Greenhouse Gas Emission Scenarios Research Articles

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.

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.

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.

Poleward migration of tropical corals inhibited by future trends of seawater temperature and calcium carbonate (CaCO3) saturation

Poleward range expansion of marine organisms is commonly attributed to anthropogenic ocean warming. However, the extent to which a single species can migrate poleward remains unclear. In this study, we used molecular data to examine the current distribution of the Pocillopora damicornis species complex in Taiwan waters and applied niche modeling to predict its potential range through the end of the 21st Century. The P. damicornis species complex is widespread across shallow, tropical and subtropical waters of the Indo-Pacific regions. Our results revealed that populations from subtropical nonreefal coral communities are P. damicornis, whose native geographical ranges are approximately between 23°N and 35°N. In contrast, those from tropical reefs are P. acuta. Our analysis of 50 environmental data layers demonstrated that the concentrations of CaCO3 polymorphs had the greatest contributions to the distributions of the two species. Future projections under intermediate shared socioeconomic pathways (SSP) 2–4.5 and very high (SSP5–8.5) scenarios of greenhouse gas emissions showed that while sea surface temperature (SST) isotherms would shift northwards, saturation isolines of two CaCO3 polymorphs, calcite (Ωcal) and aragonite (Ωarag), would shift southwards by 2100. Subsequent predictions of future suitable habitats under those conditions indicated that distinct delimitation of geographical ranges for the two species would persist, and neither would extend beyond its native geographical zones, indicating that tropical Taiwan waters are the northern limit for P. acuta. In contrast, subtropical waters are the southern limit for P. damicornis. We concluded that the decline in CaCO3 saturation would make high latitudes less inhabitable, which could be one of the boundary elements that limit poleward range expansion driven by rising SSTs and preserve the latitudinal diversity gradient (LDG) on Earth. Consequently, poleward migration of tropical reef corals to cope with warming oceans should be reevaluated.

Climate indices as predictors of global soil organic carbon stocks

ABSTRACT Global soils store more carbon than the atmosphere and terrestrial vegetation combined, with a significant proportion located in colder regions. Earth system models incorporating climate-carbon feedback suggest that a warming climate can potentially destabilize soil carbon storage, leading to carbon release into the atmosphere. However, existing models are based on limited measurements of soil organic carbon (SOC) loss and a comprehensive global-scale climate indices that effectively characterizes climate-SOC relationships is currently lacking. In this study, we present a synthetic analysis that evaluates the effectiveness of different climate indices in estimating SOC stocks using a global compilation of SOC data and the Boltzmann Sigmoidal Model (BSM). Our findings reveal that a climate index, defined as TD - Index = exp ( − 0.002 T − 0.8 D ) , where T and D are mean century temperature (MCT) and dryness respectively, serves as the most reliable predictor for SOC stocks. Furthermore, we observed temperature tipping points for SOC, ranging from −4.5 to −3°C for different soil layers. As the temperature transitions from being below to above the tipping point, the SOC shifts from a stable, high state to a rapid decline. An analysis of the projected temperatures for SOC under various future greenhouse gas emissions scenarios showed a northward shift in the northern hemisphere, potentially opening up vast areas of arctic territory to increased SOC loss from the soils, with corresponding emissions of the stored carbon into the atmosphere. Our findings open up new avenues for research on and management strategies for climate-related SOC dynamics.

Present day and future urban cooling enabled by integrated water management

The process of urbanisation has increased public health risks due to urban heat, risks that will be further exacerbated in future decades by climate change. However, the growing adoption of integrated water management (IWM) practices (coordinated stormwater management of water, land, and resources) provides an opportunity to support urban heat amelioration through water supply provision and irrigated and vegetated infrastructure that can provide cooling benefits. This study examines the thermal impacts of future implementations of IWM for nine Australian cities based on a review of Government policy documents in the present and over two future time frames (2030 and 2050) under different greenhouse gas emission scenarios (SSPs 1.2-6, 3.7-0 and 5.8-5). Statistical analysis of the future climate data using historical data shows that future warming is nuanced, with changes variable in both time and place, and with extremes becoming more pronounced in future. We have developed a unique approach to morph the future climate projections onto historical data (derived from the ERA5 Reanalysis product) for the 2010-2020 period. Additionally, we use locally appropriate Local Climate Zones (LCZs) for Australian cities, resulting from a holistic and global approach that is widely adopted by the urban climate modelling community. We developed scenarios for business-as-usual as well as implementation of moderate and high levels of IWM across each of the Australian LCZs and modelled them using TARGET (The Air temperature Response to Green infrastructure Evaluation Tool). Results generated at the LCZ level are aggregated to Australian statistical areas (SA4, the largest sub-city area) and city-wide levels. The thermal impacts associated with the various degrees of IWM were marked and geographically differentiated, depending on the climatic characteristics of the various cities. For the current climate, high IWM intervention provided reductions in annual mean daily maximum temperature ranging from -0.77°C in Darwin, up to -1.86°C in Perth. Generally, the drier southern cities of Sydney, Canberra, Albury, Melbourne, Adelaide, and Perth produced the greatest thermal response to implementation of IWM and the more tropical cities with higher rainfalls the least response. For some southern cities cooling was > -3.0°C at the time of maximum summer temperatures. Interestingly high levels of IWM in winter produced modest warming of minimum overnight temperatures, especially for the cooler southern cities. The cooling benefits of IWM were seen across all future climate scenarios and are a real opportunity to offset-projected temperature increases resulting from climate change.

The great urban shift: Climate change is predicted to drive mass species turnover in cities.

Human experiences with nature are important for our culture, economy, and health. Anthropogenically-driven climate change is causing widespread shifts in biodiversity and resident urban wildlife are no exception. We modelled over 2,000 animal species to predict how climate change will impact terrestrial wildlife within 60 Canadian and American cities. We found evidence of an impending great urban shift where thousands of species will disappear across the selected cities, being replaced by new species, or not replaced at all. Effects were largely species-specific, with the most negatively impacted taxa being amphibians, canines, and loons. These predicted shifts were consistent across scenarios of greenhouse gas emissions, but our results show that the severity of change will be defined by our action or inaction to mitigate climate change. An impending massive shift in urban wildlife will impact the cultural experiences of human residents, the delivery of ecosystem services, and our relationship with nature.

Ecological niche shift and suitable area expansion of a globally invasive species Phthorimaea operculella.

Phthorimaea operculella is a major potato pest of global importance, early warning and detection of which are of significance. In this study, we analyzed the climate niche conservation of P. operculella during its invasion by comparing the overall climate niche from three dimensions, including the differences between native range (South America) and entire invaded region (excluding South America), the differences bwtween native range (South America) and five invaded continents (North America, Oceania, Asia, Africa, and Europe), as well as the differences between native region (South America) and an invaded region (China). We constructed ecological niche models for its native range (South America) and invaded region (China). The results showed that the climatic niche of the pest has expanded to varying degrees in different regions, indicating that the pest could well adapt to new environments during the invasion. Almost all areas of South America are suitable for P. operculella. In China, its suitable area is mainly concentrated in Shandong, Hebei, Tianjin, Beijing, Henan, Hubei, Yunnan, Guizhou, Sichuan, Hainan, northern Guangxi, southern Hunan, Anhui, Guangdong, Jiangsu, southern Shanxi, and southern Shaanxi. With increasing greenhouse gas emissions and global temperature, its suitable area will decrease at low latitude and increase gradually at high latitude. Specifically, the northern boundary will extend to Liaoning, Jilin, and the southeastern region of Inner Mongolia, while the western boundary extends to Sichuan and the southeast Qinghai-Tibet Plateau. The suitable area in the southeast Yunnan-Guizhou Plateau, Hainan Island, and the south of Yangtze River, will gradually decrease. The total suitable habitat area for P. operculella in China is projected to increase under future climate condition. From 2081 to 2100, under the three greenhouse gas emissions scenarios of ssp126, ssp370, and ssp585, the suitable area is expected to increase by 27.78, 165.54, and 140.41 hm2, respectively. Therefore, it is crucial to strengtehen vigilance and implement strict measures to prevent the further expansion of P. operculella.

Forecasting changes in precipitation and temperatures of a regional watershed in Northern Iraq using LARS-WG model

Abstract Regions characterized by an arid or semi-arid climate are highly susceptible to prospective climate change impacts worldwide. Therefore, evaluating the effects of global warming on water availability in such regions must be accurately addressed to identify the optimal operation policy of water management facilities. This study used the weather generator model LARS-WG6.0 to forecast possible variations in precipitation and temperature of the Mosul Dam Reservoir in northern Iraq. Future climate change was predicted using three greenhouse gas emission scenarios (i.e., RCP2.6, RCP4.5, and RCP8.5) for four time intervals (2021–2040, 2041–2060, 2061–2080, and 2081–2100) using five Global climate models (GCMs): CSIRO-Mk3.6.0, HadGEM2-ES, CanESM2, BCC-CSM1-1, and NorESM1-M. The model’s calibration and validation were conducted using data from 2001 to 2020 from eight meteorological stations in the study area. The results showed that the weather generator model’s performance was outstanding in predicting daily climate variables. The results also showed that the highest increase in maximum and minimum temperatures was 5.70°C in July and 5.30°C in September, respectively, for the future period 2081–2100 under RCP8.5. The highly forecasted minimum and maximum temperatures were extracted from the CanESM2 and HadGEM2-ES GCM models. It was demonstrated that the study region would experience different patterns of precipitation change during the wet seasons in the evaluated periods. Finally, the variations in precipitation and temperatures in the Mosul dam region would significantly impact the amount of freshwater obtained in these areas due to rising loss rates of evaporation. This could lead to a water shortage and mismanagement of the sustainable operations of the dam.

Historical trends and future projections of annual rainfall from CMIP6 models in Ho Chi Minh City, Vietnam

Climate risks have posed a major threat to many local communities living in low-lying coastal megacities across the globe, including Ho Chi Minh City, Vietnam. Hence, this study first aimed to contribute towards a comprehensive understanding of temporal trend patterns of annual rainfall and absolute extremes in Ho Chi Minh City over the last 4 decades (1980-2022) through multiple non-parametric statistical trend tests. We employed the quantile delta mapping (QDM) method to develop daily bias-corrected rainfall data based on the outputs in the latest Coupled Model Intercomparison Project phase 6 (CMIP6) under 8 shared socio-economic pathway (SSP) greenhouse gas emission scenarios. Evaluation of model performance was implemented by repeatedly omitting 5 successive years in turn for estimating testing errors. The outcomes indicate the high applicability of well-calibrated transfer functions, even for high quantiles, to the production of future rainfall scenarios. The projected changes in annual rainfall and absolute extremes were obtained by estimating multi-model medians from CMIP6 models for future periods (i.e. 2021-2040, 2041-2060, 2061-2080, and 2081-2100), with reference to the base period (1995-2014). In general, annual rainfall in Ho Chi Minh City is projected to increase substantially, and Thu Duc station consistently shows the highest increases in annual rainfall. Projected changes are approximately 30.9% (8.3 to 77.8%) under the high-end scenario (i.e. SSP5-8.5) by the end of the 21st century. It is expected that these findings will yield several solid arguments for mitigating climate-related risks in Ho Chi Minh City.

An abrupt vegetation change on south-central Yunnan Plateau (Southwest China) during the last deglaciation

The magnitude and pace of anthropogenic climate change are increasing the likelihood of abrupt changes in terrestrial ecosystems worldwide. The Last Deglacial Transition (LDT) witnessed global and regional climate change and caused vegetation change globally. The recognition and attribution of deglacial abrupt vegetation conversion would certainly enhance our understanding on the survivability and resilience of ecosystems to climate change. Here, we conducted statistical analyses for a pollen record from Yilong Lake, southwestern China, focusing on the period of the LDT. Biome changes since the Last Glacial Maximum were reconstructed numerically by using a standard approach of pollen-plant functional type-biome assignment (biomization). Principal component analysis and structure change test were applied on pollen composition and diversity data. Modern vegetation and climate relationship was analyzed to discuss climatic conditions for the vegetation conversion in Yilong region during the LDT. Our results revealed a vegetation conversion occurring around 14.3 cal kyr BP, characterized by abrupt shifts in pollen compositions and biomes and also reduction in diversity indices. We suggested that the cumulative pressure of climate warming during the LDT was probably the critical climatic variable triggering the vegetation conversion in Yilong region, while precipitation played an important role in regulating the proportions or coverages of different vegetation types after the development of evergreen broadleaved forest. By the end of tthis century, climate under intermediate to very high greenhouse gas emission scenarios were projected to reach the thresholds for vegetation transitions from evergreen broadleaved forest to (monsoon) rainforest, or to Savanna-like vegetation. However, it is still a challenge to forecast accurately when or on what spatial scale vegetation change will happen because vegetation responses to climate change are complicated and the topography and habitats on Yunnan Plateau are extremely diverse.

Mapping current and future flood exposure using a 5 m flood model and climate change projections

Abstract. Local stakeholders need information about areas exposed to potential flooding to manage increasing disaster risk. Moderate- and large-scale flood hazard mapping is often produced at a low spatial resolution, typically using only one source of flooding (e.g., riverine), and it often fails to include climate change. This article assesses flood hazard exposure in the city of Vancouver, Canada, using flood mapping produced by flood risk science experts JBA Risk Management, which represented baseline exposure at 5 m spatial resolution and incorporated climate-change-adjusted values based on different greenhouse gas emission scenarios. The article identifies areas of both current and future flood exposure in the built environment, differentiating between sources of flooding (fluvial, pluvial, storm surge) and climate change scenarios. The case study demonstrates the utility of a flood model with a moderate resolution for informing planning, policy development, and public education. Without recent engineered or regulatory mapping available in all areas across Canada, this model provides a mechanism for identifying possible present and future flood risk at a higher resolution than is available at a Canada-wide coverage.