Frontier Research Center for Global Change Hirofumi TOMITA Masaki SATOH Tomoe NASUNO Shi-ichi IGA Hiroaki MIURA Hirofumi TOMITA Masaki SATOH Tomoe NASUNO Shi-ichi IGA Hiroaki MIURA A Cloud-Resolving Aqua Planet Experiment

Contents Brief introduction of our model Global cloud resolving model To avoid the ambiguity of cumulus parameterization New framework for AGCM Icosahedral grid non-hydrostatic dynamics Aqua Planet Experiment --- control run Method of expermental setup for high-resolution GCRM Spin-up Model setup Results Tropical variability Resolution dependency Summary

Brief introduction of our model devlopment(1) Many variations in the tropical feature in the AGCMs Wave propagation Eastward / Westward? Dominant wavenumber? Results depend on cumulus parameterization. General concerning issue for current AGCMs Cumulus parameterization One of ambiguous factors Statistical closure of cumulus convections Hovmeller diagram for precipitation on the tropics Produced by Dr. Williamson

Brief introduction of our model development(2) Avoid the uncertainties owing to cumulus parameterization Super-parameterization ( Grabowski 2001 ) Embed a 2D CRM into the each of grid box Interact with large-scale motion in the AGCM. However, problems as follows. –How is 2D-CRM configurated in the grid box? –Scale-separation between the 2D-CRM and the host AGCMs. Global cloud resolving ( our approach ) Explicit treatment of each cloud –Cumulus parameterization Large scale condensation scheme : not needed ! –Cloud microphysics : used! Direct treatment of multi-scale interactions –Each cloud scale  meso-scale  planetary scale

Brief introduction of our model development (3) Strategy of dycore development Quasi-uniform grid Spectral method : not efficient in high resolution simulations. –Legendre transformation –Massive data transfer between computer nodes Latitude-longitude grid : the pole problem. –Severe limitation of time interval by the CFL condition. The icosahedral grid: homogeneous grid over the sphere –To avoid the pole problem. Non-hydrostatic equations system Very high resolution in horizontal direction. Target resolutions 5 km or less in the horizontal direction Several 100 m in the vertical

Current Status of NICAM Model feature Governing equations Full compressible non-hydrostatic system  including acoustic wave Spatial discretization Horizontal grid configuration Vertical grid configuration Topography Finite Volume Method Icosahedral grid Lorenz grid Terrain-following coordinate Conservation Total mass, total energy Temporal scheme Slow mode － explicit scheme （ RK2 ） Fast mode － Horizontal Explicit Vertical Implicit scheme （ HEVI ) Physical parameterizationCompleted ( turbulence, radiation, cloud physics, surface flux ) Computational tuning VectorizationWell tuned for NEC SX6 architecture Parallelization2D decompostion, Flexible configuration against load imbalance Target machineWS-cluster, Linux-cluster, Earth simulator NICAM( Nonhydrostatic Icosahedral Atmospheric Model ) Model name : NICAM( Nonhydrostatic Icosahedral Atmospheric Model )

Method (1) Experimental setup follow the CONTROL RUN of Neal & Hoskins(2000) SST distribution / ozone distribution Difficult to perform the 3.5 years by GCRM. Owing to computational limitation Several months integration starting an appropriate climatology Spin up Initial condition 3.5 year integration by CCSR/NIES/FRCGC AGCM ver.5.7 with T42L59. 3 years climatology as an initial condition. Interpolate to NICAM gridpoints.

Method (2) NICAM Model setup Horizontal resolution : 14km, 7km, 3.5km Vertical layer : 54 layers 75 m at the lowest layer 750 m in the upper troposphere Time step : 30sec(14km, 7km), 15sec(3.5km) Cloud microphysics Globowski(1998) scheme –Including simple ice phase effects All the runs employ the same microphysics –To isolate the impact of resolution Turbulence : Mellor & Yamada level 2 Surface flux scheme : Louis(1979) scheme Radiation scheme : Nakajima et al.(2000) scheme Rayleigh damping z>25km : reduce the reflection of gravity waves.

Series of experiment by NICAM 0 day 60 day Spin-up time NICAM 14km grid model 7km grid model 3.5km grid model Interpolation 30days 90 day Analized term 30days Initial condition ： appropriate climatology of a conventional ＧＣＭ ( CCSR/NIES/FRCGC AGCM ver 5.7) 10days Interpolation

Reach an equilibrium state? First 60 days: Spin-up time by 14km model First 30 days: These values change.  Broclinic waves are developing. Second 30 days: Broclinic wave well developes.  Reach an equilibrium state Last 30 days : Analyzed term  Well stable.

OLR(1S-1S 平均 ) Precipitation rate [mm/day] at day 85 : log-scale by NICAM-3.5km model Super cloud cluster Mid-latitude cyclone

Propagation of convective region Produced by Dr. Williamson 2 or 3 convective regions Eastward propagation with 30- days period One strong convective region Hovmeller diagram for precipitation on the tropics westward eastward CRM-results: NICAM

Comparison with observation (1) Takayabu et al., 2000 SST distribution on May 1998 El Nino season :  Contrast between warm pool & cold pool is weak.  similar situation to CNTRL RUN. Hovmellor of precipitation One strong SCC: eastward propagation around the globe  30days period Investigation of MJO in El Nino

Comparison with observation (2) Observation result CRM result : NICAM Simlar feature: One strong SCC Peirod of 30days

Hovmoller diagram (dx=7km) 1N-1S 90d 80d 60d 90d 80d 60d OLR Temperature (tropospheric mean) Zonal Velocity (z=10m) Surface pressure L H W C Wave number 1 structure A A B C In the wavenumber 1 structure, there are several convective regions.

Vertical structure at day 80 (dx=7km) A B Cool Warm Cool Wavenumber 1 Diabatic heating / boomerang shape

Vertical structure at day 83 (dx=7km) A B C Cool Warm Strong vertical shear rear inflow Squall line cloud top : high cloud top : low

A typical Super Cloud Cluster (1) Cloud cluster :~100km Super cloud cluster : ~1000km High pressure Low pressure Westerly wind burst Convectively-Coupled Kelvin Wave

A typical Super Cloud Cluster (2) Zonal elongation as two lines off the equator  Splitting of convection area within SCC

OLR (7km-model) during day

Super Cloud Cluster and Cloud Cluster Eastward propagation of SCC a a a a b c b b b c c c d d Westward propagation of CC

Meso-scale cloud system a b c Meso-scale cloud system : ~ 10km Northeasterly Wind at SFC. Convergence line: South-edge of CC Each of MCS has cold pool  Cold pool dyanmics Liquid water pathTemperature at the surface

Resolution dependency of propagation wave NICAM-14km NICAM-7km NICAM-3.5km  Westward moving of CC  Lifetime of 2days  Eastward propagation of SCC NICAM-14km: 20~25 days  fast propagation NICAM-7km, 3.5km : days  like MJO also well organized rather than NICAM-14km.  also well organized rather than NICAM-14km. Hovmellor diagram of OLR ( 2S-2N )

Resolution dependency of ITCZ Mass-weighted T & precip. water little difference Precipitation significant difference As resolution increases, Precipitation decreases on the equator. ITCZ region is elongated in the latitudinal. For the coarser resolution, Cloud condensation hardly occurs at the sub-tropics when trade-wind converges toward the equator.  feature of low-resolution CRM  the strong convergence of water vapor on the equator. Zonal averaged field Single ITCZ, though splitting convective region within SCC sometime occurs.  SCC moves in the latitudinal direction  In the non-SCC region, preciptation occurs just on the equator.

Histograms of diurnal cycle for precipitation LT [hr] Peak : midnight Consistent with the obs in open ocean  Consistent with the obs. in open ocean Peak : early morning

Summary We have performed the control run of APE by a global cloud- resolving model. Resolution : 14km, 7km, 3.5km Hierarchical structure of cloud field can be well captured. MJO-like wavenumber 1 structure Eastward propagation of several super cloud clusters of ~1000km with 30-days period. Westward propagation of cloud clusters of ~100km with the lifetime of 2days Meso-scale cloud system of ~10km with the lifetime of several hours. Resolution dependency is found at ITCZ in CRM. As resolution increases, its intensity is smaller and its width is larger. Cloud resolving model has the reasonable diurnal cycle of precipitation rate at the tropics. Its primary peak is at the early morning, consistent with observations.