Research Report GCISC-RR-07

Validation of the Regional Climate Model RegCM3 over South Asia

Faisal S. Syed, Shahbaz Mehmood, M. Adnan Abid, M. Munir Sheikh Arshad M. Khan

June 2009

Global Change Impact Studies Centre Islamabad, Pakistan

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

ISBN: 978-969-9395-06-2

@GCISC

Copyright. This Report, or any part of it, may not be used 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: Syed, F, S., S. Mehmood, M.A. Abid, M.M. Sheikh and A.M. Khan, (2009), Validation of the Regional Climate Model RegCM3 over South Asia, GCISC-RR-07, Global Change Impact Studies Centre (GCISC), Islamabad, Pakistan

CONTENTS

Foreword

Preface 11

List of Tables 111

List of Figures IV

List of Acronyms vii

1. Introduction 1

1.1 1.2

Description of the Model RegCM3 Physics of the Model

1 2

2. Data 4

3. Seasonal Validation ofthe Model 4

3.1 3.2

Methodology Results & Discussion 3.2.1 Summer 3.2.2 Winter 3.2.3 Validation over Selected Regions Conclusions.

5 5 7 16 25 31 3.3

4. Individual Events Validation 32

4.1 Flood Event of August 1997 in the Jhelum Catchment 32 4.1.1 Data and Methodology 32 4.1.2 Analysis & Discussion 33 4.2 The Extreme Precipitation Event of 23rd July 2001 in Islamabad 36 4.2.1 Data and Methodology 36 4.2.2 Analysis & Discussion 36 4.3 Severe Cyclonic Storm in the Bay of Bengal 37

4.3.1 Data and Methodology 38 4.3.2 Analysis & Discussion 38

5.

6.

Conclusions / Summary

Acknowledgments

References

41

41

42

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, Nl, 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, National University 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 Regional Climate Models (RCMs) are mostly used to reproduce the main climatic features at the regional scale where mesoscale forcings < become important and the coarser-resolution Global Climate Models (GCMs) or re-analysis data sets are not sufficient for the more realistic assessment of local features of climate. However, despite enormous efforts made to develop and improve the RCMs, there are still many issues related to RCMs which need to be resolved such as the spin-up of the model, uncertainty of large-scale forcing data, parameterization of physical processes, choice of model domain and its spatial resolution. One physical process which has a pronounced influence on simulated results is the choice of different Convective Parameterization Schemes (CPSs) which can induce dissimilar results in regional climate simulations, since each CPS is based on some assumptions and has its own formalism. Therefore, the selection of an adequate CPS can be of particular importance in regional climate modeling.

This study focuses on the validation of the regional climate model RegCM3 developed by International Centre for Theoretical Physics (ICTP), Italy, over South Asia. The model was validated for different CPSs available in the model as well as for some local scale extreme climate events. The results show that the model captures quite well the overall pattern of summer monsoon and the systems coming from the west over northern Pakistan in winter. The model performance is also satisfactory in capturing the track of a cyclonic storm from the Bay of Bengal and the path of a monsoon depression over India and Pakistan. However, the model overestimates precipitation in most of the cases and also gives cold bias over northern areas of Pakistan which probably can be attributed to the complex topography of this region. The model also captured well the climate extreme event of severe precipitation of 23rd July 2001 in Islamabad which brought in its wake, 620 mm of rainfall in just 10 hours. It was a localized event. Emanuel convective scheme, as compared to others, was found to work reasonably well in simulating the extreme event.

ii

List of Tables

Table 1 Temperature (˚C) & Precipitation (%) Change, winter (DJFM)

Temperature (˚C) & Precipitation (%) Change, summer (JJAS)

30

Table 2 30

iii

List of Figures

Figure 1 Design of experiments 6 Figure 2 (a) Domain selected for the model showing surface 6

elevation (m), (b) Domain showing land use and laud

cover.

Figure 3 Grell Convective Scheme with Arkawa - Schubert 8

Closure, JJAS - Precipitation (mm/day), (a-b) Observed and simulated for the dry year 1987, (c-d) Observed and

simulated for the wet year 1992

Figure 4 Grell Convection Scheme with Fritsch - Chappell 9

Closure? JJAS - Precipitation (mm/day), (a-b) Observed and simulated for the dry year 1987, (c-d) Observed and simulated for the wet year 1992

Figure 5 Grell Convective Scheme with Arkawa - Schubert 10

Closure, JJAS - Temperature (˚C) (a-b) Observed and < simulated for the dry year 1987, (c-d) Observed and

simulated for the wet year 1992

Figure 6 Grell Convection Scheme with Fritsch - Chappell 11

Closure, JJAS - Temperature (˚C) (a-b) Observed and simulated for the dry year 1987, (c-d) Observed and

simulated for the wet year 1992

Figure 7 Convective Scheme, JJAS - Precipitation (mm/day), (a- 12

b) Observed and simulated for the dry year 1987, (c-d)

Observed and simulated for the wet year 1992

Figure 8 Anthes - Kuo Convective Scheme, JJAS - Temperature 13 (QC), (a-b) Observed and simulated for the dry year 1987, (c-d) Observed and simulated for the wet year 1992

Figure 9 Convective Scheme, JJAS - Precipitation (mm/day), (a- 14

b) Observed and simulated for the dry year 1987, (c-d) Observed and simulated for the wet year 1992

IV

Figure 10 Emanuel Convective Scheme, JJAS - Temperature (˚C), 15 (a-b) Observed and simulated for the dry year 1987, (c- d) Observed and simulated for the wet year 1992

Figure 11 Grell Convective Scheme with Arkawa - Schubert 17 Closure, DJFM - Precipitation (mm/day), (a-b) Observed and simulated for the dry year 1985, (c-d) Observed and simulated for the wet year 1991

Figure 12 Grell Convective Scheme with Fristch - Chappell 18 Closure, DJFM - Precipitation (rnm/day), (a-b) Observed and simulated for the dry year 1985, (c-d) Observed and simulated for the wet year 1991

Figure 13 Grell Convective Scheme with Arkawa-Schubert 19 Closure, DJFM - Temperature (˚C) , (a-b) Observed and simulated for the dry year 1985, (c-d) Observed and simulated for the wet year 1991

Figure 14 Grell Convective Scheme with Fristch - Chappell 20 Closure, DJFM - Temperature (˚C) ,(a-b) Observed and simulated for the dry year 1985, (c-d) Observed and simulated for the wet year 1991

Figure 15 Anthes - Kuo Convective Scheme, DJFM - 21 Precipitation (mmlday), (a-b) Observed and simulated for the dry year 1985, (c-d) Observed and simulated for the wet year 1991

Figure 16 Anthes - Kuo Convective Scheme, DJFM - 22 Temperature (˚C) ,(a-b) Observed and simulated for the dry year 1985, (c-d) Observed and simulated for the wet year 1991

Figure 17 Emanuel Convective Scheme, DJFM - Precipitation 23 (rnmIday), (a-b) Observed and simulated for the dry year 1985, (c-d) Observed and simulated for the wet year 1991

Figure 18 Emanuel Convective Scheme, DJFM - Temperature 24 CC), (a-b) Observed and simulated for the dry year 1985, (c-d) Observed and simulated for the wet year 1991

Figure 19(a) Selected regions of South Asia 25

v

Figure 19(b,c) Area averaged values of temperature (˚C) for winter 29 and summer

Figure 19(d,e) Area averaged values of precipitation (mm/day) for 29

winter and summer

Figure 20 Track of the observed monsoon depression 33

Figure 21 Domain showing elevation (m), selected for the flood 33

event of 1997

Figure 22 Movement of the monsoon depression developed in the 34 Bay of Bengal to the Northern Pakistan simulated by the model (a-h) position of depression from 20th August

to 27th August, 1997 respectively

Figure 23 Rainfall (mrn/day) (a) the rain spell of20-28 August 35

1997, (b) the rain spell of25-28 August 1997, (c) the whole month, (d) observed rainfall taken by CRU data

Figure 24 Surface pressure pattern on 23rd July 2001 36

Figure 25 (a) Simulated precipitation (mrn/day) for 23rd July 2001 37

(b) Simulated wind patterns at 850 hPa for 23rd July 2001

Figure 26 Observed track of the tropical cyclone in Bay of Bengal 37

May 22 - 25, 1985

Figure 27 (a-d) Simulated wind patterns from May 19 to May 26, 1985 39

respectively

Figure 27 (e-h) Simulated wind patterns from May 19 to May 26, 1985 40 respectively

Vl

List of Acronyms

Most of the Acronyms and abbreviations, wherever they appear in the text, are defined. However, some additional acronyms are included in this list.

APN Asia Pacific Network

AS Arkawa - Schubert

ASICTP Abdus Salam International Centre for Theoretical Physics

BA TS Biosphere- Atmosphere Transfer Scheme

cb centibar

CCM3 Community Climate Model version 3

CPSs Convective Parameterization Schemes

CRU Climate Research Unit

DJFM December- January- February-March

ECMWF European Centre for Medium Range Weather Forecast

ERA40 ECMWF 40 years Reanalysis

FC Fritsch & Chappel

GCM General Circulation Model

GCISC Global Change Impact Studies Centre

GHG Green House Gases

GISST Globally Interpolated Sea Surface Temperature

GLCC Global Land Cover Characterization

GTOP030 Global 30 Arc-Second Elevation Data Set

Vll

IMD

JJAS

LAMs

MM5

MOHSST6

NCAR

NCEP

PBL

PMD

PSU

PWC

RCM

SIMEX

SST

SUBEX

WGS84

Indian Monsoon Depression

June-July- August- September

Limited Area models Mesoscale

Model version 5

Met Office Historical Sea Surface Temperature

National Centre for Atmospheric Research

National Centers for Environmental Prediction

Planetary Boundary Layer

Pakistan Meteorological Department

Pennsylvania State University Physics

of the Weather and Climate Regional

Climate Model

SIMplified EXplicit cloud and precipitation scheme

Sea Surface Temperature

SUB-grid EXplicit moisture scheme

World Geodetic System of 1984

Vlll

1. Introduction

Predicting climate changes and future climate on the regional scale is a very challenging but an important task since, in practice, the impacts of climate change need to be studied on the regional rather than global scale. One of the several techniques for this task is the use of regional climate modeling, also known as dynamical downscaling (Giorgi and Mearns, 1991, 1999; McGregor, 1997, Christensen et al., 1998). In this approach, a high-resolution Regional Climate Model (RCM) is run with boundary data taken from a General Circulation Model (GCM). The technique is motivated partly by the fact that high resolution RCMs, compared to GCMs, should better account for mesoscale forcings such as complex topography and regional water bodies that modify the regional climate. In addition, increased resolution may add details to the simulated weather systems, thereby leading to a more realistic simulation of daily weather variability (Christensen et al., 1998). The idea that limited area models (LAMs) could be used for regional studies was originally proposed by Dickinson et al. (1989) and Giorgi (l990b).

RCMs have improved our understanding of the effects of global climate change on specific regions. Recently, scenarios developed using these models have been applied in a variety of impact assessments such as of temperature extremes (Hennessy et al., 1998; Mearns; 1999); water resources (Wang et al., 1999; Stone et al., 2001; Wilby et al., 1999); agriculture (Mearns et al., 1999, 2000a, 2001) and forest fires (Wotton et al. 1998).

To develop the fine scale scenarios for South Asia region, the regional climate model called RegCM3 developed by the Physics of Weather and Climate (PWC) Group of the Abdus Salam International Centre for Theoretical Physics (ASICTP) which is the third generation model of the RegCM originally developed at the National Centre for Atmospheric Research (NCAR) has been used. It has been observed that models generally perform well over their native domains but the performance needs to be improved on other domains by adjusting different parameters in the model. For this reason a number of test experiments were conducted to validate the model for South Asia region before developing the climate change scenarios.

1.1 Description of the Model RegCM3

RegCM3 is a 3-dimensional, sigma-coordinate; prumnve equation regional climate model. The first version of the RegCM series was completed by Dickinson et al. (1989), Giorgi and Bates (1989), and Giorgi (1990) at the National Center for Atmospheric Research (NCAR). It was built upon the NCAR Pennsylvania State University (PSU) Mesoscale Model version 4 (MM4) (Anthes et al., 1987). However, to adopt the MM4 to long-term climate simulations, the radiative transfer package of Kiehl et al. (1987) was added along with Biosphere-Atmosphere Transfer Scheme (BATS) version la (Dickinson et al., 1986). In addition, the existing convective precipitation (Anthes, 1977) and Planetary Boundary Layer (PBL) (Deardorff, 1972) parameterizations were improved upon.

1

The second version of the RegCM series was developed by Giorgi et al. (1993 a, b). The dynamical core was upgraded to the hydrostatic version of the NCAR-PSU Mesoscale Model version 5 (MM5) (Grell et al., 1994). The radiative transfer package was also upgraded according to that of Community Climate Model version 2 (CCM2) (described by Briegleb (1992». The Grell (1993) convective parameterization was included as an option, and the explicit cloud and precipitation scheme of Hsie et al. (1984) was used. BATS was updated from version la to Ie (Dickinson et al., 1993) and the non-local PBL parameterization of Holtslag et al. (1990) was implemented.

An intermediate version, Regional Climate Model version 2.5 (RegCM2.5),· was developed as described in Giorgi and Mearns (1999). It included an option for the Zhang and McFarlane (1995) convection scheme, the Community Climate Model version 3 (CCM3) radiative transfer package (Kiehl et al., 1996), a simplified version of the Hsie et al. (1984) explicit cloud and precipitation scheme (SIMEX) (Giorgi and Shields, 1999), and a simple interactive aerosol model (Qian and Giorgi, 1999).

RegCM3 is an integration of the main improvements that have been made to RcgCM2.5 since the description in Giorgi and Mearns (1999). These improvements are in the representation of precipitation physics,surface physics, atmospheric chemistry and aerosols, model input fields, and the user interface. In addition, the dynamical code has been modified for parallel computing. An important aspect of the RegCM3 is that it is user-friendly andoperates on a variety of computer platforms. To that end, substantial changes have been made to the pre-processing, running, and post processing of the model. Furthermore, the RegCM3 has options to interface with a variety of reanalysis and GCMs boundary conditions.

1.2 Physics of the Model

RegCM3 uses the radiation scheme of the NCAR CCM3, which is described in Kiehl et al. 1996. In this scheme, solar radiative processes are treated using the delta-Eddingtonapproximation over 18 discrete spectral intervals (7 ozone, 1 visible, 7 water, and 3 carbon dioxide )(Briegleb, 1992). The cloud scattering and absorption parameterization follow that of Slingo 1989, whereby the optical properties of the cloud droplets are expressed in terms of the cloud liquid water content and an effective droplet radius.

The surface physics calculations are performed using BATS (Biosphere-Atmosphere Transfer Scheme) which is described in detail by Dickinson et al. 1993. The model has a vegetation layer, a snow layer, a surface soil layer, 10 em thick, or root zone layer, 1-2 m thick, and a third deep soil layer 3 m thick. BATS has 20 vegetation types (soil textures ranging from coarse (sand), to intermediate (loam), to fine (clay); and different soil colors (light to dark) for the soil albedo calculations. These are described in Dickinson et al. 1986.

Atmospheric aerosols are known. to have a substantial impact on the climate system, especially at the regional scale. The RegCM3 accounts for sulfate; organic carbon, and black carbon aerosols as described by Qian et al. (2001) and Solmon et al. (2005). Both

2

direct and indirect aerosol effects are included in RegCM3. Direct radiative effects are accounted for by specifying the aerosol optical properties: extinction coefficient, singlescattering albedo, and an asymmetry parameter. Indirect effects are described by assuming that the effective cloud droplet radius depends on the aerosol mass concentration,

The formation of precipitation in RegCM3 is represented in two forms: resolvable (or large-scale) and convective (sub grid). The resolvable precipitation is generally associated to large-scale systems that move relatively slowly in the vertical and is most common in the winter hemisphere. Conversely, convective precipitation typically occurs in the summer hemisphere and tropics at scales finer than l-krn. Due to its fine scale, convective precipitation must be parameterized in most climate models. In RegCM3, the resolvable (or large-scale) precipitation is represented using the the SUB-grid EXplicit moisture scheme (SUBEX) (Pal et al., 2000).

Convective precipitation still remains one of the most important sources of errors in climate models. Three options are available in RegCM3 to represent cumulus convection: (1) the modified Anthes-Kuo scheme (Anthes, 1977; Giorgi, 1991); (2) the Grell scheme (Grell, 1993); and (3) the Massachusetts Institute of Technology (MIT) scheme (Emanuel, 1991; Emanuel and Zivkovic Rothman, 1999).

The Anthes-Kuo scheme adopts a moisture convergence approach. Convective activity is initiated when the moisture convergence in a column exceeds a given threshold and the vertical sounding is convectively unstable.

The Grell scheme is a mass flux scheme based upon the Arakawa and Schubert (1974) parameterization. More specifically, it uses a single cloud scheme with updraft and downdraft fluxes along with compensating motion that determines the heating and moistening profiles. Two closure assumptions are available: (1) that the large-scale destabilization processes are in quasi-equilibrium with convection (Arakawa and Schubert, 1974) and (2) that aJI the convective available potential energy is removed at a given time-scale (Fritsch and Chappell, 1980).

The Emanuel scheme assumes that the mixing in clouds is highly episodic and inhomogeneous (as opposed to a continuous entraining plume) and considers convective fluxes based on an idealized model of sub-cloud-scale updrafts and downdrafts. Convection is triggered when the level of neutral buoyancy is greater than the cloud base level. Between these two levels, air is lifted and a fraction of the condensed moisture forms precipitation while the remaining fraction forms the cloud.

The PBL processes are represented using the non-local formulation of Holtslag et al. (1990) and Holtslag and Boville (1993). Details of its implementation within the RegCM3 are described in Giorgi et al. (1993b). Briefly, within the PBL, the vertical eddy flux computation includes a counter gradient term that describes the non-local transport due to dry convection. An eddy diffusivity profile within the PBL is based on a diagnosed

3

PBL height and a turbulent velocity scale. Outside of the PBL, the atmospheric properties are mixed according to stability-dependent local diffusion.

The RegCM3 requires initial conditions and time-dependent lateral boundary conditions for the wind components, temperature, surface pressure, and water vapor. In addition, SSTs must be specified over oceans. An interface has been developed to easily port various reanalysis and GCM boundary conditions to the RegCM3 framework.

2. Data

To provide the initial and lateral boundary conditions, forty-year European Re-Analysis data of the Global Atmosphere (ERA40) was used. ERA40 is the computerized weather data from 1957 to August 2002, taken from relevant scientific observations worldwide. The data has been worked out by the European Centre for Medium-Range Weather Forecasts (ECMWF), U'K, This dataset is available at a resolution of 2.50 x 2.50•

For Meteorological observations over land, the Climate Research Unit, CRU TS 2.0 dataset (Mitchell et al., 2003), which includes monthly surface air temperature and precipitation for the period 1901-2000 on a regular global 0.5 degree land surface grid has been used. This dataset is a revision and extension of the original dataset described in New et al. 2000. It should be noted that in areas with sparse station coverage, the CRU dataset interpolates grid point values from the nearest available stations. This may thus add a significant element of uncertainty.

The observed SSTs were provided by the Globally Interpolated Sea Surface Temperature (GISST) dataset. GISST describes data which are derived from Met Office Historical Sea Surface Temperature (MOHSST6). Sophisticated statistical methods are used to produce a global coverage of monthly one-degree-area sea surface temperatures (Rayner, N.A, et aI1996). Data is available in monthly 10 area grids, from 1871 to February 2003.

To run the domain, the land cover and land use data set of Global Land Cover Characterization (GLCC) at a resolution of 10 min and surface elevation data called GTOP030 are used. GTOP030 is a global topography data. The horizontal grid spacing is 30-arc seconds (0.008 degrees). The horizontal coordinate system is in decimal degrees of latitude and longitude referenced to the World Geodetic System of 1984 (WGS84). The vertical units represent elevation in meters above mean sea level. In all validation experiments same dataset was used.

3. Seasonal Validation of the Model

In RegCM3, three convective schemes i.e. Anthes - Kuo, Grell with two closure assumptions and newly Implemented Emanuel scheme are incorporated. The two closure assumptions available with Grell scheme are Fritsch Chapel (FC) and Arakawa Schubert (AS).

4

To test the performance of the model, a number of simulations were carried out with different data sets like ERA40, NCEP and GCM data sets. The model output was compared with the Climatology of the region.

The model is validated for different seasons i.e. winter (December-January-FebruaryMarch; DJFM) and summer (June-July-August-September; JJAS) for two dry and wet years. Some past extreme climate events like cyclones in the Bay of Bengal and heavy precipitation events in some of the cities of Pakistan were also simulated.

3.1 Methodology

For seasonal validation, sixteen experiments were designed as shown in Fig. 1, out of which eight experiments were conducted for summer and eight for winter season. The winter season comprises December, January, February, and March (DJFM) while for summer June, July, August and September (JJAS) has been selected.

The model was run at a horizontal resolution of 50 km. In the vertical, the model was divided into 18 levels with the pressure of the model top being 50 mb. The number of grid points taken in x and y directions were 100 and 95 respectively. Experiments were designed such that the convective schemes mentioned above could be tested for the South Asia region. The domain selected for this study shows surface elevation in Fig. 2 (a). Fig. 2 (b) shows the land use and land cover of the region.

Four years (1987, 1992, 1985 and 1991) were taken to perform the seasonal experiments. 1987 and 1985 were dry years while 1991 and 1992 were wet years. The summer season was simulated for the years 1987 and 1992 and for the winter simulations, 1985 and 1991 were selected.

3.2 Results & Discussion

In summer, the precipitation in the whole region is mainly due to the phenomena of monsoon. Extra tropical storms coming from Mediterranean Sea are responsible for winter precipitation in Pakistan, North western parts of India and western parts of Nepal. Because of different weather systems, winter and summer seasons are analyzed separately. In addition to that, both seasons were simulated for dry as well as wet years to check the ability of the model to capture the inter annual variability. A comparison of simulated temperature and precipitation was made with the observed data set of Climate Research Unit (CRU).

5

1.

Figure 1: Design of experiments

(a) (b)

Figure 2: (a) Domain selected for the model showing surface elevation (m) (b) Domain showing land use and land cover.

6

3.2.1 Summer

For the summer simulations, 1987 and 1992 were selected where 1987 is a dry year while 1992 is a wet year. The analysis is done on seasonal basis for different convective schemes as mentioned earlier.

a) Grell Scheme

In case of precipitation, Grell convective scheme with AS closure assumption overestimates in the central Pakistan and deserts of India and Pakistan while underestimates in the central India for the year 1987 as shown in Fig. 3 (b). Similarly for 1992 Fig. 3 (d), it overestimates in southern Pakistan, deserts and Western Ghats and underestimates in Bangladesh and at some isolated places of India. FC closure assumption overestimates the precipitation in Western Ghats, Myanmar; however the simulated pattern in most parts of India and Pakistan is in close agreement with the observed one. Although there is an overestimation in Northern Pakistan, the monsoon path is better captured compared to the AS.

In case of temperature, the model gives cold bias over India with both closure assumptions and for both years Fig. 5 (b, d) and Fig. 6 (b, d). Similarly in FC closure scheme, there is cold bias in desert areas of Pakistan. However the pattern in rest of the region is in close agreement with the observed one.

b) Anthes - Kuo Scheme

This scheme gives reasonably good results for the precipitation in both years Fig. 7 (b, d) except for an overestimation over Bangladesh and Myanmar. It also overestimates in Western Ghats for the year 1992.

In case of temperature, the simulated pattern for both years Fig. 8 (b, d) is in close agreement with the observed one except for the cold bias over India which was present in the previous scheme as well.

c) Emanuel Scheme

For the year 1987, Fig. 9 (b), the model over estimates over Myanmar, some parts of Nepal, Western Ghats and Northern Pakistan. Similar pattern is present for 1992 Fig. 9 (d) except that the simulated precipitation in the North of Pakistan is in close agreement with the observed and there is no overestimation.

With Emanuel scheme, the cold bias over most parts of the domain is removed for both years Fig. 10 (b, d). For 1987, the simulated temperature is greater than the observed in central Pakistan. Other than this, the pattern is well captured by the model.

7

Figure 3: Grell Convective Scheme with Arkawa - Schubert Closure, JJAS - Precipitation (mm/day), (a-b) Observed and simulated for the dry year 1987, (c-d) Observed and simulated for the wet year 1992

8

(c)

Figure 4: Grell Convection Scheme with Fritsch - Chappell Closure, JJAS - Precipitation (mm/day), (a-b) Observed and simulated for the dry year 1987, (c-d) Observed and simulated for the wet year 1992

9

(a)

2.

(c)

Grell Convective Scheme with Arkawa - Schubert Closure, JJAS Temperature (˚C), (a-b) Observed and simulated for the dry year 1987, (c-d) Observed and simulated for the wet year 1992

Figure 5:

10

3. (a) -

Grell Convection Scheme with Fritsch - Chappell Closure, JJAS Temperature (˚C), (a-b) Observed and simulated for the dry year 1987, (c-d) Observed and simulated for the wet year 1992

Figure 6:

11

(a)

Anthes - Kuo Convective Scheme, JJAS - Precipitation (mmlday), (a-b) Observed and simulated for the dry year 1987, (c-d) Observed and simulated for the wet year 1992

Figure 7:

12

4.

(c) (d)

Figure 8: Anthes - Kuo Convective Scheme, JJAS - Temperature (0C), (a-b) Observed and simulated for the dry year 1987, (c-d) Observed and simulated for the wet year 1992

13

(a) (b)

(c) (d)

Emanuel Convective Scheme, JJAS - Precipitation (mm/day), (a-b) Observed and simulated for the dry year 1987, (c-d) Observed and simulated for the wet year 1992

Figure 9:

14

Emanuel Convective Scheme, JJAS - Temperature (˚C), (a-b) Observed and simulated for the dry year 1987, (c-d) Observed and simulated for the wet year 1992

Figure 10:

15

3.2.2 Winter

Like summer, we have selected one dry year (1985) and one wet year (1991) for the winter season. The analysis carried out is based on the similar pattern as is done in the case of summer.

a) Grell Scheme

Fig. 11 (b, d) shows the simulated precipitation patterns for 1985 and 1991 respectively for the closure assumption AS. The system coming from the west is very well simulated for both years. In winter, Sri Lanka experiences the winter monsoon which is also well captured by the model. However, there is an over estimation over Northern Pakistan. Almost similar behavior is observed for Fe closure as shown in Fig. 12 (b, d).

Temperature pattern is similar for both schemes and is in close agreement with the observed one for 1985 as well as 1991 as shown in Fig. 13 (b, d) and Fig. 14 (b, d). However, cold bias is still present but it now is shifted to the South of India.

b) Anthes - Kuo Scheme

The spatial pattern of precipitation (Fig. 15 (b, d) is somewhat similar to the observed one. However, the scheme overestimates over Northern Pakistan and slightly underestimates over Sri Lanka. It also shows an increased precipitation in Afghanistan for the year 1991.

The spatial variability of temperature (Fig. 16 (b, d) is well simulated but like other schemes used in the model, cold bias over Southern India persisted.

c) Emanuel Scheme

Simulated pattern of precipitation (Fig. 17 (b, d» closely resembles the pattern of previous schemes with overestimation over North of Pakistan. However unlike Kuo scheme, precipitation values over Sri Lanka closely match the observed ones.

Temperature pattern simulated by the model as shown in Fig. 18 (b, d) is in close agreement with the observed ones. However, cold bias has been observed over South of India.

16

Figure 11: Grell Convective Scheme with Arkawa - Schubert Closure, DJFM - Precipitation (mm/day), (a-b) Observed and simulated for the dry year 1985, (c-d) Observed and simulated for the wet year 1991

17

5. (a)

(d)

Figure 12: Grell Convective Scheme with Fristch - Chappell Closure, DJFM Precipitation (mm/ day), (a-b) Observed and simulated for the dry year 1985, (c-d) Observed and simulated for the wet year 1991

18

(a) (b)

Grell Convective Scheme with Arkawa - Schubert Closure, DJFM - Temperature (˚C), (a-b) Observed and simulated for the dry year 1985, (c-d) Observed and simulated for the wet year 1991

Figure 13:

19

(a) (b)

(c) (d)

Figure 14: Grell Convective Scheme, Fristch - Chappell Closure, DJFM Temperature (˚C), (a-b) Observed and simulated for the dry year 1985, (c-d) Observed and simulated for the wet year 1991

20

(c)

Figure 15: Anthes - Kuo Convective Scheme, DJFM - Precipitation (mmJday), (a-b) Observed and simulated for the dry year 1985, (c-d) Observed and simulated for the wet year 1991

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6. (a) (b)

Figure 16: Anthes - Kuo Convective Scheme, DJFM - Temperature (0C), (a-b) Observed and simulated for the dry year 1985, (c-d) Observed and simulated for the wet year 1991

22

Figure 17: Emanuel Convective Scheme, DJFM - Precipitation (mm/day), (a-b) Observed and simulated for the dry year 1985, (c-d) Observed and simulated for the wet year 1991

23

(b)

(c) (d)

Figure 18: Emanuel Convective Scheme, DJFM - Temperature (0C), (a-b) Observed and simulated for the dry year 1985, (c-d) Observed and simulated for the wet year 1991

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3.2.3 Validation over Selected Regions

For the analysis purpose, five regions were selected on the basis of their climatology. These five regions have been labeled as Box A, Box B, Box C, Box D and Box E as shown in Fig. 19 (a). Description of these boxes is given below. Area averaged values of precipitation and temperature for all the boxes have been compared with the observed ones. Results are shown in Fig. 19 (b, c) and Fig. 20 (d, e) respectively for precipitation and temperature.

Figure 19 (a): Selected regions of South Asia

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Box A

Box A covers Kashmir, Northern Areas, North West Frontier Province and upper Punjab. There is a large variation in the terrain of the region in this box. In northern side, we have high mountain ranges like Himalayas, Hindukush and Karakoram; while in southern part we have plain, arid and semi arid regions. Due to this there is large variation in temperature in this region. In winter season, minimum temperature on mountainous regions remains well below 0 °C while in plain areas can just touch the freezing point. In winter, precipitation in these regions is due to western disturbances while in summer, monsoon is responsible for the rainfall in this region.

In summer (i.e. JJAS) , Emanuel scheme gives reasonably good results for inter annual variability for Box A, whereas other schemes are unable to capture this inter annual variability. Precipitation amount is over estimated by Grell FC and Kuo, while Grell AS and Emanuel give better results (Table 2). For temperature in summer, Emanuel scheme gives good results whereas other schemes underestimate (Table 2). For winter (i.e. DJFM), precipitation is overestimated in all the schemes (Table 1) whereas inter annual variability has been captured very well by all the schemes. In case of temperature, the results of all the schemes for the winter season are in close agreement among each other. However, when we compare the results with observed values, all the schemes underestimate for this box (Table 1).

Box B

Balochistan, Sind and Southern Punjab fall into this box. From climatological point of view, it comprises arid and semi arid regions, coastal areas and deserts like Thal and Cholistan. The semi-permanent thermal low which drives the moisture from the Arabian Sea to the upper parts of Pakistan causing precipitation is also formed in this region. Mostly the climate of these areas is considered to be dry and hot. In winter, some parts of Balochistan face the cold spells and also receive heavy snowfall, while in summer, this whole region experiences hot climate. In some parts of Balochistan, Sindh and adjoining Southern Punjab, the maximum temperature in summer at times crosses 50°C. The amount of rainfall in these regions is very low.

In summer, precipitation is overestimated by all the schemes except Emanuel which gives good results (Table 2) whereas inter annual variability is captured nicely by all the schemes. Temperature in summer is simulated well by Emanuel while other schemes underestimate (Table 2). In winter, precipitation is overestimated by all the schemes (Table 1) whereas the results of temperature for all the schemes are in close agreement with each other as well as with the observed one (Table 1). Inter annual variability is captured well by all the schemes.

26

Box C

Box C contains Bangladesh, some parts of India and Myanmar. Bangladesh has a subtropical monsoon climate characterized by wide seasonal variations in rainfall, moderately warm temperatures, and high humidity. Regional climatic differences in this flat country are minor. Three seasons are generally recognized: a hot, humid summer from March to June; a cool, rainy monsoon season from June to October; and a cool, dry winter from October to March. In general, maximum summer temperatures range between 32°C and 38 "C. April is the warmest month in most parts of the country. January is the coldest month, when the average temperature for most of the country is 10°C. For summer, Emanuel scheme gives very good results for precipitation (Table 2) but is unable to simulate inter annual variability. Whereas, rest of the schemes are good in capturing inter annual variability but for precipitation, only Kuo scheme gives reasonable results while others underestimate (Table 2). Temperature in summer is simulated well by Emanuel while other schemes underestimate (Table 2). In case of winter, inter annual variability is captured well by all the schemes while in case of precipitation, Grell scheme overestimates for both of its closure assumptions i.e. AS and FC, Kuo scheme underestimates, however Emanuel scheme gives reasonably good results (Table 1). Temperature is simulated well by all the schemes except Emanuel which deviates a bit from others (Table 1).

Box D

Box D comprises Western Ghats in South of India near the Arabian Sea. This region receives rainfall for the whole year. The Southwestern monsoon winds are the main cause of precipitation in summer.

In summer, interannual variability is very well captured by all the schemes for this box but overestimates precipitation for Grell AS, Grell FC while underestimates for Kuo (Table 2). However results closely match with the observed one for Emanuel (Table 2). For temperature, all schemes underestimate, however Emanuel gives better results for 1992 in this box (Table 2). In case of winter, precipitation amount given by Emanuel and Grell FC schemes is in close agreement with the observed one whereas Kuo scheme underestimates and Grell AS overestimates (Table 1). In case of temperature, the results of all the schemes are in close agreement with each other but when compared with observed values, all schemes underestimate (Table 1). However, like summer inter annual variability is captured well.

Box E

Box E consists of Sri Lanka and southern parts of India. Sri Lanka has a warm climate, moderated by ocean winds and considerable moisture. The yearly averaged temperature for the country as a whole ranges from 26° C to 28°C. Day and night temperatures may vary by 4 to 7 "C. May, the hottest period, precedes the summer monsoon rains. The rainfall pattern is influenced by the monsoon winds of the Indian Ocean and Bay of

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Bengal and is marked by four seasons. The March to April period is known as the first inter monsoon, May to September as south west monsoon, October to November as the second inter monsoon and December to February as north-east monsoon (Puvaneswasan, K.M. and P.A. Smithson, 1993: Chandrapalla, 1996). The primary rainfall maximum for Sri Lanka occurs in the period October to December and the secondary one in May June. The hills in the centre of the country exercise profound influence on rainfall distribution in both the monsoon seasons. Humidity is typically higher in the southwest and mountainous areas and depends on the seasonal patterns of rainfall.

In winter, precipitation simulated by Emanuel and Grell FC schemes is in close agreement with the observed whereas Kuo scheme underestimates and Grell AS overestimates while for temperature, all the schemes underestimate (Table 1). Precipitation is overestimated by Grell AS, Grell FC and Emanuel for summer. Only Kuo scheme gives reasonable results for this box, whereas in case of temperature, all the schemes underestimate (Table 2).

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Figure 19 (d, e): Area averaged values of precipitation (mm/day) for winter and summer

29

Table 1: Temperature (˚C)& Precipitation (%) Change, winter (DJFM)

Convective Box A BoxB BoxC BoxD BoxE

Scheme Years ΔT ΔT ΔT ΔT ΔT (˚C) %ΔP (˚C) %ΔP (˚C) %ΔP (˚C) %ΔP (˚C) %ΔP

Grell 1985 -3.54 154.73 -0.33 35.22 -3.19 36.77 -3.31 17.6 -3.14 31.68

(AS) 1991 -2.4 117.13 -0.55 55.36 -2.57 63.3 -3.08 46.49 -3.05 5.61

Grell 1985 -3.43 143.79 -0.35 38.94 -2.97 96.04 -3.36 -29.6 -3.3 -3.12

FC 1991 -2.28 117.6 -0.54 58 -2.36 87.53 -3.06 16.7 -3.21 -19.34

Kuo- 1985 -3.31 131.04 -0.2 75.28 -2.72 -23.56 -3.15 -54.05 -2.89 -30.2

Anthes 1991 -2.14 116.02 -0.16 27.46 -1.65 -43.99 -2.72 -28 -2.69 -48.88

1985 -4.75 183.82 0.384 48.04 -1.5 -23.1 -2.57 29.84 -2.56 39.31 Emanuel

1991 -1.71 142.87 0.286 27.51 -0.92 18.6 -2.37 37.61 -2.34 -24.37

Table 2: Temperature (0C) & Precipitation (%) Change, summer (JJAS)

Convective Box A BoxB BoxC BoxD BoxE

Scheme Years ΔT ΔT ΔT ΔT ΔT (˚C) %ΔP (˚C) %ΔP (˚C) %ΔP (˚C) %ΔP (˚C) %ΔP

Grell 1987 -4.02 86.84 -8.70 203.98 -2.07 -48.29 -2.94 40.32 -3.42 125.42

(AS) 1992 -3.01 -8.73 0.15 32.55 -1.64 -42.53 -2.72 40.59 -3.34 74.41

Grell 1987 -3.21 101.9 0.54 90.76 -3.34 -9.46 -3.99 146.2 -3.42 107.24

(FC) 1992 -2.69 18.79 0.89 61.12 -2.96 -2.52 -3.46 123.80 -3.27 70.37

Kuo- 1987 -3.96 128.4 -1.06 278.58 -1.71 -55.74 -2.41 -25.6 -2.69 4.30

Anthes 1992 -3.10 3.952 -0.37 59.99 -1.19 -58.4 -2.24 -11.8 -2.50 -21.84

1987 -4.40 35.66 2.02 -66.7 0.48 -7.06 -6.5 7.44 -2.7 137.41

Emanuel 1992 -1.12 3.67 2.39 23.5 -0.19 21.26 -1.2 6.4 -2.54 131.93

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3.3 Conclusions

Three convective schemes available in RegCM3 were used to validate the model over south Asia region for summer (JJAS) and winter (DJFM) season. Emanuel convective scheme gave the precipitation and temperature results in close agreement with the observed. It captured the spatial and inter-annual variability reasonably well as compared to other convective schemes.

Emanuel scheme showed better results for the winter temperatures as compared to the summer. In case of precipitation, it overestimated more in winter as compared to the summer season. Moreover, the model, in general, gave more cold bias over northern parts of Pakistan as compared to southern parts of Pakistan in both seasons.

4. Individual Events Validation

Following events were simulated with the model:

Flood event of August 1997 in the Jhelurn catchment The extreme precipitation event of23rd July 2001 in Islamabad Severe cyclonic storm in the Bay of Bengal, May 1985

4.1 Flood Event of August 1997 in the Jhelum Catchment

A flood peak of 6, 02,780 cusecs occurred at Mangla in the Jhelum catchment on 27th of August due to a rainfall event of25-28 August, 1997. This was a prominent feature ofthe monsoon season during 1997 and was caused due to a monsoon depression developed in the Bay of Bengal on 17th of August, 1997. It reached Jodhpur (Rajasthan) on 26th of August and then re-curved in an almost northerly direction and hit the Jhelurn catchment. The path of the observed track is shown in Fig. 20.

4.1.1 Data and Methodology

The model was driven by the initial and lateral boundary conditions from Forty-year European Re-Analysis data of the Global Atmosphere (ERA40). This dataset is available at a resolution of 2.5° x 2.5°, The observed SSTs were provided by the Globally Interpolated Sea Surface Temperature (GISST) dataset. To run the domain, the global land cover and topography data at a resolution of 10 min was used. The model was run at a horizontal resolution of 30 km. The convective parameterization scheme Emanuel (1991) was used. The model was divided into 18 vertical levels with the pressure of the model top being 50 mb.

F or this particular experiment, the number of grid points taken in the x and y direction are 135 and 125 respectively. The domain extends from 55°E to 1000E and 5°N to 400N covering the whole of South Asia. The model domain showing the surface elevation of the selected region is shown in Fig. 21

7.

Figure 20: Track of the observed monsoon depressions during 1997

Figure 21: Domain showing elevation (m) selected for the flood event of 1997

4.1.2 Analysis & Discussion

Wind patterns and geo-potential heights at 850 hPa are studied on daily basis for the whole period starting from the date, the depression developed in the Bay of Bengal. Fig. 22(a) shows the development of the low pressure in the Bay of Bengal while Figs. 22(b) to 22(h) show the path followed by the depression. Simulated track closely follows the observed track shown in Fig. 20 both temporally and spatially which illustrates that model has very well captured the monsoon depression.

33

8. 9. 10.

Figure 22: Movement of the monsoon depression developed in the Bay of Bengal to the Northern Pakistan simulated by the model (a-h) position of depression from 20th August to 27th August, 1997 respectively

34

11.

Total precipitation of the event is shown in Fig. 23. Precipitation for the rain spell which caused the flood is shown in Fig. 23 (a). Fig. 23 (b) gives the precipitation for the whole period of the storm starting from the Bay of Bengal and it was seen that the rainfall pattern followed the track of the depression showing consistency of the model in simulating different variables. Fig. 23 (c) and Fig 23 (d) give a comparison of simulated rainfall with the observed (i.e. CRU) for the whole month. We can see that model overestimates the precipitation in all the regions. However, the pattern is somewhat similar.

Figure 23: Rainfall (mm/day) (a) the rain spell of 20-28 August 1997,( b) the rain spell of25-28 August 1997 (c) the whole month (d) observed rainfall taken by CRU data.

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The model was also run with convective schemes Grell and Anthes-Kuo but these schemes were unable to capture the monsoon depression. The Emanuel scheme captured the event nicely as the scheme introduces the sophisticated microphysics of water drops into the convection process. The scheme has specially been designed for tropical and subtropical regions.

4.2 The Extreme Precipitation Event of 23rd July 2001 in Islamabad

On 23rd July 2001,620 mm of rainfall was recorded in Islamabad in 10 hours from 6 am to 4 pm. It was a localized event. This resulted in catastrophic urban flooding in Lai Nullah in Rawalpindi and brought colossal loss of life and resources. Flood water in certain low-lying areas was as high as 18 feet. Every bridge on the Lai Nullah was overflown by flood water. In this event, about 100 people died and around 1400 houses were completely destroyed. The observed surface pressure pattern is shown in Fig. 24.

4.2.1 Data and Methodology

The model was driven by the initial and lateral boundary conditions from ERA40. This dataset is available at a resolution of 2.5° x 2.5°. The observed SSTs were provided by the Globally Interpolated Sea Surface Temperature (GISST) dataset. To run the domain, the global land cover and topography data at a resolution of 10 min was used. The model was run at a horizontal resolution of 25 km. The convective parameterization scheme Emanuel (1991) is used. The model is divided into 18 vertical levels with the pressure of the model top being 5 cb. The domain extends from 600E to 81°E and 25°N to 37°N

4.2.2 Analysis & Discussion

Wind patterns at 850 hPa are analyzed Fig. 25 (b). From the observed surface pressure pattern Fig. 24, we can see that the winds from the Bay of Bengal, Arabian Sea and western side are converging around Islamabad and same is simulated by the model as shown in Fig. 25 (b) which shows how well the model has captured the wind pattern.

The event was much localized and caused precipitation only in the vicinity of Islamabad. We can see that the model has also shown precipitation very close to Islamabad as shown in Fig. 25 (a). The Emanuel convective scheme has captured the event reasonably good.

Figure 24: Surface pressure pattern on 23rd July 2001

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(a) (b)

Figure 25: (a) Simulated precipitation (mm/day) for 23rd July 2001 (b) Simulated wind patterns at 850 hPa for 23rd July 2001

4.3 Severe Cyclonic Storm in the Bay of Bengal

In May 1985, a severe cyclonic storm with a core of hurricane winds developed in the Bay of Bengal. It developed on May 22 near 15.0oN and 88°E at 0600 UTC. It became intense near 16.5°N and 87.5°E at 0300 UTC on May 23. It became severe near 19.5°N and 89.0oE at 0900 UTC on May 24 and intensified further into a severe cyclonic storm with a core of hurricane winds. It crossed the coast near Chittagong early in the morning of May 25 [43]. The maximum speed, when it hit Chittagong, was 154 Kph (83 kts).

Figure 26: Observed track of the tropical cyclone in Bay of Bengal, May 22- 25, 1985

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It affected about 1,906 sq. miles of area and a population of 13,10,935. Almost 4,264 people lost their lives and 6,805 people were found missing after this event. It damaged about 1,32,860 acres of crops [43]. The observed track of the cyclone is shown in Fig. 26 [Source: www.weather.unisys.com ].

4.3.1 Data and Methodology

As it was a localized event, the model was run at a horizontal resolution of 25 km to perform the simulation. The domain extends from 600E to 1000E and OON to 36°N including the Bay of Bengal from where most of the cyclonic activity is generated. ERA40 was used as driving dataset. The model was divided into 18 vertical levels. The pressure of the model top was kept 5 cb. Wind patterns showing the simulated path of the cyclone are shown in the Fig. 27. The model has captured the event very well both in time and space.

4.3.2 Results & Discussion

Wind patterns showing the simulated path of the cyclone are shown in Fig. 27. The model has captured the event very well both in time and space.

38

12. a

(c)

Figure 27(a-d): Simulated wind pattern from May 19 to May 26, 1985 respectively (contd.)

39

13. 14.

(h) 1985 May 26

Figure 27(e-h): Simulated wind pattern from May 19 to May 26, 1985 respectively

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5. Conclusion /Summary:

Three convective schemes were used in RegCM3 to validate the model for south Asia region for summer (JJAS) and winter (DJFM) season. It was found that Grell convective scheme with Fritsch Chapel (FC) closure assumption gives the result of precipitation and temperature in close agreement with the observed one. It captures the spatial and interannual variability very well as compared to other convective schemes used in the model.

However, it has been found that there are some weaknesses in the model; it over precipitates in some regions in both summer and winter but the spatial pattern in the winter season is very good whereas there are short comings in the model in capturing Indian monsoon. Some improvements were made in the topography of the model, which helped in resolving the double peak problem in precipitation over Nepal.

The model has captured well the different extreme events. It is seen that Emanuel convective scheme is quite good in simulating extreme events as compared to other schemes.

6. Acknowledgements

We are thankful to Earth System Physics (ESP) group of the Abdus Salam International Centre for Theoretical Physics (ICTP), Trieste, Italy, for providing the model (RegCM3), datasets, and technical support to validate RegCM3 over South Asia.

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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.

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