Global Well-Posedness of the Fractional Tropical Climate Model

In this paper, we consider the Cauchy problem of 3-dimensional tropical climate model. This model reflects the interaction and coupling among the barotropic mode u, the first baroclinic mode v of the velocity and the temperature θ. The systems with fractional dissipation studied here may arise in the modeling of geophysical circumstances. Mathematically these systems allow simultaneous examination of a family of systems with various levels of regularization. The aim here is the global strong solution with the least dissipation. By energy estimate and delicate analysis, we prove the existence of global solution under three different cases: first, with the help of damping terms, the global strong solution of the system with Λ2au, Λ2βv and Λ2γ θ for;and second, the global strong solution of the system for with damping terms;finally, the global strong solution of the system for without any damping terms, which improve the known existence theory for this system.

A CMIP6-based assessment of regional climate change in the Chinese Tianshan Mountains

Climate warming profoundly affects hydrological changes,agricultural production,and human society.Arid and semi-arid areas of China are currently displaying a marked trend of warming and wetting.The Chinese Tianshan Mountains(CTM)have a high climate sensitivity,rendering the region particularly vulnerable to the effects of climate warming.In this study,we used monthly average temperature and monthly precipitation data from the CN05.1 gridded dataset(1961-2014)and 24 global climate models(GCMs)of the Coupled Model Intercomparison Project Phase 6(CMIP6)to assess the applicability of the CMIP6 GCMs in the CTM at the regional scale.Based on this,we conducted a systematic review of the interannual trends,dry-wet transitions(based on the standardized precipitation index(SPI)),and spatial distribution patterns of climate change in the CTM during 1961-2014.We further projected future temperature and precipitation changes over three terms(near-term(2021-2040),mid-term(2041-2060),and long-term(2081-2100))relative to the historical period(1961-2014)under four shared socio-economic pathway(SSP)scenarios(i.e.,SSP1-2.6,SSP2-4.5,SSP3-7.0,and SSP5-8.5).It was found that the CTM had experienced significant warming and wetting from 1961 to 2014,and will also experience warming in the future(2021-2100).Substantial warming in 1997 was captured by both the CN05.1 derived from interpolating meteorological station data and the multi-model ensemble(MME)from the CMIP6 GCMs.The MME simulation results indicated an apparent wetting in 2008,which occurred later than the wetting observed from the CN05.1 in 1989.The GCMs generally underestimated spring temperature and overestimated both winter temperature and spring precipitation in the CTM.Warming and wetting are more rapid in the northern part of the CTM.By the end of the 21st century,all the four SSP scenarios project warmer and wetter conditions in the CTM with multiple dry-wet transitions.However,the rise in precipitation fails to counterbalance the drought induced by escalating temperature in the future,so the nature of the drought in the CTM will not change at all.Additionally,the projected summer precipitation shows negative correlation with the radiative forcing.This study holds practical implications for the awareness of climate change and subsequent research in the CTM.

Differences between CMIP6 and CMIP5 Models in Simulating Climate over China and the East Asian Monsoon

We compare the ability of coupled global climate models from the phases 5 and 6 of the Coupled Model Intercomparison Project(CMIP5 and CMIP6,respectively)in simulating the temperature and precipitation climatology and interannual variability over China for the period 1961–2005 and the climatological East Asian monsoon for the period 1979–2005.All 92 models are able to simulate the geographical distribution of the above variables reasonably well.Compared with earlier CMIP5 models,current CMIP6 models have nationally weaker cold biases,a similar nationwide overestimation of precipitation and a weaker underestimation of the southeast–northwest precipitation gradient,a comparable overestimation of the spatial variability of the interannual variability,and a similar underestimation of the strength of winter monsoon over northern Asia.Pairwise comparison indicates that models have improved from CMIP5 to CMIP6 for climatological temperature and precipitation and winter monsoon but display little improvement for the interannual temperature and precipitation variability and summer monsoon.The ability of models relates to their horizontal resolutions in certain aspects.Both the multi-model arithmetic mean and median display similar skills and outperform most of the individual models in all considered aspects.

Evaluating the Dependence of Vegetation on Climate in an Improved Dynamic Global Vegetation Model

The capability of an improved Dynamic Global Vegetation Model （DGVM） in reproducing the impact of climate on the terrestrial ecosystem is evaluated. The new model incorporates the Community Land Model- DGVM （CLM3.0-DGVM） with a submodel for temperate and boreal shrubs, as well as other revisions such as the ＂two-leaf＂ scheme for photosynthesis and the definition of fractional coverage of plant functional types （PFTs）. Results show that the revised model may correctly reproduce the global distribution of temperate and boreal shrubs, and improves the model performance with more realistic distribution of di？erent vege- tation types. The revised model also correctly reproduces the zonal distributions of vegetation types. In reproducing the dependence of the vegetation distribution on climate conditions, the model shows that the dominant regions for trees, grasses, shrubs, and bare soil are clearly separated by a climate index derived from mean annual precipitation and temperature, in good agreement with the CLM4 surface data. The dominant plant functional type mapping to a two dimensional parameter space of mean annual temperature and precipitation also qualitatively agrees with the results from observations and theoretical ecology studies.

Projections of Wind Changes for 21st Century in China by Three Regional Climate Models

This paper examines the capability of three regional climate models (RCMs), i.e., RegCM3 (the International Centre for Theoretical Physics Regional Climate Model), PRECIS (Providing Regional Climates for Impacts Studies) and CMM5 (the fifth-generation Pennsylvania State University-the National Center for Atmospheric Research of USA, NCAR Mesoscale Model) to simulate the near-surface-layer winds (10 m above surface) all over China in the late 20th century. Results suggest that like global climate models (GCMs), these RCMs have the certain capability of imitating the distribution of mean wind speed and fail to simulate the greatly weakening wind trends for the past 50 years in the country. However, RCMs especially RegCM3 have the better capability than that of GCMs to simulate the distribution and change feature of mean wind speed. In view of their merits, these RCMs were used to project the variability of near-surface-layer winds over China for the 21st century. The results show that 1) summer mean wind speed for 2020-2029 will be lower compared to those in 1990-1999 in most area of China; 2) annual and winter mean wind speed for 2081-2100 will be lower than those of 1971-1990 in the whole China; and 3) the changes of summer mean wind speed for 2081-2100 are uncertain. As a result, although climate models are absolutely necessary for projecting climate change to come, there are great uncertainties in projections, especially for wind speed, and these issues need to be further explored.

Global pattern and change of cropland soil organic carbon during 1901-2010: Roles of climate, atmospheric chemistry, land use and management

Soil organic carbon(SOC)in croplands is a key property of soil quality for ensuring food security and agricultural sustainability,and also plays a central role in the global carbon(C)budget.When managed sustainably,soils may play a critical role in mitigating climate change by sequestering C and decreasing greenhouse gas emissions into the atmosphere.However,the magnitude and spatio-temporal patterns of global cropland SOC are far from well constrained due to high land surface heterogeneity,complicated mechanisms,and multiple influencing factors.Here,we use a process-based agroecosystem model(DLEM-Ag)in combination with diverse spatially-explicit gridded environmental data to quantify the long-term trend of SOC storage in global cropland area during 1901-2010 and identify the relative impacts of climate change,elevated CO2,nitrogen deposition,land cover change,and land management practices such as nitrogen fertilizer use and irrigation.Model results show that the total SOC and SOC density in the 2000s increased by 125%and 48.8%,respectively,compared to the early 20th century.This SOC increase was primarily attributed to cropland expansion and nitrogen fertilizer use.Factorial analysis suggests that climate change reduced approximately 3.2%(or 2,166 Tg C)of the total SOC over the past 110 years.Our results indicate that croplands have a large potential to sequester C through implementing better land use management practices,which may partially offset SOC loss caused by climate change.

Assessing the Impacts of Eurasian Snow Conditions on Climate Predictability with a Global Climate Model

On the basis of two ensemble experiments conducted by a general atmospheric circulation model(Institute of Atmospheric Physics nine-level atmospheric general circulation model coupled with land surface model,hereinafter referred to as IAP9L_CoLM),the impacts of realistic Eurasian snow conditions on summer climate predictability were investigated.The predictive skill of sea level pressures(SLP)and middle and upper tropospheric geopotential heights at mid-high latitudes of Eurasia was enhanced when improved Eurasian snow conditions were introduced into the model.Furthermore,the model skill in reproducing the interannual variation and spatial distribution of the surface air temperature(SAT)anomalies over China was improved by applying realistic(prescribed)Eurasian snow conditions.The predictive skill of the summer precipitation in China was low;however,when realistic snow conditions were employed,the predictability increased,illustrating the effectiveness of the application of realistic Eurasian snow conditions.Overall,the results of the present study suggested that Eurasian snow conditions have a significant effect on dynamical seasonal prediction in China.When Eurasian snow conditions in the global climate model(GCM)can be more realistically represented,the predictability of summer climate over China increases.

Compensating Errors in Cloud Radiative and Physical Properties over the Southern Ocean in the CMIP6 Climate Models

The Southern Ocean is covered by a large amount of clouds with high cloud albedo.However,as reported by previous climate model intercomparison projects,underestimated cloudiness and overestimated absorption of solar radiation(ASR)over the Southern Ocean lead to substantial biases in climate sensitivity.The present study revisits this long-standing issue and explores the uncertainty sources in the latest CMIP6 models.We employ 10-year satellite observations to evaluate cloud radiative effect(CRE)and cloud physical properties in five CMIP6 models that provide comprehensive output of cloud,radiation,and aerosol.The simulated longwave,shortwave,and net CRE at the top of atmosphere in CMIP6 are comparable with the CERES satellite observations.Total cloud fraction(CF)is also reasonably simulated in CMIP6,but the comparison of liquid cloud fraction(LCF)reveals marked biases in spatial pattern and seasonal variations.The discrepancies between the CMIP6 models and the MODIS satellite observations become even larger in other cloud macroand micro-physical properties,including liquid water path(LWP),cloud optical depth(COD),and cloud effective radius,as well as aerosol optical depth(AOD).However,the large underestimation of both LWP and cloud effective radius(regional means~20%and 11%,respectively)results in relatively smaller bias in COD,and the impacts of the biases in COD and LCF also cancel out with each other,leaving CRE and ASR reasonably predicted in CMIP6.An error estimation framework is employed,and the different signs of the sensitivity errors and biases from CF and LWP corroborate the notions that there are compensating errors in the modeled shortwave CRE.Further correlation analyses of the geospatial patterns reveal that CF is the most relevant factor in determining CRE in observations,while the modeled CRE is too sensitive to LWP and COD.The relationships between cloud effective radius,LWP,and COD are also analyzed to explore the possible uncertainty sources in different models.Our study calls for more rigorous calibration of detailed cloud physical properties for future climate model development and climate projection.

Economic Implications of Avoiding Dangerous Climate Change： An Analysis Using the AIM/CGE [Global] Model

The purpose of this study is to analyze economic impacts of reducing greenhouse gases emissions significantly. A large amount of emissions reductions are required throughout this century to avoid dangerous climate change, and understanding the economic consequences under such situations is important and meaningful. The AIM/CGE [Global] model, a recursive dynamic computable general equilibrium model on a global scale, is applied to analyze carbon prices and changes in GDP when implementing five policy scenarios represented by emissions pathways, respectively. As a result of the analysis, higher carbon prices and larger decreases in GDP compared to the baseline emissions scenario are observed when emissions are reduced more deeply. However, such GDP losses are rather small and insignificant compared to the GDP growth observed throughout the century. These results suggest that although it is challenging to reduce emissions until the level to avoid dangerous climate change, there is a sufficient possibility to achieve it from economic perspectives.

Projection of precipitation extremes over South Asia from CMIP6 GCMs

Extreme precipitation events are one of the most dangerous hydrometeorological disasters,often resulting in significant human and socio-economic losses worldwide.It is therefore important to use current global climate models to project future changes in precipitation extremes.The present study aims to assess the future changes in precipitation extremes over South Asia from the Coupled Model Intercomparison Project Phase 6(CMIP6)Global Climate Models(GCMs).The results were derived using the modified Mann-Kendall test,Sen's slope estimator,student's t-test,and probability density function approach.Eight extreme precipitation indices were assessed,including wet days(RR1mm),heavy precipitation days(RR10mm),very heavy precipitation days(RR20mm),severe precipitation days(RR50mm),consecutive wet days(CWD),consecutive dry days(CDD),maximum 5-day precipitation amount(RX5day),and simple daily intensity index(SDII).The future changes were estimated in two time periods for the 21^(st) century(i.e.,near future(NF;2021-2060)and far future(FF;2061-2100))under two Shared Socioeconomic Pathway(SSP)scenarios(SSP2-4.5 and SSP5-8.5).The results suggest increases in the frequency and intensity of extreme precipitation indices under the SSP5-8.5 scenario towards the end of the 21^(st) century(2061-2100).Moreover,from the results of multimodel ensemble means(MMEMs),extreme precipitation indices of RR1mm,RR10mm,RR20mm,CWD,and SDII demonstrate remarkable increases in the FF period under the SSP5-8.5 scenario.The spatial distribution of extreme precipitation indices shows intensification over the eastern part of South Asia compared to the western part.The probability density function of extreme precipitation indices suggests a frequent(intense)occurrence of precipitation extremes in the FF period under the SSP5-8.5 scenario,with values up to 35.00 d for RR1mm and 25.00-35.00 d for CWD.The potential impacts of heavy precipitation can pose serious challenges to the study area regarding flooding,soil erosion,water resource management,food security,and agriculture development.

Seasonal Predictions of Summer Precipitation in the Middle-lower Reaches of the Yangtze River with Global and Regional Models Based on NUIST-CFS1.0

Accurate prediction of the summer precipitation over the middle and lower reaches of the Yangtze River(MLYR)is of urgent demand for the local economic and societal development.This study assesses the seasonal forecast skill in predicting summer precipitation over the MLYR region based on the global Climate Forecast System of Nanjing University of Information Science and Technology(NUIST-CFS1.0,previously SINTEX-F).The results show that the model can provide moderate skill in predicting the interannual variations of the MLYR rainbands,initialized from 1 March.In addition,the nine-member ensemble mean can realistically reproduce the links between the MLYR precipitation and tropical sea surface temperature(SST)anomalies,but the individual members show great discrepancies,indicating large uncertainty in the forecasts.Furthermore,the NUIST-CFS1.0 can predict five of the seven extreme summer precipitation anomalies over the MLYR during 1982-2020,albeit with underestimated magnitudes.The Weather Forecast and Research(WRF)downscaling hindcast experiments with a finer resolution of 30 km,which are forced by the large-scale information of the NUIST-CFS1.0 predictions with a spectral nudging method,display improved predictions of the extreme summer precipitation anomalies to some extent.However,the performance of the downscaling predictions is highly dependent on the global model forecast skill,suggesting that further improvements on both the global and regional climate models are needed.

Modelling Impacts of Climate Change on Maize (<i>Zea mays</i>L.) Growth and Productivity: A Review of Models, Outputs and Limitations

The use of crop modelling in various cropping systems and environments to project and upscale agronomic decision-making under the facets of climate change has gained currency in recent years. This paper provides an evaluation of crop models that have been used by researchers to simulate maize growth and productivity. Through a systematic review approach, a comprehensive assessment of 186 published articles was carried out to establish the models and parameterization features, simulated impacts on maize yields and adaptation strategies in the last three decades. Of the 23 models identified, CERES-maize and APSIM models were the most dominant, representing 49.7% of the studies undertaken between 1990 and 2018. Current research shows projected decline in maize yields of between 8% - 38% under RCP4.5 and RCP8.5 scenarios by the end of the 21st century, and that adaptation is essential in alleviating the impacts of climate change. Major agro-adaptation options considered in most papers are changes in planting dates, cultivars and crop water management practices. The use of multiple crop models and multi-model ensembles from general circulation models (GCMs) is recommended. As interest in crop modelling grows, future work should focus more on suitability of agricultural lands for maize production under climate scenarios.

Evaluating effects of atmospheric CO_2 on stability of global climate:a cell-to-cell mapping approach

Atmospheric carbon dioxide concentration [CO2],incoming solar radiation and sea ice coverage are among the most important factors that control the global climate.By applying the simple cell-to-cell mapping technique to a simplified atmosphere-ocean-sea ice feedback climate model,effects of these factors on the stability of the climatic system are studied.The current climatic system is found to be stable but highly nonlinear.The resiliency of stability increases with [CO2] to a summit when [CO2] reaches 290 μL/L which is comparable to the pre-industrial level,suggesting carbon dioxide is essential to the stability of the global climate.With [CO2] rising further,the global climate stability decreases,the mean ocean temperature goes up and the sea ice coverage shrinks in the polar region.When the incoming solar radiation is intensified,the ice coverage gradually diminishes,but the mean ocean temperature remains relatively constant.Overall,our analysis suggests that at the current levels of three external factors the stability of global climate is highly resilient.However,there exists a possibility of extreme states of climate,such as a snow-ball earth and an ice-free earth.

Global Warming Control to Mitigate Climate Change

Paper describes a proposed development of empirical model of global warming fit on the collective determinants in all countries. Aim is to enable establishment and comparison of the collective effects of global determinants on global warming in the prescription of the regulations most fit for the collective deployment in each of the determinant countries to enable mitigation of the greenhouse gases build-up caused global warming.

Future meteorological drought conditions in southwestern Iran based on the NEX-GDDP climate dataset

Investigation of the climate change effects on drought is required to develop management strategies for minimizing adverse social and economic impacts.Therefore,studying the future meteorological drought conditions at a local scale is vital.In this study,we assessed the efficiency of seven downscaled Global Climate Models(GCMs)provided by the NASA Earth Exchange Global Daily Downscaled Projections(NEX-GDDP),and investigated the impacts of climate change on future meteorological drought using Standard Precipitation Index(SPI)in the Karoun River Basin(KRB)of southwestern Iran under two Representative Concentration Pathway(RCP)emission scenarios,i.e.,RCP4.5 and RCP8.5.The results demonstrated that SPI estimated based on the Meteorological Research Institute Coupled Global Climate Model version 3(MRI-CGCM3)is consistent with the one estimated by synoptic stations during the historical period(1990-2005).The root mean square error(RMSE)value is less than 0.75 in 77%of the synoptic stations.GCMs have high uncertainty in most synoptic stations except those located in the plain.Using the average of a few GCMs to improve performance and reduce uncertainty is suggested by the results.The results revealed that with the areas affected by wetness decreasing in the KRB,drought frequency in the North KRB is likely to increase at the end of the 21st century under RCP4.5 and RCP8.5 scenarios.At the seasonal scale,the decreasing trend for SPI in spring,summer,and winter shows a drought tendency in this region.The climate-induced drought hazard can have vast consequences,especially in agriculture and rural livelihoods.Accordingly,an increasing trend in drought during the growing seasons under RCP scenarios is vital for water managers and farmers to adopt strategies to reduce the damages.The results of this study are of great value for formulating sustainable water resources management plans affected by climate change.

Quantifying major sources of uncertainty in projecting the impact of climate change on wheat grain yield in dryland environments

Modelling the impact of climate change on cropping systems is crucial to support policy-making for farmers and stakeholders.Nevertheless,there exists inherent uncertainty in such cases.General Circulation Models(GCMs)and future climate change scenarios(different Representative Concentration Pathways(RCPs)in different future time periods)are among the major sources of uncertainty in projecting the impact of climate change on crop grain yield.This study quantified the different sources of uncertainty associated with future climate change impact on wheat grain yield in dryland environments(Shiraz,Hamedan,Sanandaj,Kermanshah and Khorramabad)in eastern and southern Iran.These five representative locations can be categorized into three climate classes:arid cold(Shiraz),semi-arid cold(Hamedan and Sanandaj)and semi-arid cool(Kermanshah and Khorramabad).Accordingly,the downscaled daily outputs of 29 GCMs under two RCPs(RCP4.5 and RCP8.5)in the near future(2030s),middle future(2050s)and far future(2080s)were used as inputs for the Agricultural Production Systems sIMulator(APSIM)-wheat model.Analysis of variance(ANOVA)was employed to quantify the sources of uncertainty in projecting the impact of climate change on wheat grain yield.Years from 1980 to 2009 were regarded as the baseline period.The projection results indicated that wheat grain yield was expected to increase by 12.30%,17.10%,and 17.70%in the near future(2030s),middle future(2050s)and far future(2080s),respectively.The increases differed under different RCPs in different future time periods,ranging from 11.70%(under RCP4.5 in the 2030s)to 20.20%(under RCP8.5 in the 2080s)by averaging all GCMs and locations,implying that future wheat grain yield depended largely upon the rising CO2 concentrations.ANOVA results revealed that more than 97.22% of the variance in future wheat grain yield was explained by locations,followed by scenarios,GCMs,and their interactions.Specifically,at the semi-arid climate locations(Hamedan,Sanandaj,Kermanshah and Khorramabad),most of the variations arose from the scenarios(77.25%),while at the arid climate location(Shiraz),GCMs(54.00%)accounted for the greatest variation.Overall,the ensemble use of a wide range of GCMs should be given priority to narrow the uncertainty when projecting wheat grain yield under changing climate conditions,particularly in dryland environments characterized by large fluctuations in rainfall and temperature.Moreover,the current research suggested some GCMs(e.g.,the IPSL-CM5B-LR,CCSM4,and BNU-ESM)that made moderate effects in projecting the impact of climate change on wheat grain yield to be used to project future climate conditions in similar environments worldwide.

Climate Projections and Uncertainties over South America from MRI/JMA Global Model Experiments

This paper analyses the climate change projected for the near and distant future in South America using MRI/JMA (Japanese Meteorological Agency) global model simulations with resolutions of 20 and 60 km. Changes in mean climate, as well as in the annual cycles and interannual variability of temperature and precipitation are discussed. An analysis is also made of the uncertainties of the 60 km resolution model experiments. For the near and distant future, both, the 20 km and 60 km resolution MRI/JMA models project that temperature changes will be positive in all seasons. The greatest values of change are over the Andes and over tropical and subtropical latitudes of the study region. In all the subregions analysed, the 20 km model projects greater changes in the annual cycle of mean temperature than the 60 km model. Changes in summer precipitation are positive over most of the continent, except for southern Chile. Autumn precipitation is projected to increase over northern Argentina and north-western South America and to decrease over central Chile in winter, which might be due to the southward shift of the Pacific storm-track. The most significant positive change in Southeastern South America (SESA) is projected to occur in spring precipitation. In general, projected changes in the annual cycle are greater in the rainy seasons of each subregion. No significant changes are expected in the interannual variability of temperature and precipitation. La Plata basin is projected to experience increased runoff, which would indicate that the projected rise in precipitation would have stronger effect than projected warming. The analysis of climate projection uncertainties revealed that temperature projections are more reliable than precipitation projections;and that uncertainty in near future simulations is greater than in simulations of the end of the century.

Climate Change in La Plata Basin as Seen by a High-Resolution Global Model

This paper analyses the climate change in La Plata Basin, one of the most important regions in South America due to its economy and population. For this work it has been used the Meteorological Research Institute (MRI) and the Japanese Meteorological Agency (JMA) atmospheric global model. For both near and far future, the projected changes for temperature over the entire basin were positive, although they were only statistically significant at the end of the XXI century. Changes in the annual cycle of mean temperature were also positive in all subregions of the basin. Regarding precipitation, there were no changes in the near future that were statistically significant. The summer (winter) is the only season where both models project positive (negative) changes for both periods of the future. In the transitional seasons these changes vary depending on the spatial resolution model and the area of study. The annual cycle showed that the largest changes in precipitation (positive or negative) coincide with the rainy season of each subregion. Regarding the interannual variability of temperature, it was found that the 20 km. model pro-jected a decrease of this variability for both near and far future, especially in summer and autumn. On the other hand, the 60 km. ensemble model showed a decreased of year-to-year variability for summer and an increase in winter and spring. It was also found that both models project an increase in precipitation variability for winter and summer, while in other seasons, only the 60 km. ensemble model presents the mentioned behavior.

Climate change impacts the distribution of Quercus section Cyclobalanopsis(Fagaceae),a keystone lineage in East Asian evergreen broadleaved forests

East Asian evergreen broadleaved forests(EBFLs) harbor high species richness,but these ecosystems are severely impacted by global climate change and deforestation.Conserving and managing EBLFs requires understanding dominant tree distribution dynamics.In this study,we used 29 species in Quercus section Cyclobalanopsis-a keystone lineage in East Asian EBLFs-as proxies to predict EBLF distribution dynamics using species distribution models(SDMs).We examined climatic niche overlap,similarity,and equivalency among seven biogeographical regions’ species using’ecospat’.We also estimated the effectiveness of protected areas in the predicted range to elucidate priority conservation regions.Our results showed that the climatic niches of most geographical groups differ.The western species under the Indian summer monsoon regime were mainly impacted by temperature factors,whereas precipitation impacted the eastern species under the East Asian summer monsoon regime.Our simulation predicted a northward range expansion of section Cyclobalanopsis between 2081 and 2100,except for the ranges of the three Himalayan species analyzed,which might shrink significantly.The greatest shift of highly suitable areas was predicted for the species in the South Pacific,with a centroid shift of over 300 km.Remarkably,only 7.56% of suitable habitat is currently inside protected areas,and the percentage is predicted to continue declining in the future.To better conserve Asian EBLFs,establishing nature reserves in their northern distribution ranges,and transplanting the populations with predicted decreasing numbers and degraded habitats to their future highly suitable areas,should be high-priority objectives.

大变局下的全球气候治理与中国的战略选择——基于首次全球盘点的分析

2023年迪拜气候大会完成了对《巴黎协定》的首次全球盘点,明确指出《巴黎协定》已经取得了重要进展,但各缔约方尚未集体步入实现《巴黎协定》的宗旨及其长期目标的轨道,距离实现这一目标仍存在巨大差距。这次气候大会也确定了转型脱离化石燃料的路线图,以便在2050年实现净零排放,这标志着化石燃料时代终结的开始,也表明绿色转型的时代潮流不仅不可逆转,而且转型的步伐在未来十年将持续加快。当今世界正面临百年未有之大变局,全球气候治理的紧迫性显著上升。作为国际社会的负责任大国和全球气候治理的关键行为体,中国需要从构建人类命运共同体的战略高度出发,利用首次全球盘点向国际社会发出清晰信号,坚定推进自身“双碳”目标的实现,积极推动和引领全球低碳转型,为实现全球气候治理目标贡献更大力量。