[1] Modern climate models contain numerous input parameters, each with a range of possible values. Since the volume of parameter space increases exponentially with the number of parameters N, it is generally impossible to directly evaluate a model throughout this space even if just 2-3 values are chosen for each parameter. Sensitivity screening algorithms, however, can identify input parameters having relatively little effect on a variety of output fields, either individually or in nonlinear combination. This can aid both model development and the uncertainty quantification (UQ) process. Here we report results from a parameter sensitivity screening algorithm hitherto untested in climate modeling, the Morris one-at-a-time (MOAT) method. This algorithm drastically reduces the computational cost of estimating sensitivities in a high dimensional parameter space because the sample size grows linearly rather than exponentially with N. It nevertheless samples over much of the N-dimensional volume and allows assessment of parameter interactions, unlike traditional elementary one-at-a-time (EOAT) parameter variation. We applied both EOAT and MOAT to the Community Atmosphere Model (CAM), assessing CAM's behavior as a function of 27 uncertain input parameters related to the boundary layer, clouds, and other subgrid scale processes. For radiation balance at the top of the atmosphere, EOAT and MOAT rank most input parameters similarly, but MOAT identifies a sensitivity that EOAT underplays for two convection parameters that operate nonlinearly in the model. MOAT's ranking of input parameters is robust to modest algorithmic variations, and it is qualitatively consistent with model development experience.

No abstract

Abstract. We describe an algorithm for the numerical resolution of elasto-plastic deformations in the context of antiplane shear models. The algorithm is a secondorder Godunov method. For these models the eigenvalues associated to the hyperbolic system are discontinuous. We test the algorithm on several examples.We also describe an algorithm for the resolution of the corresponding Riemann problems.

This paper describes a numerical algorithm for the Riemann solution for nonlinear elasticity. We assume that the material is hyperelastic, which means that the stress-strain relations are given by the specific internal energy. Our results become more explicit under further assumptions: that the material is isotropic and that the Riemann problem is uniaxial. We assume that any umbilical points lie outside the region of physical relevance. Our main conclusion is that the Riemann solution can be obtained by the iterative solution of functional equations (Godunov iterations) each defined in one-or two-dimensional spaces.