Physics Parameter Ensemble of MIROC 5 AOGCM H

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Physics Parameter Ensemble of MIROC 5 AOGCM H. 1 Shiogama , M. 2 Watanabe

Physics Parameter Ensemble of MIROC 5 AOGCM H. 1 Shiogama , M. 2 Watanabe , 1 Ogura , 2 Yoshiomori , 1 Yokohata , 3 Annan , T M. T J. D. J. C. M. Abe 1, S. Emori 1, T. Nozawa 1, A. Abe-Ouchi 2, and M. Kimoto 2 3 Hargreaves , 1 National Institute for Environmental Studies, Tsukuba, Japan 2 Atmosphere and Ocean Research Institute, The University of Tokyo, Kashiwa, Japan 3 Japan Agency for Marine-Earth Science and Technology, Shinsugita, Japan To investigate physics parameter uncertainty of climate sensitivity (CS), we are performing a new physics parameter ensemble (PPE) of the MIROC 5 coupled atmosphere-ocean general circulation model (CGCM). Previous studies of PPE have mainly used atmosphere-slab ocean model (ASGCM). However, CS can be different between ASGCM and CGCM. Therefore we use the CGCM in this study. Since the net radiation balance at top of atmosphere (TOA) will alter when physics parameters are swept, resulting in climate drifts in CGCM, flux corrections were applied in previous PPE studies. However, flux corrections may affect CS. In this study, we developed a new method to prevent climate drifts in the PPE experiments of CGCM without flux corrections. We simultaneously swept 10 parameters in atmosphere and surface schemes. The range of CS (estimated from our 35 ensemble members so far) was not wide (2. 2 K-3. 4 K). The shortwave cloud (SWcld) feedback relating to changes in middle-level cloud albedo dominated the variations of total feedback. We found three performance metrics of present climate simulations about middle-level cloud albedo, precipitation, and ENSO amplitude that are systematically relating to the variations of SWcld feedback. The observational constrains indicate that the SWcld feedback of standard model is more plausible than other members within this ensemble. Descriptions precz 0 Cumulus Base height for cumulus precipitation [m] clmd Cumulus Entrainment efficiency [ND] wcbmax vicec Cloud Factor for ice falling speed [m 0. 474/s] b 1 Cloud Berry parameter [m 3/kg] Factor for PBL overshooting [ND] Factor for length scale LT [ND] faz 1 Turbulence alp 1 Turbulence tnuw Aerosol Timescale for nucleation [s] ucmin Aerosol Minimum cloud droplet number (liquid) [m− 3] alb Surface Albedo for ice and snow We simultaneously vary 10 parameters in atmosphere and surface schemes. 3 cumulus, 2 cloud, 2 turbulence, 2 aerosol and 1 surface parameters. Std model and uncertainty due to natural variability TOA imbalance changes between AGCM CTL runs with max and min values of each parameter. Based on these imbalances, we can select the set of 10 parameter values having small emulated TOA imbalances. We generate 5000 Latin hypercube samples, and select 100 sets of small emulated TOA imbalances. Differences between 10 more negative SWcloud FDBK models minus 10 less negative SWcld FDBK models. More negative SWcloud FDBKs are due to more increases of middle-level cloud albedo in the tropical oceans. Differences of CTL climates between 10 more negative SWcloud FDBK models minus 10 less negative SWcld FDBK models. SW cloud FDBKs are related to (1)middle-level cloud albedeo (2)precipitation (3)ENSO amplitude E-mail: shiogama. [email protected] go. jp Climate Sensitivity Each model SW cloud FDBK CGCM CTL runs with the parameter sets of small emulated TOA imbalances. We succeed to prevent large TOA imbalances and drifts. South minus north Cumulus Maximum of cumulus updraft velocity at cloud base [m/s] Climate sensitivities are estimated by Gregory-style experiments (CTL and 4 XCO 2 runs). CSs are low and not wide, since the SW cloud feedbacks are always negative. Std model Obs. South minus north Name Nino 3. 4 SST Name Obs. Max ENSO Min ENSO Obs. Negative corr bet SW cloud FBDKs and these metrics. The obs constrains indicate that the SW cloud FDBKs of std model is more plausible than others within this ensemble