HPC for better understanding of the tropical meteorology Y. Kajikawa and H. Tomita Oct 11 th, PNU 1 Necessity of 2 History of climate modeling (1) Richardsons Dream (1910s-1920s) 3 History of climate modeling (2) ENIAC (Electronic Numerical Integrator And Computer) was the first electronic general-purpose computer. 4 [Q] Why does the climate model require the HPC?1. Increase of resolution To know more detail structure! e.g. horizontal Resolution in usual climate model : 100km 2000 / 20km (2005 3.5km on K-computer 5 2. Increase of processes To know more complex interactions. Atm. Ocn. Lnd. models Carbon cycle, aerosol, chemistry, dynamic vegetation process External forcing Variability of solar constant CO2 emission scenario by IPCC run -> Earth System model [Q] Why does the climate model require the HPC? 63. Increase of ensembles To make our results more reliable. Statistical knowledge is necessary. Importance of the cloud process (1) 7 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 8 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. 8 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! 10 Earth diameter :12740km 10km Cloud cluster 100km km Super cloud cluster 1000km MJO Cloud element: cumulus Hierarchical structure of clouds 11 Example of cloud origination meso-scale cloud Cloud drop aggregation Fall as precipitation Cooling by evaporation Cold pool Generation of new clouds understanding of cloud dynamics 12 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 ? 13 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 14 Cumulus (cloud-system ) can be resolved! To avoid the parameterization High reliability / expression of cloud dynamics (w/ cold pool) New Approach from 2004 Grid intervals a few km Numerical techniques in the new approach 15 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 ) NICAM project : ~2000 The first target machine : Earth Simulator Now, porting to K computer system Prof. Satoh (AORI, Tokyo univ.) Dr. Tomita (RIKEN AICS) 16 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 17 Ref. Satoh et al J. Comput. Phys. / Tomita & Satoh 2004 Fluid Dyn. Res. Recent DC description paper : Tomita et al. 2011, ECMWF workshop proceeding NICAM current implementation 18 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 parameterization Methodological cloud-system resolving! Objection : Cloud resolving model? (Grey zone problem) The examination of impact without Cumulus Parameterization is the most important! What can the GCRM perform? 19 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 (2010, vol.29-1, NAGARE) 20 NICAM 7km-mesh, one-month simulation: initial = 15 Dec Dec 2 31 Dec Jan Miura et al Science We can capture MJO realistically by GCRM 21 We are now in the K-computer, 10 Peta-FLOPS, era !! Earth Simulator N ow, 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 month Athena Project: (Sato et al 2012) Athena Cray XT-4 demonstrationscientific knowledge From the demonstration to scientific knowledge 10000km1000km100km10km1km100m10m cumulus Blocking Low-pressureCloud clusterstratus Tropical cyclone Grand Challenge project: GL13 (800m) GL12 (1.7km) GL11 (3.5km) GL10 (7km) GL09 (14km) GL08 (30km) 24 Successfully conducted the GL13(870m) simulation 25 Essential change of convection statistics The convection structure, number of convective cells, and distance to the nearest convective cell dramatically changed around 2.0km 26 Future direction of climate modeling Increases of resolution, model component, ensemble A key factor to sophisticate the atmospheric model Cloud modeling A new method is to express explicitly each of clouds A main topics of climate research using K computer Cumulus, cloud organization, tropical cyclone, MJO High resolution ( less than 2.0km grid spacing) can resolve convection core using multiple grid. Summary 27 28