Characterization of the Dynamics of Climate Systems and Identification of Missing Mechanisms Impacting the Long Term Predictive Capabilities of Global Climate Models Utilizing Dynamical Systems Approaches to the Analysis of Observed and Modeled Climate

Bhatt, Uma S.¹  Wackerbauer, Renate²  Polyakov, Igor V.¹  Newman, David E.²  Sanchez, Raul E.³  Univ. Carlos III de Madrid (Spain)] 

[1] Univ. of Alaska, Fairbanks, AK (United States). Dept. of Atmospheric Sciences;Univ. of Alaska, Fairbanks, AK (United States). Dept. of Physics;Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Fusion Energy Division

关键词: Climate;    Global Climate Models;    Climate indices;    Hurst Exponent;    Reyni entropy;   

DOI  :  10.2172/1225814
RP-ID  :  DOE-UAF--0001898-1
PID  :  OSTI ID: 1225814

学科分类：环境科学（综合）

美国|英语

来源: SciTech Connect

【 摘 要 】

The goal of this research was to apply fractional and non-linear analysis techniques in order to develop a more complete characterization of climate change and variability for the oceanic, sea ice and atmospheric components of the Earth System. This research applied two measures of dynamical characteristics of time series, the R/S method of calculating the Hurst exponent and Renyi entropy, to observational and modeled climate data in order to evaluate how well climate models capture the long-term dynamics evident in observations. Fractional diffusion analysis was applied to ARGO ocean buoy data to quantify ocean transport. Self organized maps were applied to North Pacific sea level pressure and analyzed in ways to improve seasonal predictability for Alaska fire weather. This body of research shows that these methods can be used to evaluate climate models and shed light on climate mechanisms (i.e., understanding why something happens). With further research, these methods show promise for improving seasonal to longer time scale forecasts of climate.