Introduction of CCCR-IITM

R. Krishnan

Centre for Climate Change ResearchIndian Institute of Tropical Meteorology, Pune

Objectives Objectives

Harness the strengths in science to better understand global and regional climate change Harness the strengths in science to better understand global and regional climate change

with particular focus on the South Asian monsoonwith particular focus on the South Asian monsoon

Understand impacts of global warming on planetary scale phenomena like monsoons and

El Niño

Understand the nature of biogeochemical interactions and their response to climate

change

Create and update information reservoirs for better assessment of climatic changes and

their impacts

Identify and explore new areas of research that will contribute to the fundamental

understanding of the Earth’s climate system

Build linkages with national and international research groups to optimally leverage

scientific capabilities for climate change research

Modeling Program Observational Program

To understand Past Changes in Monsoon Climate using Multiple Proxy Records. Reconstruction of monsoon indices going back to a few thousand years

Co-ordinate the MoES flux network program to measure GHG flux variations across diverse ecosystems and vegetation types over India

To promote Outreach and Training for Capacity Building in Climate Change Research and Dissemination of Information

To build a Global Earth System Model to address the Attribution & Projection of regional climate change – (Long-term)

To develop regional climate change scenarios over South Asia using High-resolution Regional Climate Models ; quantify uncertainties for providing reliable inputs for impact assessments. Contribute to IPCC AR5 – (Short-term)

CCCR

Administration Scientific Research Outreach

Objectives

IPCC 2007

1.0º C1.0º C

Increase in Surface TemperatureIncrease in Surface Temperature

ObservationsPredictions with Anthropogenic/Natural forcingsPredictions with Natural forcings

Attribution: Are increases in greenhouse gases responsible for global warming?

Primary synoptic and smaller-scale circulation features that affect cloudiness and precipitation in Summer monsoon region. Locations of June to September rainfall exceeding 100 cm over the land West of 100oE associated with the southwest monsoon are indicated (Rao, 1981). Those over water areas and east of 100oE are omitted.

Challenges in assessment of future changes in South Asian monsoon rainfall

•Wide variations and uncertainties among the IPCC AR4 models in capturing the mean monsoon rainfall over South Asia (eg., Kripalani et al. 2007, Annamalai et al. 2007).

•Realism of present-day climate simulation is an essential requirement for reliable assessment of future changes in monsoon

Source: Kripalani et al. 2010

Questions : On Attribution?Questions : On Attribution?

How much of the observed variability of the mean Indian Summer How much of the observed variability of the mean Indian Summer Monsoon rainfall due to Climate Change?Monsoon rainfall due to Climate Change?

How much of the observed increase in temperature over India been How much of the observed increase in temperature over India been decreased by increasing presence of aerosols?decreased by increasing presence of aerosols?

Questions : On Projections of MonsoonQuestions : On Projections of Monsoon• What will happen to the monsoon hydrological cycle 50-100 years from What will happen to the monsoon hydrological cycle 50-100 years from

now under different scenarios? In particular, will the quantum of seasonal now under different scenarios? In particular, will the quantum of seasonal mean rainfall increase or decrease and if so by how much?mean rainfall increase or decrease and if so by how much?

• What is the uncertainty in these projections? Can we quantify this What is the uncertainty in these projections? Can we quantify this uncertainty? uncertainty?

• How can we reduce this uncertainty?How can we reduce this uncertainty?

Strategy on Regional Strategy on Regional Climate Change Research at IITMClimate Change Research at IITM

Centre for Climate Change Research (CCCR)Centre for Climate Change Research (CCCR)

To build capacity in the country in high resolution coupled To build capacity in the country in high resolution coupled ocean-atmosphere modelling to address issues on ocean-atmosphere modelling to address issues on Attribution Attribution and Projection and Projection of regional Climate Changeof regional Climate Change

Earth System Model (ESM)Earth System Model (ESM)

To provide reliable input for Impact Assessment studies To provide reliable input for Impact Assessment studies Dynamic downscaling of regional monsoon climate using high Dynamic downscaling of regional monsoon climate using high

resolution models; quantification of uncertaintiesresolution models; quantification of uncertainties

Observational monitoring: Network with other Institutions Observational monitoring: Network with other Institutions

Roadmap towards Earth System Model (ESM)

development Start with an atmosphere-ocean coupled model

with realistic mean climate Fidelity in capturing the global and monsoon climate Realistic representation of monsoon interannual

variability Features of ocean-atmosphere coupled interactions Same modeling framework for seasonal monsoon

prediction …

Include components of the ESM Biogeochemistry Module (Terrestrial and Marine) Aerosol and Chemistry Transport Module …

• The NCEP CFS Components • Atmospheric GFS (Global Forecast System) model

– – T126 ~ 110 km; vertical: 64 sigma – pressure hybrid levels– – Model top 0.2 mb– – Simplified Arakawa-Schubert convection (Pan)– – Non-local PBL (Pan & Hong)– – SW radiation (Chou, modifications by Y. Hou)– – Prognostic cloud water (Moorthi, Hou & Zhao)– – LW radiation (GFDL, AER in operational wx model)– - Land surface processes (Noah land model)

• Interactive Ocean: GFDL MOM4 (Modular Ocean Model, ver.4)– – 0.5 deg poleward of 10oN and 10oS; and 0.25 deg near equator (10oS – 10oN)– – 40 levels– – Interactive sea-ice

Basic framework for global climate modelingNCEP Coupled Forecast System (CFS-2) T126L64

PRITHVI (High Performance Computing System) , IITM, Pune

Configuration of PRITHVI, HPC at IITM:

IBM P6 575 nodes totaling 117 numbers including the 2 nodes for GPFS quorum and one Login node. Each node is populated with 32 cores of IBM P 6 CPU running at 4.7 G Hz. Total of 3744 cores with Peak Performance of 70 Tflops.

High end Servers P570’s, P550’s, 20 Visual Workstations.

Interconnectivity using Infiniband Switches and Ethernet switches for Management purposes

Total of 3 Peta Bytes of Storage including Online, Near-line and Archival Storage

GPFS, Tivoli and other Management Softwares

CFSv2 precipitation (JJAS): 100-yr mean

CFSv1 precipitation (JJAS): 100-yr mean

TRMM precipitation (JJAS): 10-yr mean

CMAP precipitation (JJAS): 30-yr mean

CFSv2 runs from CCCR on PRITHVI

cold bias

Results from the CFS2 validation runs

Sea Surface TemperaturesSea Surface Temperatures (Annual Mean)(Annual Mean)

CFS2 BiasCFS2 Bias (Model minus Obs)(Model minus Obs)

Preethi et al. (2013): Under preparation

PrecipitationPrecipitation (Annual Mean)(Annual Mean)

CFS2 BiasCFS2 Bias (Model minus Obs)(Model minus Obs)

Include ESM components in the CFS-2 coupled ocean-atmosphere modelInclude ESM components in the CFS-2 coupled ocean-atmosphere model Incorporation of Ocean Biogeochemistry Component (MOM4P1) - Incorporation of Ocean Biogeochemistry Component (MOM4P1) - CompletedCompleted Incorporation of Aerosol Transport Module - Incorporation of Aerosol Transport Module - Partially completed and ongoingPartially completed and ongoing

Basic structure of ESM

The ESM development activity is a significant progress in climate modeling towards understanding the global and regional climate response to bio-geochemical processes & the mechanisms that control the ocean carbon cycle

Detailed testing and validation of ESM1.0 is in progress

Centre for Climate Change Research (CCCR), IITM, PuneResearch Highlights

Earth System Model (ESM) development: . The first version of the Earth System Model (ESM 1.0) has been successfully developed at CCCR-IITM by incorporating a Marine Biogeochemistry and Ecosystem component known as TOPAZ (Tracers of Phytoplankton with Allometric Zooplankton, GFDL, Princeton) in the CFS global coupled GCM at IITM.

Team: Swapna, Roxy, Aparna, Ketan, Ashok, Krishnan

Preliminary results from a 36-yr run of the CFSv2 and ESM1.0 starting from Dec 2009 ICThe ESM 1.0 run shows significant improvement of reduction of cold SST bias

The CFSv2 run shows a systematic SST cooling bias (blue line) during the model spin up

Global Mean Monthly Temperature (oC)

ESM

CFSv2

Global Mean Annual Temperature (oC)

Months

Year

Tropical SST

Air temp (2 m)

SST

Tropical SST

Ht cont

Comparison of ESM and CFSv2 simulations ~ 80 years free run from 2010 onward

Courtesy: Swapna, CCCR

ESM1.0

CFSv2

Annual mean SST difference (Model minus WOA)

Zonal mean annual mean temperature

Courtesy: Swapna, CCCR

Precipitation (mm day-1): JJAS mean

CFSv2

ESM1.0

TRMM

Leading pattern of SST variability in the tropical Pacific from EOF/PC analysis

Observations (HadiSST)

ESM1.0

CFSv2

Courtesy: Swapna, CCCR

Wavelet power spectra of Nino3 SST

Observations (HadiSST)

ESM 1.0

CFSv2

ENSO

ENSO

ENSO

PDO

PDO

PrecipitationPrecipitation(10N-30N; 70E-100E)(10N-30N; 70E-100E)

Indian (land +Ocean)Indian (land +Ocean)

Courtesy: Preethi, CCCR

Nino3 SSTNino3 SST

Lag/lead correlation between ISMR and Nino3 SSTLag/lead correlation between ISMR and Nino3 SST

Courtesy: Preethi, CCCR

IITM ESMFramework superstructure

Earth System ModelingFramework infrastructure

Griddedcomponents

Couplercomponents

GFS MoM4

Initialization•initialize atmosphere•read spectral fields•tracers frozen (=3)•read surface files

Run

•Clim. aerosol•radiation call

output

rest

arts

Max Planck InstituteMPI-ESM

MPI-OM ECHAM6OASISCoupler

Initialization

Run

output

•initialize atmosphere•read emissions•register submodels•allocate memory•read aerosol emission

•read boundary cond•physics calculations•diffusion•cloud & surface calc•diagnostics

Distinct featuresGFS & ECHAM have different grid structure

Parallelization & redistribution scheme differ

submodels are not standalone - derives time, date, memory structure from ECHAM

HAM variables are grouped in streams for calculations and for outputs

ECHAM handles HAM outputs

SUBMODELSHAM, MOZ ,..

rest

arts

5 Species: sulfate, seasalt, black-carbon, organic matter & dust

Emission inventory

Aerosol transport, dry & wet deposition/ sedimentationNucleation, condensation, coagulation and thermodynamicsextinction cross section, single scattering albedo, asymmetry parameter

Team: Ayantika, Jayant, Ramesh, Ingo Kirchner, Krishnan, Ashok

Incorporation of Aerosol Transport Module in CFSv2

WCRP CORDEX South Asia – led by CCCR, IITMCo-ordinated Regional Downscaling Experiment – CORDEX South Asia

South Asia

•Better understand regional climate processes and improve climate models

•Develop reliable high-resolution regional climate change scenarios globally, thereby contributing to the IPCC AR5 and to the climate community beyond the AR5

•Evaluate regional climate model performance through a set of experiments aiming at producing regional climate projections

•Quantify and understand the uncertainties in regional climate projections

•Develop regional capacity for assessment of regional climate change with higher level of confidence of model-based projections and judgment of regional experts

•Link climate modeling better with regional impact, adaptation and vulnerability assessment

•Integrate the regional downscaling activities, facilitate cross-fertilization of scientific expertise and engage the community of regional scientists for further capacity building in the region

Approach

1. Palaeoclimate reconstruction along N-S and E-W

transact.

2. Proxies:

- Tree-ring

- Speleothems

- Corals

Paleoclimate research

Courtesy: Hemant Borgaonkar

SimlaNepal

New Delhi

Tibet

Srinagar

W. Himalaya

Tree-Ring Network over India

Bhopal Central India

Peninsular India

Th’puram

B’lore

Dendro-climatic StudiesHighlights

• Longest tree-ring chronology of Cedrus deodara from Gangotri (A.D. 1450-2003; 554 Years)

• Higher growth in recent years observed in high altitude near glacier tree-ring chronologies of western Himalaya associated with increasing temperature trend.

• Longest tree-ring chronology of Tectona grandis (Teak) from Kerala (AD 1481 -2003 523 Years).

• Teak tree-ring chronologies from central & south India indicate positive relationship with monsoon and annual rainfall and PDSI suggests the important role of moisture in tree growth climate relationship.

• Tree-ring Drought Records of Indian Monsoon rainfall since past five centuries.

-0.6

-0.4

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1481

1501

1521

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1581

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1621

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1661

1681

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1721

1741

1761

1781

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1821

1841

1861

1881

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1941

1961

1981

2001

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ex A

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Year

Fig. 7. (A) Tree-ring width index anomaly of KTRC in relation to long-term mean. Smooth line is 10 year cubic spline fit. Dashed lines in all the figures indicate "Mean � Std.Dev." limit. Magenta circles indicate low growth years occurred during the deficient rainfall (droughts) years associated with the El Nino. Magenta squares are low growth years associated with El Nino years.

Fig. 7. (B and C) KTRC and ISMR anomalies respectively during the instrumental period 1871-2003. Red circles in fig. B are

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1450 1500 1550 1600 1650 1700 1750 1800 1850 1900 1950 2000

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Cooling

Warming

Figure 9 : 553 years (A.D. 1452-2004) long tree-ring index chronology of high altitude Himalayan conifer from Western Himalaya. Smooth red line is 30 years cubic spline filter. Suppressed (cooling) and released (warming) growth patterns in tree-ring chronology have also been observed to be well related to the past glacial fluctuation records of the region.

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1650 1700 1750 1800 1850 1900 1950 2000

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Tree-ring width index chronology of Cedrus deodara f rom Western Himalaya

X̄' - 2σ

X̄' + 2σ

ObservedReconstructed

Reconstructed Extreme pre-monsoon years prior to A.D.1879 over western Himalaya

1691, 1717, 1721, 1729, 1738, 1749, 1759, 1769, 1782, 1815, 1830, 1839, 1847, 1850, 1851, 1860

The strong relationship between extreme pre-monsoon climate and pointer years (narrow rings) might have been held good for past several centuries. About 16 regional pointer years were observed prior to A.D 1879., which would most probably be due to the extreme pre-monsoon summer. Such long proxy records of extreme climate information would be important to understand the long term climate change in the context of recent warming scenario.

Mahabaleswar:Mahabaleswar: Flux tower measurement over a forest canopy representatives of Western Ghats to quantify forest atmosphere exchange of CO2, water vapor and energy.

Kaziranga National ParkKaziranga National Park Soil plant atmosphere study in relation to net CO2 flux from terrestrial ecosystem of Assam

Darjeeling:Darjeeling: Study of biosphere-atmosphere exchange of GHG in a tropical high altitude forest canopy at Eastern Himalaya (Darjeeling, W.B.)

Lakshwadeep:Lakshwadeep: Study on flux of GHG governed by bio-physical processes in the Lakshadweep Island.

Pichavaram:Pichavaram: Studies on green house gas fluxes in Pichavaram Mangrove ecosystem.

Port Blair:Port Blair: Studies of carbon sequestration off Port Blair and surrounding group of islands, Andamans.

Fluxnet Project: quantifying the ecosystem fluxes Fluxnet Project: quantifying the ecosystem fluxes

Courtesy: Supriyo Chakraborty

Kaziranga National Park

Assam

Kaziranga National Park

Assam

Darjeeling, W.BDarjeeling, W.B

Port BlairAndaman Islands

Port BlairAndaman Islands

Lakshadweep IslandLakshadweep Island

Mahabaleswar, MaharashtraMahabaleswar, Maharashtra Pichavaram mangroveecosystem

Pichavaram mangroveecosystem

CO2 and GHG monitoring and inverse modeling for source / sink estimation

Source: Yogesh Tiwari, CCCR, IITM

Sinhagad observational site

Figure : Measured concentrations of CO2 (top) in air samples collected at CRI (symbol) along with fitted curve to the data points using a digital filter (black line). Smoothed fits to the Mauna Loa (blue line) and SEY data (red line) obtained by CSIRO and ESRL programs respectively, are shown for comparisons. Comparisons of inter-seasonal and inter-annual variability in CO2 (bottom) at CRI site

Current Science, 2009

CO2 and GHG monitoring and inverse modeling for source / sink estimation

Source: Yogesh Tiwari, CCCR, IITM

CO2 (ppm)

Running mean smoothing using adjacent averaging 9-points

CO2, Sinhagad (SNG) – Mauna Loa (MLO)

Ongoing and near-future plans Basic research and developmental work (ie., publications, ESM, CORDEX, Fluxnet etc)

ESM2.0: Complete incorporation of aerosol transport module in CFSv2 -- Timeline 1 yr

CORDEX Data Portal: Archival, management, sharing , distribution and publication of CORDEX data from multiple models – Time line 1 year

Paleoclimate studies: Expand the Indian tree-ring data network by using tree-ring data from South and Southeast Asia viz.,

Nepal, Myanmar, Thailand, Indonesia, etc to develop large-scale proxy climate for Monsoon Asia

Reconstruct monsoon rainfall variations covering more than 20000 years from Speleothem records

Precipitation isotope analysis across the east-west transect across the Western Ghats to identify moisture sources during the monsoon and estimation of recycled moisture over the region

Characterize amount effect in precipitation of different places in India

MoES GHG fluxnet program: Complete procurement of equipments, instruments and sensors. Setting up of fluxnet towers and GHG measurement system at six locations of India. Link fluxnet activity and background CO2 concentration measurements from Sinhagad together with climate model experiments to estimate sources and sinks; validate model estimates with fluxnet data

Macroscale hydrological modeling: Assessment of hydrological impacts of climate change on river basins of India and quantification of uncertainties - based on macroscale hydrological model simulations driven by regional climate projections

Thank you