Research On The Attribution Of Northern Hemisphere Snow Cover And Arctic Oscillation Based On CMIP5

Global warming is an indisputable fact. The attribution of global warming is a basic question of Global Change and Earth System Science. In the current, there are lots of attribution studies for temperature, precipitation and so on, but the research which is for the attribution of Northern Hemisphere snow cover is less. In this study, the temporal and spatial distributions of Northern Hemisphere snow cover were analyzed, and the simulating capability of snow cover and Arctic Oscillation (AO) of global climate models which joined the CMIP5were tested, and the results were compared with CMIP3models. Then we used the method of model ensemble average to predict the variation of Northern Hemisphere snow cover in future in RCP2.6, RCP4.5, RCP6.0and RCP8.5. The connection which existing between snow cover and sea level pressure, AO in Northern Hemisphere and the influence which radiative forcings produced on snow cover were analysised. The changes of snow cover, sea level pressure and AO which simulated by coupled models were contrasted in Historical, HistoricalNat and HistoricalGHG scenarios. Based on the results of three historical experiments, attribution of Northern Hemisphere spring snow cover and AO are analyzed. The main contents and conclusions are as the follow:(1)The significant decrease of Northern Hemisphere snow cover occurs mainly in the spring and summer, not in winter and autumn, although global warming is the most significant in winter. So, the temperature is not the only major factor that restricted the snow cover. The key area where the reduction of snow cover is the most obvious over the past few decadal is Qinghai-Tibet Plateau in the mid-latitude region. Cycle characteristics of snow cover are different in four seasons. There are mainly high frequency changes of snow cover in winter and autumn with the cycles of7-8years,3-4years in winter and4-5years in autumn, and the8years cycles of high-frequency change is represented in spring and summer.(2)The CMIP5models simulating capability of Northern Hemisphere snow cover is well. Overall the distribution characteristics of snow cover can be presented in the simulation by CMIP5models with the spatial correlations which existing between simulations and observation are more then0.9. But the simulation of snow cover in complex terrain area for example Qinghai-Tibet Plateau is poor and the significant trend towards a reduced spring snow cover extent over the1979-2005is underestimated. The multi-model ensemble has improved the effect of the simulations by CMIP5models. Through comparison, the simulation of snow cover by CMIP5models is better than CMIP3models. The projection by multi-model ensemble show that the changes of snow cover under different greenhouse gas emission scenarios. The northern Hemisphere snow cover will reduce most significantly and continue until the end of the century under RCP8.5scenario; Under RCP2.6, the snow cover will reduce in the first half of this century and remain stable after2040. Therefore, the more greenhouse gas emissions the more obviously Northern Hemisphere snow cover will reduce. It is crucial to control the discharge of GHG emissions for mitigating the disappearance of snow cover over Northern Hemisphere.(3)The basic characteristics of winter and spring AO modal can be reproduced by CMIP5models. A strong positive spatial correlation exits between the AO modal simulated by models and the observed with the correlation coefficient reaching0.6and the standard deviation of the AO modal simulated is close to the observed. The significant increasing trend and oscillation cycle of winter and spring AO index can be reproduced by models although the salient features that winter and spring AO is significant negative phase in the first30years and positive phase in the last20years aren’t shown up in the simulation. The multi-model ensemble has improved the effect of the simulations by CMIP5models. Although the simulations aren’t good enough to catch all the significant features of winter and spring AO index by CMIP5models, but relative to CMIP3models, there are significant improvement not only for the reducing trend but also for the change cycle in the simulations by CMIP5models.(4)The negative relationship exists between Northern Hemisphere spring snow cover and winter or spring AO with the relationship that existing between snow cover and winter AO is stronger. The enhancement of winter AO caused by the change of preceding winter SLP can produce significant impact on the Northern Hemisphere snow cover, the enhancement of the previous winter AO will accelerate the reduction of the Northern Hemisphere spring snow cover. The enhancement of winter AO is one of the important factors that have cause the reduction of Northern Hemisphere spring snow cover.(5)The correlations between Northern Hemisphere spring snow cover and the different radiative forcings are significantly different. A strong negative correlation exists between SCA and GHGs forcing. The correlation between SCA and natural forcing or that between SCA and precipitation is not obvious. There are different roles that different forcings have played in snow cover. The GHGs forcing played significant promoting effect in the reduction of snow cover, while the other forcing played a significant inhibitory effect; however, the effect natural forcing played is not significant. The historical simulation experiments with different forcings in CMIP5show that, the decrease of snow cover mainly occurred after1950, and in the HistoricalGHG experiment the decrease trend is the most obvious, there isn’t decrease trend in the HistoricalNat experiment. In the Historical and HistoricalGHG experiments, the distribution characteristics of the trend in sea level pressure is consistent with observation, and that in HistoricalGHG is more close to observation. There is opposite characteristics presented in the sea level pressure which simulated in HistoricalNat experiment. The same as sea level pressure, AO simulated in the HistoricalGHG experiment is the most close to observation. Based on the results of three historical experiments, statistical analysis showed that the decrease of Northern Hemisphere spring snow cover and the enhancement of winter AO were due to the Greenhouse gases.