Temperature Trends Research Articles

Observational constraint on climate model projections of global compound hot–dry events and the socioeconomic risks under climate change

Abstract The frequency of compound hot-dry events (fHD) is projected to increase significantly with future warming, yet associated uncertainties remain considerable and poorly constrained. In this study, we constrain future projections of fHD (2070-2099) using observations of recent trends in temperature (T) and precipitation (P) (1980-2014) during the warm-seasons. The physical mechanism is that the variance of fHD across climate models is dominated by their projected changes in P (ΔP), which can be constrained by recent trends in T and P. Compared to the raw projections, the observationally constrained fHD is reduced by 9.68%~18.74%, with uncertainty narrowed by 3.79%~10.66% under the high emission scenario. The highest decline of fHD is located in regions with low population and GDP, and globally, population and GDP exposures to fHD are reduced by 6.02%~10.73% and 6.51%~12.03%, respectively. The observationally constrained fHD with lower uncertainty provides more reliable information for risk management under climate change.

Rising temperatures and sinking hopes: An in-depth analysis of the interplay between climate change, land use patterns, and the desiccation of a global biosphere reserve

Global ecosystems and communities are significantly impacted by climate change and extreme events. The rapid desiccation of massive wetlands, which are essential for controlling water cycles, and biodiversity, preventing floods, and supplying essential ecosystem services, is one of the most upsetting effects. The once-largest lake in the Middle East, Lake Urmia, had a significant impact on ecology, economy, and human life contributing to climate regulation, species preservation, habitat conservation, tourism and recreation, and a wide range of other ecosystem services. The Ramsar Convention classified the lake as a Wetland of International Importance, and UNESCO designated it as a Biosphere Reserve. The ecological, agricultural, and societal challenges caused by rising temperatures, improper water resource management and overuse, enhanced salinity, and declining water levels have made Lake Urmia an acute symbol of environmental vulnerability. Using Landsat imagery, this study begins a thorough analysis of changes in the Lake Urmia basin from 1990 to 2020. The endeavor aims to develop effective conservation and restoration strategies by identifying the multiple reasons that led to its vulnerable situation. The study attempts to identify the role of precipitation, temperature trends, agricultural development, population growth, water consumption, evapotranspiration, and atmospheric salt and aerosol concentrations in the desiccation of the lake. This study presents a comprehensive knowledge of the complex interplay between climate change, human activity, and water management and may have implications for the holistic recovery of the lake. The findings have the potential to improve prognostic models and inform targeted mitigation strategies for not only Lake Urmia but also for other globally threatened wetlands.

Anomalous Arctic warming linked with severe winter weather in Northern Hemisphere continents

We have extended a recently developed metric that ingests United States station data—the accumulated winter season severity index—to a global indicator based on temperature and snowfall from reanalysis output. The expanded index is analyzed to reveal relationships between Arctic air temperatures/pressures and the probability of severe winter weather across the Northern Hemisphere midlatitudes. Here we find a direct and quasilinear relationship between anomalously high Arctic temperatures/pressures and increased severe winter weather, especially in northeastern continental regions downstream of maximum regional Arctic warming. Positive temperature trends in the Arctic are associated with positive trends in severe winter weather across the continents in mid- to late-winter, coinciding with an increase in stratospheric polar vortex disruptions. During the era of rapid Arctic warming, variability has decreased over the Arctic Ocean and Europe, but has increased in Canada, the Northern US and northeast Asia, indicating more pronounced shifts in weather conditions.

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

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

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

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

Trends in Temperatures in Latin America: A Time-Series Perspective Based on Fractional Integration

Abstract In this article, we examine the time-series properties of the temperatures in Latin America. We look at the presence of time trends in the context of potential long-memory processes, looking at the average, maximum, and minimum values from 1901 to 2021. Our results indicate that when looking at the average data, there is a tendency to return to the mean value in all cases. However, it is noted that in the cases of Guatemala, Mexico, and Brazil, which are the countries with the highest degree of integration, the process of reversion could take longer than in the remaining countries. We also point out that the time trend coefficient is significantly positive in practically all cases, especially in temperatures in the Caribbean islands such as Antigua and Barbuda, Aruba, and the British Virgin Islands. When analyzing the maximum and minimum temperatures, the highest degrees of integration are observed in the minimum values, and the highest values are obtained again in Brazil, Guatemala, and Mexico. The time trend coefficients are significantly positive in almost all cases, with the only two exceptions being Bolivia and Paraguay. Looking at the range (i.e., the difference between maximum and minimum temperatures), evidence of orders of integration above 0.5 is found in nine countries (Aruba, Brazil, Colombia, Cuba, Ecuador, Haiti, Panama, the Turks and Caicos Islands, and Venezuela), implying that shocks in the range will take longer to disappear than in the rest of the countries.

Vegetation, temperature, and Indian Summer Monsoon evolution over the past 4400 years revealed by a pollen record from Drigo Co on the southern Qinghai-Tibet Plateau

Understanding the changes in temperature and precipitation on the southern Qinghai-Tibet Plateau (QTP), which is influenced by the Indian Summer Monsoon (ISM), is crucial for predicting possible future changes in the regional ecological environment under ongoing climate change. However, due to the lack of well-dated, high-resolution paleoclimate records, the trends of temperature and ISM precipitation on the southern QTP since the mid-Holocene are poorly understood. We present a high-resolution pollen record from Dirgo Co, on the southern QTP, spanning the past 4400 years. The decrease in the pollen ratio of Artemisia to Cyperaceae (A/Cy) indicates a downward shift of altitudinal vegetation belts in the Drigo Co area over the past 4400 years, suggesting falling temperatures; and decreases in tree pollen percentages indicate the gradual weakening of the ISM. Our pollen record also indicates five cold phases with the near-simultaneous weakening of the monsoon, during: 4300–4100 cal yr BP, 3600–3400 cal yr BP, 2800–2600 cal yr BP, 1600–1400 cal yr BP, and 700–100 cal yr BP, and these climate anomalies were also recorded elsewhere on the QTP. We suggest that the changes in temperature and ISM intensity on the southern QTP over the past 4400 years were modulated by changes in summer insolation and solar activity. These changes affected the sea surface temperature (SST) over the tropical Pacific and Indian Ocean regions, and they were linked to a positive phase of the El Niño/Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD), which weakened the ISM intensity in the Drigo Co area. Additionally, due to increased evaporation related to high temperatures, combined with intensified grazing activities over the past ∼200 years, the lake level has fallen, and the vegetation cover has decreased. As global temperatures continue to rise, the alpine steppe in the Drigo Co area may increase and the vegetation density may gradually decrease, and at the same time the regional ecological environment may gradually deteriorate.

An analytical study on urban indices and land surface temperature

Any urban landscape needs to investigate the rising trend of land surface temperature (LST) with its surface materials. The present study analyzes the relationship of LST with three urban indices namely normalized difference built-up index (NDBI), urban index (UI), and built-up index (BUI) (by Pearson correlation coefficient method) using nine Landsat 8 OLI and TIRS data of May from 2013 to 2021 in a tropical Indian city, Raipur. Results show that the mean LST of the city was above 40 oC in 2013 but it is controlled in successive years by executing some eco-friendly activities. All the indices build a moderate to strong positive correlation with LST. NDBI is the least deviating index and it generates the best correlation. As surface materials are directly responsible for the rise of LST, suitable ecological planning is necessary for long-term urban thermal sustainability.

Trends and Drivers of Water Temperature Extremes in Mountain Rivers

AbstractWater temperature extremes can pose serious threats to the aquatic ecosystems of mountain rivers. These rivers are influenced by snow and glaciermelt, which change with climate. As a result, the frequency and severity of water temperature extremes may change. While previous studies have documented changes in non‐extreme water temperature, it is yet unclear how extreme water temperatures change in a warming climate and how their hydro‐meteorological drivers differ from those of non‐extremes. This study aims to assess temporal changes and spatial variability in water temperature extremes and enhance our understanding of the driving processes across European mountain rivers in the current climate, at both a regional and continental scale. First, we describe the characteristics of extreme events and explore their relationships with catchment characteristics. Second, we assess trends in water temperature extremes and compare them with trends in mean water temperature. Third, we use random forest models to identify the main driving processes of water temperature extremes. Last, we conduct a co‐occurrence analysis to examine the relationship between water temperature extremes and hydro‐climatic extremes. Our results show that mean water temperature has increased by °C per decade, leading to more extreme events at high elevations in spring and summer. While non‐extreme water temperatures are mainly driven by air temperature, water temperature extremes are also importantly influenced by soil moisture, baseflow, and meltwater. Our study highlights the complexity of water temperature dynamics in mountain rivers at the regional and continental scale, especially during water temperature extremes.

An exceptional phytoplankton bloom in the southeast Madagascar Sea driven by African dust deposition

Abstract Rising surface temperatures are projected to cause more frequent and intense droughts in the world's drylands. This can lead to land degradation, mobilization of soil particles, and an increase in dust aerosol emissions from arid and semi-arid regions. Dust aerosols are a key source of bio-essential nutrients, can be transported in the atmosphere over large distances, and ultimately deposited onto the ocean's surface, alleviating nutrient limitation and increasing oceanic primary productivity. Currently, the linkages between desertification, dust emissions and ocean fertilization remain poorly understood. Here, we show that dust emitted from Southern Africa was transported and deposited into the nutrient-limited surface waters southeast of Madagascar, which stimulated the strongest phytoplankton bloom of the last two decades during a period of the year when blooms are not expected. The conditions required for triggering blooms of this magnitude are anomalous, but current trends in air temperatures, aridity, and dust emissions in Southern Africa suggest that such events could become more probable in the future. Together with the recent findings on ocean fertilization by drought-induced megafires in Australia, our results point toward a potential link between global warming, drought, aerosol emissions, and ocean blooms.

Investigating spatial-temporal trend of snow cover over the three provinces of Northeast China based on a cloud-free MODIS snow cover product

Snow serves as a pivotal water resource in the three provinces of Northeast China (TPNC), the investigation of spatial and temporal distribution changes of snow cover is essential for the region’s efficient water resource management. The Moderate Resolution Spectroradiometer (MODIS) snow cover product has proven effective in providing detailed spatial–temporal distribution and identification of snow cover. However, its accuracy and reliability are significantly hampered by cloud obscuration. To address this issue, we have developed a novel four-step cloud removal approach, which includes Terra and Aqua combination, 5-day temporal combination, snowline method (IMS) as well as 8-pixel and 24-pixel adjacent pixel method. Results indicate that the proposed approach successfully eliminates all cloud cover, yielding a highly accurate cloud-free snow cover product with validation accuracies of 92% and 97% when using in situ snow depth measurements and cloud masking method, respectively. We investigated the spatial and temporal variations of snow cover for the TPNC during 2003–2018 with snow coverage and snow cover days (SCD) and analyzed the correlation between snow cover and climate factors. The results showed that the annual basin-averaged snow coverage increased at a rate of 1.27% yr−1 before 2009 and decreased at a rate of 1.80% yr−1 after 2009. The Mann-Kendall tests indicated that about 72% of the total area showed an increasing trend of annual SCD during 2003–2018. Conversely, 94% of the total area exhibited a decreasing trend of annual SCD for the period of 2009–2018 due to the increasing trend of temperature and decreasing trend of precipitation. Our approach shows a promising application of generating accurate cloud-free products and its potential to examine spatial–temporal variations of snow cover under climate change for other snow-covered regions globally.

Declines in Brook Trout Abundance Linked to Atmospheric Warming in Maryland, USA

Salmonid fishes provide an important indicator of climate change given their reliance on cold water. We evaluated temporal changes in the density of stream-dwelling brook trout (Salvelinus fontinalis) from surveys conducted over a 36-year period (1988–2023) by the Maryland Department of Natural Resources in Eastern North America. Nonparametric trend analyses revealed decreasing densities of adult fish (age 1+) in 19 sites (27%) and increases in 5 sites (7%). In contrast, juvenile fish (age 0) densities decreased in 4 sites (6%) and increased in 10 sites (14%). Declining adult brook trout trends were related to atmospheric warming rates during the study period, and this relationship was stronger than the effects of land use change or non-native brown trout. In contrast, juvenile fish trends generally increased with elevation but were not related to air temperature trends or land use change. Our analysis reveals significant changes in several brook trout populations over recent decades and implicates warming atmospheric conditions in population declines. Our findings also suggest the importance of temperature for adult survival rather than recruitment limitation in brook trout population dynamics.

Optimization of temperature prediction algorithms and simulation of future neighborhood-scale thermal environments in Chongqing

With climate change and urbanization, predicting the outdoor thermal environment of residential areas has become increasingly crucial, particularly during extreme weather conditions. Existing studies lack a comprehensive approach to optimizing temperature prediction algorithms for Chongqing and simulating extreme thermal environments at the neighborhood scale for future decades. This study employed various algorithms, including Levenberg-Marquardt optimization, Monte Carlo simulation, ARIMA modeling, and SARIMA prediction, to simulate and forecast temperature data for the central urban area of Chongqing from 2014 to 2023. By assessing metrics such as root mean square error (RMSE), R-squared (R2), and mean absolute percentage error (MAPE), the most effective prediction algorithm was identified. Results indicate that the Levenberg-Marquardt optimization algorithm, known for its nonlinear curve fitting capabilities, demonstrated superior performance in both the test and training sets, achieving an RMSE of 0.82 °C, MAPE of 1.81 %, and R2 of 0.75. This algorithm was subsequently used to forecast the outdoor temperature trend in Chongqing from 2024 to 2050, while also simulating the outdoor wind field, temperature distribution, building surface temperatures, and wind speeds in a residential district at neighborhood scale for 2030, 2040, and 2050 at a building density of 40 %. Findings reveal that peak pedestrian-level temperatures could reach 45.62 °C, and peak roof surface temperatures could reach 93.93 °C under extreme conditions. Furthermore, the study proposes practical recommendations for building layouts and cooling strategies. These findings are expected to improve residential planning in Chongqing, enhance residents' quality of life, and provide insights for other regions facing future temperature changes.

Variability of seasonal temperature from 1990 to 2020 within Oum Er Rbia hydraulic basin (Morocco): Study of trends and anomalies

Abstract The fifth report of the Intergovernmental Panel on Climate Change (IPCC) indicates that global temperatures have increased during the 20th century. In Morocco, numerous studies have unveiled that the nation has been contending with the effects of global warming for several decades, attributed to its geographical location. The studies have mainly focused on the evolution of rainfall and the recurrence of droughts. Nevertheless, temperature variations remain less addressed, especially at large spatial scales. This paper aims to study the seasonal temperature variability in the large hydraulic basin of Oum Er Rbia, focusing on its anomalies and trends as well as identifying their impacts on the basin’s territory. Indeed, the large basin of Oum Er Rbia occupies more than 48,.000 km2 and is characterized by an arid to semi-arid climate. Its water resources are important and play an essential role in the country’s socio-economic development. This basin is well-known for its agricultural activity. Although its water and economic potential are significant, the great basin is considered vulnerable to climatic variations, which strongly condition its development. Considering the significance of temperature trends within the framework of global warming, this current study delves into the topic of seasonal temperature variability within the expansive hydraulic basin of Oum Er Rbia. Based on statistical analysis using the temperature anomaly index and the Mann-Kendall trend test, this study reveals that strong interannual variability marked the evolution of seasonal temperatures during the period of 1990-2020, with a remarkable upward trend during the autumn and spring. When the temperature increase, the temperature increases in turn: thus, the negative impacts affect both water resources and the agriculture sector, which suffers from the combined effect of increased evapotranspiration and reduction of irrigation water.

СОВРЕМЕННЫЕ КЛИМАТИЧЕСКИЕ ОСОБЕННОСТИ АРЕАЛОВ ПРОИЗРАСТАНИЯ ПОТЕНЦИАЛЬНО ЛЕКАРСТВЕННЫХ РАСТЕНИЙ

The article considers climatic features of habitats of potentially medicinal plants are according to the data from 33 meteorological stations for the period from 1991 to 2023. Statistical analysis and visualization of the data using boxplot diagrams were carried out. Seasonal and annual trends in air temperature and precipitation amounts in the habitats of medicinal plants were analyzed. In all areas, in all seasons of the year, there is an increase in air temperature. Only in the areas of Southern Kazakhstan and Piedmont zone of Ile-Alatau there is a slight decrease in temperature in autumn. In general, there is a widespread increase in temperature by 0.29-0.62 °C/10 years per year, with significant trends for spring and summer. The analysis of the time course of monthly precipitation sums showed that there is a diverse change in precipitation, all trends are insignificant, with the exception of autumn for the Central Kazakhstan area. The study of seasonal and annual variations in the main meteorological variables in the habitats of potentially medicinal plants is an important aspect both for understanding the peculiarities of plant growth and for optimizing the processes of their cultivation, collection and use for medicinal purposes. Meteorological conditions, such as air temperature and precipitation, are playing a key role in shaping the biological characteristics of medicinal plants, their active constituents and therapeutic properties.

Thermal regime of peatlands in Western Siberia near the southern border of the permafrost

Relevance. The permafrost degradation under the effect of global warming. It determines the necessity for a predictive assessment of permafrost stability to minimize disturbances to engineering installations in the permafrost zone. Aim. To assess the stability of frozen peatlands in the southern part of the zone of insular distribution of permafrost. Objects. Soils of flat-mound and high-mound palsa mires, which preserve permafrost on the southern border of the insular permafrost zone. Methods. Measurements of the temperature of peat and mineral soil in geocryological boreholes from 0 to 10 m deep using the SAM-N automatic monitoring surveillance network; determination of surface temperature from thermal channels of MODIS satellite images from 2000 to 2022; analysis of meteorological indices and determination of air temperature trends for predictive assessment of permafrost stability; calculation of indicator values of permafrost state, such as freezing degree-days and thawing degree-days, frost index, freezing and thawing N-factors. Results. Numerous features of unstable permafrost have been observed. In particular, the mean annual temperature of the surface layer was positive in all studied boreholes (+0.8...+1.3°C), and the temperature at the depth of zero amplitudes (10 m) is close to the melting point (with the predominance of temperatures in the range of –0.2...–0.3°C). Besides, a layer of soil that does not freeze throughout the year (non-merging permafrost) was identified. The thawing degree-days and frost number values correspond to areas with unfrozen soils. Positive trends in air temperature, Earth's surface temperature, and snow depth were observed. For 2000–2022, surface air temperature increased by 0.76°C/10 years on average. The land surface temperature increased in summer by an average of 0.42°C/10 years. If the positive trend in surface air temperatures continues, frozen peatlands in the southern part of the permafrost zone will completely melt in 50–70 years.

Construction of a Model for Assessing the Comfort of Residential Indoor Environment Based on Multisensory Design

Economic development and technological progress have made people’s demand for indoor environments higher and higher, and designing safe, comfortable, and healthy indoor environments through multisensory design is worthy of serious consideration. In order to realize the assessment of indoor environment comfort under multi-sensory design, this paper carries out data assessment from three dimensions of thermal environment, light environment and air environment. Relying on the PMV model of thermal environment comfort, we analyze the trends of indoor temperature and humidity, average radiant temperature, and PMV values. Combine Weber-Fechler and lighting coefficient to study the visual comfort change of indoor light environment, and analyze the lighting illuminance of indoor light environment under different working conditions by illuminance uniformity. From the basic principle of indoor wind environment, the changes of effective temperature difference and blowing sense caused by air temperature and wind speed are investigated. The temperature difference of the south-facing room decreased between 12.14% and 15.65% before and after the heating was stopped, which is a small change. The average indoor ambient radiant temperature values at different measurement points were up to 27.59 °C, and the fluctuations of the average indoor thermal ambient PMV values were within the range of [-1.2,1.6]. When the indoor light ambient illuminance value increased from 200 lux to 600 lux, the visual comfort improvement was more significant than from 600 lux to 1000 lux. The indoor air distribution characteristic ADPI value was 100% when the air velocity was 0.15m/s and 0.25m/s. Based on the results of indoor environment comfort assessment, multisensory optimization of indoor environment comfort from heat, light and air environments is proposed to provide guidance for improving people’s satisfaction with indoor environment.

Trends of Extreme Temperature and Rainfall Indices for Addis Ababa City, Ethiopia

The extreme temperature and precipitation index trend analysis for Addis Ababa city is presented in this study. The Bole and Tikur Anbesa observatories, two stations in the capital city of Addis Ababa, provide the temperature and rainfall variation used in this analysis. The RClim-Dex software has been used to find climate extreme indices for Addis Ababa city stations based on climate data for the period 1954 to 2019. The annual total precipitation trend (PRCPTOT) at the 5% significant level exhibits a positive slope at the Addis Ababa observatory and a negative slope at the Addis Ababa Bole station. SDII trends indicate an increase at the 5% significant threshold for both stations. At the 5% significance level, the trends for both stations’ maximum and lowest temperatures show a positive slope. Conversely, both stations exhibit a negative slope in the diurnal temperature ranges. The RClim-Dex results indicate that, while rainfall intensity is decreasing, there is a tendency for annual rainfall indices to increase at the Addis Ababa observatory station, but to decrease at the Bole station. Additionally, there is an upward slope for consecutive wet days for all stations. Nevertheless, steady warming patterns are evident for both the maximum and minimum temperature indices. In contrast, declining trends are shown in the diurnal temperature ranges.

NOAAGlobalTemp version 6: An AI-based global surface temperature dataset

Abstract The National Oceanic and Atmospheric Administration (NOAA) Global Surface Temperature (NOAAGlobalTemp) dataset is widely used for scientific research, operational monitoring, and climate assessment activities. Aligning with NOAA’s mission values, NOAAGlobalTemp has been updated to version 6 (i.e., NGTv6), which includes two enhancements over its predecessor (NGTv5). The first enhancement is the expansion of the spatial coverage to encompass the entire globe and the extension of temporal coverage back to 1850 (an interim version of NOAAGlobalTemp with these features was released in February 2023). The expansion of spatial coverage is accomplished by utilizing surface air temperatures over the Arctic Ocean and by eliminating the data reconstruction mask used in NGTv5 that had suppressed interpolation in data-sparse regions. This change has important implications for global temperature trends since the Arctic region has been warming at a much faster pace, more than four times the global average, in the twenty-first century to date. The second enhancement is the implementation of a methodology based on artificial intelligence (AI) for reconstructing surface air temperature over the global land surface and the Arctic Ocean. The AI model employs an artificial neural network to fill data gaps and is demonstrated to be more robust, stable, and accurate than the previous gap-filling method, particularly in observation-sparse areas such as the polar regions. The model outperforms the previous approach across all evaluated statistical metrics, and the output reaches a stable state more quickly as observations are received, which facilitates climate monitoring.

Vapor-liquid equilibria of the ternary system of methane + n-decane + n-octacosane at high pressures

Alkane mixture phase equilibria is required for a range of industrial applications, most particularly for the petroleum industries. Mixtures containing light and heavy normal alkanes are of even further significance due to their non-ideal thermodynamic behavior. In this work, for the first time, the ternary system of C1 + C10 + C28 was investigated experimentally to obtain bubble point pressures at various concentrations and temperatures ranging from 373 up to 445 K. The synthetic method of phase equilibrium measurements was utilized, which is known to have high accuracy. The resulting bubble points for the mixtures ranged in pressure from about 3 MPa up to 6 MPa. The data indicated that methane concentrations had the greatest impact, not only on the magnitude of the bubble point pressure, but also on the slope of its temperature trend. The relative concentrations of n-decane and n-octacosane had far less impact on the bubble point curves. The results were also modelled using the Peng–Robinson equation of state, and it was found that this equation has the capability to predict the data with an AARD% of 5.1 %. When temperature-independent binary interaction parameters were optimized to the data, the AARD% reduced to 2.4 %. Although the components vary greatly in size, perhaps the success of the Peng–Robinson equation of state for this mixture could be attributed to the fact that it was developed for hydrocarbon mixtures, and so, a reason for its popularity in the petroleum industries, alongside its simplicity and ease of use.