Climate Studies Research Articles

Simulation of Urban Thermal Environment Based on Urban Weather Generator: Narrative Review

The thermal environment problem is one of the main focuses of current urban environment research. At present, there are various methods used in urban space thermal environment (USTE) research. As a simulation method to quantify the USTE, the urban weather generator (UWG) has undergone great development and achieved many progressive results. It is necessary to establish and review its current research status by synthesizing UWG multi-scale applications. This review adopts a literature review approach, leveraging the Web of Science Core Collection to obtain previous relevant publications from 2010 to 2025 using “urban weather generator” and “thermal environment” as keywords. The literature is categorized by research themes, including model development, parameter optimization, and application cases. Through innovative analyses of spatio-temporal-scale classification, parameter optimization, the integration of anthropogenic heat emissions, and the multi-domain simulation potential of the UWG, this review synthesizes the application outcomes of the UWG model in multi-scale research, addressing gaps in current urban climate studies. The paper aims to elaborate and analyze the model’s current research status considering the following six aspects. First, the basic parameters in UWG simulation are introduced, including the data and parameter determination settings used in such simulations. Secondly, we introduce the simulation model and its basic principles, the simulation process, and the main steps of this process. Third, we classify and define UWG simulations of spatial thermal environments at different time scales and spatial scales. Fourth, regarding how to improve the accuracy of the UWG model, the deterministic parameters and uncertainty parameters settings are analyzed, respectively. Then, the impacts of anthropogenic heat during the simulation process are also discussed. Fifth, the applications of the UWG model in some major fields and its possible future development directions are addressed. Finally, the existing problems are summarized, the future development trends are prospected, and research on possible expected mitigation measures for the USTE is described.

Climate and Human Behavior Studies for Our Warming World: An Introduction to the Models, Methods, and Data

Abstract This article is an introduction and guide to investigating past relationships between climate and human behavior. Improving understanding of these relationships is essential as humanity confronts the challenges of our warming world. However, how to investigate potential climatic influences on human behavior in the past is rarely presented or discussed as a distinct mode of inquiry. This article aims to fill this gap by providing a practical tool kit for students, archaeologists, anthropologists, and other historically focused social scientists. It is structured as a series of seven key steps to creating a research design for a climate and human behavior study, from identifying research questions to presenting results. Most of the conceptual models, methods, data, and examples provided have worldwide relevance and are informed by the long history of climate and human behavior studies in the North American Southwest. By expanding competence in this domain, we can enrich documentation and interpretations of the past and insights will emerge that will contribute to preparing for and responding to our warming world.

A Study of Organizational Climate among Secondary School Teachers in Kolkata

This study examines the organizational climate as perceived by secondary school teachers in Kolkata, West Bengal. A total of 400 teachers (200 government, 200 private) were selected using stratified random sampling. The Organizational Climate Scale for Teachers – Short Version (OCST–SV) developed by Dr. Venita Singh was employed to assess various dimensions of the school environment. The results indicated that while a large proportion of teachers experienced an average organizational climate, significant differences existed across the sample. One-way ANOVA confirmed these differences to be statistically significant (F = 2121.08, p < 0.001). The study concludes that organizational climate perception varies widely and may be influenced by institutional practices, leadership styles, and interpersonal relationships within the school.

Influence of Snow Redistribution and Melt Pond Schemes on Simulated Sea Ice Thickness During the MOSAiC Expedition

The observations of atmospheric, oceanic, and sea ice data from the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition were used to analyze the influence of snow redistribution and melt-pond processes on the evolution of sea ice thickness (SIT) in 2019 and 2020. To mitigate the effect of missing atmospheric observations from the time of the expedition, we used ERA5 atmospheric reanalysis along the MOSAiC drift trajectory to force the single-column sea ice model Icepack. SIT simulations from six combinations of two melt-pond schemes and three snow-redistribution configurations of Icepack were compared with observations and analyzed to investigate the sources of model–observation discrepancies. The three snow-redistribution configurations are the bulk scheme, the snwITDrdg scheme, and one simulation conducted without snow redistribution. The bulk scheme describes snow loss from level ice to leads and open water, and snwITDrdg describes wind-driven snow redistribution and compaction. The two melt-pond schemes are the TOPO scheme and the LVL scheme, which differ in the distribution of melt water. The results show that Icepack without snow redistribution simulates excessive snow–ice formation, resulting in an SIT thicker than that observed in spring. Applying snow-redistribution schemes in Icepack reduces snow–ice formation while enhancing the congelation rate. The bulk snow-redistribution scheme improves the SIT simulation for winter and spring, while the bias is large in simulations using the snwITDrdg scheme. During the summer, Icepack underestimates the sea ice surface albedo, resulting in an underestimation of SIT at the end of simulation. The simulations using the TOPO scheme are characterized by a more realistic melt-pond evolution compared to those using the LVL scheme, resulting in a smaller bias in SIT simulation.

Three Decades of Campus Racial Climate Studies and 25 New Directions for Future Research

ABSTRACT Sylvia Hurtado’s 1992 article, “The Campus Racial Climate: Contexts of Conflict.” remains a groundbreaking contribution to research on campus racial climates. In their 2007 publication, “Nine Themes in Campus Racial Climates and Implications for Institutional Transformation,” Shaun Harper and Sylvia Hurtado synthesized 15 years of studies and presented findings from qualitative climate assessments conducted at five U.S. postsecondary institutions. This article builds on those two highly-cited publications. Categorical clusters of 140 studies published in peer-reviewed academic journals within and beyond the field of higher education over three decades are presented herein. Also, 25 new topics that will deepen and modernize the study of campus racial climates are offered. The need for greater institutional type balance in the literature – specifically, increasing the number of climate studies conducted at community colleges – is repeatedly emphasized throughout this article.

Advancing the Study of Race, Racial Equity, and Racial Climates at America’s Community Colleges

ABSTRACT Like too many other topics pertaining to the study of higher education, published research on race, racial equity, and racial climates is lopsidedly situated in four-year institutional contexts. This special issue of the Community College Journal of Research and Practice aims to advance the study of these topics within community college contexts. Deepening understandings of racial issues at these institutions is especially urgent given the recent politicized attacks on diversity, equity, and inclusion in U.S. higher education.

Reflecting on the role of National Metrology Institutes on the occasion of the 125th anniversary of the National Physical Laboratory

Abstract As the National Physical Laboratory (NPL), the UK’s National Metrology Institute, celebrates its 125th anniversary, this perspectives article reflects on NPL’s history and provides some insight into the role of National Metrology Institutes in contributing towards the maintenance of the global measurement system, the International System of Units, the SI. It also provides an outlook on the challenges facing National Metrology Institutes in meeting future requirements to support the SI and disseminate metrology to scientific and industrial communities where new challenges and opportunities are emerging. The article concludes by reaffirming that, even after 125 years, metrology and the global measurement system that it supports, remains essential in providing confidence in the accuracy and worldwide comparability of measurements needed for international trade, manufacturing and industry, human health and safety, protection of the environment, global climate studies and scientific research.

A review of feature selection methods for actual evapotranspiration prediction

Accurate prediction of actual evapotranspiration (AET) is critical for hydrological modeling, agricultural planning, and climate studies. Machine learning models have emerged as powerful AET prediction tools because they can handle complex, nonlinear relationships in large datasets. However, selecting relevant input features significantly impacts model performance, efficiency, and interpretability. Feature selection techniques reduce high-dimensional datasets by identifying redundant and uncorrelated variables. This paper reviews feature selection approaches for predicting ML-based AETs by analyzing 62 studies; a total of 416 were retrieved from seven digital libraries. Our analysis shows that filtering methods are the most widely used , followed by manual selection based on domain expertise , embedded methods , and wrapper methods . Dimensionality reduction techniques, such as principal component analysis (PCA), are the least used . Among machine learning models, Random Forest (RF) and Artificial Neural Networks (ANN) are the most commonly used, with 29 and 27 instances, respectively. The study highlights the strengths and limitations of each category of feature selection, emphasizing the potential of hybrid approaches integrating filter, wrapper, embedded, and manual selection methods. These combinations improve model accuracy, robustness, and generalization, while mitigating overfitting, computational inefficiency, and sensitivity to noise. This review provides insights into optimal feature selection strategies for improving ML-based AET prediction.

Climate Change Impacts on Temperature and Water of the Soil in Iraq for the Period 1980-2022

Soil temperature is a fundamental issue in applied climate studies. It has great importance in overall plant activity in terms of the speed of seed germination and the process of water absorption by the roots. When the soil temperature rises, the effectiveness of biological organisms increases, which in turn increases the speed of decomposition of organic matter, provides organic nutrients to the plant, and determines Suitable conditions for seed germination and seedling growth in the early stages of the plant’s life. Data are taken by high vegetation Cover (HVC), soil temperature (ST), soil water (SW), total precipitation (TP), and temperature (T) from satellites recorded by the European Centre for Medium-Range Weather Forecasts (ECMWF). The choice period from 1980-2022 over Iraq extends between two latitudes (29.55° - 37.225°) North of the equator and between two longitudes (38.455° - 48.548°) East of the Corniche line. Otherwise, we have studied the behavior of monthly, seasonal, and yearly means of HVC, ST, SW, TP, and T as well as the relationship between them. The results showed that the strongest correlation was between the soil temperature and the temperature, as the relationship was very strong and positive. While the other relationship between soil temperature and water content was a very strong inverse relationship, the other relationship between soil temperature, high vegetation cover, and total precipitation was inverse and weak.

Collectivism and Climate Influence: Interpreting Indonesian Society Through the Perspective of Ibn Khaldun

<em>This study analyzes the influence of climate on the formation of social character in Indonesian society based on Ibn Khaldun’s theory. In the Muqaddimah, Khaldun argues that hot and warm climates foster collectivism through environmental adaptation. This research employs a philosophical approach and literature analysis to examine the relevance of this theory within the context of Indonesia’s tropical climate. The findings indicate that the collectivist character of Indonesian society, as seen in practices such as gotong royong (mutual cooperation), reflects an enduring adaptation to ecological challenges. The study also highlights how Indonesia’s agrarian traditions and communal resilience are rooted in climatic necessity. However, the growing impact of globalization and modernity is contributing to a shift toward individualism, disrupting traditional communal patterns. This dynamic tension between collectivist values and individualist tendencies presents new socio-cultural challenges. Ultimately, this study underscores the continued relevance of Ibn Khaldun’s environmental theory for interpreting Indonesia’s socio-cultural transformations and opens avenues for future interdisciplinary research that bridges philosophy, sociology, and climate studies.</em>

Numerical modeling and experimental study of SiO2 radiative cooling coatings on aluminum substrate in humid conditions: A case study in Morocco Mediterranean climate

Numerical modeling and experimental study of SiO2 radiative cooling coatings on aluminum substrate in humid conditions: A case study in Morocco Mediterranean climate

Decolonizing Environmental Politics: Relationality and Cosmopraxis in the Sundarbans Region, Bangladesh

ABSTRACTThe paper seeks to bridge the gap between environmental policy formulation and actual practices of the Sundarbans region in Bangladesh through a metatheoretical intervention within a decolonial framework. Ontologically, it foregrounds relationality and examines the implication of such ontological position. Epistemologically, the paper advocates standpoint epistemologies. With an effort to decolonize environmental governance, the paper specifically focusses on the epistemic erasures of traditional resource users in the Sundarbans. It engages with the ways in which specific communities engage with the mangrove forest ecosystem. Methodologically, the paper borrows the framework of Hybrid/Plural Climate Studies and Cosmopraxis. Cosmopraxis has the potential of countering epistemic erasures done by modernity and colonization of the lifeworld. Empirically, the paper examines the case of select communities in the Sundarbans, Bangladesh where their reciprocal and relational engagement with the nature goes beyond spirituality and shows an implicit presence of cosmopraxis.

Study of the North Atlantic Oscillation Influence on the Climate of Europe and Balkan Peninsula

Numerous climate studies have shown that the North Atlantic Oscillation (NAO) has a significant influence on weather conditions in Europe. Regarding the impact of the NAO on the climatic variables over the Balkans, there is a divergence of opinions among contemporary publications. The contradictory results can be attributed to several factors that make the climate of the region complex and multifaceted. This research focuses on the analysis of surface pressure and wind variability at 850 hPa, as a function of the sign and value of the NAO index, in a dense network of data provided by ERA 5, for the period 1940–2023. Using an artificial neural network, we show that: (i) the high sensitivity of northwestern Europe and the Mediterranean to NAO values is confirmed by our analysis; (ii) the impact of moderately negative NAO values is small in the Balkan region; (iii) the NAO values below the 2σ level have a significant impact on the Balkan Peninsula, due to the activation of Mediterranean cyclones.

Green Leadership Support as a Catalyst for Innovative Behavior: A Study of Green Absorptive Capacity and Work Climate in UAE Hotels

ABSTRACTThis study investigated how green absorptive capacity influences employee perceptions of a green work climate and their engagement in green innovative behavior. It also examines the moderating role of green leadership support in shaping these relationships. Guided by the resource‐based view, the research explores how environmental knowledge capabilities and supportive organizational conditions contribute to sustainability‐focused innovation. Survey data were collected from employees at twelve four‐ and five‐star hotels in the United Arab Emirates and analyzed using partial least squares structural equation modeling. The findings show that green absorptive capacity significantly enhances perceptions of a green work climate, which in turn leads to increased green innovative behavior. The results further reveal that green leadership support strengthens the effect of green work climate on green innovative behavior. These insights extend the resource‐based view by demonstrating that organizational resources must be integrated with a supportive climate to achieve innovation outcomes. The study offered practical recommendations for firms aiming to embed sustainability into their core operations by investing in environmental knowledge systems, cultivating green workplace values, and reinforcing leadership support for environmental initiatives.

Systematic global stocktake of over 50,000 urban climate change studies

Abstract Taking stock of climate change evidence is essential to helping cities address climate change. However, such efforts face challenges in appraising the growing scholarship in this fast-moving area. Here we use supervised and unsupervised machine learning to identify and classify over 53,000 urban climate studies, creating a dynamic, interactive and searchable evidence database for researchers and policymakers. Nearly 20,000 are city-specific case studies, revealing a rapidly growing yet unevenly distributed knowledge base. Notably, small and fast-growing cities, particularly in Africa and Asia, remain substantially underrepresented, contributing to topical, geographic and disciplinary biases in previous Intergovernmental Panel on Climate Change (IPCC) assessments. We propose three strategies to address this: (1) synthesizing case studies to support IPCC uptake, (2) identifying cross-city learning opportunities and (3) closing evidence gaps in the Global South. Thereby, our systematic stocktake helps inform adaptation and mitigation efforts in cities, guides future research and strengthens the IPCC’s ability to deliver robust, policy-relevant evidence.

A Sub-Hourly Precipitation Dataset from a Pluviographic Network in Central Chile

This data descriptor presents a unique high-resolution rainfall dataset derived from 14 pluviograph stations across central Chile’s Mediterranean region, covering variable periods starting from between 1969 and 1992, up to 2009. The dataset provides continuous precipitation records at a 5 min temporal resolution, obtained through the digitization and processing of pluviograph strip charts using specialized software. This high temporal resolution is unprecedented for the region and enables detailed analysis of rainfall intensity, duration, and frequency patterns critical for hydrological research, climate studies, and water resource management in general. Each station’s data was subjected to quality control procedures, including manual validation and correction of digitization errors to ensure data integrity. The dataset reveals the significant temporal variability of rainfall in central Chile, capturing both short-duration high-intensity events and longer precipitation patterns. By making this dataset publicly available, we provide researchers with a valuable resource for studying rainfall behavior in a Mediterranean climate zone subject to significant climate variability and change. The dataset supports various applications, including the development of intensity–duration–frequency curves, analysis of rainfall erosivity, calibration of hydrological models, and investigation of precipitation trends in the context of climate change.

Recovery and Reconstructions of 18th Century Precipitation Records in Italy: Problems and Analyses

Precipitation is one of the main meteorological variables in climate research and long records provide a unique, long-term knowledge of climatic variability and extreme events. Moreover, they are a prerequisite for climate modeling and reanalyses. Like all meteorological observations, in the early period, every observer used a personal measuring protocol. Instruments and their locations were not standardized and not always specified in the observer’s metadata. The situation began to change in 1873 with the foundation of the International Meteorological Committee, though the complete standardization of protocols, instruments, and exposure was reached in 1950 with the World Meteorological Organization. The aim of this paper is to present and discuss the methodology needed to recover and reconstruct early precipitation records and to provide high-quality dataset of precipitation usable for climate studies. The main issues that have to be addresses are described and critically analyzed based on the longest Italian precipitation series to which the methodology was successfully applied.

An Investigation of GNSS Radio Occultation Data Pattern for Temperature Monitoring and Analysis over Africa

Climate change monitoring and analysis is a critical task that involves the consideration of both spatial and temporal dimensions. Theimproved spatial distribution of the global navigation satellite system (GNSS) ground-based Continuous Operating Reference (COR) stations can lead to enhanced results when coupled with a continuous flow of data over time. In Africa, a significant number of COR stations do not operate continuously and lack collocation with meteorological sensors essential for climate studies. Consequently, Africa faces challenges related to inadequate spatial distribution and temporal data flow from GNSS ground-based stations, impacting climate change monitoring and analysis. This research delves into the pattern of GNSS radio occultation (RO) data across Africa, addressing the limitations of the GNSS ground-based data for climate change research. The spatial analysis employed Ripley’s F-, G-, K-, and L-functions, along with calculations of nearest neighbour and Kernel density. The analysis yielded a Moran’s p-value of 0.001 and a Moran’s I-value approaching 1.0. For temporal analysis, the study investigated the data availability period of selected GNSS RO missions. Additionally, it examined seasonal temperature variations from May 2001 to May 2023, showcasing alignment with findings from other researchers worldwide. Hence, this study suggests the utilisation of GNSS RO missions/campaigns like METOP and COSMIC owing to their superior spatial and temporal resolution.

Refining linear trend estimates from one dimensional time series data with autoregressive covariance modelling: an application to GRACE total water storage time series data

Abstract Deriving trend functions from observed time series is one of the main tasks in the field of signal processing. Separating the observation into a deterministic function, a stochastic residual signal and a stochastic noise component using dedicated model representations enables further study of these individual components, e.g. screening for climate signals. Whereas the deterministic part is modelled as a linear combination of basis functions, the use of autoregressive processes to model the noise and signal is proposed. Within an iterative estimation scheme, the uncertainty information of the observed variables is properly modelled and carefully propagated to the resulting parameters. This enables the use of statistical testing and Least-Squares Collocation in further investigations of the separated signal components. In this study, the proposed iterative procedure is applied to relatively short total water storage time series derived from measurements of the satellite mission GRACE. The trend in total water storage is, for instance, relevant for climate studies, identifying regions getting drier or wetter. Accounting for the aforementioned covariance information based on autoregressive processes allows the use of Hypothesis tests to identify regions with significant trends. On the contrary, the smoothed stochastic signal components are required to identify extreme/anomalous events like floods and droughts in the observed time series. Additionally, to improve the estimation of the stochastic signal, data from numerical models are used to estimate the process characteristics.

Improvements to the Met Office's global ocean–sea ice forecasting system including model and data assimilation changes

Abstract. The Forecast Ocean Assimilation Model (FOAM) is the Met Office's operational, coupled ocean–sea ice system, which produces analyses and short-range forecasts at global and regional scales each day for various stakeholders, including defence, marine navigation and science users. This paper describes and evaluates the impacts of recent model and data assimilation (DA) updates on global FOAM when compared to its current operational version. The model updates include the use of the TEOS10 formulation for the seawater equation of state, with improved ocean model settings in the Southern Ocean and the implementation of a new sea ice model. Updates to the DA include an increase in the number of DA minimisation iterations, an improved specification of observation errors for sea surface temperature and sea level anomaly (SLA), and optimisations of the DA computational efficiency. Large-scale DA corrections for temperature have also been removed to prevent an inconsistent projection of the SLA DA signal onto large-scale temperature at depth. For 1-year runs at 1/12° resolution, the new FOAM system shows a 40 % improvement in observation-minus-background (OmB) statistics for SLA and subsurface temperatures relative to the current system in eddy-rich regions, which result in a similar level of improvement for ocean currents. To evaluate potential impacts on the pre-Argo period, 1-year experiments at 1/4° resolution are run withholding profiles of temperature and salinity observations in both new and current FOAM systems. Limited to the assimilation of only surface data, OmB statistics for SLA, temperature and salinity in the new FOAM system can reach improvements up to 90 % in the Southern Hemisphere relative to the current system, resulting in more temporally consistent ocean transport and heat content results. Therefore, it is expected that the model and DA updates will lead to more potential for use of FOAM reanalyses in climate studies, particularly in the pre-Argo period, and will provide improved ocean–sea ice initial conditions to FOAM as well as to the Met Office short-range and seasonal coupled ocean–atmosphere–land–sea ice forecasting systems.