Seasonal Hydroclimatology of the Continental United States: Forecasting and its Relevance to Water Management

Water Management;hydroclimatology;Climate forecasts;ENSO;predictor state;Multimodel

Devineni, Naresh ; Sankar Arumugam, Committee Chair,Ranji Ranjithan, Committee Member,Downey Brill, Committee Member,Fred Semazzi, Committee Member,Devineni, Naresh ; Sankar Arumugam ; Committee Chair ; Ranji Ranjithan ; Committee Member ; Downey Brill ; Committee Member ; Fred Semazzi ; Committee Member

University:North Carolina State University

关键词: Water Management;    hydroclimatology;    Climate forecasts;    ENSO;    predictor state;    Multimodel;   

Others  :  https://repository.lib.ncsu.edu/bitstream/handle/1840.16/6268/etd.pdf?sequence=1&isAllowed=y

美国|英语

来源: null

【 摘 要 】

Recent research in seasonal climate prediction has focused on combining multiple atmospheric General Circulation Models (GCMs) to develop multimodel ensembles. A new approach to combine multiple GCMs is proposed by analyzing the skill of candidate models contingent on the relevant predictor(s) state.To demonstrate this approach, we combine historical simulations of winter (December-February, DJF) precipitation and temperature from seven GCMs by evaluating their skill – represented by Mean Square Error (MSE) – over similar predictor (DJF Nino3.4) conditions. The MSE estimates are converted into weights for each GCM for developing multimodel tercile probabilities. A total of six multimodel schemes are considered that includes combinations based on pooling of ensembles as well as based on the long-term skill of the models. To ensure the improved skill exhibited by the multimodel scheme is statistically significant, we perform rigorous hypothesis tests comparing the skill of multimodels with individual models’ skill. The multimodel combination contingent on Nino3.4 show improved skill particularly for regions whose winter precipitation and temperature exhibit significant correlation with Nino3.4. Analyses of weights also show that the proposed multimodel combination methodology assigns higher weights for GCMs and lesser weights for climatology during El Nino and La Nina conditions. On the other hand, due to the limited skill of GCMs during neutral conditions over the tropical Pacific, the methodology assigns higher weights for climatology resulting in improved skill from the multimodel combinations.Thus, analyzing GCMs’ skill contingent on the relevant predictor state provide an alternate approach for multimodel combination such that years with limited skill could be replaced with climatology.