Air Temperature Research Articles

The impact of changing climate on the spread of the widely expanding species Ambrosia artemisiifolia in Slovakia

AbstractThe invasion of common ragweed in Slovakia was analysed based on 1635 collected records. More than 70 years have passed since the first record was sampled in 1949. Since then, it has spread in two waves, first in the lowlands, next to farmland and human settlements, and later in the mountains, on the roadsides. In the present, it is probably expanded in half of the territory of Slovakia. It is changing habitat preferences and adapting to new environments. Using eleven environmental and geographical variables, the species distribution modelling was performed to obtain probability of occurrence in high resolution (30 s) in Slovakia. Four predictions were calculated based on two models. The first prediction was based on the first model trained on ragweed records before the start of the second expansion wave in 2000. The second prediction is based on the first model and predicts the present distribution according to current climatic conditions, which have changed in twenty years. The third prediction was based on the second model, trained on all 1635 collected records from 1949 to 2022. The fourth prediction based on the second model shows the potential distribution in 2040–2061. Comparing the predictions, the climatic changes explain current expansion of common ragweed in mountains since the air temperature is the main factor shaping distribution of common ragweed in Slovakia.

A hybrid deep learning approach to predict hourly riverine nitrate concentrations using routine monitored data

With high-frequency data of nitrate (NO3–N) concentrations in waters becoming increasingly important for understanding of watershed system behaviors and ecosystem managements, the accurate and economic acquisition of high-frequency NO3–N concentration data has become a key point. This study attempted to use coupled deep learning neural networks and routine monitored data to predict hourly NO3–N concentrations in a river. The hourly NO3–N concentration at the outlet of the Oyster River watershed in New Hampshire, USA, was predicted through neural networks with a hybrid model architecture coupling the Convolutional Neural Networks and the Long Short-Term Memory model (CNN-LSTM). The routine monitored data (the river depth, water temperature, air temperature, precipitation, specific conductivity, pH and dissolved oxygen concentrations) for model training were collected from a nested high-frequency monitoring network, while the high-frequency NO3–N concentration data obtained at the outlet were not included as inputs. The whole dataset was separated into training, validation, and testing processes according to the ratio of 5:3:2, respectively. The hybrid CNN-LSTM model with different input lengths (1d, 3d, 7d, 15d, 30d) displayed comparable even better performance than other studies with lower frequencies, showing mean values of the Nash-Sutcliffe Efficiency 0.60–0.83. Models with shorter input lengths demonstrated both the higher modeling accuracy and stability. The water level, water temperature and pH values at monitoring sites were main controlling factors for forecasting performances. This study provided a new insight of using deep learning networks with a coupled architecture and routine monitored data for high-frequency riverine NO3–N concentration forecasting and suggestions about strategies about variable and input length selection during preprocessing of input data.

Using clustering to understand intra-city warming in heatwaves: insights into Paris, Montreal, and Zurich

We introduce a novel methodological advancement by clustering paired near-surface air temperature with the planetary boundary layer height to characterize intra-city clusters for analytics. To illustrate this approach, we analyze three heatwaves (HWs): the 2019 HW in Paris, the 2018 HW in Montreal, and the 2017 HW in Zurich. We assess cluster-based characteristics before, during, and after heatwave events. While the urban clusters identified by this clustering align well with built-up areas obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) land cover data, additional local hot spots spanning several kilometers can also be recognized, extending outside the built-up areas. Using the objective hysteresis model, we further determine the overall strength coefficient of the hysteresis loop between ground storage flux and all-wave downward radiative flux, ranging from 0.414 to 0.457 for urban clusters and from 0.126 to 0.157 for rural clusters during the heatwave periods. Across all cities, we observe a consistent refueling-restoration mode in the cumulative ground heat flux as the heatwaves progress. Future developments of this proposed two-component clustering approach, with the integration of more influential physics and advances in spatial and temporal resolutions, will offer a more comprehensive characterization of cities for urban climate analytics.

Fabrication and Characterization of Sustained-Release Eudragit RSPO Microsphere of Lornoxicam by Spray Drying Technique

Background: Lornoxicam is a potent non-steroidal anti-inflammatory drug (NSAID) with strong analgesic properties; however, its short half-life necessitates frequent administration for sustained efficacy. Objective: The objective of the present study was to identify critical formulation parameters affecting sustained release Eudragit RSPO microsphere of lornoxicam employing the concept of design of experiments Method: The lornoxicam-loaded microspheres were prepared using a spray drying technique by optimizing the drug:polymer ratio(X1) and inlet air temperature (X2) using a 32 full factorial design. The percentage drug entrapment (Y1), percentage yield (Y2) and cumulative percentage of the drug dissolved at 18 hr (Y3) were selected as dependent variables. Microspheres were evaluated for percentage drug entrapment, mean particle size, percentage yield, in vitro drug release study and scanning electron microscopy (SEM). Result: The optimized batch showed 84.51% drug release after 18 h with significantly higher drug entrapment and percentage yield. The optimization was further verified using overlay and desirability plots. The sustained release behavior of the drug from microspheres in GIT was displayed. The predicted and observed responses for the optimized formulation were in close agreement with the release profile of the drug. Conclusion: The sustained release microspheres of lornoxicam may prove to be an effective analgesic with excellent anti-inflammatory potential.

Evaluating the effects of different tree species on enhancing outdoor thermal comfort in a post-industrial landscape

Abstract A frequently emphasized strategy to reduce the burden of heat in cities across the world is the implementation of street trees. Here, we examine the effects of deciduous and coniferous tree deployment on meteorological variables and pedestrian themal comfort through analysis of the new dynamic thermal comfort (dPET) index, using the latest version of the computational fluid dynamics model ENVI-met. We performed on site observational measurements of air temperature (Ta), relative humidity (Rh), wind speed (Ws), and mean radiant temperature (MRT) at 5 different locations on the hottest day of summer 2023, in a post-industrial urban landscape located in Tehran, Iran. Observations were used to evaluate ENVI-met simulation performance and served as a baseline against which sensitivity experiments – based on a minimum (35%) and maximum (75%) intervention scenario for deciduous and coniferous trees – were compared against. Our analysis indicates that 35% and 75% deployment reduced Ta by 1.2°C and 4.2°C, respectively, for deciduous tree species, compared to a 0.9°C and 3.1°C reduction for coniferous species, during the hottest day of summer 2023. The maximum deployment scenario decreased MRT by approximately 60°C and 43°C for deciduous and coniferous tree deployment, respectively. The maximum tree deployment scenario decreased dPET by nearly 16°C and 14°C for deciduous and coniferous trees, respectively, during the time of day that diurnal heating is maximized. Our findings highlight micrometeorological and personalized thermal comfort effects associated with variable tree species type and extent through examination of a pedestrian's ambulatory experience across diverse urban microclimates in a region of the world that is particularly understudied.

PRISMA Hyperspectral Satellite Imagery Application to Local Climate Zones Mapping

Abstract. The urban heat island effect exacerbates the vulnerability of cities to climate change, emphasizing the need for sustainable urban planning driven by data evidence. In the last decade, the Local Climate Zone (LCZ) model emerged as a key tool for categorizing urban landscapes, aiding in the development of urban temperature mitigation strategies. In this work, the contribution of hyperspectral satellite imagery to LCZ mapping, leveraging the Italian Space Agency (ASI)’s PRISMA satellite, is investigated. Mapping performances are compared with traditional multispectral-based LCZ mapping using Sentinel-2 satellite imagery. The Random Forest algorithm is utilized for LCZ classification, with evaluation conducted through spectral separability analysis and accuracy assessment between PRISMA and Sentinel-2 derived LCZ maps as well as with the benchmark LCZ Generator mapping tool. An initial experiment on the effect of PRISMA image pan-sharpening on LCZ spectral separability is also presented. Results obtained for Milan (Northern Italy) demonstrate the potential of hyperspectral imagery in enhancing LCZ identification compared to multispectral data, with promising improvements in LCZ maps overall accuracy. Finally, air temperature patterns within each LCZ class are explored, qualitatively confirming the influence of urban morphology on thermal comfort.

A Comparative Model of Thermal Performances Between a Modern and Classic Habitat for A Hot and Humid Tropical Climate: Case of Guinea

The objective of this research is to carry out a comparative study between a classic habitat and a modern habitat in order to understand which of the two is more efficient for a humid tropical area. To do this, a digital modeling of the heat transfer phenomena in the different components of the habitat was highlighted for a habitat made of stabilized earth bricks, a roof made of transparent tiles, false ceilings made of rectangular panels containing phase change material, concrete floor and a classic habitat made from local materials is presented. To show the reliability of this study, we validated our habitat model integrating phase change material through an experimental study carried out by researchers B. ZIVKOVIC and I. FUJII. A study on the temperature distributions at the level of certain components of the habitat done, and the influence of the variation of certain parameters was highlighted to know the thermal behavior of the two habitats. Furthermore, a comparative study was performed between the interior and exterior temperatures of the south walls of habitats incorporating PCM and without PCM, which are the components most exposed to solar radiation whose maximum values are respectively 33°C and 34, 5°C and 33.15°C against 47°C. Keywords: Performances, thermal, bioclimatic, PCM, Tropical climate, Guinea.

Exploring the Seasonal Comparison of Land Surface Temperature Dominant Factors in the Tibetan Plateau

Abstract. LST (Land Surface Temperature) is a significant parameter that represents the ground energy balance and plays a crucial role in understanding climate change. The LST of the Tibetan Plateau (TP) has a direct influence on the climate and environmental changes of the TP, and it also has a significant impact on global climate and atmospheric circulation. Although there are various factors that drive the spatial and temporal distribution of LST on the TP, the primary driving forces and its seasonal variations of LST are not yet well understood. The research focuses specifically on the TP region, selecting three types of LST data, using geodetector model, to analyze the driving factors affecting the spatial pattern of LST in different seasons. The results indicate that the three factors, Air Temperature (AT), Elevation (Ele), and Permafrost Thermal Stability (PTS), have a significant influence on LST throughout all seasons, whereas other variables demonstrate varying contributions to LST depending on the season. This study contributes to the understanding of the spatial variability of surface thermal conditions and the intricate relationships between their driving factors. It also emphasizes the potential changes in these relationships throughout the year.

Accuracy of Simscape Solar Cell Block for Modeling a Partially Shaded Photovoltaic Module

With half-cut photovoltaic (PV) modules being the dominant technology on the market, there is an increasing necessity for accurate modeling of this module type. Circuit simulators such as Simulink are widely used to study different topics regarding photovoltaics, often employing a solar cell block available from the Simcape library. The purpose of this work is to validate this model against measurements for a partially shaded half-cut PV module. Diverse shading scenarios are created by varying the number of shaded substrings, the number of shaded solar cells in the substring, and the shading level. For every shading scenario, the PV module’s I-V curve is measured, along with in-plane irradiance, air temperature, and module temperature. A comprehensive evaluation of simulation accuracy is presented. The results confirm a high accuracy of the model with mean nRMSE values of 2.2% for I-V curves and 2.8% when P-V curves are considered. It is found that the simulation errors tend to increase when increasing the number of shaded substrings. At the same time, no obvious dependency of simulation accuracy on the shading level or the number of shaded solar cells in the substring is found.

Influence of meteorological factors on the moisture content of fine forest fuels: responses of fire danger class to different microclimates on the example of European beech (Fagus sylvatica L.) stands

Abstract Due to the increased number of forest fires, a detailed examination and knowledge of the effects of the microclimatic conditions of forests is currently significant. The study carried out in Arboretum Borova hora (Slovak Republik), investigates the influence of meteorological factors (air temperature, relative humidity), the value of the Angström index, and the danger class of the Angström index on the moisture content of fine fuel at the edge of a beech forest stand, but also in its interior. We tested three working hypotheses: a) meteorological conditions differ significantly between the edge and the interior of the beech forest, b) the moisture content of fine fuel is higher in the beech forest interior than at the forest edge, c) the Angström index fire danger class is higher at the edge of the beech forest than in its interior. We created a calibration curve that was also used to measure the humidity of beech leaves with the help of the ME 2000 hygrometer. Our results show that edge beech stands are significantly more susceptible to fires, lower air humidity and fine fuel moisture content, and higher air temperatures than forest interiors. From our point of view, the microclimate is considered the main factor that explains the difference between the vegetation structure of the forest edge and the forest interior.

Analysis of Internal Conditions and Energy Consumption during Winter in an Apartment Located in a Tenement Building in Poland

The residential sector of existing buildings has great potential in energy savings and the improvement of indoor conditions. The modernization of buildings is of particular concern to the policies of the European Union, local governments, and building users. The aim of this paper is to present an analysis of indoor parameters and energy consumption for heating for an apartment located in a pre-war tenement building before and after thermomodernization. The analysis was conducted for winter conditions and was based on measurements and simulations. Originally, the building had not undergone any thermomodernization actions since its reconstruction after WWII. Interior, exterior, and surface temperatures were recorded to describe the thermal conditions of the apartment, while gas meter readings were used to estimate energy consumption for heating purposes. WUFI Plus software (v.3.2.0.1) was used to estimate energy consumption and perform energy simulations for the apartment over an extended period of time. The best thermomodernization effect resulted from the replacement of windows and the inefficient heating system, avoiding surface condensation and reducing final energy consumption by more than 50%. The extended options resulted in energy savings higher than 70%. The presented analysis shows the importance of retrofit measures and proves that even a small improvement can bring significant benefits.

Examining UV Radiation Patterns in Relation to Particulate Matter and Atmospheric Conditions in Arid, Unclouded Skies

This study investigates the influences of air temperature (T), relative humidity (RH), wind speed (WS), clearance index (KT), particulate matter (PM) concentrations (PM10 and PM2.5), and aerosol optical depth (AOD) on ultraviolet (UV) radiation during clear skies in Riyadh, central Saudi Arabia. The observational dataset utilized in this study comprises global solar radiation (G), UVA radiation, AOD measurements, and PM concentrations. The data were collected from 2014 to 2015 at the King Abdulaziz City for Science and Technology (KACST) campus in Riyadh. Regression analyses were conducted to investigate the relationships between UV radiation and the considered variables. The methodology is based on the least square method and associated statistical tests. The findings of this study provide valuable insights into the impacts of meteorological variables and aerosols on UV radiation, contributing to the understanding of environmental and industrial applications in the Arabian Peninsula. The analyses showed that the strengths and magnitudes differed from one variable to another. No significant correlations between UVA radiation (315–400 nm) and hourly and daily values of PM2.5 were found. Moreover, no significant correlations were seen between daily values of the UVA radiation and RH and between the UVB (280–315 nm) and PM10. The rest of the correlations (between UV radiation and the PM10 and meteorological variables) were found to be significant. While WS, the ratio of the PM concentrations (PM2.5/PM10), KT, and T exhibited positive correlations with UV radiation, the rest of the variables had anti-correlations with UV radiation. The influences of T, WS, and RH on ambient PM concentrations during the considered period were taken into account, and it was found that the PM concentrations correlate, with different magnitudes and strengths, positively with T and negatively with RH and WS.

Influence of Supraglacial Lakes on Accuracy of Inversion of Greenland Ice Sheet Surface Melt Data in Different Passive Microwave Bands

The occurrence of Supraglacial Lakes (SGLs) may influence the signals acquired with microwave radiometers, which may result in a degree of uncertainty when employing microwave radiometer data for the detection of surface melt. Accurate monitoring of surface melting requires a reasonable assessment of this uncertainty. However, there is a scarcity of research in this field. Therefore, in this study, we computed surface melt in the vicinity of Automatic Weather Stations (AWSs) by employing Defense Meteorological Satellite Program (DMSP) Ka-band data and Soil Moisture and Ocean Salinity (SMOS) satellite L-band data and extracted SGL pixels by utilizing Sentinel-2 data. A comparison between surface melt results derived from AWS air temperature estimates and those obtained with remote sensing inversion in the two different bands was conducted for sites below the mean snowline elevation during the summers of 2016 to 2020. Compared with sites with no SGLs, the commission error (CO) of DMSP morning and evening data at sites where these water bodies were present increased by 36% and 30%, respectively, and the number of days with CO increased by 12 and 3 days, respectively. The omission error (OM) of SMOS morning and evening data increased by 33% and 32%, respectively, and the number of days with OM increased by 17 and 21 days, respectively. Identifying the source of error is a prerequisite for the improvement of surface melt algorithms, for which this study provides a basis.

Thermal Environment Analysis and Optimization for Large Space Buildings with Radiant Cooling Floors: A Case Study of Xianyang International Airport

Radiant cooling floors combined with ventilation systems have been widely applied in large space buildings. However, there has been a lack of research on system control strategies for their adaptation to weather changes. This study aimed to find control strategies for radiant cooling floors combined with displacement ventilation systems used in large space buildings in order to achieve energy conservation and environmental improvement. Supply air temperature and cooling surface temperature were determined to be the control variables. It was found that cooling capacity of the combined system and the comfort index, PMV (predicted mean vote), were linear in relation to the supply air temperature and cooling surface temperature. The linear equations regarding cooling capacity and PMV were established separately using environment data, and then the optimal region was determined. A case study on Terminal 3 of Xi’an Xianyang International Airport was conducted. The thermal environment was investigated through on-site measurements, questionnaires, and numerical simulations with CFD (computational fluid dynamics). It was found that supply air temperature and cooling surface temperature had a significant impact on PMV, and less impact on the cooling capacity. Therefore, it was determined that the supply air temperature should be altered first when the indoor temperature exceeds the upper limit, and then the cooling surface temperature should be changed if the indoor environment continues to overheat with the supply air temperature set to 18 °C. Thus, the supply air temperature was kept at 18 °C, and the floor surface temperature was set to be 22 °C on a high-temperature day. The average PMV was 0.87, and the cooling capacity of the combined system was 200 W/(m2·K), according to the CFD simulation. In addition, the surface heat transfer coefficient of the cooling floor was found to be 10.26 W/(m2·K). This research provides important references for the design and operational management of radiant cooling floors in large space buildings.

Overheating in the Tree Shade of Urban Parks: A Field Study of Thermal Adaption in China

With increased atmospheric temperature, temperatures in the shade of trees in parks also increase, and people are faced with high temperature challenges. In this study, thermal comfort in the shade of the trees of an urban park during summer in China was assessed. The subjective responses of the respondents were recorded via questionnaires, and environment parameters were measured. The results show that the air temperature in the shade was 31.1 ± 3.0 °C during the day, and that it peaked at 36.9 °C; the globe temperature was 31.3 ± 3.1 °C, and it peaked at 40.1 ℃. Respondents’ clothing insulation was 0.31 ± 0.08 clo, and the effect of clothing adjustment on thermal adaptation was limited. Thermal sensation is linearly related to standard effective temperature (SET), and the upper limit of 80% acceptable SET was 32.1 °C. At different temperature values, the proportion of expected airflow enhancement exceeded 50%. The respondents preferred a neutral-warm sensation. Moreover, there was an obvious thermal adaptation, with thermal history and psychological adaptation being the main factors affecting thermal comfort. This study confirmed the value of shade and provided us with guidance for park planning and design.

Interaction of black carbon surface mass with meteorological variables and spatial pattern across the 36 states of tropical Nigeria

Black carbon (BC) has been linked to cardio-pulmonary diseases and lung cancer. It is also the second largest contributor to climate change, after carbon dioxide (CO2), Effective mitigation of BC is of much benefits, but it requires detailed knowledge of its spatial distribution, as well as the influence of local meteorology on the spread of the pollutant. Many countries in sub-Sahara Africa including Nigeria, lack data on BC level. There is also limited information on meteorological impact on BC spatial distribution. This study therefore assessed the spatial and temporal distribution of BC across the 36 states of Nigeria and the influence of meteorology on the pollutant spread. Data on BC surface mass (BCMASS) and seven meteorological variables (air temperature, relative humidity, rainfall, wind speed, planetary boundary layer height (PBLH), atmospheric pressure and wind direction) were obtained from Modern Era Retrospective analysis for Research (MERRA-2). Spatial distribution of BCSMASS across 36 states of Nigeria, and interaction with meteorology was studied using correlation analyses, Bayesian ridge regression with marginal maximum likelihood estimation, and a Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model. There was significant (p < 0.05) variation in BCSMASS across the states. The meteorological variables, except wind direction, significantly influenced BCSMASS and they accounted for 63% of the variation. Five-day backward trajectory modelled for each month of the year revealed that northern Nigeria is majorly under the influence of the dry and dusty tropical continental wind, and this could partially account for the lower BCSMASS concentration in the region, compared to southern Nigeria. Based on population-weighted exposure, states in southern Nigeria, especially the south-eastern part, should be prioritized in planning mitigation, for effective climate action.

Does the Amount of Pre-Sowing Nitrogen Fertilization Affect Sugar Beet Root Yield and Quality of Different Genotypes?

There has always been a specific focus on nitrogen fertilization in sugar beet production due to its important effect on sugar beet root yield and quality. For stable sugar beet growth and satisfactory root yield and quality, balanced N fertilization is crucial. Thus, this study aimed to investigate spring N fertilization in two seasons as the following treatments: N0—control, N1—only pre-sowing fertilization, and N2—pre-sowing with topdressing. Four different genotypes were included in the study (Serenada, Colonia, Fred, and Danton). The experiment was set up in a plain area, belonging to the temperate climate zone in Eastern Croatia (Županja and Vrbanja), with the long-term mean (LTM) (March–October) air temperature around 16 °C and the total precipitation of 515 mm. Pre-sowing N fertilization had a smaller impact on root yield in the year with higher precipitation (31% higher than LTM). Therefore, the average yields with pre-sowing fertilization (N1) and pre-sowing fertilization with top dressing (N2) were very similar and were only 7% higher than those of the control. In a season with less rainfall (29% less than LTM), pre-sowing fertilization with top dressing (N2) had a more pronounced effect on the increase in sugar beet root yield, which was 17% higher compared to that of the control treatment. The sugar beet sucrose content and quality parameters (brei impurities, loss of sugar in molasses, extractable sugar) differed when N fertilization was applied among locations in both seasons. The white sugar yield was the highest at N2 treatment with pre-sowing and topdressing N fertilization. In general, according to the average of all locations and years of research, the Serenada hybrid achieved the highest average root yield (81.1 t ha−1), while Colonia exhibited the highest root sugar content (14.5%) and white sugar yield (9.7 t ha−1).

A climate-driven, altitudinal transition in rock glacier dynamics detected through integration of geomorphological mapping and synthetic aperture radar interferometry (InSAR)-based kinematics

Abstract. In dry southwestern South Tyrol, Italy, rock glaciers are dominant landforms of the high-mountain cryosphere. Their spatial distribution and degree of activity hold critical information on the current state of discontinuous permafrost and consequently on the response potential to climate warming. Traditional geomorphologic mapping, however, owing to the qualitative expert-based nature, typically displays a high degree of uncertainty and variability among operators with respect to the dynamic classification of intact (permafrost-bearing) and relict (permafrost-devoid) rock glaciers. This limits the reliability of geomorphologic rock glacier inventories for basic and applied purposes. To address this limitation, (i) we conduct a systematic evaluation of the improvements that synthetic aperture radar interferometry (InSAR) can afford to the detection and dynamic classification of rock glaciers and (ii) build an integrated inventory that combines the strengths of geomorphologic- and InSAR-based approaches. To exploit fully InSAR-based information towards a better understanding of the topo-climatic conditions that sustain creeping permafrost, we further explore how velocity and the spatial distribution of moving areas (MAs) within rock glaciers may vary as a function of simple topographic variables known to exert first-order controls on incoming solar radiation, such as elevation and aspect. Starting from a geomorphologic inventory (n=789), we characterize the kinematics of InSAR-based MAs and the relevant hosting rock glaciers on 36 Sentinel-1 interferograms in the 2018–2019 period. With respect to the original inventory, InSAR analysis allowed us to identify 14 previously undetected rock glaciers. Further, it confirmed that 246 (76 %) landforms, originally interpreted as intact, do exhibit detectable movement (i.e., ≥1 cm yr−1) and that 270 (60 %) of the relict labeled counterparts do not, whereas 144 (18 %) were kinematically undefined due to decorrelation. Most importantly, InSAR proved critical for reclassifying 121 (15 %) rock glaciers, clarifying that 41 (13 %) of those interpreted as being intact do not exhibit detectable movement and that 80 (17 %) of the original relict ones do move. Reclassification (i) allowed us to identify a cluster of intact rock glaciers below 2000 m a.s.l. associated with positive mean annual air temperature (MAAT), and (ii) by increasing the altitudinal overlap between intact and relict rock glaciers, it depicts a broad transition belt in the aspect–elevation space, which varies from 50 m on west-facing slopes to 500 m on easterly ones. This finding deteriorates the significance of elevation and aspect as topographic proxies for modeling permafrost occurrence and highlights the importance of using InSAR to inform such models. From a process-oriented standpoint, InSAR information proves fundamental for imaging how this altitudinal transition manifests through changing rates and styles of rock glacier surface deformation. Specifically, we find that, as rock glaciers move faster, an increasingly larger proportion of their surface becomes kinematically involved (i.e., percent MA cover) and that this proportion increases with elevation up to 2600–2800 m, beyond which an inflection occurs and consistent average values are attained. Considering that the inflection falls between the −1 and −2 °C MAAT – the lower boundary for discontinuous permafrost – and is independent of slope gradient, we conclude that this altitudinal pattern represents a geomorphic signature: the dynamic expression of increasing permafrost distribution, from sporadic to discontinuous.

Residual canopy cover provides buffering of near-surface temperatures, but benefits are limited under extreme conditions

Increasing summer temperatures and higher probabilities of extreme heat events have led to concerns about tree damage and mortality. However, insufficient attention has been given to conditions leading to heat-related regeneration failures in temperate forests. To address this managers need to understand how microclimate varies under a range of overstory conditions. We measured air temperatures at 2cm above-ground underneath a gradient of canopy cover on south-facing slopes in recently thinned Douglas-fir stands in western Oregon, USA. To expand the ecological relevance of these data to impacts on regeneration, we created the stress-degree hours (SDH) metric, representing the amount of time - and by how much – temperatures exceeded biologically relevant stress thresholds. Overall, for every 10% increase in canopy cover, maximum temperatures at 2cm were 1.3oC lower, the odds of temperatures exceeding stress thresholds for conifer regeneration declined by a multiplicative factor of 0.26, and the total of SDH decreased by 40%. These reductions are large enough to be worthy of attention when managing for tree regeneration. However, data collected during the Pacific Northwest Heat Dome in June 2021 indicate that with various climate change scenarios and heatwave occurrences, temperatures will be unfavorable for regeneration regardless of overstory cover.

Predicting the geographical distribution and niche characteristics of Cotoneaster multiflorus based on future climate change

IntroductionArid and semi-arid regions are climate-sensitive areas, which account for about 40% of the world’s land surface area. Future environment change will impact the environment of these area, resulting in a sharp expansion of arid and semi-arid regions. Cotoneaster multiflorus is a multi-functional tree species with extreme cold, drought and barren resistance, as well as ornamental and medicinal functions. It was found to be one of the most important tree species for ecological restoration in arid and semi-arid areas. However, bioclimatic factors play an important role in the growth, development and distribution of plants. Therefore, exploring the response pattern and ecological adaptability of C. multiflorus to future climate change is important for the long-term ecological restoration of C. multiflorus in arid and semi-arid areas.MethodsIn this study, we predicted the potential distribution of C. multiflorus in China under different climate scenarios based on the MaxEnt 2.0 model, and discussed its adaptability and the major factors affecting its geographical distribution.ResultsThe major factors that explained the geographical distribution of C. multiflorus were Annual precipitation (Bio12), Min air temperature of the coldest month (Bio6), and Mean air temperature of the coldest quarter (Bio11). However, C. multiflorus could thrive in environments where Annual precipitation (Bio12) &amp;gt;150 mm, Min air temperature of the coldest month (Bio6) &amp;gt; -42.5°C, and Mean air temperature of the coldest quarter (Bio11) &amp;gt; -20°C, showcasing its characteristics of cold and drought tolerance. Under different future climate scenarios, the total suitable area for C. multiflorus ranged from 411.199×104 km² to 470.191×104 km², which was 0.8~6.14 percentage points higher than the current total suitable area. Additionally, it would further shift towards higher latitude.DiscussionThe MaxEnt 2.0 model predicted the potential distribution pattern of C. multiflorus in the context of future climate change, and identified its ecological adaptability and the main climatic factors affecting its distribution. This study provides an important theoretical basis for natural vegetation restoration in arid and semi-arid areas.