climate simulation and modelling at ministry of earth...

First annual NKN workshop, IIT-B, 31 Oct. -2 Nov., 2012

Indian Institute of Tropical Meteorology (IITM)

A.K.Sahai

Climate Simulation and Modelling at Ministry of Earth Sciences


Major Objectives of MoES

To provide the country best possible weather

forecast (short range ) and climate prediction

(long range )

To conduct the R & D required to improve the

skill of both weather and climate forecasts

To conduct regional climate change research to

provide reliable projection of monsoon under

climate change


Weather Climate

Climate: A statistical description of weather


Global climate Modeling

F = Friction (turbulent dissipation) Q = Non Adiabatic Heating = Net Radiation + Latent heat (clouds) + Sensible heat

.

pp C

Q

p

T

C

RTTV

t

T

pV

p

RT

p

FVkfDt

VD

.

.

0.

ˆ

Basic equations for Weather and Climate Models in pressure coordinate system

--------------(1)

--------------(2)

--------------(3)

--------------(4)

Complexities involved in a Climate Modelling System


Calculation of Heating Distribution

AGCM/OGCM NS Eqs.

Global 3-D + time

Radiation Incoming SW Outgoing LW

Clouds •Convective •Startiform

Land-Surface Processes •Vegetation

•Soil moisture

Boundary Layer Turbulence

•Fluxes •Mixing

•Dissipation

Stratospheric Chemistry

•Heating •Stability

Aerosols •Direct Rad Eff

•Indir eff thr clouds

Key Uncertainties for Climate :

High Clouds:

Dominant effect is that they Trap heat (warm)

More Clouds=Warming Fewer Clouds=Cooling

Source: Schär


All physical processes involved in heating

arise from complex small scale processes, that

need to be parameterized in the model

Accuracy of paramereization determines

heating distribution and hence weather and

climate

More complex paramerization requires more

computation

Improvement of parameterization need R & D

Scale Interaction and Parameterization

Horizontal Resolution of the Contemporary AGCM/OGCM

500 km 300 km

75 km 150 km


Simulations at 200km and 50 km

Resolution is of key importance for the representation of hydrological Cycle and extreme rainfall events

First annual NKN workshop, IIT-B, 31 Oct. -2 Nov., 2012 Source: Kinter


Weather & Climate Prediction

Chaotic System; Probabilistic prediction

Large number of ensemble of each prediction

Initial value problem; 4-D data Assimilation

Variational Assimilation ; Adjoint of the model; Extremely computation intensive;

It is found that preparation of the I.C. for operational weather prediction at high resolution takes more computer time than actually making the prediction!


The Butterfly Effect

• Discovery of the

“butterfly effect” (Lorenz 1963)

• Simplified climate

model… When the

integration was

restarted with 3 (vs 6)

digit accuracy,

everything was going

fine until… Time


• Solutions began to diverge

Solutions diverge

Time



• Soon, two “similar” but clearly unique solutions

Solutions diverge

Time



• Eventually, results

revealed two uncorrelated and completely different solutions (i.e., chaos)

Solutions diverge

Time

Chaos



• Ensembles can be used to provide information on forecast uncertainty

• Information from the

ensemble typically

consists of…

(1) Mean

(2) Spread

(3) Probability

Ensembles useful in

this range!

Solutions diverge

Time

Chaos



• Ensembles extend predictability…

• A deterministic

solution is no longer

skillful when its error

variance exceeds

climatic variance

• An ensemble remains

skillful until error

saturation (i.e., until

chaos occurs)

Solutions diverge

Chaos

Time

Ensembles extend predictability

Ensembles especially

useful in this range!


Data Assimilation

Univariate SI Multivariate SI 3DVAR 4DVAR 4DVAR/EnKF 4DVAR/EnKF

Adv. sounders Adv. sounders Adv. sounders IR/MW sounders IR sounders

Scatterometer Scatterometer Scatterometer Scatterometer

TRMM TRMM TRMM

Rainfall assimilation

Rainfall assimilation

Mesoscale assimilation

Chemical species

1975 1985 1990 1997 1999 2005 -

Increasing

complexity

Vast increase in data


Success story of Numerical Weather Forecasting! Great Improvement in medium-range forecast skill.

12-month running mean of anomaly correlation (%) of 500 hPa height forecasts

Note the convergence of skill in NH and SH

From ECMWF


Improving the Forecasts

Must improve the MODEL and Data Assimilation

Ex: Consider seasonal prediction with a CGCM

100 yr integration for testing mean climate

Hindcast experiments to test prediction skill;

25 member ensemble x six month prediction x 20 years = 250 year integration

Must turn around within a few days so that other improvement could be tested!

Evolution of Climate Models in last 5 decades at renowned climate centers

India’s Present status

Leading climate centers’ status


Computational requirements of climate models.

[

Range: Assumed efficiency of 10-40%. 0 -

Atmospheric General Circulation Model

(AGCM; 100 vertical levels) 1 - Coupled

Ocean-Atmosphere-Land Model (CGCM; ~

2X AGCM) 2 - Earth System Model (with

biogeochemical cycles) (ESM; ~ 2X CGCM)]

[Range: Assumed efficiency of 10-40%. 0 - Atmospheric General Circulation Model (AGCM;

100 vertical levels) 1 - Coupled Ocean-Atmosphere-Land Model (CGCM; ~ 2X AGCM)

2 - Earth System Model (with biogeochemical cycles) (ESM; ~ 2X CGCM)]

How a code in a coupled model works?

Source: Anne Roches & Piccinali

International Centres HPC Current Capacity

NERSCC, USA Cray XT5 ~1.17PF (peak)

UKMet Office IBM P6 IBM P7

~150TF ~900TF (by 2011)

NCAR, USA IBM P5/P6 ~80TF

NCEP, USA IBM P6 ~90TF

German Met Office IBM P6 ~165TF

ECMWF IBM P6 IBM P7

~300TF ~1PF (by 2011)

JAMSTEC Earth Simulator ~131TF

KMA Cray XT5 ~600TF

National Supercomputing Center in Tianjin China

NUDT ~4.7PF

Oak Ridge National Laboratory USA

Cray XT5 Supercomputer(JAGUAR)

~2PF

These centres are also having additional HPC for operational/other usage.


Phase 1

Number of Clusters

2 compute clusters

(272 nodes each)

Compute Nodes

272 x 32-core

POWER6 (SMT)

Peak Performance

~300TF (Total)

Sustained

Performance ~20TF

HPC at ECMWF

National Weather and Climate Centres

HPC Current Capacity

NCMRWF, Noida

IBM P6 ~23TF

IMD, New Delhi IBM P6 ~15TF

INCOIS, Hyderabad IBM P6 ~7TF

IITM, Pune IBM P6 ~70TF 2010 July Ranking 94th

2010 November Ranking 137th

2011 November Ranking 403rd

In India where do we stand?

No. of Systems in Top 500 from Different countries

India:2 (0.4%)

Source: Top 500 list, Nov. 2011

China:74 USA:263

T62L64 T126L64

Dynamical Seasonal Prediction of Indian Monsoon JJAS Rainfall – 2010 (CFS V1.0) Issues in April

Central Indian drought predicted by CFS model Above normal rainfall over southern peninsular India

IITM CFS T62 IITM CFS T126

IMD

T62L64 T126L64

Dynamical Seasonal Prediction of Indian Monsoon With Initial Conditions generated within India at (INCOIS & NCMRWF)

JJAS Rainfall – 2011 (Issued in March)

Central Indian above normal rain predicted by CFS model Below normal rainfall over southern peninsular India

IITM CFS T62 IITM CFS V2.0 T126

IMD Upto 10th

September

Monsoon Performance = 100±4.5 %

Predictions for 2012: Predicted Vs. Observed

Monsoon Performance = 92 %

Actual Rainfall Departure (IMD)


Extended range Prediction of Active

Break Cycles of Monsoon • Forecast of seasonal mean rainfall may not be very useful and

meaningful when the mean is close to normal (70%). The regional/temporal distribution of rainfall anomalies is very inhomogeneous. Therefore, in addition to the seasonal mean All India rainfall, we need to predict some aspects of monsoon 3-4 weeks in advance on a relatively smaller spatial scale that will be useful for farmers.

• The extended range prediction refers to a meteorological

forecast more than 10 days in advance which is the normal predictability range of weather systems (storms, cyclones etc.)

• In two slides to follow some efforts for extended range

prediction during summer monsoon season 2012 over India is presented.

Evolution of daily rainfall and wind at 850 hPa From 29th Aug, 2012 IC

from

20-25 June


Climate Change Simulation

Proposed System of MoES Computing

Facilities (2012-2015)

IITM, Pune 1250 TF

NCMRWF, Delhi 700 TF

INCOIS, Hyderabad

80 TF

IMD, Delhi 350 TF

NKN Connectivity

1 GBPS 10/40 GBPS

Earth Science Grid

Internet 2

US-India-EU (Monsoon Mission)

Universities & Academic Institutes

Data Transfer among MoES Institutes/Day

(Present requirement)

IITM, Pune (16 TB)

NCMRWF, Delhi 4 TB

INCOIS, Hyderabad

1 TB

IMD, Delhi 2 TB

NKN Connectivity

1 GBPS 10/40 GBPS

Earth Science Grid

5 TB

1 T

B

0.5

TB

Data Transfer among MoES Institutes/Day

(Projections)

IITM, Pune (16 TB)

NCMRWF, Delhi 4 TB

INCOIS, Hyderabad

1 TB

IMD, Delhi 2 TB

NKN Connectivity

10/40 GBPS

Earth Science Grid

10 TB

4TB

2 T

B

Universities & Academic Institutes

Estimated Data to be generated by planned

MoES HPC Systems (2012-2015)

IITM, Pune 30 Petabytes

NCMRWF, Delhi 10 Petabytes

INCOIS, Hyderabad 1 Petabyte

IMD, Delhi 5 Petabytes

NKN Connectivity

1 GBPS 10/40 GBPS

Earth Science Grid

Internet 2

US-India-EU (Monsoon Mission)


Data Transfer requirements at MoES

Initial Conditions Required for Making Seasonal and Extended range predictions are being prepared at INCOIS and NCMRWF, however, the real predictions are made at IITM super computer. The data required for the prediction is required to be transferred from INCOIS and NCMRWF to IITM.

Similarly, the observed data being collected at IMD is transferred to NCMRWF to prepare initial data for prediction.

Out of the Estimated MoES HPC Data of 36 Petabytes approx 25 % are Model outputs which are required to transfered to and fro between MoES Institutes for research collaborations, validations and forecasting purposes.


Plans through NKN

Connecting MoES HPC clusters and to consolidate the computational resources thus forming an Earth Science HPC grid.

To share HPC data through common shared file systems in enabling deduplication of Model data as well as computational time for similar runs.

Extending MoES HPC facilities to other Academic and Research Institutes through NKN.

Other IP based Applications like VOIP, MoES Intranet based applications, automations, training..etc


Enabling Earth Science NKN Grid for MoES.

More number of Internet Public IP’s for International Collaborations (At least 256) per Institute.

Onsite support from NIC/NKN team for flawless, seamless migration on to NKN .

Requirements through NKN


Thank you

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