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The data are taken from a height of 3195 m and are averaged in the horizontal to scale of 24 km. The mass fluxes have been converted to velocities in units of m s −1 by normalization with density. (a) Scatterplots of cloudy mass flux against large-scale mass flux and (b) minus dry mass flux against cloudy updraft mass flux. Error bars indicate interannual variability of global- and annual-mean surface temperature. The spatial resolution of the atmosphere model is T31 with 19 layers.

For each time step size listed on the x axis, the global-mean surface temperature change is computed as the difference between a 10-yr present-day simulation and the last 10 years of a 50-yr simulation with doubled CO 2. Red and blue markers indicate high- and low-sensitivity models, which differ only in a few uncertain parameters in the physics parameterizations ( Klocke et al. Global-mean surface temperature change (K) resulting from a doubling of CO 2 in simulations conducted with the ECHAM5 atmosphere model ( Roeckner et al. The manner in which the results and conclusions from the experimentation can inform the production runs ranges from “difficult” (results are expected in the form of guidance or informing a choice that needs to be made in the design phase) to “direct” (a benefit can be demonstrated straightaway by producing an improved forecast). The complexity of the analysis increases from one to the other.

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(b) Four-tier scheme of investigation, ranging from (by necessity) abstract analysis via reduced equation sets (with less necessity for abstraction) to simplified physics tests and finally full model runs. All of this exists in front of a background problem of thermodynamics, which ultimately governs them all (or ought to, anyhow). These parameterizations also occupy potentially-or almost certainly-different areas on the spatial plane and temporal axis. Then, each of these models has its ecosystem of parameterizations (an arbitrary set of processes was chosen here for illustration only), which interact with the model and themselves via coupling. In the spatial plane, aspects such as grid type, fixed vs variable resolution, one-dimensional vs three-dimensional, and fine vs coarse are shown as some of the aspects of the spatial resolution that can vary between models and do not necessarily have a straightforward mapping. These are coupled (thick lines) that means one domain in the spatial plane maps into the spatial plane of the other model (thick red line) and similarly in the temporal axis (thick blue line). Both of these have spatial scales (here indicated by the plane with red lines) and temporal scales (indicated by the blue axis). (a) Two models: an ocean model and an atmosphere model. Schematic representation of physics–dynamics coupling.

Compatibility of Parameterizations with the Laws of Thermodynamics.

Emerging challenges in physics–dynamics coupling with multiscale models

Spatial physics–dynamics coupling with element-based high-order Galerkin methods The inclusion of moisture-related quantities

Compatibility of parameterizations with the laws of thermodynamics Reducing physics–dynamics inconsistency and splitting errors Theoretical limitations of some of the current ocean–atmosphere coupling methods

1) The different nature and sources of error.

Idealized testing of global circulation and weather models Analyzing the coupling of dynamical cores with a hierarchy of GCM test cases Interaction of the boundary layer with balanced dynamics Interaction of convection with balanced dynamics Insights from models with simplified equation sets Time-stepping errors introduced by splitting

Spectrum cfl.rr.com email setup for outlook 2013