Regional Climate Modeling in CORDEX South Asia for Climate Change Research

Regional Climate Modeling In The Framework Of

Regional Climate Modeling In The Framework Of

CORDEX South Asia

CORDEX South Asia

Sanjay Jayanarayanan

Sanjay Jayanarayanan

Centre for Climate Change Research (CCCR)

Centre for Climate Change Research (CCCR)

Indian Institute of Tropical Meteorology (IITM), Pune, India

Indian Institute of Tropical Meteorology (IITM), Pune, India

An Autonomous Institute of the Ministry of Earth Sciences, Govt.of India

An Autonomous Institute of the Ministry of Earth Sciences, Govt.of India

High Resolution (50 km)

Dynamical Downscaling of

CMIP5  Climate Projections

based on RCP Scenarios

during 1950-2100 using

multiple RCMs

Regional Climate Information for Application Studies

 CORDEX South Asia

is leading CORDEX (Coordinated

is leading CORDEX (Coordinated

Regional Climate Downscaling

Regional Climate Downscaling

Experiment) over South Asia Region

Experiment) over South Asia Region

More information for CORDEX South Asia data access from CCCR-IITM Climate Data Portal and

ESGF datanode are provided at

: 

http://cccr.tropmet.res.in/home/cordexsa_datasets.jsp

https://cordex.org/domains/region-6-south-asia-2

CORDEX South Asia (WAS) Activities

CORDEX South Asia (WAS) Activities

Opportunities and Challenges for better assessment of regional climate change

Opportunities and Challenges for better assessment of regional climate change

•

Future global 

Future global 

meteorological drought 

meteorological drought 

hot spots: A

hot spots: A

study based on CORDEX Data 

study based on CORDEX Data 

(Spinoni et al., 2020)

(Spinoni et al., 2020)

•

Contrasting regional and global climate simulations

Contrasting regional and global climate simulations

over South Asia 

over South Asia 

(Rana et al., 2020)

(Rana et al., 2020)

•

Added value 

Added value 

of CORDEX-SA experiments in simulating

of CORDEX-SA experiments in simulating

monsoon precipitation 

monsoon precipitation 

over India 

over India 

(Choudhary etal.2018)

(Choudhary etal.2018)

•

Understanding the cascade of GCM and downscaling

Understanding the cascade of GCM and downscaling

(dynamical versus statistical) 

(dynamical versus statistical) 

uncertainties

uncertainties

 in

 in

capturing the spatio-temporal variability of 

capturing the spatio-temporal variability of 

hydro-

hydro-

climatic projections

climatic projections

 over India 

 over India 

(Sharma et al., 2017)

(Sharma et al., 2017)

•

Do dynamic regional models 

Do dynamic regional models 

add value 

add value 

to the global

to the global

model projections of 

model projections of 

Indian monsoon

Indian monsoon

? 

? 

(Singh 

(Singh 

etal, 2017)

etal, 2017)

•

Climatic uncertainty in RCMs is far larger than

Climatic uncertainty in RCMs is far larger than

observations over the 

observations over the 

Himalayan water towers

Himalayan water towers

(Mishra 2015)

(Mishra 2015)

•

Reliability of regional and global climate models to

Reliability of regional and global climate models to

simulate 

simulate 

precipitation extremes 

precipitation extremes 

over India

over India

(Mishra et al., 2014)

(Mishra et al., 2014)

Grid cells (in red) where models show bias (±10%) for 1

Grid cells (in red) where models show bias (±10%) for 1

day precip maxima at 25 year return period and (d)

day precip maxima at 25 year return period and (d)

models and the area (%) (Fig. 9, Mishra et al. 2014)

models and the area (%) (Fig. 9, Mishra et al. 2014)

•

More than 40 research publications (2014 onwards) analysed the RCM outputs from the CORDEX

More than 40 research publications (2014 onwards) analysed the RCM outputs from the CORDEX

South Asia ensemble  (see  

South Asia ensemble  (see  

http://cccr.tropmet.res.in/home/cordexsa_pub.jsp

  )

  )

Ensemble mean JJAS 1951-2007

Ensemble mean JJAS 1951-2007

(Fig. 4, Mishra  2015)

(Fig. 4, Mishra  2015)

ISMR (Fig. 16, Singh et al.,  2017)

ISMR (Fig. 16, Singh et al.,  2017)

AV JJAS mean precip (Fig. 2,

AV JJAS mean precip (Fig. 2,

Choudhary et al.,  2018)

Choudhary et al.,  2018)

Source:

Source:

Krishnan et al., (2020) 

Krishnan et al., (2020) 

SpringerNature;

SpringerNature;

https://doi.org/10.1007/978-981-15-

https://doi.org/10.1007/978-981-15-

4327-2

4327-2

•

CORDEX South Asia

CORDEX South Asia

future projections

future projections

of regional climate

of regional climate

change over India

change over India

(In Krishnan et al., 2020)

(In Krishnan et al., 2020)

https://cordex.org/wp-

https://cordex.org/wp-

content/uploads/2020/12/CORDEX_simulations_Dec_2020.xlsx

content/uploads/2020/12/CORDEX_simulations_Dec_2020.xlsx

CORDEX simulations are stored in a distributed archive

CORDEX simulations are stored in a distributed archive

(the Earth System Grid Federation, ESGF) after standardization &

(the Earth System Grid Federation, ESGF) after standardization &

curation: 

curation: 

https://cordex.org/data-access/regional-climate-change-simulations-for-cordex-domains/

https://cordex.org/data-access/regional-climate-change-simulations-for-cordex-domains/

Climate Data Store (CDS)

Climate Data Store (CDS)

CORDEX data subset

CORDEX data subset

https://cds.climate.copernicus.eu/cdsapp#!/dat

https://cds.climate.copernicus.eu/cdsapp#!/dat

aset/projections-cordex-domains-single-

aset/projections-cordex-domains-single-

levels?tab=overview

levels?tab=overview

•

The CDS subset of CORDEX data

The CDS subset of CORDEX data

is an effort done by Copernicus to

is an effort done by Copernicus to

consolidate a World-wide CORDEX

consolidate a World-wide CORDEX

dataset, and has also contributed

dataset, and has also contributed

to the IPCC-AR6 WGI activities

to the IPCC-AR6 WGI activities

Spatial distribution of annual maximum

Spatial distribution of annual maximum

1-day precipitation (Rx1day, mm) over

1-day precipitation (Rx1day, mm) over

South Asia and adjoining regions

South Asia and adjoining regions

averaged for the historical reference

averaged for the historical reference

period 1986-2005 from the multi-model

period 1986-2005 from the multi-model

ensemble (MME) mean of global (CMIP6

ensemble (MME) mean of global (CMIP6

and CMIP5) and downscaled regional

and CMIP5) and downscaled regional

(CORDEX South Asia) historical climate

(CORDEX South Asia) historical climate

simulations.

simulations.

Rx1day is an index for CID category

Rx1day is an index for CID category

Heavy Precipitation & Pluvial Floods

Heavy Precipitation & Pluvial Floods

The spatial maps are drawn online using the

The spatial maps are drawn online using the

IPCC Interactive Atlas

IPCC Interactive Atlas

(Gutiérrez et al., 2021;

(Gutiérrez et al., 2021;

https://interactive-atlas.ipcc.ch/

https://interactive-atlas.ipcc.ch/

 )

 )

Climate Models Simulated

Climate Models Simulated

Regional to Local Precipitation Extremes

Regional to Local Precipitation Extremes

The spatial maps are drawn using the data extracted from IPCC Interactive Atlas

The spatial maps are drawn using the data extracted from IPCC Interactive Atlas

(Gutiérrez et al., 2021; 

(Gutiérrez et al., 2021; 

https://interactive-atlas.ipcc.ch/

https://interactive-atlas.ipcc.ch/

 )

 )

•

Climatological spatial distribution of Rx1day magnitudes

over the Kerala State and adjoining regions shows that

the downscaled high resolution CORDEX MME mean

simulated the intense precipitation extremes relatively

closer to observed (APHRODITE) estimates than the

coarse resolution CMIP5 and CMIP6 MME means.

I

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M

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•

Analyses of MME mean

global and downscaled

regional climate projections

implies that scenarios with

low GHG emissions (RCP2.6

and SSP1-2.6) would lead to

substantially smaller changes

in annual Rx1day index

beyond 2040 than under

very high GHG emissions

scenarios (RCP8.5 and SSP5-

8.5) over the Kerala State

and adjoining regions

Climate Change Driven Changes in Precipitation Extremes

Climate Change Driven Changes in Precipitation Extremes

The spatial maps are drawn using the data

The spatial maps are drawn using the data

extracted from IPCC Interactive Atlas

extracted from IPCC Interactive Atlas

(Gutiérrez et al., 2021;

(Gutiérrez et al., 2021;

https://interactive-atlas.ipcc.ch/

https://interactive-atlas.ipcc.ch/

 )

 )

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Summary & Knowledge Gaps……

Summary & Knowledge Gaps……

•

The evaluation of global and downscaled regional climate model

performance will provide confidence to the analyses of projected changes

from these model outputs.

•

Climate information as provided by the IPCC may be too coarse, too broad

or too disciplinary to directly inform decision-making at the national, local

or sectoral level where adaptation planning measures are taken

•

IPCC AR6 had assessed that

there is high confidence that

Climate Services has set new

scientific challenges to physical

climate research

•

Understanding and modelling of

weather and climate extremes is

of great relevance for Climate

Services, and is continuing to set

challenges for research, such as

modelling changes in impact-

relevant threshold exceedance

and return periods for a variety

of extremes

      CCCR-IITM is a modeling partner in this five year (2020-2024) International

      CORDEX Flagship Pilot Study (FPS) project

•

This FPS aims to better understand the regional characteristics of water cycle and its

variabilities and changes over the TP and adjoining regions using a set of coordinated high

resolution regional climate downscaling experiments carried out by international participants

with a focus on convection-permitting simulations (2-5 km) using different models or model

setups.

CORDEX-FPS: High resolution climate modeling with a focus on

CORDEX-FPS: High resolution climate modeling with a focus on

mesoscale convective systems and associated precipitation

mesoscale convective systems and associated precipitation

over the Third Pole region

over the Third Pole region

Convection-Permitting Third Pole (CPTP)

Convection-Permitting Third Pole (CPTP)

http://rcg.gvc.gu.se/cordex_fps_cptp/

https://doi.org/10.1007/s00382-022-06543-3

https://doi.org/10.1007/s00382-022-06543-3

•

The first results from multi-model, multi-physics ensemble

The first results from multi-model, multi-physics ensemble

simulations of three case studies show high performance

simulations of three case studies show high performance

across a range of meteorological situations and are close to

across a range of meteorological situations and are close to

available observational estimates in simulating precipitation

available observational estimates in simulating precipitation

and near-surface temperature.

and near-surface temperature.

https://www.wcrp-climate.org/wgcm-cmip/wgcm-cmip6

https://cordex.org/data-access/how-to-access-the-data/

http://cccr.tropmet.res.in/home/data_portals.jsp