scholarly journals Modeling Physical-Biological Responses to Climate Change in the California Current System

Oceanography ◽  
2013 ◽  
Vol 26 (3) ◽  
pp. 26-33 ◽  
Author(s):  
Peter Franks ◽  
Emanuele Di Lorenzo ◽  
Nicole Goebel ◽  
Fanny Chenillat ◽  
Pascal Riviere ◽  
...  
Keyword(s):  
Climate Change ◽  
Current System ◽  
California Current ◽  
California Current System ◽  
Biological Responses

A Closer Look at 15N in Relation to Climate Change: Example from the California Current System

2021 ◽  
Author(s):  
Z. Erdem ◽  
P. Riekenberg ◽  
M.T.J. Van der Meer ◽  
N. Ohkouchi ◽  
N. Ogawa ◽  
...  
Keyword(s):  
Climate Change ◽  
Current System ◽  
California Current ◽  
California Current System

Climate change, reproductive performance and diet composition of marine birds in the southern California Current system, 1969–1997

2001 ◽  
Vol 49 (1-4) ◽  
pp. 309-329 ◽  
Author(s):  
William J Sydeman ◽  
Michelle M Hester ◽  
Julie A Thayer ◽  
Franklin Gress ◽  
Paige Martin ◽  
...  
Keyword(s):  
Climate Change ◽  
Reproductive Performance ◽  
Southern California ◽  
Diet Composition ◽  
Current System ◽  
California Current ◽  
Marine Birds ◽  
California Current System

scholarly journals Attributing Causes of Future Climate Change in the California Current System With Multimodel Downscaling

2020 ◽  
Vol 34 (11) ◽  
Author(s):  
Evan M. Howard ◽  
Hartmut Frenzel ◽  
Fayçal Kessouri ◽  
Lionel Renault ◽  
Daniele Bianchi ◽  
...  
Keyword(s):  
Climate Change ◽  
Current System ◽  
California Current ◽  
Future Climate ◽  
Future Climate Change ◽  
California Current System

scholarly journals Reconstruction of Diffusion Coefficients and Power Exponents from Single Lagrangian Trajectories

Fluids ◽  
2021 ◽  
Vol 6 (3) ◽  
pp. 111
Author(s):  
Leonid M. Ivanov ◽  
Collins A. Collins ◽  
Tetyana Margolina
Keyword(s):  
Black Sea ◽  
Diffusion Coefficients ◽  
Current System ◽  
Mean Square Displacement ◽  
California Current ◽  
The Black Sea ◽  
Mean Square ◽  
California Current System ◽  
The Mean ◽  
Non Gaussian

Using discrete wavelets, a novel technique is developed to estimate turbulent diffusion coefficients and power exponents from single Lagrangian particle trajectories. The technique differs from the classical approach (Davis (1991)’s technique) because averaging over a statistical ensemble of the mean square displacement (<X2>) is replaced by averaging along a single Lagrangian trajectory X(t) = {X(t), Y(t)}. Metzler et al. (2014) have demonstrated that for an ergodic (for example, normal diffusion) flow, the mean square displacement is <X2> = limT→∞τX2(T,s), where τX2 (T, s) = 1/(T − s) ∫0T−s(X(t+Δt) − X(t))2 dt, T and s are observational and lag times but for weak non-ergodic (such as super-diffusion and sub-diffusion) flows <X2> = limT→∞≪τX2(T,s)≫, where ≪…≫ is some additional averaging. Numerical calculations for surface drifters in the Black Sea and isobaric RAFOS floats deployed at mid depths in the California Current system demonstrated that the reconstructed diffusion coefficients were smaller than those calculated by Davis (1991)’s technique. This difference is caused by the choice of the Lagrangian mean. The technique proposed here is applied to the analysis of Lagrangian motions in the Black Sea (horizontal diffusion coefficients varied from 105 to 106 cm2/s) and for the sub-diffusion of two RAFOS floats in the California Current system where power exponents varied from 0.65 to 0.72. RAFOS float motions were found to be strongly non-ergodic and non-Gaussian.

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