HPC for better understanding of the tropical meteorology

Y. Kajikawa and H. TomitaOct 11th, 2013, @GMCL, PNU

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Necessity of …

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History of climate modeling (1)

Richardson’s Dream (1910s-1920s)

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History of climate modeling (2)ENIAC (Electronic Numerical Integrator And Computer) was

the first electronic general-purpose computer.

http://en.wikipedia.org/wiki/ENIAC

1947-1955@Maryland

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[Q] Why does the climate model require the HPC?

http://www.nies.go.jp/kanko/kankyogi/19/04-09.html

1. Increase of resolutionTo know more detail structure!

e.g. horizontal Resolution in usual climate model :100km （ 2000 ） / 20km (2005 ）3.5km on K-computer

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2. Increase of processesTo know more complex interactions.

Atm. Ocn. Lnd. modelsCarbon cycle, aerosol, chemistry, dynamic

vegetation processExternal forcing

Variability of solar constantCO2 emission scenario by IPCC run

-> Earth System model

[Q] Why does the climate model require the HPC?

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[Q] Why does the climate model require the HPC?

http://www.jma.go.jp/jma/kishou/know/kisetsu_riyou/glossary/ensenble.html

3. Increase of ensemblesTo make our results more reliable.Statistical knowledge is necessary.

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Importance of the cloud process (1)

1. Engine for general circulation :– Cumulus has an important role

for atmospheric heat transfer over the globe. (latitudinal direction).

2. Hierarchical structure generates many phenomena.» Cloud cluster , super cloud

cluster, tropical cyclone, MJO, … 7

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• Large impact on the energy balance in climate: • Parasol effect :

reduce the incoming solar incidence.• Green house effect :

cloud emits infrared radiation into the surface and space.– Difficulty :

the interaction with aerosol and chemistry through radiation process• Indirect effect of aerosol : optical

thickness of cloud and cloud life time.• Direct effect of aerosol is also important.

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Importance of the cloud process (2)

Parasol effect Reflection of solar incident

Greenhouse effect Emission of infrared

99…Very difficult to model the cloud!

cumulus

Shallow cloud

cirrus

Various cloud types exist in our earth!

10Earth diameter :12740km10km

10km

Cloud cluster ～ 100km

１ km

Super cloud cluster ～ 1000km 　 MJOCloud element: cumulus

Hierarchical structure of clouds

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Example of cloud origination meso-scale cloud

Cloud drop aggregation

Fall as precipitation

Cooling by evaporation

Cold poolGeneration of

new clouds

Generation of new clouds

understanding of cloud dynamics

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Cloud has many features and large impact on the climate through the

complicated processes.

What should we start to study the cloud

processes by modeling

in the age of HPC ?

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Cumulus: Each of cumuli cannot be expressed directly due to

too coarse grid The effect of cumulus is taken a count as parameterization

Grid intervals： 100 km

Each of clouds < 10 km

Uncertainty ： many methods generate many results!

Expression of the clouds 10 years ago

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Cumulus (cloud-system ) can be resolved! To avoid the parameterizationHigh reliability / expression of cloud dynamics (w/ cold

pool)

New Approach from 2004

Grid intervals： a few km

Numerical techniques in the new approach

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• Global cloud-system resolving model– Icosahedral grid

• To get a quasi-homogeneous grid– nonhydrostatic DC

• To resolve cloud scale– explicit cloud expression:

• To avoid the ambiguity of cumulus parameterization.

NICAM ( Tomita & Satoh 2004, Satoh et al. 2008 )

• NICAM project : ~2000 – The first target machine :

Earth Simulator– Now, porting to K computer system Prof. Satoh (AORI, Tokyo univ.) Dr. Tomita (RIKEN AICS)

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Icosahedral grid system?

Regular Icosahedron = Polyhedron with 20 triangular faces.

By dividing each triangles in to 4 small triangle, we can obtain one-higher resolution.

e.g. a -> b -> c-> d …

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DynamicsGoverning equations Fully compressible non-hydrostatic system Spatial discretizationHorizontal grid configuration

Vertical grid configurationTopography

Finite Volume Method

Icosahedral grid with spring dynamics smoothing 　(Tomita et al. 2001/2002)Lorenz gridTerrain-following coordinate

Conservation Total mass, total energy 　 (Satoh 2002, 2003)Temporal scheme Slow mode 　－　 explicit scheme 　（ RK2, RK3 ）

Fast mode －　 Horizontal Explicit Vertical Implicit scheme Physics Turbulence/shallow clouds MYNN 2.0,2.5(Nakanishi and Niino 2004) modified by Noda(2009)Surface flux Louis (1979), Uno et al. (1995)Radiation MSTRNX (Sekiguchi and Nakajima, 2005)Cloud microphysics NSW6 (Tomita 2008) --- 6 caegories of water ( 1moment-bulk)Cloud parameterization NONESurface process MATSIRO(Takata et al.)

Ref. Satoh et al. 2008 J. Comput. Phys. / Tomita & Satoh 2004 Fluid Dyn. Res.

Recent DC description paper : Tomita et al. 2011, ECMWF workshop proceeding

NICAM current implementation

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• NICAM high resolution run:– 14km, 7km, 3.5km, – 1.8km, 800m, 400m

• Many terms :– Cloud permitting?– Cloud resolving?– Cloud system-resolving? (GCRM)– Meso-scale resolving?

• In the terms of methodology,– To avoid the ambiguity of cumulus parameterizationMethodological cloud-system resolving!

Objection : Cloud resolving model? (Grey zone problem)

The examination of impact

without Cumulus Parameterization is the most important!

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What can the GCRM perform?Explicit expression of cloud clusters from the basic dynamical

mechanism ( Cold pool dynamics )Explicit expression of lifecycle of typhoon (onset &development)

　　 e.g .NICAM 7-km simulation

筑波大・田中博教授 (2010, vol.29-1, NAGARE)

20NICAM 7km-mesh, one-month simulation: initial = 15 Dec. 2006

23 Dec. 2006

報道発表資料　図2

31 Dec. 2006 8 Jan. 2007

Miura et al. 2007 Science

We can capture MJO realistically by GCRM

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We are now in the K-computer, 10 Peta-FLOPS, era !!

Earth Simulator Now, we can run such simulations of several decades with “K”, and make a

breakthrough from the case study

Case study(Miura et al 2007)

Several weeks and monthAthena Project: (Sato et al 2012)

　 Athena Cray XT-4

From the demonstration to scientific knowledge

10000km 1000km 100km 10km 1km 100m 10m

cumulusBlocking

Low-pressure Cloud cluster stratus

Tropical cyclone

Grand Challenge project:

GL13 (800m)

GL12 (1.7km)

GL11 (3.5km)

GL10 (7km)

GL09 (14km)

GL08 (30km)

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Successfully conducted the GL13(870m) simulation

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Essential change of convection statistics

The convection structure, number of convective cells, and distance to the nearest convective cell dramatically changed around 2.0km

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Future direction of climate modelingIncreases of resolution, model component, ensemble

A key factor to sophisticate the atmospheric modelCloud modelingA new method is to express explicitly each of clouds

A main topics of climate research using K computerCumulus, cloud organization, tropical cyclone, MJOHigh resolution ( less than 2.0km grid spacing) can resolve

convection core using multiple grid.

Summary

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감사합니다

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