Social and Economic Considerations for Creating Sustainable Climate Change Haven Communities

As Climate Change Haven Communities are constructed across the Northern Hemisphere, it will be necessary to attract two types of migrants to populate them. The first group consists of professionals and companies in eco-sustainable businesses, such as law firms, insurance companies, investment firms, banking, technological innovation, mass media, medical research and pharmaceutical research. The second group will consist of persons engaged in organic/eco-sustainable agriculture whose crops and animal husbandry practices can be transferred successfully to Climate Change Haven regions. The present research focuses on the social and economic variables that must be taken into account to insure that each new Climate Change Haven Community becomes successfully integrated with the local population and forms a cohesive, harmonious social structure. Examples are given from the United States, France, Spain, Portugal and Italy.

Application of an Economy-Climate Model to Assess the Impact of Climate Change

An interdisciplinary investigation was conducted to assess the impact of climate change on grain yields using an economy-climate model （C-D-C）. The model was formulated by incorporating climate factors into the classic Cobb-Douglas （C-D） economic production function model. The economic meanings of the model output elasticities are described and elucidated. The C-D-C model was applied to the assessment of the impact of climate change on grain yields in China during the past 20 years, from 1983 through 2002. In the study, the land of China was divided into eight regions, and both the C-D-C and C-D models were applied to each individual region. The results suggest that the C-D-C model is superior to the classic C-D model, indicating the importance of climate factors. Prospective applications of the C-D-C model are discussed.

Toward a Learnable Climate Model in the Artificial Intelligence Era

Artificial intelligence(AI)models have significantly impacted various areas of the atmospheric sciences,reshaping our approach to climate-related challenges.Amid this AI-driven transformation,the foundational role of physics in climate science has occasionally been overlooked.Our perspective suggests that the future of climate modeling involves a synergistic partnership between AI and physics,rather than an“either/or”scenario.Scrutinizing controversies around current physical inconsistencies in large AI models,we stress the critical need for detailed dynamic diagnostics and physical constraints.Furthermore,we provide illustrative examples to guide future assessments and constraints for AI models.Regarding AI integration with numerical models,we argue that offline AI parameterization schemes may fall short of achieving global optimality,emphasizing the importance of constructing online schemes.Additionally,we highlight the significance of fostering a community culture and propose the OCR(Open,Comparable,Reproducible)principles.Through a better community culture and a deep integration of physics and AI,we contend that developing a learnable climate model,balancing AI and physics,is an achievable goal.

New Record Ocean Temperatures and Related Climate Indicators in 2023

The global physical and biogeochemical environment has been substantially altered in response to increased atmospheric greenhouse gases from human activities.In 2023,the sea surface temperature(SST)and upper 2000 m ocean heat content(OHC)reached record highs.The 0–2000 m OHC in 2023 exceeded that of 2022 by 15±10 ZJ(1 Zetta Joules=1021 Joules)(updated IAP/CAS data);9±5 ZJ(NCEI/NOAA data).The Tropical Atlantic Ocean,the Mediterranean Sea,and southern oceans recorded their highest OHC observed since the 1950s.Associated with the onset of a strong El Niño,the global SST reached its record high in 2023 with an annual mean of~0.23℃ higher than 2022 and an astounding>0.3℃ above 2022 values for the second half of 2023.The density stratification and spatial temperature inhomogeneity indexes reached their highest values in 2023.

The coordinated evolution of ecological environment,public service,and tourism economy along the Silk Road Economic Belt,using the Dual-Carbon Targets

Carbon peak and carbon neutrality(dual-carbon)are important targets for the international response to climate change.The Silk Road Economic Belt is a strategic resource region and is important for future ecological environment and tourism development.Based on the“dual-carbon”targets,the Single index quantification,Multiple index synthesis,and Poly-criteria integration evaluation model were used in this study to measure the coordinated development index of the ecological environment,public service,and tourism economy along the Silk Road Economic Belt and to analyze its spatial and temporal evolution.Further,it explores the dynamic evolution and development trend of the three systems using the Kernel Density and Grey Markov Prediction Model.The results show that the coordinated development index along this region needs to be improved during the study period.Furthermore,the coordinated development index of the Southwest region is relatively higher than that of the Northwest region.From the development trend of the three systems,all of them develop in a stable manner;however,the tourism economy system is easily affected by external disturbances.The coordinated development index of the three systems changes dynamically and tends to be in a good state of coordination.There is a certain spatial and temporal heterogeneity.The gravity center of the coordinated development index has been in the Southwest region.During the forecast period,the coordinated development index along this region will improve significantly,while insufficient and unbalanced development will continue.

Future changes in precipitation and water availability over the Tibetan Plateau projected by CMIP6 models constrained by climate sensitivity

Precipitation projections over the Tibetan Plateau(TP)show diversity among existing studies,partly due to model uncertainty.How to develop a reliable projection remains inconclusive.Here,based on the IPCC AR6–assessed likely range of equilibrium climate sensitivity(ECS)and the climatological precipitation performance,the authors constrain the CMIP6(phase 6 of the Coupled Model Intercomparison Project)model projection of summer precipitation and water availability over the TP.The best estimates of precipitation changes are 0.24,0.25,and 0.45 mm d^(−1)(5.9%,6.1%,and 11.2%)under the Shared Socioeconomic Pathway(SSP)scenarios of SSP1–2.6,SSP2–4.5,and SSP5–8.5 from 2050–2099 relative to 1965–2014,respectively.The corresponding constrained projections of water availability measured by precipitation minus evaporation(P–E)are 0.10,0.09,and 0.22 mm d^(−1)(5.7%,4.9%,and 13.2%),respectively.The increase of precipitation and P–E projected by the high-ECS models,whose ECS values are higher than the upper limit of the likely range,are about 1.7 times larger than those estimated by constrained projections.Spatially,there is a larger increase in precipitation and P–E over the eastern TP,while the western part shows a relatively weak difference in precipitation and a drier trend in P–E.The wetter TP projected by the high-ECS models resulted from both an approximately 1.2–1.4 times stronger hydrological sensitivity and additional warming of 0.6℃–1.2℃ under all three scenarios during 2050–2099.This study emphasizes that selecting climate models with climate sensitivity within the likely range is crucial to reducing the uncertainty in the projection of TP precipitation and water availability changes.

Climate-growth relationships of Pinus tabuliformis along an altitudinal gradient on Baiyunshan Mountain,Central China

A set of standard chronologies for tree-ring width(TRW),earlywood width(EWW)and latewood width(LWW)in Pinus tabuliformis Carr.along an altitudi-nal gradient(1450,1400,and 1350 m a.s.l.)on Baiyunshan Mountain,Central China to analyze the effect of varying temperature and precipitation on growth along the gradi-ent.Correlation analyses showed that at all three altitudes and the TRW and EWW chronologies generally had signifi-cant negative correlations with mean and maximum tem-peratures in the current April and May and with minimum temperatures in the prior July and August,but significant positive correlations with precipitation in the current May.Correlations were generally significantly negative between LWW chronologies and all temperatures in the prior July and August,indicating that the prior summer temperature had a strong lag effect on the growth of P.tabuliformis that increased with altitude.The correlation with the standard-ized precipitation evapotranspiration index(SPEI)confirmed that wet conditions in the current May promoted growth of TR and EW at all altitudes.Significant altitudinal differences were also found;at 1400 m,there were significant positive correlations between EWW chronologies and SPEI in the current April and significant negative correlations between LWW chronologies and SPEI in the current September,but these correlations were not significant at 1450 m.At 1350 m,there were also significant negative correlations between the TRW and the EWW chronologies and SPEI in the prior October and the current July and between LWW chronology and SPEI in the current August,but these cor-relations were not significant at 1400 m.Moving correlation results showed a stable response of EWW in relation to the SPEI in the current May at all three altitudes and of LWW to maximum temperature in the prior July-August at 1400 m from 2002 to 2018.The EWW chronology at 1400 m and the LWW chronology at 1450 m were identified as more suitable for climate reconstruction.These results provide a strong scientific basis for forest management decisions and climate reconstructions in Central China.

Climate change drives flooding risk increases in the Yellow River Basin

The Yellow River Basin(YRB)has experienced severe floods and continuous riverbed elevation throughout history.Global climate change has been suggested to be driving a worldwide increase in flooding risk.However,owing to insufficient evidence,the quantitative correlation between flooding and climate change remains illdefined.We present a long time series of maximum flood discharge in the YRB dating back to 1843 compiled from historical documents and instrument measurements.Variations in yearly maximum flood discharge show distinct periods:a dramatic decreasing period from 1843 to 1950,and an oscillating gentle decreasing from 1950 to 2021,with the latter period also showing increasing more extreme floods.A Mann-Kendall test analysis suggests that the latter period can be further split into two distinct sub-periods:an oscillating gentle decreasing period from 1950 to 2000,and a clear recent increasing period from 2000 to 2021.We further predict that climate change will cause an ongoing remarkable increase in future flooding risk and an∼44.4 billion US dollars loss of floods in the YRB in 2100.

A progress review of black carbon deposition on Arctic snow and ice and its impact on climate change

The rapid warming of the Arctic,accompanied by glacier and sea ice melt,has significant consequences for the Earth’s climate,ecosystems,and economy.Black carbon(BC)deposition on snow and ice can trigger a significant reduction in snow albedo and accelerate melting of snow and ice in the Arctic.By reviewing the published literatures over the past decades,this work provides an overview of the progress in both the measurement and modeling of BC deposition and its impact on Arctic climate change.In summary,the maximum value of BC deposition appears in the western Russian Arctic(26 ng·g^(–1)),and the minimum value appears in Greenland(3 ng·g^(–1)).BC records in the Arctic ice core already peaked in 1920s and 1970s,and shows a regional difference between Greenland and Canadian Arctic.The different temporal variations of Arctic BC ice core records in different regions are closely related to the large variability of BC emissions and transportation processes across the Arctic region.Model simulations usually underestimate the concentration of BC in snow and ice by 2–3 times,and cannot accurately reflect the seasonal and regional changes in BC deposition.Wet deposition is the main removal mechanism of BC in the Arctic,and observations show different seasonal variations in BC wet deposition in Ny-Ålesund and Barrow.This discrepancy may result from varying contributions of anthropogenic and biomass burning(BB)emissions,given the strong influence by BC from BB emissions at Barrow.Arctic BC deposition significantly influences regional climate change in the Arctic,increasing fire activities in the Arctic have made BB source of Arctic BC more crucial.On average,BC in Arctic snow and ice causes an increase of+0.17 W·m^(–2)in radiative forcing and 8 Gt·a^(–1)in runoff in Greenland.As stressed in the latest Arctic Monitoring and Assessment Programme report,reliable source information and long-term and high-resolution observations on Arctic BC deposition will be crucial for a more comprehensive understanding and a better mitigation strategy of Arctic BC.In the future,it is necessary to collect more observations on BC deposition and the corresponding physical processes(e.g.,snow/ice melting,surface energy balance)in the Arctic to provide reliable data for understanding and clarifying the mechanism of the climatic impacts of BC deposition on Arctic snow and ice.

A study on the simulation of carbon and water fluxes of Dangxiong alpine meadow and its response to climate change

The alpine meadow ecosystem accounts for 27%of the total area of the Tibetan Plateau and is also one of the most important vegetation types.The Dangxiong alpine meadow ecosystem,located in the south-central part of the Tibetan Plateau,is a typical example.To understand the carbon and water fluxes,water use efficiency(WUE),and their responses to future climate change for the alpine meadow ecosystem in the Dangxiong area,two parameter estimation methods,the Model-independent Parameter Estimation(PEST)and the Dynamic Dimensions Search(DDS),were used to optimize the Biome-BGC model.Then,the gross primary productivity(GPP)and evapotranspiration(ET)were simulated.The results show that the DDS parameter calibration method has a better performance.The annual GPP and ET show an increasing trend,while the WUE shows a decreasing trend.Meanwhile,ET and GPP reach their peaks in July and August,respectively,and WUE shows a“dual-peak”pattern,reaching peaks in May and November.Furthermore,according to the simulation results for the next nearly 100 years,the ensemble average GPP and ET exhibit a significant increasing trend,and the growth rate under the SSP5–8.5 scenario is greater than that under the SSP2–4.5 scenario.WUE shows an increasing trend under the SSP2–4.5 scenario and a significant increasing trend under the SSP5–8.5 scenario.This study has important scientific significance for carbon and water cycle prediction and vegetation ecological protection on the Tibetan Plateau.

Prevalence of vegetation browning in China’s drylands under climate change

Vegetation greening has long been acknowledged,but recent studies have pointed out that vegetation greening is possibly stalled or even reversed.However,detailed analyses about greening reversal or increased browning of vegetation remain scarce.In this study,we utilized the normalized difference vegetation index(NDVI)as an indicator of vegetation to investigate the trends of vegetation greening and browning(monotonic,interruption,and reversal)through the breaks for the additive season and trend(BFAST)method across China’s drylands from 1982 to 2022.It also reveals the impacts of ecological restoration programs(ERPs)and climate change on these vegetation trends.We find that the vegetation displays an obvious pattern of east-greening and west-browning in China’s drylands.Greening trends mainly exhibits monotonic greening(29.8%)and greening with setback(36.8%),whereas browning shows a greening to browning reversal(19.2%).The increase rate of greening to browning reversal is 0.0342/yr,which is apparently greater than that of greening with setback,0.0078/yr.This research highlights that,under the background of widespread vegetation greening,vegetation browning is pro-gressively increasing due to the effects of climate change.Furthermore,the ERPs have significantly increased vegetation coverage,with the increase rate in 2000-2022 being twice as much as that of 1982-1999 in reveg-etation regions.Vegetation browning in southwestern Qingzang Plateau is primarily driven by adverse climatic factors and anthropogenic disturbances,which offset the efforts of ERPs.

A New Economic Assessment Index for the Impact of Climate Change on Grain Yield

The impact of climate change on agriculture has received wide attention by the scientific community. This paper studies how to assess the grain yield impact of climate change, according to the climate change over a long time period in the future as predicted by a climate system model. The application of the concept of a traditional ＂yield impact of meteorological factor （YIMF）＂ or ＂yield impact of weather factor＂ to the grain yield assessment of a decadal or even a longer timescale would be suffocated at the outset because the YIMF is for studying the phenomenon on an interannual timescale, and it is difficult to distinguish between the trend caused by climate change and the one resulting from changes in non-climatic factors. Therefore, the concept of the yield impact of climatic change （YICC）, which is defined as the difference in the per unit area yields （PUAY） of a grain crop under a changing and an envisaged invariant climate conditions, is presented in this paper to assess the impact of global climate change on grain yields. The climatic factor has been introduced into the renowned economic Cobb-Douglas model, yielding a quantitative assessment method of YICC using real data. The method has been tested using the historical data of Northeast China, and the results show that it has an encouraging application outlook.

Climate change adaptation, greenhouse gas mitigation and economic profitability of conservation agriculture: Some examples from cereal systems of Indo-Gangetic Plains

Achieving sustainability of the cereal system in the Indo-Gangetic Plains（IGP）of India under progressive climate change and variability necessitates adoption of practices and technologies that increase food production,adaptation and mitigation in a sustainable way.This paper examines conservation agriculture（CA）from the perspective of：（i）increased yield and farm income,（ii）adaptation to heat and water stresses,and（iii）reduction in greenhouse gas（GHGs）emissions.The analyses and conclusions are based on the literature and evidences from a large number of on-station as well as farmers’field trials on CA in the cereal systems of IGP.Our analyses show that CA-based system substantially reduces the production cost（up to 23%）but produces equal or even higher than conventional system;thereby increasing economic profitability of production system.CA-based production systems also moderated the effect of high temperature（reduced canopy temperature by 1–4°C）and increased irrigation water productivity by 66–100%compared to traditional production systems thus well adapting to water and heat stress situations of IGP.Our continuous monitoring of soil flux of CO2,N2O and CH4 revealed that CA-based rice-wheat systems emit 10–15%less GHGs than conventional systems.This is the first time that CA and its components are synthesized and analyzed from food security-climate change nexus.From this holistic analysis,we suggest that wide-scale promotion of suitable CA practices by integrating into national agriculture development strategy is a way forward to address food security,climate change adaptation and mitigation challenges faced by present agriculture.

Influence of Climate and Socio-Economic Factors on the Spatio-Temporal Variability of Soil Organic Matter: A Case Study of Central Heilongjiang Province, China

For the scientific management of farmland, it is significant to understand the spatio-temporal variability of soil organic matter and to study the influences of related factors. Using geostatistical theory, GIS spatial analysis, trend analysis and a Geographically Weighted Regression （GWR） model, this study analyzed the response of soil organic matter to climate and socio-economic factors in central Heilongjiang Province during the past 25 years. Second soil survey data of China for 1979-1985, 2005 field sampling data, climate observations and socio-economic data for 1980-2005 were analyzed. First, soil organic matter in 2005 was spatially interpolated using the Co-Kriging method along with auxiliary data sets of soil type and pH. The spatio-temporal variability was then studied by comparison with the 1980s second soil census data. Next, the temporal trends in climate and socio-economic factors over the past 25 years were investigated. Finally, we examined the variation of the response of soil organic matter to climate and socio-economic factors using the GWR model spatially and temporally. The model showed that 53.82% area of the organic matter content remained constant and 29.39% has decreased during the past 25 years. The impact of precipitation on organic matter content is mainly negative, with increasing absolute values of the regression coefficient. The absolute value of regression coefficient of annual average temperature has decreased, and more areas are now under its negative effects. In addition, the areas of positive regression coefficient of annual sunshine hours have northward shifted, with the increasing absolute value of positive coefficient and decreasing absolute value of negative coefficient. The areas of positive regression coefficient of mechanized farming as a socio-economic factor have westward shifted, with the increasing absolute value of negative coefficient and decreasing absolute value of positive coefficient. The area of regions with the positive regression coefficient of irrigation has expanded. The regions with positive regression coefficient of fertilizer use have shrinked. The positive regression coefficient of mulch film consumption has significantly increased. The regression coefficient of pesticide consumption was mainly positive in the west of the study area, while it was negative to the east. Generally, GWR model is capable to investigate the influence of both climatic and socio-economic factors, avoided the insufficiency of other research based on the single perspective of climatic or socio-economic factors. Therefore, we can conclude that GWR model could provide methodological support for global change research and serve as basic reference for cultivated land quality improvement and agricultural decision making.

Spatiotemporal changes of gross primary productivity and its response to drought in the Mongolian Plateau under climate change

Gross primary productivity(GPP)of vegetation is an important constituent of the terrestrial carbon sinks and is significantly influenced by drought.Understanding the impact of droughts on different types of vegetation GPP provides insight into the spatiotemporal variation of terrestrial carbon sinks,aiding efforts to mitigate the detrimental effects of climate change.In this study,we utilized the precipitation and temperature data from the Climatic Research Unit,the standardized precipitation evapotranspiration index(SPEI),the standardized precipitation index(SPI),and the simulated vegetation GPP using the eddy covariance-light use efficiency(EC-LUE)model to analyze the spatiotemporal change of GPP and its response to different drought indices in the Mongolian Plateau during 1982-2018.The main findings indicated that vegetation GPP decreased in 50.53% of the plateau,mainly in its northern and northeastern parts,while it increased in the remaining 49.47%area.Specifically,meadow steppe(78.92%)and deciduous forest(79.46%)witnessed a significant decrease in vegetation GPP,while alpine steppe(75.08%),cropland(76.27%),and sandy vegetation(87.88%)recovered well.Warming aridification areas accounted for 71.39% of the affected areas,while 28.53% of the areas underwent severe aridification,mainly located in the south and central regions.Notably,the warming aridification areas of desert steppe(92.68%)and sandy vegetation(90.24%)were significant.Climate warming was found to amplify the sensitivity of coniferous forest,deciduous forest,meadow steppe,and alpine steppe GPP to drought.Additionally,the drought sensitivity of vegetation GPP in the Mongolian Plateau gradually decreased as altitude increased.The cumulative effect of drought on vegetation GPP persisted for 3.00-8.00 months.The findings of this study will improve the understanding of how drought influences vegetation in arid and semi-arid areas.

Analysing the Potential Impact of Climate Change on the Hydrological Regime of the Upper Benue River Basin (North Cameroon)

In this study, we analyse the climate variability in the Upper Benue basin and assess its potential impact on the hydrology regime under two different greenhouse gas emission scenarios. The hydrological regime of the basin is more vulnerable to climate variability, especially precipitation and temperature. Observed hydroclimatic data (1950-2015) was analysed using a statistical approach. The potential impact of future climate change on the hydrological regime is quantified using the GR2M model and two climate models: HadGEM2-ES and MIROC5 from CMIP5 under RCP 4.5 and RCP 8.5 greenhouse gas emission scenarios. The main result shows that precipitation varies significantly according to the geographical location and time in the Upper Benue basin. The trend analysis of climatic parameters shows a decrease in annual average precipitation across the study area at a rate of -0.568 mm/year which represents about 37 mm/year over the time 1950-2015 compared to the 1961-1990 reference period. An increase of 0.7°C in mean temperature and 14% of PET are also observed according to the same reference period. The two climate models predict a warming of the basin of about 2°C for both RCP 4.5 and 8.5 scenarios and an increase in precipitation between 1% and 10% between 2015 and 2100. Similarly, the average annual flow is projected to increase by about +2% to +10% in the future for both RCP 4.5 and 8.5 scenarios between 2015 and 2100. Therefore, it is primordial to develop adaptation and mitigation measures to manage efficiently the availability of water resources.

Responses of Annual Variability of Vegetation NPP to Climate Variables Using Satellite Techniques in Gadarif State, Sudan

Plants play an essential role in matter and energy transformations and are key messengers in the carbon and energy cycle. Net primary productivity (NPP) reflects the capability of plants to transform solar energy into photosynthesis. It is very sensible for factors affecting on vegetation variability such as climate, soils, plant characteristics and human activities. So, it can be used as an indicator of actual and potential trend of vegetation. In this study we used the actual NPP which was derived from MODIS to assess the response of NPP to climate variables in Gadarif State, from 2000 to 2010. The correlations between NPP and climate variables (temperature and precipitation) are calculated using Pearson’s Correlation Coefficient and ordinary least squares regression. The main results show the following 1) the correlation Coefficient between NPP and mean annual temperature is Somewhat negative for Feshaga, Rahd, Gadarif and Galabat areas and weakly negative in Faw area;2) the correlation Coefficient between NPP and annual total precipitation is weakly negative in Faw, Rahd and Galabat areas and somewhat negative in Galabat and Rahd areas. This study demonstrated that the correlation analysis between NPP and climate variables (precipitation and temperature) gives reliably result of NPP responses to climate variables that is clearly in a very large scale of study area.

Discovery of the Significant Impacts of Swell Propagation on Global Wave Climate Change

This study compared the differences in the wave climate in the South China Sea and North Indian Ocean under these two datasets:ERA-40 wave reanalysis and Mei’s hindcast wave data.In the numerical calculation of regional ocean waves,the wave climate characteristics exhibited significant bias if the influence of external swells(swells from afar)was not fully considered,which may provide an incorrect basis for global climate change analysis.1)The trends of the significant wave height(SWH)obtained from the two datasets showed significant differences,such as those of the Bay of Bengal and the Java Sea in June-July-August.For the past 45 years,SWH from ERA-40(SWH-ERA)exhibited a significant annual increase in low-latitude waters of the North Indian Ocean(0.2-0.6 cm yr^(-1))and South China Sea(0.2-0.8 cm yr^(-1)).2)In the Bay of Bengal,the SWH-ERA in each month was generally 0.5 m higher than the SWH from Mei’s hindcast wave data(SWH-Mei)and can reach 1.0 m higher in some months.3)In the Bay of Bengal,SWH-ERA and SWH-Mei increased significantly at annual rates of 0.13 and 0.27 cm yr^(-1),respectively.This increasing trend was mainly reflected after 1978.SWH-ERA showed a trough in 1975(1.33 m)and a crest in 1992(1.83 m),which were not reflected in SWH-Mei.

Differential response of radial growth and δ^(13)C in Qinghai spruce(Picea crassifolia) to climate change on the southern and northern slopes of the Qilian Mountains in Northwest China

Tree radial growth can have significantly differ-ent responses to climate change depending on the environ-ment.To elucidate the effects of climate on radial growth and stable carbon isotope(δ^(13)C)fractionation of Qing-hai spruce(Picea crassifolia),a widely distributed native conifer in northwestern China in different environments,we developed chronologies for tree-ring widths and δ^(13)C in trees on the southern and northern slopes of the Qilian Mountains,and analysed the relationship between these tree-ring variables and major climatic factors.Tree-ring widths were strongly influenced by climatic factors early in the growing season,and the radial growth in trees on the northern slopes was more sensitive to climate than in trees on the southern.Tree-ring δ^(13)C was more sensitive to climate than radial growth.δ^(13)C fractionation was mainly influenced by summer temperature and precipitation early in the growing season.Stomatal conductance more strongly limited stable carbon isotope fractionation in tree rings than photosynthetic rate did.The response between tree rings and climate in mountains gradually weakened as climate warmed.Changes in radial growth and stable carbon isotope fractionation of P.crassifolia in response to climate in the Qilian Mountains may be further complicated by continued climate change.

Regional Climate Models in the Simulation of the Drought of the 1970’s and 1980’s Years in Senegal (In West Africa)

West Africa was hit by an unprecedented drought in the 1970’s and 1980’s years, with dramatic consequences for surface and groundwater resources. In the context of climate change, there are many studies for the prediction of the increase in the occurrence of these droughts. To predict this situation in the Senegalese region, it is necessary to use regional climate models, which carrying out the study. This work deals with the interest to examine the capacity of the RCMs (regional climate models) in order to reproduce the deficit on the 1970’s year rainfall in Senegal. In this work, we used daily precipitation data from five (5) regional climate models to characterize the droughts in Senegal by using the SPI (Standardized Precipitation Index) on different time scales (3, 6, 12 and 24 months). For this purpose, the index was calculated over two distinct periods: 1951-1969 and 1970-1990. The results show that the period 1970-1990 was drier than the period 1951-1969. For the zonal average, the results show that the North of Senegal was more affected by this deficit rainfall than the South part. The analysis of the interannual variability of rainfall for some stations in Senegal shows that the drought did not start at the same time throughout the zone.