Land surface temperature (LST) plays an important role in Earth energy balance and water/carbon cycle processes and is recognized as an Essential Climate Variable (ECV) and an Essential Agricultural Variable (EAV). LST products that are issued from satellite observations mostly depict landscape-scale temperature due to their generally large footprint. This means that a pixel-based temperature integrates over various components, whereas temperature individual components are better suited for the purpose of evapotranspiration estimation, crop growth assessment, drought monitoring, etc. Thus, disentangling soil and vegetation temperatures is a real matter of concern. Moreover, most satellite-based LSTs are contaminated by directional effects due to the inherent anisotropy properties of most terrestrial targets. The characteristics of directional effects are closely linked to the properties of the target and controlled by the view and solar geometry. A singular angular signature is obtained in the hotspot geometry, i.e., when the sun, the satellite and the target are aligned. The hotspot phenomenon highlights the temperature differences between sunlit and shaded areas. However, due to the lack of adequate multi-angle observations and inaccurate portrayal or neglect of solar influence, the hotspot effect is often overlooked and has become a barrier for better inversion results at satellite scale. Therefore, hotspot effect needs to be better characterized, which here is achieved with a three-component model that distinguishes vegetation, sunlit and shaded soil temperature components and accounts for vegetation structure. Our work combines thermal infrared (TIR) observations from the Sea and Land Surface Temperature Radiometer (SLSTR) onboard the LEO (Low Earth Orbit) Sentinel-3, and two sensors onboard GEO (geostationary) satellites, i.e. the Advanced Himawari Imager (AHI) and Spinning Enhanced Visible and Infrared Imager (SEVIRI). Based on inversion with a Bayesian method and prior information associated with component temperature differences as constrained, the findings include: 1) Satellite observations throughout East Asia around noon indicate that for every 10 degrees change in angular distance from the sun, LST will on average vary by 0.6 K; 2) As a better constraint, the hotspot effect can benefit from multi-angle TIR observations to improve the retrieval of LST components, thereby reducing the root mean squared error (RMSE) from approximately 3.5 K, 5.8 K, and 4.1 K to 2.8 K, 3.5 K, and 3.1 K, at DM, EVO and KAL sites, respectively; 3) Based on a dataset simulated with a threedimensional radiative transfer model, a significant inversion error may result if the hotspot is ignored for an angular distance between the viewing and solar directions that is smaller than 30 degrees. Overall, considering the hotspot effect has the potential to reduce inversion noise and to separate the temperature difference between sunlit and shaded areas in a pixel, paving the way for producing stable temperature component products.

River riparian basins play a crucial role in mitigating greenhouse gas (GHG) emissions through carbon sequestration and nitrogen sinks. However, increased ecological stresses led to the release of CO2, CH4 and N2O. This study aimed to investigate how extreme temperatures, water levels, moisture content, land use changes and soil composition influence GHG emissions in the riparian corridor and to recommend mitigation techniques. It was carried out at the Yangtze River Riparian zone, China, using soil column testing. It used soil column testing. The results showed that extreme temperatures caused the highest emissions of CO2 (29-45%), CH4 (24-43%) and N2O (27-33%). This was due to increased soil temperatures and accelerated organic carbon/nitrogen decomposition. Conversely, control and wet-dry cycles absorbed CO2 (1-3%), CH4 (3-10%) and N2O (1-21%) by improving soil aeration, increased oxygen availability, soil structure, stable water table and low temperature change. Grasses in riparian areas also improved carbon sinks. Highest water levels had lowest gas concentrations and emissions due to low oxygen level. Adaptive wet-dry cycles, grass cover and better water table management can restore riparian areas, maintain soil moisture, balance soil carbon/nitrogen levels and mitigate climate change by improving soil quality. Dissolved organic matter fluorescence (DOMFluor) components are essential for soil carbon dynamics, aquatic biome safety, nutrient cycling and ecological balance in riparian zones. The study recommends implementing restoration practices, managing soil moisture, afforestation, regulating temperature and monitoring water tables to mitigate GHG emissions and address climate change. Future policies should focus on promoting resilient land use and ecosystems.

The existing traditional creep models have obvious shortcomings when describing the accelerated creep properties of the viscoplastic stage of rock. As a way to describe the whole process of rock creep and different creep stages and characterize the deformation characteristics of the accelerated creep stage, the creep damage during the creep loading process can be considered to be caused by the development of internal defects in the rock and soil materials. According to the theory of internal variable thermodynamics, the internal variable evolution equation in the form of self -consistent differential equations is established. The creep curve of rock under different temperatures is in good agreement with the model curve. But there are some errors. This is due to the inevitable differences between the samples. However, the changing trend of strain and creep time is the same. Therefore, the rock creep model established in this paper is reasonable and feasible to describe the creep characteristics under different stresses and different temperatures. The variation of the rock creep model curve under different parameters is analyzed. Then, the clear physical meaning of different parameters is obtained. Finally, the established rock creep model can predict the creep deformation under different temperatures and stress. It has theoretical significance for practical engineering.

Irrigation has distinct impacts on extreme temperatures. Due to the carryover effect of soil moisture into other seasons, temperature impacts of irrigation are not limited to irrigated seasons. Focusing on the North China Plain, where irrigation occurs in both spring (March-April-May) and summer (June-July-August), with a higher proportion of irrigation water applied during spring, we investigate the impact of spring irrigation on summer extreme heat events. Based on partial correlation analysis of data products, we find positive correlations between spring and summer soil moisture, suggesting that spring irrigation-induced water surplus persists into the following summer and affects regional climate by impacting surface energy partitioning. Regional climate simulations confirm cross-seasonal climatic effects and show that spring irrigation reduces the frequency and intensity of summer extreme heat events by approximately -2.5 days and -0.29 degrees C, respectively. Our results highlight the importance of the cross-seasonal climatic effect of irrigation in mitigating climate extremes. Irrigation exerts a stronger impact on extreme temperatures than on mean temperatures. The North China Plain (NCP) is a typical winter wheat-summer maize rotation planting area, where irrigation is necessary in both spring and summer, but with a higher proportion of irrigation water applied during spring. The climatic effects of spring and summer irrigation in the NCP are intertwined due to the carryover effects of soil moisture. Recently, the climatic effect of irrigation in the NCP has been extensively explored, whereas the cross-seasonal effects of irrigation on summer extreme heat events have never been quantified. In this study, we employ the Weather Research and Forecasting model coupled with a demand-driven irrigation algorithm to discern the effects of spring and/or summer irrigation on summer extreme heat events by means of idealized climate simulations. The results show that spring and summer irrigation significantly reduces the frequency and intensity of summer extreme heat events by approximately -6.5 days and -1.0 degrees C, of which spring irrigation contributes about 38% and 30%, respectively. Our findings underline the importance of irrigation-induced climate impacts in mitigating extreme heat events and emphasize that climate change adaptation planning in terms of irrigation must account for cross-seasonal climatic effects. Effect of multi-seasonal irrigation on summer extreme heat events is investigated Spring irrigation is beneficial for reducing summer extreme heat events Irrigation modulates the relationship between spring and summer soil moisture

Temperature measurements in boreholes are the most common method allowing the quantitative and direct observation of permafrost evolution in the context of climate change. Existing boreholes and monitoring networks often emerged in a scientific context targeting different objectives and with different setups. A standardized, well-planned and robust instrumentation of boreholes for long-term operation is crucial to deliver comparable, high-quality data for scientific analyses and assessments. However, only a limited number of guidelines are available, particularly for mountain regions. In this paper, we discuss challenges and devise best practice recommendations for permafrost temperature measurements at single sites as well as in a network, based on two decades of experience gained in the framework of the Swiss Permafrost Monitoring Network PERMOS. These recommendations apply to permafrost observations in mountain regions, although many aspects also apply to polar lowlands. The main recommendations are (1) to thoroughly consider criteria for site selection based on the objective of the measurements as well as on preliminary studies and available data, (2) to define the sampling strategy during planification, (3) to engage experienced drilling teams who can cope with inhomogeneous and potentially unstable subsurface material, (4) to select standardized and robust instrumentation with high accuracy temperature sensors and excellent long-term stability when calibrated at 0 degrees C, ideally with double sensors at key depths for validation and substitution of questionable data, (5) to apply standardized maintenance procedures allowing maximum comparability and minimum data processing, (6) to implement regular data control procedures, and (7) to ensure remote data access allowing for rapid trouble shooting and timely reporting. Data gaps can be avoided by timely planning of replacement boreholes. Recommendations for standardized procedures regarding data quality documentation, processing and final publication will follow later.

Knowledge of the spatiotemporal dynamics of the soil temperature in cold environment is key to understanding the effects of climate change on land-atmosphere feedback and ecosystem functions. Here, we quantify the recent thermal status and trends in shallow ground using the most up-to-date data set of over 457 sites in Russia. The data set consists of in situ soil temperatures at multiple depths (0.8, 1.6, and 3.2 m) collected from 1975 to 2016. For the region as a whole, significant soil warming occurred over the period. The mean annual soil temperature at depths of 0.8, 1.6, and 3.2 m increased at the same level, at ca 0.30-0.31 degrees C/decade, whereas the increase in maximum soil temperature ranged from 0.40 degrees C/decade at 0.8 m to 0.31 degrees C/decade at 3.2 m. Unlike the maximum soil temperature, the increases in minimum soil temperature did not vary (ca 0.25 degrees C/decade) with depth. Due to the overall greater increase in maximum soil temperature than minimum soil temperature, the intra-annual variability of soil temperature increased over the decades. Moreover, the soil temperature increased faster in the continuous permafrost area than in the discontinuous permafrost and seasonal frost areas at shallow depths (0.8 and 1.6 m depth), and increased slower at the deeper level (3.2 m). The warming rate of the maximum soil temperature at the shallower depths was less than that at the deeper level over the discontinuous permafrost area but greater over the seasonal frost area. However, the opposite was found regarding the increase in minimum soil temperature. Correlative analyses suggest that the trends in mean and extreme soil temperatures positively relate to the trends in snow cover thickness and duration, which results in the muted response of intra-annual variability of the soil temperature as snow cover changes. This study provides a comprehensive view of the decadal evolutions of the shallow soil temperatures over Russia, revealing that the temporal trends in annual mean and extreme soil temperatures vary with depth and permafrost distribution.

Permafrost is a key element of the cryosphere and an essential climate variable in the Global Climate Observing System. There is no remote-sensing method available to reliably monitor the permafrost thermal state. To estimate permafrost distribution at a hemispheric scale, we employ an equilibrium state model for the temperature at the top of the permafrost (TTOP model) for the 2000-2016 period, driven by remotely-sensed land surface temperatures, down-scaled ERA-Interim climate reanalysis data, tundra wetness classes and landcover map from the ESA Landcover Climate Change Initiative (CCI) project. Subgrid variability of ground temperatures due to snow and landcover variability is represented in the model using subpixel statistics. The results are validated against borehole measurements and reviewed regionally. The accuracy of the modelled mean annual ground temperature (MAGT) at the top of the permafrost is +/- 2 degrees C when compared to permafrost borehole data. The modelled permafrost area (MAGT 0) is around 21 x 10(6) km(2) (22% of exposed land area), which is approximately 2 x 10(6) km(2) less than estimated previously. Detailed comparisons at a regional scale show that the model performs well in sparsely vegetated tundra regions and mountains, but is less accurate in densely vegetated boreal spruce and larch forests.

Soil temperatures, water temperatures, and weather parameters were monitored at a variety of locations in the vicinity of King Sejong station, King George Island, Antarctica, during summer 2010-2011. Thermal characteristics of soil and water were analysed using time-series analyses, apparent thermal diffusivity (ATD), and active layer thickness. The temperatures of pond water and nearby seawater showed the distinctive diurnal variations and correlated strongly with solar radiation (r = 0.411-0.797). Soil temperature (0.1-0.3 m depth) also showed diurnal fluctuations that decreased with depth and were directly linked to air temperature (r = 0.513-0.783) rather than to solar radiation; correlation decreased with depth and the time lag in the response increased by 2-3 hours per 0.1 m of soil depth. Owing to the lack of snow cover, summertime soil temperature was not decoupled from air temperature. Estimated ATD was between 0.022 and 29.209 mm2/sec, showed temporal and spatial variations, and correlated strongly with soil moisture content. The maximum estimated active layer thickness in the study area was a 41-70 cm, which is consistent with values reported in the previous work.

As the climate warms, winters with less snow and therefore more soil freeze-thaw cycles are likely to become more frequent in oceanic mountain areas. It is a concern that this might impair the soil's ability to store carbon and nutrients, and lead to increased leaching losses of dissolved C and nutrients and subsequent changes in nutrient cycling and ecosystem productivity. Through a combination of laboratory and field experiments, we studied short-term effects of changing winter conditions on carbon and nutrient leaching from two plant-soil systems with contrasting snow conditions (shallow/intermittent vs. deep/persistent snow). In the laboratory we exposed cores (soil and vegetation) from sites with either intermittent or persistent winter snow cover to five different freeze-thaw scenarios of realistic frequency and duration. Additionally, we set up a transplant experiment at our field site by reciprocally transplanting soil-plant monoliths between sites with intermittent and persistent snow. Together, the field and laboratory experiments aimed to assess how carbon and nutrient leaching was affected by both historical snow conditions and short-term (through freeze-thaw scenarios and transplantation) changes in snow cover and thermal conditions. Both a greater number of freeze-thaw cycles and longer duration of sub-zero temperatures increased carbon and nutrient leaching from incubated soil cores. Cores from sites with persistent snow generally had lower nutrient losses under control conditions, but greater losses following induced freeze-thaw cycles than cores from intermittent snow sites. The character of the leached dissolved organic carbon (DOC) suggested fresh organic material, such as live plant roots or microbes, as the source of carbon and nutrients. Nutrient losses from the plant-soil systems in the field were greater at sites with persistent winter snow due to greater volumes of percolating water in spring. This suggests that increasingly severe and frequent soil freeze-thaw events in oceanic mountain ecosystems can enhance the mobilization of C, N and P in labile forms but, in the absence of water fluxes, these nutrients would remain available for in-situ cycling. Thus, under future warmer winter conditions, increased carbon and nutrient losses from oceanic mountain ecosystems could occur if winters with little snow coincide with wet spring conditions.

Winter is a key driver of individual performance, community composition, and ecological interactions in terrestrial habitats. Although climate change research tends to focus on performance in the growing season, climate change is also modifying winter conditions rapidly. Changes to winter temperatures, the variability of winter conditions, and winter snow cover can interact to induce cold injury, alter energy and water balance, advance or retard phenology, and modify community interactions. Species vary in their susceptibility to these winter drivers, hampering efforts to predict biological responses to climate change. Existing frameworks for predicting the impacts of climate change do not incorporate the complexity of organismal responses to winter. Here, we synthesise organismal responses to winter climate change, and use this synthesis to build a framework to predict exposure and sensitivity to negative impacts. This framework can be used to estimate the vulnerability of species to winter climate change. We describe the importance of relationships between winter conditions and performance during the growing season in determining fitness, and demonstrate how summer and winter processes are linked. Incorporating winter into current models will require concerted effort from theoreticians and empiricists, and the expansion of current growing-season studies to incorporate winter.