Research Report GCISC-RR-06

High Resolution Climate Change Scenarios over South Asia Region Downscaled by Regional Climate Model

PRECIS for IPCC SRES A2 Scenario

Siraj ul Islam, Nadia Rehman, M. Munir Sheikh Arshad M. Khan

June 2009

Published by: Global Change Impact Studies Centre (GCISC) National Centre for Physics (NCP) Complex Quaid-i-Azarn University Campus P.O. Box 3022, Islamabad-44000 Pakistan

ISBN: 978-969-9395-05-5

@GCISC

Copyright. This Report, or any part of it, may not be Llsed for resale or any other commercial or gainful purpose without prior permission of Global Change Impact Studies Centre, Islamabad, Pakistan. For educational or non-profit use, however, any part of the Report may be reproduced with appropriate acknowledgement.

Published in: June 2009

This Report may be cited as follows: Islam, S., N. Rehman, M. M. Sheikh, and A.M. Khan (2009), High Resolution Climate Change Scenarios over South Asia Region Downscaled by Regional Climate Model PRECIS for IPCC SRES A2 Scenario. GCISC-RR-06, Global Change Impact Studies Centre (GCISC), Islamabad, Pakistan

CONTENTS

Foreword i

Preface ii

List of Tables iii

List of Figures iv

List of Acronyms vi

1. Introduction

2. Description of the PRECIS RCM

2.1 Data sets used with PRECIS 2

2.1.1 ERA40 2

2.1.2 HadAM3P Data 2

3. Methodology 2

4. Nesting Techniques 4

5. Validation 4

5.1 Validation of PRECIS using ERA40 Data 4

5.2 Validation of HadAM3P Downscaled Data 6 5.2.1 Validation over Pakistan 9

6. Future Climate Change Projections 13

6.1 Future Projections for Climatic Zones 18

6.2 Future Projections for Agriculture Regions 20

6.3 Future Projections for Watershed Regions 22

7. Conclusions 24

References 26

FOREWORD

Global Change Impact Studies Centre (GCISC) was established in 2002 as a dedicated research centre for climate change and other global change related studies, at the initiative of Dr. Ishfaq Ahmad, NI, HI, SI, the then Special Advisor to Chief Executive of Pakistan. The Centre has since been engaged in research on past and projected climate change in different sub regions of Pakistan; corresponding impacts on the country's key sectors; in particular Water and Agriculture; and adaptation measures to counter the negative impacts.

The work described in this report was carried out at GCISC and was supported in part by APN (Asia Pacific Network for Global Change Research), Kobe, Japan, through its CAPaBLE Programme under a 3-year capacity enhancement cum research project titled "Enhancement of national capabilities in the application of simulation models for assessment of climate change and its impacts on water resources, and food and agricultural production", awarded to GCISC in 2003 in collaboration with Pakistan Meteorological Department (PMD).

It is hoped that the report will provide useful information to national planners and policymakers as well as to academic and research organizations in the country on issues related to impacts of climate change on Pakistan.

The keen interest and support by Dr. Ishfaq Ahmad, Advisor (S & T) to the Planning Commission and useful technical advice by Dr. Amir Muhammed, Rector, NationalUniversity for Computer and Emerging Sciences and Member, Scientific Planning Group, APN, throughout the course of this work are gratefully acknowledged.

Dr. Arshad M. Khan Executive Director, GCISC

PREFACE

The assessment of global warming caused by enhanced greenhouse gas concentrations due to anthropogenic activities has increased the demand of high resolution climate change scenarios at regional scales to study more realistically the impact of climate change on various socio-economic sectors. The starting point is the various SRES global climate scenarios which are consistent with the assumptions of IPCC for future emissions of greenhouse gases, population growth, use of technology etc. These global scenarios are available from IPCC Data Distribution Centre in the form of outputs of Global Climate Models (GCMs), also known as General Circulation Models. These models are the most complex of climate models, since they attempt to represent the main components of the climate system in three dimensions. GCMs are able to simulate fairly well the most important mean climate variables, but at a coarse resolution (~300 km x 300 km). That's why they can not capture the effects of local and regional forcings in the areas of complex surface physiography and provide information suitable for any impact assessment study. To overcome this problem, Regional Climate Models (RCMs) are developed having a higher resolution (around 50 km x 50 km) for assessing the climate of a particular region within greater details especially in the regions where forcings due to complex topography and land use are more important. RCMs are widely used to study the regional climate over different parts of the world for developing climate change scenarios.

In this report, high resolution climate change scenarios are developed for South Asia region focusing particularly over Pakistan. PRECIS, a regional climate model developed by the Hadley Centre UK is nested within the HadAM3P GCM to simulate the baseline (1961-1990) climatology and the future time-slice of 2071-2100 under SRES A2 scenario. PRECIS was first validated with observed CRU data sets. The model simulated baseline climate compares quite well with the local distribution and characteristics of surface air temperature over South Asia while in the case of precipitation, sizeable biases are seen with respect to the observed values, as precipitation is sensitive to topography and local details. Overall, the model performance in simulating temperature is better than for precipitation. The simulated climate change in 2080s for A2 scenario shows spatially an increase in summer precipitation over the monsoon belt of South Asia and an increase in winter precipitation over northern parts of Pakistan, whereas precipitation is seen decreased over southern parts of Pakistan. The rise of temperature in winter is more than that in summer in Pakistan.

Further, this report presents the results for average annual and seasonal changes in temperature and precipitation over the following regions: (i) Climatic zones of Pakistan (ii) Agro climatic zones of Pakistan and (iii) Watershed basins in Northern Pakistan.

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List of Tables

Table 1 Annual and seasonal correlation, root mean square error and 13

differences for temperature (˚C)over Pakistan

Table 2 Annual and seasonal correlation, root mean square error and 13

differences for precipitation (%) over Pakistan

Table 3 Projected changes of temperature (˚C)in (2071-2100) over three 17

individual regions of Pakistan and over whole Pakistan

Table 4 Projected changes of precipitation (%) in (2071-2100) over three 18

individual regions of Pakistan and over whole Pakistan

Table 5: Projected temperature changes (˚C)over climatic regions 19

Table 6: Projected precipitation changes (%) over climatic regions 20

Table 7: Projected temperature changes (˚C)over agriculture regions 21

Table 8: Projected precipitation changes (%) over agriculture regions 22

Table 9: Projected temperature changes (˚C)over water shed regions 23

Table 10: Projected precipitation changes (%) over water shed regions 24

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List of Figures

Figure 1 South Asia domain used in the simulations: (a) Elevation (in 3 meters) (b) Standard Deviation of elevation (in meters)

Figure 2 30-years (1961-1990) annual mean temperature of (a) CRU 5 data, (b) ERA40 downscaled data and ( c) their difference (0C)

Figure 3 30-years (1961-1990) annual mean precipitation of (a) CRU 6 data, (b) ERA40 downscaled data (c) their differences (%)

Figure 4 30-years (1961-1990) mean annual and seasonal biases for 7 temperature (˚C) and precipitation (%) with CRU data over South Asia region

Figure 5 Correlation of monthly (a) temperature and (b) precipitation 8 values for the period 1961-1990 obtained with PRECIS and CRU data

Figure 6 Root mean square error of PRECIS based (a) temperature and 8 (b) precipitation monthly values for the period 1961-1990 compared to CRU data

Figure 7 Grids covering Pakistan 9

Figure 8a Monthly probability distribution functions (PDFs) of 10 temperature over Pakistan between station, CRU, ERA40 and HadAM3P data for the period 1961-1990

Figure 8b Monthly probability distribution functions (PDFs) of 10 precipitation over Pakistan between station, CRU, ERA40 and HadAM3P data for 1961-1990

Figure 9 Annual and seasonal biases of temperature and precipitation 11 over Pakistan for the period 1961-1990

Figure 10 Annual cycles of temperature and precipitation for PRECIS 12 and CRU data over Pakistan for the period 1961-1990

Figure 11 Projected future changes in mean annual, summer and winter 14 temperature (˚C) and precipitation (%) over South Asia in (2071-2100)

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Figure 12 Projected future changes in mean annual, summer and winter 15 temperature CC) and precipitation (%) over Pakistan in (2071- 2100)

Figure 13 Annual cycles of precipitation and temperature for the period 16

1961-1990 and 2071-2100 over Pakistan

Figure 14 Three regions selected over Pakistan as contained within BOX 17

A (Northern part), within BOX B (Central part) and within BOX C (Southern part)

Figure 15 Climatic zones of Pakistan 19

Figure 16 (a) Humid, (b) Sub-humid, (c) Semi-arid and (d) Arid agro 21

climatic zones over Pakistan

Figure 17 Watershed region comprises (a) Upper Indus Basin, (b) Jhelum 23 river catchments and (c) Kabul River

v

List of Acronyms

Most of the Acronyms and abbreviation, wherever they appear in text, are defined as follows:

AM April-May

AOGCM Atmosphere/Ocean General Circulation Model

APN Asia Pacific Network for global change research

CRU Climate Research Unit

DDC Data Distribution Centre

DJFM December-January-February-March

ECMWF European Centre for Medium Range Weather Forecast

ERA European Re-Analysis

GCISC Global Change Impact Studies Centre

GCM General Circulation Model

GHG Greenhouse Gas

IPCC Intergovernmental Panel on Climate Change

JJAS June-July-August-September

mm Millimeter

NWP Numerical Weather Prediction

PDF Probability Distribution Function

PMD Pakistan Meteorological Department

PRECIS Providing REgional Climates for Impacts Studies

RCM Regional Climate Model

RMSE Root Mean Square Error

S.D Standard Deviation

SRES Special Report on Emissions Scenarios

SST Sea Surface Temperature

1. Introduction

A Regional Climate Model (RCM) is a tool to add small-scale detailed information of future climate change to the large-scale projections of a GCM. RCMs are full climate models and as such are physically based and represent most or all of the processes, interactions and feedbacks among different components of the climate system that are represented by GCMs. They take coarse resolution information from a GCM and then develop fine-scale information (both temporally and spatially) using their higher resolution representation of the climate system. The typical horizontal resolution of an RCM is about 50 km. It covers an area (domain) of typically 5000 km x 5000 km, over a particular region of interest. It is a comprehensive physical model, usually of the atmosphere and land surface, containing representations of the important processes in the climate system (e.g. clouds, radiation, rainfall, soil hydrology) as are found in a GCM. An RCM does not generally include an ocean component; as this can increase complexity and would need more computing power. Further most applications for impact assessments require only the land-surface or atmospheric data. At its boundaries, an RCM is driven by atmospheric winds, temperatures and humidity output from a GCM. RCM predictions of ideally 30 years (e.g. the period 2071-2100) are needed to provide robust climate statistics, e.g. distributions of daily rainfall or intra-seasonal variability for a region generally consistent with the continental-scale climate changes predicted in the GCM ( Jones et a1., 2004).

2. Description of the PRECIS RCM

The third-generation Hadley Centre RCM (PRECIS) is based on the latest GCM, HadCM3 (Gordon et al., 2000). It has a horizontal resolution of 50 km with 19 vertical levels in the atmosphere (from the surface to 30 km in the stratosphere) and four vertical levels in the soil (Cullen, 1993). The PRECIS is an atmospheric and land surface model of limited area and high resolution, and is locatable over any part of the globe and can be nested over a limited area in a GCM (Jones et al., 2004).

Dynamic atmospheric flows, clouds and precipitation formation, radiation processes, land surface and deep soil characteristics are all included in the model. The PRECIS RCM system incorporates the current version of the HadRM3H RCM, which has similar dynamic and physics employed by the HadCM3 GCM. Both the RCM and GCM employ identical representations of both the grid scale dynamics and the sub-grid physics. In this way the RCM produces high-resolution simulations for a defined region, which are consistent with the large-scale simulation of the GCM.

The atmospheric component of the PRECIS RCM system is a hydrostatic version of the full primitive equations, i.e. vertical acceleration in the atmosphere is assumed to be very small for the hydrostatic equilibrium to be achieved and hence vertical motions are diagnosed separately from the equations of state. It has a complete representation of the Coriolis force and employs a regular latitude-longitude grid in the horizontal and a hybrid vertical coordinate system.

A terrain following sigma (σ)coordinates (sigma=pressure/surface pressure) is considered at the lower four levels with purely pressure coordinates at the top three levels (Simons et al., 1981). The model equations are solved in spherical polar coordinates and the latitude-

1

longitude grid is rotated so that the equator lies inside the region of interest in order to obtain quasi-uniform grid box area throughout the region. Dynamical flow, the atmospheric sulphur cycle, clouds and precipitation, radiation processes, the land surface and the deep soil are all described and information from every aspect is diagnosed from within the model (Gregory et al., 1990).

The PRECIS RCM system can run at two different horizontal resolutions, namely 0.44°and 0.22° (giving grid boxes of approximately 50 km x 50 km and 25 km x 25 km,respectively). Whilst a more realistic. land-sea mask and fine scale detail is expected at 25 km resolution, the time to complete such a simulation takes approximately six times longer than the time to complete a 50 km resolution run over the same area.

The PRECIS RCM requires prescribed surface and lateral boundary conditions. For present-day simulations, surface boundary conditions are only required over water, where the model needs time series of SSTs and sea-ice. For the lateral boundary conditions different GCMs output data sets are used

2.1 Data sets used with PRECIS

The descriptions of driving data sets used for simulation by PRECIS are as follows:

2.1.1 ERA40

ECMWF 40 Years Re-Analysis data, usually called as ERA40, is the new reanalysis data which covers the period from mid-1957 to 200 I including the earlier ECMWF reanalysis ERA15 data from 1979-1993.

2.1.2 HadAM3P Data

This boundary data is provided by 30-year integration of HadAM3P (Gordon, C. et al., 2000), a 150 km-resolution version of the Hadley Center's global atmosphere-only model. Observed time series of HadISST sea-surface temperatures and sea-ice for 1960-1990 are used in this integration.

PRECIS RCM is forced by model-derived boundary conditions output data on an idealized 360-day calendar.

3. Methodology

The horizontal resolution used for the simulation is taken to be 0.44° (~50 km) with the domain covering South Asia from 5° to 50° North and 55° to 100° East. The model was driven by input data of HadAM3P and with the reanalysis data ofERA40. These data setswere downscaled for the time periods comprising 1961-1990 in base line and 2071-2100in future for HadAM3P data set and 1961-1990 for ERA40 data set.

To validate and compare downscaled data, CRU data set (New et al, 1999) of monthly mean temperature on 0.5 degree resolution is used. Model output is first regridded to regular latitude / longitude grids (dx=dy=50km) to make the comparison possible. In time series analysis for validation, station data from all the available stations over Pakistan is also used. This is done by first averaging all the stations data into one time series and is

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then compared with the simulated one. Firstly for model validation, downscaled ERA40 data is compared with CRU climatology for annual mean precipitation and temperature. Then as a second step, validation ofHadAM3P data is performed with CRU climatology.

The topography of the domain selected for scenario runs is shown in Fig. lea). As we go to the north of South Asia, the topography becomes more and more complex as the altitude reaches to 5000 m. Fig. 1 (b) presents the standard deviation (S.D) of the elevation. The RCM domain has elevation values averaged for each grid box, so peaks are indeed smoothed out. The S.D. of the orography is, however, calculated from the original 10 minute resolution global data and then averaged to the grid of the regional model.

Figure 1: South Asia domain used in the simulations: (a) Elevation (in meters) (b) Standard Deviation of elevation (in meters)

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4. Nesting Techniques

The use of GCM output providing initial and lateral boundary conditions to drive the high-resolution RCM, (for selected time periods) is called nested regional climate modeling teclmique (Dickinson et al., 1989). Sea surface temperature (SST), sea ice, greenhouse gases (GHG) and aerosol-forcing, as well as initial soil conditions, are also provided by the driving AOGCM. Some variations of this technique include forcing of the large-scale component from the GCMs into the entire RCM domain (Kida et al., 1991) and (Zorita et al., 1999). This technique has been used only in one-way mode, i.e. with no feedback from the RCM simulation to the driving GCM. The basic strategy underlying this one-way nesting approach is that the GCM is used to simulate the response of the global circulation to large scale forcing and the RCM is used to account for sub-GCM grid scale forcing (e.g. complex topographical features and land cover homogeneity) in a physically-based way, and to enhance the simulation of atmospheric circulations and climatic variables at fine spatial scales (Jones et al., 2004).

5. Validation

A measure of the confidence to be placed in projections of climate change from a particular climate model (global or regional) comes in part from its ability to simulate recent climate. This validation can be done in two ways:

a) The RCM is driven, not by output from a GCM, but by a re-analysis data of actual global observations over the same time period. In re-analysis data sets, a Numerical Weather Prediction (NWP) model is used, taking in observational data and assimilating it to provide the optimum estimate of the state of the atmosphere on any given day (or shorter time interval, e.g. 6 hours). Comparison of the model simulation can then be made with observations for the same time, providing a more rigorous test of the model.

b) The regional climate model is run for a recent climate period with GCM data (for example, 1961-1990; this run is also required for projections of climate change) and results are compared with a climatology formed from observations over the same period. Comparison can be made for annual or seasonal means i.e. summer (JJAS) and winter (DJFM).

Needless to say, no model will give a perfect validation against climatology or observations. It is best to validate two or more climate models (GCM or RCM) as it will then enable a choice to be made of the most appropriate model(s) to be used in scenario generation for that region.

5.1 Validation of PRECIS using ERA40 Data

To validate the performance of the PRECIS model, 30 year simulation was carried out with the re-analysis driving data sets. Initial and lateral boundary conditions were from the European Centre for Medium-range Weather Forecasts re-analysis data set ERA-40, available at 1.25˚ x 1.88° spatial resolution. The model was integrated from Dec 1959 to Dec 1990 with a horizontal grid spacing of approximately 50 km over the domain 5˚N to 45°N and 5S0E to 95˚E.

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In Fig. 2, thirty years average climatology of CRU data and model (driven with ERA4D) output are shown for temperature.

It is observed from the analysis of model output that the model simulated realistically the 3D-years area-averaged spatial pattern of temperature. Biases as compared with CRU data set, are within the range of 0 to 3°C in many parts of South Asia and the biases are more over high mountains, because of complex topography.

Figure 2: 30-years (1961-1990) annual mean temperature of (a) CRU data, (b) ERA40 downscaled data and (c) their difference (0e)

Fig. 3 presents 30-years average climatology of CRU and model (driven with ERA40) output for precipitation and their percentage difference as shown in the figure In case of precipitation, biases are again more at complex topographic regions but if we look at the precipitation patterns, these are seen to be well captured by PRECIS. The monsooncirculation and its penetration from India into Pakistan are also reproduced fairly well as shown in the figure.

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Figure 3: 30-years (1961-1990) annual mean precipitation of (a) CRU data, (b) ERA40 downscaled data (c) their differences (%)

5.2 Validation of HadAM3P Downscaled Data

In Fig. 4, 30-years mean temperature (left column) and precipitation (right column) of HadAM3P down scaled data is compared with the annual, summer (JJAS) and winter (DJFM) averages of observed data i.e. CRU and biases are calculated. Spatial patterns of simulated temperature from model are compared with CRU data. The model's response in simulating temperature is always better than precipitation.

In case of temperature, model shows a good agreement with CRU over the whole region mainly over Bangladesh and most parts of Pakistan and India whereas small amount of cold bias is observed over northern parts of Nepal and Pakistan. The difference of 5˚C is observed over central part of India for annual temperature (Fig. 4).

The pattern of precipitation is well captured by the model over most parts of South Asia particularly Pakistan, India, Nepal and Bangladesh. There is negligible bias observed over most parts of Pakistan and India. Model is also able to capture the monsoon path. Model overestimates the precipitation over northern parts of Pakistan and Nepal.

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In winter, the model shows less biases as compared to annual and summer precipitation and in case of temperature, the biases over northern parts of Pakistan are more in winter than in other parts of Pakistan, whereas biases are somewhat less as compared to annual and summer season as shown in Fig. 4 (right column).

Figure 4: 30-years (1961-1990) mean annual and seasonal biases for temperature ˚C and precipitation (%) with CRU data over South Asia region

Correlation between model data (i.e. HadAM3P downscaled data) and CRU dataset for temperature and precipitation is calculated for 30-years (1961-1990) and is shown in

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Fig. 5.0 Model and CRU temperature is very strongly correlated over whole South Asia domain particularly over northern parts of Pakistan where it is 0.98.

In case of precipitation, less correlation is observed over northern and western parts of Pakistan whereas the correlation is very high approaching 0.9 over central parts of India.

Figure 6: Root mean square error of PRECIS based (a) temperature and (b) precipitation monthly values for the period 1961-1990 compared to CRU data

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Correlation of monthly (a) temperature and (b) precipitation values for the period 1961-1990 obtained with PRECIS and CRU data

Figure 5:

Root mean square error (RMSE) with respect to CRU data is also calculated for precipitation and temperature as shown in Fig. 6. Temperature errors are in the range of 0°C to 4°C in whole of the South Asia except for some regions like Bangladesh and Western Ghats where the RMSE is in the range of l0°C to 15°C. In northern areas of South Asia, the error is in the range of 9 to 12 mm/day whereas in the rest of region, the error is up to 6 mm/day for precipitation.

5.2.1 Validation over Pakistan

Analysis of temperature and precipitation patterns is now carried out over Pakistan for model biases and validation. Grids covering Pakistan are shown in Fig. 7.

Figure 7: Grids covering Pakistan

For comparison, not only CRU dataset is used but also the available monthly time series data of the several stations across Pakistan (data averaged over Pakistan) is used. Four probability Distribution Functions (PDFs) based on monthly time series of station, CRU, ERA40 (downscaled with PRECIS) and HadAM3P (downscaled data with PRECIS) for the baseline period (1961-1990) are shown (Fig. 8a, 8b). For both temperature and precipitation, model simulation is in good agreement with the observed climatology over Pakistan. Also the upper (90th) and lower (l0th) percentile threshold values are calculated for both the variables.

Station time series and PRECIS data have almost the same pattern in the PDP oftemperature. The values of 10th and 90th percentiles for both the data sets are almost same. In case of temperature only lower thresh-hold values of HadAM3P data differ from the rest of three (Fig. 8a) whereas for the precipitation case, CRU data shows a slight difference from all the other three percentile values (Fig. 8b).

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Figure 8a: Monthly probability distribution functions (PDFs) of temperature over Pakistan between station, CRU, ERA40 and HadAM3P data for the period 1961-1990

Figure 8b: Monthly probability distribution functions (PDFs) of precipitation over Pakistan between station, CRU, ERA40 and HadAM3P data for 1961- 1990

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In Fig. 9, spatial patterns of model for temperature (left column) and precipitation (right column) are shown over Pakistan. The precipitation biases on the annual basis are in the range of -50 to 50 % over most parts of the Pakistan whereas in some southern parts, these biases are above 250 %. For the temperature case, biases range from 0 to 2° C. Seasonal biases are also shown in this figure.

Figure 9: Annual and seasonal biases of temperature and precipitation over Pakistan for the period 1961-1990

Annual cycles of precipitation and temperature over Pakistan are shown in Fig. 11. Model has over estimated precipitation in summer, whereas in the rest of months CRU and model data values are quite close. In the case of temperature, the biases differ in different months particularly for April, May and June where the warm bias is observed up to 5° C.

Figure 10: Annual cycles of temperature and precipitation for PRECIS and CRU data over Pakistan for the period 1961-1990

In Tables 1 and 2, area averaged values over Pakistan are shown. Annual correlation of temperature is higher than precipitation whereas root mean square error (RMSE) is higher for temperature. Annual average bias over Pakistan is zero but the precipitation is 50 % overestimated by the model.

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Table 1: Annual and seasonal con-elation, root mean square error and differences for temperature (˚C) over Pakistan

TEMPERATURE

PAKISTAN Correlation RMSE (˚C) Difference (˚C)

(model-cru)

Annual 0.95 3.30 -0.32 Summer 0.29 2.11 0.10 Winter 0.15 7.95 -1.73

Table 2: Annual and seasonal con-elation, root mean square error and differences for precipitation (%) over Pakistan

PRECIPITATION

PAKISTAN Correlation RMSE (mm/d) % Difference

(model-cru)/cru * I 00

Annual 0.61 1.02 54.54 Summer 0.15 2.83 127.22 Winter 0.10 2.02 -14.12

6. Future Climate Change Projections

Climate change scenarios provide the best-available means of exploring how human activities in future may change the composition of the atmosphere, how this may affect global climate, and how the resulting climate changes may impact upon the environment and human activities. They should not be viewed as predictions or forecasts of future climate but as internally-consistent pictures of possible future climates, each dependent on a set of prior assumptions.

For future scenario, PRECIS was nested into HadAM3P GCM data set forced with the SRES A2-emission scenario for the time slices 1961-1990 and 2071-2100 in order to downscale the regional response of the GCM projections over South Asia. Temperature and precipitation changes were analyzed over the whole of South Asia and Pakistan. Fig. II presents future changes of temperature (left column) and precipitation (right column) on annual as well as on seasonal basis. Spatial patterns show an increase in precipitation over the monsoon belt and some central parts of India whereas over rest of the region, there is no significant change. On seasonal basis, there is increase in winter precipitation over central India and northern parts of Pakistan whereas in summer there is no significant change. In the case of temperature, the annual mean temperature rise is in the range of 3.5° to 5.5˚C over Pakistan and India. Temperature change in winter is greater as compared to the temperature change in summer.

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Figure 11: Projected future changes in mean annual, summer and winter temperature (˚C) and precipitation (%) over South Asia in (2071-2100)

In Fig. 12, the future changes of temperature and precipitation are shown only overPakistan. Over central Punjab, the annual temperature rise is 5.5˚C, over costal areas, therise is 3.S to 4°C and over rest of the region, the rise is 4.5˚C to 5˚C by the end of this century. Over northern areas of Pakistan, the change in temperature is up to 6°C in summer whereas in winter this rise is in the range of 4 to 6°C.

Annual precipitation percentage change shows no net increase of precipitation overPakistan. In summer season, precipitation decreased by 20% over Punjab and Sindh

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province and over some northern areas. Winter precipitation increased over northern areasup to 40 to 60 % but over southern parts of Pakistan, the precipitation decreased as shownin Fig. 12.

Figure 12: Projected future changes in mean annual, summer and winter temperature (˚C) and precipitation (%) over Pakistan in (2071-2100)

In Fig. 13, annual cycles of temperature and precipitation are calculated by averaging the values over Pakistan. The temperature cycle in base and future has the same patterns but with the difference in temperature increase around SoC. For precipitation, the net increase

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or decrease is not visible but one can see that the precipitation pattern over Pakistan is same in future showing that there is no net shift in the future monsoon rainfall pattern.

Figure 13: Annual cycles of precipitation and temperature for the period 1961-1990 and 2071-2100 over Pakistan

For future change analysis, Pakistan is divided into three regions namely the region within Box A (Northern Pakistan, 34°N - 37.2°N), the region within Box B (Central Pakistan, 300N - 34°N) and the region within Box C (Southern Pakistan, 24°N - 300N) as is shown in Fig. 14. Results are obtained by masking the actual geographical regions within the boxes.

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(l2E

Figure 14: Three regions selected over Pakistan as contained within BOX A (Northern part), within BOX B (Central part) and within BOX C (Southern part)

In Tables 3 and 4, average values of future changes over the three selected regions of Pakistan and over whole Pakistan are shown. The rise of temperature in winter is more than in summer for all three regions and also for whole Pakistan. In case of precipitation,there is increase over northern areas (Box A) and decrease over southern areas (Box C)whereas no net increase of precipitation is simulated over Pakistan.

Table 3: Projected changes of temperature (˚C) in (207 t -2100) over three individual regions of Pakistan and over whole Pakistan

PROJECTED TEMPERATURE ΔT(°C)

Region Annual Summer (JJAS) Winter (DJFM)

BOXA 4.76 4.81 4.97

BOXB 4.98 4.86 5.20

BOXC 4.68 4.56 4.71

WHOLE 4.77 4.68 4.88 PAKISTAN

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Table 4: Projected changes of precipitation (%) in (2071-2100) over three individual regions of Pakistan and over whole Pakistan

PROJECTED PRECIPITATION ΔP%

Region Annual Summer (JJAS) Winter (DJFM)

BOXA 11.35 -0.88 20.43

BOXB 0.78 0.97 -7.74

BOXC -0.51 -3.37 -24.53

WHOLE 3.99 -1.48 24.95 PAKISTAN

6.1 Future Projections for Climatic Zones

In order to make this exercise more meaningful for the policy makers and users, future projections have been worked out further on annual, seasonal and monthly basis over different climatic zones as were used for the assessment of past climate changes over Pakistan as well as over the regions of particular interest to GCISC for its Agriculture and Water Resources sections.

Future projections for temperature and precipitation for different climatic zones are also worked out on annual and seasonal basis. These values are tabulated in Table 5 and Table 6 for temperature and precipitation' respectively. A map showing different climate zones of Pakistan is given in Fig. 15. Details related to these different climatic zones are available in the research report, GCISC RR 1: Climate Profile and Past Climate Changes in Pakistan.

Table 5 and 6 show the projected temperature and precipitation changes over the different climatic regions of Pakistan for the period 2071-2100.

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JIN

JON

2'lN

~8N

27N

16N

25N

24N ~O[ 6'[

Figure 15: Climatic zones of Pakistan

Table 5: Projected temperature changes (˚C) over climatic regions

PROJECTED TEMPERATURE ΔT(°C)

Climatic Regions Annual Summer (JJAS) Winter (DJFM) I(a): Greater Himalayas 4.83 4.95 4.96 (Winter dominated) I(b): Sub-montane region and 4.77 4.45 5.23 Monsoon dominated

II: Western Highlands 4.67 4.35 5.18

III: Central & Southern Punjab 5.42 5.46 5.34

IV: Lower Indus Plains 4.61 4.45 4.70

V(a) : Balochistan Plateau Northern) 4.78 4.82 4.80 Suleman & Kirthat Ranges)

V(b): Baluchistan Plateau 4.73 4.76 4.63 kwestcrn)

VI: Coastal Belt 3.91 3.33 4.29

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Table 6: Projected precipitation changes (%) over climatic regions

PROJECTED PRECIPITATION ΔP (%)

Climatic Regions Annual Summer (JJAS) Winter (DJFM) I(a): Greater Himalayas 15.75 6.08 27.20 (Winter dominated) I(b): Sub-montane region and 7.46 4.19 24.13 Monsoon dominated II: Western Highlands 8.33 8.06 34.33

III: Central & Southern Punjab -12.06 -13.50 314.17

IV: Lower Indus Plains 1.12 -4.95 113.36

V(a) : Balochistan Plateau Northern) 4.26 0.80 163.48 Suleman & Kirthat Ranges)

V(b): Baluchistan Plateau 24.59 29.60 261.51 kwestcrn) VI: Coastal Belt 12.09 7.52 58.46

Tables 5 and 6 reflect that the projected temperature trends on annual and seasonal basis for both summer and winter seasons are, by the end of the current century, mostly higher(except in the case of coastal areas) than the highest value of 4°C as envisaged in IPCC's AR4. However highest temperature value is seen over the central and southern Punjab for annual, summer and winter periods and lowest over the coastal areas. Precipitation trends are higher during winter as compared to summer.

6.2 Future Projections for Agriculture Regions

Similar analysis for different agricultural regions, as given by the GCISC Agriculture Section, is done for different Agro Climatic Zones of Pakistan, as shown in Fig. 16

Four agro climatic zones of Pakistan namely: Humid, Sub Humid, Semi-arid and Arid are shown in Fig. 16.

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Figure 16: (a) Humid, (b) Sub-humid, (c) Semi-arid and (d) Arid agro climatic zones over Pakistan

Tables 7 and 8 respectively represent the projected temperature and precipitation trends over the different agricultural regions for the period 2071-2100.

Table 7: Projected temperature changes (0C) over agriculture regions

PROJECTED TEMPERATURE ΔT(˚C)

Agriculture Regions Annual Summer (JJAS) Winter (DJFM) Humid agro-climatic-zone 4.61 4.39 4.97

Sub-humid -Agro-climatic-zone 4.73 4.61 5.11

Semi-arid-agro climatic-zone 4.97 4.86 5.28

Arid-Agro-climatic-zone 4.78 4.64 4.87

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Table 8: Projected precipitation changes (%) over agriculture regions

PROJECTED PRECIPITATION ΔP (%)

Agriculture Regions Annual Summer (JJAS) Winter (DJFM) Humid agro-climatic-zone 17.25 9.92 30.70

Sub-humid -Agro-climatic-zone 11.91 5.69 23.44

Semi-arid-agro climatic-zone 3.74 0.24 20.48

Arld-Agro-climatic-zone -2.22 -5.86 99.15

Tables 7 and 8 reflect that the temperature trends in all the agricultural regions are higherin winter than summer by around 10%. Uncertainties attached to these values are lessthan 5%. Rains during winter are higher in all the regions compared to summer.

6.3 Future Projections for Watershed Regions

The analysis is also extended to three watershed regions given by the water section. Fig. 17 presents the three watershed basins on Indus river system. These are: (a) Upper Indus Basin, (b) Jhelum and (c) Kabul basins. Tables 9 and 10 respectively represent the projected temperature and precipitation changes for the period 2071-2100.

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Figure 17: Watershed region comprises (a) Upper Indus Basin, (b) Jhelum river catchments and (c) Kabul River

Table 9: Projected temperature changes (˚C) over watershed regions

PROJECTED TEMPERATURE ΔT(°C)

Water Shed Regions Annual Summer (JJAS) Winter (DJFM)

Upper Indus basin 4.58 2.66 2.54

Jhelum river basin 4.67 4.12 5.32

Kabul river basin 5.00 4.79 5.46

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Projected precipitation changes (%) over watershed regions Table 10:

PROJECTED PRECIPITATION ΔP (%)

Water Shed Regions Annual Summer (JJAS) Winter (DJFM)

Upper Indus basin 19.62 14.83 -4.33

Jhelum river basin 5.58 5.20 20.05

Kabul river basin 4.26 2.54 16.20

Summer and winter temperatures trends over the Upper Indus Basin are lower compared to other basins. Temperature trends during winter are slightly lower in winter in case of Upper Indus Basin, but higher for Jhe1um and Kabul rivers catchments. Precipitation trends are positive in summer and negative in winter for Upper Indus Basin. Trends are higher in winter compared to summer in the other two basins.

7. Conclusions

When driven by re-analysis ERA40 data, PRECIS simulated realistic structure and evolution of synoptic events. Averaged over 30-years base period (1961-1990), temperature biases remained mostly in the range of a few tenths of a degree centigrade to a few degrees centigrade, and precipitation biases were remained in the range of 10-40% of observed values. PRECIS has simulated the monsoon patterns fairly well over South Asia in summer. Biases generally increased as the size of the region decreased and also due to the complex topography of South Asia. The biases are higher in the case of precipitation.

The PRECIS performance was critically affected by the quality of the driving large-scale fields i.e. HadAM3P GCM data, and tended to deteriorate when the models were driven by GCM output, mostly because of the poorer quality of the driving large-scale data compared to the re-analysis data.

Compared to the driving GCMs, PRECIS produced more realistic regional detail of surface climate as forced by topography, large lake systems, or narrow land masses. Overall, the model performed better for temperature than precipitation.

An important problem in the validation of RCMs has been the lack of adequately dense observational network of meteorological stations. This problem is especially relevant in mountainous areas, where only a small number of high-elevation stations are usually available. .

For future projections, spatial patterns show an increase of precipitation over the monsoon belt and some central parts of India whereas over rest of the region, there is no significant change. If we look at seasonal detail, there is an increase in winter precipitation over central India and northern parts of Pakistan whereas in summer there is no significant change. Winter temperature change is greater as compared to summer temperature.

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The rise of temperature in winter is more than in summer for all three regions and also for whole Pakistan but there is no net increase of precipitation over Pakistan. In winter, precipitation increases over northern areas but over southern parts of Pakistan, the precipitation decreases.

All the four agro climatic zones show changes in temperature higher in winter and in case of precipitation the winter precipitation changes are higher than annual and summer precipitation changes. Over the Indus Basin, the summer and winter temperature changes are less as compared to the Jhelum and Kabul River basins.

, '

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References

Cullen, M. J. P., 1993: The unified forecast/climate model. Meteorol. Mag., 122, 81- 94.

Dickinson, R.E., R.M. Errico, F. Giorgi and G.T. Bates, 1989: A regional climate model for western United States. Clim. Change, 15,383-422.

Gordon, C., C. Cooper, C. A. Senior, H. Banks, J. M. Gregory, T. C. Johns, 1. F. B. Mitchell and R. A. Wood, 2000: The simulation of SST, sea ice extents and ocean heat transports in a version of the Hadley Centre coupled model without flux adjustments. Clim, Dyn., 16:147-168.

Gregory, D., and P. R. Rowntree, 1990: A mass-flux convection scheme with representation of cloud ensemble characteristics and stability dependent closure. Mon. Wea. Rev., 118:1483-1506.

Jones R., Noguer M., Hassell D., Hudson D., Wilson S., Jenkins G. and Mitchell J., 2004: Generating high resolution climate change scenar-ios using PRECIS, Hadley Centre for Climate Prediction and Re-search, Met Office Hadley Centre, UK, pp. 40.

Kida, H., T. Koide, H. Sasaki and M. Chiba, 1991: A new approach to coupling a limited area model with a GCM for regional climate simulations. J. Met. Soc. Japan., 69, 723-728.

New, M., Hulme, M. and Jones, P.D., 1999: Representing twentieth century space- time climate variability. Part 1: development of a 1961-90 mean monthly terrestrial climatology. Journal of Climate 12, 829-856

Simmons, A. J., and D. M. Burridge, 1981: An energy and angular-momentum conserving vertical finite difference scheme and hybrid vertical coordinates.Mon. Wea. Rev. 109:758-766.

Zorita, E. and H. von Storch, 1999: The analog method - a simple statistical downscaling technique: comparison with more complicated methods. - 1. Climate 12: 2474-2489

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Global Change Impact Studies Centre (GCISC)

Global change science is being aggressively pursued around the world. The Global Change Impact Studies Centre was created in May 2002 to initiate this multidisciplinary effort in Pakistan. The main objective of the Centre is to comprehend the phenomenon of global change, scientifically determine its likely impacts on various socio-economic sectors in Pakistan and develop strategies to counter the adverse effects, if any. Another function of the Centre is to establish itself as a national focal point for providing cohesion to global change related activities at the national level and for linking it with international global research. An important function of the Centre is to help develop manpower that is capable of studying and participating in the international effort to study the global change phenomenon. The Centre also works to increase the awareness of the public, the scientific community and the policy planners in the country to global change.

Global Change Impact Studies Centre (GCISC) National Centre for Physics (NCP) Complex

Quaid-i-Azam University Campus P.O. Box 3022, Islamabad

Pakistan

Telephone: (+92-51) 9230226 - 8, 2077386 Fax: (+92-51) 2077385

E-mail: gcisc@comsats.net.pk Web: www.gcisc.org.pk