Asymmetry of Surface Climate Change under RCP2.6 Projections from the CMIP5 Models

The multi-model ensemble （MME） of 20 models from the Coupled Model Intercomparison Project Phase Five （CMIP5） was used to analyze surface climate change in the 21st century under the representative con- centration pathway RCP2.6, to reflect emission mitigation efforts. The maximum increase of surface air temperature （SAT） is 1.86℃ relative to the pre-industrial level, achieving the target to limit the global warming to 2℃. Associated with the ＂increase-peak-decline＂ greenhouse gases （GHGs） concentration path- way of RCP2.6, the global mean SAT of MME shows opposite trends during two time periods： warming during 2006-55 and cooling during 2056-2100. Our results indicate that spatial distribution of the linear trend of SAT during the warming period exhibited asymmetrical features compared to that during the cool- ing period. The warming during 2006-55 is distributed globally, while the cooling during 2056-2100 mainly occurred in the NH, the South Indian Ocean, and the tropical South Atlantic Ocean. Different dominant roles of heat flux in the two time periods partly explain the asymmetry. During the warming period, the latent heat flux and shortwave radiation both play major roles in heating the surface air. During the cooling period, the increase of net longwave radiation partly explains the cooling in the tropics and subtropics, which is associated with the decrease of total cloud amount. The decrease of the shortwave radiation accounts for the prominent cooling in the high latitudes of the NH. The surface sensible heat flux, latent heat flux, and shortwave radiation collectively contribute to the especial warming phenomenon in the high-latitude of the SH during the cooling period.

Response of North Pacific and North Atlantic decadal variability to weak global warming

The Pacific Decadal Oscillation （PDO） and the Atlantic Multidecadal Variability （AMV） are the two dominant low-frequency modes in the climate system. This research focused on the response of these two modes under weak global warming. Observational data were derived from the Hadley Center Sea Ice and Sea Surface Temperature dataset （HadISST） and coupled model outputs from the Coupled Model Intercomparison Project Phase 5 （CMIP5）. Changes in PDO and AMV were examined using four models （bcc-csml-1, CCSM4, IPSL-CM5A-LR, and MPI- ESM-LR） with long weak global warming scenarios （RCP2.6）. These models captured the two low-frequency modes in both pre-industrial run and RCP2.6 run. Under weak global warming, the time scales of PDO and AMV significantly decreased while the amplitude only slightly decreased. Interestingly, the standard deviation of the North Pacific sea surface temperature anomaly （SSTA） decreased only in decadal time scale, and that of the North Atlantic SSTA decreased both in interannual and decadal time scales. The coupled system consists of a slow ocean component, which has a decadal time scale, and a fast atmospheric component, which is calculated by subtracting the decadal from the total. Results suggest that under global warming, PDO change is dominated by ocean dynamics, and AMV change is dominated by ocean dynamics and stochastic atmosphere forcing.

Mapping the current and future distributions of Onosma species endemic to Iran

Climate change may cause shifts in the natural range of species especially for those that are geographically restricted and/or endemic species.In this study,the spatial distribution of five endemic and threatened species belonging to the genus Onosma(including O.asperrima,O.bisotunensis,O.kotschyi,O.platyphylla,and O.straussii)was investigated under present and future climate change scenarios:RCP2.6(RCP,representative concentration pathway;optimistic scenario)and RCP8.5(pessimistic scenario)for the years 2050 and 2080 in Iran.Analysis was conducted using the maximum entropy(MaxEnt)model to provide a basis for the protection and conservation of these species.Seven environmental variables including aspect,depth of soil,silt content,slope,annual precipitation,minimum temperature of the coldest month,and annual temperature range were used as main predictors in this study.The model output for the potential habitat suitability of the studied species showed acceptable performance for all species(i.e.,the area under the curve(AUC)>0.800).According to the models generated by MaxEnt,the potential current patterns of the species were consistent with the observed areas of distributions.The projected climate maps under optimistic and pessimistic scenarios(RCP2.6 and RCP8.5,respectively)of 2050 and 2080 resulted in reductions and expansions as well as positive range changes for all species in comparison to their current predicted distributions.Among all species,O.bisotunensis showed the most significant and highest increase under the pessimistic scenario of 2050 and 2080.Finally,the results of this study revealed that the studied plant species have shown an acute adaptability to environmental changes.The results can provide useful information to managers to apply appropriate strategies for the management and conservation of these valuable Iranian medicinal and threatened plant species in the future.

基于多目标双层规划模型的汛期分期研究

针对以往分期方法建模时仅从某一角度构建模型,且未对分期结果在未来情景中的适用性做出评价等不足,研究基于多目标双层规划模型的汛期分期.以汾河下游为例,基于历史降水资料,建立多目标双层规划的汛期分期模型,并应用NSGA-II-POS算法进行模型求解.根据历史与未来降水资料,采用加权灰色关联度评价法对双层规划模型和Fisher最优分割法的划分结果进行评价,确定最优分期方案为前汛期6月1~27日,主汛期6月28日~9月1日,后汛期9月2~30日.结果表明,与Fisher最优分割法相比,文中方法合理可信,以期为我国流域汛期分期提供支撑.

中亚五国未来棉花和冬小麦作物需水量模拟预测数据集(2020-2100)

中亚五国地处亚洲中部,是世界最大的干旱和半干旱区之一,水资源匮乏严重,农业灌溉用水是最主要的水资源利用方式,因此研究未来主要农作物的作物需水量对探究中亚地区农业水资源的发展极其重要。本研究基于CMIP5(Fifth Coupled Model Intercomparison Project)的RCP2.6和RCP4.5气候变化情景,利用作物系数法估算2020-2100年中亚五国棉花和冬小麦的作物需水量,生成了RCP2.6和RCP4.5情景下中亚五国棉花和冬小麦逐年需水量数据集。数据的时间跨度为2020-2100年,时间分辨率为1年,空间分辨率为0.5度,数据格式为.tif。