Abstract
AbstractThe cloud‐permitting model (CPM) of the superparameterized Community Atmosphere Model (SP‐CAM) is a stochastic parameterization. As reported in a companion paper, we have created a variant of SP‐CAM, called MP‐CAM, that uses the averaged feedback of 10 independent two‐dimensional CPMs in each global model column, in place of the single CPM of SP‐CAM. This ensemble‐averaged feedback is interpreted as an approximation to the feedback from a deterministic parameterization. We present evidence that the multiple‐instance superparameterization of MP‐CAM is indeed more deterministic than SP‐CAM. The climates of the SP and MP configurations are compared, giving particular attention to extreme precipitation events and convectively coupled large‐scale tropical weather systems, such as the Madden‐Julian Oscillation. A number of small but significant changes in the mean state climate are uncovered, and the deterministic parameterization slightly degrades the Madden‐Julian Oscillation simulation.
Highlights
Realistic simulations of precipitation, including probability, timing, location, type, and amount, are of great practical importance
We focus on a particular type of stochastic parameterization: superparameterization, in which the conventional parameterizations are replaced by a two‐dimensional cloud‐ permitting model or cloud‐permitting model (CPM) (Grabowski, 2001; Grabowski & Smolarkiewicz, 1999; Khairoutdinov & Randall, 2001; Khairoutdinov et al, 2005), with a horizontal grid spacing on the order of a few kilometers and periodic lateral boundary conditions
Following a brief description of the models and how they are configured, we investigate to what degree MP‐Community Atmosphere Model (CAM) behaves like a more deterministic version of superparameterized Community Atmosphere Model (SP‐CAM)
Summary
Realistic simulations of precipitation, including probability, timing, location, type, and amount, are of great practical importance. Parameterizations that are designed to give expected values can be called deterministic They cannot represent the “stochastic” variability of convection that arises from the intrinsically uncertain nature of small‐scale motions, through sensitive dependence on initial conditions (Arakawa, 2004; Jones & Randall, 2011; Hohenegger & Schär, 2007; Lorenz, 1969; Randall & Ding, 1997; Randall et al, 1994; Shutts & Palmer, 2007; Xu et al, 1992; Xu & Randall, 1998). Subramanian and Palmer (2017) examined both stochastic and SP methods in simulations using the European Centre for Medium‐Range Weather Forecasts Integrated Forecasting System They reported that, relative to stochastic ensemble forecasts, SP ensemble forecasts give more realistic simulations of tropical waves and intraseasonal variability and improvements in the modeling of uncertainty in tropical convection, MJO initiation, and tropical cyclones.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
