JpGU-AGU Joint Meeting 2017

Presentation information

[EE] Oral

A (Atmospheric and Hydrospheric Sciences) » A-AS Atmospheric Sciences, Meteorology & Atmospheric Environment

[A-AS02] [EE] Cloud-Resolving Model Simulations for Cloud-Related Processes in Climate and Weather Studies

Mon. May 22, 2017 3:30 PM - 5:00 PM 301B (International Conference Hall 3F)

convener:Toshi Matsui(NASA GSFC & ESSIC UMD), Masaki Satoh(Atmosphere and Ocean Research Institute, The University of Tokyo), Wei-Kuo Tao(Mesoscale Atmospheric Processes Laboratory), Chairperson:Toshihisa Matsui(NASA GSFC / ESSIC UMD), Chairperson:Masaki Satoh(Atmosphere and Ocean Research Institute, The University of Tokyo), Chairperson:Wei-Kuo Tao(NASA GSFC)

3:45 PM - 4:00 PM

[AAS02-08] RCEMIP: Radiative Convective Equilibrium Model Inter-comparison Project

*Masaki Satoh1, Tomoki Ohno1, Allison Wing2, Sandrine Bony3, Bjorn Stevens4, Kevin Reed5 (1.Atmosphere and Ocean Research Institute, The University of Tokyo, Kashiwa, Japan, 2.Department of Earth, Ocean, and Atmospheric Science, Florida State University, USA, 3.Laboratoire de Météorologie Dynamique, IPSL, CNRS, Paris, France, 4.Max-Planck Institute for Meteorology, Hamburg, Germany, 5.School of Marine and Atmospheric Science, Stony Brook University, NY, USA)

Keywords:radiative convective equilibium, cloud resolving model, general circulation model, model intercomparison project, RCEMIP

Radiative Convective Equilibrium Model Inter-comparison Project (RCEMIP) is proposed. Radiative-convective equilibrium (RCE) is referred to as atmospheric quasi statistical balance between convection and radiation processes (Manabe and Strickler 1964). Historically, RCE has been argued mainly with one-dimensional models, but in recent years more computationally intensive numerical calculations of RCE have been conducted with three-dimensional numerical models with explicitly resolved convection and domain lengths of 100-1000 km. A simple horizontally uniform boundary condition is prescribed with a constant sea surface temperature (SST) or a slab ocean model with uniform solar insolation. Since clouds are a most ambiguous part of climate models, the simple framework of RCE is suitable for understanding how clouds are simulated in numerical models. RCE is also useful to clarify the sensitivities of clouds to the details of cloud schemes implemented in numerical models (e.g., Satoh and Matsuda 2008). A number of RCE numerical studies have been conducted until recently with their own various configurations. One category is RCE on the sphere either with or without a cumulus parameterization scheme (e.g., Popke et al., 2013; Arnold and Randall, 2015; Reed et al., 2015; Bony et al., 2016; Satoh et al., 2016; Ohno and Satoh, 2016). The other category is RCE with regional models in an arbitrary domain size , primarily with explicitly resolved convection (e.g., Wing and Cronin 2016; Silvers et al. 2016). In order to systematically understand differences or similarities of various model results, a more coordinated framework for RCE numerical studies is demanded as “RCEMIP”. Possible choices for experimental settings of RCE are listed as follows:
Geometry: sphere / plane (square or channel)
Domain size: Earth radius R / length=40,000 km × factor (e.g., 0.1–1.0)
Horizontal resolution: Δx = 1–10 km for explicit convection, or coarser resolution (1–2 degree) with a cumulus parameterization
Boundary condition: fixed SST (e.g., 296, 300, 304K) or a slab ocean
CO2: a current value or increased (e.g., 4×CO2)
Physics dependency: cloud microphysics, turbulence, radiation,; switch on/off of cumulus parameterization
Interactive radiation or non-interactive, with/without clouds
With or without diurnal cycle
Without rotation, or with rotation
Among a lot of varieties listed above, we will discuss the experimental design of RCEMIP, scientific targets, and how to proceed. One strength of RCEMIP is the numerous scientific questions that could be explored, such as better understanding of uncertainties of climate sensitivities and changes in clouds and circulations, or convective aggregation, associated with global warming.

Arnold, N. P., and D. A. Randall, 2015: Global-scale convective aggregation: Implications for the Madden-Julian Oscillation. J. Adv. Model. Earth Syst., 7, 1499-1518.
Bony, S. et al., 2016: Thermodynamic control of anvil cloud amount. PNAS, 113, 8927-8932.
Manabe, S., and R. F. Strickler, 1964: Thermal equilibrium of the atmosphere with a convective adjustment. J. Atmos. Sci., 21, 361-385.
Ohno, T., and M. Satoh, 2016: Sensitivity studies of cloud responses on SSTs in RCE experiments using a high-resolution global nonhydrostatic model. The 4th International Workshop on Nonhydrostatic Models., Hakone, Japan. Nov. 30 - Dec. 2, 2016.
Popke, D. et al., 2013: Climate and climate change in a radiative-convective equilibrium version of ECHAM6. J. Adv. Model. Earth Syst., 5, 1-14.
Reed, K. et al., 2015: Global Radiative–Convective Equilibrium in the Community Atmosphere Model, Version 5. J. Atmos. Sci., 72, 2183-2197.
Satoh, M., and Y. Matsuda, 2009: Statistics on high-cloud areas and their sensitivities to cloud microphysics using single-cloud experiments. J. Atmos. Sci., 66, 2659-2677.
Satoh, M. et al., 2016: Structure of tropical convective systems in aqua-planet experiments: Radiative-convective equilibrium versus the Earth-like experiment. SOLA, 12, 220-224.
Silvers, L. G. et al., 2016: Radiative convective equilibrium as a framework for studying the interaction between convection and its large-scale environment. J. Adv. Model. Earth Syst., 8, 1330–1344.
Wing, A. A., and T. W. Cronin., 2016: Self-aggregation of convection in long channel geometry. Q. J. Roy. Meteor. Soc., 142, 1-15.