Climate Change Scenarios for Nepal based on Regional Climate Model RegCM3

Transcription

1 FINAL Climate Change Scenarios for Nepal based on Regional Climate Model RegCM3 Jagadishwor Karmacharya, Archana Shrestha, Rupak Rajbhandari, Madan L. Shrestha July, 2007 Department of Hydrology and Meteorology Kathmandu - Nepal

2 Preface To anticipate the future climate change, we need to project how green house gases will change in the future. A range of emission scenarios has been developed in the IPCC Special Report on Emissions Scenarios (SRES) that reflect a number of different ways in which the world might develop (storylines) as the consequences of population and economic growth; energy use and technology evolution. To estimate the effect of the Green House Gases (GHGs) emissions on the global climate, global climate models (GCMs) are employed. GCMs describe important physical elements and processes in the atmosphere, oceans and land surface that make up the climate system. One disadvantage of GCMs is that their scale is typically a few hundred kilometers in resolution. In order to study the impacts of climate change, we need to predict changes on much finer scales. One of the techniques for doing so is to use the Regional Climate Models (RCMs), which have the potential to improve the representation of the climate information which is important for assessing a country s vulnerability to climate change for appropriate adaptation measures. To conduct thorough assessments, impact researchers need regional details of how future climate might change, which in general should include information on changes in variability and extreme events. An 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 the climate system components that are represented by GCMs. They take coarse resolution information from GCMs and then develop temporal and spatial fine scale information consistent with their high resolution. The typical resolution of an RCM is about 50 km in the horizontal. It covers an area (domain) typically of 5000 km by 5000 km in size, located over a particular region of interest. This study focuses on the validation of the regional climate model RegCM3 and climate change projection over Nepal for mid 21 st century using ECHAM5 GCM driving dataset. The work described in this report was carried out under the frame work of APN CAPaBLE project entitled: Enhancement of National Capacities in the Application of Simulation Models for Assessment of Climate Change and its Impacts on water Resources and Food and Agricultural Production (2005-CRPOCMY-Khan) awarded to Global Change Impact Studies Centre (GCISC) and Pakistan Meteorological Department (PMD) with Department of Hydrology and Meteorology (DHM) as one of the partner..

3 Table of Contents 1. Regional Climate Model RegCM3: An Introduction 1.1 Physics of the Model 2. Experiments on Selection of Parameters for Model Configuration 2.1 Domain Selection 2.2 Convective Parameterization 2.3 Terrain Smoothening 2.4 Errata 3. Model Configuration for Simulation 4. Validation of RegCM3 for Nepal 4.1 Spatial Pattern Validation Annual Climate Winter Climate Pre-Monsoon Climate Monsoon Climate Post-Monsoon Climate 4.2 Area Average Validation Temperature Bias Precipitation Bias 4.3 Annual Cycle Validation 5. Climate Change Projection for Nepal in Mid 21 st Century 5.1 Spatial pattern of Climate Change Annual Climate Change Winter Climate Change Pre-Monsoon Climate Change Monsoon Climate Change Post-Monsoon Climate Change 5.2 Area Average Climate Change 5.3 Annual Cycle 6 Conclusion References ii

4 List of Figures Fig. 1: Fig. 2i: Fig. 2ii: South Asia Domain (elevation in meter) Monsoon precipitation (in mm/day) for the year 1996 :- (a) CRU observation; model simulation (b) MIT Scheme (c) Grell Scheme for (b) MIT scheme (c) Grell with FC Difference in elevation (in meter) between smoothened terrain and default model terrain Fig. 3 (a, b): Mean Annual Temperature Climatology Base Line (Model) and Bias with CRU Fig. 3 (c, d): Mean Annual Precipitation Climatology Base Line (Model) and Bias with CRU Fig. 3e: Mean Annual precipitation Bias with Station data Fig. 4 (a, b): Mean winter Temperature Climatology Base Line (Model) and Bias with CRU Fig. 4 (c, d): Mean winter Precipitation Climatology Base Line (Model) and Bias with CRU Fig. 4e: Mean winter precipitation bias with station data Fig. 5 (a, b): Mean Pre-monsoon Temperature Climatology Base Line (Model) and Bias with CRU Fig. 5 (c, d): Mean Pre-monsoon Precipitation Climatology Base Line (Model) and Bias with CRU Fig. 5e: Mean Pre-monsoon precipitation bias with station data Fig. 6 (a, b): Mean Monsoon Temperature Climatology Base Line (Model) and Bias with CRU Fig. 6 (c, d): Mean Monsoon Precipitation Climatology Base Line (Model) and Bias with CRU Fig. 6e: Mean Monsoon precipitation bias with station data Fig. 7 (a, b): Mean Post-monsoon Temperature Climatology Base Line (Model) and Bias with CRU iii

5 Fig. 7 (c, d): Mean Post-monsoon Precipitation Climatology Base Line (Model) and Bias with CRU Fig. 7e: Fig. 8: Fig. 9: Mean Post-monsoon precipitation bias with station data Area of grid boxes in west and east Nepal for Area average analysis Model bias in mean temperature with CRU Fig.10 (a, b): Area average bias in model precipitation with CRU and station data Fig.10 (c, d): Comparison of area average bias in precipitation between CRU and station data in west and east Nepal Fig.11 (a, b): Annual cycle of mean temperature for base period in west and east Nepal Fig.11 (c, d): Annual cycle of mean precipitation for base period in west and east Nepal Fig.12 (a, b): Mean annual temperature and precipitation change Fig.13 (a, b): Mean winter temperature and precipitation change Fig.14 (a, b): Mean pre-monsoon temperature and precipitation change Fig.15 (a, b): Mean monsoon temperature and precipitation change Fig.16 (a, b): Mean post-monsoon temperature and precipitation change Fig.17(a, b): Comparison in change in mean temperature and precipitation in west and east Nepal Fig.18 (a, b): Comparison of Annual cycle of mean temperature in west and east Nepal Fig.18 (c, d): Comparison of Annual cycle of mean precipitation in west and east Nepal iv

6 List of Tables Table 1: Bias in model mean temperature ( C) Table 2: Bias in Model Precipitation (%) Table 3: Climate Change in mid-century ( ) compare to base line ( ) v

7 1. Regional Climate Model RegCM3: An Introduction RegCM3 is a 3-dimensional, sigma-coordinate, primitive 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 1a (Dickinson et al., 1986). In addition, the existing convective precipitation (Anthes, 1977) and planetary boundary layer (PBL) (Deardorff, 1972) parameterizations were improved upon. The second version of the RegCM series was developed by Giorgi et al. (1993a, 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 version2 (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 1a to 1e (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 RegCM2.5 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 and operates 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 General Circulation Model (GCM) boundary conditions. Other improvements in RegCM3 involve the input data. The U.S. Geological Survey (USGS) Global Land Cover Characterization and Global 30 Arc-Second Elevation datasets are now used to create the terrain files. In addition, NCEP and ECMWF global reanalysis datasets are used for the initial and boundary conditions. 1

8 1.1 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-eddington approximation 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 (extinction optical depth, single scattering albedo, and asymmetry parameter) are expressed in terms of the cloud liquid water content and an effective droplet radius. The surface physics are performed using BATS (Biosphere-Atmosphere Transfer Scheme) which is described in detail by Dickinson et al The model has a vegetation layer, a snow layer, a surface soil layer, 10 cm thick, or root zone layer, 1-2 m thick, and a third deep soil layer 3 m thick. Prognostic equations are solved for the soil layer temperatures using a generalization of the force-restore method of Deardoff (1978). The temperature of the canopy and canopy foliage is calculated diagnostically via an energy balance formulation including sensible, radiative, and latent heat fluxes. The soil hydrology calculations include predictive equations for the water content of the soil layers. These equations account for precipitation, snowmelt, canopy foliage drip, evapotranspiration, surface runoff, infiltration below the root zone, and diffusive exchange of water between soil layers. The soil water movement formulation is obtained from a fit to results from a high-resolution soil model (Climate Processes and Climate Sensitivity, 1984) and the surface runoff rates are expressed as functions of the precipitation rates and the degree of soil water saturation. Snow depth is prognostically calculated from snowfall, snowmelt, and sublimation. Precipitation is assumed to fall in the form of snow if the temperature of the lowest model level is below 271 K. Sensible heat, water vapor, and momentum fluxes at the surface are calculated using a standard surface drag coefficient formulation based on surface-layer similarity theory. The drag coefficient depends on the surface roughness length and on the atmospheric stability in the surface layer. The surface evapotranspiration rates depend on the availability of soil water. BATS has 20 vegetation types(see Table 2) and soil textures ranging from coarse (sand), to intermediate (loam), to fine (clay); and different soil colors (light to dark) for the soil aledo calculations. These are described in Dickinson et al In the RegCM3, water bodies can be categorized as open (e.g. oceans) and as enclosed (e.g. lakes). The energy fluxes from open bodies are computed from prescribed sea surface temperatures (SSTs) with no two-way interaction. The ocean affects the atmosphere, but the atmosphere does not affect the ocean. The energy fluxes from enclosed water bodies can be computed using one of two methods: a column lake model with two-way interaction or the prescribed SST method used for open bodies. In the RegCM3, there are two parameterization options for computing fluxes from open water bodies: the BATS formulation and the newly implemented Zeng scheme (Zeng et al., 1998). BATS scheme uses standard Monin-Obukhov similarity relations to compute the fluxes with no special treatment of convective and very stable conditions. In addition, the roughness length is set to a constant (not a function of wind and stability). For these reasons, according to Zeng et al. (1998), the ocean flux computations of BATS tend to 2

9 overestimate evaporation in both high wind and low wind conditions. The Zeng scheme describes all stability conditions and includes a gustiness velocity to account for the additional flux induced by boundary layer scale variability. Tests by Francisco et al.(2005) show that the RegCM3 coupled with the Zeng scheme better estimates evaporation fluxes over the South Pacific Ocean. As mentioned above, the RegCM3 also includes a one-dimensional, energy balance lake model (Hostetler and Bartlen, 1990). It is a physically based eddy diffusion model designed to simulate the seasonal variations of lake temperature, evaporation, and ice cover. The original implementation and testing of the lake model is described by Hostetler et al. (1993) and further modifications and improvements are described by Small et al. (1999). Since the lake model is onedimensional in the vertical, it may not be accurate for larger bodies of water, where horizontal dynamical processes are present. 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 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 the 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 1-km. Due to its fine scale, convective precipitation must be parameterized in most climate models. In the RegCM3, the resolvable (or large-scale) precipitation is represented using 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 3

10 Schubert, 1974) and (2) that all 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 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. To date, several global reanalysis products and GCMs have provided boundary conditions to the RegCM3 including NCEP/NCAR Reanalysis Product (NNRP), ECMWF 40 year Reanalysis (ERA40), CCM3, ECHAM, Hadley Centre Atmospheric Model version 3H (HadAM3H), and NASA Data Assimilation Office atmospheric finite-volume general circulation model (fvgcm). 2. Experiments on Selection of Parameters for Model Configuration A number of experiments have been performed as part of model transferability and validation over South Asia. These involve experimentation on domain selection, selection of convective parameterization, selection of atmosphere and ocean driving data and finally modification (smoothening) of terrain. 2.1 Domain Selection In running regional climate model over an area proper attention should be paid on the domain selection as it affects the simulation output. It is well accepted fact that the domain should be big enough to develop its own circulation in the interior of the domain and at the same time small enough so that RCM results does not diverse too much from that of GCM. Also a number of factors have to be considered in location of the domain boundary such as it should not cross the complex terrain, be far from active convective zone like ICTZ, does not intersect a climate regime etc. Obviously it is difficult to satisfy all these conditions specially in South Asia region having complex terrain. 4

11 Selection of model resolution is another important aspect in RCM simulation. Technically RegCM3 can be run at any resolution but jump from driving data resolution to RCM resolution has to be taken into consideration. Also, simulation period as well as data output volume should also be consideration as time of simulation increases by approximately 6 times and data volume by 4 times with doubling of the resolution. Taking these factors into consideration and after a number of test we have selected a domain with 111x120 grids; 18 vertical levels at 50 km resolution. The domain extends roughly from 50 E to 110 E and 5 S to 45 N (fig.1) Figure 1: South Asia Domain (elevation in meter) 2.2 Convective Parameterization Convective precipitation still remains one of the most important source of error in climate models so care should be taken in selecting convective scheme in model simulation. Three options are available in RegCM3 to represent cumulus convection: (1) the modified Anthes-Kuo scheme; (2) the Grell scheme ; and (3) the Massachusetts Institute 5

12 of Technology (MIT) Scheme. We have conducted a series of experiments to identify appropriate scheme over the South Asia. These experiments mainly focused on two scheme viz. Grell and MIT and the simulation were done mainly for the summer months since it has been seen that model generates sporadic precipitation during summer. After evaluating a series of simulation from different convective parameterization and some optimization of the parameter in MIT scheme we have chosen MIT scheme for the scenario simulation. Fig 2i shows the plot for one of the experiments that compare the observed (CRU) and model simulated precipitation for the monsoon of 1996 (a) CRU (b) MIT Scheme (c) Grell Scheme Fig 2i: Monsoon precipitation (in mm/day) for the year 1996 :- (a) CRU observation; model simulation (b) MIT Scheme (c) Grell Scheme 2.2 Terrain Smoothening Some problem remained in simulation of monsoon precipitation over South Asia even after tuning of the two parameters in MIT convection scheme over the domain we have selected. There was excess precipitation over the domain in general and more so over the Himalayan belt and one unusual pattern was generation of double band of precipitation in vicinity of the Himalayan range. Under the guidance of the experts from PWC, ICTP we conducted a number of experiments taking consideration of various factor and we are able to remove the double band of precipitation with slight modification of the model terrain. This modification involves slight smoothening of the terrain only over the steep terrain. This in effect smoothen the terrain only around the foothill of the Himalayan range and Tibetan plateau leaving the rest of the area unaffected. Fig 2ii shows the difference in elevation between smoothened terrain and default model terrain. As seen in the figure, this modification raise elevation of the low land and drop the height of summit, but leaves rest of the domain virtually unaffected. 6

13 Fig 2ii: difference in elevation (in meter) between smoothened terrain and default model terrain 2.4 Errata A bug was reported in the current version RegCM3 which was used throughout this modeling exercise. The bug is in the calculation of the eccentricity; which sets the eccentricity at the value of the initial day and then does not change it any more. For example, if simulation is started on Jan 1, the eccentricity will remain that of Jan 1 for the whole run. The eccentricity affects the value of incoming solar radiation at the top of the atmosphere. This values varies by about 6-7% from a maximum in January to a minimum in July. So, depending on when simulation is initialized it might have an error of up to 6-7% in the incoming solar radiation. In climate change simulation runs done at DHM; both for base period and future scenario simulations were initialized on the month of October so its presumed that the error in incoming solar radiation varies from 0 to +/- 3/4 % depending upon model month of simulation. Though it is unfortunate to have this error but we hope that this error will not greatly effect the model results. 7

14 At the same time another bug was reported related with calculation of declination angle. Due to this bug model run should always be initialized on 00:00 GMT, otherwise the model has some problems in calculating the declination angle with the proper timing. Fortunately all our runs are starts at 00:00 GMT. At the time of preparation of this report this problem still need to be shorted out. 3. Model Configuration for Simulation Regional Climate model RegCM3 developed and supported by PWC/ICTP is run over the South Asia domain. The model is driven by ECHAM5 GCM and simulation is run over the period of roughly 30 year time slice each with couple of months spin up for the base line and future period. Monthly averages of the output files are computed and climatological average are computed from those files. 4. Validation of RegCM3 for Nepal The model outputs were validated for four seasons for Nepal for the base line period ( ). Two major parameters, mean temperature and precipitation, were used for the validation. The model outputs were compared with Climate Research Unit (CRU) data. Gridded station data is also available for precipitation. Therefore, precipitation model output was also validated with station data. The validation analysis is done for spatial distribution of bias in various seasons, area average bias over the eastern and western Nepal in various seasons and comparison of annual cycle. 4.1 Spatial Pattern Validation This section presents the comparison of model output and observed datasets CRU for temperature and precipitation over Nepal. Moreover, the model precipitation output is also compared with the station data. The comparison is done on seasonal and annual climatology. The seasons are defined as winter (December - February), pre-monsoon (March May), monsoon (June - September) and post-monsoon (October- November) Annual Climate Fig. 3a shows the mean annual temperature climatology for the base line period. The north south gradient of temperature is captured well by the model. Mean temperature lies in the range of +24 C to 4 C for the grid boxes within Nepal. Fig. 3b shows the mean annual temperature climatology bias with CRU. There is cold bias over most of the grids within the range 2 to 8 C, while there is warm bias over the Himalayas in the north up to 10 C. This warm and cold bias is partly due to the extra smoothening of the complex terrain that resulted in flattening of the Himalayas in the north and lifting of Terai plain in the south. Fig. 3c shows the annual precipitation of model output. Annual precipitation increases from the west to the east. However, the observed annual precipitation pattern is different. 8

15 It is highest between the latitudes 27.5 N and 28.5 N and east of 83 E. Annual precipitation bias (Fig. 3d) shows over estimation by model over the entire country, with maximum bias in the northwestern Nepal. Fig. 3e shows the precipitation bias with station data in mm/day. It also shows over estimation over the entire country. Fig. 3a: Mean Annual Temperature Climatology Base Line (Model) Fig. 3b: Mean Annual Temperature Climatology - Bias with CRU Fig. 3c: Mean Annual Precipitation Climatology Base Line (Model) Fig.3d: Mean Annual Precipitation Climatology Bias with CRU Fig. 3e: Mean Annual precipitation bias with station data 9

16 4.1.2 Winter Climate Fig. 4a presents the model climatology of winter mean temperature. The Fig shows that winter temperature varies from 14 C in south to -14 C over the northwest. The bias with CRU observation is presented in Fig. 4b. Warm bias is noted in few grids in the north and cold bias over most of the grids in south in the Terai plains and middle mountains. Warm bias is only up to 6 C and cold bias is up to -8 C. The magnitude of cold bias in winter is similar to that of annual, while warm bias is lower in winter compare to the annual temperature. Winter precipitation as depicted by model is shown in Fig. 4c. Winter precipitation is maximum in the west and decreases towards east. The observation also shows the similar pattern. However, the comparison with CRU shows overestimation over entire country with highest positive bias in the southeast Nepal Fig. 4d. Precipitation bias with station data is shown in Fig. 4e. It also shows over estimation in the entire country, with maximum bias more than 6mm/day over western Nepal. 10

17 Fig. 4a: Mean winter Temperature Climatology Base Line (Model) Fig. 4b: Mean winter Temperature Climatology- Bias with CRU Fig. 4c: Mean winter Precipitation Climatology Base Line (Model) Fig. 4d: Mean winter Precipitation Climatology Bias with CRU Fig. 4e: Mean winter precipitation bias with station data Pre-Monsoon Climate The mean pre-monsoon temperature produced by model shows 28 C in the southern Nepal to -4 C in the northern Nepal (Fig. 5a). This pattern of spatial gradient is close to the observed pattern. However, the bias with CRU shows cold bias in most of the grids 11

18 up to -10 C in the southern Nepal to warm bias in few grids up to +10 C in the northern Nepal (Fig. 5b). The cold bias is higher than the annual and winter cold biases. The model output shows that pre-monsoon precipitation increases from the west towards east (Fig. 5c), which is similar to the observed pattern. The bias with CRU data shows over estimation over the entire country and the bias is higher in the eastern Nepal compared to the western Nepal (Fig. 5d). Bias with station data also shows over estimation of precipitation, mainly over eastern Nepal with bias more than 20mm/day (Fig. 5e). Fig. 5a: Mean pre-monsoon temperature climatology Base Line Fig. 5b: Mean pre-monsoon temperature climatology- Bias with CRU Monsoon Climate Fig. 5c: Mean pre-monsoon precipitation climatology Base Line Fig. 5d: Mean pre-monsoon precipitation climatology Base Line Fig. 5e:Mean pre-monsoon precipitation bias with station data 12

19 Fig. 6a: Mean monsoon temperature climatology Base line Fig. 6b: Mean monsoon temperature climatology- bias with CRU Fig. 6c: Mean monsoon precipitation climatology Base Line Fig. 6d: Mean monsoon precipitation climatology Base Line Fig. 6e:Mean monsoon precipitation bias with station data Monsoon climate Model mean temperature for monsoon is presented in Fig. 6a. The Fig. shows that the mean temperature is higest in Terai over southern Nepal up to 28 C and it decreases towards north up to 6 C over Himalayas. Again this pattern is quite close to the observation. A comarison with CRU observations shows larger area of cold bias up to - 13

20 10 C (Fig. 6b). In the northern part of the country over Himalayas the model produced warm bias of up to +10 C in few grids. The area of cold bias is largest in monsoon season. The precipitation model output is shown in Fig. 6c. The model shows the highest precipitation in the eastern Nepal and it decreases towards west and is minimum in the northwestern Nepal. However, observation shows the highest precipitation in the southern part of central Nepal and lowest in the northern part of the central Nepal around 84 E. Comparison with CRU observation shows overestimation over the entire country except over the southern part of central Nepal (Fig. 6d). The positive bias is higher in the Northern Nepal and maximum in the northwestern Nepal. The bias with station data is shown in Fig 6e. The Fig shows over estimation over eastern and few grids in the northwestern Nepal. However, it shows underestimation in the southwestern Nepal. Fig. 7a: Mean Post Monsoon Temperature Climatology Base Line Fig. 7b: Mean Post Monsoon Temperature Climatology- Bias with CRU Fig. 7c: Mean Post Monsoon Precipitation Climatology Base Line Fig. 7d: Mean Post Monsoon Temperature Climatology- Bias with CRU Fig. 7e:Mean Post monsoon precipitation bias with station data 14

21 4.1.5 Post-Monsoon Climate The mean temperature during post-monsoon produced by model is shown in Fig. 7a. The temperature decreases from the southern Nepal +20 C to -6 C in the northern Nepal. The bias with CRU observation is shown in Fig. 7b. In this season also the cold bias is noted over most part of the country and warm bias over northern Nepal. The warm bias was up to +10 C and the cold bias was up to -10 C. The area of cold bias of more than -8 C is largest during the post-monsoon season. Post-monsoon is the driest season in Nepal. The post-monsoon precipitation as depicted by model is shown in Fig. 7c. The Fig. shows the highest precipitation in the west. However, observed distribution is of different pattern. The highest observed precipitation is in the eastern Nepal. The bias with CRU observation is presented in Fig. 7d. The model overestimated the precipitation over the entire country, with highest bias in the western Nepal. Comparison with station data is shown in Fig. 7e. It also shows the highest bias in the west. 4.2 Area Average Validation Spatially, the climatic pattern in the western Nepal is different than eastern Nepal. The eastern Nepal is influenced mainly by the south-west monsoon, while the western Nepal is dominated by the western disturbances in winter. For detailed analysis of model validation and climate change aspect, Nepal is divided into two parts East Nepal- east of 84 N and West Nepal- west of 84 N (Fig. 8). There are 36 grid boxes in East Nepal and 41 over West Nepal. Area average analysis is done over these grid boxes for comparison between model simulation and observation for validation. Fig. 8: Area of grid boxes in west and east Nepal for Area average analysis 15

22 4.2.1 Temperature Bias Area average bias shows that in all the seasons the model produces cold bias both in the eastern and western Nepal (Fig. 9). Moreover, except in winter, bias is higher in the eastern Nepal compare to the western Nepal. For both eastern and western Nepal bias is highest in the post-monsoon season. The absolute values of bias is shown in Table 1 and it varies from -1.3 C to -3.3 C. 0.0 Bias in Mean Temperature -0.5 Difference in o C Winter Bias-West Bias East Annual Monsoon Postmonsoon Pre- Monsoon Fig. 9: Model bias in mean temperature with CRU Table 1: Bias in model mean temperature ( C) West East Winter Pre-Monsoon Monsoon Post-monsoon Annual Precipitation Bias Area average seasonal and annual precipitation bias with CRU observations is shown in Fig. 10a. In all the seasons, precipitation is over estimated by the model. Except in annual and in post-monsoon season, bias is higher in the east than in the west. Similarly, Fig. 10b shows precipitation bias with station data. The model over estimates the precipitation compare to the station data for all the seasons in the east as well as in the west. Contrary to bias with CRU in winter and pre-monsoon, the bias with station is higher in the west than in the east. It is interesting to note that the bias with station is much less compare to the bias with CRU data in the eastern Nepal in all the seasons (Fig. 10c). While in the west the biases with both data sets are similar except for monsoon and annual, which is lesser with the station (Fig. 10d). 16

23 % Difference Winter Precipitation Bias With CRU Data Bias with CRU-West Bias with CRU-East Pre- Monsoon Monsoon Annual % Difference Winter Precipitation Bias With Station Data Bias with Station-West Bias with Station-East Postmonsoon Pre- Monsoon Monsoon Postmonsoon Annual Fig. 10a: Area average bias in model precipitation with CRU Fig. 10b: Area average bias in model precipitation with station data Bias with CRU-West Bias with Station-West Precipitation Bias in the West Precipitation Bias in the East Bias with CRU-East Bias with Station-East % Difference % Difference Winter Pre-Monsoon Monsoon Postmonsoon Annual Fig. 10c: Comparison of area average bias in precipitation between CRU and station data in west 0 Winter Pre- Monsoon Monsoon Postmonsoon Annual Fig. 10d: Comparison of area average bias in precipitation between CRU and station data in east The absolute values of area average bias in precipitation with station data and CRU is shown in Table 2. The bias with station data varies from 21.5% in monsoon in west Nepal to 366.8% in post monsoon in west Nepal. The bias with station data in eastern Nepal is less than 70% in all the season. The bias with CRU data varies from 51.2% in monsoon to 359.1% in post-monsoon season. Moreover, bias with station data is less than with CRU data in all seasons both in west and east Nepal. Table 2: Bias in Model Precipitation (%) West Nepal East Nepal Bias with Station Bias with CRU Bias with Station Bias with CRU Winter Pre-Monsoon Monsoon Post-monsoon Annual Annual Cycle Validation The annual cycle of mean temperature for eastern and western Nepal is presented in Fig. 11a. It shows that the temperature cycle of the model follows same pattern as that of the CRU data, with maximum in June and minimum in January in the western Nepal. However, in the eastern Nepal the model mean temperature is maximum in June while it 17

24 is maximum in July in CRU data. Minimum values are in January for both model output and CRU (Fig. 11b). The annual cycle of precipitation for eastern Nepal and western Nepal is shown is Fig. 11c. The figures show that the precipitation pattern of model does not follow neither of the observations (CRU and station data). Observed data show the highest precipitation is in July and the lowest in November. But the model data shows the highest in June and the lowest in December in the east as well as in the west (Fig. 11d). Tem perature C Annual Cycle of Mean Temperature in West Nepal CRU Base Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Fig. 11a: Annual cycle of mean temperature for base period in west Nepal Precipitation in mm/day Annual Cycle of Precipitation in West Nepal Jan Feb Mar Apr May Jun Fig. 11c: Annual cycle of mean precipitation for base period in west Nepal Jul CRU Base Station Aug Sep Oct Nov Dec Temperature in C Annual Cycle of Mean Temperature in East Nepal CRU Base Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Fig. 11b: Annual cycle of mean temperature for base period in east Nepal Precipitation in mm/day Annual Cycle of Precipitation in East Nepal 25.0 CRU 20.0 Base Jan Feb Mar Apr May Jun Jul Station Fig. 11d: Annual cycle of mean precipitation for base period in east Nepal Aug Sep Oct Nov Dec 5. Climate Change Projection for Nepal in Mid 21 st Century This section presents the climate change analysis based on the RegCM3 output for A2 Scenario for the mid-century period The change was studied compared to the base line climate for Spatial pattern of Climate Change 18

25 The spatial comparison is done on seasonal and annual climatology. The seasons are defined as winter (December-February), pre-monsoon (Mar May), monsoon (June- September) and post-monsoon (October-November). Similarly, the area average analysis for seasonal and annual climatology is done for western and eastern Nepal as depicted in Fig Annual Climate Change Fig. 12a shows annual mean temperature change in mid-century. It depicts warming over entire country, with the range of 1.7 C in the south to 2.5 C in the north. Annual precipitation change is presented in Fig. 12b. It shows decrease in precipitation in large parts of the country, mainly in the eastern and southern Nepal (up to -30%); and there is no change in precipitaion over north center and north west Nepal. Fig. 12a: Mean annual temperature change ( C) Fig. 12b: Mean annual precipitation change (%) Winter Climate Change The winter temperature seems to increase in future by C. The warming rate increases from the south east towards northwest (Fig. 13a). The winter precipitation change is presented in Fig. 13b. It shows decrease in precipitation up to by 20%, mainly in the eastern Nepal and increase in the southwestern Nepal up to 20%. There is no change in precipitation over rest of the country. 19

26 Fig. 13a: Mean winter temperature change ( C) Fig. 13b: Mean winter precipitation change (%) Pre-monsoon Climate Change The mean pre-monsoon temperature change in mid-21 st century is shown Fig. 14a. The Fig. shows increase in temperature over the entire country with 1.5 C-2.5 C. Contrary to other seasons the increase is higher in the southeastern part of the country. Regarding change in pre-monsoon precipitation, signal is more significant in the western Nepal (Fig. 14b). Precipitation seems to increase (+40%) in the northern part and decrease (-40%) in southern part in the western Nepal. In the eastern Nepal there is possibility of slight decrease in precipitation (-5% to -10%). Fig. 14a: Change in pre-monsoon mean temperature ( C) Fig. 14b: Change in pre-monsoon mean precipitation (%) 20

27 5.1.4 Monsoon Climate Change Change in mean temperature in monsoon is presented in Fig. 14a. As in other seasons monsoon temperature seems to increase in the mid century all over the country. The Fig. shows that the mean monsoon temperature seems to increase by 1.7 C in the eastern Nepal to 3.1 C in the western Nepal. Change in monsoon precipitation is presented in Fig. 14b. The Fig. indicates decrease in precipitation in most parts of the country. The decrease is maximum in the east, up to -40%. Fig. 15a: Change in monsoon mean temperature ( C) Post-Monsoon Climate Change Fig. 15b: Change in monsoon mean precipitation (%) The mean post-monsoon temperature change is shown in Fig. 16a The Fig. indicates warming in mid-century over the entire country, with the highest increase in the northwestern parts. The precipitation change in post-monsoon is shown in Fig. 16b. It indicates increase in precipitation southwestern parts of the country and decrease in precipitation in the eastern parts. Fig. 16a: Change in post-monsoon mean temperature ( C) Fig. 16b: Change in post-monsoon mean precipitation (%) 21

28 5.2 Area Average Climate Change Except in pre-monsoon season the increase in mean temperature in the mid-century is higher in the west compare to the east in all seasons (Fig. 17a). The precipitation change shows that except in post and pre-monsoon seasons, precipitation seems to decrease in both west and east (Fig. 17b). The decreasing rate is higher in the east than in west. In the east precipitation seems to decrease in all the seasons while in the west, precipitation seems to decrease only in the annual, monsoon and winter seasons and increase in the post and pre-monsoon seasons. The area average values of climate change in the east and west Nepal is shown in Table 3. Difference in oc Change Comparison in Mean Tempearature Change-w est Change-east Annual Monsoon Post-monsoon Winter Pre-Monsoon Fig. 17a: Comparison in change in mean temperature in west and east Nepal % Difference Change Comparison in Precipitation Winter Fig. 17b: Comparison in change in mean precipitation in west and east Nepal Change %-West Change %-East Annual Monsoon Postmonsoon Pre- Monsoon Table 3: Climate Change in mid-century ( ) compare to base line ( ) Precipitation Change (%) Temperature Change ( C) West East West East Winter Pre-Monsoon Monsoon Post-monsoon Annual Annual Cycle Comparison of annual cycles of mean temperature and precipitation in east and west Nepal is presented in Fig. 18a through 18d respectively. The annual cycle of mean temperature shows no shift in peaks, but only incremental increase in all the months (Fig. 18a and Fig. 18b). The precipitation annual cycle shows change in peaks in future. In west Nepal the peak seems to shift forward by a month to July from June (Fig. 18c) and no change in east Nepal (Fig. 18d). 22

29 Temperature C Jan Feb Comparison of Annual Cycle of Mean Temperatures in West Nepal Mar Apr May Jun Jul Aug Sep Fig. 18a: Comparison of Annual cycle of mean temperature in west Nepal Precipitation in m m /day Jan Oct Base Future Nov Annual Cycle of Precipitation Change in West Nepal Feb Mar Apr May Jun Jul Aug Sep Fig. 18c: Comparison of Annual cycle of mean precipitation in west Nepal Oct Nov Dec Future Base Dec Temperature in C Comarison of Annual Cycle of Mean Temperature in East Nepal Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Fig. 18b: Comparison of Annual cycle of mean temperature in East Nepal Precipitation in mm/day Annual Cycle of Precipitation Change in East Nepal Jan Feb Mar Apr May Jun Jul Aug Sep Fig. 18d: Comparison of Annual cycle of mean precipitation in east Nepal Oct Base Future Nov Future Base Dec 6. Conclusion RegCM3 regional climate model was run over South Asian domain for base period and for future period (mid 21 st century) with ECHAM5 GCM dataset for A2 scenario. The validation and climate change analysis was done for Nepal. Validation and climate change analysis was done for two major climate parameters mean temperature and precipitation. Analyses include analysis of spatial distribution, area average comparison and annual cycle comparison. Validation of temperature was done only with CRU observation and validation of precipitation was done with CRU and station observation. Validation of mean temperature shows that the spatial distribution of mean temperature of the model in all the seasons is quite close to observed distribution. However, most of the gird boxes depicted cold bias and few grid boxes in the northern part in the Himalayas depicted warm bias. The area average bias is negative in all the season in the east as well as in the west. It is highest in the post-monsoon both in east and west Nepal. Except in winter and pre-monsoon, this cold bias is higher in west than in the east. Analysis of annual cycle shows similar model mean temperature distribution with the CRU data. 23

30 Validation of precipitation shows that spatial distribution in most of the seasons does not match with the observed distribution. In all the seasons the model over estimated precipitation compared to CRU as well as to station data. Wet bias with CRU is higher than wet bias with station data. Area average analysis for bias with CRU and station shows wet bias over east as well as over west Nepal in all the seasons. Wet bias with CRU is higher in the east than in the west, except in post-monsoon and annual and this wet bias is highest in the post-monsoon in the west. Wet bias with station is higher in the west than in the east except in monsoon and this bias is highest in the post-monsoon in the west. Analysis of annual cycle shows that the model produces shift in high peak to June while observation has high peak in July. Temperature change analysis shows the warming in all the seasons in the mid-21 st century compared to base period. The warming is higher in the northern part over high Himalayas than in the southern part. More over, the warming is highest in the winter season and minimum in the pre-monsoon season both in the west and east Nepal. Precipitation change analysis shows decrease in precipitation in the eastern Nepal in all the seasons and significant increase in southwestern and northwestern parts in the monsoon and pre-monsoon seasons. The annual cycle analysis shows the delay in precipitation peak in the western Nepal by a month. 24

31 Acknowledgments We would like to Acknowledge APN project, Global Change Impact Studies Centre Islamabad Pakistan, Physics of weather and climate group, International Centre for Theoretical Centre Italy for their support in implementation of this research work. 25

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