Historical Climate Conditions Research Articles

Triple coupling random forest approach for bias correction of ensemble precipitation data derived from Earth system models for Divandareh‐Bijar Basin (Western Iran)

AbstractClimate change is expected to change the frequency, duration, intensity, and pattern of precipitation, underscoring the need for accurate predictive tools. Earth system models (ESMs) serve as invaluable instruments in this endeavour, simulating climate variable variations across temporal and spatial dimensions. This study aims to develop a methodology for generating precise daily precipitation maps by rectifying biases inherent in ESM outputs. The proposed methodology includes downscaling ESM outputs to simulate historical daily grid‐based precipitation, thereby enhancing the fidelity of daily precipitation representation. For this purpose, 14 models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) were employed. Random forest (RF) machine learning method was used to correct biases in these ESM outputs. This study's novelty lies in integrating results of a grid‐based RF classification model, employed to distinguish between rainy and non‐rainy days, with those obtained by two RF regression models, to estimate precipitation amounts for grid cells receiving extreme and non‐extreme precipitation, to generate an ensemble of ESM outputs. The resulting method, termed the triple coupling method (EN‐RF), was validated using precipitation data from the Divandareh‐Bijar Basin in western Iran to simulate historical climate conditions. Furthermore, the accuracy of the developed triple coupling approach was compared with that of a commonly used single machine learning‐based downscaling model (EN‐Single‐RF). Comparative analysis against a commonly used single machine learning‐based downscaling model (EN‐Single‐RF) revealed the superior performance of the EN‐RF approach in replicating the intensity and distribution of daily precipitation. Furthermore, within the triple coupling framework, support vector machine (SVM) was utilized to simulate daily historical precipitation (EN‐SVM), while the quantile mapping (QM) method served as a benchmark. Comparison of the results showed superiority of the EN‐RF to other methods (EN‐Single‐RF, EN‐SVM, and QM) in terms of various accuracy metrics (Kling‐Gupta Efficiency = 0.95, mean square error = 0.22). The findings indicated the capability of the proposed triple coupling framework using the RF algorithm to simulate the spatio‐temporal distribution of precipitation using the ESM precipitation outputs. The developed framework can be used to produce reliable projections to gain deeper insights into the potential impacts of climate change on regional precipitation patterns.

Harnessing Artificial Intelligence for Early Detection and Management of Infectious Disease Outbreaks

Infectious diseases pose ongoing threats to global public health, demanding advanced detection methods for effective outbreak management. This study explores integrating artificial intelligence (AI) for early detection and management. AI algorithms analyze diverse datasets, including electronic health records and social media, to identify potential outbreaks. Machine learning models predict disease spread and severity, aiding proactive resource allocation. The implementation of AI extends beyond detection, encompassing predictive analytics for disease spread and severity assessment. Furthermore, the paper discusses AI's role in predictive modeling, enabling public health officials to anticipate the spread of infectious diseases and allocate resources proactively. Machine learning algorithms can analyze historical data, climatic conditions, and human mobility patterns to predict potential hotspots and optimize intervention strategies. The study also evaluates the current landscape of AI applications in infectious disease surveillance and proposes a comprehensive framework for their integration into existing public health infrastructures. Ethical considerations, privacy protection, and data security are paramount in developing a framework that balances the benefits of AI with the protection of individual rights. Ethical considerations are crucial, emphasizing collaboration between public health agencies, healthcare providers, and technology experts. The study evaluates the current landscape of AI applications in infectious disease surveillance and proposes a comprehensive framework for their integration into existing public health infrastructures. The implementation of an AI-driven early detection system requires collaboration between public health agencies, healthcare providers, and technology experts. This paper advocates for AI integration to enhance infectious disease surveillance, offering a proactive response to safeguard public health.

Improving our understanding of future tropical cyclone intensities in the Caribbean using a high-resolution regional climate model

The Caribbean region is prone to the strong winds and low air pressures of tropical cyclones and their corresponding storm surge that driving coastal flooding. To protect coastal communities from the impacts of tropical cyclones, it is important to understand how this impact of tropical cyclones might change towards the future. This study applies the storyline approach to show what tropical cyclones Maria (2017) and Dorian (2019) could look like in a 2 °C and 3.4 °C warmer future climate. These two possible future climates are simulated with a high-resolution regional climate model using the pseudo global warming approach. Using the climate response from these simulations we apply a Delta-quantile mapping technique to derive future changes in wind speed and mean sea level pressure. We apply this Delta technique to tropical cyclones Maria and Dorian’s observed wind and pressure fields to force a hydrodynamic model for simulating storm surge levels under historical and future climate conditions. Results show that the maximum storm surge heights of Maria and Dorian could increase by up to 0.31 m and 0.56 m, respectively. These results clearly show that future changes in storm surge heights are not negligible compared to end-of-the-century sea level rise projections, something that is sometimes overlooked in large-scale assessments of future coastal flood risk.

Impacts of climate change and host plant availability on the potential distribution of Bradysia odoriphaga (Diptera: Sciaridae) in China.

Chinese chives (Allium tuberosum Rottler ex Sprengel) are favored by consumers because of its delicious taste and unique fragrance. Bradysia odoriphaga (Diptera: Sciaridae) is a main pest that severely harms Chinese chives and other Liliaceae's production. Climate change may change the future distribution of B. odoriphaga in China. In this study, the CLIMEX was employed to project the potential distribution of B. odoriphaga in China, based on China's historical climate data (1987-2016) and forecast climate data (2021-2100). Bradysia odoriphaga distributed mainly between 19.8° N-48.3° N and 74.8° E-134.3° E, accounting for 73.25% of the total mainland area of China under historical climate conditions. Among them, the favorable and highly favorable habitats accounted for 30.64% of the total potential distribution. Under future climate conditions, B. odoriphaga will be distributed mainly between 19.8° N-49.3° N and 73.8° E-134.3° E, accounting for 84.89% of China's total mainland area. Among them, the favorable and highly favorable habitats will account for 35.23% of the total potential distribution, indicating an increase in the degree of fitness. Areas with relatively appropriate temperature and humidity will be more suitable for the survival of B. odoriphaga. Temperature was a more important determinant of the climatic suitability of the pest B. odoriphaga than humidity. Host plants (Liliaceae) availability also had impact on climate suitability in some regions. These projected potential distributions will provide supportive information for monitoring and early forecasting of pest outbreaks, and to reduce future economic and ecological losses. © 2024 Society of Chemical Industry.

Opportunities for Leveraging Existing Hydrologic and Hydraulic Models Developed for Water Quantity Management to Mitigate Flooding Due to Extreme Precipitation

With a focus on a specific flood-prone community in Camden, NJ, this research utilizes a detailed hydraulic and hydrologic (H&H) model to assesses the impacts of climate change on Combined Sewer Overflows (CSOs) and localized flooding under two different infrastructure scenarios. In the US, the Clean Water Act compels regulated utilities to develop Long-Term Control Plans to reduce combined sewer overflows (CSOs), but there is no parallel mandate to simultaneously reduce flooding within the associated service areas. With different control measures in place, H&H models are frequently used to evaluate CSO volumes and frequencies under historical climate conditions. However, precipitation intensification and sea level rise (SLR) will also modify CSO volumes. This study uses a calibrated and validated 1D and 2D Personal Computer Stormwater Management Model (PCSWMM) simulation to predict both CSO discharges and flooding under different climate and infrastructure scenarios. A total of ten climate change scenarios comprising a range of plausible climate futures are considered. The infrastructure scenario that is tested would divert stormwater generated in an upstream municipality (Pennsauken, NJ) away from Camden’s combined sewer system. Without the disconnection, increases in precipitation will increase CSOs, whereas SLR primarily increases flooding. The proposed mitigation strategy can immediately reduce both CSOs and flooding, but with diminishing effectiveness over time, as climate change demonstrates the need for supplemental measures. Areas for further analysis regarding alternative mitigation methods and future research are outlined.

Remarkable contamination characteristics, potential hazards and source apportionment of heavy metals in surface dust of kindergartens in a northern megacity of China

It is essential to understand the impact of heavy metals (HMs) present in the surface dust (SD) of kindergartens on children, who are highly sensitive to contaminated dust in cities in their growth stage. A study was conducted on 11 types of HMs present in the SD of 73 kindergartens in Beijing. This study aims to assess the pollution levels and sources of eleven HMs in Beijing’s kindergartens surface dust (KSD), and estimate the potential health risks in different populations and sources. The results indicate that Cd has the highest contamination in the KSD, followed by Pb, Zn, Ni, Ba, Cr, and Cu. The sources of these pollutants are identified as industrial sources (23.7%), natural sources (22.1%), traffic sources (30.4%), and construction sources (23.9%). Cancer risk is higher in children (4.02E-06) than in adults (8.93E-06). Notably, Cr is the priority pollutant in the KSD, and industrial and construction activities are the main sources of pollution that need to be controlled. The pollution in the central and surrounding areas is primarily caused by historical legacy industrial sites, transportation, urban development, and climate conditions. This work provides guidance to manage the pollution caused by HMs in the KSD of Beijing. Environmental implicationChildren within urban populations are particularly sensitive to pollutants present in SD. Prolonged exposure to contaminated SD significantly heightens the likelihood of childhood illnesses. The pollution status and potential health risks of HMs within SD from urban kindergartens are comprehensively investigated. Additionally, the contributions from four primary sources are identified and quantified. Furthermore, a pollution-source-oriented assessment is adopted to clearly distinguish the diverse impacts of different sources on health risks, and the priority pollutants and sources are determined. This work holds pivotal importance for risk management, decision-making, and environmental control concerning HMs in KSD.

Predicted Dynamic of Biodeterioration in Cultural Heritage Stones Due to Climate Changes in Humid Tropical Regions—A Case Study on the Rhodotorula sp. Yeast

The recent global warming started at the end of the 19th century, causing an increase in the average temperature of Earth and posing environmental, social, economic, and cultural repercussions. Much tangible cultural heritage is composed of natural stones, which decay due to the combination of chemical, physical, and biological factors. Biodeterioration leads to a loss of the performance requirements and socio-economic value of stone building materials. In the future, the dynamics of biodeterioration will hypothetically vary. This study aims to shed light on this variation by comparing biodeterioration under historical climatic conditions (1995–2014) with a future scenario defined by the IPCC SSP5-8.5 for the reference period 2080–2099. The material tested is Pedra de Ançã (PA), a candidate for World Heritage Stone. Climatic chambers were used to simulate the historical and predicted environmental conditions. The scope of this investigation is to understand the growth dynamic of the biodeteriogen Rhodotorula sp. and to study the morphological and aesthetic variations of stone surfaces. Biochemical and micro-topographic analyses highlighted the metabolic activity of the population proliferating under distinct environmental conditions, revealing better adaptability of Rhodotorula sp. and higher biocorrosion in the historical climate status with respect to the future.

Integrated crop and livestock systems increase both climate change adaptation and mitigation capacities

Integrated crop-livestock systems (ICLS) are proposed as key solutions to the various challenges posed to present-day agriculture which must guarantee high and stable yields while minimizing its impacts on the environment. Yet the complex relationships between crops, grasslands and animals on which they rely demand careful and precise management. In this study, from a 18-year ICLS field experiment in Brazil, that consists in annual no-till soybean-pastures grazed by beef cattle, we investigated the impacts of contrasted pastures grazing intensities (defined by sward heights of 10, 20, 30 and 40 cm, plus an ungrazed treatment) on the agroecosystem productivity and soil organic carbon (SOC) under both historical and future (2040–2070, RCP8.5) climatic conditions. We used an innovative methodology to model the ICLS with the STICS soil-crop model, which was validated with field observations. Results showed that the total system production increased along with grazing intensity because of higher stocking rates and subsequent live weight gains. Moderate and light grazing intensities (30 and 40 cm sward heights) resulted in the largest increase in SOC over the 18-year period, with all ICLS treatments leading to greater SOC contents than the ungrazed treatment. When facing climate change under future conditions, all treatments increased in productivity due to the CO2 fertilization effect and the increases in organic amendments that result from the larger stocking rate allowed by the increased pasture carrying capacity. Moderate grazing resulted in the most significant enhancements in productivity and SOC levels. These improvements were accompanied by increased resistance to both moderate and extreme climatic events, benefiting herbage production and live weight gain. Globally, our results show that adding a trophic level (i.e. herbivores) into cropping systems, provided that their carrying capacities are respected, proved to increase their ability to withstand climate change and to contribute to its mitigation.

Spatio-temporal shifts in British wild bees in response to changing climate.

Climate plays a major role in determining where species occur, and when they are active throughout the year. In the face of a changing climate, many species are shifting their ranges poleward. Many species are also shifting their emergence phenology. Wild bees in Great Britain are susceptible to changes in climatic conditions but little is known about historic or potential future spatio-temporal trends of many species. This study utilized a sliding window approach to assess the impacts of climate on bee emergence dates, estimating the best temperature window for predicting emergence dates for 88 species of wild bees. Using a 'middle-of-the-road' (RCP 4.5) and 'worst-case' (RCP 8.5) climate scenario for the period 2070-2079, predictions of future emergence dates were made. In general, the best predicting climate window occurred in the 0-3 months preceding emergence. Across the 40 species that showed a shift in emergence dates in response to a climate window, the mean advance was 13.4 days under RCP 4.5 and 24.9 days under RCP 8.5. Species distribution models (SDMs) were used to predict suitable climate envelopes under historic (1980-1989), current (2010-2019) and future (2070-2079 under RCP 4.5 and RCP 8.5 scenarios) climate conditions. These models predict that the climate envelope for 92% of studied species has increased since the 1980s, and for 97% and 93% of species under RCP 4.5 and RCP 8.5 respectively, this is predicted to continue, due to extension of the northern range boundary. While any range changes will be moderated by habitat availability, it highlights that Great Britain will likely experience northward shifts of bee populations in the future. By combining spatial and temporal trends, this work provides an important step towards informing conservation measures suitable for future climates, directing how interventions can be provided in the right place at the right time.

Comparative phylogeography reveals dissimilar genetic differentiation patterns in two sympatric amphibian species.

Global climate change is expected to have a profound effect on species distribution. Due to the temperature constraints, some narrow niche species could shift their narrow range to higher altitudes or latitudes. In this study, we explored the correlation between species traits, genetic structure, and geographical range size. More specifically, we analyzed how these variables are affected by differences in fundamental niche breadth or dispersal ability in the members of two sympatrically distributed stream-dwelling amphibian species (frog, Quasipaa yei; salamander, Pachyhynobius shangchengensis), in Dabie Mountains, East China. Both species showed relatively high genetic diversity in most geographical populations and similar genetic diversity patterns (JTX, low; BYM, high) correlation with habitat changes and population demography. Multiple clustering analyses were used to disclose differentiation among the geographical populations of these two amphibian species. Q. yei disclosed the relatively shallow genetic differentiation, while P. shangchengensis showed an opposite pattern. Under different historical climatic conditions, all ecological niche modeling disclosed a larger suitable habitat area for Q. yei than for P. shangchengensis; these results indicated a wider environment tolerance or wider niche width of Q. yei than P. shangchengensis. Our findings suggest that the synergistic effects of environmental niche variation and dispersal ability may help shape genetic structure across geographical topology, particularly for species with extremely narrow distribution.

High-resolution (1 km) Köppen-Geiger maps for 1901–2099 based on constrained CMIP6 projections

We introduce Version 2 of our widely used 1-km Köppen-Geiger climate classification maps for historical and future climate conditions. The historical maps (encompassing 1901–1930, 1931–1960, 1961–1990, and 1991–2020) are based on high-resolution, observation-based climatologies, while the future maps (encompassing 2041–2070 and 2071–2099) are based on downscaled and bias-corrected climate projections for seven shared socio-economic pathways (SSPs). We evaluated 67 climate models from the Coupled Model Intercomparison Project phase 6 (CMIP6) and kept a subset of 42 with the most plausible CO2-induced warming rates. We estimate that from 1901–1930 to 1991–2020, approximately 5% of the global land surface (excluding Antarctica) transitioned to a different major Köppen-Geiger class. Furthermore, we project that from 1991–2020 to 2071–2099, 5% of the land surface will transition to a different major class under the low-emissions SSP1-2.6 scenario, 8% under the middle-of-the-road SSP2-4.5 scenario, and 13% under the high-emissions SSP5-8.5 scenario. The Köppen-Geiger maps, along with associated confidence estimates, underlying monthly air temperature and precipitation data, and sensitivity metrics for the CMIP6 models, can be accessed at www.gloh2o.org/koppen.

Seasonal forecast-informed reservoir operation. Potential benefits for a water-stressed Mediterranean basin

The increased seasonal demand for water puts pressure on Mediterranean water resources, which are often exploited in a non-renewable way. Besides, climate change can alter hydroclimatic patterns and exaggerate freshwater stress. Flexible operation of existing water reservoirs is one of the most cost-effective ways to mitigate water-related stress by storing water when it is abundant and releasing it when droughts persist. In this context, hydroclimatic forecasts can be central in properly informing reservoir operation. Nevertheless, the link between forecast skill and forecast value is neither easily predictable nor necessarily positive. Each system requires specific forecasts according to its characteristics, and the skill of existing forecast systems does not necessarily translate into a significant gain in system performance. In this work, we develop downscaled seasonal forecasts of reservoir inflow for the Faneromeni irrigation dam on Crete island. We then quantify the value of these seasonal forecasts in informing the reservoir operations. While the current operation of this reservoir is based on the available storage at the beginning of the irrigation season, we investigate alternatives by using the Evolutionary Multi Objectives Direct Policy Search method, which allows the design of flexible rules to cope with the variability of the hydrologic conditions as well as to include forecast information for conditioning operational decisions. Under historical climate conditions, results demonstrate a notable enhancement in performance solely by implementing more flexible operating policies. Incorporating perfect forecasts results in an additional improvement of 4% on average throughout the period from 1993 to 2019. However, when using actual forecasts, this gain diminishes to 1%. These outcomes support the exploration of potential trade-off solutions that effectively balance the competing demands within the region.

On model selection criteria for climate change impact studies

Climate change impact studies inform policymakers on the estimated damages of future climate change on economic, health and other outcomes. In most studies, an annual outcome variable is observed, e.g. agricultural yield, along with a higher-frequency regressor, e.g. daily temperature. Applied researchers then face a problem of selecting a model to characterize the nonlinear relationship between the outcome and the high-frequency regressor to make a policy recommendation based on the model-implied damage function. We show that existing model selection criteria are only suitable for the policy objective if one of the models under consideration nests the true model. If all models are seen as imperfect approximations of the true nonlinear relationship, the model that performs well in the historical climate conditions is not guaranteed to perform well at the projected climate. We therefore propose a new criterion, the proximity-weighted mean squared error (PWMSE) that directly targets precision of the damage function at the projected future climate. To make this criterion feasible, we assign higher weights to historical years that can serve as “weather analogs” to the projected future climate when evaluating competing models using the PWMSE. We show that our approach selects the best approximate regression model that has the smallest weighted squared error of predicted impacts for a projected future climate. A simulation study and an application revisiting the impact of climate change on agricultural production illustrate the empirical relevance of our theoretical analysis.

Attribution of the Record‐Breaking Extreme Precipitation Events in July 2021 Over Central and Eastern China to Anthropogenic Climate Change

AbstractIn July 2021, Typhoon In‐Fa produced record‐breaking extreme precipitation events (hereafter referred to as the 2021 EPEs) in central and eastern China, and caused serious socioeconomic losses and casualties. However, it is still unknown whether the 2021 EPEs can be attributed to anthropogenic climate change (ACC) and how the occurrence probabilities of precipitation events of a similar magnitude might evolve in the future. The 2021 EPEs in central (eastern) China occurred in the context of no linear trend (a significantly increasing trend at a rate of 4.44%/decade) in the region‐averaged Rx5day (summer maximum 5‐day accumulated precipitation) percentage precipitation anomaly (PPA), indicating that global warming might have no impact on the 2021 EPE in central China but might have impacted the 2021 EPE in eastern China by increasing the long‐term trend of EPEs. Using the scaled generalized extreme value distribution, we detected a slightly negative (significantly positive) association of the Rx5day PPA time series in central (eastern) China with the global mean temperature anomaly, suggesting that global warming might have no (a detectable) contribution to the changes in occurrence probability of precipitation extremes like the 2021 EPEs in central (eastern) China. Historical attributions (1961–2020) showed that the likelihood of the 2021 EPE in central/eastern China decreased/increased by approximately +47% (−23% to +89%)/+55% (−45% to +201%) due to ACC. By the end of the 21st century, the likelihood of precipitation extremes similar to the 2021 EPE in central/eastern China under SSP585 is 14 (9–19)/15 (9–20) times higher than under historical climate conditions.

Evaluation of Gridded Meteorological Data for Crop Sensitivity Assessment to Temperature Changes: An Application with CERES-Wheat in the Mediterranean Basin

In areas with a limited or non-existent network of observing stations, it is critical to assess the applicability of gridded datasets. This study examined the agreement of Agri4Cast and E-OBS at two spatial resolutions (10 km (EOBS-0.1) and 25 km (EOBS-0.25)) in 13 Mediterranean stations nearby to wheat crops and how this agreement may influence simulated potential development and production with the crop simulation model (CSM) CERES-Wheat in historical and near-future (2021–2040) (NF) periods. A wide range of sensitivity tests for maximum and minimum air temperatures and impact response surfaces were used for the future projections. EOBS-0.1 showed the lowest discrepancies over observations. It underestimated statistical measures of temperature and precipitation raw data and their corresponding extreme indices and overestimated solar radiation. These discrepancies caused small delays (5–6 days, on average) in crop development and overestimations (8%) in grain production in the reference period. In the NF, the use of EOBS-0.1 reduced by a few (2–3) days the biases in crop development, while yield responses differed among stations. This research demonstrated the ability of EOBS-0.1 for agricultural applications that depend on potential wheat development and productivity in historical and future climate conditions expected in the Mediterranean basin.

Greenhouse Warming Reduces Global Energy Conversion Into Oceanic Lee Waves

AbstractOceanic lee waves play an important role in dissipating wind‐driven ocean circulations and powering turbulent diapycnal mixing. Here we investigate impacts of the greenhouse warming on global energy conversion into lee waves using a linear theory of lee wave generation and output from a high‐resolution (0.1° for the ocean) coupled global climate model. The global energy conversion rate into lee waves under the historical (1930s) climate condition is estimated to be 193.0 ± 3.0 GW. Under the high carbon emission scenario, this conversion rate is projected to decrease by about 20% by the end of 21st century, due to weakened bottom large‐scale mean flows, mesoscale eddies and stratification. The decrease of the conversion rate is widespread and particularly pronounced in the Gulf Stream and Drake Passage.

A comprehensive analysis of CO2 exchanges in agro-ecosystems based on a generic soil-crop model-derived methodology

Carbon emissions in agriculture play a major role in climate change. Modelling studies enable to investigate the impacts of climate change in crops, accounting for soil organic carbon feedbacks and CO2 concentrations. But it is primordial that crop models properly consider the CO2 exchanges at the level of crop rotations beyond the cycle of a single crop. With this goal in mind, we used the outputs of the soil-crop model STICS in its standard pre-parameterized version to model (i) the Gross Primary Productivity (GPP), derived from the autotrophic respiration and the Net Primary Productivity, which is computed through the daily change in plant carbon (C) pools; (ii) the Ecosystem Respiration (RECO), with the autotrophic component being derived from the plant biomass, plant nitrogen concentration and GPP, and the heterotrophic component from the mineralization of residues and organic matter; and (iii) the Net Ecosystem Exchange, equal to the sum of GPP and RECO. The comparison of simulations with field observations indicates that the model is able to simulate accurately daily CO2 fluxes originating from a long-term and diversified crop rotation (efficiency EF equal to 0.79 for GPP, 0.59 for RECO and 0.67 for NEE). Concerning the evaluation of the cumulated fluxes over the 16-year rotation, the model is able to evaluate it accurately for RECO, with a slight underestimation (normalized deviation ND = 15.7%), and very accurately for GPP (ND = 5.12%). But for NEE, the relative overestimation is higher (ND = 62.2%), indicating that a more precise estimation of HR is required to obtain reliable net C budgets. The model also succeeds to capture the trends in the influence of several environmental drivers on CO2 fluxes. It globally proves to be a valuable tool in the investigation of CO2 exchanges of crop rotations in historical and future climatic conditions.

Contemporary biodiversity pattern is affected by climate change at multiple temporal scales in steppes on the Mongolian Plateau

Abstract. Present and historical climate conditions jointly determine contemporary biodiversity patterns and ecosystem functions. However, it remains unclear how contemporary climate and paleoclimate changes together affect the three dimensions of biodiversity (i.e., taxonomic diversity, functional diversity and phylogenetic diversity) and their relationship with ecosystem functions. Here, we assess the impact of current climate, paleoclimate and its anomalies on contemporary biodiversity and ecosystem functions. We estimated the taxonomic, functional and phylogenetic diversity of grassland on the Mongolian Plateau using vegetation survey data and trait information. We then used random forest and structural equation models to assess the relative importance of the present, the Mid-Holocene and the Last Glacial Maximum climate as well as paleoclimate changes as determinants of diversity and aboveground biomass. Our results showed that paleoclimate changes and modern climate jointly determined contemporary biodiversity patterns, while community biomass was mainly affected by modern climate, namely the aridity index. Modern aridity and temperature were two major influences on all three dimensions of biodiversity. Mid-Holocene climate anomalies had a strong effect on species richness and phylogenetic diversity, while functional diversity had mainly been affected by temperature anomalies since the Last Glacial Maximum. These findings suggest that contemporary biodiversity patterns may be affected by processes at divergent temporal scales. Our results show that simultaneously exploring the response of the three dimensions of biodiversity in different periods of climate change and the theoretical framework for its impact on community biomass is helpful to provide a more comprehensive understanding of patterns of biodiversity and its relationship with ecosystem functions.

On the issue of the development of religious tourism in the Republic of Dagestan

Aim. The article deals with the development of religious tourism in the Republic of Dagestan as excursions, educational and religious studies and pilgrimage tours. Discussion. The recreational potential of the republic includes numerous places of worship, monuments of spiritual culture and cultural and historical complexes, favourable natural and climatic conditions, the warm Caspian Sea and sandy beaches and unique natural objects, such as the Sulak Canyon, the Sary‐Kum dune, the Saltinsky underground waterfall, the Samur forest, the Tobotsky waterfall, majestic mountains, which have attracted a large number of guests to Dagestan in recent years. During 2022, more than one and a half million people visited the republic, while the population in the republic itself is three million. In view of the fact that paganism, Zoroastrianism, Judaism and Christianity were practiced on the territory of modern Dagestan before the spread of Islam, cult objects associated with these religions have been preserved. Of course, the largest group of places of worship is associated with Islam. Conclusion. The presence in Dagestan of shrines associated not only with Islam, but also with Christianity, makes it possible to develop religious tourism in the republic not only in the form of excursions and educational and religious studies, but also pilgrimage tours for believing citizens of the country, which will undoubtedly contribute to the education of religious tolerance and mutual understanding of representatives of different religions.

Assessment of the applicability of large‐scale climate ensemble simulations for analysing future extreme rainfall

AbstractExtreme rainfall events are a major cause of natural disasters and have been projected to increase in frequency and intensity under future climate change scenarios. Previous studies have used outputs of climate model simulations to evaluate changes in future extreme rainfall, but results have been uncertain due to the use of only a limited number of climate simulations. To address this issue, this study aims to evaluate the applicability of the d4PDF database (Database for Policy Decision‐Making for Future Climate Change), produced through large‐scale climate ensemble simulations, for analysing changes in extreme rainfall values between historical and future climatic conditions. A large‐scale sample size of the d4PDF allows the investigation of high‐magnitude rainfalls with long return periods. To utilize the d4PDF as a large‐scale sample for estimating extreme rainfall, the homogeneity between ensembles was assessed using the t test and Mann–Whitney U test, and the historical extreme rainfall with the d4PDF dataset was validated through comparison with observed data and using the Kolmogorov–Smirnov test. It was confirmed that the d4PDF ensembles could be used as a large‐scale sample, and the historical rainfall series of the d4PDF showed a similar distribution pattern to the observed rainfall series. Subsequent investigation of extreme rainfall for future ensembles during 2051–2110 indicated an increase by 20%–30% compared to historical climatic conditions in South Korea due to climate change. The results demonstrate the applicability of large‐scale climate ensemble simulations, such as d4PDF, for analysing changes in extreme rainfall and suggest that such information can be used to counter future extreme rainfall‐related disasters. Therefore, this study contributes to the knowledge gap in this area and highlights the need for further assessment and utilization of large‐scale climate ensemble simulations for future climate change research.