Shared Socioeconomic Pathways Research Articles

Sustainable urban planning to control flood exposure in the coastal zones of China

ContextSustainable development in coastal zones faces escalating flood risk in the context of climate change and urbanization, and the rapid urban growth in flood zones has been one of the key drivers. Therefore, understanding the Urban Exposure to Flooding (UEF) and its future scenarios is important in coastal zones.ObjectivesThe objectives of this study were: (1) to assess the future dynamics of UEFs in China's coastal zones, and (2) to identify a sustainable way of urban planning in controlling the growth of UEFs.MethodsFuture UEFs in coastal China were assessed during 2020–2050 by combining urban expansion model, scenario analysis, and flood exposure assessment. Alternative scenarios were considered of shared socioeconomic pathways (SSPs), representative concentration pathways (RCPs), strategies of urban planning.ResultsThe results show that the 1000-year flood UEFs along coastal China was expected to grow under SSP2-RCP4.5 from 9,879 km2 in 2020 to 13,424 (12,997–13,981) km2 in 2050, representing an increment of 35.88% (31.56%–41.52%). Alternatively, the strategy of sustainable development planning could reduce the newly added UEF by 16.98% (15.63%–18.67%) in a 1000-year flood scenario.ConclusionsThe findings proved that the ways of urban growth matters in terms of affecting food exposure and risk and flood risk should be incorporated into urban planning for a sustainable landscape. The study could offer methodology and support for sustainable development strategies in reducing future urban flood risk.

Assessing the impacts of temperature extremes on agriculture yield and projecting future extremes using machine learning and deep learning approaches with CMIP6 data

Climate change, particularly extreme weather events, has significantly affected various sectors, including agriculture, human health, water resources, sea levels, and ecosystems. It is anticipated that the intensity, duration, and frequency of these extremes will escalate in the future. This study aims to discover the association between temperature extremes and agricultural yield and to project these extremes using machine learning (ML) and deep learning (DL) models with CMIP6 (Coupled Model Intercomparison Project Phase 6) data under two SSPs (Shared Socioeconomic Pathways). A bi-wavelet coherence technique is employed to investigate the association, providing detailed information in both the frequency and time domains for the period of 1980–2014. Various ML and DL models are trained and tested for the periods of 1985–2004 and 2005–2014, respectively, with gradient boosting machine chosen for projecting temperature extremes based on its superior performance. Mann-Kendall test is used for trend analysis in the projected temperature extremes. The results indicate strong negative and positive associations between TN10p (Cold nights) and TN90p (Warm nights), respectively, with wheat production. Additionally, there is a long-term negative association of CSDI (Cold Spell Duration Indicator) and strong positive association of WSDI (Warm Spell Duration Indicator) with rice yield. Projected results show an increase and decrease under SSP2-4.5 and SSP5-8.5, respectively, in DTR (Diurnal Temperature Range) at most stations. TN10p will increase in the future at most stations, with exceptions such as Muree station where it decreases during 2025–2049 and then increases under both SSPs. Projections show that TXn (annual or monthly minimum value of daily maximum temp) will increase in the future, with Muree station exhibiting the lowest value close to zero, while the average maximum value is around 20 °C at Khanpur station. Trend analysis reveals significantly increasing trend in TR20 (Tropical nights) and decreasing trend in CSDI in future durations under both SSPs. These findings hold implications for policymakers and stakeholders in various departments, including agriculture, health, and water resources management.

Quantile Delta-Mapped Spatial Disaggregation Analysis for Climate Variations over the Yangtze River Basin

Abstract Significant increases in temperature and precipitation due to global warming affect socioeconomics. Accurate analysis is needed for future temperature and precipitation variations across the Yangtze River basin (YRB). A novel quantile delta-mapped spatial disaggregation (QDMSD) approach was developed in this study to analyze temperature and precipitation changes for the first time. The evaluation results show that the QDMSD has a similar performance in simulating temperature with the bias correction and spatial disaggregation (BCSD) model, while it shows improvement in reproducing precipitation. Projections indicate the annual-mean temperature will increase from 2020 to 2080 under shared socioeconomic pathway (SSP) scenarios SSP2-4.5 and SSP5-8.5. The projected temperature obtained from five downscaled GCMs has the smallest range of differences in summer. Conversely, annual-mean temperatures significantly decrease from 2081 to 2100 under SSP2-4.5. In terms of spatial distribution characteristics, most of the positive changes tend to expand across the YRB. The annual-mean precipitation will increase from 2020 to 2080 but decrease from 2081 to 2100 under SSP2-4.5 over the YRB. In terms of spatial distribution, precipitation in the southeast region of the YRB will increase, and the maximum variations in precipitation will occur downstream of the YRB. The QDMSD method reproduces observed precipitation trends well and enhances simulation accuracy in the YRB. For projected temperature, there will be a widespread increase across the YRB; for projected precipitation, significant increases will occur in the eastern YRB. These findings support policymaking to address potential risks from temperature and precipitation changes across multiple sectors (e.g., agriculture and industry).

Effects of climate warming on energetics and habitat of the world's largest marine ectotherm

Responses of organisms to climate warming are variable and complex. Effects on species distributions are already evident and mean global surface ocean temperatures are likely to warm by up to 4.1 °C by 2100, substantially impacting the physiology and distributions of ectotherms. The largest marine ectotherm, the whale shark Rhincodon typus, broadly prefers sea surface temperatures (SST) ranging from 23 to 30 °C. Whole-species distribution models have projected a poleward range shift under future scenarios of climate change, but these models do not consider intraspecific variation or phenotypic plasticity in thermal limits when modelling species responses, and the impact of climate warming on the energetic requirements of whale sharks is unknown. Using a dataset of 111 whale shark movement tracks from aggregation sites in five countries across the Indian Ocean and the latest Earth-system modelling produced from Coupled Model Intercomparison Project Phase 6 for the Intergovernmental Panel on Climate Change, we examined how SST and total zooplankton biomass, their main food source, may change in the future, and what this means for the energetic balance and extent of suitable habitat for whale sharks. Earth System Models, under three Shared Socioeconomic Pathways (SSPs; SSP1–2.6, SSP3–7.0 and SSP58.5), project that by 2100 mean SST in four regions where whale shark aggregations are found will increase by up to 4.9 °C relative to the present, while zooplankton biomass will decrease. This reduction in zooplankton is projected to be accompanied by an increase in the energetic requirements of whale sharks because warmer water temperatures will increase their metabolic rate. We found marked differences in projected changes in the extent of suitable habitat when comparing a whole-species distribution model to one including regional variation. This suggests that the conventional approach of combining data from different regions within a species' distribution could underestimate the amount of local adaptation in populations, although parameterising local models could also suffer from having insufficient data and lead to model mis-specification or highly uncertain estimates. Our study highlights the need for further research into whale shark thermal tolerances and energetics, the complexities involved in projecting species responses to climate change, and the potential importance of considering intraspecific variation when building species distribution models.

Estimating and forecasting suppressed electricity demand in Ghana under climate change, the informal economy and sector inefficiencies

Suppressed demand arises from inadequate energy access, resulting in unmet basic needs. Therefore, this study investigates the impact of the informal economy, rising temperatures, and electricity transmission losses on suppressed demand in Ghana from 2000 to 2020, using a quantile autoregressive distributed lag (QARDL) approach. The study forecasts suppressed demand using Shared Socioeconomic Pathway (SSP) scenarios, offering insights for energy system planning. The results indicate that all the variables significantly affect suppressed demand in the mid-quantiles. Notably, transmission losses and growth of informal economy variables significantly impact suppressed demand within the 50th to 75th quantiles but have minimal impact before the 50th and after the 75th quantiles in the long run. Additionally, rising temperatures substantially increase suppressed demand by increasing electricity demand for cooling. All future scenarios project this growth trend will continue through 2050, albeit at varying rates. In the business-as-usual (BAU) case, suppressed demand is expected to steadily increase from 1782 MW in 2020 to 8636 MW in 2050. This trajectory aligns well with historical growth trends, which saw suppressed demand increase from 659 GWh to 1782 GWh between 2000 and 2020. SSP scenarios suggest that suppressed demand could grow substantially through 2050, driven by high losses and informal sector growth. Despite sustainable development narratives like SSP1, suppressed demand remains high without major grid and governance improvements. Comparing the results with past studies shows that our findings align with previous research but provide more nuanced insights by incorporating the effects of the informal economy and using advanced forecasting techniques. Practical policy implications include investing in green infrastructure, upgrading grid infrastructure, and formalising the informal economy to alleviate suppressed demand. These actions are critical for sustainable energy access and meeting future electricity needs effectively.

Improving hydrological modeling to close the gap between elevated CO2 concentration and crop response: Implications for water resources

Rising atmospheric carbon dioxide concentrations ([CO2]) affect crop growth and the associated hydrological cycle through physiological forcing, which is mainly regulated by reducing stomatal conductance (gs) and increasing leaf area index (LAI). However, reduced gs and increased LAI can affect crop water consumption, and the overall effects need to be quantified under elevated [CO2]. Here we develop a SWAT-gs-LAI model by incorporating a nonlinear gs-CO2 equation and a missing LAI-CO2 relationship to investigate the responses of water consumption of grain maize, maize yield, and losses of water and soil to elevated [CO2] in the Upper Mississippi River Basin (UMRB; 492,000 km2). Results exhibited enhanced maize yield with decreased water consumption for increases in [CO2] from 495 ppm to 825 ppm during the historical period (1985–2014). Elevated [CO2] promoted surface runoff but suppressed sediment loss as the predominant impact of LAI-CO2 leading to enhanced surface cover. A comprehensive analysis of future climate change showed increased maize water consumption in comparison to the historical period, driven by the more pronounced effects of overall climate change rather than solely elevated [CO2]. Generally, future climate change promoted maize yield in most regions of the UMRB for three Shared Socioeconomic Pathway (SSP) scenarios. Surface runoff was shown to increase generally in the future with sediment loss increasing by an average of 0.39, 0.42, and 0.66 ton ha-1 for SSP1–2.6, SSP2–4.5, and SSP5–8.5, respectively. This was due to negative climatic change effects largely surpassing the positive effect of elevated [CO2], particularly in zones near the middle and lower stream. Our results underscore the crucial role of employing a physically-based model to represent crop physiological processes under elevated [CO2] conditions, improving the reliability of predictions related to crop growth and the hydrological cycle.

Regional carbon inequality prediction in China based on shared socioeconomic pathways: A human well-being equity perspective

Carbon inequality is strongly related to economic development and human well-being (HWB) improvements. However, relatively little research has been undertaken to predict future interregional carbon inequality in China from an HWB equity perspective based on scenarios combining shared socioeconomic pathways (SSPs) and carbon reduction policies. The biproportional scaling method named after economist Richard Stone (RAS) was used to predict China's 2020 to 2050 multiregional input–output (MRIO) tables. Then, these MRIO tables were used to simulate future net carbon emissions (NCEs) and net human well-being (NWB) transfers under the SSP1–1.5 °C, SSP1-NEU, SSP2-2 °C and SSP5-BAU scenarios. China's interregional carbon inequality was predicted under different scenarios to clarify the ideal path for mitigating carbon inequality. In the SSP1–1.5 °C, SSP1-NEU, SSP2-2 °C and SSP5-BAU scenarios, the total carbon emissions (CEs) clearly decrease, whereas the total HWB clearly increases. Transfers of NCEs between regions increases, and transfers of NWB between regions decreases in each of the four scenarios. According to the mean regional environmental inequality (REI) value, China's interregional carbon inequality is relatively low under the SSP1–1.5 °C and SSP1-NEU scenarios and relatively high under the SSP2–2 °C and SSP5-BAU scenarios. Mitigating interregional carbon inequality is a long-term and arduous task that requires commitments from governments, businesses and society. These findings clarify the optimal path for China to reduce carbon inequality in the future and provide a theoretical basis for government agencies to rationally adjust the current development model. Furthermore, they provide a supportive reference to help other economies achieve equitable and sustainable development.

The Key Role of Temporal Stratification for GCM Bias Correction in Climate Impact Assessments

AbstractCharacterizing climate change impacts on water resources typically relies on Global Climate Model (GCM) outputs that are bias‐corrected using observational data sets. In this process, two pivotal decisions are (a) the Bias Correction Method (BCM) and (b) how to handle the historically observed time series, which can be used as a continuous whole (i.e., without dividing it into sub‐periods), or partitioned into monthly, seasonal (e.g., 3 months), or any other temporal stratification (TS). Here, we examine how the interplay between the choice of BCM, TS, and the raw GCM seasonality may affect historical portrayals and projected changes. To this end, we use outputs from 29 GCMs belonging to the CMIP6 under the Shared Socioeconomic Pathway 5–8.5 scenario, using seven BCMs and three TSs (entire period, seasonal, and monthly). The results show that the effectiveness of BCMs in removing biases can vary depending on the TS and climate indices analyzed. Further, the choice of BCM and TS may yield different projected change signals and seasonality (especially for precipitation), even for climate models with low bias and a reasonable representation of precipitation seasonality during a reference period. Because some BCMs may be computationally expensive, we recommend using the linear scaling method as a diagnostics tool to assess how the choice of TS may affect the projected precipitation seasonality of a specific GCM. More generally, the results presented here unveil trade‐offs in how BCMs are applied, regardless of the climate regime, urging the hydroclimate community to carefully implement these techniques.

Prediction of long-term photovoltaic power generation in the context of climate change

Accurate long-term prediction of power generation in photovoltaic (PV) power stations is crucial for preparing generation plans and future planning. Quantitative prediction of future power generation from PV stations not only contributes to the stable operation of the local power system but also assists managers in formulating regional energy policies to promote renewable energy consumption. We utilized the NEX-GDDP-CMIP6 high-resolution climate dataset and employed the Vine Copula method for post-downscaling. This approach enabled high-resolution forecasts of key meteorological factors under different shared socioeconomic pathways (SSPs) scenarios (SSP245 and SSP585) for a PV power station in Yunnan, China. Additionally, we developed the KM-PSO-SVR power generation prediction model, which enables future accurate long-term PV power generation prediction. The results show that the Vine Copula multi-model ensemble downscaling model can effectively simulate the changes in key meteorological factors in the PV power station area. The KM-PSO-SVR model exhibited good simulation performance, with a mean absolute error of 0.843, root mean square error of 1.136, and correlation coefficient of 0.874 during the validation period. The results indicate that during the decade spanning from January 1, 2025, to December 31, 2034, radiation and wind speed will be decrease, while the temperature is expected to increase. In the SSP245 scenario, there is a 1.585% increase in the average annual power generation during the future carbon peaking period (2025-2034). However, the SSP585 scenario, representing higher future emissions, shows a lower increase of 1.479%.

Projected global sulfur deposition with climate intervention

Even with immediate implementation of global policies to mitigate carbon dioxide emissions, the impacts of climate change will continue to worsen over the next decades. One potential response is stratospheric aerosol injection (SAI), where sulfur dioxide is released into the stratosphere to block incoming solar radiation. SAI does not reduce the level of carbon dioxide in the atmosphere, but it can slow warming and act as a stopgap measure to give the world more time to pursue effective carbon reduction strategies. While SAI is controversial, it remains a technically feasible proposition. It ought to be thoroughly modeled both to characterize global risks better and to further the scientific community’s understanding of stratospheric aerosol dynamics. SAI relies on sulfate aerosols which have a lifetime of several years in the stratosphere but will eventually be deposited back onto Earth’s surface. While sulfate is an important nutrient for many ecosystems, high concentrations can cause acidification, eutrophication, and biodiversity loss. We use model outputs from the Geoengineering Model Intercomparison Project (GeoMIP) to track the impacts of sulfur deposition from SAI to various ecoregions through comparison with historical climate and future Shared Socioeconomic Pathway (SSP) scenarios. Our results demonstrate that dry sulfur deposition will continue to decline worldwide, regardless of scenario, from a high of 41 Tg S/yr in 1981 to under 20 Tg S/yr by 2100. Wet sulfur deposition, however, is much more uncertain and further work needs to be done in this area to harmonize model estimates. Under SAI, many ecoregions will experience notably different sulfur deposition regimes by the end of the century compared to historical trends. In some places, this will not be substantially different than the impacts of climate change under SSP2–4.5 or SSP5–8.5. However, in some ecoregions the model projections disagree dramatically on the magnitude of future trends in both emissions and deposition, with, for example, UKESM1–0-LL projecting that SO42- deposition in deciduous needleleaf forests under G6 Sulfur will reach 394 % of SSP2–4.5 deposition by the 2080 s while CESM2-WACCM projects that SO42- deposition will remain at 170 % of SSP2–4.5 deposition during that same time period. Our work emphasizes the lack of agreement between models and the importance of improving our understanding of SAI impacts for future climate decision-making.

Analysis of carbon peak achievement at the provincial level in China: Construction of ensemble prediction models and Monte Carlo simulation

As China advances toward its carbon peaking goals, many regions face the challenge of balancing rapid economic growth with sustainable development. Evaluating carbon emissions at the provincial level is crucial for formulating effective strategies to achieve China's carbon peak targets. This study aims to construct an accurate model for predicting carbon emissions and to explore the evolution of these emissions across Chinese provinces, as well as their contributions to national carbon peak targets. Using the Environmental Kuznets Curve (EKC) theory, the 30 provinces were categorized into groups. An ensemble carbon emissions forecasting model was developed by integrating time-series models with multifactor models. Three scenarios were established within the framework of Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs). Monte Carlo simulations were employed to explore potential pathways to achieve carbon peaks. The results indicate that China will reach its carbon emission peak between 2030 and 2031, with peak values expected to range between 11,499.65 and 11,629.51 Mt. Significant differences were observed among the provincial groups in their contributions to carbon peaking. Groups II and III are projected to peak in 2030 and 2022, respectively, while Groups I and IV face greater challenges, with peak years projected between 2032–2035 and 2031–2034, respectively. Four tiers with different emission reduction responsibilities were identified by comparing the peak times of the 30 provinces under the three scenarios, and optimal recommendations for achieving carbon peaks were proposed for each province. The accurate prediction models and Monte Carlo simulations provide reliable results for achieving carbon peak targets across Chinese provinces, offering a scientific basis for optimizing national carbon emission reduction policies.

A quantitative analysis framework for analyzing impacts of climate change on water-food-energy-ecosystem nexus in irrigation areas based on WEAP-MODFLOW

Agricultural irrigation areas are vital for ensuring food security under climate change conditions with extensive water and energy consumption, which will inevitably exert pressures on ecosystem especially in arid and semi-arid areas normally with higher ecosystem degradation risk. The key to realize sustainable development lies in a quantitative analysis framework for analyzing impacts of climate change on water-food-energy-ecosystem nexus. Climate change exacerbates these challenges by impacting food production, water resources utilization, energy consumption for water supply and ecological conditions in irrigation areas. In this work, a quantitative analysis framework to analyze the trade-offs and synergies among various scenarios within the water-food-energy-ecosystem nexus in irrigation area was proposed. Two climate models (BCC and MIR1) were selected from the Coupled Model Intercomparison Project Phase 6 (CMIP6), and three Shared Socioeconomic Pathway (SSP) scenarios (SSP245, SSP370, and SSP585) were employed to quantitatively evaluate different sustainable development strategies among water, food, energy and ecosystem sectors from five key aspects: irrigation demand, food production, groundwater supply for irrigation, ecological base flow and irrigation water shortage. The results show that the sustainability index (SI) of combined management scenarios CC1-IM, CC5-IM, CC1-WSI and CC5-WSI could be 12% to 25% higher than that of the corresponding single management scenario. Among the four climate change and management scenarios, the SI of the integrated measures scenario (CC1-IM, CC5-IM) was the highest, with values of 1.39 and 1.12 respectively. This work highlights the pomising results of the integrated measures scenario in alleviating food production challenges, protecting ecological base flows, reducing groundwater extraction, alleviating irrigation water shortages and improving water supply efficiency when dealing with climate change challenges.

Controlling factors of wildfires in Australia and their changes under global warming

Abstract This study investigates a fire weather index (FWI) and its associated components in Australia using the downscaled projects for the Coupled Model Intercomparison Project phase 6 (CMIP6) dataset, aiming to understand how they respond to global warming, particularly associated with different phases of the El Niño-Southern Oscillation (ENSO). In the historical simulation, multimodel mean composite results show positive anomalies of FWI during El Niño and negative anomalies during La Niña over most of Australia relative to the neutral year. At the end of the 21st century under the Shared Socioeconomic Pathways (SSP585 scenario), FWI anomalies increased across Australia; however, ENSO wildfire teleconnections weakened (-4.4%) during El Niño but strengthened (+6.0%) during La Niña, especially in northern Australia. Further examination of the contribution from individual environmental variables that enter the FWI shows that increased temperature and drought conditions with warming in La Niña strengthen positive FWI anomalies, thus making fire more favorable in north and central Australia. The impacts of relative humidity and wind speed anomaly changes also favor fire activity toward the north. These results suggest a more robust modulation of FWI in northern Australia by ENSO in a warmer climate; future efforts to predict wildfire will depend on the model’s ability to predict local climate conditions.

Carbon Reduction Pathways for Chinese Provinces: Considering Carbon Intensity Convergence, Regional Development Plans, and Shared Socioeconomic Pathways

Carbon Reduction Pathways for Chinese Provinces: Considering Carbon Intensity Convergence, Regional Development Plans, and Shared Socioeconomic Pathways

Intercomparison of deep learning models in predicting streamflow patterns: insight from CMIP6

This research was carried out to predict daily streamflow for the Swat River Basin, Pakistan through four deep learning (DL) models: Feed Forward Artificial Neural Networks (FFANN), Seasonal Artificial Neural Networks (SANN), Time Lag Artificial Neural Networks (TLANN) and Long Short-Term Memory (LSTM) under two Shared Socioeconomic Pathways (SSPs) 585 and 245. Taylor Diagram, Random Forest, and Gradient Boosting techniques were used to select the best combination of General Circulation Models (GCMs) for Multi-Model Ensemble (MME) computation. MME was computed via the Random Forest technique for Maximum Temperature (Tmax), Minimum Temperature (Tmin), and precipitation for the aforementioned three techniques. The best MME for Tmax, Tmin, and precipitation was rendered by Compromise Programming. The DL models were trained and tested using observed precipitation and temperature as independent variables and discharge as dependent variables. The results of deep learning models were evaluated using statistical performance indicators such as root mean square error (RMSE), mean square error (MSE), mean absolute error (MAE), and coefficient of determination (R2). The TLANN demonstrated superior performance compared to the other models based on RMSE, MSE, MAE, and R2 during training (65.25 m3/s, 4256.97 m3/s, 46.793 m3/s and 0.7978) and testing (72.06 m3/s, 5192.95 m3/s, 51.363 m3/s and 0.7443) respectively. Subsequently, TLANN was utilized to make predictions based on MME of SSP245 and SSP585 scenarios for future streamflow until the year 2100. These results can be used for planning, management, and policy-making regarding water resources projects in the study area.

CMIP6-based global estimates of future aridity index and potential evapotranspiration for 2021-2060

The “Future Global Aridity Index and PET Database” provides high-resolution (30 arc-seconds) average annual and monthly global estimates of potential evapotranspiration (PET) and aridity index (AI) for 22 CMIP6 Earth System Models for two future (2021-2041; 2041-2060) and two historical (1960-1990; 1970–2000) time periods, for each of four shared socio-economic pathways (SSP). Three multimodel ensemble averages are also provided (All; Majority Consensus, High Risk) with different level of risks linked to climate model uncertainty. An overview of the methodological approach, geospatial implementation and a technical evaluation of the results is provided. Historical results were compared for technical validation with weather station data (PET: r 2 = 0.72; AI: r 2 = 0.91) and the CRU_TS v 4.04 dataset (PET: r 2 = 0.67; AI: r 2 = 0.80). Within the context of projected significant change in the near- and medium-term, the “Future_Global_AI_PET Database” provides a set of data projections and tools available for a variety of scientific and practical applications, illustrating trends and magnitude of predicted climatic and eco-hydrological impacts on terrestrial ecosystems. The Future_Global_AI_PET Database is archived in the ScienceDB repository and available online at: https://doi.org/10.57760/sciencedb.nbsdc.00086

Projecting LUCC dynamics and ecosystem services in an emerging urban agglomeration under SSP-RCP scenarios and their management implications

Improving our knowledge of future dynamics of ecosystem services (ESs) in the face of climate change and human activities provides a crucial foundation to navigate complex environmental challenges, which are essential to attaining sustainable development particularly in urban regions. However, an existing dearth persists in thoroughly forecasting the intricate interplay of trade-offs and synergies, as well as ecosystem services bundling under distinct future scenarios. This study adopts an integrated research framework to understand the spatiotemporal dynamics of ESs in the Changsha-Zhuzhou-Xiangtan Urban Agglomeration (CZTUA) under three Shared Socioeconomic Pathway and Representative Concentration Pathway (SSP-RCP) scenarios (i.e., SSP126, SSP245 and SSP585). Our future scenarios suggest that the core urban area of CZTUA is projected to expand at the cost of forests and croplands by 2050. Furthermore, human-induced urbanization, particularly the high-intensity LUCC along the Xiangjiang river, significantly impacts ESs, resulting in lower ESs values. The trade-off effects between ESs are primarily observed between WY (water yield) and other ESs. Ecosystem service bundles (ESB) previously dominated by WY have significantly transitioned to CS (carbon storage)-HQ (habitat quality) bundle, especially in the urban core of CZTUA, which serves as an early warning of potential challenges related to water resources. Our study utilizes the latest climate and land use change predictions to evaluate ecosystems in urban agglomerations, and adopts a layered zoning strategy based on ESs, which provides decision-makers with reproducible tools to explore ecosystem changes.

Quantifying future climate extreme indices: implications for sustainable urban development in West Africa, with a focus on the greater Accra region

Climate change leading to Climate extremes in the twenty-first century is more evident in megacities across the world, especially in West Africa. The Greater Accra region is one of the most populated regions in West Africa. As a result, the region has become more susceptible to climate extremes such as floods, heatwaves, and droughts. The study employed the Coupled Model Intercomparison Project 6 models in simulating climate extreme indices under the Shared Socioeconomic Pathway scenarios (SSPs) over West Africa between 1979 and 2059 as exemplified by the Greater Accra region. The study observed a generally weak drought in the historical period and expected to intensify especially under SSP585 in Greater Accra. For instance, continuous dry days (CDD) reveal an increasing trend under the SSPs. Similarly, the overall projected trend of CDD over West Africa reveals an increase signifying a more frequent and longer drought in the future. The flood indices revealed a surge in the intensity and duration of extreme precipitation events under the SSPs in the region. For instance, R99pTOT and Rx5days are expected to significantly increase under the SSPs with intensification under the SSP245, SSP370, and SSP585. A similar trend has been projected across West Africa, especially along the Guinean coast. The study foresees a gradual and intensifying rise in heatwave indices over the Greater Accra region. The warming and cooling indices reveal an increasing and decreasing trend respectively in the historical period as well as under the SSPs particularly within urban centers like Accra and Tema. Most West African countries are projected to observe more frequent warm days and nights with cold nights and days becoming less frequent. Expected effects of future climate extreme indices pose potential threats to the water, food, and energy systems as well as trigger recurrent floods and droughts over Greater Accra. The findings of the study are expected to inform climate policies and the nationally determined contribution of the Paris Agreement as well as address the sustainable development goal 11 (Sustainable cities) and 13 (Climate action) in West Africa.

Large-scale ozone episodes in Europe: Decreasing sizes in the last decades but diverging changes in the future

Episodes of high near-surface ozone concentrations tend to cover large areas for several days. They are strongly dependent on meteorology, precursor emissions, and the ambient photochemical conditions. This study introduces a new pseudo-Lagrangian algorithm that identifies the spatiotemporal patterns of episodes, allowing for a good characterization of their areal extent and an assessment of their drivers. The algorithm has been used to identify ozone episodes in Europe from April to September over the last twenty years (2003–2022) in the Copernicus Atmosphere Monitoring Service (CAMS) reanalysis as well as in the historical simulation (1950–2014) and four shared socio-economic pathways (SSPs, spanning 2015–2100) of three Earth system models (UKESM1-0-LL, EC-Earth3-AerChem and GFDL-ESM4). While the total number of episodes has increased in recent years, the frequency of large episodes has decreased following European precursor emission reductions. The analysis of the 100 largest episodes shows that they tend to occur in Northern Europe during spring and in the center and south of the continent from June onwards. Most of the top 10 episodes occurred in the first years of the century and were associated with high temperatures, enhanced solar radiation, and anticyclonic conditions. Despite the decrease in large episodes in recent years, there is uncertainty regarding future European episodes. Episodes of reduced size are found for SSPs with weak greenhouse forcing and low precursor emissions, whereas episode sizes increase in scenarios with high methane concentrations and enhanced radiative forcing, even exceeding the maximum historical size. However, the three models project episodes of different sizes for any given scenario, probably associated with their differing warming trends and the varying level of complexity in the implementation of processes. These results point to the need to implement both effective climate and air quality policies to address the ozone air pollution problem in Europe in a warming climate.

Projected Increase in Heatwaves under 1.5 and 2.0 °C Warming Levels Will Increase the Socio-Economic Exposure across China by the Late 21st Century

The impending challenge posed by escalating heatwave events due to projected global warming scenarios of 1.5 and 2.0 °C underscores the critical need for a comprehensive understanding of their impact on human health and socio-economic realms. This study delves into the anticipated implications of elevated global temperatures, specifically the 1.5 and 2.0 °C warming scenarios under the SSP2-4.5 and SSP5-8.5 pathways, on population and GDP exposure to heatwaves in China. We also evaluated the aggregated impacts of climate, population, and GDP and their interactions on future socio-economic exposure across China. We leveraged data sourced from the climatic output of Coupled Model Intercomparison Project Phase 6 (CMIP6) for heatwave analysis and integrated population and GDP projections under divergent Shared Socio-economic Pathways (SSPs), including SSP2-4.5 (low emission) and SSP5-8.5 (high-emission). Results indicate a drastic surge in the number of heatwave days under both warming scenarios, particularly in regions like Xinjiang (XJ), North China (NC), and South China (SC) subregions, with a notable disparity in the elevation of heatwave days among different levels. There is an alarming surge in population exposure, escalating approximately 7.94–8.70 times under the 1.5 °C warming scenario and markedly increasing by 14.48–14.75 times by the 2100s relative to the baseline (1985–2014) under the more extreme 2.0 °C warming level. Likewise, the study unveils a substantial elevation in GDP exposure, ranging from 40.65 to 47.21 times under the 1.5 °C warming level and surging dramatically by 110.85–113.99 times under the 2.0 °C warming level. Further analyses disclose that the climate effect predominantly influences changes in population exposure, constituting 72.55–79.10% of the total change. Meanwhile, the interaction effect notably shapes GDP exposure alterations, contributing 77.70–85.99% to the total change. The comprehensive investigation into alterations in population and GDP exposure under varying warming scenarios, coupled with the quantification of each contributing factor, holds paramount importance in mitigating the detrimental repercussions of heatwaves on both human life and socio-economic landscapes.