| Climate change on the Tibetan Plateau will have a great impact on human society,economy,and ecological environment.Compared with other parts of the world,the accelerated warming of the Tibetan Plateau in the past few decades,which is called the amplification effect of temperature change on the Tibetan Plateau,has become a hot spot in the field of climate change research and attracted the attention of the scientific community.The amplification degree of plateau temperature change,possible reasons and how to change in the future are of great application value to deeply understand the impact,risk and response of climate change on the Tibetan Plateau,and are key scientific problems to be solved urgently.In view of this,this paper analyzed the amplification effect of the observed temperature change on the Tibetan Plateau by using the multi-sliding time series analysis method from 1961 to 2018.Combined with the observation data of meteorological stations in China and the model data of the Sixth International Coupled Model Comparison Program(CMIP6),the simulation ability of the model for the amplification effect of plateau temperature change is systematically evaluated.On this basis,the optimal fingerprint method is used to attribute the amplification effect of plateau temperature change.From the perspective of climate feedback,this study analyzed the possible mechanism of plateau temperature change amplification.Finally,based on the constraint prediction method of observation results,this study tried to constrain and predict the amplification effect of future plateau temperature change.The main conclusions are as follows:It is found that during the period of 1961-2018,the annual average temperature change in the plateau region is 0.13℃/10 years compared with the global land,and the temperature change in the central,western and northeastern parts of Tibetan Plateau is the most significant.Judging from the seasonal changes,the plateau temperature changes in autumn and winter have a strong amplification.It should be pointed out that the time scale of temperature change has an important influence on the plateau amplification effect: on the 30-year time scale,the plateau temperature amplification effect began in the mid-1970 s.After that,the amplification value of temperature change in winter and spring on the plateau is strong.On the time scale of less than 30 years,there are interdecadal differences in plateau amplification,especially in winter.CMIP6 model has a certain simulation ability for plateau temperature amplification effect,but there are differences between different models in the simulation ability of plateau amplification effect,and the amplification effect of plateau temperature change is underestimated as a whole.When all 47 models are used to analyze the trend of temperature change,the amplification effect of temperature change on Tibetan Plateau can hardly be reproduced.The biased estimation of the model on the amplification of plateau warming is mainly caused by the obvious underestimation of the amplification effect of plateau winter warming.Based on the comprehensive evaluation methods such as Taylor diagram and DISO index,the simulation ability of climate model is evaluated,and 15 models with the best simulation results are selected,which obviously improve the simulation ability of the amplification effect of plateau warming and can basically reflect the amplification effect of plateau temperature change on the time scale of more than 30 years.The amplification of the surface temperature change in the Tibetan Plateau relative to the global land surface temperature change can be attributed to human activities.The historical simulation results of CMIP6 from 1961 to 2014 show that the amplification effect of plateau temperature change is mainly influenced by human forcing,and the natural external forcing may weaken the amplification effect of plateau temperature.The plateau amplification caused by anthropogenic forcing is mainly influenced by greenhouse gas forcing,and the influence of aerosol forcing on plateau amplification is weak.In terms of spatial distribution,anthropogenic forcing,mainly anthropogenic greenhouse gas forcing,has a stronger positive contribution to the western plateau than to the eastern plateau,while natural forcing and anthropogenic aerosol forcing may have a negative contribution to the temperature enhancement in the southwestern plateau.According to the results of detection and attribution by the optimal fingerprint method,CMIP6 model can stably detect the artificially forced signals in the observed global land and Tibetan Plateau temperature changes.However,it performs better in simulating the global land surface air temperature change,but underestimates the warming simulation of the Tibetan Plateau,which leads to the plateau amplification effect in the model simulation results compared with the real observation results.In addition,the CMIP6 simulation is unable to effectively detect the influence signals of natural forcing and anthropogenic aerosol forcing on the amplification effect of plateau temperature change.Therefore,there are still uncertainties in the understanding of the role of natural forcing and artificial aerosol forcing in the amplification of plateau warming.The feedback process of snow albedo has a significant influence on the temperature amplification effect of plateau in winter and spring,and the feedback of snow albedo in winter and spring has spatial heterogeneity in plateau area.The areas with strong snow feedback in Tibetan Plateau in spring are mainly concentrated in the Pamir Plateau,northern Kunlun Mountains and the vicinity of Himalayas.The spatial distribution of snow cover/surface albedo feedback in Tibetan Plateau is different,and the big difference is mainly near the Karakorum Mountains.The winter snow feedback on the Tibetan Plateau is mainly concentrated in the vicinity of Kunlun Mountain,Himalayas and Tanggula Mountain.From the feedback of regional contribution,the spring warming near Pamirs and Karakorum Mountains is mainly due to the decrease of snow in these areas,while the spring warming in Kunlun Mountains,Himalayas and parts of the southeast plateau is mainly due to the decrease of snow and the change of snow phase.The winter warming and strengthening in Kunlun Mountain,Himalayan Mountain,Tanggula Mountain and northern Hengduan Mountain are caused by the decrease of snow cover area and the change of snow phase.The strengthening of winter warming in northwest Qinghai is mainly due to the reduction of snow in the changed area;The strengthening of winter warming in parts of southeastern Qinghai,near Qilian Mountain in northern Qinghai and near Yarlung Zangbo River is mainly caused by the change of snow phase in these areas.At the same time,the results show that the two feedback processes of snow reduction and snow phase change can partially capture the physical process of controlling the total snow albedo feedback in winter and spring.The prediction results based on observation constraints reflect that the amplification effect of future temperature changes in the Tibetan Plateau will increase with the increase of emission scenarios.The climate model has a good simulation effect on the global average temperature,and the temperature changes before and after the constraint prediction are relatively consistent.However,the model generally underestimates the temperature change in the Tibetan Plateau.In the short-term forecast period(2021-2040),the amplification effect of temperature change on Tibetan Plateau will continue,and the plateau amplification intensity is basically the same under three SSP scenarios.In the medium-term forecast period(2041-2060),the higher the emission,the more intense the plateau enlargement.In the long-term forecast period(2081-2100),the amplification of temperature change on the Tibetan Plateau under SSP5-8.5 scenario is more obvious than that under SSP1-2.6 scenario and SSP2-4.5 scenario.In the long-term forecast period,there is no obvious difference between the surface temperature change of the Tibetan Plateau and the global land surface air temperature change under the SSP1-2.6 scenario and the SSP2-4.5 scenario. |
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