Climate Classification Research Articles

Water Balance and Climate of the Ipojuca River Water Basin – Pernambuco, Brazil

The objective is to carry out the analysis of the climatic water balance and make a comparison between the municipalities surrounding the Ipojuca River basin, using the applications of climatic indices and aridity. The study area is the Ipojuca River – Pernambuco river basin, made up of twenty-seven municipalities. The rainfall data were provided by the Water and Climate Agency of the State of Pernambuco and thermal data were estimated by the Estima_T software corresponding to the years 1962 to 2018. For the climatic and agricultural suitability analysis, climatic indices of aridity and water availability were applied to the vegetation. The aridity index was calculated using the UNEP formulation, the climatic classifications, using the aridity index or dry month, recommended by Gaussen. Municipalities with higher altitudes provide conditions for better natural development of vegetation followed by greater potential for the development of agricultural activities, when compared to municipalities at lower altitudes. A marked change was observed between the climatic conditions of municipalities with high and low altitudes in the basin area, with emphasis on water deficits and high temperatures. Marked disputes between climate variability in municipalities at higher and lower altitudes were recorded in water deficits and inter-municipal thermal fluctuations.

Thornthwaite Moisture Index for the Triângulo Mineiro, Brazilian Cerrado Region, Under Climate Change

Climate change represents one of the main challenges of the 21st century for planning and sustainable development. However, little is known about how climate change can affect a region's climate zones. The objective was to evaluate probable changes in climatic zones using the Thornthwaite climate classification (1948). Historical series between 1981 and 2021 of rainfall and air temperature were used. The water balance was calculated from Thornthwaite and Mather. Thornthwaite's humidity index (1948) was used to classify localities according to their level of humidity and the scenarios RCP 2.6, RCP 4.5, RCP 6.0 and RCP 8.5 to analyze projections for the 21st century (period 2081–2100). The current characterization, with historical data, of the region's climate presented air temperature, rainfall and average potential evapotranspiration, respectively, of 22.4ºC, 1,318.8 mm and 1,123.74 mm, in addition to a water surplus of 391.04 mm and water deficit of 195.04 mm. The region currently has five climate indices, with a prevalence of more humid classes (B1, B2 and B3), corresponding to 62% of the territory. The results derived from the projections indicate reductions in climate classes and an increase in the area occupied by drier climates. For example, the percentage of area occupied by class C1 (dry subhumid) would increase from the current 8.4% to 69.68% in the RCP 8.5 scenario. The study of these change projections is important since profound consequences for the hydrology, ecology and social area of the region will take place, potentially harming agriculture, the region's main economic activity.

Adaptive Thermal Comfort in the Different Buildings of Temperate Climates—Comparison Between High-Latitude Europe and Mountainous Himalayas in India

Thermal comfort in buildings is essential for occupant well-being and energy efficiency, particularly in naturally ventilated environments where indoor conditions are closely influenced by outdoor climates. Current studies have not fully explored how thermal comfort varies across regions with similar climatic classifications but distinct geographic and cultural contexts. Addressing this gap, we analyzed and compared the adaptive thermal comfort responses in different naturally ventilated buildings located in temperate oceanic regions arising due to the high latitude in Europe and the elevated Himalayan region of Darjeeling, India. A mixed-methods approach was used with data from classrooms, offices, and residential buildings with adaptive thermal comfort modeling. The results show that European buildings exhibit narrower thermal comfort ranges compared to Darjeeling, for example, 21.2~24.8 °C versus 16.0~21.6 °C for 80% comfortability in classroom settings, respectively. Statistical analysis revealed significant differences in clothing insulation levels, with occupants in Darjeeling buildings demonstrating higher variability (mean rank 2103.31) compared to their European counterparts (mean rank 1207.30, p < 0.001). Additionally, a stronger correlation between indoor and outdoor air temperature was observed in Darjeeling (R: 0.785, p < 0.001), reflecting limited thermal buffering compared to European buildings (R: 0.372, p < 0.001). The paper advances adaptive thermal comfort models that account for regional differences and links these finding to sustainable building practices. The findings provide actionable insights for energy-efficient, climate-responsive building practices while supporting global sustainable development goals.

Long-term forecasting for climate classification using hybrid backpropagation relevance vector machine algorithm

Abstract Climate forecasting and classification are essential in planning cropping patterns. Therefore, the purpose of this study is to evaluate the condition of hydro-climatological data for the last 10 years and determine the trend of the data and determine the climate classification. The forecasting process utilizes a long-term forecasting method based on a hybrid backpropagation-relevance vector machine (BP-RVM) algorithm. The architecture parameters consist of three hidden layers (36-73-38-19-1), learning rate 0.1, momentum 0.9, and RBF gamma of 0.01. The forecasting results show that the BP-RVM algorithm has an average accuracy level with a category of “high accurate forecasting”. The climate classification in the Central Lombok region, Indonesia is D4 climate (slightly dry category). The increase in rainfall volume and temperature from year to year has a strong correlation with a decrease in air humidity, wind speed, and length of sunshine. The results of this study can be utilized as a basis for planning cropping patterns and handling the water needs of crops grown in the dry season. Therefore, farmers can determine the amount of water needed from dams or watersheds due to evaporation that occurs during the growing season.

CMIP6 GCMs Projected Future Koppen‐Geiger Climate Zones on a Global Scale

AbstractThis study compared the future global climate zones based on four radiative forcings ranging from low‐end (SSP1‐2.6) to high‐end (SSP5‐8.5) using the Köppen‐Geiger climate classification. To reduce uncertainties in future projected precipitation and temperature, multimodel projections comprising 25 general circulation models (GCMs) were sourced from the recent Coupled Model Intercomparison Project phase six (CMIP6) and used to create a Multi‐Model Ensemble. The changes in historical climate zones on CMIP6 simulations were divided into six periods considering data availability (1954–1964; 1964–1974; 1974–1984; 1984–1994; 1994–2004; and 2004–2014). Furthermore, the climate zone reproducibility of 25 CMIP6 GCMs was compared with the reference data sourced from Global Precipitation Climatology Centre precipitation and Climatic Research Unit temperature. The future climate zones were projected into seven periods using monthly precipitation and surface temperature under four main SSP scenarios. Consequently, the climate variables from GCMs were overestimated compared to the reference data, and the composition of the climate zones was less complex. While temperature discrepancies of 1–2°C may not drastically alter the Köppen‐Geiger climate zone classifications, precipitation‐based classifications are significantly impacted by the observed errors. Thus, it is crucial to recognize that despite the advancements in GCMs, they still possess limitations in accurately predicting “real” future climate changes. The projected future climate zones are simpler compared to the historical periods across six continents, with tundra and ice caps expected to disappear. This study highlights potential risks by projecting future climate zones based on varying greenhouse gas concentration levels, stressing the importance of using these projections with caution given the inherent uncertainties and limitations of GCMs.

Robust spatial changes in climate classes: insights from bias-corrected CMIP6 models across Chile

Abstract The climate in Continental Chile is marked by strong latitudinal and elevation gradients, exacerbated by diverse geographical features, such as the Andes. Despite previous studies projecting warmer and dryer conditions for most of the territory, there is concern about the robustness (i.e. level of agreement among models) of changes projected for its magnitude, not only for the impact on climate indices across this domain but also to identify changes in the spatial distribution of climate classes. Hence, we statistically downscaled and bias-corrected daily CMIP6 model outputs for continental Chile, using a multivariate bias correction method, to project climate changes under the SSP5-8.5 scenario. The results reveal that General Circulation Models (GCMs) project increased dryness across the study domain by the end of the 21st century, especially in Central Chile (−30% in precipitation), with notable sensitivities of precipitation projections to the implementation of bias correction methods in the northern and austral macrozones. Temperature projections show less dispersion, with higher increments in northern Chile and the Andes (4 ∘C–5 ∘C). Notable shifts in the extension of Köppen–Geiger climate classes are projected for the next decades, with the expansion of deserts in northern Chile and the prevalence of temperate climates with dry summers in central Chile. The Andes subdomain is expected to face the most dramatic changes in Köppen–Geiger classes (inter-model agreement > 70%). Surprisingly, despite the large spread in GCM projections, there is high agreement among models regarding spatial changes in climate classes. Additionally, our results project drastic reductions in snowfall across the Andes, with higher freezing level heights that may exacerbate flooding and landslide risk across the country.

A Comparison of the Effects of Climate and Human Variability on the Thermal Resistance of Clothing

We used a clothing thermal resistance model to investigate and compare the effects of climate and human variability on human thermal load. To investigate the effect of climate variability, we introduced the mean clothing thermal resistance rcl¯. For characterizing the effect of human variability, we used the standard deviation of clothing thermal resistance ∆rcl. We distinguished people based on their body type. We also defined the average human, a man and a woman, with thermal resistances of rcl,m and rcl,f. The investigation was carried out for the European region in the cold season for the period of 1981–2010. The climate variables were taken from the ERA5 reanalysis database. Our most important results are the following. (1) The macroscale pattern of the rcl¯ and ∆rcl fields are very similar, based on which it can be stated that human variability does not modify the spatial distribution of rcl¯. (2) The ∆rcl values are roughly a quarter of the rcl¯ values. The highest rcl¯ values (3.2–3.4 clo) are in Lapland, and the smallest (1–1.2 clo) in Andalusia. (3) The macroscale pattern of the rcl,m and rcl,f fields is similar to the macroscale pattern of the rcl values of the mesomorphic person rcl,2. The field of rcl,2 can be used for climate classification purposes.

Sugar Content in Arbutus unedo L. Fruit and Its Relationship with Climatic and Edaphic Characteristics.

This research was carried out as part of a program for the conservation and improvement of the strawberry tree. Accessions' prospecting was conducted in different Portuguese provenances. Accessions (204) were identified, and mature fruits were collected in autumn. The sugar contents in the fruit pulp (glucose, fructose, maltose, sucrose) were analyzed by HPLC and the correlation between the average sugar content, climatic classification (Thornthwaite Method and Xerothermic Index), and edaphic characteristics was investigated. The predominant sugar was fructose, which ranged from 7.89 ± 0.55% to 17.01 ± 1.46% (f.w.), respectively, under an Attenuated Thermomediterranean climate at limestone-derived soil and an Accentuated Mesomediterranean climate at schist-derived soil. The lowest sugar contents, linked to a reduction in photosynthesis, were found: (1) in the north, despite soil water availability, due to the low temperature, high precipitation and short photoperiod, particularly during the fruit maturation (autumn); (2) in the south, due to the excessive number of dry days, linked to low soil water availability during the active growth period, particularly at limestone zones prone to water retention. The relationship found between the total sugar content and climate classification by the Xerothermic Index allows to enhance fruit production, advise on the establishment of new orchards and restore natural areas.

Organisms with high dispersal ability as a proxy for biogeographical characterisation of the Mediterranean biome

The Mediterranean biome offers a diverse range of habitats, particularly rocky environments, known for their long-term stability and persistence. Adapted to these rocky surfaces, chasmophytes thrive in this region, benefiting from reduced competition and human interference. The distribution of stress-tolerant cryptogams with generally broad niches represents a significant gap in the biogeography of the region. This study investigates whether complex symbiotic organisms with a potential for long-distance dispersal could serve as biogeographic indicators for delimitation of the Mediterranean biome's boundaries. Through comprehensive chorology data research, saxicolous lichen congeners of the genus Solenopsora were mapped across the Mediterranean Basin as a model area. Environmental predictors (climate and geology) were employed to predict suitable habitats, shedding light on the lichens' ecological niches. Habitat suitability models consistently aligned with the Mediterranean's geographical area, indicating a tight link between lichen distribution and Mediterranean climate classification. While climate change may pose vulnerabilities, no significant range shifts beyond the latitudinal limit of the Mediterranean were predicted. Despite overlapping climatic niches, differences in microhabitat preferences in terms of insolation were observed among studied taxa. Vulnerable regions with potential habitat decreases were identified under future scenarios, emphasizing the importance of environmental heterogeneity in securing local species persistence. This study supports the hypothesis that these lichen symbiotic systems are restricted to the Mediterranean Basin, highlighting their potential to reflect Mediterranean climate, environmental stability, and indicate the boundaries of the Mediterranean biome. Our investigation demonstrates that collection-based research can yield significant contributions to current biogeography.

Using random forests to explore the feasibility of groundwater knowledge transfer between the contiguous US and Denmark

Abstract Groundwater is our largest freshwater reservoir, playing an important role in the global hydrologic cycle. Lack of reliable groundwater data restricts the development of global groundwater monitoring systems linking observations with modeling at spatial scales relevant for local decision making. Despite the growing interests in machine learning (ML) for groundwater resource modeling, taking ML models to the global scale is still outstanding due to sparse groundwater data. The contiguous US (CONUS) has extensive groundwater information covering a wide range of hydrogeologic settings. We hypothesize that a ML model trained on the CONUS is transferable to other regions, and thus can be used to produce a global water table depth (WTD) map within the bounds of transferability. To test this hypothesis, we conduct a study on transferring groundwater knowledge between the CONUS and Denmark, using several random forest models trained against ∼30 m resolution long-term mean WTD data. The joint model trained on data from the CONUS and Denmark outperforms the individual models trained separately, implying similarities within global groundwater systems. The largest improvement occurs in Denmark, where the testing Nash-Sutcliffe efficiency rises from 0.68 to 0.95. SHapley Additive exPlanations (SHAP) values are utilized to express the importance of input variables. While annual mean precipitation plays a key role in the joint model and the model for the CONUS, it is the second least important input variable in the model for Denmark where local processes dominate. Moreover, Köppen-Geiger climate classification shows a significant impact on the model testing performance and the importance ranking of input variables, which might be a missing input variable in the applied random forest models. This study provides unique insights into future ML model developments towards global groundwater monitoring and improves our confidence in producing a hyper-resolution global WTD map for sustainable freshwater management.

Quantitative Impact of Monthly Precipitation on Urban Vegetation, Surface Water and Potential Evapotranspiration in Baghdad Under Wet and Dry Conditions

Precipitation is a fundamental variable that is widely used in the organization of water resources and has a great influence on hydrological processes and ecological assessment. This study investigated the quantitative effect of monthly precipitation on surface water area (denoted by the Modified Normalized Difference Water Index, MNDWI), vegetation area (denoted by Normalized Difference Vegetation Index, NDVI), and potential evapotranspiration (PET) during two years (2018 and 2021) in the city of Baghdad, Iraq. Using the Thornthwaite aridity index, the annual aridity was first assessed to quantify the climate category of these years. The result shows that they were semi-arid and very arid, respectively. The empirical relationships between precipitation and areas of MNDWI and NDVI, and between rainfall and PET, were also examined. Due to less precipitation in 2021, no relationship was found in arid climates, while in 2018 for semi-arid climates, precipitation had a positive non-linear correlation with MNDWI and NDVI areas and a negative correlation with PET.

On the closure relationship among shortwave radiometric measurements under a cold climate during winter

To support the Baseline Surface Radiation Network initiative, a new radiometric station was set up in Qiqihar, China, which has a monsoon-influenced hot-summer humid continental climate (Dwa) under the Köppen–Geiger climate classification, which is extremely rare in the world. Through a two-month operation in winter, it was observed that the station suffered from some nonclosure problems that impacted the quality of its measurements—the summation of the beam and diffuse components do not agree well with the global one. This work therefore investigates the possible causes of the problems and presents a set of quality checks to identify the causes. It is found that the problems originate from the mis-leveling of the sun tracker and frost deposition on the pyrheliometer, and once they are fixed, the closure relationship is maintained.

Threshold Changes in Winter Temperature and Precipitation Drive Threshold Responses Across Nine Global Climate Zones and Associated Biomes

Globally, winter temperatures are rising, and snowpack is shrinking or disappearing entirely. Despite previous research and published literature reviews, it remains unknown whether biomes across the globe will cross important thresholds in winter temperature and precipitation that will lead to significant ecological changes. Here, we combine the widely used Köppen–Geiger climate classification system with worst-case-scenario projected changes in global monthly temperature and precipitation to illustrate how multiple climatic zones across Earth may experience shifting winter conditions by the end of this century. We then examine how these shifts may affect ecosystems within corresponding biomes. Our analysis demonstrates potential widespread losses of extreme cold (<−20°C) in Arctic, boreal, and cool temperate regions. We also show the possible disappearance of freezing temperatures (<0°C) and large decreases in snowfall in warm temperate and dryland areas. We identify important and potentially irreversible ecological changes associated with crossing these winter climate thresholds.

Thermal Transmittance Limits Dataset for New and Existing Buildings Across EU Regulations

Building energy regulations are essential for reducing energy consumption in the European Union (EU) and achieving climate neutrality goals. This data article supplements the “Overview of EU Building Envelope Energy Requirement for Climate Neutrality” by presenting a detailed dataset on building regulations across all 27 EU member states, with a focus on building envelope efficiency. The data include thermal transmittance limits for windows, walls, floors, and roofs, offering insights into regulatory differences and potential opportunities for harmonization. Information was sourced from the Energy Performance of Buildings Directive (EPBD) database, national reports, and scientific literature to ensure comprehensive coverage. Key aspects of each country’s regulations are summarized in tables, covering both new constructions and renovations. The inclusion of Köppen–Geiger climate classifications allows for climate-specific analyses, providing valuable context for researchers, policymakers, and construction professionals. This dataset enables comparative studies, helping to identify best practices and inform policy interventions aimed at enhancing energy efficiency across Europe. It also supports the development of tailored strategies to improve building performance in different environmental conditions, ultimately contributing to the EU’s energy and climate targets.

Passive House Design: A Possible Energy Efficient Option in the Building Sector of Nepal

The diversified geography of Nepal creates huge variations in the country's climatic zones; however, the building industry has so far used standardized methods that tend to neglect local climate conditions. Most of these standards then rely on energy-intensive mechanical systems to maintain indoor thermal comfort, without considering more viable and climate-responsive design methods. In this light, the development of a climate classification related to building design will help develop and encourage energy- and climate-effective building architecture in Nepal. The existing energy-saving practice in the building sector of the country is reviewed in this paper, and it outlines ways to improve the adaptation of energy-efficient methodology. It shows that passive houses are performing much better in comparison with modern constructions that are behind in terms of energy efficiency when compared with traditional homes. The study outlines the climate-specific design criteria and methodologies for various regions and sets the path for exploration of the passive house design process challenges and opportunities that might exist for wider diffusion. Also discussed are strategies to overcome the barriers and promote passive house construction, offering a pathway toward sustainable building practices in Nepal.

Design and Use of a Stratum-Based Yield Predictions to Address Challenges Associated with Spatial Heterogeneity and Sample Clustering in Agricultural Fields Using Remote Sensing Data

Machine learning (ML) models trained with remote sensing data have the potential to improve cereal yield estimation across various geographic scales. However, the complexity and heterogeneity of agricultural landscapes present significant challenges to the robustness of ML-based field-level yield estimation over large areas. In our study, we propose decomposing the landscape complexity into homogeneous zones using existing landform, agroecological, and climate classification datasets, and subsequently applying stratum-based ML to estimate cereal yield. This approach was tested in a heterogeneous region in northern Morocco, where wheat is the dominant crop. We compared the results of the stratum-based ML with those applied to the entire study area. Sentinel-1 and Sentinel-2 satellite imagery were used as input variables to train three ML models: Random Forest, Extreme Gradient Boosting (XGBoost), and Multiple Linear Regression. The results showed that the XGBoost model outperformed the other assessed models. Furthermore, the stratum-based ML approach significantly improved the yield estimation accuracy, particularly when using landform classifications as homogeneous strata. For example, the accuracy of XGBoost model improved from R2 = 0.58 and RMSE = 840 kg ha−1 when the ML models were trained on data from the entire study area to R2 = 0.72 and RMSE = 809 kg ha−1 when trained in the plain area. These findings highlight that developing stratum-based ML models using landform classification as strata leads to more accurate predictions by allowing the models to better capture local environmental conditions and agricultural practices that affect crop growth.

Performance assessment of a 30.26 kW grid-connected photovoltaic plant in Egypt

Abstract This paper presents an experimental analysis and performance evaluation of a grid-connected photovoltaic plant installed on the rooftop of the Electronics Research Institute in Cairo, Egypt. Cairo is classified as a hot-desert climate region according to the standard Koppen-Geiger climate classification system. Over a year, we monitored real-time data to assess key system performance metrics, such as energy yield, efficiencies, performance ratio, capacity factor, and losses. Based on the obtained experimental results, the highest final yield of 5.2498 hr/day was observed in the summer, whereas the lowest yield of 3.439 hr/day occurred in the winter months. The photovoltaic plant had an average annual system efficiency of 15.8%, while the photovoltaic and inverter had mean yearly efficiencies of 17.1% and 97.2%, respectively. The average annual performance ratio is 83.03%, and the capacity factor is 18.72%. The monthly total loss exhibited a linear rise alongside increasing ambient temperature and solar irradiance. The ambient temperature affected the system efficiency, photovoltaic efficiency, and performance ratio. The findings can help strengthen forecasts of future large-scale photovoltaic plants in hot desert climates. Moreover, they can guide the design, optimization, operation, and maintenance of new grid-connected photovoltaic systems.

Community structure of tropics emerging from spatio-temporal variations in the Intertropical Convergence Zone dynamics

The Intertropical Convergence Zone (ITCZ) is a narrow tropical belt of deep convective clouds, intense precipitation, and monsoon circulations encircling the Earth. Complex interactions between the ITCZ and local geophysical dynamics result in high climate variability, making weather forecasting and prediction of extreme rainfall or drought events challenging. We unravel the complex spatio-temporal dynamics of the ITCZ and the resulting teleconnection patterns via a novel tropical climate classification achieved using complex network analysis and community detection. We reduce the high-dimensional complex ITCZ dynamics into a simple yet insightful community structure that classifies the tropics into seven regions representing distinct ITCZ dynamics. The two largest communities, encompassing landmasses over the Northern and Southern hemispheres, are associated with coherent seasonal ITCZ dynamics and have significant long-range connections. Temporal analysis of the community structure highlights that the tropical Pacific and Atlantic Oceans communities exhibit substantial variation on multidecadal scales. Further, these communities exhibit incoherent dynamics due to atmosphere-ocean interactions driven by equatorial and coastal oceanic upwelling.

Projection of climate change impact on main climate variables and assessment of the future of Köppen–Geiger climate classification in Iran

AbstractHuman society and the environment are facing significant challenges due to climate change. Climate change is projected to impact main climate variables, such as temperature and precipitation. Changes in main climate variables affect climate classification and alter climate zone maps. In this research, first, the projection of temperature and precipitation in 30 main stations of Iran under the Shared Socioeconomic Pathways scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5) from Coupled Model Intercomparison Project Phase Six (CMIP6) for the end of the twenty-first century (2071–2099) was carried out. Then, the future of climate zone maps was assessed in Iran by Köppen-Geiger climate classification. The evaluation of the model data based on observation data for the period of 1991–2020 showed an acceptable correlation, with R-square and RMSE values in the ranges of 0.67–0.96 and 2.44–8.38, respectively. Results showed that the temperature in the future period (2071–2099) will increase by 1–4.7 °C under all scenarios compared to the historical study period (1991–2020), while the precipitation will either increase or decrease depending on the season and the specific climate change scenario. Assessment of future climate classifications revealed that the BW (arid desert) and BS (semi-arid steppe) categories will increase, as classified by Köppen-Geiger, will increase. At the same time, Ds and Cs (dry summer) classifications will decrease in during the study period over Iran. These findings provide policymakers with some insights into how to deal with the impacts of climate change in the future and implement some measures now.

Monthly rainfall prediction for different climatic zones in south Africa for 2024 using a random forest model

This study predicted 2024 rainfall in different climatic zones in South Africa using a random forest model. Previous studies have shown that random forests performed better than other models for rainfall prediction. South Africa was divided into nine using the Koppen-Geiger climate classification system, and three cities were selected for each climatic zone. Atmospheric datasets from the South African Weather Service and the National Aeronautics and Space Agency were used for this study. The datasets were trained, tested, and validated to assess the model's accuracy. With good forecast ability observed, the random forest was then used for monthly rainfall for 2024. The result of this prediction was then compared with 2022 and 2023 rainfall. The result indicated months where much rain should be expected in various cities and cities that may likely experience droughts. This result is particularly important for agriculture, water resource management, and early drought/flooding warning systems.