Search Results

You are looking at 1 - 3 of 3 items for :

  • Polar Climate Stability x
  • All content x
Clear All
Garry K. C. Clarke, Etienne Berthier, Christian G. Schoof, and Alexander H. Jarosch

assume that the process that transforms Fig. 1a to Fig. 1c can be represented by a multilayer feedforward ANN ( Fig. 1b ) and use the MATLAB Neural Network Toolbox ( Demuth et al. 2006 ) to train and then apply the ANN. Following the simplest options of the MATLAB Toolbox, we adopt the standard Levenberg–Marquardt back-propagation training algorithm ( Levenberg 1944 ; Marquardt 1963 ) and the Widrow–Hoff least squares learning rule. Only 60% of the data in the training set are used for training

Full access
M. Eby, K. Zickfeld, A. Montenegro, D. Archer, K. J. Meissner, and A. J. Weaver

exponential formula of the form A 0 exp(− t / A 1 ) + A 2 . The parameter A 0 gives an estimate of the amount a quantity is reduced, A 1 is the average lifetime, and A 2 is the amount of any very long-lived residual. We restrict our analysis to experiments with total emissions greater than 1500 PgC. In simulations with lower emissions, the response curve is often contaminated by noise, making curve fitting imprecise ( Figs. 6 , 9 ). A gradient-expansion algorithm was used to compute the least

Full access
Martin Sharp and Libo Wang

resolution, slice-based (2.225-km gridcell spacing; effective resolution ∼5 km) backscatter images derived from the SeaWinds scatterometer on QS using the Scatterometer Image Reconstruction (SIR) algorithm ( Early and Long, 2001 ; Long and Hicks 2005 ). We use descending pass images with horizontal polarization, which have effective measurement times of 1700–2100 LST for Svalbard, Severnaya Zemlya, and Novaya Zemlya. Ice cap outlines were taken from the circum-Arctic map of permafrost and ground ice

Full access