Mathematical climate studies
from K to Fugaku
Computational Climate Study Research TeamTL: Hirofumi Tomita

Feb 2020 The 2nd R-CCS international SymposiumIntroduction of our Team
Two activities
1.

Model library development :construction the basic libraryfor meteorology and climate simulation as a research infrastructure
Using the library, we can ...

Compare the simulated results by different models,inter-exchanging “model components” and “numerical method”⚫ Construct a more advanced climate modelwith highly efficiency in massive parallel computers⚫

2.

Climate science research:
Using our model based on the library, we want ...

To understand the feedback mechanism between cloud, aerosol andradiation⚫ To understand the role of cloud and their organization⚫ To develop the assessment method of future climate⚫ To construct the theory for the moist process with the turbulence
process⚫

SCALE: a highly sophisticated common library formeteorological/climatic simulations
Scalable Computing for Advanced Library and Environment.
Many components and, including CFD & computerscience techniques

Free software! : under BSD2-license
https://www.r-ccs.riken.jp/software_center/software/scale/overview/⚫ SCALE is a library, but it has also models⚫ SCALE-RM : regional model:

Nishizawa et al.(2015, Geophys. Model Dev. )Satoh et al.(2015, Progress in Earth and Planetary Scicence)⚫ SCALE-GM: global model(NICAM-DC clone)⚫

Porting from NICAM(Tomita & Satoh(2004,Fluid Dyn.Res.)⚫ Data assimilation has been already equippedin cooperation with Data Assimilation Research Team⚫ SCALE-LETKF
2Highlights of our scientific achievement
on K computer～selected two topics~

1. Cloud convection convergency in highresolution simulation
2. Regional climate assessment method3

Sub km global atmospheric model simulation
Background & Motivation:
15 years ago, Global Cloud Resolving Simulation
started!(NICAM: the first GCRM in the world)--- Tomita et al.(2005, GRL) / Miura et al.(2007,Science)

Debatable issue : Cloud permitting? or resolving?

Miyamoto et al.(2015, Geophys.Res.Lett.)--- Research highlight--- Eos research spotlight--- Press release from RIKEN

Simulation with much higher resolution on K Computer By using grid refinement
approach
Q. How the convection aspects change?
5numbernumber101041010(b) (b)distance
betweenconvection
distancebetween
convection
0.25
0.25
D14.0
D14.0
D7.0
D7.0
0.20.2
D3.5
D3.5
D1.7
D1.7
0.15
0.15
D0.8
D0.8

frequency/total number
frequency/total number(a) numberof convection(a) numberof convection

5

0.10.1

4

0.05

0.05

10310

3

0.8

0.8

1.7

1.7

3.5

3.5D (km)
7D (km)

714

140

0 00

4 5

104number5of gird 10
number of gird

Conclusion
1. Convection aspects (# of density,convection distance) change aroundΔx=2km drastically.
2. The core of deep convections
becomes resolved from this resolution
4

Much higher resolution in cloud cluster!
The convection aspects in the sub-km GCM was still perfectly converged.
Q. If the resolution becomes much higher,
1. How is statistics of deep convection converged by grid refinement?
2. Which is the convergence point?
⚫ Downscaling the cloud organized system!
Sueki et al.(2019, Geophys. Res. Lett.)from : Global model result(NICAM/3.5km)
to : Regional model (SCALE-RM).
⚫ High resolution run : LES(promising for high resolution)⚫ Medium resolution run :RANS(conventional method)⚫ Simulation Target: a well organized cloud system,

Madden Julian Oscillation.
⚫ Evaluation Metrics:
distance of “scale -categorized” convection cluster.

Schematic illustration of the experiments. (A)Outline of the experiments. Timeseries is expressed inuniversal time coordinated (UTC). (B) Snapshots of
total column condensate for each simulation at 18:00
UTC on 24 November 2011.

5

Convergence result by downscaling
Dash-line : RANS
Solid-line : LES⚫ In less than 200m resolution,

the convection system withhorizontal scale greater than 500m1km radius is almost converged.
⚫ Surprisingly, convergence ofRANS is quite similar to thatof LES.

※ Still debatable point :RANS is available even in highresolution, or not?

Future analysis:
Comparison of turbulence structure in the PBL btw LES & RANS.
also, an important point is convergence of moisture mass flux6

Development of New downscaling procedure
for regional climate

Basic idea :dividing GCM results into components,

Nishizawa et al.(2018,PEPS)
Adachi et al.(2017, Nat. Commun.)--- Press release⚫

Climatology (Mean state) : e.g. monthly mean atmospheric state⚫

Perturbation (Fluctuation) : e.g. synoptic low pressure system/ tropical cyclone

Appling this downscaling method
to western japan climate,

Schematic figure for evaluation of each effect

Severe rainfall cannot be assessed byconventional only-climatology-change
downscaling because of large
contribution of perturbation change
and its interaction with climatology change.

Domain1 （7.5km）

Domain2（2.5km）(m)

Main result: we can extract thecontribution of climatology change andperturbation change and its interaction.

Development of New downscaling procedure
for regional climate(2)Adachi et al.(2018,Atmos.Sci.Lett.)

We Apply the A2017 method for
uncertainty evaluation of regional
climate, using multi GCM results⚫ Focus point:

which of change in climatology or change inperturbation is dominant for uncertainty.
-> Almost same contribution for precipitation
Mean Temp

A2017, A2018 are proof-of-concept papers
Maximum daily temp
for methodology⚫ We will assess the regional climate, fully using

this method
ΔFuture change

ΔP0 Contrib. of Perturbation

ΔC0 Contrib. of Climatology
Δcp Contrib. of Nonlinearity
Mean Precip.

Maximum daily precip.

8Other epoch making results on K Computer(with Press release)⚫ High prediction skill of Madden Jullian

Oscillation by GCRM:(Miyakawa et al. 2015, Nat. Commun.)⚫ Possibility of underestimation of ice-al

bedo feedback due toblack carbon transport(Sato et al. 2016, Sci. Rep.)⚫ Precise sensitivity of cloud lifecycle byinteracting with aerosol(Sato et al. 2018, Nat. commun.)⚫ Investigation of dust devil statistics

in the Martial PBL(Nishizawa et al.2015, Geophys.Res.Lett.)

9Future plan of our team on Fugaku
supercomputer

10

Future plan toward Fugaku(1)1.Extension and sophistication of basic library SCALE⚫ Pursue higher usability for the outside users.
⚫ Provide useful analysis methods of simulation results for enhancement ofscientific output and social outcome.

2. Improvement of simulation codefor big data assimilation system
toward the future weather prediction system
Coupling our models with data assimilation
for high resolution, large ensembles, and observational big datawith Data Assimilation Research Team.
We have already prepared

NICAM-LETKF : global mid-range prediction ( large scale disturbance )⚫ SCALE (RM)-LETKF : regional short-range prediction ( each of deep convections )⚫

NICAM-LETKF is one of main models
in the FUGAKU prior subject 4
11Future plan toward Fugaku(2)3. Enhancement of model parameter estimation bydata science⚫ Already, we have started the basic study.
⚫ A parameter for rainfall (Cr: one of the most important parameters in

the Numerical Weather prediction)
is tuned by Ensemble Kalman Filter

We want to establish this techniquein Fugaku era, and install it to SCALE
as the model estimation tool.
12Future plan toward Fugaku(3)4. Investigation of uncertainties in environment assessment
by regional climate model
Current issues:⚫ To clarify the cause of uncertainties for the future prediction
both in RCM itself and GCM boundary conditions⚫ To establish a new perspective for use of regional climate model
From the viewpoint of N2018 & A2017,
we reorganize
all of the past downscaling methods

Adachi and Tomita(2020, J.Geophys.Res.) in revision--- Methodology review paper

13

Future plan toward Fugaku(4)⚫

⚫

Conventional LES: Lack of water
condensation/evaporation:
violation of dry theory
Direct Numerical Simulation
with particle-level microphysics isuseful:⚫

The data science approach
using big simulation data wouldhelp us
for construction of moist LES theory(e.g. equation discovery field)

Nishizawa et al.2015, GMD

Sato et al.2018（ASL）

Moist caseNot -5/3 power but-6/5? in cloud area
101

100

Dry case

10-1

E(k)

5. Establishment of moist LES(theory and validation)

k-6/5

10-2

10-3

-5/3 power

k-5/3
10-4

10-5

10-6
1/5000Ex6

1/2000 1/1000 1/500

Ex6(without feedback)

1/200 1/100

k1/50

Ex12

1/20

Ex25

1/10

1/5

Ex50

Toward the Global Large Eddy Simulation!!
6. Scientific subject in Fugaku era : based on the modeling subjects,
Mechanism of Self-organization and hierarchical structure in the atmosphere.
i.e., Go back again to idealized simulations
Radiation-convection equilibrium exp / Aqua planet exp.

1/2-1

1/1 [m ]Ex10014