Climate change impact projections at the catchment scale in Tunisia using the multi-model ensemble mean approach

In this work, we developed a mean projection for climate change and assessed its impact on some hydro-meteorological indicators relevant to climatic condition, precipitation extremes magnitude and frequency for the Siliana catchment in Tunisia based on an ensemble of seven combinations of global circulation models (GCMs) and regional climate models (RCMs) derived from the EU-FP6 ENSEMBLES project. We performed quantile-based mapping (QM) bias correction technique of climate model projection using local observations. Because there is no warranty that the best climate model based on its performances in reproducing historic climate will be superior to other models in simulating future climate, we used the multi-model ensemble (MME) mean approach to derive a mean projection as the best guess for climate change projection for the Siliana catchment. We also quantified the uncertainty of the MME in the projected change in the selected indicators by comparing their values in the reference period (1981–2010) to these in the future period (2041–2070). Results reveal that the Siliana catchment will be prone to drier and warmer climate in the future with less rainy days for each month. The uncertainty associated with the MME projection suggests that no clear general tendency for extreme rainy days in the future is expected. These findings highlight the need to consider an ensemble of multi-climate models with an uncertainty framework if reliable climate change impact study is sought at the catchment scale.     

Latent Variable Modeling for Integrating Output from Multiple Climate Models

Numerical models of atmosphere–ocean circulation are widely used to understand past climate and to project future climate change. Although the same laws of physics, chemistry, and fluid dynamics govern any general circulation model, each model’s formulations and parameterizations are different, yielding different projections. Notwithstanding, models within an ensemble will have varying degrees of similarity for different outputs of interest. Multi-model ensembles have been used to increase forecast skill by using simple or weighted averages where weights have been obtained by considering factors such as estimated model bias and consensus with other models (Giorgi and Mearns, J. Clim. 15:1141–1158, 2002, Geophys. Res. Lett. 30:1629–1632, 2003; Tebaldi et al., Geophys. Res. Lett. 31:L24213, 2004, J. Clim. 18:1524–1540, 2005). This paper considers an alternative view of multi-model ensembles. For use with the North American Regional Climate Change Assessment Program (NARCCAP), multivariate statistical models are employed to characterize modes of similarity within the members of an ensemble. Specifically, we propose a spatially-correlated latent variable model which facilitates the exploration of when, where, and how regional climate models are similar, and what factors best predict observed locations of model convergence.     

Assessment of climate change impact on the stream-flow in a typical debris flow watershed of Jianzhuangcuan catchment in Shaanxi Province, China

The study on the stream-flow change associated with future climate change scenarios has a practical significance for local socio-economic development and eco-environmental protection. A study on the Jianzhuangcuan catchments was carried out to quantify the expected impact of climate change on the stream-flow using a multi-model ensemble approach. Climate change scenarios were developed by ensemble four Global Climate Models, which showed good performance for Jianzhuangcuan catchment. Soil and Water Assessment Tool (SWAT), a physically based distributed hydrological model, was used to investigate the impacts on stream-flow under climate change scenarios. The model was calibrated and validated using daily stream-flow records. The calibration and validation results showed that the SWAT model was able to simulate the daily stream-flow well, with Nash–Sutcliffe efficiency >0.83 for Yaoping Long station, for calibration and validation at daily and monthly scales. Their difference in simulating the stream-flow under future climate scenarios was also investigated. The results indicate a 0.6–0.9 °C increase in annual temperature and changes of 12.6–18.9 mm in seasonal precipitation corresponded to a change in stream-flow of about 0.62–3.67 for 2020 and 2030 scenarios. The impact of the climate change increased in both scenarios.     

CMIP5多模式集合对南亚印度河流域气候变化的模拟与预估

利用印度河流域CRU、APHRODITE和CMIP5多模式逐月气温、降水格点数据集, 评估了CMIP5模式集合对印度河流域气候变化的模拟能力; 对多模式集合数据进行了偏差订正, 并对流域2046-2065年和2081-2100年气候变化进行了预估. 结果表明: 气候模式对流域年平均气温时间变化和空间分布特征有着较强的模拟能力, 时间空间相关系数均达到了0.01的显著性水平, 尤其对夏季气温的模拟要优于其他季节; 模式对降水的季节性波动也有着较好的模拟能力. 偏差订正后的预估结果表明, RCP2.6、4.5、8.5情景下, 相对于基准期(1986-2005年), 21世纪中期(2046-2065年)和末期(2081-2100年)整个流域年平均气温都有一定上升, 且流域上游增幅较大; 除RCP4.5情景下21世纪中期流域有弱减少趋势外, 年降水量都将有一定增长. 未来夏季持续升温将引起源区冰川的进一步消融, 春季降水对于中高海拔地区水资源的贡献将减弱; 流域北部高海拔区域冬季降水的增加有助冰川累积和上游水资源的增加, 东部高海拔区域冬季降水的减少会减少上游水资源. 两时期夏季降水都有一定的增长, 洪涝的发生风险加大; 流域暖事件和强降水事件也将可能增多.     

Multi-Physics ensemble prediction of tropical cyclone movement over Bay of Bengal

Ensemble prediction methodology based on variations in physical process parameterizations in tropical cyclone track prediction has been assessed. Advanced Research Weather Research and Forecasting model with 30-km resolution was used to make 5-day simulation of the movement of Orissa super cyclone (1999), one of the most intense tropical cyclones over the North Indian Ocean. Altogether 36 ensemble members with all possible combinations of three cumulus convection, two planetary boundary layer and six cloud microphysics parameterization schemes were produced. A comparison of individual members indicated that Kain–Fritsch cumulus convection scheme, Mellor–Yamada–Janjic planetary boundary layer scheme and Purdue Lin cloud microphysics scheme showed better performance. The best possible ensemble formulation is identified based on SPREAD and root mean square error (RMSE). While the individual members had track errors ranging from 96–240 km at 24 h to 50–803 km at 120 h, most of the ensemble predictions show significant betterment with mean errors less than 130 km up to 120 h. The convection ensembles had large spread of the cluster, and boundary layer ensembles had significant error disparity, indicating their important roles in the movement of tropical cyclones. Six-member ensemble predictions with cloud microphysics schemes of LIN, WSM5, and WSM6 produce the best predictions with least of RMSE, and large SPREAD indicates the need for inclusion of all possible hydrometeors in the simulation and that six-member ensemble is sufficient to produce the best ensemble prediction of tropical cyclone tracks over Bay of Bengal.     

Experimental real-time multi-model ensemble (MME) prediction of rainfall during monsoon 2008: Large-scale medium-range aspects

Realistic simulation/prediction of the Asian summer monsoon rainfall on various space–time scales is a challenging scientific task. Compared to mid-latitudes, a proportional skill improvement in the prediction of monsoon rainfall in the medium range has not happened in recent years. Global models and data assimilation techniques are being improved for monsoon/tropics. However, multi-model ensemble (MME) forecasting is gaining popularity, as it has the potential to provide more information for practical forecasting in terms of making a consensus forecast and handling model uncertainties. As major centers are exchanging model output in near real-time, MME is a viable inexpensive way of enhancing the forecasting skill and information content. During monsoon 2008, on an experimental basis, an MME forecasting of large-scale monsoon precipitation in the medium range was carried out in real-time at National Centre for Medium Range Weather Forecasting (NCMRWF), India. Simple ensemble mean (EMN) giving equal weight to member models, bias-corrected ensemble mean (BCEMn) and MME forecast, where different weights are given to member models, are the products of the algorithm tested here. In general, the aforementioned products from the multi-model ensemble forecast system have a higher skill than individual model forecasts. The skill score for the Indian domain and other sub-regions indicates that the BCEMn produces the best result, compared to EMN and MME. Giving weights to different models to obtain an MME product helps to improve individual member models only marginally. It is noted that for higher rainfall values, the skill of the global model rainfall forecast decreases rapidly beyond day-3, and hence for day-4 and day-5, the MME products could not bring much improvement over member models. However, up to day-3, the MME products were always better than individual member models.     

Multi-model ensemble schemes for predicting northeast monsoon rainfall over peninsular India

The northeast (NE) monsoon season (October, November and December) is the major period of rainfall activity over south peninsular India. This study is mainly focused on the prediction of northeast monsoon rainfall using lead-1 products (forecasts for the season issued in beginning of September) of seven general circulation models (GCMs). An examination of the performances of these GCMs during hindcast runs (1982–2008) indicates that these models are not able to simulate the observed interannual variability of rainfall. Inaccurate response of the models to sea surface temperatures may be one of the probable reasons for the poor performance of these models to predict seasonal mean rainfall anomalies over the study domain. An attempt has been made to improve the accuracy of predicted rainfall using three different multi-model ensemble (MME) schemes, viz., simple arithmetic mean of models (EM), principal component regression (PCR) and singular value decomposition based multiple linear regressions (SVD). It is found out that among these three schemes, SVD based MME has more skill than other MME schemes as well as member models.     

未来气候情景下长江上游区域积雪时空变化分析——基于CMIP5多模式集合数据

基于21个CMIP5全球气候模式集合数据,耦合VIC模型,预估了未来30年（2011-2040年）RCP2.6、RCP4.5和RCP8.5 三个情景下长江上游区域积雪的时空变化。结果表明:与基准期1970-1999年相比,长江上游区域未来30年的多年平均气温和各月平均气温都将升高1~2℃,其中冬季和春季升温较大;平均年降水量将增加3%~4%,但秋、冬季降水有所减小。未来30年平均积雪深相对于基准期将减小37.8%左右,在积雪过程中达到最大积雪深的时间与基准期基本相同,而融雪开始的时间略有延后;从空间变化来看,冬季（1月份）长江上游区域大部分地区的积雪深都呈现减小趋势,部分地区积雪深减小超过了50%。     

Surface air temperature changes over the Tibetan Plateau: Historical evaluation and future projection based on CMIP6 models

With its amplification simultaneously emerging in cryospheric regions, especially in the Tibetan Plateau, global warming is undoubtedly occurring. In this study, we utilized 28 global climate models to assess model performance regarding surface air temperature over the Tibetan Plateau from 1961 to 2014, reported spatiotemporal variability in surface air temperature in the future under four scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5), and further quantified the timing of warming levels (1.5, 2, and 3 °C) in the region. The results show that the multimodel ensemble means depicted the spatiotemporal patterns of surface air temperature for the past decades well, although with differences across individual models. The projected surface air temperature, by 2099, would warm by 1.9, 3.2, 5.2, and 6.3 °C relative to the reference period (1981–2010), with increasing rates of 0.11, 0.31, 0.53, and 0.70 °C/decade under the SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 scenarios for the period 2015–2099, respectively. Compared with the preindustrial periods (1850–1900), the mean annual surface air temperature over the Tibetan Plateau has hit the 1.5 °C threshold and will break 2 °C in the next decade, but there is still a chance to limit the temperature below 3 °C in this century. Our study provides a new understanding of climate warming in high mountain areas and implies the urgent need to achieve carbon neutrality.     

A review of observed and projected changes in climate for the islands in the Caribbean

Observed and projected changes in climate have serious socio-economic implications for the Caribbean islands. This article attempts to present basic climate change information—based on previous studies, available observations and climate model simulations—at spatial scales relevant for islands in the Caribbean. We use the General Circulation Model (GCM) data included in the Coupled Model Intercomparison Project phase 3 (CMIP3) and the UK Hadley Centre regional climate model (RCM) data to provide both present-day and scenario-based future information on precipitation and temperature for individual island states. Gridded station observations and satellite data are used to study 20th century climate and to assess the performance of climate models. With main focus on precipitation, we also discuss factors such as sea surface temperature, sea level pressure and winds that affect seasonal variations in precipitation. The CMIP3 ensemble mean and the RCM successfully capture the large-scale atmospheric circulation features in the region, but show difficulty in capturing the characteristic bimodal seasonal cycle of precipitation. Future drying during the wet season in this region under climate change scenarios has been noted in previous studies, but the magnitude of change is highly uncertain in both GCM and RCM simulations. The projected decrease is more prominent in the early wet season erasing the mid-summer drought feature in the western Caribbean. The RCM simulations show improvements over the GCM mainly due to better representation of landmass, but its performance is critically dependent on the driving GCM. This study highlights the need for high-resolution observations and ensemble of climate model simulations to fully understand climate change and its impacts on small islands in the Caribbean.     

Construction of Statistical-Dynamic Prediction Model for the Asian-Australian Summer Monsoon Based on the Predictable Mode Analysis Method and Assessment of Its Predictive Skills

Due to the limitations of model performances, the predictive skills of current climate models for the Asian-Australian summer monsoon precipitation are still poor. The prediction based on the combination of statistical and dynamic approaches is an effective way to improve the predictive skills. We used such method to identify the predictable modes of the Asian-Australian summer monsoon precipitation with clear physical interpretation from the historical observational data. Then we combined the principal components time series of these modes predicted by the coupled models, which is derived from the seasonal prediction experiments in the ENSEMBLES project, and the corresponding spatial patterns derived from the above observational analysis to reconstruct the precipitation field. These formed a statistical-dynamic seasonal prediction model for the Asian-Australian summer monsoon precipitation. We analyzed the predictive skills of the model at 1-, 4-and 7-month leads. The result shows that the forecast skills of the statistical-dynamic prediction model are higher than those of the simple dynamic predictions. In addition, the predictive skills of the Multi-Model Ensemble (MME) mean are superior to those of any individual models. Therefore, it is very necessary to implement multi-model ensemble prediction for the monsoon precipitation.     

Application of Bias Correction and Spatial Disaggregation in Removing Model Biases and Downscaling over China

Global Climate Models (GCM) are the primary tools for studying past climate change and evaluating the projected future response of climate system to changing atmospheric composition. However, the state of art GCMs contain large biases in regional or local scales and are often characterized by low resolution which is too coarse to provide the regional scale information required for regional climate change impact assessment. A popular technique, Bias Correction and Spatial Disaggregation (BCSD), are widespreadly employed to improve the quality of the raw model output and downscaling throughout the world. Unfortunately, this method has not been applied in China. Consequently, the detailed principle and procedure of BCSD are introduced systematically in this study. Furthermore, the applicability of BCSD over China is also examined based on an ensemble of climate models from phase five of the Coupled Model Intercomparison Project (CMIP5), though the excellent performance of it has been validated for other parts of the world in many works. The result shows that BCSD is an effective, model independent approach to removing biases of model and downscaling. Finally, application scope of BCSD is discussed, and a suite of fine resolution multimodel climate projections over China is developed based on 34 climate models and two emissions scenarios (RCP4.5 and RCP8.5) from CMIP5.     

À l'échelle des continents : le regard des modèles

The credibility of models simulating climate change over different continental regions is based upon analysis of the dispersion of results of simulation ensembles. This analysis, associated to the analysis of model biases, shows that there is no systematic link between these biases and the simulated climate changes. The reduction of uncertainties on the scales of different regions implies a better definition of anthropogenic emission scenarios and the development of regional climate models. Climate change detection on regional scales appears to be a promising way of reinforcing the reliability of these models and scenarios. To cite this article: S. Planton, C. R. Geoscience 335 (2003).     

基于CMIP6模式数据的1961—2100年青藏高原地表气温时空变化分析

基于第六次国际耦合模式比较计划（CMIP6）的22个地球气候/系统模式模拟数据,分析了1961—2100年期间青藏高原年均地表气温在不同情景下的时空变化。结果表明,多模式集合平均的模拟结果优于大多数单个模式。由于共享社会经济路径（SSP）和辐射强迫的不同,在SSP1-2.6、SSP2-4.5、SSP3-7.0和SSP5-8.5四种情景下,2015—2100年间青藏高原年均地表气温的增温趋势分别为0.10 ℃·（10a）^-1、0.29 ℃·（10a）^-1、0.53 ℃·（10a）^-1和0.69 ℃·（10a）^-1,帕米尔高原、藏北高原中西部和巴颜喀拉山区为三个升温中心。相对于1995—2014年参考时段,到本世纪中期（2041—2060年）,青藏高原区域年均地表气温将分别增加1.37 ℃、1.72 ℃、1.98 ℃和2.30 ℃,而到本世纪末期（2081—2100年）,年均地表气温将分别增加1.42 ℃、2.65 ℃、4.28 ℃和5.38 ℃。与《巴黎协定》提出的到本世纪末全球平均气温升高不超过2 ℃目标相比,无论在哪种情景下,到本世纪中期时青藏高原年均地表气温相对于工业革命前均升高超过2 ℃,这会造成极大的气候生态环境问题。     

气候变化对地表水资源的影响

总结了气候变化对水文水资源影响方面的研究方法, 分析了气候变化条件下水文水资源变化的研究现状和存在问题.并以山西省和黄河源区为研究对象, 以分布式水文模型为工具、GCMs输出的气候情景为输入条件, 针对不同的下垫面特征建立不同的分布式水文模型, 分别采用气候情景趋势分析结果和直接利用GCMs输出结果两类方法确定气候变化的数据源, 对研究区域未来的地表径流过程和地表水资源可能的变化趋势进行了研究.从气候情景的预测结果来看, 未来50年山西省的气温和降水都呈增加趋势, 但由于各自对水资源带来的影响不同, 将使山西省水资源呈现先增加后减少的趋势; 且由于冬季气温和降水的增幅比夏季大, 使得未来山西省的水资源年内分布有略微平缓的趋势.对黄河源区而言, 虽然未来100年内的降水和气温都呈增加趋势, 但由于降水增长引起的地表水资源的增加不足以抵消气温升高带来的影响, 因此将导致径流量不断降低的总体趋势, 并使径流年内分布略趋平缓, 而年际分布将越来越不均匀, 旱涝威胁日趋严峻.      

气候变化下淮河流域极端洪水情景预估

利用IPCC第4次评估公开发布的22个全球气候模式在A1B、A2和B1三种典型排放情景下的未来气温和降水预测结果,结合新安江月分布式水文模型,在对模型验证效果良好的基础上,参照集合预报方法,对未来90年(2010～2099年)气候变化下淮河流域的极端洪水进行预估。研究结果表明,从出现概率来看,淮河流域未来可能发生极端洪水年份的密集程度从大到小依次为A2情景、A1B情景、B1情景。A1B情景下,21世纪下半叶出现极端洪水的可能性增大,A2情景在2035～2065年以及2085年以后是极端洪水发生较为集中的时期。B1情景在21世纪70年代左右发生极端洪水的可能性较大。综合各种极端事件的定义方法,将极端洪水划定3个洪水量级。A2情景预估极端洪水的平均洪量在3种情景中最大,B1情景最小。3种情景未来一级极端洪水发生比例都比历史上偏大,A2情景下增加最多。二级极端洪水都较历史略有减少,三级极端洪水减少最显著。3种情景下各个量级极端洪水所占比例各不相同,A1B和A2情景二级以上极端洪水出现比例较大,B1情景下极端洪水量级多为三级,超1954年的一级极端洪水所占比例较小。     

基于CMIP6模式的黄河上游地区未来气温模拟预估

利用第六次国际耦合模式比较计划（CMIP6）提供的5个气候模式,并结合基于地面气象站的CN05.1气象资料,评估了CMIP6模式对黄河上游地区1961—2014年气温变化的模拟能力。基于7个共享社会经济路径及代表性浓度路径（SSP-RCP）组合情景,结合多模式集合平均预估了2015—2100年黄河上游地区年均气温和季平均气温的时空变化规律。结果表明：多模式集合平均能较好地模拟黄河上游地区历史平均气温的空间分布格局与年变化。7个未来情景一致表明,2015—2100年黄河上游地区年平均气温呈现波动上升趋势［0.03～0.82 ℃?（10a）^-1］。其中,低辐射强迫情景下（SSP1-1.9、SSP1-2.6及SSP4-3.4）气温先呈现增加趋势,21世纪中期到达增幅峰值,之后增温呈现放缓趋势;而中、高辐射强迫情景下（SSP2-4.5、SSP3-7.0、SSP4-6.0及SSP5-8.5）气温表现为持续上升态势。空间上,未来气温增幅显著的区域位于黄河上游西部地区;时间上,呈现夏季增温快,春季增温慢。四季增温的空间分布呈现出一致特征,表现为西部增温强于东部,北部增温强于南部。研究结果可为黄河流域水资源管理及气候变化的适应性研究提供科学依据。     

气候变化对黄河流域生态径流影响预估

人类活动和气候变化严重改变了黄河水文情势和生态径流,分析未来气候变化对河流生态的影响对流域水资源管理和长期规划意义重大。本文对第六次国际耦合模式比较计划（CMIP6）的13个全球气候模式数据进行偏差订正,驱动水文模型进行径流模拟,应用流量历时曲线方法分析SSP1-2.6、SSP2-4.5、SSP5-8.5情景下2026年至21世纪末年、季节尺度的花园口生态径流变化。结果表明:订正能明显降低降水、气温模拟偏差;人类活动严重影响了1986-2010年花园口生态径流;2026-2100年年均气温和年降水量增加趋势显著,低排放情景增速慢,高排放情景增速快;气候变化可在一定程度上缓解水库调控、水土保持等人类活动对生态径流的负面影响,SSP5-8.5情景缓解程度最高,冬季缓解程度最高,夏、秋季最低。     

To what extent did changes in temperature affect China's socioeconomic development from the Western Han Dynasty to the Five Dynasties period?

Analysing the contribution of climate and non-climate change factors to social development and the occurrence of historical events represents important research on the impact of climate change. This study identifies combinations of social subsystem indices affected by temperature changes using the conceptual framework of food security, a priori knowledge and logical reasoning to statistically analyse three 10-year data series (grain harvest grades, famine indices and economic levels) from the Western Han Dynasty to the Five Dynasties period of ancient China (210 bc to 960 ad ). The results are as follows. For 94 of the 118 decades in the study period, social development was relatively directly related to temperature effects. On a decadal scale, against a cold background, grain production was closely related to temperature conditions in 40.7% of all decades. Economic prosperity and depressions in 5.1 and 21.2% of these decades, respectively, were directly related to temperature effects. Against a warm background, grain production was closely related to temperature conditions in 39% of all decades. Economic prosperity and depressions in 22 and 8.5% of these decades, respectively, were directly related to the temperature effects. The century and decadal-scale characteristics were the same. Specifically, when mostly negative combinations of natural–socioeconomic factors dominated, the proportion of decades was slightly higher in cold than in warm periods. This case study enables a scientific understanding of the effect of changes in mean climate values/trends on social development and further demonstrates the different effects of the climate change process and mechanism. Climate cooling and warming may bring more positive than negative impacts in some regions and more negative than positive impacts in others. Complex feedback may amplify or reduce the impact of climate cooling and warming. Climate that evolves unfavourably has an impact more strongly correlated with the socioeconomic system's vulnerability and adaptability. © 2020 John Wiley & Sons, Ltd.     

Progress in Researches on Ensemble Forecasting of Extreme Weather Based on Numerical Models

Under the background of climate change, extreme weather events (e.g., heavy rainfall, heat wave, and cold damage) in China have been occurring more frequently with an increasing trend of induced meteorological disasters. Therefore, it is of great importance to carry out research on forecasting of extreme weather. This paper systematically reviewed the primary methodology of extreme weather forecast, current status in development of ensemble weather forecasting based on numerical models and their applications to forecast of extreme weather, as well as progress in approaches for correcting ensemble probabilistic forecast. Nowadays, the forecasting of extreme weather has been generally dominated by methodology using dynamical models. That is to say, the dynamical forecasting methods based on ensemble probabilistic forecast information have become prevailing in current operational extreme weather forecast worldwide. It can be clearly found that the current major directions of research and development in this field are the application of ensemble forecasts based on numerical models to forecasting of extreme weather, and its improvement through bias correction of ensemble probabilistic forecast. Based on a relatively comprehensive review in this paper, some suggestions with respect to development of extreme weather forecast in future were further given in terms of the issues of how to propose effective approaches on improving level of identification and forecasting of extreme events.