On the basis of two ensemble experiments conducted by a general atmospheric circulation model(Institute of Atmospheric Physics nine-level atmospheric general circulation model coupled with land surface model,hereinaft...On the basis of two ensemble experiments conducted by a general atmospheric circulation model(Institute of Atmospheric Physics nine-level atmospheric general circulation model coupled with land surface model,hereinafter referred to as IAP9L_CoLM),the impacts of realistic Eurasian snow conditions on summer climate predictability were investigated.The predictive skill of sea level pressures(SLP)and middle and upper tropospheric geopotential heights at mid-high latitudes of Eurasia was enhanced when improved Eurasian snow conditions were introduced into the model.Furthermore,the model skill in reproducing the interannual variation and spatial distribution of the surface air temperature(SAT)anomalies over China was improved by applying realistic(prescribed)Eurasian snow conditions.The predictive skill of the summer precipitation in China was low;however,when realistic snow conditions were employed,the predictability increased,illustrating the effectiveness of the application of realistic Eurasian snow conditions.Overall,the results of the present study suggested that Eurasian snow conditions have a significant effect on dynamical seasonal prediction in China.When Eurasian snow conditions in the global climate model(GCM)can be more realistically represented,the predictability of summer climate over China increases.展开更多
The paper describes the application of SDSM （statistical downscaling model） and ANNs （artificial neural networks） models for prediction of the hydrological trend due to the climate-change. The SDSM has been calibr...The paper describes the application of SDSM （statistical downscaling model） and ANNs （artificial neural networks） models for prediction of the hydrological trend due to the climate-change. The SDSM has been calibrated and generated for the possible future scenarios of meteorological variables, which are temperature and rainfall by using GCMs （global climate models）. The GCM used is SRES A2. The downscaled meteorological variables corresponding to SDSM were then used as input to the ANNs model calibrated with observed station data to simulate the corresponding future streamflow changes in the sub-catchment of Kurau River. This study has discovered the hydrological trend over the catchment. The projected monthly streamflow has shown a decreasing trend due to the increase in the, mean of temperature for overall months, except the month of August and November.展开更多
With trends indicating increase in temperature and decrease in winter precipitation, a significant negative trend in snow-covered areas has been identified in the last decade in the Himalayas. This requires a quantita...With trends indicating increase in temperature and decrease in winter precipitation, a significant negative trend in snow-covered areas has been identified in the last decade in the Himalayas. This requires a quantitative analysis of the snow cover in the higher Himalayas. In this study, a nonlinear autoregressive exogenous model, an artificial neural network （ANN）, was deployed to predict the snow cover in the Kaligandaki river basin for the next 30 years. Observed climatic data, and snow covered area was used to train and test the model that captures the gross features of snow under the current climate scenario. The range of the likely effects of climate change on seasonal snow was assessed in the Himalayas using downscaled temperature and precipitation change projection from - HadCM3, a global circulation model to project future climate scenario, under the AIB emission scenario, which describes a future world of very rapid economic growth with balance use between fossil and non-fossil energy sources. The results show that there is a reduction of 9% to 46% of snow cover in different elevation zones during the considered time period, i.e., 2Oll to 2040. The 4700 m to 52oo m elevation zone is the most affected area and the area higher than 5200 m is the least affected. Overall, however, it is clear from the analysis that seasonal snow in the Kaligandaki basin is likely to be subject to substantialchanges due to the impact of climate change.展开更多
基金supported by the Special Public Sector Research of Meteorology (Grant No. GYHY200906018)the National Basic Research Program of China (Grant No. 2009CB421407)the National Key Technologies R&D Program of China (Grant No. 2007BAC29B03)
文摘On the basis of two ensemble experiments conducted by a general atmospheric circulation model(Institute of Atmospheric Physics nine-level atmospheric general circulation model coupled with land surface model,hereinafter referred to as IAP9L_CoLM),the impacts of realistic Eurasian snow conditions on summer climate predictability were investigated.The predictive skill of sea level pressures(SLP)and middle and upper tropospheric geopotential heights at mid-high latitudes of Eurasia was enhanced when improved Eurasian snow conditions were introduced into the model.Furthermore,the model skill in reproducing the interannual variation and spatial distribution of the surface air temperature(SAT)anomalies over China was improved by applying realistic(prescribed)Eurasian snow conditions.The predictive skill of the summer precipitation in China was low;however,when realistic snow conditions were employed,the predictability increased,illustrating the effectiveness of the application of realistic Eurasian snow conditions.Overall,the results of the present study suggested that Eurasian snow conditions have a significant effect on dynamical seasonal prediction in China.When Eurasian snow conditions in the global climate model(GCM)can be more realistically represented,the predictability of summer climate over China increases.
文摘The paper describes the application of SDSM （statistical downscaling model） and ANNs （artificial neural networks） models for prediction of the hydrological trend due to the climate-change. The SDSM has been calibrated and generated for the possible future scenarios of meteorological variables, which are temperature and rainfall by using GCMs （global climate models）. The GCM used is SRES A2. The downscaled meteorological variables corresponding to SDSM were then used as input to the ANNs model calibrated with observed station data to simulate the corresponding future streamflow changes in the sub-catchment of Kurau River. This study has discovered the hydrological trend over the catchment. The projected monthly streamflow has shown a decreasing trend due to the increase in the, mean of temperature for overall months, except the month of August and November.
文摘With trends indicating increase in temperature and decrease in winter precipitation, a significant negative trend in snow-covered areas has been identified in the last decade in the Himalayas. This requires a quantitative analysis of the snow cover in the higher Himalayas. In this study, a nonlinear autoregressive exogenous model, an artificial neural network （ANN）, was deployed to predict the snow cover in the Kaligandaki river basin for the next 30 years. Observed climatic data, and snow covered area was used to train and test the model that captures the gross features of snow under the current climate scenario. The range of the likely effects of climate change on seasonal snow was assessed in the Himalayas using downscaled temperature and precipitation change projection from - HadCM3, a global circulation model to project future climate scenario, under the AIB emission scenario, which describes a future world of very rapid economic growth with balance use between fossil and non-fossil energy sources. The results show that there is a reduction of 9% to 46% of snow cover in different elevation zones during the considered time period, i.e., 2Oll to 2040. The 4700 m to 52oo m elevation zone is the most affected area and the area higher than 5200 m is the least affected. Overall, however, it is clear from the analysis that seasonal snow in the Kaligandaki basin is likely to be subject to substantialchanges due to the impact of climate change.
