Atlantic Meridional Overturning Circulation(AMOC)plays a central role in long-term climate variations through its heat and freshwater transports,which can collapse under a rapid increase of greenhouse gas forcing in c...Atlantic Meridional Overturning Circulation(AMOC)plays a central role in long-term climate variations through its heat and freshwater transports,which can collapse under a rapid increase of greenhouse gas forcing in climate models.Previous studies have suggested that the deviation of model parameters is one of the major factors in inducing inaccurate AMOC simulations.In this work,with a low-resolution earth system model,the authors try to explore whether a reasonable adjustment of the key model parameter can help to re-establish the AMOC after its collapse.Through a new optimization strategy,the extra freshwater flux(FWF)parameter is determined to be the dominant one affecting the AMOC’s variability.The traditional ensemble optimal interpolation(EnOI)data assimilation and new machine learning methods are adopted to optimize the FWF parameter in an abrupt 4×CO_(2) forcing experiment to improve the adaptability of model parameters and accelerate the recovery of AMOC.The results show that,under an abrupt 4×CO_(2) forcing in millennial simulations,the AMOC will first collapse and then re-establish by the default FWF parameter slowly.However,during the parameter adjustment process,the saltier and colder sea water over the North Atlantic region are the dominant factors in usefully improving the adaptability of the FWF parameter and accelerating the recovery of AMOC,according to their physical relationship with FWF on the interdecadal timescale.展开更多
[Objective] This study was to establish an optimized model for the allocation of agricultural fertilizer resources in Southern Xinjiang from the perspective of sustainable development.[Method] An optimized model for t...[Objective] This study was to establish an optimized model for the allocation of agricultural fertilizer resources in Southern Xinjiang from the perspective of sustainable development.[Method] An optimized model for the allocation of agricultural fertilizer resources was established based on their allocation structure.Combined with the actual agricultural production in Aksu areas of Southern Xinjiang,by establishing a rational evaluation index system,under the premise of considering the planting area constraints,the total water resources constraints and the security constraints of food production,we established the empirical optimal allocation model of the regional agricultural fertilizer resources in Aksu area of Southern Xinjiang.Moreover,we solved the model by using the search algorithm of computer and lingo programming.[Result] The increased economic benefit was near to 1.8 billion Yuan by adopting the optimal allocation methods,with a relative increment of about 34.4%.[Conclusion] Our results provided theoretical basis for achieving the sustainable development of agricultural economy in Southern Xinjiang.展开更多
For the prediction of ENSO, the accuracy of the model including the parameters, initial value and others of the model is important, which can be retrieved by the variational data assimilation methods developed in rece...For the prediction of ENSO, the accuracy of the model including the parameters, initial value and others of the model is important, which can be retrieved by the variational data assimilation methods developed in recent years. However, when the nonlinearity of the model is quite strong, the effect of the improvement made by the 4-D variational data assimilation may be poor due to the bad approximation of the tangent linear model to the original model. So in the paper the ideas in the optimal control is introduced to improve the effect of 4-DVAR in the inversion of the parameters of a nonlinear dynamic ENSO model. The results indicate that when the terminal controlling term is added to the cost functional of 4DVAR, which originated from the optimal control, the effect of the inversion may be largely improved comparing to the traditional 4DVAR, as can be especially obvious from the phase orbit of the model variables. The results in the paper also suggest that the method of 4DVAR in combination with optimal control cannot only reduce the error resulting from the inaccuracy of the model parameters but also can correct the parameters itself. This gives a good method in modifying the model and improving the quality of prediction of ENSO.展开更多
基金supported by the National Key R&D Program of China [grant number 2023YFF0805202]the National Natural Science Foun-dation of China [grant number 42175045]the Strategic Priority Research Program of the Chinese Academy of Sciences [grant number XDB42000000]。
文摘Atlantic Meridional Overturning Circulation(AMOC)plays a central role in long-term climate variations through its heat and freshwater transports,which can collapse under a rapid increase of greenhouse gas forcing in climate models.Previous studies have suggested that the deviation of model parameters is one of the major factors in inducing inaccurate AMOC simulations.In this work,with a low-resolution earth system model,the authors try to explore whether a reasonable adjustment of the key model parameter can help to re-establish the AMOC after its collapse.Through a new optimization strategy,the extra freshwater flux(FWF)parameter is determined to be the dominant one affecting the AMOC’s variability.The traditional ensemble optimal interpolation(EnOI)data assimilation and new machine learning methods are adopted to optimize the FWF parameter in an abrupt 4×CO_(2) forcing experiment to improve the adaptability of model parameters and accelerate the recovery of AMOC.The results show that,under an abrupt 4×CO_(2) forcing in millennial simulations,the AMOC will first collapse and then re-establish by the default FWF parameter slowly.However,during the parameter adjustment process,the saltier and colder sea water over the North Atlantic region are the dominant factors in usefully improving the adaptability of the FWF parameter and accelerating the recovery of AMOC,according to their physical relationship with FWF on the interdecadal timescale.
基金Supported by National Natural Science Foundation of China(30960188)Natural Science Fund of Principal Program from Tarim University(TDZKSS09010)+1 种基金Key Principal Program from Tarim University(TDZKZD09001)Quality Engineering Program from TarimUniversity(TDZGTD09004&DZGKC09085)~~
文摘[Objective] This study was to establish an optimized model for the allocation of agricultural fertilizer resources in Southern Xinjiang from the perspective of sustainable development.[Method] An optimized model for the allocation of agricultural fertilizer resources was established based on their allocation structure.Combined with the actual agricultural production in Aksu areas of Southern Xinjiang,by establishing a rational evaluation index system,under the premise of considering the planting area constraints,the total water resources constraints and the security constraints of food production,we established the empirical optimal allocation model of the regional agricultural fertilizer resources in Aksu area of Southern Xinjiang.Moreover,we solved the model by using the search algorithm of computer and lingo programming.[Result] The increased economic benefit was near to 1.8 billion Yuan by adopting the optimal allocation methods,with a relative increment of about 34.4%.[Conclusion] Our results provided theoretical basis for achieving the sustainable development of agricultural economy in Southern Xinjiang.
基金supported by the National Science Foundation of China (40775023)the Science Foundation for Doctor of the Institute of Meteorology of PLA University of Sci.and Tech
文摘For the prediction of ENSO, the accuracy of the model including the parameters, initial value and others of the model is important, which can be retrieved by the variational data assimilation methods developed in recent years. However, when the nonlinearity of the model is quite strong, the effect of the improvement made by the 4-D variational data assimilation may be poor due to the bad approximation of the tangent linear model to the original model. So in the paper the ideas in the optimal control is introduced to improve the effect of 4-DVAR in the inversion of the parameters of a nonlinear dynamic ENSO model. The results indicate that when the terminal controlling term is added to the cost functional of 4DVAR, which originated from the optimal control, the effect of the inversion may be largely improved comparing to the traditional 4DVAR, as can be especially obvious from the phase orbit of the model variables. The results in the paper also suggest that the method of 4DVAR in combination with optimal control cannot only reduce the error resulting from the inaccuracy of the model parameters but also can correct the parameters itself. This gives a good method in modifying the model and improving the quality of prediction of ENSO.
