Similarity Among Atmospheric Thermal Stratifications Over Elevated Surfaces Under Radiative‐Convective Equilibrium. Issue 6 (22nd March 2019) - Nie, Ji; Xia, Yan; Hu, Shineng; Yuan, Wei; Yang, Jun; Ma, Ding
Abstract: An air column under radiative‐convective equilibrium is studied here to understand equilibrium climate over different surface elevations. Cloud‐resolving model (CRM) simulations show that atmospheric thermal stratifications exhibit similar structures when expressed in sigma coordinates over varying surface elevations under radiative‐convective equilibrium. A zero‐buoyancy plume model that reproduces CRM results is used to interpret related processes. As surface pressure decreases, decreased pressure on a certain σ level leads to decreases of moist adiabatic temperature lapse rate, which largely cancel the increases of moist adiabatic lapse rate by temperature decreases. Meanwhile, the invariance of convective entrainment/detrainment rates keeps the deviation of environmental temperature lapse rate from moist adiabat approximately invariant, result in similar thermal stratifications under varying surface pressures. A comparison of thermodynamic profiles over the Tibetan Plateau with those over plains in reanalysis confirms the similarity found in CRM simulations. This similarity provides a useful simplification for representing the effects of convection. Plain Language Summary: What constrains the vertical structure of the atmosphere over different surface elevations? Here it is found that the atmospheric vertical stratification exhibits similar structures in a specific vertical coordinate normalized by the mass of the column. Cloud‐resolving numerical models are used to understand this similarity, which has significant implications for modeling convection in global climate models. Key Points: Atmospheric thermal stratifications show similar structures over varying surface elevations under RCE This similarity is largely due to the invariance of entrainment/detrainment rates This similarity is supported by reanalysis data and provides a useful simplification for climate research