<p>When is good enough, good enough? The spatio-temporal variability of precipitation makes measurements extremely challenging, particularly in the mountains. &#160;Simultaneously, the improvements in physical realism of atmospheric models makes them increasingly valuable for fields such as hydrology, particularly in the mountains. &#160;However, the computational cost of such models renders them impractical for many applications, in or out of the mountains. &#160;Here we describe an intermediate complexity atmospheric model (ICAR) capable of capturing around 90% of the variability in orographic precipitation for 1% of the computational cost of a state of the science non-hydrostatic atmospheric simulation. &#160;ICAR uses an analytical solution for flow perturbations created by topography and simulates the core processes responsible for orographic precipitation (e.g. orographic lifting, advection, cloud microphysical processes). We show that key aspects of orographic precipitation spatial patterns are well simulated in ICAR, including some that gridded observation based products are missing. We then show some early results when using ICAR to simulate regional climate changes forced by global models at higher spatial resolutions than it is currently practical to run traditional regional climate models. These simulations quantify plausible shifts in precipitation resulting in the transition from snow to rain, as well as elevation dependent warming caused by the snow albedo feedback. &#160;Further, the computational efficiency of ICAR permits us to run these simulations with many different physics configurations to better explore the sensitivity of these changes to assumptions in the microphysics and land surface model components.&#160;</p>
Reduced‐precision parametrization: lessons from an intermediate‐complexity atmospheric model
