Geiger Climate Classification Research Articles

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.

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.

Evaluation of Thermal Comfort Conditions in the Working Environments of Seasonal Agricultural Workers in Csa Koppen Climate Type

This study focuses on determining the thermal comfort conditions of seasonal agricultural workers during the hot periods of the year when agricultural production is intense in the Aksu/Türkiye region, which is characterized by the Csa climate type according to the Köppen–Geiger climate classification. In this study, the thermal comfort conditions of seasonal agricultural workers working on open farmlands were evaluated in ten-day, monthly, and seasonal periods for 6 months between 5:00 and 21:00 h using the modified Physiological Equivalent Temperature (mPET) index in the Rayman Pro software according to their activity energy during work. The results of the study reveal that increased activity energy leads to a decrease in thermal comfort conditions of agricultural workers, mPET values of agricultural workers engaged in soil cultivation (Group II) are 2.1 to 2.9 °C higher than the mPET values of workers engaged in plant care and harvesting (Group I), and the agricultural workers in Group II are exposed to more heat stress. The thermal comfort conditions of agricultural workers in Group I deteriorate between 09:00 and 16:00 h with mPET values between 34.1 and 35.3 °C and those of agricultural workers in Group II deteriorate between 08:00 and 17:00 h with mPET values between 34.3 and 37.7 °C. In this context, the daily comfortable working time in the morning and afternoon was found to be 9 h for Group I and 7 h for Group II. Overall, determining the comfortable working hours of agricultural workers in regions with different climate types in future studies will be an important resource for decision-makers in developing strategies to protect the health and increase the productivity of agricultural workers.

Reviews and syntheses: A scoping review evaluating the potential application of ecohydrological models for northern peatland restoration

Abstract. Peatland restoration and rehabilitation action has become more widely acknowledged as a necessary response to mitigating climate change risks and improving global carbon storage. Peatland ecosystems require restoration time spans of the order of decades and, thus, cannot be dependent upon the shorter-term monitoring often carried out in research projects. Hydrological assessments using geospatial tools provide the basis for planning restoration works as well as analysing associated environmental influences. “Restoration” encompasses applications to pre-restoration and post-restoration scenarios for both bogs and fens, across a range of environmental impact fields. The aim of this scoping review is to identify, describe, and categorize current process-based modelling uses in peatlands in order to investigate the applicability and appropriateness of ecohydrological and/or hydrological models for northern peatland restoration. Two literature searches were conducted using the entire Web of Science database in September 2022 and August 2023. Of the final 211 papers included in the review, models and their applications were categorized according to this review's research interests in seven distinct categories aggregating the papers' research themes and model outputs. Restoration site context was added by identifying 229 unique study site locations from the full database, which were catalogued and analysed against raster data for the Köppen–Geiger climate classification scheme. A majority of northern peatland sites were in temperate oceanic zones or humid continental zones that experienced snow. Over one in five models from the full database of papers were unnamed and likely intended for single use. Key themes emerging from topics covered by papers in the database included the following: modelling restoration development from a bog growth perspective, the prioritization of modelling greenhouse gas (GHG) emissions dynamics as a part of policymaking, the importance of spatial connectivity within or alongside process-based models to represent heterogeneous systems, and the increased prevalence of remote sensing and machine learning techniques to predict restoration progress with little physical site intervention. Models are presented according to their application to peatlands or broader ecosystem and organized from most to least complex. This review provides valuable context for the application of ecohydrological models in determining strategies for peatland restoration and evaluating post-intervention development over time.

Climate Classification for Major Cities in China Using Cluster Analysis

Climate classification plays a fundamental role in understanding climatic patterns, particularly in the context of a changing climate. This study utilized hourly meteorological data from 36 major cities in China from 2011 to 2021, including 2 m temperature (T2), relative humidity (RH), and precipitation (PRE). Both original hourly sequences and daily value sequences were used as inputs, applying two non-hierarchical clustering methods (k-means and k-medoids) and four hierarchical clustering methods (ward, complete, average, and single) for clustering. The classification results were compared using two clustering evaluation indices: the silhouette coefficient and the Calinski–Harabasz index. Additionally, the clustering was compared with the Köppen–Geiger climate classification based on the maximum difference in intra-cluster variables. The results showed that the clustering method outperformed the Köppen–Geiger climate classification, with the k-medoids method achieving the best results. Our research also compared the effectiveness of climate classification using two variables (T2 and PRE) versus three variables, including the addition of hourly RH. Cluster evaluation confirmed that incorporating the original sequence of hourly T2, PRE, and RH yielded the best performance in climate classification. This suggests that considering more meteorological variables and using hourly observation data can significantly improve the accuracy and reliability of climate classification. In addition, by setting the class numbers to two, the clustering methods effectively identified climate boundaries between northern and southern China, aligning with China’s traditional geographical division along the Qinling–Huaihe River line.

Köppen–Geiger climate classification across France based on an ensemble of high-resolution climate projections

Köppen–Geiger climate classification across France based on an ensemble of high-resolution climate projections

Modelling tools for including climate change in pest risk assessments

AbstractThis paper provides a comprehensive overview of the modelling tools available for integrating climate change impacts into pest risk assessments (PRA), elucidating the existing methodologies and models employed to understand the potential distributions of pests based on historical data and under future climate change scenarios. We highlight the strengths and weaknesses of these models and provide commentary on their ability to identify emerging threats due to climate change accurately and adequately, considering pest establishment likelihood, host crop exposure and the distribution of impacts. The simplest methods are based on climate‐matching models, degree‐day development models and Köppen–Geiger climate classification. Correlative species distribution models derive species–environment relationships and have been applied to PRA with mixed success. When fitted models are applied to different continents they are usually challenged to extrapolate climate suitability patterns outside the climate space used to train them. Global climate change is creating novel climates, exacerbating this ‘transferability’ problem. Some tools have been developed to reveal when these models are extrapolating. Process‐oriented models, which focus on mechanisms and processes rather than distribution patterns, are inherently more reliable for extrapolation to novel climates such as new continents and future climate scenarios. These models, however, require more skill and generally more knowledge of the species to craft robust models.

Köppen–Geiger Climate Classification in the Pannonian Basin According to SSP5-8.5 Scenario

Köppen–Geiger Climate Classification in the Pannonian Basin According to SSP5-8.5 Scenario

Heat stress in South America over the last four decades: a bioclimatic analysis

The observed continuous rise in the frequency of extreme heat events in South America (SA) poses a serious challenge for public health. However, there is a lack on the understanding of the large-scale and long-term variability and trends of thermal stress in this continent. Accordingly, here we developed the first comprehensive bioclimatology of thermal stress over SA during the past four decades. Consecutive heat stress hours were analyzed using the Universal Thermal Climate Index (UTCI) from ERA5-HEAT reanalysis according to the Köppen–Geiger climate classification and also focusing on the 31 most populated cities of SA. Results show an inland/coastline contrast and a marked latitudinal northward increase in the number of hours under heat stress. Heat stress hotspots are located mostly around the Amazon, northern and central parts of SA with 26–35% of the hours between 1979 and 2020 under strong heat stress. The annual number of hours within heat stress increased significantly between 1979 and 2020, varying from + 1.16 h/year to + 8.25 h/year depending on the Köppen–Geiger class. The past 20 years (2000 forward) presented not only more consecutive hours under heat stress than the previous two decades in all the analyzed cities, but also a higher persistence of such conditions. The bioclimatology of thermal stress developed here may provide important guidelines to decision-makers for exploring adaptation strategies to increase societal resilience.

Phenotypic, Geological, and Climatic Spatio-Temporal Analyses of an Exotic Grevillea robusta in the Northwestern Himalayas

The last five decades (since 1980) have witnessed the introduction of exotic trees as a popular practice in India to fulfill the demand of forest-based products for utilization in afforestation programmes. This study examines the distribution and habitat suitability of exotic Grevillea robusta trees in the northwestern Himalayas (state: Uttarakhand), focusing on the interaction between G. robusta and abiotic factors, such as climate, soil, and habitat suitability. This multipurpose agroforestry species is mainly grown by farmers as a boundary tree, windbreak, or shelterbelt and among intercrops on small farms in agroforestry systems worldwide. The results indicate that phenotypic plasticity is determined by tree height and diameter, indicating a higher frequency of young and adult trees. The study also highlights spatio-temporal modeling coupled with geological analysis to address the current distribution pattern and future habitat suitability range through MaxEnt modeling. The AUC ranged from 0.793 ± 3.6 (RCP 6.0_70) to 0.836 ± 0.008 (current) with statistical measures, such as K (0.216), NMI (0.240), and TSS (0.686), revealing the high accuracy of the model output. The variables, which include the minimum temperature of the coldest month (Bio 6), the slope (Slo), the mean temperature of the driest quarter (Bio 9), and the precipitation of the driest quarter (Bio 17), contribute significantly to the prediction of the distribution of the species in the Himalayan state. The model predicts a significant habitat suitability range for G. robusta based on bio-climatic variables, covering an area of approximately ~1641 km2 with maximal occurrence in Pauri (~321 km2) and Almora (~317 km2). Notably, the future prediction scenario corroborates with the regions of Tons (Upper Yamuna, Uttarkashi), Kalsi (Mussoorie, Dehradun), the Kedarnath Wildlife Sanctuary, and the Badrinath Forest Division for the potentially suitable areas. The climate was found to have a strong influence on the species’ distribution, as evidenced by its correlation with the Köppen–Geiger climate classification (KGCC) map. While the species demonstrated adaptability, its occurrence showed a high correlation with bedrocks containing an elevated iron content. Furthermore, the study also provides the first trees outside forests (TOF) map of G. robusta in the region, as well as insight into its future habitat suitability.

Features of the Earth’s seasonal hydroclimate: characterizations and comparisons across the Köppen–Geiger climates and across continents

Detailed investigations of time series features across climates, continents and variable types can progress our understanding and modelling ability of the Earth’s hydroclimate and its dynamics. They can also improve our comprehension of the climate classification systems appearing in their core. Still, such investigations for seasonal hydroclimatic temporal dependence, variability and change are currently missing from the literature. Herein, we propose and apply at the global scale a methodological framework for filling this specific gap. We analyse over 13,000 earth-observed quarterly temperature, precipitation and river flow time series. We adopt the Köppen–Geiger climate classification system and define continental-scale geographical regions for conducting upon them seasonal hydroclimatic feature summaries. The analyses rely on three sample autocorrelation features, a temporal variation feature, a spectral entropy feature, a Hurst feature, a trend strength feature and a seasonality strength feature. We find notable differences to characterize the magnitudes of these features across the various Köppen–Geiger climate classes, as well as between continental-scale geographical regions. We, therefore, deem that the consideration of the comparative summaries could be beneficial in water resources engineering contexts. Lastly, we apply explainable machine learning to compare the investigated features with respect to how informative they are in distinguishing either the main Köppen–Geiger climates or the continental-scale regions. In this regard, the sample autocorrelation, temporal variation and seasonality strength features are found to be more informative than the spectral entropy, Hurst and trend strength features at the seasonal time scale.

A Satellite-Based Approach for Quantifying Terrestrial Water Cycle Intensity

The terrestrial water cycle intensity (WCI) is a widely used tool to quantify the impact of climate change on the distribution of global water resources. In this study, a satellite-based WCI was tested by comparing the parameter-elevation regressions on independent slopes model (PRISM) precipitation estimates with those of the Global Precipitation Measurement (GPM) satellite mission across the contiguous United States (CONUS), based on an existing Köppen–Geiger climate classification for the CONUS. Both precipitation products were not statistically different across all climate classes. Consequently, satellite-based WCI changes between two multiannual periods (2001 to 2009 and 2010 to 2019) were calculated at a 0.1-degree spatial resolution using the GPM and a validated global evapotranspiration dataset. This study showed that: (1) The water cycle is speeding up in many parts of the CONUS, particularly the West, driven by recent increases in both precipitation and evapotranspiration through much of the region. (2) The El Niño-Southern Oscillation (ENSO) may be influencing the WCI of the CONUS by driving precipitation in the west, southeast, and parts of the north, and dryness in the northeast regions. The hydrological impacts of these results cannot be generalized. However, flood and drought risks, water availability and quality issues remain key primary concerns.

Population dynamics shifts by climate change: High-resolution future mid-century trends for South America

Population dynamics and climate change are the main challenges for the 21st century, especially in South America. Human populations will increase their exposure to novel climatic conditions in their territories, entangling and complicating health and social problems. We analyze how socioeconomic and climatic future pathways will evolve in South America, a land with high climatic and social heterogeneity. We use the Köppen–Geiger climate classification, population growth, and climate projections for the most likely climate change scenarios for the 2050s based on the CHELSA dataset. We found that tropical and arid climates extend between 4.2%–2.5% and 2.6%–3.9%, replacing temperate climate zones, which will be reduced between 5.3%–4.5% for the Representative Concentration Pathways (RCP) 4.5 and 8.5 respectively. This implies a reduction of the Mediterranean, oceanic and polar climates. Population growth shows a significant relationship to increasing tropical and arid climates extension in almost all countries, meaning a higher exposure to more severe conditions for humans. This work opens up the chance of using possible guidelines to assist environmental management with key background information on expected climate types and population changes and address the potential effects of climate change on human settlements in the near future.

The Eurasian shrew and vole tick Ixodes trianguliceps: geographical distribution, climate preference, and pathogens detected

The Eurasian shrew and vole tick Ixodes trianguliceps Birula lives in the nests and burrows of its small mammalian hosts and is—along with larvae and nymphs of Ixodes ricinus or Ixodes persulcatus—one of the most commonly collected tick species from these hosts in its Eurasian range. Ixodes trianguliceps is a proven vector of Babesia microti. In this study, up-to-date maps depicting the geographical distribution and the climate preference of I. trianguliceps are presented. A dataset was compiled, resulting in 1161 georeferenced locations in Eurasia. This data set covers the entire range of I. trianguliceps for the first time. The distribution area between 8^circ W–105^circ E and 40–69^circ N extends from Northern Spain to Western Siberia. To investigate the climate adaptation of I. trianguliceps, the georeferenced locations were superimposed on a high-resolution map of the Köppen–Geiger climate classification. The Köppen profile for I. trianguliceps, i.e., a frequency distribution of the tick occurrence under different climates, shows two peaks related to the following climates: warm temperate with precipitation all year round (Cfb), and boreal with warm or cold summers and precipitation all year round (Dfb, Dfc). Almost 97% of all known I. trianguliceps locations are related to these climates. Thus, I. trianguliceps prefers climates with warm or cold summers without dry periods. Cold winters do not limit the distribution of this nidicolous tick species, which has been recorded in the European Alps and the Caucasus Mountains up to altitudes of 2400 m. Conversely, I. trianguliceps does not occur in the Mediterranean area with its hot and dry summers.

Macro-analysis of climatic factors for COVID-19 pandemic based on Köppen–Geiger climate classification

This study integrated dynamic models and statistical methods to design a novel macroanalysis approach to judge the climate impacts. First, the incidence difference across Köppen-Geiger climate regions was used to determine the four risk areas. Then, the effective influence of climate factors was proved according to the non-climate factors' non-difference among the risk areas, multi-source non-major component data assisting the proof. It is found that cold steppe arid climates and wet temperate climates are more likely to transmit SARS-CoV-2 among human beings. Although the results verified that the global optimum temperature was around 10 °C, and the average humidity was 71%, there was evident heterogeneity among different climate risk areas. The first-grade and fourth-grade risk regions in the Northern Hemisphere and fourth-grade risk regions in the Southern Hemisphere are more sensitive to temperature. However, the third-grade risk region in the Southern Hemisphere is more sensitive to relative humidity. The Southern Hemisphere's third-grade and fourth-grade risk regions are more sensitive to precipitation.

Temporal and spatial variability of energy intensity for atmospheric water generators

Atmospheric water generators (AWGs) produce potable water from the moisture in the air, providing a potentially viable water source in austere locations or emergency response scenarios. In this study, the operating constraints of three existing commercially available AWG devices are investigated, compared to historical weather data from across the continental United States. Utilizing linear regression modeling and weather station data for the years of 1985–2019, the monthly and spatial trends of energy demand to produce water from these devices are estimated. Energy and water production efficiencies for the devices are highly dependent on environmental conditions with relative humidity (RH) and temperature as the two driving factors. Publicly available manufacturer specifications for each AWG system were modeled to predict yield and specific energy consumption (SEC). A spatial analysis depicts the change in SEC in kilowatt-hours per liter (kWh l−1) across the country at a monthly scale. SEC for refrigeration AWG ranged between 0.02 and 3.64 kWh l−1 and solar driven sorption was between 3.19 and 5.29 kWh l−1, significantly larger than conventional water treatment energy demands. Additionally, the results are synthesized based on the Köppen–Geiger climate classification system, to approximate projected water production and energy demand for each environment, with arid climates demanding larger energy consumption per unit volume of water produced. Excluding arid and cold climate classes, solar powered refrigeration devices have the potential to operate more efficiently than solar driven sorption due to advances in photovoltaic solar panel technology, but still require more energy than alternatives.

Aesthetic Possibilities of Building Thermal Control through Colored Envelopes

The possibilities of obtaining efficient building thermal control by means of colored envelopes while maintaining broad aesthetic options have been evaluated. The use of colored bilayers, where the inner layer presents strong solar absorbent or reflectance properties, while the outer layer provides visible coloration, represents a feasible way. A representative set of twenty bilayers spanning a broad color palette has been studied in two configurations, static and dynamic. The dynamic configuration allows for the switching of the inner layer between absorptive and reflective states. Simulations have been carried out in a total of nineteen locations around the world, representing the main habitable climates according to Köppen–Geiger climate classification. Comparative results between climate zones where this approach may have a significant impact have been obtained, and identification of feasible climate zones has been carried out. The use of the simpler static configuration is recommended for tropical climates, whereas the dynamic configuration could be effective in moderate climate locations (within arid or temperate climate classification). Possible savings for cold climates are negligible.

Planning Strategy for Urban Building Energy Conservation Supported by Agent-Based Modeling

As a systematic, preventive, and structural adjustment method of improving building energy conservation and carbon emission reduction, urban planning has received extensive attention. However, due to the insufficient interface between energy-saving technology and urban planning systems, urban planning has not properly played a role in building energy conservation. Scientific and innovative technical methods are urgently needed to explore the role of coordinating multiple effective planning elements in overall building energy conservation through urban planning means. Due to climate conditions, there is high demand for conserving building energy in severe cold regions, but research into this has not been thoroughly carried out. Harbin, located in the northeast of China, belongs to the Dwa zone of the Köppen–Geiger Climate Classification, and is also a typical city of severe cold regions where the daily average temperature is lower than 5 °C for more than 145 days in a year. This study takes Harbin as an example and uses agent-based modeling to establish an urban-scale building energy consumption simulation model. The model contains four types of agents (a global agent, building agent, residential agent, and household equipment agent) and two types of influence factor modules (an urban form module and a climate module). Three simulation scenarios were designed, including a baseline scenario, an urban form scenario, and a climate scenario. The baseline scenario provided an overview of the urban-scale building energy consumption distribution characteristics of Harbin and served as a reference group for the simulation results of other scenarios. The urban form scenario results show that when the elements with a highly significant impact change by 1 unit, the retail building block has the most obvious change in energy consumption, up to 44.7 × 106 kWh/105 m2/year, while the office building block has the lowest change, with 34.5 × 103 kWh/105 m2/year. The fluctuation of electricity is the most obvious, but the total change is lower than the heating energy consumption. The climate scenario shows that the energy consumption of residential land in urban centers will consistently rise in the next 50 years, up to 5.3 × 105 kWh/105 m2/year. Based on these results, this study puts forward future building energy conservation planning strategies for Harbin, focusing on three aspects: the planning and control of urban form, the optimization and adjustment of the climate, and the building energy conservation planning system. These research results are expected to provide scientific support for transforming Harbin into a low-carbon city.

Screening of potato cv. Florice against potato wart and Fusarium oxysporum using different planting densities based on the weight of the tubers

Potato wart and fusarium diseases are increasing rapidly in the potato producing areas of Turkey and are gradually becoming a cause of concern potato-based Turkish economy. Therefore, taking of early measures against both diseases are of great importance. Seed mini tubers regenerated under in vitro conditions have a significant role to produce disease-free tubers for use as plants. This research was conducted at Ankara province, in the Central Anatolia (hot-humid continental climate conditions (Dsb- as per koppen Geiger climate classification), in 2015-2016 growing seasons for 2 years. The mini tubers were planted in 5 different planting densities (70×10 cm, 70×15 cm, 70×20 cm, 70×25 cm, 70 × 30 cm) by using in vitro regenerated mini tubers belonging to 3 categories (5-10 g, 11-20 g and 21 -30 g) during 2015 and 2016 at Ankara in the Central Anatolia (hot-humid continental climate conditions (Dsb- as per koppen Geiger climate classification), belonging to the cv. Florice. A comparison was made for mean tuber weight per plant (g), number of tubers per plant (number/plant), Total weight of all tubersper plant (g/plant) and yield (kg/ha) at the time of harvest. The findings of the study showed that average tuber weight and number per plant, Total tuber weight per plant increased positively, and yield decreased by increasing the seed tuber weight and row to plant-to-plant distance between plants, when the results of the two years were compared. It was found that the weight changed from tuber weight was 91.3 g and 48.8 g/plant, number of tubers per plant was 5.5 and 5.3, Total tuber weight per plant was and 373.8 and 201.0 g/plant for first and 2nd year respectively without showing any signs of wart and fusarium regardless of the density of the plants. The maximum seed yields per hectare was 32,272 kg and 15,082 kg, during 2015 and 2016, respectively. When the tuber distribution was compared, the maximum of 40% and 39% tubers attained diameter of 35-60 mm and 25-35 mm in the first year and 2nd years in the same order. No wart and fusarium rot was noted on the tubers irrespective of the row to plant to plant distance in each plot. Maximum yield was obtained by planting large tubers (21-30 g) using planting density of 70 ×10 cm. The results recommended use of Ankara as an alternative place for the production of wart and fusarium rot free potato seed production area

Evaluation and Comparison of Spatial Clustering for Solar Irradiance Time Series

This work exposes an innovative clustering method of solar radiation stations, using static and dynamic parameters, based on multi-criteria analysis for future objectives to make the forecasting of the solar resource easier. The innovation relies on a characterization of solar irradiation from both a quantitative point of view and a qualitative one (variability of the intermittent sources). Each of the 76 Spanish stations studied is firstly characterized by static parameters of solar radiation distributions (mean, standard deviation, skewness, and kurtosis) and then by dynamic ones (Hurst exponent and forecastability coefficient, which is a new concept to characterize the “difficulty” to predict the solar radiation intermittence) that are rarely used, or even never used previously, in such a study. A redundancy analysis shows that, among all the explanatory variables used, three are essential and sufficient to characterize the solar irradiation behavior of each site; thus, in accordance with the principle of parsimony, only the mean and the two dynamic parameters are used. Four clustering methods were applied to identify geographical areas with similar solar irradiation characteristics at a half-an-hour time step: hierarchical, k-means, k-medoids, and spectral cluster. The achieved clusters are compared with each other and with an updated Köppen–Geiger climate classification. The relationship between clusters is analyzed according to the Rand and Jaccard Indexes. For both cases (five and three classes), the hierarchical clustering algorithm is the closest to the Köppen classification. An evaluation of the clustering algorithms’ performance shows no interest in implementing k-means and spectral clustering simultaneously since the results are similar by more than 90% for three and five classes. The recommendations for operating a solar radiation clustering are to use k-means or hierarchical clustering based on mean, Hurst exponent, and forecastability parameters.