temperature in simulating high‐latitude ecosystem processes and call for a community effort to measure plant temperatures across canopy heights and seasons to reduce uncertainties in estimating high‐latitude plant responses and feedback to climate.

Global assessment of interannual variability in coastal urban areas and ecosystems

Abstract Both seasonal and extreme climate conditions are influenced by long-term natural internal variability. However, in general, long-term hazard variation has not been incorporated into coastal risk assessments. There are coastal regions of high interest, such as urban areas, where a large number of people are exposed to hydrometeorological hazards, and ecosystems, which provide protection, where long-term natural variability should be considered a design factor. In this study, we systematized climate analysis to identify high-interest regions where hazard long-term variability should be considered in risk assessment, disaster reduction, and future climate change adaptation and protection designs. To achieve this goal, we examined the effect of the leading modes of climate variability (Arctic Oscillation, Southern Annular Mode, and El Niño–Southern Oscillation) on the variation in the recurrence of extreme coastal hazard events, including as a first step sea surface temperature, winds, and waves. Neglecting long-term variability could potentially lead to the underperformance of solutions, or even irreversible damage that compromises the conditions of ecosystems for which nature-based solutions are designed.

Impact of North Atlantic Tripole and Extratropical North Pacific Extreme SSTs on the 2023/24 El Niño

AbstractObservations revealed notable discrepancies in the 2023/24 El Niño compared to earlier events, despite registering moderate Niño3.4 index magnitudes. Essential indicators such as the westerly wind burst, thermocline zonal tilting, and eastward propagation of oceanic Kelvin waves were conspicuously weak, indicating a weak air‐sea coupled in contrast to past occurrences, the 2023/24 El Niño coincided with unusually high North Atlantic Tripole and extratropical North Pacific sea surface temperatures (SSTs). The elevated North Atlantic Tripole SST triggered a strong negative Pacific meridional mode and easterly anomalies in the equatorial western‐central Pacific. At the same time, the extratropical North Pacific SST induced a negative Pacific Decadal Oscillation‐like pattern. These anomalies potentially dampened SST‐wind coupling during the developmental stages of El Niño. The negative Pacific meridional mode distinguished the 2023/24 El Niño from previous events and substantially altered its local and remote influences.

Quantifying the influence of dominant factors on the long-term sandstorm weather - A case study in the Yellow River Basin during 2000–2021

Sandstorm is a disastrous weather phenomenon that often occurs in arid and semi-arid areas, endangering the ecological environment and affecting people's lives and property safety seriously. Since the 21st century, the sandstorm weather in the Yellow River Basin has ameliorated obviously. However, the causes of the long-term trends in sandstorms during 21st century were still unknown. In this study, fifteen influencing factors from five aspects: ecology, meteorology, hydrology, geography and man-made were selected to comprehensively analyze the driving mechanism of sandstorm activities in the Yellow River Basin since the 21st century, and the effect of each influencing factor on sandstorm weather was quantified. The results indicated that ecological, meteorological and geographical factors had dominant impacts on the spatio-temporal variation of sandstorms during 2000–2021, while hydrological and human factors played little role in the long-term variation of sandstorms. Sandstorms frequently occurred in semi-desert or grassland or non-high vegetation covered areas in spring. Vegetation coverage, precipitation, surface pressure, surface roughness, and soil moisture content were negatively correlated with sandstorms, while wind speed, friction velocity, evaporation, and soil temperature were positively correlated with sandstorms. Precipitation, runoff, evaporation, soil moisture content, soil temperature, and surface temperature indirectly acted on normalized brightness temperature dust index (NBTDI) and sandstorms by changing soil texture. Normalized Vegetation Index (NDVI) had direct negative effects on NBTDI, while wind speed 10 m (WS10m), slope of sub-gridscale orography (SOSGO), and forecast surface roughness (FSR) had direct positive effects on NBTDI. This study comprehensively revealed the dominant factors and their driving mechanism of sandstorm weather in the Yellow River Basin since the 21st century, which had practical application value for the prevention of sandstorms.

Methods to mitigate the impact of work environment on the measurement of building surface temperature by unmanned aerial vehicle onboard thermal imaging system

With the widespread adoption of UAVs and compact infrared cameras, UAV infrared remote sensing technology has extensive applications in qualitative research fields such as finding thermal bridges and air tightness detection in construction projects, but less in quantitative research, mainly due to its large temperature measurement error. When measuring temperature with UAV-mounted thermal imaging cameras, factors such as operational wind conditions and operation methods, in addition to the inherent accuracy of the instrument, can introduce measurement errors. To investigate the impact of operational wind conditions on temperature measurements by UAV-mounted thermal imaging cameras, both field and laboratory experiments were conducted to analyze the error characteristics caused by wind conditions. Devices were designed to mitigate the impact of wind on temperature measurements, and their effectiveness was validated through experiments. Based on these findings, methods were developed for reducing the influence of wind conditions on temperature measurements in UAV operations. The results indicate that wind condition errors primarily affect the stability of temperature data. Under the influence of wind conditions, the temperature data recorded by thermal infrared cameras exhibit periodic and significant fluctuations, with a temperature difference of approximately 4.7 °C between the highest and lowest points within a single cycle, constituting the main source of measurement error. However, by following the prescribed measurement procedures, the temperature measurement error can be reduced by 32 %, from ±5 °C to ±1.6 °C, approaching the accuracy of tripod-mounted thermal imaging cameras. This establishes a foundation for quantitative research in low-altitude infrared remote sensing.

Lithium isotopes track changes in continental weathering regimes across the end-Permian mass extinction in Southwest China

It has been assumed there was a massive amount of volcanic CO2 injection into the Permian-Triassic atmosphere and ocean systems, leading to rapid climatic warming and expansion of marine anoxia. However, it remains intriguing how Earth recovered from such a CO2-driven hyperthermal condition. One potential mechanism involves the negative feedback between continental silicate weathering and atmospheric CO2, which could have helped maintain habitability across the end-Permian mass extinction (EPME) interval. This process can be examined using lithium isotopes (δ7Li), which reflect the balance of physical erosion and chemical weathering, and chemical weathering indices such as the Chemical Index of Alteration (CIA), which indicates the chemical alteration of parent materials during weathering. In this study, we analyze siliciclastic sedimentary rocks from the Lopingian to Lower Triassic depositional sequences in the HK-1 drill core at the Lengqinggou section, a terrestrial coastal depositional environment in Southwest China, to reconstruct changes in continental chemical weathering intensity across the EPME. We observed a significant ∼4 ‰ positive shift in δ7Li, accompanied by a marked decrease in Li content from 26 ppm to 6 ppm. Our corrected CIA data (CIAcorr) also exhibits a considerable decrease from 94 to 59 across the EPME. The new δ7Li and CIAcorr data from the terrestrial section indicate a decrease in overall chemical weathering intensity in Southwest China, alongside an increase in physical erosion rates, suggesting a shift from a transport-limited to a kinetically limited weathering regime across the EPME. These changes in the continental weathering regime appear to be linked to active volcanic activity near the South China Block, which led to massive deforestation and the collapse of soil systems. Dramatic reductions in chemical weathering intensity may result in inefficient atmospheric CO2 consumption through silicate weathering if other climatic and tectonic conditions remain constant, potentially contribute to maintaining high global surface temperatures and prolonged marine anoxia into the Early Triassic.

Tree Rings Reveal ENSO in the Last Millennium

AbstractWe present new climate field reconstructions (CFR) of tropical Pacific ENSO sea surface temperatures (HadISST) for the boreal winter season using a circum‐Pacific tree‐ring network from known El Niño rainfall impact regions. We use two different CFR methods: Point‐by‐Point Regression (PPR) and reduced‐space Orthogonal Spatial Regression (OSR). Both methods produce reconstructions with high validation skill, but OSR is preferred because it has less spatial noise and is more efficient. Only the leading EOF of the SST field (EOF1) can be skillfully reconstructed by either method; EOF2 does not validate. The success of EOF1 reflects its importance for ENSO rainfall impacts over land; the failure with EOF2 is from the lack of these impacts. EOF1 allows for the reconstruction of many ENSO indices, including the ENSO Longitudinal Index (ELI). We also find evidence in our reconstructions for a recent increase in ENSO activity.

Sea surface wind retrieval from CFOSAT Rotating fan-beam scatterometer in ocean cyclones

ABSTRACT Rotating fan-beam scatterometer (RFSCAT) is a radar scatterometer system for sea surface wind vector measurement. Compared with other available scatterometers, RFSCAT can provide more combination of azimuth angles and incidence angles for a single WVC (wind vector cell), this observation mechanism is more conducive to the sea surface wind direction retrieval. In this paper, the NSCAT-4DS GMF (geophysical model function) with SST (sea surface temperature) correction, and the MSS (Multiple Solution Scheme) combination with a 2DVAR (2-dimensional variational analysis) are adopted to retrieve the sea surface winds from RFSCAT on the CFOSAT (Chinese-French Oceanography Satellite). The retrieved RFSCAT sea surface winds and ASCAT (advanced scatterometer) sea surface winds are compared and tested, and the feasibility of the RFSCAT measuring sea surface winds under high wind speeds is analysed. The results show that the RMSE of the RFSCAT sea surface wind speed using improved algorithm has decreased by 0.292 m s−1, the correlation coefficient has increased by 0.032, and the residual standard deviation has decreased by 0.194 m s−1. The RMSE of RFSCAT sea surface wind direction has decreased by 5.950°, the correlation coefficient has increased by 0.002, and the residual standard deviation has decreased by 5.567°. It is shown that the changes in winds RMSE using pre-improved and improved algorithms have statistical significance. In a word, the spaceborne Ku-band rotating fan-beam scatterometer can capture the winds structure of ocean cyclones. Although there may be high wind speeds saturation phenomena, the detecting sea surface winds at high wind speeds show preferable performance.

Impact of Habitat Quality Changes on Regional Thermal Environment: A Case Study in Anhui Province, China

Biodiversity degradation and loss represent critical global challenges, primarily driven by the urban heat island effect, which results from elevated surface temperatures. As urbanization and climate change continue to progress, these phenomena have a profound impact on both habitats and human residential environments. This study focuses on Anhui Province as a case study to systematically investigate the effects of changes in habitat quality (HQ) on the evolution of the regional thermal environment. The objective is to provide a scientific basis for addressing regional thermal environment issues and promoting biodiversity conservation. This paper employs the InVEST-HQ model to analyze HQ in Anhui Province from 2000 to 2020 and integrates surface temperature data to assess the response of HQ changes to variations in the regional thermal environment. The results show that: (1) From 2000 to 2020, the HQ index in Anhui Province exhibited a general decline, characterized by pronounced spatial heterogeneity, with lower values observed in the northern regions and higher values in the southwestern and southern areas. (2) Concurrently, the relative surface temperature in Anhui Province continued to rise, particularly in central urban areas such as Hefei, where the increase in impermeable surfaces has facilitated the expansion of high-temperature zones. (3) Different types of HQ had distinctly varying effects on regional thermal environments: habitats classified as poor HQ or worse HQ were associated with noticeable warming effects, while those categorized as good HQ or excellent HQ exhibited significant cooling effects. (4) The contribution index of varying HQ to relative surface temperature ranged from −0.2 to 0.3, indicating that poor HQ and worse HQ positively contributed to regional thermal environments, whereas good HQ and excellent HQ exerted a negative contribution. City-level analyses revealed that cities such as Suzhou, Chizhou, Wuhu, Anqing, Xuancheng, and Lu’an were associated with positive contributions to relative surface temperature, while cities including Bengbu, Fuyang, Chuzhou, Huaibei, Tongling, Ma’anshan, and Hefei demonstrated negative contributions. This study provides valuable insights for optimizing the spatial distribution of urban cold islands and promoting ecological sustainable development.

Stability of cloud detection methods for Land Surface Temperature (LST) Climate Data Records (CDRs)

The stability of a climate data record (CDR) is essential for evaluating long-term trends in surface temperature using remote sensing products. In the case of a satellite-derived CDR of land surface temperature (LST), this includes the stability of processing steps prior to the estimation of the target climate variable. Instability in the masking of cloud-affected observations can result in non-geophysical trends in a LST CDR. This paper provides an assessment of cloud detection performance stability over a 25-year LST CDR generated using data from the second Along-Track Scanning Radiometer (ATSR-2), the Advanced Along-Track Scanning Radiometer (AATSR), the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Sea and Land Surface Temperature Radiometer (SLSTR). We evaluate three cloud detection methodologies, one fully Bayesian, one naïve probabilistic and the operational threshold-based cloud mask provided with each sensor, at four in-situ ceilometer sites. Of the 12 algorithm-site combinations assessed, only two (17 %) were stable across the full timeseries with respect to both cloud contamination and missed clear-sky observations. Five (42 %) were stable with respect to missed clear-sky observations only. The associated impacts on LST trends in the CDR could be as large as (+/−)0.73 K per decade (0.43 K per decade above the target stability), which means that attention needs to be paid to this aspect of stability in order to understand uncertainty in long-term observed trends. Given that cloud detection stability has not to our knowledge been previously assessed for any target climate variable, this conclusion may apply more broadly to other satellite-derived CDRs.

Antarctic sea ice surface temperature bias in atmospheric reanalyses induced by the combined effects of sea ice and clouds

Sea-ice surface temperature from atmospheric reanalysis has been used as an indicator of ice melt and climate change. However, its performance in atmospheric reanalyses is not fully understood in Antarctica. Here, we quantified biases in six widely-used reanalyses using satellite observations, and found strong and persistent warm biases in most reanalyses examined. Further analysis of the biases revealed two main culprits: incorrect cloud properties, and inappropriate sea-ice representation in the reanalysis products. We found that overestimated cloud simulation can contribute more than 4 K warm bias, with ERA5 exhibiting the largest warm bias. Even in reanalysis with smaller biases, this accuracy is achieved through a compensatory relationship between relatively lower cloud fraction bias and overestimated sea ice insulation effect. A dynamic downscaling simulation shows that differences in sea-ice representation can contribute a 2.3 K warm bias. The representation of ice concentration is the primary driver of the spatial distribution of biases by modulating the coupling between sea ice and clouds, as well as surface heat conduction. The lack of a snow layer in all reanalyses examined also has an impact on biases.

Monitoring of Vegetation Drought Index in Laibin City Based on Landsat Multispectral Remote Sensing Data

Due to the impact of global warming, drought has caused serious damage to China’s ecological environment and social status. This article selects Laibin City in the Guangxi Zhuang Autonomous Region as the research area, utilizing multispectral remote sensing data as the data source and Landsat series image data for relevant preprocessing. It calculates the monthly normalized vegetation index (NDVI) and surface temperature (LST) data for Laibin City. Based on the ecological environment and surface coverage conditions of the research area, the ratio vegetation index (RVI), normalized vegetation moisture index (NDWI), temperature vegetation drought index (TVDI), and conditional vegetation temperature drought index (VTCI) were selected to calculate and invert the drought monitoring results of Laibin City. The drought monitoring results were obtained and overlaid with the vegetation area map to generate the vegetation drought monitoring results of Laibin City. Based on the climate, geography, and ecological characteristics of the monitored area in Laibin City, a specific analysis will be conducted to develop an appropriate TVDI index drought level, and generate vegetation drought level result maps for Laibin City in 2021, 2022, and 2023. Then, a detailed analysis of the vegetation drought situation in Laibin City is conducted according to time and space. Among them, in the past three years, the vegetation areas in Laibin City have experienced drought seasons mostly in summer and autumn. The interannual drought is mainly mild drought, and the proportion of areas with mild drought shows a relatively stable trend. In conclusion, TVDI proves to be a valuable tool for monitoring vegetation drought in Laibin City, offering insights for efficient water resource management strategies.

Effect of filer content on the thermal characteristics of underfill materials for Ball-Grid-Array component package

High-density integration and fast processing speed in the semiconductor industry have increased heat generation in electronic devices. Underfill materials, known for their high thermal conductivity and low coefficient of thermal expansion (CTE), offer a potential solution to dissipate heat and improve device reliability. In this study, we investigate the effect of filler content on the thermal reliability of underfill materials for ball grid array (BGA) component packaging. Mainly, we investigate the thermal conductivity, CTE, and mechanical properties of different filler contents of Al2O3 and Al2O3–BN hybrid underfills to facilitate heat dissipation and improve device reliability. The thermal conductivity of the underfill materials was evaluated by measuring the surface temperatures of underfill molded flip-chip light-emitting diodes (FCLEDs). The mechanical properties and thermo-mechanical reliability of the underfill materials were evaluated via a three-point bending test of the underfill packaged BGA components after the thermal shock test. The results showed that optimizing underfill properties based on specific application environments is crucial for obtaining enhanced thermal reliability and mechanical properties of underfill packaged BGA components.

Daytime land surface temperature and its limits as a proxy for surface air temperature in a subtropical, seasonally wet region

Land surface temperatures (LSTs) captured via satellite remote sensing are widely used as a proxy for the surface air temperatures (SATs) experienced outdoors, a key component of human heat exposure. However, LST’s accuracy in capturing SAT can vary through space and time across climate types and geographies and has been less explored in subtropical, seasonally wet regions (where summer precipitation exceeds 570 mm). Utilizing daytime (11 AM/12 PM local time, ET/EST) Landsat 8 remote sensing data, this study derived LST and evaluated its spatiotemporal patterns, as well as its relationship with SAT retrieved from local weather stations, using the case of Miami-Dade County, Florida, USA. Over 2013–2022, a surface urban heat island effect is distinctly present (mean SUHII = 3.43°C)—most intense during spring months rather than summer months (mean spring SUHII = 4.09°C). As such, LST peaks in May/June as opposed to July/August for many other parts of the northern hemisphere. In contrast, Miami-Dade SAT is greatest in August, and the strength of its relationship with LST varies by season. LST and SAT are most correlated in winter (R = 0.91) and spring (R = 0.59) months and least correlated during the wetter fall (R = 0.40) months. The relationship between LST and SAT during the summer is statistically insignificant. In this subtropical region with a seasonally wet climate, LST effectively reflects the spatial heterogeneity of the urban thermal landscape, consistent with the literature across urban regions globally. However, because the strength of the LST-SAT relationship considerably weakens during wet season months, LST data therefore have limits as a proxy for the heat exposure people experience outdoors annually, as they may not accurately represent the magnitude of localized potential heat risks. These findings underscore important considerations in using LST data to identify urban heat exposures and inform potential adaptive responses in seasonally wet, subtropical-to-tropical regions.

An Experimental Validation-Based Study of Airport Pavement Icing Mechanisms in Saline Environments and the Development of a Simplified Prediction Model

Runway icing presents a significant challenge to aviation safety, especially in saline environments, where comprehending the icing mechanisms and predicting the icing onset are crucial for efficient airport operations. This study developed a specialized experimental apparatus to examine the mechanisms of airport pavement icing under controlled conditions. The apparatus, comprising an environmental chamber, a data acquisition system, and a scaled pavement structure, allowed for detailed simulations of various environmental factors. The experiments specifically examined the effects of the air temperature (−3 °C to −20 °C), wind speed (2 m/s to 6 m/s), and deicing salt concentration (0% to 80%) on the icing process. The results demonstrated that higher wind speeds and lower temperatures significantly accelerated the pavement surface cooling, leading to earlier icing onset. Under the most extreme conditions, the pavement reached critical icing temperatures within 15 min. In contrast, higher deicing salt concentrations delayed the icing onset by up to 67 min and 33 s at an 80% concentration, effectively lowering the pavement surface temperature. A comparison of the experimental data with the theoretical predictions showed initial consistency, although the discrepancies increased over time. This study culminated in the development of a simplified prediction model, which was validated against the experimental results, offering a practical tool for airport operators to manage runway safety during winter conditions.

A 40-Year Time Series of Land Surface Emissivity Derived from AVHRR Sensors: A Fennoscandian Perspective

Accurate land surface temperature (LST) retrieval depends on precise knowledge of the land surface emissivity (LSE). Neglecting or inaccurately estimating the emissivity introduces substantial errors and uncertainty in LST measurements. The emissivity, which varies across different surfaces and land uses, reflects material composition and surface roughness. Satellite data offer a robust means to determine LSE at large scales. This study utilises the Normalised Difference Vegetation Index Threshold Method (NDVITHM) to produce a novel emissivity dataset spanning the last 40 years, specifically tailored for the Fennoscandian region, including Norway, Sweden, and Finland. Leveraging the long and continuous data series from the Advanced Very High Resolution Radiometer (AVHRR) sensors aboard the NOAA and MetOp satellites, an emissivity dataset is generated for 1981–2022. This dataset incorporates snow-cover information, enabling the creation of annual emissivity time series that account for winter conditions. LSE time series were extracted for six 15 × 15 km study sites and compared against the Moderate Resolution Imaging Spectroradiometer (MODIS) MOD11A2 LSE product. The intercomparison reveals that, while both datasets generally align, significant seasonal differences exist. These disparities are attributable to differences in spectral response functions and temporal resolutions, as well as the method considering fixed values employed to calculate the emissivity. This study presents, for the first time, a 40-year time series of the emissivity for AVHRR channels 4 and 5 in Fennoscandia, highlighting the seasonal variability, land-cover influences, and wavelength-dependent emissivity differences. This dataset provides a valuable resource for future research on long-term land surface temperature and emissivity (LST&E) trends, as well as land-cover changes in the region, particularly with the use of Sentinel-3 data and upcoming missions such as EUMETSAT’s MetOp Second Generation, scheduled for launch in 2025.

Drying trend in land and sea in East Asia during the warm season over the past four decades

Abstract The East Asian region is typically characterized by warm and humid conditions from late spring to summer. However, in recent decades, this region has experienced an increase in severe drying conditions, deviating from historical climatological patterns. This study investigated the precipitation-evaporation (P-E) trends across land and sea regions in East Asia (EA) during the extended summer season (April to September) from 1980 to 2022, and the key physical processes driving these trends through moisture budget decomposition and numerical experiments. The results reveal pronounced drying trends in southeastern China and the Yellow Sea and parts of the Korea Strait and Korean Peninsula over the past 43 years. The underlying physical processes driving these drying conditions differ between land and sea in EA. In southeastern China, the drying is driven by dynamic processes, particularly moisture divergence related to wind changes. This is linked to anomalous strengthening of descending motion due to the Indo-Pacific warm pool warming induced by both anthropogenic global warming and natural Pacific Decadal Oscillation-like sea surface temperature (SST) patterns. Conversely, drying in the Yellow Sea and adjacent areas is influenced by thermodynamic moisture advection. The altered humidity distribution due to global warming-induced SST patterns, which are higher over the Northwest Pacific marginal sea and lower in inland China, drives dry air transport from inland China to the Yellow Sea via background southwesterly wind. These findings enhance our understanding of the drying trend and their distinct processes in EA’s land and sea areas during the extended summer.

Atmospheric variability drives anomalies in the Bering Sea air-sea heat exchange

Abstract High-latitudes, including the Bering Sea, are experiencing unprecedented rates of change. Long-term Bering Sea warming trends have been identified, and marine heatwaves (MHWs), event-scale elevated sea surface temperature (SST) extremes, have also increased in frequency and longevity in recent years. Recent work has shown that variability in air-sea coupling plays a dominant role in driving Bering Sea upper ocean thermal variability, and that surface forcing has driven an increase in the occurrence of positive ocean temperature anomalies since 2010. In this work, we characterize the drivers of the anomalous surface air-sea heat fluxes in the Bering Sea over the period 2010-2022 using ERA5 fields. We show that the surface turbulent heat flux dominates the net surface heat flux variability from September-April, and is primarily a result of near-surface air temperature and specific humidity anomalies. The air-mass anomalies that account for the majority of the turbulent heat flux variability are a function of wind direction, with southerly (northerly) wind advecting anomalously warm (cool), moist (dry) air over the Bering Sea, resulting in positive (negative) surface turbulent flux anomalies. During the remaining months of the year, anomalies in the surface radiative fluxes account for the majority of the net surface heat flux variability, and are a result of anomalous cloud coverage, anomalous lower tropospheric virtual temperature, and sea ice coverage variability. Our results indicate that atmospheric variability drives much of the Bering Sea upper ocean temperature variability through mediation of the surface heat fluxes during the analysis period.

Population genetic structure of Montipora digitata coral-algal symbiosis in the South China Sea

Species genetic diversity can reflect their adaptability to environmental changes. Coral reefs worldwide are in rapid decline due to climate change and human activities, highlighting the need for conservation intervention. The South China Sea (SCS) is an important biodiversity hotspot, particularly in the Xisha Islands (XS) that contain the majority of coral species in the SCS. However, few studies exist on coral genetic diversity in the SCS. Montipora digitata is the dominant reef-building coral species in the XS. In this study, two mitochondrial DNA fragments and 11 microsatellite markers were employed to investigate the genetic diversity of coral host of M. digitata, and ITS amplicon sequencing was employed to investigate the Symbiodiniaceae community structure. We sampled five wild populations of M. digitata in Hainan Island and the Xisha Islands. Montipora digitata showed low genetic diversity across populations in the SCS. We found significant genetic differentiation between the Sanya (SY) and XS populations but no significant genetic differentiation within XS populations. We identified four Symbiodiniaceae genera, with Cladocopium and Durusdinium being the most common, as well as Gerakladium and Fugacium. The Symbiodiniaceae types of SY and XS are significantly different, C15 is abundant in all populations, while D1a is also abundant in SY. We analyzed the correlation between 16 environmental factors and the genetic diversity of symbionts, genetic diversity of coral host and Symbiodiniaceae community was significantly correlated with sea surface temperature (SST) and other environmental factors. Our study provided a detailed paradigm from the perspective of genetic diversity of how a dominant coral species can be indicative of poor environmental adaptation potential.

PRELIMINARY DATA ON COAT COVERAGE AND GROOMING INTERFERING WITH THE THERMAL COMFORT OF LHASA APSO AND SHIH TZU DOGS

The objective of this report was to investigate the effects of grooming on thermal comfort in Lhasa Apso and Shih Tzu dogs exposed to different ambient temperatures. A randomized block design in a 3 x 2 factorial scheme was used, with 3 different temperatures associated with whether the animal was groomed or not. Six adult dogs, consisting of two Lhasa Apsos and four Shih Tzus, were used. The dogs were exposed to temperatures of 17°C, 25°C, and 33°C both before and after grooming. Physiological parameters (blood pressure, heart rate, respiratory rate, rectal temperature, ambient temperature, ITU, and body surface temperature) were measured using infrared thermography with a portable IRT camera. The images were analyzed using FLIR Tools™ software with an emissivity of 0.97. Variance analysis was performed to evaluate the isolated effects and interactions between the factors of coat coverage and ambient temperature at the 5% significance level. No interaction was found between the treatments, nor were there isolated effects of the factors on the physiological parameters evaluated. A temperature of 33°C was associated with a higher rectal temperature (p<0.05) in the animals. Ungroomed dogs exposed to temperatures of 25°C and 33°C had ITU values above the thermal comfort limit. In terms of body temperature, there was a significant interaction effect between coat coverage and the environment under different climatic conditions (p<0.05). The surveyed grooming dogs exhibited greater body temperatures at all temperatures tested, namely, 17°C, 25°C, and 33°C. Grooming did not reduce the effects of thermal stress caused by different climatic conditions, indicating that grooming is not advisable for these animals