Future Climate Uncertainty Research Articles

ISMIP6-based Antarctic projections to 2100: simulations with the BISICLES ice sheet model

Abstract. The contribution of the Antarctic Ice Sheet is one of the most uncertain components of sea level rise to 2100. Ice sheet models are the primary tool for projecting future sea level contribution from continental ice sheets. The Ice Sheet Model Intercomparison for the Coupled Model Intercomparison Phase 6 (ISMIP6) provided projections of the ice sheet contribution to sea level over the 21st century, quantifying uncertainty due to ice sheet model, climate model, emission scenario, and uncertain parameters. We present simulations following the ISMIP6 framework with the BISICLES ice sheet model and new experiments extending the ISMIP6 protocol to more comprehensively sample uncertainties in future climate, ice shelf sensitivity to ocean melting, and their interactions. These results contributed to the land ice projections of Edwards et al. (2021), which formed the basis of sea level projections for the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (AR6). Our experiments show the important interplay between surface mass balance processes and ocean-driven melt in determining Antarctic sea level contribution. Under higher-warming scenarios, high accumulation offsets more ocean-driven mass loss when sensitivity to ocean-driven melt is low. Conversely, we show that when sensitivity to ocean warming is high, ocean melting drives increased mass loss despite high accumulation. Overall, we simulate a sea level contribution range across our experiments from 2 to 178 mm. Finally, we show that collapse of ice shelves due to surface warming increases sea level contribution by 25 mm relative to the no-collapse experiments, for both moderate and high sensitivity of ice shelf melting to ocean forcing.

Scalable, data-assimilated models predict large-scale shoreline response to waves and sea-level rise

Coastal change is a complex combination of multi-scale processes (e.g., wave-driven cross-shore and longshore transport; dune, bluff, and cliff erosion; overwash; fluvial and inlet sediment supply; and sea-level-driven recession). Historical sea-level-driven coastal recession on open ocean coasts is often outpaced by wave-driven change. However, future sea-level-driven coastal recession is expected to increase significantly in tandem with accelerating rates of global sea-level rise. Few models of coastal sediment transport can resolve the multitude of coastal-change processes at a given beach, and fewer still are computationally efficient enough to achieve large-scale, long-term simulations, while accounting for historical behavior and uncertainties in future climate. Here, we show that a scalable, data-assimilated shoreline-change model can achieve realistic simulations of long-term coastal change and uncertainty across large coastal regions. As part of the modeling case study of the U.S. South Atlantic Coast (Miami, Florida to Delaware Bay) presented here, we apply historical, satellite-derived observations of shoreline position combined with daily hindcasted and projected wave and sea-level conditions to estimate long-term coastal change by 2100. We find that 63 to 94% of the shorelines on the U.S. South Atlantic Coast are projected to retreat past the present-day extent of sandy beach under 1.0 to 2.0 m of sea-level rise, respectively, without large-scale interventions.

Response of drought to climate extremes in a semi-arid inland river basin in China

Against the backdrop of global warming, climate extremes and drought events have become more severe, especially in arid and semi-arid areas. This study forecasted the characteristics of climate extremes in the Xilin River Basin (a semi-arid inland river basin) of China for the period of 2021–2100 by employing a multi-model ensemble approach based on three climate Shared Socioeconomic Pathway (SSP) scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5) from the latest Coupled Model Intercomparison Project Phase 6 (CMIP6). Furthermore, a linear regression, a wavelet analysis, and the correlation analysis were conducted to explore the response of climate extremes to the Standardized Precipitation Evapotranspiration Index (SPEI) and Streamflow Drought Index (SDI), as well as their respective trends during the historical period from 1970 to 2020 and during the future period from 2021 to 2070. The results indicated that extreme high temperatures and extreme precipitation will further intensify under the higher forcing scenarios (SSP5-8.5>SSP2-4.5>SSP1-2.6) in the future. The SPEI trends under the SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios were estimated as −0.003/a, −0.004/a, and −0.008/a, respectively, indicating a drier future climate. During the historical period (1970–2020), the SPEI and SDI trends were −0.003/a and −0.016/a, respectively, with significant cycles of 15 and 22 a, and abrupt changes occurring in 1995 and 1996, respectively. The next abrupt change in the SPEI was projected to occur in the 2040s. The SPEI had a significant positive correlation with both summer days (SU) and heavy precipitation days (R10mm), while the SDI was only significantly positively correlated with R10mm. Additionally, the SPEI and SDI exhibited a strong and consistent positive correlation at a cycle of 4–6 a, indicating a robust interdependence between the two indices. These findings have important implications for policy makers, enabling them to improve water resource management of inland river basins in arid and semi-arid areas under future climate uncertainty.

Google Earth Engine application for mapping and monitoring drought patterns and trends: A case study in Arkansas, USA

Drought is a prolonged dry period that can have severe impacts on the environment, human health, economies, agriculture, and energy resources. It can lead to water shortages, ruin crops, dry out forests, and reduce the availability of food and water for wildlife and livestock. The primary objectives of this study are to: 1) quantify the variability and distributions of drought patterns in Arkansas, United States (US), 2) use remotely sensed indices to investigate the correlation between drought and vegetation cover in the area, and 3) develop a cloud-based framework (user-friendly app) to facilitate the assessment of drought impact in Arkansas over the past decades. A correlation analysis was also performed between the Vegetation Health Index (VHI) and meteorological indices to better understand the impact of meteorological drought on vegetation stress. In addition, Mann-Kendall trend analysis was used to assess trends in meteorological drought indices. The results indicate that drought is most prevalent during March and August months. The results of this study revealed that approximately 31% of the study area fell under the four drought classes (i.e., 1% Extreme drought, 4% Severe drought, 9% moderate drought, and 19% mild drought), with spring and the growing season experiencing moderate drought, particularly in agricultural areas, most notably within the Mississippi Alluvial Valley Plain at both state and county levels. In August, approximately 31% of the study area fell under the Four drought classes (i.e., 1% Extreme drought, 4% Severe drought, 9% moderate drought, and 19% mild drought), with spring and the growing season experiencing moderate drought, particularly in agricultural areas, most notably within the Mississippi Alluvial Valley Plain at both state and county levels. This study provides an essential foundation for policymakers, environmental scientists, and agricultural stakeholders aiming to mitigate drought impacts and safeguard against future climate uncertainties.

Climate-adaptative management strategies for soybean production under ENSO scenarios in Southern Brazil: An in-silico analysis of crop failure risk

CONTEXTSoybeans (Glycine max L.) are a crucial crop for global food security, and the state of Rio Grande do Sul (RS), Brazil, plays a significant role. However, climate instability, particularly water stress (WS), is a major concern in this region, causing large interannual yield variability. OBJECTIVEThis study aims to address this issue using an in-silico approach to: i) characterize WS and spatial patterns and their frequency within ENSO events; and ii) explore climate-adaptative management strategies such as planting dates and maturity groups to mitigate the risk of crop failure and maximize seed yield and profits. METHODSCrop growth simulations were performed testing three soybean maturity groups (MG, 5.0, 5.8, and 6.4) and eight planting dates (from October 5th to January 20th) over 30 years at 187 locations in RS, Brazil, using APSIM Next Generation. Failure risk was calculated as the percentage of simulations that yielded less than the economic break-even soybean yield in a given scenario. RESULTS AND CONCLUSIONSThe simulated yields were clustered into four regions: Northeast, North, Central, and Southwest. Four WS seasonal patterns were then defined (no stress, early stress, late stress, and whole season stress). On average, WS reduced yields up to 2 Mg ha-1 (∼50 % relative to the maximum). WS varied among regions, with the SW experiencing more frequent and severe stress (up to 50 % of whole season stress during La Nina). ENSO events influenced WS frequency, with El Niño events associated with reduced stress and La Niña events to increased stress. The MG 5.0 resulted in a higher probability of failure risk in all regions. Early planting dates resulted in the highest yield variability (up to 5 Mg ha-1). Climate-adaptative management strategies, such as optimizing planting dates and maturity groups, resulted in a 15 % reduction in crop failure. SIGNIFICANCEOur findings provide valuable insights for developing targeted approaches to enhance soybean yield stability, thereby increasing the resilience of agriculture in the face of future climate uncertainties.

Future Climate Projections and Uncertainty Evaluations for Frost Decay Exposure Index in Norway

To implement the geographical and future climate adaptation of building moisture design for building projects, practitioners need efficient tools, such as precalculated climate indices to assess climate loads. Among them, the Frost Decay Exposure Index (FDEI) describes the risk of freezing damage for clay bricks in facades. Previously, the FDEI has been calculated for 12 locations in Norway using 1961–1990 measurements. The purpose of this study is both updating the FDEI values with new climate data and future scenarios and assessing how such indices may be suitable as a climate adaptation tool in building moisture safety design. The validity of FDEI as an expression of frost decay potential is outside the scope of this study. Historical data from the last normal period as well as future estimated climate data based on 10 different climate models forced by two emission scenarios (representative concentration pathways 4.5 and 8.5) have been analyzed. The results indicate an overall decline in FDEI values on average, due to increased winter temperatures leading to fewer freezing events. Further, the variability between climate models and scenarios necessitates explicit uncertainty evaluations, as single climate model calculations may result in misleading conclusions due to high variability between models.

Hygrothermal resilience of typical lightweight steel-framed wall assemblies in hot-humid areas under future climate uncertainty

Ensuring the hygrothermal performance of lightweight steel-framed (LSF) wall assemblies is critical for the sustainability of LSF buildings in hot-humid regions; however, climate change poses new challenges. Therefore, this study aimed to bridge the gap in understanding the characteristics of hygrothermal load changes and the hygrothermal resilience of different LSF wall assemblies. Future climate data for each decade of this century (from 2030 to 2090), under various carbon emission scenarios, were generated based on a typical meteorological year (TMY) for hot-humid areas to evaluate the impact of climate change on hygrothermal loads. The hygrothermal resilience of four typical LSF wall assemblies, with either ventilated rainscreen cladding or face-sealed stucco cladding, with or without external insulation, was evaluated through simulation and comparative analysis. The results indicated that the thermal load would increase with increasing carbon emissions and over time, with the annual average external temperature peaking at 25.8 °C by the end of this century. The frequency of months with a high or critical moisture load would increase, although the moisture load may fluctuate across scenarios and years. Consequently, the cooling and dehumidification loads may increase by 52–60 % and 40–88 %, respectively. The hygrothermal risk within the walls may increase, with maximum increases of 2.8–8.0 % in the annual average relative humidity, 2.7–3.0 °C in the annual average temperature, and 10–35 % in the wetness duration. However, the impacts could be reduced by using ventilated rainscreens and external insulation, with further precautions needed to mitigate the increasing hygrothermal risk in areas with thermal bridges and rainwater leakage, as well as at the outer interface of the external insulation. These findings could provide a reference for the design of LSF buildings adapted to climate change in hot-humid areas.

Forecasting daily total pollen concentrations on a global scale.

There is evidence that global anthropogenic climate change may be impacting floral phenology and the temporal and spatial characteristics of aero-allergenic pollen. Given the extent of current and future climate uncertainty, there is a need to strengthen predictive pollen forecasts. The study aims to use CatBoost (CB) and deep learning (DL) models for predicting the daily total pollen concentration up to 14 days in advance for 23 cities, covering all five continents. The model includes the projected environmental parameters, recent concentrations (1, 2 and 4 weeks), and the past environmental explanatory variables, and their future values. The best pollen forecasts include Mexico City (R2(DL_7) ≈ .7), and Santiago (R2(DL_7) ≈ .8) for the 7th forecast day, respectively; while the weakest pollen forecasts are made for Brisbane (R2(DL_7) ≈ .4) and Seoul (R2(DL_7) ≈ .1) for the 7th forecast day. The global order of the five most important environmental variables in determining the daily total pollen concentrations is, in decreasing order: the past daily total pollen concentration, future 2 m temperature, past 2 m temperature, past soil temperature in 28-100 cm depth, and past soil temperature in 0-7 cm depth. City-related clusters of the most similar distribution of feature importance values of the environmental variables only slightly change on consecutive forecast days for Caxias do Sul, Cape Town, Brisbane, and Mexico City, while they often change for Sydney, Santiago, and Busan. This new knowledge of the ecological relationships of the most remarkable variables importance for pollen forecast models according to clusters, cities and forecast days is important for developing and improving the accuracy of airborne pollen forecasts.

Cloud Responses to Abrupt Solar and CO2 Forcing: 1. Temperature Mediated Cloud Feedbacks

AbstractThere are many uncertainties in future climate, including how the Earth may react to different types of radiative forcing, such as CO2, aerosols, and even geoengineered changes in the amount of sunlight absorbed by Earth's surface. Here, we analyze model simulations where the climate system is subjected to an abrupt change of the solar constant by ±4%, and where the atmospheric CO2 concentration is abruptly changed to quadruple and half its preindustrial value. Using these experiments, we examine how clouds respond to changes in solar forcing, compared to CO2, and feedback on global surface temperature. The total cloud response can be decomposed into those responses driven by changes in global surface temperature, called the temperature mediated cloud feedbacks, and responses driven directly by the forcing that are independent of the global surface temperature. In this paper, we study the temperature mediated cloud changes to answer two primary questions: (a) How do temperature mediated cloud feedbacks differ in response to abrupt changes in CO2 and solar forcing? And (b) Are there symmetrical (equal and opposite) temperature mediated cloud feedbacks during global warming and global cooling? We find that temperature mediated cloud feedbacks are similar in response to increasing solar and increasing CO2 forcing, and we provide a short review of recent literature regarding the physical mechanisms responsible for these feedbacks. We also find that cloud responses to warming and cooling are not symmetric, due largely to non‐linearity introduced by phase changes in mid‐to‐high latitude low clouds and sea ice loss/formation.

Leveraging climate and remote sensing metrics for predicting forest carbon stock using Bayesian geostatistical modelling under a projected climate warming in Zimbabwe

Climate change, driven by escalating carbon dioxide (CO2) emissions, poses a significant threat to forest ecosystems and the livelihoods of communities reliant on them, especially for the global south countries and regions like the eastern highlands of Zimbabwe. The 2000 land redistribution programme reduced buffer zones between ecologically sensitive forests and land reform beneficiaries near major carbon reservoirs. In light of these challenges, this study aimed to assess the potential effects of climate change on a strategically important plantation forest ecosystem in Zimbabwe's eastern highlands. Using data from the Coupled Model Inter-comparison Project Phase 6 (CMIP6) of the Intergovernmental Panel on Climate Change (IPCC), we modelled and predicted changes in forest carbon (C) stock density under different climate scenarios: current (1970–2000), SSP5–4.5, and SSP5–8.5. Employing a hierarchical Bayesian geostatistical approach, we compared the baseline scenario (1970–2000) with projected scenarios (RCP4.5 and RCP8.5) for 2075 to estimate changes in forest carbon stock distribution. Our results indicated a decline in carbon stock concentration under future climate scenarios, reflecting the adverse impact of greenhouse gas emissions on forest growth. We found that the projected range of forest carbon stock under the RCP8.5 scenario for 2075 is notably lower (2≤MgCha−1≤44.9) than that of the baseline period (1970–2000) (1≤MgCha−1≤97), suggesting a substantial reduction in carbon storage. As the difference in posterior mean C stock (μ̅1−μ̅2), 52.1 MgCha-1 is well above zero, we deduce that the posterior mean C stock distribution of the projected future RCP8.5 2075 climate projection is indeed credibly different from the current (1970–2000) climate scenario. Additionally, there is a high probability (>90%) that forest plantations will be adversely affected by the business-as-usual climate warming projection. Overall, our findings highlight the urgent need for climate change mitigation strategies, such as reforestation programs and careful selection of tree species for plantations, to safeguard forest ecosystems and the communities dependent on them. These insights are crucial for informing effective adaptation measures in the face of future climate uncertainties.

Business strategies to counter climate change risks to long lived production assets

The business community is concerned about how to account for long-lived production assets in an era of climate change. Globally, accounting standards and guidance are emerging but these are yet to provide the level of clarity required by the business and accounting profession on the uncertainty of future climate and how this can compromise some long-lived assets. Very few organisations have the necessary expertise and data to integrate climate change scenarios into their long-lived production asset accounting. This poses an additional risk to business and society. We explore different approaches for including risk and uncertainty in business accounting and reporting. Seven desk-based case studies of emerging practice in the food sector demonstrate how these varied climate risk and uncertainty models for infrastructure are being deployed around the world. Through review of data from company annual reports, documents and websites from 2021 to 2023, we find that specific climate-impacted long-lived asset accounting is relatively rare, notwithstanding that risk and uncertainty models are in effective use in other emerging areas. We provide recommendations on the development of data and modelling frameworks for climate-related financial data and an improved accounting discipline for climate-related financial impacts.

Rainfall reliability and maize production in the Bamenda Highlands of Cameroon

The long-term average rainfall for a given period (month, season or year) scarcely indicates reliability because rainfall in low latitudes varies significantly from one year to the other. The less variable rainfall is, the more reliable it is, as the index of variability measures the likelihood of repetition in the mean amount of rainfall. This study bridges some gaps in related studies in the Bamenda Highlands, such as a study assessing climate change impacts on food security, where the Rainfall Anomaly Index was used and another study on the impact of rainfall on maize production using the Standardized Precipitation Index. The objectives of this study are to assess rainfall reliability and establish the impact of rainfall reliability on maize production. Rainfall data were collected from 15 stations, while maize output was collected for four localities. Results revealed that rainfall is still reliable for 13 stations, with a coefficient of variations of 9.62 to 18.54 %, while Ndop and Ndawara recorded unreliable rainfall of 23.14 % and 30.97 % respectively. Rainfall reliability was complemented by the Standardized Precipitation Index, which showed that only 53.45 % of rainfall episodes were normal to sustain maize production, while 46.55 % were events of rainfall deficits. Maize production has been decreasing in Ndu, Oku and Nkum while increasing in Ndop. These findings reflect the realities of other tropical mountainous regions in the world. Faced with future climatic uncertainties, farmers should embrace agroecological practices, climate-smart agriculture, conservation agriculture, Nature-based Solutions, Ecosystem-based Adaptation and diversification of production systems and livelihood sources to ensure food security.

An ensemble-based approach for pumping optimization in an island aquifer considering parameter, observation and climate uncertainty

Abstract. In coastal zones, a major objective of groundwater management is often to determine sustainable pumping rates which avoid well salinization. Understanding how model and climate uncertainties affect optimal management solutions is essential for providing groundwater managers with information about salinization risk and is facilitated by the use of optimization under uncertainty (OUU) methods. However, guidelines are missing for the widespread implementation of OUU in real-world coastal aquifers and for the incorporation of climate uncertainty into OUU approaches. An ensemble-based OUU approach was developed considering parameter, observation and climate uncertainty and was implemented in a real-world island aquifer in the Magdalen Islands (Quebec, Canada). A sharp-interface seawater intrusion model was developed using MODFLOW-SWI2 and a prior parameter ensemble was generated containing multiple equally plausible realizations. Ensemble-based history matching was conducted using an iterative ensemble smoother which yielded a posterior parameter ensemble conveying both parameter and observation uncertainty. Sea level and recharge ensembles were generated for the year 2050 and were then used to generate a predictive parameter ensemble conveying parameter, observation and climate uncertainty. Multi-objective OUU was then conducted, aiming to both maximize pumping rates and minimize the probability of well salinization. As a result, the optimal trade-off between pumping and the probability of salinization was quantified considering parameter, historical observation and future climate uncertainty simultaneously. The multi-objective, ensemble-based OUU led to optimal pumping rates that were very different from a previous deterministic OUU and close to the current and projected water demand for risk-averse stances. Incorporating climate uncertainty into the OUU was also critical since it reduced the maximum allowable pumping rates for users with a risk-averse stance. The workflow used tools adapted to very high-dimensional, nonlinear models and optimization problems to facilitate its implementation in a wide range of real-world settings.

Examining current bias and future projection consistency of globally downscaled climate projections commonly used in climate impact studies

The associated uncertainties of future climate projections are one of the biggest obstacles to overcome in studies exploring the potential regional impacts of future climate shifts. In remote and climatically complex regions, the limited number of available downscaled projections may not provide an accurate representation of the underlying uncertainty in future climate or the possible range of potential scenarios. Consequently, global downscaled projections are now some of the most widely used climate datasets in the world. However, they are rarely examined for representativeness of local climate or the plausibility of their projected changes. Here we explore the utility of two such global datasets (CHELSA and WorldClim2) in providing plausible future climate scenarios for regional climate change impact studies. Our analysis was based on three steps: (1) standardizing a baseline period to compare available global downscaled projections with regional observation-based datasets and regional downscaled datasets; (2) bias correcting projections using a single observation-based baseline; and (3) having controlled differences in baselines between datasets, exploring the patterns and magnitude of projected climate shifts from these datasets to determine their plausibility as future climate scenarios, using Hawaiʻi as an example region. Focusing on mean annual temperature and precipitation, we show projected climate shifts from these commonly used global datasets not only may vary significantly from one another but may also fall well outside the range of future scenarios derived from regional downscaling efforts. As species distribution models are commonly created from these datasets, we further illustrate how a substantial portion of variability in future species distribution shifts can arise from the choice of global dataset used. Hence, projected shifts between baseline and future scenarios from these global downscaled projections warrant careful evaluation before use in climate impact studies, something rarely done in the existing literature.

Improving building resilience in the face of future climate uncertainty: A comprehensive framework for enhancing building life cycle performance

This research introduces an innovative resilient design framework, addressing gaps in building performance optimization by considering a holistic life cycle perspective and factoring in climate projection uncertainties from the 2020s to 2090s. The study concludes that in the case study, future climate scenarios in different Shared Socio-Economic Paths significantly impact building life cycle performance, with wall U-value, windows U-value, and wall density identified as major factors affecting building performance under future climate uncertainty. The ensemble learning model achieves high fidelity in predicting life cycle carbon emissions (LCCE), life cycle cost (LCC), and indoor discomfort hours (IDH) through input feature screening and hyperparameter optimization. Comparing optimization algorithms, Two-Archive Evolutionary Algorithm for Constrained multi-objective optimization (C-TAEA) demonstrates better convergence degree and optimization effect. Among the tested multi-criteria decision making methods, the VIKOR method selects the best building resilient design scheme from the Pareto data set, effectively reducing LCCE, LCC, and IDH, and leading to an overall improvement in building life cycle performance. Applying this framework, the finalized scheme for an office building in cold region optimizes 44.0% of LCCE, 8.2% of LCC, and 4.3% of IDH. This framework develops a new approach to improve building life cycle performance under future climate uncertainty and supports informed decision-making for resilient building design. To promote carbon neutrality in construction, it is urged that future studies incorporate additional sources of uncertainty, such as occupant behavior, into the proposed framework.

Designing climate change dynamic adaptive policy pathways for agricultural water management using a socio-hydrological modeling approach

Deep climate uncertainties create challenges to long-term planning in the management of complicated water resources and agricultural systems. The use of an innovative decision-making approach under deep climate uncertainties is helpful to deal with these challenges in such systems. The research aims to develop an appropriate methodology for designing and evaluating robust adaptable plans in the agricultural sector in the face of deep climate uncertainties which covers hydrological aspects and stakeholders’ preferences and behaviors. For this purpose, Dynamic Adaptive Policy Pathways (DAPP) approach in conjunction with socio-hydrological modeling is utilized to design and evaluate comprehensive robust adaptable plans under future climate uncertainties in the Hablehroud River Basin, Iran. In this context, an agent-based modeling (ABM) approach is linked to the Soil and Water Assessment Tool (SWAT) model to account for the social behavior of various stakeholders as well as the impact of climate change on water resources and agricultural systems and their interactions. The results indicated that encouraging farmers to cooperate in changing cropping patterns and deficit irrigation should be proposed as a main plan in both short- and long-term planning. If the implementation issues are also considered, penalty-based adaptation actions (robustness range of 56–72 %) can be prioritized in the early years to gradually educate farmers about the benefits of cooperative behavior. Therefore, taking into account all issues: 1) sustainability of water resources, 2) agricultural income, 3) climate change impacts, and 4) implementation plans in the time period, it is suggested to impose a penalty on deficit irrigation (56–76 % of robustness) during the early time periods and then shift to changes in cropping patterns through training of farmers (76–82 %). Considering limits to allowable water withdrawal through penalties (56–72 %) can be implemented as another suggested alternative in the early time periods, but imposing penalties on deficit irrigation (62–68 %) should be implemented in the long-term.

Spatio-temporal modelling suggests that some dung beetle species (Coleoptera: Geotrupidae) may respond to global warming by boosting dung removal

In the current framework of changes to the global climate, information on the thermal tolerance of dung beetles is crucial to understand how they might cope with increases in land temperature in terms of survival and ecosystem service provision. In this spatio-temporal modelling study, we investigated the thermal tolerance and effect of temperature changes on dung removal by three dung beetle species (Coleoptera: Geotrupidae) living within the 600–1400 m altitudinal belt in the Italian Alps. We chose large tunneler beetles because of their pivotal role in dung removal and nutrient recycling, important ecosystem services for maintaining the viability and profitability of the Alpine pastoral system. Our study used experimental data on dung removal at different temperatures to predict changes to this ecosystem service in the future considering different climatic scenarios and changes in land use for the specific study area. The results show that the temperature increases incurred between 1981 and 2005 may have boosted rates of spring dung removal across the entire study area (expressed as average dung removal per pair per month), partially compensating for the reduction in grassland extent within pasture-based livestock farming systems. Despite the limitations related to modelling future climate change scenarios and uncertainties deriving from several interacting factors (e.g., the sensitivity of large-bodied species to land-use changes), our results suggest that the predicted increases in temperature over the next 80 years would continue to boost dung removal, revealing a resilience of this service. The increase in dung removal rates, for all three species, is mainly related to the most extreme scenario of carbon emissions and for the months spanning from May to October of the interval 2041–2100. Focusing on large tunnelers and adopting a dynamic approach that considers changes in dung removal over space and time can assist ecosystem service conservation planning.

Effects of species mixture on understory vegetation, soil properties and bacterial diversity of Acacia cincinnata, Eucalyptus robusta and Acacia mangium plantations in Southeastern China

The establishment of mixed forests is gaining increasing attention as a way to optimize forest production, improve ecological benefits, and serve as a safety net for impacts of future climate uncertainties. However, practical knowledge about which species and what proportion of them should be mixed is still lacking. Thus, this study was conducted to identify suitable species for a mixture with Acacia cincinnata. The mixtures tested in this study were A. cincinnata and Eucalyptus robusta (6:4), A. cincinnata and Acacia mangium (3:1), and monoculture plantation of A. cincinnata established in 2014. After seven years of growth, we analyzed the effects of species mixing on tree species growth, understory vegetation, and soil physicochemical properties as well as bacterial community structure and diversity. The results showed that species mixing had no significant effect on the diameter and individual volume of A. cincinnata. However, mixed planting increased the total stocking volume compared to monoculture plantation of A. cincinnata. The species diversity, biomass and nutrient stocks in the understory vegetation were significantly increased by stand mixing. The soil of mixed stand of A. cincinnata and A. mangium had the highest C and N contents, whereas the soil of monoculture A. cincinnata stands had the highest P content. The diversity of soil bacterial community was highest in the mixing stand of A. cincinnata and E. robusta, followed by monoculture A. cincinnata stand and mixed stand of A. cincinnata and A. mangium. The relative abundance of Proteobacteria and Actinobacteria was highest in mixed stand soil of A. cincinnata and E. robusta. Furthermore, the relative abundance of Firmicutes was high in the mixed stand soils of A. cincinnata and A. mangium while the relative abundance of Verrucomicrobia was high in the mixed stand of A. cincinnata and E. robusta stand. In general, the study revealed that the establishment of mixed-species forest enhances the diversity and composition of understory vegetation, soil physicochemical and soils bacterial community; thereby increasing biodiversity, nutrient cycling and carbon sequestration in the biomass and soil. From the viewpoints of forest productivity and ecological benefits, it is advisable to establish a mixed forest of A. cincinnata and A. mangium in southern China. Overall, our work revealed that the sustainability of mixed-species plantations relies on the interactions between soil attributes, vegetation, and bacterial community.

Middle-class risk perception of disasters and land reclamation in Metro Manila, Philippines

The United Nations estimate that by 2030 about half of the world’s population would be comprised of the middle-class, who mostly live in the increasing number of megacities around the world. Southeast Asian megacities, such as Metropolitan Manila, have long been troubled by rapid urbanization, increasing disaster risk, and the looming impacts of climate change. As a response, there is a growing focus on disaster and climate resilient policies in megacities, most of which have only centered on how future disasters and climate uncertainty would impact vulnerable communities. This has resulted in policies that cater towards relocation of the poor to combat disasters and climate change. This exploratory study attempts to elucidate how the middle-class views disasters and land reclamation in Metro Manila, the Philippines. Using an online questionnaire survey of 425 middle-class respondents, the study shows that middle-class perception of risk potentially amplifies vulnerability and reduces the resilience of the poor. While knowledge about the risks is high, the capacity of the middle class to act is low, especially compared to vulnerable communities. Also, climate change and disasters are viewed primarily as environmental issues, which is compounded by inadequate defenses. Land reclamation, along with coastal informal settlements, are viewed as an intrusion into the environment. This study finds that the middle-class’s perception of risk may marginalize the poor by favoring eviction of vulnerable communities in coastal areas, including those targeted for land reclamation, under the pretext of controlling the city’s population growth and environmental impact.

Building Thermal and Energy Performance of Subtropical Terraced Houses under Future Climate Uncertainty

Due to global temperature increases, terraced house (TH) residents face a threat to their health due to poor indoor thermal environments. As buildings are constructed by low-income residents without professional guidance, this study aims to investigate the indoor thermal comfort and energy resilience of THs under the future climate and determine the optimal passive design strategies for construction and retrofitting. By exploring the effects of building envelope structures, adjusting the window-to-wall ratio (WWR) and designing shading devices, EnergyPlus version 22.0 was used to optimize the thermal environment and cooling load of THs throughout their life cycle under future climate uncertainties. Unimproved THs will experience overheating for nearly 90% of the hours in a year and the cooling load will exceed 60,000 kWh by 2100 under the Representative Concentration Pathways (RCP) 8.5 scenario. In contrast, optimization and improvements resulted in a 17.3% reduction in indoor cooling load by increasing shading devices and the WWR, and using building envelope structures with moderate thermal insulation. This study can guide TH design and renovation, significantly reducing indoor cooling load and enabling residents to better use active cooling to combat future overheating environments.