Presentation at ARRCC-CARISSA workshop 29 Jan 2019 to 31 Jan 2019

1. Overview of Climate Change
Projections for Pakistan
Dr. Jehangir Ashraf Awan
jehangir_awan@hotmail.com
Pakistan Meteorological Department

2. • Pakistan is very high on vulnerability scale
–135th in GHG Emissions
–7th most vulnerable nation to Climate Change Impacts (Global
Climate Risk Index 2017)
• Geographical Location
–Heat surplus zone
• Population
• Limited Capacity
• Infrastructure
2
Climate Vulnerability in Pakistan

3. Pakistan is historically prone to Natural Disasters such as;
Extreme Rainfall (Monsoon)
Tropical Cyclones (Pre & Post Monsoon)
Extreme Weather Events – High Impact Weather
Natural Disasters in Pakistan
Snow-melt Flooding
Heavy Rains/River Flooding
Torrential Rain/Urban & Flash Flooding
Cyclones/Coastal Flooding
Extreme Heat in May/June
Extreme Rainfall (Monsoon)
Droughts Deficient Rainfall (Winter & Monsoon)
SMOG Deficient Rainfall & Pollution
In Pakistan, more than 70% Natural Disasters (High Impact Weather or Extreme Weather
Events) are associated with Monsoon Season
Severe Dust storms Dry Summer Months

4. Downscaling Climate Change Projections
• Study of long-term climate trends of Pakistan using observation datasets,
gridded products and reanalysis.
• Future climate projections using GCMs (CMIP3 & CMIP5) and RCMs.
• Downscaling of GCMs output at Regional Scale using both statistical and
dynamical methods.
• Tailored climate products for user needs (e.g., climate projections for
impact models)

5. Dynamically Downscaled Projections based on
IPCC AR4 SRES
RCMs
Global Data
RegCM4 PRECIS
RF A1B RF A1B
ECHAM5 (CMIP3 Family) 1970-2000 2010-2100 1970-2000 2010-2100
ERA40
(observed SSTs)
1970-2000 1970-2000
 RegCM4 –
International Centre for Theoretical
Physics, Italy
(Model and GCMs boundary Data downloaded from ICTP)
 PRECIS 1.9 - Hadley Centre, UK
(Model and GCMs boundary Data provided by Met Office UK)
Horizontal resolution – 50 km & 25 km
Vertical Levels – 18

6. Statistically Downscaled Projections for Indus Basin
using CMIP5 GCMs
(For Hydrological impacts assessment)
• GCMs data accessed via ESGF Portal
• Model Selection among a suite of 24 GCMs.
• Four GCMs were selected based on their
goodness of fit criteria:
– Having Correlation Coefficient (>= 0.80) with
baseline APHRODITE time-series.
– Normalized root mean square <= 0.15
– Normalized standard deviations within ±0.4 to that
of observed datasets.
Model Center Spatial Resolution RMSE_T RMSE_P SD_T SD_P CC_T CC_P
CCSM4 NCAR 1.25x0.94 0.05 0.15 0.31 0.28 0.99 0.89
CanESM2 CCCMA 2.81x2.81 0.05 0.11 0.31 0.28 0.99 0.94
GFDL-ESM2M GFDL 2.5x2.011 0.05 0.15 0.31 0.25 0.99 0.86
HadGEM2-ES MOHC 1.87 x 1.25 0.04 0.11 0.30 0.24 0.99 0.92

7. RCP4.5
RCP8.5
Winter Summer
RCP4.5
RCP8.5
Temperature
Rainfall

8. Tailored Climate Projections
(rice-wheat & cotton-wheat cropping zone, Pakistan)
• Agricultural Model Intercomparison and Improvement Project (AgMIP) - a major
international effort linking the climate, crop, and economic modelling communities
with cutting-edge information technology to produce improved crop and economic
models and the next generation of climate impact projections for the agricultural
sector.
Methodology (Delta Downscaling with mean & Variability Changes)
• Collection of historical climate information at farm level.
• Generating climate sensitivity experiments (Sensitivity of agricultural production to
climate changes
• CMIP5 GCM-selection approach that better captures the range and uncertainty of
modeled climate changes in a site-specific way (so each site will be sampling a unique
set of 5 GCMs)
 Mean-change-only “Delta” scenarios
 Mean-and-variability “Enhanced Delta” scenarios
 RCP4.5 and RCP8.5
• Agro-climatological analysis for rice-wheat & cotton-wheat cropping zone.
• The procedure removes some inaccuracies and the biases associated with GCM data.

9. Projected warmer conditions could impact agricultural production, with large
declines in cotton yield and stagnant or slight declines in wheat yield. Uncertain
changes in rainfall could impact the magnitude of these changes.
CLIMATE 2050s
Increase in temperature of at least 2.5 °C
up to 3.6 °C
Uncertain projected changes in rainfall –
median increases but potential for up to
52% decline in cotton growing season and
42% in wheat growing season
IMPACTS
Mean cotton yield losses of
47% under higher temperatures
Wheat yields stable or decline by 2%
to 4.5%
Tailored Climate Projections
(rice-wheat & cotton-wheat cropping zone, Pakistan)

10. Tailored Climate Projections
(Climate Change Impacts on Crop Yields under 1.5 and 2 degree scenarios)
• UAF and PMD partnership
• Started in 2018 and in progress
• 1.5 and 2 degree scenarios for 5 GCMs
• Impacts assessment of Crop yields in Punjab Pakistan.

11. Dynamically Downscaled Projections based on IPCC AR5
RCPs at 0.22 degree resolution
• Downscaling HadGEM2-ES (Met Office Hadley Centre Earth System Model)
using PRECIS 2.0
• Emission scenarios RCP 4.5 and RCP 8.5
• Horizontal grid resolution 0.22°
• Domain: 3–38°N and 58–90° E
• Historical 1950 – 2006
• Future 2020 – 2100

12. How to make Climate Projections User Friendly?

13. Capacity building of Academia on Climate Change
(Training Workshops on Climate Modelling & Gridded Datasets)
• Training workshop on “Analyzing Global Climate Datasets using
GrADs at ALLAMA IQBAL OPEN UNIVERSITY, ISLAMABAD
April, 2018
• Summer School on Managing Shared River Basins: Connecting
Science & Policy for Integrated Water Resources Management
July 2018
• Second Summer School on Integrated Water Resources
Management: Connecting Science and Policy
August, 2018
• Training workshop on use of Climate Change Projections Gridded
Datasets at BAHRIA UNIVERSITY, ISLAMABAD
October, 2017

14. How are the projections you produce being used? Is it
used for long term planning and policy making?
• The projections are being used to formulate National Climate
Change Policy implementation framework, to inform INDC, to
conduct hazard and climate vulnerability assessment for
national projects, by academia, researchers, students
• Ministry of Planning Development and Reforms
– Revision of PC-1 template
– Climate change analysis at projection formulation stage.
• Top Users: Impact based modeling community including
Hydrological, Crop, Economic, Demographics

15. What challenges do you face in the provision of climate
change projections?
• Required horizontal resolution not possible to achieve due to
limitation of computing resources and/or model limitations
• Users not conversant with climate data formats
• GCMs/RCMs data is sometimes missing from ESGF
• Required temporal resolution of CORDEX data not available
(daily or 6-hourly).

16. What are the gaps in provision of climate change
projections?
• Range of projections using different methodologies/models
makes selection difficult for users.
• Lack of coordination at regional level and between relevant
institutions and agencies which is vital to succeed in
development of and effective application of climate products
and projections by various socio-economic sectors including
agriculture.

17. What are the ways of improving quality and coverage of
data availability?
• Regional gridded climate product based on in-situ
observations should be produced which will provide baseline
climate for future projections.
• CORDEX data on all available temporal/spatial resolutions
should be made available. Currently monthly data is available
only.
• Regional and country level climate change portal should be
developed.
• Extracted Climate datasets and projections for South Asia
region for all the GCMs, tools and targeted products should be
made available through an online portal.
• Data sets of huge volume should be made available through
Hard-drive.

18. THANKS