Assessing the suitability of unconditioned buildings for adaptive reuse as archival storages, particularly with respect to their microclimate behaviour, remains a complex and evolving methodological challenge. This study proposes a data-driven method using indoor and outdoor climate data to assess whether unconditioned buildings can be adaptively reused for specific functions (e.g., archival), based on their microclimate behaviour. The multi-step approach includes quality check, stationarity analysis, temporal decomposition, and buffering capacity analysis. This is applied to 5 years (2020-2024) of air temperature (T) and relative humidity (RH) data (approximately 14 600 observations) from 10 thermohygrometers in the "Library Section for Special Collections (LSSC)" within "Dora I" (Trondheim, Norway), where conservation conditions of paper-based objects are considered satisfactory. Results indicate a very stable indoor climate with no significant trends or abrupt changes, with a mean temperature (T) and mixing ratio (MR) values of 17 ± 2°C and 6 ± 1g/kg respectively, and very low short-term variability, suggesting consistent conservation conditions and reduced need for active climate control. Indoor T and MR show delayed responses to outdoor conditions (up to 70 and 50 days), and buffering factors of 0.2 and 0.4, respectively, reflecting thermal inertia and moisture buffering. The proposed method captures the limited impact of site management on indoor climate and reveals structure-specific dynamics often overlooked in commonly applied evaluations.

• A parsimonious soil temperature model using minimal inputs shows strong performance in predicting frozen ground conditions. • The model demonstrates high accuracy with a true frozen rate of 0.90 and false frozen rate of 0.06. • The model provides a computationally efficient solution for representing frozen ground effects in hydrological models. Seasonal soil freezing and thawing processes significantly influence runoff generation dynamics during cold periods, affecting various hydrological and agricultural systems, including flood generation, soil erosion, and plant health. Representing frozen soil conditions in land surface or hydrological models is therefore crucial. While fully distributed models implement the process by solving energy-mass balance equations to obtain soil temperature profiles, parsimonious models using “snow tanks” or frozen ground states can provide suitable modeling solutions with reduced computational demands. However, even these parsimonious approaches to representing frozen ground typically require some additional complexity through additional inputs or surface energy balance calculations. This study evaluates the applicability of a simplified soil temperature prediction model that determines frozen/unfrozen ground states using only air temperature and snow cover data, reducing model complexity. We first validate the model performance using AmeriFlux network in-situ measurements across the United States and Canada. Furthermore, we provide a comprehensive assessment at the global scale with ERA5-LAND reanalysis data (1980–2020). The model demonstrates robust performance globally, achieving an average true frozen rate of 0.90 and false frozen rate of 0.06. We also investigate the model performance by month, and, while monthly analyses show drops in model performance for certain months, these lower scores are primarily due to the limited number of freeze–thaw events during these periods, which makes the model appear less accurate than it actually is. In terms of spatial performance, the model shows reduced accuracy in mountainous regions, including the Tibetan Plateau, Rocky Mountains, and Andes, suggesting the need for region-specific parameter calibration in orographic settings. Nevertheless, this parsimonious soil temperature model demonstrates significant potential as a computationally efficient solution for incorporating frozen ground effects in distributed hydrological models with simple conceptual runoff generation schemes; the implications of this work are toward improved flood prediction in cold regions, such as the Yukon-Kuskokwim Delta region during its record 2025 flooding, where the hydrologic response is largely controlled by frozen soil

Understanding of urban weather and extreme events remains severely limited by data poverty resulting from a dearth of true urban weather stations. As a result, land surface temperature (Ts), obtained from remote sensing platforms, has been widely used as a stand-in for near-surface air temperature (Ta) despite their fundamental differences, especially in urban areas. Although Ts provides important scientific insights and practical utility for studying theurban thermal environment, this substitution risks introducing large uncertainties and biased characterization of impact-relevanturban heat stress. Here we develop an urban transfer-learning framework (U-TL) to address this critical gap and to provide urban high-resolution air temperature (U-HAT) data at large scales across the contiguous United States (CONUS). U-TL demonstrates high accuracy and strong robustness in predicting urban Ta, even with limited training data. The resulting U-HAT is a high-resolution urban Ta dataset capable of accurately reproducing observed and well-established urban climatology. U-HAT reveals substantial Ts-Ta discrepancies and therefore cautions the use of Ts to characterize urban heat. We show that satellite-measured Ts substantially overestimates both urban heat stress magnitude and intra-city spatial variability, which have consequential implications for urban heat exposure, vulnerability, and adaptation policy making.

Accurate regional climate information is vital for hydrological applications, impact studies, and adaptation planning. However, Global Climate Model (GCM) outputs are constrained by coarse resolution and systematic biases, limiting their utility over the climatically diverse and topographically complex Indian subcontinent. To address this, we present INDRA-CMIP6, a high-resolution (0.1° × 0.1°) daily dataset of precipitation, maximum temperature (Tmax), and minimum temperature (Tmin) for the historical period (1951-2014) and future projections (2015-2100) under four emission scenarios (SSP126, SSP245, SSP370, SSP585). The dataset, derived from 14 CMIP6 GCMs and their multi-model ensemble (MME) mean, was developed using the Double Bias-Corrected Constructed Analogue (DBCCA) approach with MSWEP and MSWX as reference datasets. Evaluation against reference data shows reduced warm and cold biases and improved representation of regional climate. INDRA-CMIP6 enables assessments of climate extremes and supports water resource planning.

ABSTRACT Objectives To explore associations between air temperature and light in the sleeping environment and sleep health in individuals with neurological conditions. Methods The sleep health of 46 individuals with neurological conditions (mean age 51.37 ± 14.24 years; 20 males, 26 females) was measured using the Sleep Health Index (SHI). Air temperature and light data were captured across seven days using a light/temperature data logger positioned next to the participant’s bed. Data recorded during the participants’ sleeping periods (determined using a sleep diary) were analyzed. Linear regression models were used to assess the associations between air temperature and light and sleep health (including domains). Results This study showed that for every additional minute of low (10 to 50 lux) light exposure during sleep periods, sleep quality decreased by 9%. Conclusions Our findings suggest that low light exposure during sleep periods may be detrimental to the sleep quality of individuals with neurological conditions.

Abstract. Modelling current permafrost distribution and response to a changing climate depends on understanding which factors most strongly control ground temperatures. The Temperature at the Top of Permafrost (TTOP) model provides an analytical framework for estimating permafrost presence and thermal state, yet its sensitivity to key parameters remains poorly quantified across diverse northern environments. This study evaluates the relative influence of TTOP model parameters using ground and air temperature data from 330 sites across northern Canada. A leave-one-out cross-validation approach to determine model sensitivity was combined with random forest analysis to rank variable importance. Results show that TTOP performance is dominated by freezing-season conditions – particularly the freezing n-factor and freezing degree days – while thaw-season parameters exert less control. Sensitivity varies by region, with thawing parameters becoming more influential where the duration of the freezing and thawing seasons is similar. Machine learning results also highlighted the importance of thermal offset and mean surface temperatures which are strongly influenced by substrate properties. While the model generally reproduces observed ground temperatures well (RMSE of 0.2 °C), parameters derived from landcover classes were not transferable between sites, underscoring the importance of locally calibrated inputs. Overall, this study is the first empirically-based Canada-wide assessment of how different climatic and environmental factors affect the accuracy of permafrost temperature modelling and provides practical guidance for improving parameterization in regional and global permafrost models.

Air temperature data registered from 1959 to 2020 by the Chacras de Coria meteorological station (32°59' S Lat.; 68°52' W Long.) were analyzed to characterize the site and identify trends considering the climate change scenario. We ensured quality and homogeneity of the time series following the procedures of the Standard World Meteorological Organization, calculating reference (1961-1990) and regulatory (1991-2020) climatological normals. The CLIMPACT package detected trends in temperature-related indices, including extreme values, daily thermal amplitude, and degree days. The results reveal that the 1991-2020 period was 0.6°C warmer than 1961-1990. Significant increases were observed in average minimum (0.12°C/decade) and average maximum (0.20°C/decade) temperatures. Extreme minimum and maximum temperatures also increased by 0.11°C/decade and 0.33°C/decade, respectively, resulting in fewer cold nights (-0.49%/decade) and more hot days (1.6%/decade). Daily temperature ranges increased by 0.11°C/decade, and degree days by 52 DD/decade. These findings are consistent with global warming evidence. A corrected and homogenized database spanning over 60 years is available for future climatological studies. Highlights: A 62-year (1959–2020) homogenized and quality-controlled daily temperature database was established for Chacras de Coria using WMO standards and the CLIMPACT package. The regulatory climate normal period (1991–2020) was 0.6 °C warmer than the reference normal period (1961–1990). The Daily Thermal Range (DTR) increased by 0.11 °C per decade; maximum temperatures rose significantly faster (0.20 °C/decade) than minimum temperatures (0.12 °C/decade), an asymmetrical increase of 66.6%. Analysis of extreme indices reveals a decrease in cold nights (-0.49%/decade) and a significant rise in hot days (1.6%/decade). Annual Grow Degree Days (GDD) increased by 52 DD per decade, indicating higher energy availability that may shorten the cycles of ectothermic organisms.

High-resolution air temperature (Ta) data are essential for environmental monitoring, public health evaluation, and urban climate adaptation, particularly in mountainous megacities with sharp spatial gradients. This study presents a gridded daily Ta dataset at 30 m resolution for the Chongqing Metropolitan Circle, China, spanning 2016 to 2024. This area features a unique topography of alternating ridge-valley corridors, creating strong microclimatic contrasts within densely populated urban areas. The dataset was generated using a Spatially Varying Coefficient Model with Sign Preservation (SVCM-SP) framework that integrates multi-year Landsat-derived land surface temperature, digital elevation, and observations from an average of 215 meteorological stations per year, with an average inter-station distance of 37.7 km. Validation at both daily and monthly scales confirms high spatial and temporal consistency across complex terrain and seasonal conditions. The dataset provides fine-scale daily maximum and minimum temperature estimates and supports diverse applications such as heatwave risk assessment, urban climate research, and adaptation policy design in rapidly urbanizing mountainous regions.

Understanding how the internal structure of precipitation events evolves and responds to antecedent thermal conditions is essential for revealing the mechanisms of extreme precipitation in plateau-margin mountainous regions. Using hourly precipitation and air temperature data from 14 national reference meteorological stations in the Hehuang Valley during the warm seasons (May–September) of 2015–2024, this study constructed an event-based precipitation database and introduced the inter-event maximum temperature (Tmax_inter) as an indicator of antecedent thermal accumulation. The Theil–Sen slope estimator, Mann–Kendall trend test, K-means clustering, and binary logistic regression were applied to examine changes in precipitation-event structure and their nonlinear response to antecedent high temperature. Results show that warm-season precipitation was characterized by fluctuating frequency but increasing intensity. Precipitation events were classified into three types—uniform, front-peaked, and rear-peaked—with the proportion of uniform events decreasing and the proportions of front-peaked and rear-peaked events increasing. Tmax_inter was significantly positively associated with extreme precipitation occurrence: for every 1 °C increase in Tmax_inter, the odds of extreme precipitation increased by 13.4% (OR = 1.134, 95% CI: 1.10–1.17, p < 0.001). These findings provide a reference for extreme precipitation risk identification and disaster prevention in plateau-margin mountainous areas.

Water is a vital resource whose availability is increasingly uneven due to climate variability, land-use changes, and rising water demand. This condition leads to water surplus during the rainy season and deficit during the dry season, including in the Prumpung Watershed, Tuban Regency. This study aims to analyze the monthly and annual water balance and evaluate the relationship between water availability and water demand in the study area. A descriptive quantitative approach was employed using the Thornthwaite–Mather method, based on rainfall and air temperature data from 2015 to 2024, combined with domestic and non-domestic water demand data for 2024. The results indicate that potential evapotranspiration fluctuates in accordance with air temperature, while actual evapotranspiration is influenced by groundwater availability. Groundwater storage increases during the rainy season and decreases during the dry season. The annual water balance reveals a dominance of deficit conditions in most observation years, although surplus occurs during certain periods. The Prumpung Watershed is vulnerable to water deficits due to uneven rainfall distribution. Sustainable water resource management strategies, focusing on conservation, are required to maintain a long-term hydrological balance.

Identifying geothermal resources typically requires time-consuming extensive geophysical surveys and deep drilling, which are costly and logistically challenging. This study illustrates the use of readily available shallow groundwater temperature data combined with surface air temperature records as a fast, low-cost, exploration screening tool for geothermal anomalies in deep sedimentary basins. The data were collated from existing piezometers used to monitor water-table fluctuations in groundwater monitoring wells, which are far more common than the bores typically used for geothermal temperature measurements. Geothermal anomalies can be identified by spatial analysis of the difference between the phreatic surface temperature and long-term averaged air temperature data. The study evaluates the workflow using known deep geothermal anomalies in two geologically distinct sedimentary basins: the onshore Perth Basin in Western Australia and the Hunter Valley in New South Wales. The analysis demonstrates that the widely available and commonly acquired hydrogeological data, along with average surface air temperature data, can be used for exploration for deeper geothermal anomalies. While the data are commonly measured to provide information about subsurface water levels, collating a more comprehensive national database of borehole temperature data would further facilitate geothermal exploration programs that are seeking deep sources of heat for electricity generation or shallow sources of heat for ground-source heat pumps.

The article presents the results of an assessment of the agro-climatic resources of the Zhetysu region, which was conducted on the basis of field research and GIS data. Maps of climatic meteorological parameters on a scale of 1:1,000,000 have been developed. To prepare thematic climate maps, an information and analytical database was compiled for a multi-year period of time for meteorological stations located in various agro-climatic regions of the region, which presents the results of climatological processing of the results at meteorological stations over a multi-year observation period (from 1891 to 2000, as well as new data from 2001-2024.). The assessment of moisture availability was based on the Selyaninov hydrothermal index (GTK), and heat availability was based on the sum of temperatures above 10°C. The results of the assessment of changes in the average annual air temperature by weather stations showed that over the past 25 years, there has been a slight increase in air temperature in the region, especially in summer. An increase in air temperature in summer can have a serious impact on pasture biodiversity, plant life cycle and productivity. Based on the average annual air temperature data, we assessed the heat supply of the growing season, which affects the productivity of pasture lands, and assessed the aridity of the region, which contributes to the processes of land degradation and desertification of the territory. Four zones have been identified in terms of moisture conditions: very dry (GTK=0.2-0.3); dry (GTK=0.3-0.5); very arid foothills (GTK=0.6-0.9); mountainous zone (GTK=>1.9).

The Regional Frequency Analysis (RFA) method is widely used to improve probabilistic assessments of extreme hydrometeorological events. However, its classical formulation assumes stationarity - an assumption often violated under climate change. To overcome this limitation, we propose the Non-Stationary Additive Regional Frequency Analysis (NS-Add-RFA), an extension of the additive RFA that integrates nonstationary probabilistic models to represent temporal changes in air temperature frequency distributions at the regional scale. Application of the NS-Add-RFA in controlled simulation experiments and to daily extreme maximum air temperature data from São Paulo State, Brazil (1991–2024), showed that it outperforms nonstationary at-site approaches in capturing the probabilistic structure of extremes under diverse climate and climate change scenarios. The new regionalization method revealed widespread increases in extreme air temperatures across 93% of the state, capturing changes in both the central tendency and dispersion of the series. Furthermore, it provides a more detailed and reliable trend assessment than traditional approaches such as the regional Mann–Kendall trend test. To facilitate broader adoption, we developed the R package NSTempRFA ( https://github.com/gabrielblain/NSTempRFA ), which computes all NS-Add-RFA statistics. In conclusion, the NS-Add-RFA offers a scientifically grounded framework for nonstationary regional frequency analysis, enhancing the capacity to assess and interpret extreme air temperature events under changing climate conditions.

Abstract Studying urban heat islands (UHIs) in Southern Europe is crucial, as they amplify heat risks under climate change. UHIs and their temporal variability at seven urban–rural pair locations in Spain were analysed from 1970 to 2023. The UHI was defined as the air temperature difference between each urban site and its neighbouring rural sites, and trends were analysed using the non-parametric Mann–Kendall test with Sen’s slope estimator. Based on daily minimum air temperature data, results indicated a mean UHI intensity ranging from −0.15 °C in Alicante to 2.28 °C in A Coruña. The UHI annual trend was significant, increasing in Valladolid (0.023 °C/year) and Alicante (0.009 °C/year) and decreasing in Santander (-0.015 °C/year). Seasonal analysis showed statistically significant trends in Valladolid, particularly in spring and summer (0.029 °C/year). In Alicante, an increase of around 0.012 °C/year was observed in spring and summer, while Madrid showed a trend of 0.012 °C/year in winter. However, a warming effect at the rural site was identified in Barcelona (−0.028 ºC/year in autumn) and in Santander −0.025 °C/year in spring and summer), corresponding to negative UHI trends. The influence of synoptic patterns on UHI yielded values between 3 and 4 °C in A Coruña and Madrid for anticyclonic southeasterly, anticyclonic southerly, and southeasterly air flows. Lower intensities were found in Barcelona (2.5 °C) and were associated with hybrid anticyclonic westerly flows. UHI intensities below 2 °C were obtained at the other locations, with the lowest values being linked to hybrid cyclonic westerly and cyclonic north-westerly flows.

This study develops an integrated bifacial photovoltaic (PV) performance and orientation-optimization framework to determine the optimal fixed tilt and azimuth angles that maximize annual energy yield under desert climate conditions. The methodology combines established solar geometry formulations, advanced diffuse irradiance modeling, and bifacial performance considerations, and is applied using Tier-I hourly ground-measured solar irradiation and air temperature data (±2% accuracy) for 18 cities across Saudi Arabia. Unlike conventional latitude-based tilt rules, the proposed framework systematically identifies optimal bifacial orientations, revealing that the optimal tilt angle is typically 4°–30° higher than site latitude, depending on location and climatic conditions. Monthly analysis for Riyadh shows that bifacial modules maintain higher summer tilt angles than monofacial systems, enhancing rear-side contribution. The rear side contributes approximately 8–12% of total energy yield, with higher contributions during spring and autumn due to increased ground-reflected irradiance. A nationwide comparison indicates a relatively narrow bifacial gain range (9.3–11.5%), with higher gains observed in regions characterized by greater diffuse irradiance. The results are validated through consistency with previous monofacial PV orientation optimization studies and independent GIS-based suitability analyses. Overall, the proposed framework provides a transparent and robust decision-support tool for early-stage bifacial PV planning and comparative assessment in arid and desert environments.

The relationship between ambient temperatures and health outcomes has been extensively studied, yet long-term analyses of mortality responses to extreme temperatures, particularly in the Mediterranean region, remain scarce. This study leverages daily all-cause mortality and air temperature data in the city of Madrid from 1890 to 2019 to examine adaptation patterns to extreme and moderate heat and cold. Using a distributed lag non-linear modeling framework, we explored temperature-mortality relationships and estimated decade-specific adaptation metrics. Results indicate a general reduction in temperature-attributable mortality, primarily due to decreased cold-related risks across all population groups. While moderate heat impact declined over time, extreme heat effects remained stable, with a slight increase towards the study’s end. Our findings highlight the dual impact of climate change, i.e. reducing cold-related mortality while stabilizing heat-related risks, which emphasizes the complex interplay of climate change and health outcomes and offers new insights into adaptation dynamics using long-term temperature-mortality time series.

This study provides a comprehensive dataset (FAIRUrbTemp) that addresses the lack of high-resolution urban air temperature data across Europe. It compiles sub-hourly street-level air temperature data from 811 low-cost to commercial sensors across several European cities and offers data in a quality-controlled, standardized format in sub-hourly, hourly, and daily resolutions. In addition, detailed metadata, as an important source of information in urban studies, is provided at network, station, and measurement levels. This pan-European dataset is rigorously quality-controlled using a serially automatic method applicable to diverse city-scale air temperature data, which identifies systematic and minor inconsistencies to enhance reliability. Expert-based validation shows that the QC reliably identifies problematic measurements, while its performance varies across urban and climatic settings due to local environmental and instrumental effects. To ensure transparency, the results of the quality control are provided to the user together with the original value in the dataset. The validated FAIRUrbTemp is a valuable resource for urban climate studies, with direct applications in validating microclimate models, assessing heat-health risks, and informing climate-adaptive urban planning.

Purpose: assessment of climatic factors influencing the dynamics of Amur pink salmon catch and determination of the mechanisms of this influence during periods of high and low salmon runs to the Amur estuary. Research Material: salmon catch data from the Amur estuary, water temperature observations from the ESIMO electronic database, air temperature and precipitation data on coastal GMS were obtained from the archives of the VNIIGMI MDC and the archives of the Reanalysis (NCEP/NCAR Reanalysis Monthly Mean and Other Derived Variables) of atmospheric pressure P 0 , geopotential H 500 . Methods used: to identify critical levels of salmon catches, an interval recognition algorithm was used, previously developed by a team of authors and demonstrated its effectiveness in solving fisheries problems. Results: the possibility of determining in two years whether the conditions for the survival of pink salmon during the incubation period will be favorable or not, as well as a forecast for a year of favorable conditions for the survival of pink salmon based on an estimate of the water temperature during the slope, which depends on the characteristics of atmospheric circulation.

Ice cover on the Great Lakes plays an important role in regional climate, supports tourism and recreation, and provides ecological habitat. As the climate warms, ice cover in the Great Lakes is expected to decline, which in turn will create more lake effect precipitation, reduce ice cover for recreation, and alter habitat for aquatic species. While it is important to understand the historical ice patterns to better understand past distributions of aquatic species and improve the accuracy of forecasts for future ice cover on the lakes, Great Lakes ice cover data prior to 1973 is scarce, due to the limited routine satellite observations. We used weather station data around the Great Lakes to compile daily air temperature, calculate cumulative freezing degree-days and net melting degree-days from 1897-2023, and develop raster layers estimating ice duration and variability spatially during the historical period from 1897-1960.

Abstract. Ice cover of water bodies in the northern high latitudes (NHL) is highly sensitive to the changing climate, and its dynamics exert substantial impacts on the NHL ecosystems, hydrological processes, and the carbon cycle. Yet, operational quantification of ice cover dynamics for smaller water bodies (e.g., ≤25 km2) over vast, remote NHL regions remains limited. Here, we developed an ice fraction dataset for small water bodies (ponds, lakes, and rivers; 900 m2 to 25 km2) across the Arctic Coastal Plain of Alaska (ACP) from 2017 through 2023, using Sentinel-1 Synthetic Aperture Radar (SAR) imagery, texture features, and Daymet air temperature data. The dataset has a spatial resolution of 1 km and a temporal resolution of approximately 6 d. Compared with the Google Dynamic World (DW) product derived from Sentinel-2 optical remote sensing, our dataset shows high consistency with DW (R=0.91, RMSE=0.19) while having enhanced temporal coverage due to less SAR constraints from solar illumination, cloud cover, and atmospheric conditions. Validation against in-situ observations suggests that our dataset is more capable of capturing small water body ice phenology (e.g., freeze-up and break-up dates) relative to DW, with an 11 d reduction in mean absolute error. Our ice fraction dataset reveals high spatial heterogeneity in ice conditions mainly occurring in June for small water bodies across the ACP. The ice phenology analysis over three selected subregions further shows that a warmer transition period generally leads to earlier ice break-up and later freeze-up, while the responses of ice fraction to warming climate vary among and within individual water bodies. The resulting dataset is anticipated to fill a gap in ice phenology studies for small water bodies, improve our understanding on the interactions between ice dynamics and climate change, and enhance the coupled modelling of ice and carbon processes. The S1 ice fraction dataset is publicly available at https://doi.org/10.5281/zenodo.17033546 (Lin et al., 2025).