Figure 5 shows the possibility distribution of the LCC prediction model output, where only parameter uncertainty is propagated.
Our study pin-points the difference between parameters that have a quantitative, modulatory impact on model output, where the perturbation size is correlated with the change in model output, and parameters that are essential to the system in a qualitative manner.
Where model outputs were missing due to a land mask, SSTs were interpolated in the zonal direction.
For example, Frigg et al. (2015) urge caution on the over-reliance of model projections in informing decision-making, particularly where model outputs are subject to errors and uncertainties, as these may undermine ability to make robust decisions.
We evaluate the approach and the resulting products by comparison with simulation model outputs and point measurements, where obtainable, over the Earth's surface.
UPGMA is a simple hierarchical clustering method that, by assuming a constant rate of evolution, hence no implicit evolutionary model, outputs a rooted tree where the sum of times down a path to the leaves from any node is the same, regardless of the chosen path.
Model 3 used 6 outputs where inpatient discharges and day care were further split into subgroups: medical, surgical and other cases in the inpatient care and medical and surgical for day care.
In contrast to conventional nonlinear fitting or minimisation algorithms that only estimate parameter values, this method provides an overview of the parameter space and identifies regions in the parameter space where model outputs match measured data.
A state-space model has states, inputs, and outputs, where hidden states contain complete information of the system driven by the inputs, and the outputs are the measurements made by scientists.
