Impacts of future climate change(2030-2059)on debris flow hazard:A case study in the Upper Minjiang River basin,China

An increase in extreme precipitation events due to future climate change will have a decisive influence on the formation of debris flows in earthquake-stricken areas. This paper aimed to describe the possible impacts of future climate change on debris flow hazards in the Upper Minjiang River basin in Northwest Sichuan of China, which was severely affected by the 2008 Wenchuan earthquake. The study area was divided into 1285 catchments, which were used as the basic assessment units for debris flow hazards. Based on the current understanding of the causes of debris flows, a binary logistic regression model was used to screen key factors based on local geologic, geomorphologic, soil,vegetation, and meteorological and climatic conditions. We used the weighted summation method to obtain a composite index for debris flow hazards, based on two weight allocation methods: Relative Degree Analysis and rough set theory. Our results showed that the assessment model using the rough set theory resulted in better accuracy. According to the bias corrected and downscaled daily climate model data, future annual precipitation(2030-2059) in the study area are expected to decrease, with an increasing number of heavy rainfall events. Under future climate change, areas with a high-level of debris flow hazard will be even more dangerous, and 5.9% more of the study area was categorized as having a high-level hazard. Future climate change will cause an increase in debris flow hazard levels for 128 catchments, accounting for 10.5% of the total area. In the coming few decades, attention should be paid not only to traditional areas with high-level of debris flow hazards, but also to those areas with an increased hazard level to improve their resilience to debris flow disasters.

Carbon Dynamics in Woody Biomass of Forest Ecosystem in China with Forest Management Practices under Future Climate Change and Rising CO_2 Concentration

It is critical to study how different forest management practices affect forest carbon sequestration under global climate change regime. Previous researches focused on the stand-level forest carbon sequestration with rare investigation of forest carbon stocks influ- enced by forest management practices and climate change at regional scale. In this study, a general integrative approach was used to simulate spatial and temporal variations of woody biomass and harvested biomass of forest in China during the 21st century under dif- ferent scenarios of climate and CO2 concentration changes and management tasks by coupling Integrated Terrestrial Ecosystem Carbon budget （InTEC） model with Global Forest Model （G4M）. The results showed that forest management practices have more predominant effects on forest stem stocking biomass than climate and CO2 concentration change. Meanwhile, the concurrent future changes in cli- mate and CO2 concentration will enhance the amounts of stem stocking biomass in forests of China by 12%-23% during 2001-2100 relative to that with climate change only. The task for maximizing stem stocking biomass will dramatically enhance the stem stocking biomass from 2001~100, while the task for maximum average increment will result in an increment of stem stocking biomass before 2050 then decline. The difference of woody biomass responding to forest management tasks was owing to the current age structure of forests in China. Meanwhile, the sensitivity of long-term woody biomass to management practices for different forest types （coniferous forest, mixed forest and deciduous forest） under changing climate and CO2 concentration was also analyzed. In addition, longer rotation length under future climate change and rising CO2 concentration scenario will dramatically increase the woody biomass of China during 2001~100. Therefore, our estimation indicated that taking the role of forest management in the carbon cycle into the consideration at regional or national level is very important to project the forest carbon sequestration under future climate change and rising atmospheric CO2 concentration.

conomic losses from reduced freshwater under future climate scenarios: An example from the Urumqi River,Tianshan Mountains

As important freshwater resources in alpine basins,glaciers and snow cover tend to decline due to climate warming,thus affecting the amount of water available downstream and even regional economic development.However,impact assessments of the economic losses caused by reductions in freshwater supply are quite limited.This study aims to project changes in glacier meltwater and snowmelt of the Urumqi River in the Tianshan Mountains under future climate change scenarios(RCP2.6(RCP,Representative Concentration Pathway),RCP4.5,and RCP8.5)by applying a hydrological model and estimate the economic losses from future meltwater reduction for industrial,agricultural,service,and domestic water uses combined with the present value method for the 2030 s,2050 s,2070 s,and 2090 s.The results indicate that total annual glacier meltwater and snowmelt will decrease by 65.6%and 74.5%under the RCP4.5 and RCP8.5 scenarios by the 2090 s relative to the baseline period(1980-2010),respectively.Compared to the RCP2.6 scenario,the projected economic loss values of total water use from reduced glacier meltwater and snowmelt under the RCP8.5 scenario will increase by 435.10×10^(6) and 537.20×10^(6) CNY in the 2050 s and 2090 s,respectively,and the cumulative economic loss value for 2099 is approximately 2124.00×10^(6) CNY.We also find that the industrial and agricultural sectors would likely face the largest and smallest economic losses,respectively.The economic loss value of snowmelt in different sectorial sectors is greater than that of glacier meltwater.These findings highlight the need for climate mitigation actions,industrial transformation,and rational water allocation to be considered in decision-making in the Tianshan Mountains in the future.

Climate Suitability of Schima superba in Subtropical Zone of China under Future Climate Scenario

[ Objective] The research aimed to study climate suitability of S. superba in subtropical zone of China under future climate scenario and response of its regional distribution on climate change. [ Metbed] Based on climate- vegetation related Kira model, Holdridge model and ecological suitability theory, climate suitability model of S. superba was established by using fuzzy mathematics. Based on the daily meteorological data at 246 stations of the subtropical zone from 1960 to 2005, by using spatial interpolation method, suitability of S. superba on temperature, precipitation, po- tential evapotranspiration rate was analyzed. According to Intergovernmental Panel on Climate Change （IPCC） Special Report on Emissions Sce- nario （SRES）, future scenario simulation result was used. Under IPCC A2 scenario, climate suitability of S. superba and its change were studied in subtropical zone of China under future climate scenario. Climate suitability of S. superba in future was classified. [ Result] Under future climate scenario, climate suitability of S. superba was stronger in most of areas in Hunan, north Guangdong, northeast Guangxi and east coast of Zhe- jiang. It was suitable for growth of S. superba in central Guangxi, east Guizhou, central Jiangxi and Fuzhou. Growth suitability of S. superba was still lower in the north of Gongshan - Weixi - Lijiang - Yuanjiang - Huize - Leibo - Emei - Neijiang - Nanchong - Bazhong - Zhongxiang - zaoyang - Xinyang -Lu＇an -Chuzhou -Gaoyou -Taitong. Climate suitability in west Yunnan and Sichuan had big change. Future climate suitability change of S. superba was greatly affected by temperature and potential evapotranspiration rate. [ Conclusion] Future climate suitability decreased toward west and north from Hunan. The climate suitability had a decline trend as time went by under future climate scenario. The research provided theoretical basis for studying geographic distribution of the vegetation population.

Future climate change decreases multi-pathway but increases respiratory human health risks of PAHs across China

Future climate change will affect the environmental fate of hydrophobic organic contaminants(HOCs)and associated human health risks,yet the extent of these effects remains unknown.Here,we couple a high-resolution environmental multimedia model with a bioaccumulation model to study the multimedia distribution of 16 priority polycyclic aromatic hydrocarbons(PAHs),a group of HOCs,and assess future PAH-related human health risks under varying climate change scenarios over China at a continental scale.After removing the effects of PAH emission changes,we find that the total PAH concentrations would decrease in the air,freshwater,sediment,soil,and organisms,while the high-molecular-weight PAH would increase in the air with climate warming from 1.5°C to 4°C.Consequently,the multi-pathway exposure human health risks predominately influenced by dietary ingestion are expected to decrease by 1.7%–20.5%,while the respiratory risks are projected to rise by 0.2%–5.8%in the future.However,the persistently high multi-pathway human health risks underscore the need for reducing future PAH emissions by 69%compared with 2009 levels in China.Our study demonstrates the urgency of limiting PAH emissions under future climate change for public health and highlights the importance of including the contribution of dietary ingestion in human health risk assessment.

Machine learning ensemble model prediction of northward shift in potato cyst nematodes(Globodera rostochiensis and G.pallida)distribution under climate change conditions

Potato cyst nematodes(PCNs)are a significant threat to potato production,having caused substantial damage in many countries.Predicting the future distribution of PCN species is crucial to implementing effective biosecurity strategies,especially given the impact of climate change on pest species invasion and distribution.Machine learning(ML),specifically ensemble models,has emerged as a powerful tool in predicting species distributions due to its ability to learn and make predictions based on complex data sets.Thus,this research utilised advanced machine learning techniques to predict the distribution of PCN species under climate change conditions,providing the initial element for invasion risk assessment.We first used Global Climate Models to generate homogeneous climate predictors to mitigate the variation among predictors.Then,five machine learning models were employed to build two groups of ensembles,single-algorithm ensembles(ESA)and multi-algorithm ensembles(EMA),and compared their performances.In this research,the EMA did not always perform better than the ESA,and the ESA of Artificial Neural Network gave the highest performance while being cost-effective.Prediction results indicated that the distribution range of PCNs would shift northward with a decrease in tropical zones and an increase in northern latitudes.However,the total area of suitable regions will not change significantly,occupying 16-20%of the total land surface(18%under current conditions).This research alerts policymakers and practitioners to the risk of PCNs’incursion into new regions.Additionally,this ML process offers the capability to track changes in the distribution of various species and provides scientifically grounded evidence for formulating long-term biosecurity plans for their control.

ClimateAP: an application for dynamic local downscaling of historical and future climate data in Asia Pacific

While low-to-moderate resolution gridded climate data are suitable for climate-impact modeling at global and ecosystems levels, spatial analyses conducted at local scales require climate data with increased spatial accuracy. This is particularly true for research focused on the evaluation of adaptive forest management strategies. In this study, we developed an application, Climate AP, to generate scale-free(i.e., specific to point locations) climate data for historical(1901–2015) and future(2011–2100)years and periods. Climate AP uses the best available interpolated climate data for the reference period 1961–1990 as baseline data. It downscales the baseline data from a moderate spatial resolution to scale-free point data through dynamic local elevation adjustments. It also integrates and downscales the historical and future climate data using a delta approach. In the case of future climate data, two greenhouse gas representative concentration pathways(RCP 4.5 and 8.5) and 15 general circulation models are included to allow for the assessment of alternative climate scenarios. In addition, Climate AP generates a large number of biologically relevant climate variables derived from primary monthly variables. The effectiveness of the local downscaling was determined based on the strength of the local linear regression for the estimate of lapse rate. The accuracy of the Climate AP output was evaluated through comparisons of Climate AP output against observations from 1805 weather stations in the Asia Pacific region. The local linear regression explained 70%–80% and 0%–50% of the total variation in monthly temperatures and precipitation, respectively, in most cases. Climate AP reduced prediction error by up to27% and 60% for monthly temperature and precipitation,respectively, relative to the original baselines data. The improvements for baseline portions of historical and futurewere more substantial. Applications and limitations of the software are discussed.

Potential global distribution of the guava root-knot nematode Meloidogyne enterolobii under different climate change scenarios using MaxEnt ecological niche modeling

In recent years,Meloidogyne enterolobii has emerged as a major parasitic nematode infesting many plants in tropical or subtropical areas.However,the regions of potential distribution and the main contributing environmental variables for this nematode are unclear.Under the current climate scenario,we predicted the potential geographic distributions of M.enterolobii worldwide and in China using a Maximum Entropy(MaxEnt)model with the occurrence data of this species.Furthermore,the potential distributions of M.enterolobii were projected under three future climate scenarios(BCC-CSM2-MR,CanESM5 and CNRM-CM6-1)for the periods 2050s and 2090s.Changes in the potential distribution were also predicted under different climate conditions.The results showed that highly suitable regions for M.enterolobii were concentrated in Africa,South America,Asia,and North America between latitudes 30°S to 30°N.Bio16(precipitation of the wettest quarter),bio10(mean temperature of the warmest quarter),and bio11(mean temperature of the coldest quarter)were the variables contributing most in predicting potential distributions of M.enterolobii.In addition,the potential suitable areas for M.enterolobii will shift toward higher latitudes under future climate scenarios.This study provides a theoretical basis for controlling and managing this nematode.

Fair Plan 8: Earth’s Climate Future—Pope Francis’ Population Mistake

Pope Francis wrote in his Encyclical Letter Laudato Si’: On Care for Our Common Home: “Instead of resolving the problems of the poor and thinking of how the world can be different, some can only propose a reduction in the birth rate.” … “To blame population growth instead of extreme and selective consumerism on the part of some is one way of refusing to face the issues.” Here, we test the hypothesis that population size does not matter. We do so in terms of the effect of the size of the human population on its emission of greenhouse gases. We find that the hypothesis is false = PO-PULATION MATTERS. Ceteris paribus, the larger the population of human beings on Planet Earth, the more difficult it will be to reduce, and finally eliminate, the emission of greenhouse gases by humanity and, thereby, constrain human-caused climate change = Anthropogenic Global Warming.

Projecting the Summer Climate of China's Mainland in the Middle 21st Century: Will the Droughts in North China Persist?

Since no consensus has been reached in previous studies about how the summer climate in China will evolve in the first half of the 21st century, this issue is addressed here through sensitivity experiments by forcing an atmospheric general circulation model （AGCM）, the Geophysical Fluid Dynamics Laboratory （GFDL）＇s Atmospheric Model Version 2.0 （AM2） with projected sea surface temperature （SST） trend. A total of two SST trends from the Intergovernmental Panels on Climate Change （IPCC） Special Report on Emissions Scenario （SRES） AlB are used. The two trends are from two coupled climate system models, the National Center for Atmospheric Research （NCAR） Community Climate System Model Version 3.0 （CCSM3） and the GFDL Climate Model Version 2.0 （CM2）, respectively. Results consistently suggest a substantial warming and drying trend over much of China, with a surface air temperature increase of 1.0-2.0℃ and a 10%-20% decrease in rainfall. Exceptions are the areas from northwestern China to western North China as well as the southern Tibetan Plateau, which are projected to be wetter with a rainfall anomaly percentage increase of 10%-50%. The drying in eastern North China has not been documented to date but appears to be reasonable. Physically, it is attributed to anomalous northeasterly winds at the rear of a low-level cyclone over the South China Sea, the Philippines and the subtropical western North Pacific. These conditions, which govern the climate of eastern China, are forced by the northward shift of convection over warm waters due to additional warming.

Past, Present and Future Climatic Forcing due to Greenhouse Gases

An advanced one-dimensional radiative-convective model (RCM) is used to estimate the past, present and future climatic forcings induced by greenhouse gases of anthropogenic origin, such as CO2, CH4, N2O and CFCs, in this paper. The results show that the decadal climatic forcing for the last decade is one-order bigger than that prior to the year 1900, and in the case of no control on the emission of the greenhouse gases the climatic forcing for the year 2100 will be almost 4 times as much as now.

Climate change and Aedes albopictus risks in China:current impact and future projection

Background Future distribution of dengue risk is usually predicted based on predicted climate changes using general circulation models(GCMs).However,it is difficult to validate the GCM results and assess the uncertainty of the predictions.The observed changes in climate may be very different from the GCM results.We aim to utilize trends in observed climate dynamics to predict future risks of Aedes albopictus in China.Methods We collected Ae.albopictus surveillance data and observed climate records from 80 meteorological stations from 1970 to 2021.We analyzed the trends in climate change in China and made predictions on future climate for the years 2050 and 2080 based on trend analyses.We analyzed the relationship between climatic variables and the prevalence of Ae.albopictus in different months/seasons.We built a classification tree model(based on the average of 999 runs of classification and regression tree analyses)to predict the monthly/seasonal Ae.albopictus distribution based on the average climate from 1970 to 2000 and assessed the contributions of different climatic variables to the Ae.albopictus distribution.Using these models,we projected the future distributions of Ae.albopictus for 2050 and 2080.Results The study included Ae.albopictus surveillance from 259 sites in China found that winter to early spring(November–February)temperatures were strongly correlated with Ae.albopictus prevalence(prediction accuracy ranges 93.0–98.8%)—the higher the temperature the higher the prevalence,while precipitation in summer(June–September)was important predictor for Ae.albopictus prevalence.The machine learning tree models predicted the current prevalence of Ae.albopictus with high levels of agreement(accuracy>90%and Kappa agreement>80%for all 12 months).Overall,winter temperature contributed the most to Ae.albopictus distribution,followed by summer precipitation.An increase in temperature was observed from 1970 to 2021 in most places in China,and annual change rates varied substantially from-0.22℃/year to 0.58℃/year among sites,with the largest increase in temperature occurring from February to April(an annual increase of 1.4–4.7℃ in monthly mean,0.6–4.0℃ in monthly minimum,and 1.3–4.3℃ in monthly maximum temperature)and the smallest in November and December.Temperature increases were lower in the tropics/subtropics(1.5–2.3℃ from February–April)compared to the high-latitude areas(2.6–4.6℃ from February–April).The projected temperatures in 2050 and 2080 by this study were approximately 1–1.5℃ higher than those projected by GCMs.The estimated current Ae.albopictus risk distribution had a northern boundary of north-central China and the southern edge of northeastern China,with a risk period of June–September.The projected future Ae.albopictus risks in 2050 and 2080 cover nearly all of China,with an expanded risk period of April–October.The current at-risk population was estimated to be 960 million and the future at-risk population was projected to be 1.2 billion.Conclusions The magnitude of climate change in China is likely to surpass GCM predictions.Future dengue risks will expand to cover nearly all of China if current climate trends continue.

Extreme Rainfall Indices in the Hydrographic Basins of Brazil

The authors analyze climate extremes indices (CEI) of rainfall over the largest basins of the Brazilian territory: Amazon (AMA), S?o Francisco (SF), Tocantins (TO) and Paraná (PAR) rivers. The CEI represent the frequency of heavy precipitation events (R30mm and R95p) and short duration extreme rainfall (RX5day and RX1day). Droughts (CDDd) are identified based on two indicators: The longest dry period (CDD) and the annual cycle. The results demonstrate that CDDd, RX1day and RX5day occurred with more frequency and intensity in SF basin during El Ni?o events. CDDd was of greater magnitude in the TO basin during La Ni?a events, while an increase of RX1day occurred in El Ni?o. The strong El Ni?o events (1983 and 1997) caused more intense and frequent RX1day and R30mm over the PAR basin. Amazon droughts occurred in two out of the six El Ni?o events. Moreover, the relationship between the positive (negative) sea superficial temperatures anomalies in North (South) Tropical Atlantic and drought in AMA basin was corroborated. A gradual warming of SST was observed at the start of 2003 until it achieved a maximum in 2005 associated with the southwestern Amazon drought. The second highest anomaly of SST was in 2010 linked with drought that was more spatially extensive than the 2005 drought. The spatial distribution of annual trends showed a significant increase of CDD in south-eastern AMA, Upper SF, northern PAR and throughout the TO basins. R20mm, RX1day and RX5day tend to increase significantly in southwestern (northeast) PAR (AMA) and northwestern TO basins. Comparisons between CEI derived from daily precipitation data from Climate Prediction Center (CPCp) and of the ETA_HadCM3 model showed that the model overestimated RX1day, RX5day and CDD, in the four basins. Future scenarios show that dry periods will occur with greatest magnitude in all the basins until 2071-2099 time slice, while RX1day will be more intense in the TO and SF basins.

Potential geographical distribution of Anopheles gambiae worldwide under climate change

Background:Anopheles gambiae(An.gambiae)is considered the most effective malaria vector worldwide and is widely distributed in Africa.The purpose of this study is to determine the potential impact of climate change on An.gambiae and predict the present and future potential suitable habitat globally.Methods:In this study,environmental variables,global occurrence data of An.gambiae,and the maximum entropy model(MaxEnt)were used to evaluate the contribution of environmental factors and predict the habitat suitability of An.gambiae under the current and future scenarios.Results:Among all environmental variables,isothermality(Bio3,34.5%)contributed the most to An.gambiae distribution.Under current climate conditions,the potential suitable areas for An.gambiae are mainly located near the equator(approximately 30°N-30°S),with a total area of 16.58 million km2,including central and northern South America,a fraction of areas near the equator of North America,central and southern Africa,some tropical regions of southern Asia,and small areas of Oceania.The areas of potential suitable habitats would be reduced to varying degrees in future climate scenarios.Conclusions:Potential suitable habitats for An.gambiae may not be limited to Africa.Necessary surveillance and preventive measures should be undertaken in high-risk regions,including those outside Africa,to monitor and control the spread of An.gambiae.