Winter Temperatures Research Articles

Drivers of summer Arctic sea-ice extent at interannual time scale in CMIP6 large ensembles revealed by information flow

Arctic sea-ice extent has strongly decreased since the beginning of satellite observations in the late 1970s. While several drivers are known to be implicated, their respective contribution is not fully understood. Here, we apply the Liang-Kleeman information flow method to five different large ensembles from the Coupled Model Intercomparison Project Phase 6 (CMIP6) over the 1970-2060 period to investigate the extent to which fluctuations in winter sea-ice volume, air temperature and ocean heat transport drive changes in subsequent summer Arctic sea-ice extent. This allows us to go beyond classical correlation analyses. Results show that air temperature is the most important controlling factor of summer sea-ice extent at interannual time scale, and that winter sea-ice volume and Atlantic Ocean heat transport play a secondary role. If we replace air temperature by net shortwave and downward longwave radiations, we find that the sum of influences from both radiations is almost similar to the air temperature influence, with the longwave radiation being dominant in driving changes in summer sea-ice extent. Finally, we find that the influence of air temperature is more prominent during periods of large sea-ice reduction and that this temperature influence has overall increased since 1970.

Linking housing, socio-demographic, environmental and mental health data at scale

Mental disorders are a growing problem worldwide, putting pressure on healthcare systems and wider society. Anxiety and depression are estimated to cost the global economy US$1 trillion per year, yet only 2% of global median government healthcare expenditure goes towards mental health. There is growing evidence linking housing, socio-economic status and local environmental conditions with mental health inequalities. The aim of this paper is to link several open-access datasets at the local area level (N = 32,844) for England to clinical mental health metrics and describe initial statistical findings. Two mental health metrics were used: Small Area Mental Health Index (SAMHI) and diagnosed depression prevalence. To demonstrate the utility of the longitudinal mental health data, changes in depression prevalence were investigated over two study periods (2011–19, i.e. austerity; and 2019–22, i.e. COVID-19). These data were linked to housing data (energy efficiency, floor area, year built, type and tenure) from Energy Performance Certificates (EPCs); socio-demographic data (age, sex, income and education deprivation, household size) from administrative records; and local environment data (winter temperature, air pollution and access to green space). The linked dataset provides a useful resource with which to investigate the social and environmental determinants of mental health. Practice relevance Initial observations of the data revealed a non-linear relationship between home energy efficiency (EPC band) and the mental health metrics, with depression prevalence higher in local areas where the mode EPC bands were C and D, compared with B and E. Researchers can further investigate this relationship using the dataset through robust statistical analysis, adjusting for confounding variables. National and local governments may use the dataset to help allocate resources to prevent and treat mental health conditions. Practitioners can map and interrogate the data to describe their local areas and make preliminary conclusions about the relationships between the built environment and mental health. This preliminary analysis of the data demonstrated a gradient in SAMHI and depression prevalence with income and employment deprivation at the local area level.

Effects of winter soil warming on crop biomass carbon loss from organic matter degradation

Global warming poses an unprecedented threat to agroecosystems. Although temperature increases are more pronounced during winter than in other seasons, the impact of winter warming on crop biomass carbon has not been elucidated. Here we integrate global observational data with a decade-long field experiment to uncover a significant negative correlation between winter soil temperature and crop biomass carbon. For every degree Celsius increase in winter soil temperature, straw and grain biomass carbon decreased by 6.6 ( ± 1.7) g kg-1 and 10.2 ( ± 2.3) g kg-1, respectively. This decline is primarily attributed to the loss of soil organic matter and micronutrients induced by warming. Ignoring the adverse effects of winter warming on crop biomass carbon could result in an overestimation of total food production by 4% to 19% under future warming scenarios. Our research highlights the critical need to incorporate winter warming into agricultural productivity models for more effective climate adaptation strategies.

Asymmetric winter warming reduces microbial carbon use efficiency and growth more than symmetric year-round warming in alpine soils

Asymmetric seasonal warming trends are evident across terrestrial ecosystems, with winter temperatures rising more than summer ones. Yet, the impact of such asymmetric seasonal warming on soil microbial carbon metabolism and growth remains poorly understood. Using 18O isotope labeling, we examined the effects of a decade-long experimental seasonal warming on microbial carbon use efficiency (CUE) and growth in alpine grassland ecosystems. Moreover, the quantitative stable isotope probing with 18O-H2O was employed to evaluate taxon-specific bacterial growth in these ecosystems. Results show that symmetric year-round warming decreased microbial growth rate by 31% and CUE by 22%. Asymmetric winter warming resulted in a further decrease in microbial growth rate of 27% and microbial CUE of 59% compared to symmetric year-round warming. Long-term warming increased microbial carbon limitations, especially under asymmetric winter warming. Long-term warming suppressed the growth rates of most bacterial genera, with asymmetric winter warming having a stronger inhibition on the growth rates of specific genera (e.g., Gp10, Actinomarinicola, Bosea, Acidibacter, and Gemmata) compared to symmetric year-round warming. Bacterial growth was phylogenetically conserved, but this conservation diminished under warming conditions, primarily due to shifts in bacterial physiological states rather than the number of bacterial species and community composition. Overall, long-term warming escalated microbial carbon limitations, decreased microbial growth and CUE, with asymmetric winter warming having a more pronounced effect. Understanding these impacts is crucial for predicting soil carbon cycling as global warming progresses.

Alternative products for breaking dormancy in ‘Reubennel’ plum in subtropical regions

Abstract Plum is a temperate fruit tree and thus shows abundant vegetative bud percentage and flowering following extended periods of low winter temperatures. When adequate periods of low temperatures do not occur, it is necessary to apply products to stimulate bud dormancy break and uniform leaf and flower appearance. This application is more frequently needed when plum trees are grown in the tropics. The objective of this study was to evaluate alternative products for breaking dormancy in ‘Reubennel’ plum in subtropical regions. The experiment was conducted at a site located at 22°22’S, 43°77’W and 1,173 m altitude, with a Cwa climate type. The following treatments were applied to eight-year-old ‘Reubennel’ plum trees in the 2018 and 2019 production cycles: 1) a negative standard composed of only water (control), a positive standard composed of 2) 1.5% hydrogen cyanamide (HC) (Dormex® commercial product) plus 4.5% mineral oil, 3) Erger G® organomineral fertilizer supplemented with 3% calcium nitrate, 4) 5% potassium nitrate and 5) 0.3% copper sulfate. Two liters of the treatment solutions were applied per plant with a sprayer. The effect of the tested products on vegetative bud percentage and flowering capacity, production cycle reduction and production and quality of ‘Reubennel’ plums was evaluated. It was concluded that the chemicals used to break dormancy affected only the phenology of plum trees in this subtropical region and that the application of copper sulfate can be used to break dormancy in these trees.

Reconstruction of the Temperature Index Series of China in 1368–1911 based on REACHES database

This study reports the methodology for reconstructing anomalous temperature index series of China in 1368–1911 based on the REACHES database which digitizes the Chinese records quoted in the Compendium of Meteorological Records of China in the Last 3000 Years. The reconstruction adopts an ordinal scale index approach ranging from −2 (very cold) to 1 (warm). Based on the grading criteria, a total of 12,871 records were retrieved through a standard coding system established at REACHES. Sensitivity experiments were performed to test robustness of the index system and a reasonability test was conducted to develop an appropriate method for deriving areal mean temperature index. The reconstructed series were validated through comparison with early instrumental data from Global Historical Climatology Network which shows good correlations and reliability of the REACHES reconstructed index data. Annual and semi-annual (winter and summer) temperature index data series were released for the whole domain as well as the 3- and 15-subregion geographical domains in China.

Spatio-temporal modeling of asthma-prone areas: Exploring the influence of urban climate factors with explainable artificial intelligence (XAI)

Urbanization's impact on climate is increasingly recognized as a significant public health challenge, particularly for respiratory conditions like asthma. Despite progress in understanding asthma, a critical gap remains regarding the interaction between urban environmental factors and asthma-prone areas. This study addresses this gap by applying innovative spatio-temporal modeling techniques with explainable artificial intelligence (XAI). Using data from 872 asthma patients in Tehran, Iran, and 19 factors affecting asthma exacerbations, including climate and air pollution, spatio-temporal modeling was conducted using XGBoost (eXtreme Gradient Boosting) algorithm optimization by the Bat algorithm (BA). Evaluation of asthma-prone area maps using receiver operating characteristic (ROC) curves revealed accuracies of 97.3 % in spring, 97.5 % in summer, 97.8 % in autumn, and 98.4 % in winter. Interpretability analysis of the XGBoost model utilizing the SHAP (Shapley Additive exPlanations) method highlighted rainfall in spring and autumn and temperature in summer and winter as having the most significant impacts on asthma. Particulate matter (PM2.5) in spring, carbon monoxide (CO) in summer, ozone (O3) in autumn, and PM10 in winter exhibited the most substantial effects among air pollution factors. This research enhances understanding of asthma dynamics in urban environments, informing targeted interventions for urban planning strategies to mitigate adverse health consequences of urbanization.

Abundance, ecology and dispersal of wood bison (Bison bison athabascae Rhoads) in Yakutia

In 2006, 2011, 2013, and 2020, four groups of young wood bison, each made up of 30 individuals from Canada, were introduced to Yakutia. The bison began to reproduce at ages 2 to 3 in 2008. Their mating behavior was first noted in 2007 when they were 2 to 3 years old, and calving took place in the spring of 2008 when they were 3 to 4 years old. Between 2008 and 2023, 16 generations of calves were born from both Canadian and Yakutia-born bison. The percentage of females that gave birth varied each year, ranging from 46.2% to 92.3%, with an average of 69.3 ± 5.4%. In 2017 and 2018, some bison were released in the middle section of the Sinyaya River. By 2023, the total population of bison in the nurseries “Ust-Buotama,” “Tympynai,” and in free-ranging groups reached 348 individuals. We discuss the characteristics of pastures, plant nutrition, feeding, reproduction, reintroduction to the wild, and the distribution of bison in Yakutia and surrounding areas. Bison have adapted well to the region’s climate, which features long winters, short summers, and extremely low winter temperatures. Central Yakutia offers a broad ecological niche for bison, with shallow valley lands that have meadow and shrub vegetation. This environment supports the potential for successfully restoring the historical habitat of the Bison genus in Yakutia and nearby regions. To avoid inbreeding and preserve genetic diversity, it is crucial to regularly release animals into the wild and occasionally introduce new individuals from various parks in Canada.

Combined Impacts of Autumn Snow Cover on the Tibetan Plateau and Northeast Asia on the Winter Eurasian Temperature

ABSTRACTThis study explores the combined effects of autumn snow cover anomalies on the Tibetan Plateau (TP) and Northeast Asia (NEA) on winter Eurasian temperature using observational data for the period 1972–2021 and a linear baroclinic model (LBM). Distinctive wintertime temperature patterns are found across the Eurasian continent corresponding to increased autumn snow cover in NEA when TP experiences normal, above‐normal, or below‐normal snow cover condition. In the scenario with an anomalous increase in autumn snow cover in NEA in combination with normal snow cover condition in TP, the overall winter temperature anomalies tend to be generally weak across the Eurasian continent. In years when autumn TP snow cover is above normal, the spatial distribution of winter temperature anomalies over the Eurasian continent associated with more NEA snow cover exhibits a ‘cold Arctic‐warm Eurasia’ (CAWE) pattern. The emergence of this CAWE pattern can be attributed to the low‐pressure system induced by the intensified NEA snow cover, which is further reinforced by the atmospheric wave train generated by negative North Atlantic sea surface temperature anomalies (SSTAs) in winter. This low‐pressure system amplifies the polar vortex and causes cooling in polar regions. Simultaneously, southeasterly winds along the southwestern flank of the North Pacific high‐pressure system contribute to warming in the mid‐latitude regions of Eurasia. While in years when autumn snow cover in TP is less than normal, more snow cover over NEA is accompanied by a ‘warm Arctic‐cold Eurasia’ (WACE) temperature anomaly pattern prevalent during the winter season. The decrease in autumn Barents‐Kara Sea ice is accompanied by a circulation pattern akin to the negative phase of the Arctic Oscillation during winter, favouring the southward intrusion of cold air, thus contributing to this WACE temperature anomaly pattern. Further analysis reveals that the impact of snow cover on the WACE temperature pattern is, for the most part, independent of the Arctic sea ice.

Numerical analysis of rack-based data center cooling with rear door heat exchanger (RDHx): Interrelationship between thermal performance and energy efficiency

This study presents a comprehensive numerical analysis of thermal performance and energy efficiency, IT operating environment, and thermal performance of the RDHx system, a rack-based cooling solution for high-density IT power using air-cooled equipment. A 150 kW IT module and RDHx cooling system were designed and evaluated, and CFD numerical analysis confirmed that RDHx maintains a stable IT environment within ASHRAE standards at a supply air temperature of 24 °C. Additionally, the chilled water supply temperature for the secondary CDU can be increased to 14 °C, and for the primary chiller to 12 °C. A central chilled water system for a 30 MW reference data center was designed and analyzed, showing increased efficiency with a 12 °C supply temperature and varying condenser water return temperatures in winter, resulting in an annual PUEcooling range of 1.25–1.33, with an average of 1.27. The annual energy consumption distribution for rack-based cooling is approximately 46 % from chillers, 21 % from fans, and 33 % from pumps. These results provide crucial insights into data center cooling and support the application of the RDHx system in high-density data centers, with future validation involving a field mock-up test of a 150 kW IT module.

Genomic variation of European beech reveals signals of local adaptation despite high levels of phenotypic plasticity

Local adaptation is key for ecotypic differentiation and species evolution. Understanding underlying genomic patterns can allow the prediction of future maladaptation and ecosystem stability. Here, we report the whole-genome resequencing of 874 individuals from 100 range-wide populations of European beech (Fagus sylvatica L.), an important forest tree species in Europe. We show that genetic variation closely mirrors geography with a clear pattern of isolation-by-distance. Genome-wide analyses for genotype-environment associations (GEAs) identify relatively few potentially adaptive variants after correcting for an overwhelming signal of statistically significant but non-causal GEAs. We characterize the single high confidence genomic region and pinpoint a candidate gene possibly involved in winter temperature adaptation via modulation of spring phenology. Surprisingly, allelic variation at this locus does not result in any apparent fitness differences in a common garden. More generally, reciprocal transplant experiments across large climate distances suggest extensive phenotypic plasticity. Nevertheless, we find indications of polygenic adaptation which may be essential in natural ecosystems. This polygenic signal exhibits broad- and fine-scale variation across the landscape, highlighting the relevance of spatial resolution. In summary, our results emphasize the importance, but also exemplify the complexity, of employing natural genetic variation for forest conservation under climate change.

Comparison of 2-m surface temperature data between reanalysis and observations over the Arabian Peninsula

The 2-m temperature data is a significant indicator for studying the weather extremes and the exchange of water and energy fluxes between the surface and atmosphere. This study compared three reanalysis datasets, i.e., ERA5, ERA5-Land, and MERRA-2, with observations from in-situ sources from 1990 to 2022 using various statistical error metrics and extreme temperature indices over the Arabian Peninsula (AP) region. We selected these reanalysis datasets due to the continuous improvements and higher spatiotemporal output better capturing the temperature variability. The spatiotemporal climatology shows lower temperatures in winter (<15 °C) and maximum in summer (>35 °C); however, the reanalysis data show more deviations in temperature during the cold season than in the warm season. The reanalysis data underestimated the frequency of the cold (<10 °C) and hot (>30 °C) days across the four regions, except ERA5-Land, which closely followed observed data. The strength of the correlation shows better performance (>0.90) in the cold extremes (cold nights and cold days) frequency than the hot extremes. On an interannual scale, reanalysis products exhibit strong correlations (>0.90) with in-situ data across most regions, particularly in winter and autumn, moderate in spring, and weaker in summer. The reanalysis data shows negative biases in the inland regions and positive biases in the coastal areas with consistent root mean square differences (RMSD) spatiotemporally. The differences in performance are due to the topography and poor representation of the energy fluxes, especially in MERRA-2 as well as missing data in observations. This study recommends ERA5-Land as the first choice for extreme weather simulations in the region, followed by MERRA-2 and ERA5 on the same scale, but proper attention is needed when using reanalysis data for cold and hot extremes.

Random Forests’ Postprocessing Capability of Enhancing Predictive Skill on Subseasonal Time Scales—A Flow-Dependent View on Central European Winter Weather

Abstract Weather predictions 2–4 weeks in advance, called the subseasonal time scale, are highly relevant for socioeconomic decision-makers. Unfortunately, the skill of numerical weather prediction models at this time scale is generally low. Here, we use probabilistic random forest (RF)-based machine learning models to postprocess the subseasonal to seasonal (S2S) reforecasts of the European Centre for Medium-Range Weather Forecasts (ECMWF). We show that these models are able to improve the forecasts slightly in a 20-winter mean at lead times of 14, 21, and 28 days for wintertime central European mean 2-m temperatures compared to the lead-time-dependent mean bias-corrected ECMWF’s S2S reforecasts and RF-based models using only reanalysis data as input. Predictions of the occurrence of cold wave days are improved at lead times of 21 and 28 days. Thereby, forecasts of continuous temperatures show a better skill than forecasts of binary occurrences of cold wave days. Furthermore, we analyze if the skill depends on the large-scale flow configuration of the atmosphere at initialization, as represented by weather regimes (WRs). We find that the WR at the start of the forecast influences the skill and its evolution across lead times. These results can be used to assess the conditional improvement of forecasts initialized during one WR in comparison to forecasts initialized during another WR. Significance Statement Forecasts of winter temperatures and cold waves 2–4 weeks in advance done by numerical weather prediction (NWP) models are often unsatisfactory due to the chaotic characteristics of the atmosphere and limited predictive skill at this time range. Here, we use statistical methods, belonging to the so-called machine learning (ML) models, to improve forecast quality by postprocessing predictions of a state-of-the-art NWP model. We compare the forecasts of the NWP and ML models considering different weather regimes (WRs), which represent the large-scale atmospheric circulation such as the typical westerly winds in Europe. We find that the ML models generally yield better temperature forecasts for 14, 21, and 28 days in advance and better forecasts of cold wave days 21 and 28 days in advance. The quality of forecasts depends on the WR present at the forecast start. This information can be used to assess the conditional improvement of forecasts.

Relationships between freezing resistance and biochemicals in grapevine buds and canes: Different soluble carbohydrates accumulate in several cultivars during cold acclimation

Owing to recent warming trends during the growing season, wine grapevine (Vitis vinifera) production has increased in subarctic areas such as Hokkaido. However, high freezing resistance remains essential for grapevines due to the severe winter temperatures in these regions. Here, we investigated the seasonal variation in freezing resistance, as well as the water, soluble carbohydrate, proline, and total phenolic content (TPC) in the buds and canes of two V. vinifera cultivars and a Vitis riparia hybrid, Maeve. We found that freezing resistance in the buds and canes increased from fall to winter, with Maeve exhibiting higher freezing resistance than the V. vinifera cultivars during both seasons. Maeve experienced a faster rate of deacclimation, leading to comparable spring freezing resistance among all cultivars. The water content in the buds and canes of all three cultivars decreased during the fall, with Maeve showing an earlier reduction compared to the others. Most soluble carbohydrates increased in winter and decreased in spring. Notably, Maeve, which had higher winter freezing resistance, accumulated more sucrose in its buds and a higher concentration of soluble carbohydrates, particularly monosaccharides, in its canes compared to the other two cultivars. Proline and TPC levels were not linked to freezing resistance. Grapevine buds and canes appeared to accumulate soluble carbohydrates, especially monosaccharides, during winter, while reducing water content in fall to enhance freezing resistance. This study suggests that grapevine cultivars in Hokkaido may utilize different types of soluble carbohydrates to improve the freezing resistance of their buds and canes.

The Role of Antecedent Winter Soil Moisture Carryover on Spring Runoff Predictability in Snow-Influenced Western U.S. Catchments

Abstract Spring runoff is critical to agricultural, industrial, and municipal water supply as well as environmental uses in the western United States. Although spring runoff in this region has long been predicted based on late winter and early spring montane snow water storage, the role of soil moisture carryover from the previous winter is less understood and utilized in forecasts. We quantify the relationship between antecedent winter soil moisture and spring runoff for 85 unmanaged catchments in the western United States. Using a regression-based approach, we estimate the proportional reduction in error in seasonal runoff forecasts associated with soil moisture. We classify the catchments into two regimes: interior (cold and relatively dry) and maritime (warmer and wetter), based on seasonal variations in soil moisture. Soil moisture generally is relatively unchanged through the winter period in interior catchments, whereas it increases (mostly gradually, but occasionally rapidly) in maritime catchments. We find that winter temperature dominates soil moisture variability in both types of catchments. We find two patterns in the predictability of spring runoff. First, including antecedent winter soil moisture as a predictor enhances forecast accuracy and variance explanation in spring runoff for both interior and maritime catchments, particularly and with greater significance in interior catchments. Second, spring runoff prediction skill from antecedent winter soil moisture increases with elevation. Overall, we find strong evidence that the use of antecedent soil moisture can improve spring runoff forecast skill for catchments in the western United States. Significance Statement Spring runoff forecasts are crucial for water supply in the western United States, but typically exclude information about soil moisture despite its key role in the water balance. Our study explores how and why incorporating information about winter’s soil moisture levels affects spring runoff forecasts across 85 catchments in the western United States. We find that doing so can produce modest improvements in the prediction skill of spring runoff for both interior and maritime catchments but is particularly noticeable in interior catchments and at higher elevations where temperatures are lower. Overall, our research strongly supports the idea that using information about soil moisture from the previous winter can improve accuracy in spring runoff forecasts for the western United States.

Climatic Impact on the Air Pollutant Concentrations Emitted from Gas Stations in Fallujah, Al-Anbar, Western Iraq

The research investigates the influence of climatic elements like temperature, relative humidity, rainfall, evaporation, dust storms, wind direction, and speed on the air pollutant concentrations at gas stations in Fallujah city while examining their impact on humans and the environment. This was achieved by measuring concentrations of CO, NO2, and CH4, suspended particles (TSP, PM10), and Cu, and Pb. It was found that the impact increased in July compared to January, because of the influence of the measured climatic elements. Rising temperatures increase air pollutants' concentrations in the atmosphere, while winds spread them from emission sites to distant locations. Moreover, summer temperatures significantly impacted air pollutant concentrations at the study stations, particularly at Al-Rehab gas station, with the highest recorded temperatures of 48.4°C. Some gas stations also exceeded local environmental limits for TSP, PM10, CO, NO2, and Pb. The study revealed that summer temperatures with wind speeds of 2.1 m/s resulted in the highest air pollutants values, while winter temperatures with wind speeds of 1.9 m/s decreased these values. Higher temperatures increase chemical reactions and temperature inversions, leading to increased pollution concentrations and exacerbating air quality problems in the study area. Strong winds disperse pollutants, reducing concentration in the study area, while low wind speed increases concentrations and stagnation, resulting in poor air quality in the studied sites. The importance of the current study lies in evaluating pollutants in the Fallujah city, determining their causes, the extent of the impact, and contribution of natural and human sources in causing changes in air pollutant concentrations; caused by emission from the source. Moreover, the current study showed its impact on human’s health, and trying to find solutions to limit these effects or reduce their levels to ensure the safety of the environment and health.

Long-term geomagnetic activities and stratospheric winter temperature

In this research, impact of long term solar forcing on stratospheric winter temperature is checked. The 11- year sunspot activity and geomagnetic indices (AE, Kp, Dst) are used as an indicator for solar forcing, as geomagnetic activity indices show good correlation with solar variability. To understand the impact of solar forcing through high latitude, stratospheric winter (November to March) time North Polar Region (60N–90N) temperature anomalies are considered. The findings showed that temperature changes in the stratosphere are significantly correlated with solar activity, as evidenced by a significant positive correlation between the 11-year moving mean of stratospheric (10 hPa) temperature anomalies and sunspot number. Approximately from 1970 to 2000, the North Polar Region saw positive anomalous stratospheric winter temperatures. During the same time, the geomagnetic activity also showed a substantial increase. The year-to-year correlation between stratospheric pole temperature and geomagnetic activity is significant (about 0.5). The Empirical Mode Decomposition analysis reveals a highly significant correlation (around 0.9) between the long-term component of stratospheric winter temperature (IMF-4) and the long-term component of geomagnetic activity (IMF-3 and IMF-4). One of the reasons for the increase in lower stratospheric temperature is an increase in ozone concentration during the same period when geomagnetic activity is higher. Empirical orthogonal function (EOF) and correlation analysis of stratospheric winter temperature with large-scale circulation patterns are also carried out. The spatial correlation is checked for stratospheric winter temperature at North Pole and lower atmospheric levels (250 hPa and 850 hPa) followed by pre-monsoon and monsoon season. This study includes statistical analysis, however, also highlights the necessity of in-depth dynamical analysis to improve our understanding of how solar activity impacts Earth's atmospheric layers, which may be helpful in predicting the weather and climate.

In pursuit of change: Divergent temporal shifts in climate sensitivity of Norway spruce along an elevational and continentality gradient in the Carpathians

Across much of Europe, climate change has caused a major dieback of Norway spruce (Picea abies L.), an economically important tree species. However, the southeasternmost fringe of this tree species – the Eastern Carpathians – has not yet suffered large-scale dieback. Studying temporal shifts of climate sensitivity (TSCS) over time may elucidate the degree to which Norway spruce may be vulnerable to climate-change induced decline in upcoming decades. Under this framework, we analyzed a regional tree-ring network comprising >3000 trees, with the aim of quantifying TSCS since 1950. We mathematically defined TSCS as the slope parameter of the regression of climate sensitivity (the correlation coefficient) over time. Given the often-observed contrasting shift of climate sensitivity at low versus high elevations, we were particularly interested in studying potentially divergent TSCS along elevational and spatial gradients. Our analyses revealed several indications of TSCS for Norway spruce in the Eastern Carpathians. First, at high elevations (>1100 m a.s.l.), we found that the positive link between summer temperature and spruce growth decreased significantly over the study period. In turn, these trees, over time, featured an increasing positive relationship with late winter temperatures. At low elevations (<800 m a.s.l.), the signal of positive summer Standardised Precipitation-Evapotranspiration Index (SPEI) correlation became more frequent among sites towards 2021, while the strength of the positive winter SPEI correlation from the previous growing season weakened. Our results revealed that TSCS was driven significantly by an elevational climate gradient and a longitudinal continentality gradient. Overall, our findings indicate that Norway spruce is increasingly affected by water limitations under climate change at low elevations, highlighting a potentially rising risk of decline of this species in the Eastern Carpathians.

Substantial Cold Bias During Wintertime Cold Extremes in the Southern Cascadia Region in Historical CMIP6 Simulations

AbstractGlobal climate models often simulate atmospheric conditions incorrectly due to their coarse grid resolution, flaws in their dynamics, and biases resulting from parameterization schemes. Here we document a bias in the magnitude and extent of minimum temperature extremes in the CMIP6 model ensemble, relative to ERA5. The bias is present in the southern Cascadia region (i.e., Pacific Northwestern United States and southwestern British Columbia, Canada, spanning from the coast to the Rocky Mountains), with some models showing a bias magnitude in excess of −10°C in the first percentile of daily winter minimum temperature. The sea level pressure pattern for these events is similar in CMIP6 models and ERA5, showing high anomalies in the Northeast Pacific that are indicative of an atmospheric blocking pattern and consequently more northerly flow. Though this atmospheric blocking pattern is typically concurrent with cold winter temperatures across much of North America, Rocky and Cascade mountain ranges prevent the cold air from reaching the southern Cascadia region as confirmed by the observations and reanalysis. Our results suggest that the bias in CMIP6 minimum temperatures is a result of unresolved topography in the Rockies and Cascade mountain ranges, such that the terrain does not adequately block cold air advection from reaching the southern Cascadia region.

Feasibility Study of Temperature Control Measures during the Construction of Large-Volume Concrete Gravity Dams in Cold Regions: A Case Study

Effective temperature control measures are crucial for achieving temperature regulation and preventing cracking in the dam body during the construction of large-volume concrete gravity dams. Due to the low ambient temperatures in winter, it is especially important to focus on temperature control measures for concrete dam construction in cold regions. This paper employs a numerical simulation method that takes into account dam temperature control measures to simulate and predict the overall temperature and stress fields of the Guanmenzuizi Reservoir Dam, and validates these simulations with field monitoring results. This study finds that the ambient temperature significantly impacts the temperature and stress of the dam body’s concrete. The internal temperature of the dam reaches its highest value approximately 7 days after pouring, followed by periodic fluctuations, with the dam body’s temperature changes exhibiting a certain lag compared to the ambient temperature. The interior of the dam is under compression, while the upstream and downstream surfaces experience significant tensile stress. This project adopts targeted temperature control measures for the cold environmental conditions of the region, which are reasonably implemented and effectively reduce the temperature rise of the concrete during construction, achieving the temperature control objectives. This study also explores the impact of the cooling water pipe density on the dam body. The research results offer valuable references for the implementation of temperature control measures and the establishment of temperature control standards for concrete gravity dams in cold regions.