Bifurcations in a low-order nonlinear model of tropical Pacific sea surface temperatures derived from observational data

2013 ◽  
Vol 23 (2) ◽  
pp. 023104 ◽  
Author(s):  
Mei Hong ◽  
Ren Zhang ◽  
Hui-Zan Wang ◽  
Jing-jing Ge ◽  
Ao-Da Pan
Keyword(s):  
Observational Data ◽  
Nonlinear Model ◽  
Tropical Pacific ◽  
Sea Surface ◽  
Sea Surface Temperatures ◽  
Surface Temperatures ◽  
Low Order

scholarly journals Do climate simulations from models forced by averaged sea surface temperatures represent actual dynamics?

1997 ◽  
Vol 4 (2) ◽  
pp. 93-100 ◽  
Author(s):  
P. J. Roebber ◽  
A. A. Tsonis ◽  
J. B. Elsner
Keyword(s):  
General Circulation ◽  
Climatic Variability ◽  
General Circulation Models ◽  
Ocean Model ◽  
Coupled Systems ◽  
Sea Surface ◽  
Sea Surface Temperatures ◽  
Surface Temperatures ◽  
Fully Coupled ◽  
Low Order

Abstract. Recently atmospheric general circulation models (AGCMs) forced by observed sea surface temperatures (SSTs) have offered the possibility of studying climate variability over periods ranging from years to decades. Such models represent and alternative to fully coupled asynchronous atmosphere ocean models whose long term integration remains problematic. Here, the degree of the approximation represented by this approach is investigated from a conceptual point of view by comparing the dynamical properties of a low order coupled atmosphere-ocean model to those of the atmospheric component of the same model when forced with monthly values of SST derived from the fully coupled simulation. The low order modelling approach is undertaken with the expectation that it may reveal general principles concerning the dynamical behaviour of the forced versus coupled systems; it is not expected that such an approach will determine the details of these differences, for which higher order modelling studies will be required. We discover that even though attractor (global) averages may be similar, local dynamics and the resultant variability and predictability characteristics differ substantially. These results suggest that conclusions concerning regional climatic variability (in time as well as space) drawn from forced modelling approaches may be contaminated by an inherently unquantifiable error. It is therefore recommended that this possibility be carefully investigated using state-of-the-art coupled AGCMs.

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