Climate Classification Research Articles

Liver Cancer Mortality Disparities at a Fine Scale Among Subpopulations in China: Nationwide Analysis of Spatial and Temporal Trends.

China has the highest number of liver cancers worldwide, and liver cancer is at the forefront of all cancers in China. However, current research on liver cancer in China primarily relies on extrapolated data or relatively lagging data, with limited focus on subregions and specific population groups. The purpose of this study is to identify geographic disparities in liver cancer by exploring the spatial and temporal trends of liver cancer mortality and the years of life lost (YLL) caused by it within distinct geographical regions, climate zones, and population groups in China. Data from the National Death Surveillance System between 2013 and 2020 were used to calculate the age-standardized mortality rate of liver cancer (LASMR) and YLL from liver cancer in China. The spatial distribution and temporal trends of liver cancer were analyzed in subgroups by sex, age, region, and climate classification. Estimated annual percentage change was used to describe liver cancer trends in various regions, and partial correlation was applied to explore associations between LASMR and latitude. In China, the average LASMR decreased from 28.79 in 2013 to 26.38 per 100,000 in 2020 among men and 11.09 to 9.83 per 100,000 among women. This decline in mortality was consistent across all age groups. Geographically, Guangxi had the highest LASMR for men in China, with a rate of 50.15 per 100,000, while for women, it was Heilongjiang, with a rate of 16.64 per 100,000. Within these regions, the LASMR among men in most parts of Guangxi ranged from 32.32 to 74.98 per 100,000, whereas the LASMR among women in the majority of Heilongjiang ranged from 13.72 to 21.86 per 100,000. The trend of LASMR varied among regions. For both men and women, Guizhou showed an increasing trend in LASMR from 2013 to 2020, with estimated annual percentage changes ranging from 10.05% to 29.07% and from 10.09% to 21.71%, respectively. Both men and women observed an increase in LASMR with increasing latitude below the 40th parallel. However, overall, LASMR in men was positively correlated with latitude (R=0.225; P<.001), while in women, it showed a negative correlation (R=0.083; P=.04). High LASMR areas among men aligned with subtropical zones, like Cwa and Cfa. The age group 65 years and older, the southern region, and the Cwa climate zone had the highest YLL rates at 4850.50, 495.50, and 440.17 per 100,000, respectively. However, the overall trends in these groups showed a decline over the period. Despite the declining overall trend of liver cancer in China, there are still marked disparities between regions and populations. Future prevention and control should focus on high-risk regions and populations to further reduce the burden of liver cancer in China.

Hydrological Changes in a Fruit Growing Area Under the Influence of Climate Change: on the Amazon Coast

Objective: The objective of this study is to investigate the water consumption of the mango orchard using the method proposed by Thornthwaite and Mather (1955), called Climatological Water Balance, with the aim of assisting in agricultural planning and decision making. Theoretical Framework: The Climatological Water Balance was initially developed with the aim of characterizing the climate of a region, in order to be used in the climate classification developed by Thornthwaite in the 1940s. Later, this method began to be used for agronomic purposes given the great interrelationship of agriculture with climatic conditions. Method: The methodology adopted for this research comprises the information used in the preparation of the water balance and was acquired by instrumentation implemented at the experimental hose site in the village of Cuiarana, district of Salinópolis in the northeast of the state of Pará. The meteorological data were obtained in the Project tower LBA where are measured, by automatic instrumentation, such as: average air temperature (Tar) the relative air humidity (RH) through the HMP45 probe, the Rain acquired by the CSI MODEL TB4-L RAIN GAGE ​​from Campbell Scientific, the collection of data was carried out using a datalogger with recording every 10s and storage of the average every 10 min. Results and Discussion: The results obtained revealed an increase in air temperature mainly in El-niño years and with a strong reduction in rainfall in these years of approximately 30 to 40% in the annual accumulation. These results imply a reduction in productivity and the regional and local impact of the hydrological cycle. Research Implications: The practical and theoretical implications of this research are discussed, providing insights into how the results can be applied or influence practices in the field of fruit growing. These implications may include the importance of the vegetation cover of the mango orchard on the socio-environmental quality of the village of Cuiarana. Originality/Value: This study contributes to the literature on mango crops that are not irrigated, it also makes it possible to optimize sowing times, taking into account the average water availability in the studied region, resulting in a more efficient use of rainfall for agricultural production. The relevance and value of this research are evidenced by the fact that the study in the northeast of Pará is still precarious in terms of water consumption by the plant in a region with water deficiency.

Microclimatic Zonation and Climatic Variability of Sikkim Himalaya

The Köppen classification of climate integrates precipitation and temperature information with natural vegetation patterns to create a precise representation of any particular region's climate. This integration depends on the empirical relationship of climate and vegetation, which indicates that distinct locations in the same class have similar vegetation attributes. Köppen climatic classification factors are explained and Sikkim's climate characteristics are regionalized based on it. The method for making representations of air temperatures and precipitation has been described, and an illustration of Sikkim's climatic zones with variability is generated as a result of these changes. The geographic pattern of climatic types and subtypes in Sikkim has been briefly addressed using an available high-resolution gridded dataset (ERA5-Land). This is described that the constraints of microclimatic zonation emanate from the empirical prerequisite of climate classifications, as well as the nature of data selection and the methodologies employed for climate variability analysis and classification. Based on the Köppen classification for the long term (1980–2021), the Sikkim Himalaya contains three primary climatic classes, particularly ETc (cold, tundra, and cool summer), Cfc (moderately warm, humid, and cool summer), and Cfb (moderately warm, humid, and warm summer). Climate variability on the basis of temperature and precipitation change with respect to 1980–2021 over the entire Sikkim Himalaya concludes that the climatic pattern of the Sikkim has been changed from cold-dry to warm-wet. The alteration in the corresponding climatic pattern is further supported by changes in LULC and NDVI. The results suggest that the precipitation change in the past two decades (1980–2000) is negative, while a significant positive change has been noticed in the recent two decades (2001–2021). Subsequently, the number of extremely wet days decreases in the entire ETc and Cfc climate zones. Regardless, the southern part of the Cfb climatic zone has experienced an increase in extremely wet days. This study's findings will significantly contribute to the development of future policies and initiatives by providing insights crucial for achieving sustainable development, environmental protection, and climate change adaptation.

Urban form and surface temperature inequality in 683 European cities

Despite rapid urbanization and growing heat stress, comprehensive insights into the association between urban form and environmental inequality are lacking. We study the population-weighted Gini coefficient, the measurement of inequality, of the average land surface temperature (LST) in summer in 683 European cities (i.e. Functional Urban Areas) against thirty-four urban form indicators, i.e. population, area, compact ratio, local climate zones (LCZ), presence of grass, impervious surfaces, trees, water and albedo, elevation, aspect, Köppen-Geiger climate classification (KG) and latitude. We calculate density, Moran's I and Entropy measures to quantify cities' spatial structures and diversity. The area of each city is divided into 1 × 1 km spatial units, and inequality among such units is calculated. Using geographically weighted regression (GWR), we found that the impact of factors varies across the cities and that almost 70 % of overall inequality can be explained by urban form. The variables occasionally decreasing LST inequality were population density and concentration, LCZ 9, dry climate, and high latitude. The ones increasing it were urban size, LCZ 3, 5, 6, 8, entropy LCZ, impervious surfaces' density and concentration, trees, high elevation, aspect southwest, and polar climate. Ultimately, the study discusses the findings and proposes spatial strategies for mitigating inequality.

Crack Length of Elastomeric Sealants and Their Service Life in Contrasting Canadian Climates: Effects of Climate Change.

The longevity of polymer-based sealant and jointing products, including elastomers, significantly depends on the level of exposure to sunlight and joint movement. These factors are particularly crucial in the application of polymers in construction due to their susceptibility to degradation under environmental conditions. For instance, diurnal cycles of contraction and dilation, arising from daily temperature fluctuations, impose significant stress on sealants and joints, impacting their durability over time. The elastic nature of polymeric sealants enables them to endure these cyclic mechanical loads. Athough there is considerable information on sealant durability obtained from laboratory accelerated aging, there is limited knowledge about the effect of climatic factors using historical and projected weather data on the durability and expected service life of these products. This study employed the Shephard crack growth model to predict the performance of sealants in a Canadian context; the crack growth and time-to-failure of hypothetical silicone sealants were investigated across 564 locations, for which historical climate data were obtained from 1998 to 2017, including gridded reanalysis data for the period of 1836-2015. The historical climate data were classified into four climate categories, and crack growth was estimated based on historical climatic data within the valid range for the Shephard model, revealing that locations in colder climates with lower levels of precipitation typically exhibit higher cumulative crack growth. The impact of climatic variation and environmental stressors on the longevity of sealants in the context of climate change was also investigated using future projected data.

Short-term ozone exposure and cancer mortality in Brazil: A nationwide case-crossover study.

Exposure to ambient ozone (O3) is linked to increased mortality risks from various diseases, but epidemiological investigations delving into its potential implications for cancer mortality are limited. We aimed to examine the association between short-term O3 exposure and site-specific cancer mortality and investigate vulnerable subgroups in Brazil. In total 3,459,826 cancer death records from 5570 Brazilian municipalities between 2000 and 2019, were included. Municipal average daily O3 concentration was calculated from a global estimation at 0.25°×0.25° spatial resolution. Thetime-stratified case-crossover design was applied to assess the O3-cancer mortality association. Subgroup analyses by age, sex, season, time-period, region, urban hierarchy, climate classification, quantiles of GDP per capita and illiteracy rates were performed. A linear and non-threshold exposure-response relationship was observed for short-term exposure to O3 with cancer mortality, with a 1.00% (95% CI: 0.79%-1.20%) increase in all-cancer mortality risks for each 10-μg/m3 increment of three-day average O3. Kidney cancer was most strongly with O3 exposure, followed by cancers of the prostate, stomach, breast, lymphoma, brain and lung. The associated cancer risks were relatively higher in the warm season and in southern Brazil, with a decreasing trend over time. When restricting O3 concentration to the national minimum value during 2000-2019, a total of 147,074 (116,690-177,451) cancer deaths could be avoided in Brazil, which included 17,836 (7014-28,653) lung cancer deaths. Notably, these associations persisted despite observed adaptation within the Brazilian population, highlighting the need for a focus on incorporating specific measures to mitigate O3 exposure into cancer care recommendations.

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.

Mesoclimate scale effects of river intermittency on aquatic insects in Seto Inland Sea watersheds, Western Japan

AbstractThe responses of assemblages and ecosystems to river intermittency are often considered on large spatial scales, such as those associated with the Köppen climate classification. However, river intermittency may naturally occur even in humid regions, and the effects may differ from those where intermittency occurs throughout the area. This study aimed to examine the differences in taxonomic and trait composition of aquatic insects between intermittent rivers in the Seto Inland Sea watersheds and neighboring humid-temperate rivers in Japan using national census data. While the climate in the most part of the Japanese Archipelago is categorized as humid-temperate, and rivers are usually perennial, local climate in the Seto Inland Sea watersheds has low summer precipitation compared with that in neighboring regions, thereby resulting in river intermittency. Consistent with the previous studies in the regions where rivers are intermittent on a large spatial scale, we observed that aquatic insects with multivoltinism and/or aerial respiration notably tolerated river intermittency. In contrast, the aquatic insect fauna in Japanese intermittent rivers represented subsets of those in the humid-temperate rivers, although previous studies such in Mediterranean regions have reported high endemism and species diversity in intermittent rivers. These results highlight the importance of exploring the role of river intermittency on small to medium spatial scales, as well as on large scales, to project effects of ongoing and future climate changes on species diversity.

Mapping the Potential of Zero-Energy Building in Greece Using Roof Photovoltaics

The present study investigates the incorporation of renewable rooftop photovoltaic systems in fully electrified residential buildings and estimates the zero-energy demand building potential in relation to the climatic data of Greece. Specifically, the aim of the analysis is to calculate the maximum possible number of stories and therefore the total building height for a complete transformation to zero-net-energy building. The energy analysis, which is conducted using the DesignBuilder software, focuses on single-floor up to seven-story buildings. The importance of the present work lies in the acknowledgment of the diversity of the Greek residential sector, the adherence to national energy policies, and the European goal of fully electrified buildings. The examined case studies are equipped with electrically driven air-to-air heat pumps serving the space heating and cooling demands and with an air-to-water heat pump covering the domestic hot water requirements. The investigated locations are the four main cities of Greece, Athens, Thessaloniki, Chania, and Kastoria, which represent the country’s four climatic categories. The conducted analysis allows for the mapping of the zero-energy building potential for the climatic data of Greece, demonstrating the possibility of striking a positive building energy balance through the integration of on-site renewable energy sources and the production of necessary electrical energy. The novelty of the present work lies in the identification of a key factor, namely, the building height, which determines the feasibility of transforming multifamily buildings into zero-energy buildings. According to the analysis results, the critical number of stories is calculated at six for Chania, five for Athens, four for Thessaloniki, and two for Kastoria. Regarding a three-story residential building, the incorporation of a renewable photovoltaic system can result in an annual surplus electricity production of 13,741 kWh (Chania), 10,424 kWh (Athens), and 6931 kWh (Thessaloniki), and a corresponding coverage of 100% (Chania), 69.0% (Athens), 38.9% (Thessaloniki) and 0% (Kastoria).

Analysis of the non-uniform thermal environment of local floor and ceiling radiant heating: Numerical simulation and thermal comfort experiment

Local radiant heating is a crucial method for avoiding inefficient heating and reducing energy consumption in buildings. To supplement existing research, which primarily focuses on local vertical-wall radiant heating, this paper investigates the non-uniform thermal environment of local floor and ceiling radiant heating rooms under various radiant positions, areas, and temperatures. The study utilizes numerical analysis by Ansys Fluent and thermal comfort experiments in Xi'an, China, which has a Dwa Köppen climate classification. When the local heating zone is located near windows and doors, the radiant film preferentially exchanges heat with them, resulting in a decrease in air temperature and mean radiant temperature. In such cases, it is recommended to increase the radiant temperature or area. With a radiant area of 5 m2 and a radiant temperature of 25 °C, expanding the radiant area to 15 m2 increases the average air temperature in the local heating zone by 5.0 °C, while raising the radiant temperature to 29 °C increases it by 1.4 °C. Moreover, enlarging the radiant area can effectively improve occupants' skin temperature, thermal sensation, and overall thermal comfort. When the radiant area and temperature are determined, priority should be given to local floor radiant heating over local ceiling radiant heating due to its lower air temperature, larger temperature gradients, and smaller mean radiant temperatures. This study aims to provide references for proposing refined design and regulation parameters of local floor and ceiling radiant heating systems in the future.

Solar irradiance component separation benchmarking: The critical role of dynamically-constrained sky conditions

The decomposition of global horizontal irradiance into its direct and diffuse components is critical in many applications. To guarantee accurate results in practice, the existing separation techniques need to be validated against reference ground measurements from a variety of stations. Here, four versions of the recent GISPLIT model are compared to a strong benchmark constituted from nine leading models of the literature. The validation database includes ≈24 million data points and is constituted of one calendar year of 1-min high-quality data from 118 research-class world stations covering all continents and all five major Köppen-Geiger climates. The results are analyzed with various statistical metrics to be as generalizable and explicative as possible. It is found that even the simpler GISPLIT version reduces the mean site RMSE of the best benchmark model by ≈11 % for the direct component and ≈17 % for the diffuse component. The improvement reaches ≈17 % and ≈25 %, respectively, when using the best GISPLIT version. The improvements are more important in cases of highly variable sky cloudiness, per the CAELUS sky classification scheme. A ranking analysis shows that all four versions of GISPLIT ranked higher than the benchmark models, and that the use of machine learning significantly improves the separation performance. In contrast, only marginal improvements are obtained through preliminary conditioning by Köppen-Geiger climate class. Overall, it is concluded that GISPLITv3, which is not dependent on climate class but makes use of machine learning for the most challenging sky conditions, can be asserted as the new high-performance quasi-universal separation model.

Impact assessment of agricultural droughts on water use efficiency in different climatic regions of Punjab Province Pakistan using MODIS time series imagery

AbstractDrought is the most destructive phenomenon that distresses the terrestrial carbon cycle balance and crop production. The variation in evapotranspiration (ET) and gross primary productivity (GPP) is a significant cause of agricultural drought effects on water use efficiency. This study aims to evaluate the impact of agricultural drought on WUE and it's anomalies in different climate regions. The standard vegetation index was used to measure the extent of agricultural drought. WUE was calculated using the ratio of ET, GPP, and climate classification using the De Martonne method. This study was conducted over the last 22 years, from 2001 to 2022. Meanwhile, 2001, 2002, 2014, and 2018 were considered high drought years based on a 22‐year analysis. According to remote sensing analysis of ET and GPP, the WUE increased throughout drought in all climate regions and more strongly in the arid zone than in humid regions. Humid areas were more vital due to drought than arid ones. WUE anomalies badge with increased drought severity across all climates except the very humid zone. Humid climates saw faster recovery times than arid ones, and areas experienced severe droughts. The findings of this research are essential for understanding the carbon and water cycles for agriculture management. The analysis of drought severity and WUE across arid climate regions helped analyse the varying impact of drought on WUE due to climate change. The significance of this study includes informing agricultural, water resource, and drought management planning in the Punjab Province, an agricultural region vulnerable to climate change. This study holds important learnings for agricultural regions worldwide. It has practical and scientific importance regarding agricultural systems' specific stresses and responses to droughts.

Identifying Climate Patterns Using Clustering Autoencoder Techniques

Abstract The complexity of growing spatiotemporal resolution of climate simulations produces a variety of climate patterns under different projection scenarios. This paper proposes a new data-driven climate classification workflow via an unsupervised deep learning technique that can dimensionally reduce the vast volume of spatiotemporal numerical climate projection data into a compact representation. We aim to identify distinct zones that capture multiple climate variables as well as their future changes under different climate change scenarios. Our approach leverages convolutional autoencoders combined with k-means clustering (standard autoencoder) and online clustering based on the Sinkhorn–Knopp algorithm (clustering autoencoder) across the conterminous United States (CONUS) to capture unique climate patterns in a data-driven fashion from the Geophysical Fluid Dynamics Laboratory Earth System Model with GOLD component (GFDL-ESM2G). The developed approach compresses 70 years of GFDL-ESM2G simulation at 0.125° spatial resolution across the CONUS under multiple warming scenarios to a lower-dimensional space by a factor of 660 000 and then tested on 150 years of GFDL-ESM2G simulation data. The results show that five climate clusters capture physically reasonable and spatially stable climatological patterns matched to known climate classes defined by human experts. Results also show that using a clustering autoencoder can reduce the computational time for clustering by up to 9.2 times when compared to using a standard autoencoder. Our five unique climate patterns resulting from the deep learning–based clustering of the lower-dimensional space thereby enable us to provide insights on hydrometeorology and its spatial heterogeneity across the conterminous United States immediately without downloading large climate datasets. Significance Statement This paper presents a data-driven climate classification approach using unsupervised deep learning to dimensionally reduce climate model outputs and to identify distinct climate regions for their future changes. Our approach compresses climate information for 70 years of Geophysical Fluid Dynamics Laboratory Earth System Model data across the conterminous United States (CONUS) at 0.125° spatial resolution. The results reveal that five climate clusters capture reasonable and stable climatological patterns matched to known climate patterns. The embedded clustering process in deep learning provides ×9.2 times faster execution than the k-means clustering technique. These results give us insight about climate spatial patterns and heterogeneity of hydrological patterns across the conterminous United States without downloading large climate datasets.

Essential contribution of habitats in non-protected areas to climate-driven species migration in China

Given the reality of climate-driven migration, the net effectiveness of existing spatially fixed protected areas (PAs) to biodiversity conservation is expected to decline, while the potential of non-PA habitats (non-PAs, i.e., natural, altered, or artificial ecosystems that are not formally designated as PAs) for biodiversity conservation is gaining attention. However, the contribution of non-PAs to biodiversity conservation remains poorly understood. With the aim of comprehensively assessing the effectiveness of non-PAs as transient refugia and steppingstones during future climate-change-induced migration of species in China, a six-metric integrated framework was applied and statistics of these metrics for PAs and non-PAs are compared. Results reveal that, a greater area of non-PAs have a low velocity of climate change (VoCC) compared to that of PAs, and can therefore serve as temporary refugia for species. The disappearing climate index (DCI) and novel climate index (NCI) results show that some 17% of the subdivided climate classes within the PAs have changed. However, the displacement index (DI) results imply that nearly half (48.98%) of the PAs need non-PAs to provide transient refugia for climate-driven migration of species in PAs. The higher ratio of effective steppingstones measured using the climate corridor score (CCS) and landscape current flow (LCF) further emphasizes that non-PAs play a more significant role as steppingstones for climate-driven migration than do PAs in terms of both their structural and functional connectivity. Our research further demonstrates that a conservation approach that improves connectivity among PAs and considers Other Effective area-based Conservation Measures (OECMs) is essential for long-term biodiversity adaptation to climate change.

The Future Sustainability of the São Francisco River Basin in Brazil: A Case Study

The viewpoint and reaction of a country towards climate change are shaped by its political, cultural, and scientific backgrounds, in addition to the distinct characteristics of its evolving climate and the anticipated and actual consequences of the phenomenon in the times ahead. A region’s climate has a significant impact on how water is managed and used, mostly in the primary sector, and both the distribution of ecosystem types and the amount and spreading of species on Earth. As a result, the environment and agricultural practices are affected by climate, so evaluating both distribution and evolution is extremely pertinent. Towards this aim, the climate distribution and evolution in the São Francisco River basin (SFRB) is assessed in three periods (1970–2000, 1981–2022) in the past and 2041–2060 in the future from an ensemble of GCMs under two SSPs (Shared Socioeconomic Pathways), SSP2-4.5 and SSP5-8.5. The Köppen-Geiger (KG) climate classification system is analyzed, and climate change impacts are inferred for this watershed located in central-eastern Brazil, covering an area equivalent to 8% of the country. Results predict the disappearance of the hot summer (Csa) and warm summer (Csb) Mediterranean climates, and a reduction/increase in the tropical savanna with dry winter (Aw)/dry summer (As). A striking increase in the semi-arid hot (BSh-steppe) climate is predicted with a higher percentage (10%) under SSP5-8.5. The source and the mouth of SFRB are projected to endure the major impacts of climate change that are followed by a predicted increase/decrease in temperature/precipitation. Future freshwater resource availability and quality for human use will all be impacted. Consequences on ecosystems, agricultural, and socioeconomic sectors within the SFRB might deepen the current contrasts between regions, urban and rural areas, and even between population groups, thus translating, to a greater extent, the inequality that still characterizes Brazilian society. Maps depicting land use and cover changes in SFRB from 1985 to 2022 highlight tendencies such as urbanization, agricultural expansion, deforestation, and changes in shrubland and water bodies. Urban areas fluctuated slightly, while cropland significantly increased from 33.57% to 45.45% and forest areas decreased from 3.88% to 3.50%. Socioeconomic data reveals disparities among municipalities: 74.46% with medium Human Development Index (HDI), 0.59% with very high HDI, and 9.11% with low HDI. Most municipalities have a Gross Domestic Product (GDP) per capita below US$6000. Population distribution maps show a predominance of small to medium-sized urban and rural communities, reflecting the basin’s dispersed demographic and economic profile. To achieve sustainable adaptation and mitigation of climate change impacts in SFRB, it is imperative that integrated measures be conducted with the cooperation of stakeholders, the local population, and decision-makers.

Historic Changes and Future Projections in Köppen–Geiger Climate Classifications in Major Wine Regions Worldwide

Historic Changes and Future Projections in Köppen–Geiger Climate Classifications in Major Wine Regions Worldwide

Elevation Regulates the Response of Climate Heterogeneity to Climate Change

AbstractClimate change represents a profound threat to the diversity and stability of global climate zones. However, the complex interplay between climate change and elevation in shaping climate heterogeneity is not yet fully understood. Here, we combine Shannon's diversity index (SHDI) with the Köppen‐Geiger climate classification to explore the altitudinal distributions of global climate heterogeneity; and their responses to climate change. The study reveals a distinctive pattern: SHDI, a proxy for climate heterogeneity tends to slow down or decline at lower elevations with increasing temperatures, while at higher elevations, it continues to rise due to continuing cold conditions. Examination of climate simulations, both with and without anthropogenic forcing, confirms that observed changes in climate heterogeneity are primarily attributable to anthropogenic climate change within these high‐elevation regions. This study underscores the importance of high‐elevation regions as not only custodians of diverse climate types but also potential refuges for species fleeing warmer climates.

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

Effect of Depositional Environment and Climate on Organic Matter Enrichment in Sediments of the Upper Miocene—Pliocene Kampungbaru Formation, Lower Kutai Basin, Indonesia

The Upper Miocene–Pliocene Kampungbaru Formation crops out in the easternmost part of the Lower Kutai Basin, Indonesia. The sedimentological analysis of seven outcrops was carried out, and a total of twenty-five samples from these outcrops was analyzed for bulk geochemistry, organic petrography, and bulk and clay mineralogy to assess the effect of the climate and depositional environment on organic matter enrichment. The Kampungbaru Formation consists of interbedded sandstone, siltstone, claystone, and thick coal beds, which were classified into eleven lithofacies. Subsequently, seven facies associations were identified, namely the fluvial-dominated distributary channel, sheet-like sandstone, tide-influenced distributary channel, mouth bar, crevasse splay, delta plain, and delta front. The coal facies generally have a high amount of total organic carbon (TOC, 5.1–16.9; avg. 10.11 wt.%), and non-coal layers range from 0.03 to 4.22 wt.% (avg. 1.54 wt.%). The dominant maceral is vitrinite, while liptinite occurs only rarely in the samples. Organic matter is inferred to have originated from terrestrial plants growing in mangrove swamps. Identified clay minerals include varying proportions of kaolinite, illite, chlorite, and mixed layer illite/smectite (I/S). Kaolinite, which commonly constitutes up to 30% of the clay volume, indicates intensive chemical weathering during a warm and humid climate. In accordance with the Köppen climate classification, the paleoclimate during the deposition of the Kampungbaru Formation is classified as type Af, which is a tropical rainforest. Tropical climate was favorable for the growth of higher plants and deposition of organic matter under anoxic conditions and led to higher amounts of TOC in the Kampungbaru Formation.