Greenhouse Gas Emission Scenarios Research Articles

Quantifying the historical and future heat-related mortality above the heat alert thresholds of the inaugural Chinese national heat-health action plan.

China published its inaugural national heat-health action plan (HHAP) in 2023, but the mortality burden associated with temperatures exceeding the heat alert thresholds specified by this HHAP (maximum temperatures >35, 37, or 40 °C) remains unknown. We aimed to estimate the historical and future mortality burden associated with temperatures above the heat alert thresholds of the Chinese national HHAP. We conducted time-series analyses to estimate the mortality burden associated with temperatures exceeding the three heat alert thresholds from 2016-2019 in Jiangsu Province (including 13 cities, population ∼80.7 million), China. A quasi-Poisson regression in conjunction with a distributed lag non-linear model was used to estimate the dose-response association between maximum temperature and mortality risk from 2016-2019, adjusting for potential covariates. We then projected the future mortality burden associated with temperatures exceeding these thresholds under three distinct levels of greenhouse gas (GHG) emission scenarios via scenario shared socioeconomic pathways [SSP] 1-2.6 (low), SSP2-4.5 (intermediate), and SSP5-8.5 (high), respectively, by assuming that there will be no adaptation to heat. Climate scenarios derived from the General Circulation Model (GCM) under the Coupled Model Intercomparison Project Phase 6 (CMIP6) were used. From 2016-2019, temperatures above 35 °C were associated with 0.51% of mortality, including 0.40% associated with 35 °C - 37 °C and 0.11% associated with >37 °C. Heat-related mortality risk was most prominent in those who were single/divorced/widowed and had <10 years of education. Under SSP2-4.5, compared with the 2020s, the excess mortality associated with >37 °C would increase by 1.4 times in the 2050s and 1.7 times in the 2090s. Under SSP5-8.5, the annual number of days with maximum temperature >37 °C would approximately double every 20 years (67 days annually in the 2090s). Consequently, compared with the 2020s, the excess mortality associated with >37 °C would increase by 2.8 times in the 2050s and 18.4 times in the 2090s. Significant mortality risk is associated with temperatures above the lowest heat alert threshold of the Chinese national HHAP (35 °C). If the high GHG emission scenario occurred, the annual number of days and excess mortality associated with maximum temperatures >37 °C would largely increase in the coming decades.

Observation-inferred resilience loss of the Amazon rainforest possibly due to internal climate variability

Abstract. Recent observation-based studies suggest that the Amazon rainforest has lost substantial resilience since 1990, indicating that the forest might undergo a critical transition in the near future due to global warming and deforestation. The idea is to use trends in a lag-1 auto-correlation of leaf density as an early-warning signal of an imminent critical threshold for rainforest dieback. Here we test whether the observed change in auto-correlations could arise from internal variability using historical and control simulations of nine sixth-generation Earth system model ensembles (Phase 6 of the Coupled Model Intercomparison Project, CMIP6). We quantify trends in the leaf area index auto-correlation from both models and satellite-observed vegetation optical depth from 1990 to 2017. Four models reproduce the observed trend with at least one historical realization whereby the observations lie at the upper limit of model variability. Three out of these four models exhibit similar behavior in control runs, suggesting that historical forcing is not necessary for simulating the observed trends. Furthermore, we do not observe a critical transition in any future runs under the strongest greenhouse gas emission scenario (SSP5-8.5) until 2100 in the four models that best reproduce the past observed trends. Hence, the currently observed trends could be caused simply by internal variability and, unless the data records are extended, have limited applicability as an early-warning signal. Our results suggest that the current rapid decline in the Amazon rainforest coverage is not foremost caused by global warming.

Future climate doubles the risk of hydraulic failure in a wet tropical forest.

Future climate presents conflicting implications for forest biomass. We evaluate how plant hydraulic traits, elevated CO2 levels, warming, and changes in precipitation affect forest primary productivity, evapotranspiration, and the risk of hydraulic failure. We used a dynamic vegetation model with plant hydrodynamics (FATES-HYDRO) to simulate the stand-level responses to future climate changes in a wet tropical forest in Barro Colorado Island, Panama. We calibrated the model by selecting plant trait assemblages that performed well against observations. These assemblages were run with temperature and precipitation changes for two greenhouse gas emission scenarios (2086-2100: SSP2-45, SSP5-85) and two CO2 levels (contemporary, anticipated). The risk of hydraulic failure is projected to increase from a contemporary rate of 5.7% to 10.1-11.3% under future climate scenarios, and, crucially, elevated CO2 provided only slight amelioration. By contrast, elevated CO2 mitigated GPP reductions. We attribute a greater variation in hydraulic failure risk to trait assemblages than to either CO2 or climate. Our results project forests with both faster growth (through productivity increases) and higher mortality rates (through increasing rates of hydraulic failure) in the neo-tropics accompanied by certain trait plant assemblages becoming nonviable.

Predicting the Future Distribution and Habitat Suitability of Ilex latifolia Thunb. in China under Climate Change Scenarios

Ilex latifolia Thunb., a plant of significant economic and medicinal value, is both edible and medicinal. Assessing the climate suitability for I. latifolia has profound implications for advancing medical progress and enhancing the quality of human life. This study comprehensively utilized data on the field distribution of I. Latifolia, as well as corresponding climatic, topographical, and soil data at these distribution points, with the aid of future climate data predicted by global climate models, and employed the MaxEnt model to predict and analyze the climate suitability areas of I. latifolia under three greenhouse gas emission scenarios (SSP126, SSP245, and SSP585). The research covers the spatiotemporal distribution characteristics, suitable growth range, and influencing factors from the present to the end of the 21st century (2041–2100). The predictive results of the MaxEnt model indicate that, under current climatic conditions, the main suitable growth areas for I. latifolia are concentrated in the southeastern part of China, especially in the provinces of Fujian and Zhejiang. However, facing the challenges of future climate change, it is expected that the moderately high suitable growth areas for I. latifolia will show a trend of gradual reduction. The primary climatic factors crucial for I. latifolia’s growth are annual precipitation (1469.05 to 4499.50 mm), the lowest temperature in the coldest month (−18.72 to 3.88 °C), seasonal precipitation changes (11.94 to 64.69 mm), and topographic slope (0.37 to 3.00 °), with annual precipitation being the most influential. The findings of this study provide a scientific basis for the introduction of I. latifolia and offer important reference information for the artificial cultivation, resource development, and achievement of sustainable industrial development of this species.

Estimate of Temperature Rise in the 21st Century for Various Scenarios

The International Panel on Climate Change (IPCC) published a lengthy report on climate change in early 2023. This report hypothesizes five potential scenarios of greenhouse gas emissions from 2015 to the end of the century (2100) and estimates the global average temperature gain in the year 2100 from the mid-1800s for each scenario. The method of calculation in the IPCC report is obscure. The results are merely stated. The present paper provides a clear method for estimating the temperature gain each year from 2015 until 2100, along with yearly estimates of ppm of CO2. To facilitate the calculations, a set of scenarios of future emissions was chosen that is analogous to the scenarios used by the IPCC but is more amenable to computation. The basic assumption in this paper is that most of the temperature gain from the mid-1800s to 2015 (1.15 C – as reported by the IPCC) was due to rising CO2 concentration in the atmosphere and a relationship is thereby derived between warming and gigatons of CO2 emitted for the period: 1800s to 2015. If it is assumed that the amount of warming per gigaton CO2 from the past persists into the 21st century, then future warming in the 21st century can be estimated for any assumed future scenario of CO2 emissions. This paper provides a simple and clear estimate of yearly CO2 ppm and temperature rise from 2015 to 2100 since the 1800s for a set of scenarios that cover the likely range of future emissions.

Performance and projections of the NEX‐GDDP‐CMIP6 in simulating precipitation in the Brazilian Amazon and Cerrado biomes

AbstractThe objective of this work is to provide projections of mean annual and monthly precipitation for the Brazilian Amazon and Cerrado biomes, in the near‐term (2021–2040), medium‐term (2041–2060) and long‐term (2081–2100). The intermediate and most pessimistic Intergovernmental Panel on Climate Change (IPCC) greenhouse gas emissions scenarios were considered. Thus, 34 high‐resolution global climate models (GCMs) from the NASA Earth Exchange Global Daily Downscaled Projections (NEX‐GDDP) Phase 6 of the Coupled Model Intercomparison Project (CMIP6) were evaluated. The base period evaluated was from 1981 to 2010. The NEX‐GDDP simulations are bias‐corrected and spatially disaggregated. The Climate Hazards Group InfraRed Precipitation with Station v2.0 was chosen as the source of observed data due to low availability in situ data. The Kling‐Gupta efficiency (KGE) and the global performance indicator were implemented in Google Earth Engine to evaluate the GCMs. The results show that the GCMs perform satisfactorily, except for KACE‐1‐0‐G and IITM‐ESM. The median KGE is 0.86 for the biomes. Thus, the Ensemble Model of 32 GCMs (EM‐32) indicates a reduction in precipitation in the biomes, except the northern Cerrado. In the most pessimistic scenario, changes in annual precipitation range from 3% to −33% until the end of the century. The north‐central Amazon and the northwestern Cerrado are the most affected regions. In general, the monthly precipitations between September and November show the most intense decreasing rates. It is estimated that 91% and 23% of areas in the Amazon and Cerrado biomes, respectively, show robust signs of reduction in mean annual precipitation. Thus, EM‐32 shows more intense and robust climate projections, in comparison to the total annual precipitation of the subset of 33 raw CMIP6 models from Working Group I of the IPCC Sixth Assessment Report. Therefore, the EM‐32 precipitation projections can be applied to future hydrological and hydrosedimentological investigations.

Predicting climate change impacts on the distribution of endemic fish Cyprinion muscatense in the Arabian Peninsula.

Freshwater fishes are facing considerable threats in the Arabian Peninsula which is considered as a highly stressed region in the Middle East. It is predicted that northern Oman is likely to face decreasing rainfall and increasing temperature in coming decades. In this study, we focused on an endemic cyprinid fish Cyprinion muscatense, as a model to investigate impacts of climate change on the mountain fishes inhibiting in this arid region. This species is expected to be strongly affected by climate change because of its limited distribution range in a montane area surrounded by lowlands and sea, limiting the species in shift to other areas. We used an ensemble approach by considering two regressions-based species distribution modeling (SDM) algorithms: generalized linear models (GLM), and generalized additive models (GAM) to model the species habitat suitability and predict the impacts of climate change on the species habitat suitability. Based on the distribution models, the montane area located in northeastern Oman was identified as the most suitable habitat for this species. Our results indicate that, even under the minimum greenhouse gas emissions scenario (RCP 2.6), climate change will produce a high reduction in its potential future habitats. According to the results of percent contribution, elevation and annual minimum temperature were the most important variables in predicting the species suitable habitats. Results also showed that only a small percentage of suitable habitats for the species within boundaries of protected areas. Therefore, the impact of climate change on the species appears particularly alarming. Although our study was restricted to a single cyprinid freshwater species, decreases in potential habitats are likely predicted for other cyprinid fish species restricted to the mountains of this region, suggesting severe consideration is needed for aquatic systems in future conservation planning, especially for endemic freshwater fishes.

Current and future burden of Ross River virus infection attributable to increasing temperature in Australia: a population-based study

Ross River virus (RRV), Australia's most notifiable vector-borne disease transmitted through mosquito bites, has seen increased transmission due to rising temperatures. Quantifying the burden of RRV infection attributable to increasing temperatures (both current and future) is pivotal to inform prevention strategies in the context of climate change. As RRV-related deaths are rare in Australia, we utilised years lived with disability (YLDs) associated with RRV infection data from the Australian Institute of Health and Welfare (AIHW) Burden of Disease database between 2003 and 2018. We obtained relative risks per 1°C temperature increase in RRV infection from a previous meta-analysis. Exposure distributions for each Köppen-Geiger climate zone were calculated separately and compared with the theoretical-minimum-risk exposure distribution to calculate RRV burden attributable to increasing temperatures during the baseline period (2003-2018), and projected future burdens for the 2030s and 2050s under two greenhouse gas emission scenarios (Representative Concentration Pathways, RCP 4.5 and RCP 8.5), two adaptation scenarios, and different population growth series. During the baseline period (2003-2018), increasing mean temperatures contributed to 35.8 (±0.5) YLDs (19.1%) of the observed RRV burden in Australia. The mean temperature attributable RRV burden varied across climate zones and jurisdictions. Under both RCP scenarios, the projected RRV burden is estimated to increase in the future despite adaptation scenarios. By the 2050s, without adaptation, the RRV burden could reach 45.8 YLDs under RCP4.5 and 51.1 YLDs under RCP8.5. Implementing a 10% adaptation strategy could reduce RRV burden to 41.8 and 46.4 YLDs, respectively. These findings provide scientific evidence for informing policy decisions and guiding resource allocation for mitigating the future RRV burden. The current findings underscore the need to develop location-specific adaptation strategies for climate-sensitive disease control and prevention. Australian Research Council Discovery Program.

Study of the future evolution of the urban climate of Paris by statistical-dynamical downscaling of the EURO-CORDEX ensemble

Abstract High-resolution urban climate projections are needed for local decision-making on climate change adaptation. Regional climate models have resolutions that are too coarse to simulate the urban climate at such resolutions. A novel statistical-dynamical downscaling approach (SDD) is used here to downscale the EURO-CORDEX ensemble to a resolution of 1 km while adding the effect of the city of Paris (France) on air temperature. The downscaled atmospheric fields are then used to drive the Town Energy Balance urban canopy model to produce high-resolution temperature maps over the period 1970-2099, while maintaining the city’s land cover in its present state. The different steps of the SDD are evaluated for the summer season. The regional climate models simulate minimum(maximum) temperatures (TN/TX) that are too high(low). After correction and downscaling, the urban simulations inherit some of these biases, but give satisfactory results for summer urban heat islands (UHI), with average biases of −0.6 K at night and +0.3 K during the day. Changes in future summer temperatures are then studied for two greenhouse gas emission scenarios, RCP4.5 and RCP8.5. Outside the city, the simulations project average increases of 4.1 K and 4.8 K for TN and TX for RCP8.5. In the city, warming is lower, resulting in a decrease in UHIs of −0.19 K at night (from 2.1 K to 1.9 K) and −0.16 K during the day. The changes in UHIs are explained by higher rates of warming in rural temperatures due to lower summer precipitation and soil water content, and are partially offset by increased ground heat storage in the city.

Patterns and correlates of potential range shifts of bat species in China in the context of climate change.

Climate change may diminish biodiversity; thus, it is urgent to predict how species' ranges may shift in the future by integrating multiple factors involving more taxa. Bats are particularly sensitive to climate change due to their high surface-to-volume ratio. However, few studies have considered geographic variables associated with roost availability and even fewer have linked the distributions of bats to their thermoregulation and energy regulation traits. We used species distribution models to predict the potential distributions of 12 bat species in China under current and future greenhouse gas emission scenarios (SSP1-2.6 and SSP5-8.5) and examined factors that could affect species' range shifts, including climatic, geographic, habitat, and human activity variables and wing surface-to-mass ratio (S-MR). The results suggest that Ia io, Rhinolophus ferrumequinum, and Rhinolophus rex should be given the highest priority for conservation in future climate conservation strategies. Most species were predicted to move northward, except for I. io and R. rex, which moved southward. Temperature seasonality, distance to forest, and distance to karst or cave were the main environmental factors affecting the potential distributions of bats. We found significant relationships between S-MR and geographic distribution, current potential distribution, and future potential distribution in the 2050s. Our work highlights the importance of analyzing range shifts of species with multifactorial approaches, especially for species traits related to thermoregulation and energy regulation, to provide targeted conservation strategies.

Estimation of Potential Nitrous Oxide Emissions from Landfills in the United States: 2010–2020

Nitrous oxide (N2O), a major greenhouse gas, has the potential to be emitted from waste landfills. Previous studies have demonstrated the propensity of landfilling facilities to emit significant quantities of N2O, a fact underscored by the IPCC Guidelines, which emphasize the importance of researching this phenomenon. However, due to the absence of established international guidelines for quantifying N2O emissions from landfills, many countries, including the United States, have excluded N2O from greenhouse gas inventories. Therefore, this study aims to estimate N2O emissions from landfills in the United States, a country with a significant landfill waste volume. In this study, N2O emissions from U.S. landfills over an 11-year period (2010–2020) are estimated by using the emission estimation formula provided in CDM AM0083 and emission factors from the 2006 IPCC Guidelines. Additionally, emissions were calculated spatially for each state and individual landfill facility. As a result, the impact of integrating N2O emissions from landfills into the national greenhouse gas inventory was assessed. The average annual landfill N2O emission in the United States over the 11-year period was estimated to be 3,214,693 ton-CO2-equivalent/year, with an overall decreasing trend. In 2020, Indiana, Michigan, and Oregon exhibited high landfill N2O emissions per capita, while the Virgin Islands, Connecticut, and Massachusetts demonstrated lower emissions. When incorporated into the U.S. greenhouse gas inventory, landfill N2O emissions represent 10.41% of the total sector N2O emissions. Although N2O emissions are declining alongside reduced waste landfilling in the United States, the quantity remains significant and should be factored into greenhouse gas inventory calculations and emission scenarios for the next CMIP6. Further research investigating N2O emission coefficients across different regions and waste types is necessary. Ultimately, this study aims to support the United Nations (UN)’s Sustainable Development Goals (SDGs), particularly SDG 11 (Sustainable Cities and Communities), 12 (Responsible Consumption and Production), and 13 (Climate Action), by enhancing the tools for accurate greenhouse gas inventory and promoting sustainable waste management.

Current and future distribution of Forsythia suspensa in China under climate change adopting the MaxEnt model.

This study evaluated the potential impact of climate change on the distribution of Forsythia suspensa, a valuable traditional Chinese medicinal plant, using the MaxEnt model integrated with Geographic Information System (GIS). By analyzing occurrence data from various databases and environmental variables including climate and soil factors, we forecasted the present and future (2050s and 2070s) habitat suitability of F. suspensa under different greenhouse gas emission scenarios (RCP8.5, RCP4.5, RCP2.6). Results indicated that the suitable habitats for F. suspensa were primarily located in North, East, Central, Northwest, and Southwest China, with a significant potential expansion of suitable habitats anticipated by the 2070s, particularly under the high emission scenario. The study identified precipitation and temperature as the primary environmental drivers impacting the distribution of F. suspensa. Furthermore, a northward shift in the centroid of suitable habitats under future climate scenarios suggested a potential migration response to global warming. This work provides crucial insights into the future conservation and cultivation strategies for F. suspensa amidst changing climatic conditions.

Projecting Changes in the Frequency and Magnitude of Ozone Pollution Events Under Uncertain Climate Sensitivity

AbstractClimate change is projected to worsen ozone pollution over many populated regions, with larger impacts at higher concentrations. More intense and frequent ozone episodes risk setbacks to human health and environmental policy achievements. However, assessing these changes is complicated by uncertain climate sensitivity, closely related to climate model response, and internal variability in simulations projecting climate's influence on air quality. Here, leveraging a global modeling framework that one‐way couples a human activity model, an Earth system model of intermediate complexity, and an atmospheric chemistry model, we investigate the role of climate sensitivity in climate‐induced changes to high ozone pollution episodes in the United States using multiple greenhouse gas emissions scenarios, representations of climate sensitivity, and initial condition members. We bias correct and evaluate historical model simulations, identifying modeled and observed O3 episodes using extreme value theory, and extend the approach to projections of mid‐ and end‐century climate impacts. Results show that the influence of climate sensitivity can be as significant as that of greenhouse gas emissions scenario absent precursor emissions changes. Climate change is projected to increase the magnitude of the highest annually occurring O3 concentrations by over 2.3 ppb on average across the U.S. at mid‐century under a high climate sensitivity and moderate emissions scenario, but the increase is limited to less than 0.3 ppb under lower climate sensitivity. Further, we show that areas in the U.S. currently meeting air quality standards risk being pushed into non‐compliance due to a climate‐induced increase in frequency of high ozone days.

Beyond the First Tipping Points of Southern Hemisphere Climate

Analysis of observations, reanalysis, and model simulations, including those using machine learning methods specifically designed for regime identification, has revealed changes in aspects of the Southern Hemisphere (SH) circulation and Australian climate and extremes over the last half-century that indicate transitions to new states. In particular, our analysis shows a dramatic shift in the metastability of the SH climate that occurred in the late 1970s, associated with a large-scale regime transition in the SH atmospheric circulation, with systematic changes in the subtropical jet, blocking, zonal winds, and storm tracks. Analysis via nonstationary clustering reveals a regime shift coincident with a sharp transition to warmer oceanic sea surface temperatures and increased baroclinicity in the large scales of the Antarctic Circumpolar Circulation (ACC), extending across the whole hemisphere. At the same time, the background state of the tropical Pacific thermocline shoaled, leading to an increased likelihood of El Niño events. The SH climate shift in the late 1970s is the first hemispheric regime shift that can be directly attributed to anthropogenic climate change. These changes in dynamics are associated with additional regional tipping points, including reductions in mean and extreme rainfall in south-west Western Australia (SWWA) and streamflow into Perth dams, and also with increases in mean and extreme rainfall over northern Australia since the late 1970s. The drying of south-eastern Australia (SEA) occurred against a background of accelerating increases in average and extreme temperatures across the whole continent since the 1990s, implying further inflection points may have occurred. Analysis of climate model simulations capturing the essence of these observed shifts indicates that these systematic changes will continue into the late 21st century under high greenhouse gas emission scenarios. Here, we review two decades of work, revealing for the first time that tipping points characteristic of regime transitions are inferred to have already occurred in the SH climate system.

Exceptional atmospheric conditions in June 2023 generated a northwest European marine heatwave which contributed to breaking land temperature records

The Northwest European shelf experienced unprecedented surface temperature anomalies in June 2023 (anomalies up to 5 °C locally, north of Ireland). Here, we show the shelf average underwent its longest recorded category II marine heatwave (16 days). With state-of-the-art observation and modelling capabilities, we show the marine heatwave developed quickly due to strong atmospheric forcing (high level of sunshine, weak winds, tropical air) and weak wave activity under anticyclonic weather regimes. Once formed, this shallow marine heatwave fed back on the weather: over the sea it reduced cloud cover and over land it contributed to breaking June mean temperature records and to enhanced convective rainfall through stronger, warmer and moister sea breezes. This marine heatwave was intensified by the last 20-year warming trend in sea surface temperatures. Such sea surface temperatures are projected to become commonplace by the middle of the century under a high greenhouse gas emission scenario.

Climate change effects on animal presence in the Massaciuccoli Lake basin

Big-data mining approaches based on Artificial Intelligence models can help forecast biodiversity changes before they happen. These approaches can predict macroscopic species distribution patterns and trends that can inform preventive measures to avoid the loss of ecosystem functions and services. They can, therefore, help study and mitigate climate change implications on biodiversity conservation in fragile ecosystems. Wetlands are particularly fragile ecosystems where climate change poses severe risks and has dramatically reduced their size over the past century, with profound consequences on biodiversity and ecosystem services. Through big-data mining approaches, we can predict future wetland biodiversity trends in the context of climate change. This paper proposes such predictive analysis for a specific wetland: The Massaciuccoli Lake basin in Tuscany, Italy. This basin is a critical tourist attraction due to its rich biodiversity, making it an area of interest for citizens, tourists, and scientists. However, the region's suitability for native and non-native species is at risk due to climate and land-use change. Using machine-learning models, we predict the potential effects of climate change on animal spatial distribution in the basin under different greenhouse gas emission scenarios. The results suggest that habitat suitability has generally improved from 1950 to today, presumably owing to the targeted conservation strategies adopted in the area, but climate change will severely reduce bird biodiversity by 2050 while favouring several insect species' proliferation and other species' habitat change, even under a medium-emission scenario. This will lead to significant changes in the basin's biodiversity. Our methodology is adaptable to other wetland basins, being fully based on open data and models. The spatially explicit modelling used in this research provides valuable information for policymakers and spatial planners, complementing traditional biodiversity trend analyses.

A Detailed Examination of Türkiye's Projected Precipitation and Growth Season Trends under Climate Change Condition

This study presents a comprehensive analysis of Türkiye's changing precipitation patterns and growing season dynamics in the context of global warming, utilizing the Coupled Model Intercomparison Project Phase 5 (CMIP5) and various Representative Concentration Pathway (RCP) scenarios. In light of global warming, this study provides a thorough analysis of Türkiye's evolving precipitation patterns and growing season dynamics using multiple RCP scenarios and CMIP5. The research aims to fill a crucial gap in climate research by combining historical data and future projections to assess Türkiye's precipitation path under different greenhouse gas emission scenarios. The study employs linear regression for trend analysis and uses data from the Climate Change Knowledge Portal (CCKP), with a focus on precipitation data from 1986 to 2100. According to various RCP scenarios, this study’s findings show a considerable variation in precipitation trends over the 21st century. The RCP 8.5 scenario predicts a significant decrease in precipitation, which would present difficulties for the management of water resources and agricultural productivity. In contrast, the least severe RCP 2.6 pathway shows a fairly stable pattern of precipitation. Complex seasonal hydrological responses to climate change are revealed by monthly precipitation analysis; RCP8.5 predicts an increase in the frequency of periods of drought and heavy precipitation events. The impact of these changes in precipitation on Türkiye's agricultural growing seasons was further investigated in this study. In high-emission scenarios, there was an initial tendency towards longer growing seasons, which were subsequently followed by shorter ones. This suggests that although global warming might initially result in an extended growing season, it might ultimately cause a reduction in it, particularly in situations where mitigation efforts are minimal. The need for adaptable strategies that can respond to long-term climate trends as well as seasonal variability was highlighted by this research. It draws attention to the fact that to mitigate the effects of climate variability, informed policy decisions and integrated resource management are essential. The results highlight the need for quick action to lower the risks associated with water and highlight the potential advantages of intensive mitigation efforts in stabilizing and extending growing seasons.

Prediction of ambulance transport system collapse under extremely high temperatures induced by climate change

The heat-related health impacts of extremely high temperatures have become increasingly severe worldwide. Quantitative information on these impacts is necessary to plan and implement adaptation measures. The present study aimed to predict the number of heatstroke ambulance calls (HAC) and ambulance occupancy rate of patients with heatstroke (AORPH) under extremely high temperatures in Tokyo, Japan. Extremely high temperatures were defined as the annual highest value of daily maximum temperature that occurs once every 30 y (30 y temp) or 50 y (50 y temp). Combinations of three greenhouse gas (GHG) emission scenarios and five climate models were adopted to predict the impacts during the base years (1985–2014), mid-21st century (2021–2050), and late 21st century (2071–2100). Furthermore, two types of populations were adopted: fixed and changing. In the case of 50 y temp, the HAC during the peak hour in the base years ranged from 52 to 130 persons across the climate models. In the case of a fixed population, in the late 21st century it ranged from 123 to 2618 persons across the climate models and GHG scenarios. In the case of a changing population, the corresponding values in late 21st century ranged from 128 to 3078 persons. The AORPH during the peak hour ranged from 32% to 80% during the base years for 50 y temp, assuming that all ambulances were dispatched for heatstroke only. In the late 21st century, it ranged from 75% to 1605% in the fixed population case. With population change, the corresponding values in late 21st century ranged from 83% to 1781%. In many scenarios, not all patients with heatstroke could be transported (AORPH > 100%) even if all ambulances have been dispatched. Therefore, adaptation measures that mitigate such an ambulance transport system collapse must be planned and implemented.

Magnesium Isotopes of Carbonate Reveal Seasonal Climate Variation in the Central East Asia During the Middle Eocene

AbstractIt is debated whether there was strong climate seasonality during the Eocene, which provides a close geological analogy for near‐future scenarios of greenhouse gas emissions. Lithological data suggest the existence of a broad arid zone centered around 30°N paleo‐latitude, while a humid climate was supported by palaeobotanic assemblages in East Asia. Here, we report the occurrence of massive primary lacustrine dolomite and magnesite in the central East Asia during the middle Eocene. We provide a novel perspective from magnesium isotopes to link the formation of Mg‐carbonates to seasonal dry‐wet cycles. Rapid magnesium input during the rainy season and intense evaporation in the dry season likely caused the formation of magnesium carbonates in an enclosed lake. These findings provide insights into hydroclimatic seasonality during the Eocene, contributing to our understanding of the hydrological cycle response to a greenhouse climate.

Projection of dengue fever transmissibility under climate change in South and Southeast Asian countries.

Vector-borne infectious disease such as dengue fever (DF) has spread rapidly due to more suitable living environments. Considering the limited studies investigating the disease spread under climate change in South and Southeast Asia, this study aimed to project the DF transmission potential in 30 locations across four South and Southeast Asian countries. In this study, weekly DF incidence data, daily mean temperature, and rainfall data in 30 locations in Singapore, Sri Lanka, Malaysia, and Thailand from 2012 to 2020 were collected. The effects of temperature and rainfall on the time-varying reproduction number (Rt) of DF transmission were examined using generalized additive models. Projections of location-specific Rt from 2030s to 2090s were determined using projected temperature and rainfall under three Shared Socioeconomic Pathways (SSP126, SSP245, and SSP585), and the peak DF transmissibility and epidemic duration in the future were estimated. According to the results, the projected changes in the peak Rt and epidemic duration varied across locations, and the most significant change was observed under middle-to-high greenhouse gas emission scenarios. Under SSP585, the country-specific peak Rt was projected to decrease from 1.63 (95% confidence interval: 1.39-1.91), 2.60 (1.89-3.57), and 1.41 (1.22-1.64) in 2030s to 1.22 (0.98-1.51), 2.09 (1.26-3.47), and 1.37 (0.83-2.27) in 2090s in Singapore, Thailand, and Malaysia, respectively. Yet, the peak Rt in Sri Lanka changed slightly from 2030s to 2090s under SSP585. The epidemic duration in Singapore and Malaysia was projected to decline under SSP585. In conclusion, the change of peak DF transmission potential and disease outbreak duration would vary across locations, particularly under middle-to-high greenhouse gas emission scenarios. Interventions should be considered to slow down global warming as well as the potential increase in DF transmissibility in some locations of South and Southeast Asia.