To explore the interactions among column processes in the Community Atmosphere Model (CAM), the single‐column version of CAM (SCAM) is integrated for 1000 days in radiative‐convective equilibrium (RCE) with tropical values of boundary conditions, spanning a parameter or configuration space of model physics versions (v5 vs. v6), vertical resolution (standard and 60 levels), sea surface temperature (SST), and some interpretation‐driven experiments. The simulated time‐mean climate is reasonable, near observations and RCE of a cyclic cloud‐resolving model. Updraft detrainment in the deep convection scheme produces distinctive grid‐scale structures in humidity and cloud, which also interact with radiative transfer processes. These grid artifacts average out in multi‐column RCE results reported elsewhere, illustrating the nuts‐and‐bolts interpretability that SCAM adds to the hierarchy of model configurations. Multi‐day oscillations of precipitation arise from descent of warm convection‐capping layers starting near the tropopause, eventually reset by a burst of convective deepening. Experiments reveal how these oscillations depend critically on an internal parameter that controls the number of neutral buoyancy levels allowed for determining cloud top and computing dilute convective available potential energy in the deep convection scheme, and merely modified a little by disabling cloud‐base radiation (heating of cloud base). This strong dependence of transient behavior in 1D on this parameter will be tested in the second part of this work, in which SCAM is coupled to a parameterized dynamics of two‐dimensional, linearized gravity wave, and in the 3D simulations in future study.
Precipitation amount (A), frequency (F), intensity (I), and duration (D) are important properties of precipitation, but their estimates are sensitive to data resolution. This study investigates this resolution dependence, and the influences of different model physics, by analyzing simulations by the Community Atmospheric Model (CAM) version 4 (CAM4) and version 5 (CAM5) with varying grid sizes from ~0.25 to 2.0°. Results show that both CAM4 and CAM5 greatly overestimate F and D but underestimate I at all resolutions, despite realistic A. These biases partly result from too much parameterized (convective) precipitation with high F and D but low I. Different cloud microphysics schemes contribute to the precipitation differences between CAM4 and CAM5. The A, F, I, and D of convective and nonconvective precipitation react differently to grid‐size decreases, leading to the large decreases in F and D but increases in the I for total precipitation as model resolution increases. This resolution dependence results from the increased probability of precipitation over a larger area (area aggregation effect, which is smaller than in observations) and the varying performance of model physics under changing resolution (model adjustment effect), which roughly enhances the aggregation‐induced dependence. Finer grid sizes not only increase resolved precipitation, which has higher intensity and thus improves overall precipitation intensity in CAM, but also reduce the area aggregation effect. Thus, the long‐standing
- NSF-PAR ID:
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Journal of Advances in Modeling Earth Systems
- Page Range / eLocation ID:
- p. 2352-2374
- Medium: X
- Sponsoring Org:
- National Science Foundation
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