Calibration of PRECIS in employing future scenarios in Bangladesh

Transcription

1 INTERNATIONAL JOURNAL OF CLIMATOLOGY Int. J. Climatol. 28: (2008) Published online 1 June 2007 in Wiley InterScience ( Calibration of PRECIS in employing future scenarios in Bangladesh Md. Nazrul Islam, a * M. Rafiuddin, a Ahsan Uddin Ahmed b and Rupa Kumar Kolli c a Department of Physics, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh b Centre for Global Change, 12-Ka/A/1 Shaymoli, Dhaka-1207, Bangladesh c Institute of Tropical Meteorology, Pune, India ABSTRACT: Providing Regional Climates for Impacts Studies (PRECIS) is a regional climate model, which is used for the simulation of regional-scale climatology at high resolution (i.e. 50-km horizontal resolution). The calibration of rainfall and temperature simulated by PRECIS is performed in Bangladesh with the surface observational data from the Bangladesh Meteorological Department (BMD) for the period The Climate Research Unit (CRU) data is also used for understanding the performance of the model. The results for the period are used as a reference to find the variation of PRECIS-projected rainfall and temperature in 2071, in and around Bangladesh, as an example. Analyses are performed using the following two methods: (1) grid-to-grid and (2) point-to-point analyses. It is found that grid-to-grid analysis provides overestimation of PRECIS in Bangladesh because of downscaling of observed data when gridded from asymmetric low-density data network of BMD. On the other hand, model data extracted at observational sites provide better performance of PRECIS. The model overestimates rainfall in dry and pre-monsoon periods, whereas it underestimates it in the monsoon period. Overall, PRECIS is found to be able to estimate about 92% of surface rainfall. Model performance in estimating rainfall increases substantially with the increase in the length of time series of datasets. Systematic cold bias is found in simulating the annual scale of the surface temperature. In the annual scale, the model underestimates temperature of about 0.61 C that varies within a range of C to 3.89 C in different months. This analysis reveals that rainfall and temperature will be increased in Bangladesh in On the basis of the analyses, look-up tables for rainfall and temperature were prepared in a bid to calibrate PRECIS simulation results for Bangladesh. The look-up tables proposed in this analysis can be employed in the application of the projected rainfall and temperature in different sectors of the country. These look-up tables are useful only for the calibration of PRECIS simulation results for future climate projection for Bangladesh. Copyright 2007 Royal Meteorological Society KEY WORDS regional climate model; precipitation; temperature; calibration; simulation; future scenarios Received 17 July 2006; Revised 26 March 2007; Accepted 5 April Introduction In a country like Bangladesh ( E, N), where about 60% of the population finds employment from agriculture, the importance of predicted rainfall and temperature towards planning for the sector and ensuring food security of 140 million people is paramount. Bangladesh is amongst the most densely populated areas of the world where proper planning and management of water resources are essential. Model-simulated climate scenarios can play an important role in developing such types of plans. Bangladesh is regarded as one of the most vulnerable countries under climate change. A climate change is likely to exacerbate frequently occurring climatic hazards such as floods, cyclones, storm surges, droughts, and heavy rain (Huq et al., 1998; Karim et al., 1998; Ali, 1999). Since the * Correspondence to: Md. Nazrul Islam, Department of Physics, Bangladesh University of Engineering and Technology, Dhaka-1000, Bangladesh. mnislam@phy.buet.ac.bd country is primarily agrarian, the projection of rainfall and temperature, and their effects on water-related hazards and subsequent implications for peoples lives and livelihoods are very important (Ahmed et al., 1998; Ahmed, 2000). Climate models are the main tools available for developing projections of climate change in the future (Houghton et al., 1995, 2001). In recent years, atmosphere ocean general circulation models (AOGCMs) have been used to predict the climatic consequences of increasing atmospheric concentrations of greenhouse gases (McGuffie and Henderson-Sellers, 1997; McCarthy et al., 2001). These predictions may be adequate for areas where the terrain is reasonably flat, uniform, and away from coasts. However, in areas where coasts and mountains have significant effects on weather, scenarios based on global models are unable to capture the locallevel details needed for assessing impacts at national and regional scales. Also, at such coarse resolutions, extreme events such as cyclones or heavy rainfall episodes are either not captured or their intensities are unrealistically Copyright 2007 Royal Meteorological Society

2 618 MD. NAZRUL ISLAM ET AL. low. The highest horizontal resolution of any AOGCM published is around 300 km (Murphy and Mitchell, 1995). Yet, in order to assess potential impacts of climate change, regional information at a scale of 100 km or finer is needed (Robinson and Finkelstein, 1991). A regional climate model (RCM), therefore, is the best tool for dynamic downscaling of climate features in case of obtaining detailed information for a particular region (Georgi and Hewitson, 2001; Jones et al., 2004). A regional model generally covers a limited area of the globe at a higher resolution (typically around 50 km) for which conditions at its boundary are specified from an AOGCM (Dickinson et al., 1989; Hack et al., 1993; Grell et al., 1994). The RCM is better able to resolve mesoscale forcings associated with coastlines, mountains, lakes, and vegetation characteristics that exert a strong influence on the local climate (Giorgi and Mearns, 1991; Vernekar, 1995; Pal et al., 2000). In particular, previous investigations (Giorgi et al., 1994; Jones et al., 1995) have shown that the precipitation distributions simulated by RCMs contain a strong orographically related component on scales not resolved by the AOGCM. The RCM simulates a strong precipitation signal, which appears to represent an orographic component of the response to circulation anomalies associated with the intra-seasonal oscillation (ISO), whereas this precipitation signal is absent in the AOGCM (Bhaskaran et al., 1998). Several observational studies have been carried out to understand the spatial structure and phase propagation of the 30- to 50-day mode (e.g. Yasunari, 1980, 1981; Krishnamurti and Subrahmanyam, 1982) of the ISO. Bhaskaran et al. (1996) demonstrated the superior ability of an RCM to capture fine scale details of the observed rainfall distribution. The spatial patterns of precipitation and temperature over Europe are well simulated by RCM and are validated against the observed climatology for Great Britain (Jones et al., 1995). However, there is very little research work carried out so far using the climate model in Bangladesh. Since climate scenarios will determine the response options for agricultural and water management for Bangladesh in future decades, it is expected that the available RCMs will be employed towards the development of such scenarios. Therefore, calibration of an RCM such as Providing Regional Climates for Impacts Studies (PRECIS) is essential in order to develop future climate scenarios for the country. As the Earth s surface, on average, gets warm because of the increase in the concentrations of greenhouse gases, it will not become warm uniformly. The pattern of climate response in any given area due to the increased radiative forcing depends substantially on how the main atmospheric circulation patterns as a whole respond to the forcing. The distribution of atmospheric temperature and precipitation depends substantially on this local climate. RCMs are appropriate tools for elaborating this local climate. The United Kingdom Hadley Centre has developed a regional climate model named PRECIS that can be run on a PC and applied easily to any area of the globe to generate detailed climate change projections. One may find examples of PRECIS applications in China (Yinlong et al., 2006), in Niger (Beraki, 2005), and in India (Kolli et al., 2006). Projection on future climate using PRECIS leads to substantially improved assessments of a country s vulnerability to climate change, which in turn allows policy makers to decide on adaptation options. Since the adverse implication of climate change will be of paramount importance to water- and agriculture-sector planning, it is equally important to develop PRECISgenerated climate scenarios for Bangladesh. In this pursuit, the model outputs need to be calibrated with the observational data. Once the calibration is completed and the performance is reasonable, model projected scenarios can be generated and utilized for application purposes. This article explains where the calibration of PRECIS outputs will be required and how they may be useful towards the development of future climate scenarios for Bangladesh. 2. Model description and methodology 2.1. Model description The PRECIS is a hydrostatic, primitive equation gridpoint model containing 19 levels described by a hybrid vertical coordinate (Simmons and Burridge, 1981; Simon et al., 2004). The present version of PRECIS has a horizontal resolution of 50 km with the option of downscaling to 25 km horizontal resolution. It has the provision to include the sulphur cycle and it can generate outputs for more than 150 parameters. PRECIS is made freely available for use by scientists of developing countries involved in vulnerability and adaptation studies. PRECIS runs with 50-km horizontal resolution for the present climate ( ) using different base-line lateral boundary conditions (LBCs) and for future scenarios ( ) using the special report on emissions scenarios (SRES) of the Intergovernmental Panel on Climate Change (IPCC). The model domain (65 103E, 6 35N) is selected on the basis of the following considerations: (1) sufficiently large area is covered so that synoptic and mesoscale circulations generated within the RCM are not undesirably damped and, simultaneously, (2) the chosen domain is sufficiently small so that the deviation of the large-scale seasonally averaged RCM circulation from the driving AOGCM is not overwhelmingly large to imply a significant perturbation to the planetary-scale divergent circulation. These conditions are necessary to ensure physical consistency between the RCM solution and the pre-determined AOGCM solution external to the RCM domain (Jones et al., 1995) Methodology Observational data of BMD throughout Bangladesh (Figure 1) have been used for the purpose of calibration. The BMD observation network density is low and the distribution is poor; in some cases, observation sites are located about 25 km apart, whereas these are about 145 km apart in some other areas. When the coverage of

3 CALIBRATION OF PRECIS IN EMPLOYING FUTURE SCENARIOS IN BANGLADESH 619 then converted to monthly, seasonal, annual, decadal, and long-term values. Figure 1. Location of the BMD observation sites throughout Bangladesh. For regional analysis, four domains namely NE, NW, SW, and SE are considered, as shown by dashed lines. Bangladesh is gridded at 0.5 by 0.5, a number of grids are found that do not contain any observation site. For the application of PRECIS for climate change impact studies in Bangladesh, it is important to find out the appropriate calibration method. Having this in mind, analyses have been performed on both grid-to-grid and point-to-point basis. 1. Grid-to-grid basis: In this method, observational data collected at 26 locations are gridded using the Kriging average technique. The regional value is obtained for both observation and model data at four regions, namely north west (NW: E; N), north-east (NE: E; N), south-east (SE: E; N), and south-west (SW: E; N), as shown by dashed lines in Figure 1. Averages obtained from the four regional values are considered as equivalent to the coverage of Bangladesh (BD: E; N). Monthly, seasonal, annual, decadal, and long-term analyses are performed using rainfall and temperature data. 2. Point-to-point basis: In this procedure, observed data at a particular site are considered as being the representative of that location (Islam and Uyeda, 2007). Grid value of the model data is compared with the observed data representing that grid. If more than one observation site exists within a grid, the average value of all the observational sites is considered as being the representative value for that grid. Rain-gauge rainfall and temperature data collected daily by BMD are processed to obtain monthly, seasonal, annual, decadal, and long-term values. The model data of rainfall and temperature are extracted at 26 sites of BMD and are Rainfall is simulated by PRECIS for different ensembles (a, b and c) of LBCs, which are (1) blsula (baseline with the sulphur cycle and ensemble category (a), (2) blnosula (baseline without the sulphur cycle and ensemble category (a), (3) blsulb (baseline with the sulphur cycle and ensemble category (b), (4) blnosulb (baseline without the sulphur cycle and ensemble category (b), (5) blsulc (baseline with the sulpher cycle and ensemble category (c), (6) blnosulc (baseline without the sulphur cycle and ensembles category (c), and (7) ERA15 [ECMWF (European Centre for Medium-Range Weather Forecasting) re-analyses]. On the other hand, temperature data are simulated for blsula and blnosula. Future scenarios are generated for a2sul, a2nosul, b2sul, and b2nosul for Look-up tables are prepared for the two analysed parameters at different sites to ascertain the amount that needs to be added or subtracted from the model resolved values for future scenarios in order to obtain the projected value. Correction expressions were developed to obtain the projected rainfall and temperature from model scenarios with the help of look-up tables. The performance of a model option described above is defined by the difference between the model and observed values divided by the observed value, which is expressed in percentage. 3. Results 3.1. Calibration of rainfall The model outputs are available from 1961 to 1990, whereas the ERA15 option has provided values for ERA15 and blsula output data are analysed in detail Monthly rainfall Rainfall obtained from observation (BMD) and simulation (blsula) using the point-to-point analysis method is shown in Figure 2. The model overestimates rainfall from the dry month of December to the monsoon month of June. During July September, it underestimates rainfall. In the post-monsoon months October and November, the model estimates are almost closer to the observed rainfall. These results are consistent with the Tropical Rainfall Measuring Mission (TRMM) reported by Islam and Uyeda (2005, 2006). The fact is that the characteristics of precipitation systems, especially the vertical height and precipitation strength, in this region are different in different rainy periods, whereas the use of the same cloud parameterization cannot represent variable atmospheric conditions in different periods Seasonal rainfall Seasonal rainfall averaged for NW, NE, SE, and SW regions and for the whole of Bangladesh (BD) using the

4 620 MD. NAZRUL ISLAM ET AL. Figure 2. Comparison of monthly average rainfall (mm/day) obtained from observation (RNG) and simulation (blsula), considering values from 26 BMD observation sites and averaged for the period Table I. Seasonal rainfall estimated by model and rain-gauges using the grid-to-grid analysis method in Bangladesh. Rainfall (mm/day) averages from 1961 to 1990 NW NE SE SW BD BD Bias DJF RNG Model MAM RNG Model JJAS RNG Model ON RNG Model the model underestimates about 3.2 mm/day at the NE, whereas it overestimates about 2.1 mm/day at the SW. The fact is that the spatial distribution of rainfall in Bangladesh is region dependent (Islam et al., 2004). Figure 3 shows the comparison of seasonal rainfall obtained from observation (RNG) and model simulation using the point-to-point analysis method for The results are similar to that for the monthly values as explained in Figure 2 and Table I: during winter and pre-monsoon periods, the model overestimates, whereas during monsoon it underestimates the observed rainfall. During the post-monsoon period, model simulated values are almost the same as the observed values Annual rainfall The time sequence of the annual rainfall calculated by the BMD rain-gauge (RNG) and PRECIS (blsula and ERA40) using the point-to-point analysis method is shown in Figure 4. Out of 30 years, blsula overestimates for 10 years (1966, 1969, 1971, 1976, 1977, , and 1987). In case of ERA15, the model overestimates for 2 years (1980 and 1982) out of 10 years. Here, it is clear that the annual rainfall varies from year to year and the simulation does not always provide the same trend i.e. grid-to-grid analysis method are obtained as tabulated in Table I. It is seen that the model has overestimated rainfall in winter (DJF) and pre-monsoon (MAM) periods, whereas it has underestimated in monsoon (JJAS) and postmonsoon (ON) periods for BD. Large spatial differences are also found. For example, during the monsoon period, Figure 3. Comparison of the seasonal average rainfall (mm/day) obtained from observation (RNG) and model simulation (blsula). Figure 4. Comparison of the annual rainfall obtained from observation (RNG) and model simulation (blsula and ERA15). Amounts are averaged from all observational sites throughout Bangladesh.

5 CALIBRATION OF PRECIS IN EMPLOYING FUTURE SCENARIOS IN BANGLADESH 621 the bias (= model observation) is not systematic. So, an average of long-term data may provide a reasonable calibration Decadal rainfall The decadal average of the extracted rainfall at the BMD observation sites are compared with observed decadal values (RNG). Using the point-to-point analysis method, it is found that RNG(blsula) values are 6.88(6.57), 6.34(6.03), and 6.91(6.05) mm/day in , , and respectively. From observations, rainfall has decreased from 1971 to 1980 but has increased again from 1981 to These trends are detected well by the model (values in parenthesis), whereas the extent of changes are not equal for all the three analysed decades Long-term rainfall The simulated rainfall data for the entire analysis domain are compared with the CRU data (Figure 5) for Model and CRU rainfall patterns are found to be almost similar. The model detects heavy rain in the NE of Bangladesh (Shilong hill of India), NE of the Bay of Bengal, and Western Ghat of India, which are verified by the CRU data. Even the lack of rainfall along the western parts of India and southern parts of Pakistan are well captured. Figure 6 shows the long-term average ( ) rainfall estimated by PRECIS (blsula) and observation (RNG) over Bangladesh. For RNG, rainfall values from all observation sites are gridded and displayed in the same procedure as applied for the model data. Applying the grid-to-grid analysis method, the obtained patterns are similar with a few exceptions. This may be partly attributed to the lack of observational data sites throughout the country. This may also have resulted from the inherent uncertainties of the model. Significantly, rainfall in the north-eastern parts of the country is well simulated by the model, which are the heaviest rainfall areas in and around Bangladesh (Islam et al., 2005). Considering rainfall averages for all analysed stations using the pointto-point analysis method and for 30 years ( ), RNG and blsula estimate 6.71 and 6.22 mm/day respectively. Overall, the model slightly underestimates longterm rainfall over Bangladesh Calibration of temperature In this section, model output temperature for blsula is compared with the observational data Monthly and seasonal temperature Figure 7 shows the monthly averaged temperature ( C) obtained from PRECIS (sula) and BMD (Obs) using the point-to-point analysis method. It is found that there exists a hot bias in favour of the model for 5 months (March, April, May, June, and July), while for the rest of the year there exists a cold bias. One can find comparable seasonal temperatures for both observational and model values (the latter in parenthesis) for DJF, MAM, JJAS, and ON as 19.94(17.52), 27.23(28.44), 28.33(28.4), and 25.4(23.44) C respectively. Hence, the model underestimates 2.42 and 1.96 C for winter (DJF) and post-monsoon (ON) respectively. On the other hand, the model overestimates 1.21 and 0.06 C for summer (MAM) and monsoon (JJAS) respectively. The variation in temperature (i.e. cold bias in the dry season and hot bias in the rainy season) may be due to the decrease and increase of latent heat flux for the two seasons respectively, (Uchiyama et al., 2006) which may not be well distinguished by the model. The temperature in different seasons and at different regions obtained using the grid-to-grid analysis method is tabulated in Table II. It is seen that, in DJF and ON, the temperature is underestimated by PRECIS for the northern parts (NW and NE), whereas it is overestimated for the southern parts (SE and SW) of the country. In MAM and JJAS, the model overestimates throughout all regions. Overall, for BD, it is overestimated. The magnitude of overestimation is higher during premonsoon (5.68 C) and monsoon (4.69 C) as compared Figure 5. Spatial distribution of rainfall (mm/day) obtained from the model (left panel) and CRU (right panel) for the period

6 622 MD. NAZRUL ISLAM ET AL. Figure 6. Distribution of average rainfall (mm/day) obtained from the model (left panel) and observation (right panel) for the period Table II. Comparison of model and observed temperature obtained by the grid-to-grid analysis method at different regions. Average Temp in C NW NE SE SW BD BD Bias DJF Obs Model MAM Obs Model JJAS Obs Model ON Obs Model all regions for BD, whereas point-to-point analysis shows underestimation of temperature during DJF and ON. The fact is that when observational data is gridded, the actual value is reduced. Figure 7. Comparison of PRECIS-simulated monthly average temperature with observational data for the period This figure is available in colour online at to post-monsoon and dry periods. Here, one observes the difference between the results of point-to-point and gridto-grid analyses. Regional analysis using the grid-to-grid analysis method shows overestimation of temperature in Annual and decadal temperature Annual temperatures provided by the model are compared with that obtained from observations, as shown in Figure 8. The time sequence of both datasets obtained using the point-to-point analysis method is similar in trend with a few exceptions. The model underestimates temperature in all the years between 1961 and On an average, the model simulated temperature is 24.8 C, whereas the observed value appears to be C. A similar cold bias is found for decadal temperatures when values from observations are compared with model values (in parenthesis) as (24.74), (24.77), and (24.89) C for , , and respectively. A gradual increase in temperature with time is also evident. This result is consistent with IPCC findings (McCarthy et al., 2001) as well as the recent results

7 CALIBRATION OF PRECIS IN EMPLOYING FUTURE SCENARIOS IN BANGLADESH 623 Figure 8. Comparison of average annual temperatures and temperature trends obtained from the model and observation. of Uchiyama et al. (2006), who found that mean temperature has increased worldwide. Hence, PRECIS can detect the rise in temperature in Bangladesh well, which may be considered as a signature of global warming having significant implications for monsoon-influenced regions (Chase et al., 2003). Figure 9 shows the spatial distribution of temperature simulated by PRECIS (blsula) and CRU for the period and processed by using the grid-to-grid method. It is seen that the patterns are almost similar: low temperature regions are in the east and north of Bangladesh, whereas high temperature regions are in the south-eastern parts. Overall, a cold bias persists in model simulation Long-term temperature Figure 10 shows the spatial distribution of long-term average temperatures obtained from the model (blsula, left panel) and from observation (BMD, right panel) for the period Simulation shows low temperatures in and around the Shillong hill in India, north and east to Bangladesh. A high-temperature zone is observed in the western parts of the country. The distribution of long-term observed temperature is obtained from the gridto-grid method, which shows somewhat similarities to the simulated distribution. For decadal analysis, as explained in Figure 9, distribution patterns of simulated temperature are also very similar to CRU data. As shown in Figure 8, point-to-point analysis also shows that the temperature distribution patterns for the model and observed values are similar. The model outputs based on the point-topoint method are better correlated to the observed values in comparison with values obtained from the grid-to-grid method. Therefore, it may be inferred that, point-to-point analysis can provide a better understanding of modelsimulated temperature data at any location, which can be utilized with confidence in many applications, especially in planning for the agriculture sector of the country. 4. Discussion 4.1. Rainfall bias and model performance Both dry and wet biases are found in rainfall analysis where a bias may be defined as the difference between the model and observed value (bias = model observed). Model outputs for rainfall are also found to vary with model options as well as timeframe of analysis. Figure 11(a) shows seasonal and annual biases for ERA15 and blsula options, while the latter has been resolved for two distinctly different time frames (blsula for and blsula for ). The simulation overestimates during DJF and MAM, and underestimates during JJAS. The model overestimates during ON except for blsula On an annual scale, dry biases amount to 0.36, 0.69, and 0.10 mm/day for ERA15, blsula and blsula options respectively. From the above analysis, it may be inferred that long-term data analysis Figure 9. Spatial distribution of average temperature obtained from the model (left panel) and CRU (right panel), averaged for the period

8 624 MD. NAZRUL ISLAM ET AL. Figure 10. Spatial distribution of temperature obtained from the model and BMD observation averages for the period Figure 11. (a) Rainfall biases and (b) performance of PRECIS. using the point-to-point method can provide a consistent calibration factor for a region. Figure 11(b) shows the PRECIS performance in estimating rainfall in Bangladesh using the point-to-point method. It shows that PRECIS underperforms by 9.91, 10.86, and 7.32% for ERA15, blsula , and blsula options respectively. This represents that PRECIS can calculate about 90.09, 89.14, and 92.68% of the observed rainfall for ERA15, blsula , and blsula options respectively. The performance of PRECIS increases (underestimation diminishes) when averages from a longer simulation period (i.e ) are considered. Using the gridto-grid method, it is found that for blsula option the estimated rainfall is 111, 97, 111, and 149% with respect to observed values for NW, NE, SE, and SW regions respectively. For the whole of Bangladesh, the estimated rainfall amount is 114%. Hence, the estimation becomes overrated for grid-to-grid analysis. In SW, PRECIS overrates by about 49% and for BD it overrates only by about 14%. Therefore, point-to-point analysis and averages for all observation sites throughout the country give better performance of PRECIS in estimating rainfall in Bangladesh. PRECIS can be used to generate future climate scenarios and those scenarios can be used in rainfall forecasting with some tolerance of biases at different locations of Bangladesh. To assess the future scenario, one can adjust the biases by adding or subtracting, as needed, monthly, seasonal, and annual average rainfall from 1961 to 1990 at any particular location. The proposed correction equation for rainfall in Bangladesh is given as follows: Obs RF = Model RF C RF (1) where C RF is the constant amount of rainfall at a certain location. As an example, C RF values for ten stations obtained by point-to-point method are shown in Table III.

9 CALIBRATION OF PRECIS IN EMPLOYING FUTURE SCENARIOS IN BANGLADESH 625 Such type of look-up table (Table III) may be helpful for future planning PRECIS projected rainfall PRECIS projected rainfall for the year 2071 is shown in Figure 12 (left panel). The right panel of Figure 12 shows the anomaly of average rainfall of 2071 with respect to the average rainfall of the period The rainfall is expected to increase almost throughout Bangladesh in The rate of increase will be about mm/day. The rainfall seems to decrease in the SW and NW tips of the country. The rate of decrease in rainfall will be mm/day. These amounts are obtained without model calibration. One can estimate the projected amount of rainfall at a particular location for any specific timeframe in future using Equation 1 for planning purposes Temperature bias and future scenario As explained earlier, there is a cold bias as shown by the simulation results. Similar to the corrective treatment for rainfall, a correction equation for temperature is developed, which is proposed as follows: Obs T = Model T C T (2) where C T is a constant value that is obtained by the pointto-point method and tabulated for different locations and months (in Table IV). One can prepare a look-up table for temperature biases at monthly, seasonal, and annual scales for different sites of the country as shown in Table IV. The temperature projection in 2071 and the difference of average temperatures of 2071 and are shown in Figure 13. It is seen that the average temperature in and around Bangladesh will be increased by C in 2071 with respect to the period. The rate of increase in temperature in the northern sides is higher than that in the southern sides. Using Equation (2) and Table IV, one can calculate the projected temperature from PRECIS simulation at different locations of the country and these values can be used in many applications, especially for agricultural planning in future. 5. Conclusions Results from simulations using various options of a regional climate model, PRECIS, with horizontal resolution of 50 km, the following conclusions can be drawn: 1. The blsula option of PRECIS is able to estimate about 92.68% of surface rainfall in Bangladesh. 2. Data extracted and averaged from all observational sites and analysed using the point-to-point method provides reasonable calibration of PRECIS in Bangladesh. 3. The regional analysis with gridded observational data and analysed in the grid-to-grid method provides an overestimation of PRECIS resolved data towards calculating rainfall at different regions of the country. 4. The blsula option of PRECIS shows a cold bias for temperature. On an average, PRECIS underestimates temperature by about 0.61 C. However, there is a month-wise variability in the model resolved temperature, which varies within a range of to 3.89 C with respect to observed monthly average temperatures. 5. PRECIS projected rainfall and temperature indicates that in Bangladesh rainfall and temperature will be increased throughout the country in The rate of increase in rainfall will vary from 1 to 2.5 mm/day, Table III. C RF for ten locations using the point-to-point analysis method throughout Bangladesh. C RF for Rainfall (blsula) in mm/day Barisal Bhola Chittagong Coxsbazar Dhaka Jessore Rangpur Satkhira Srimongal Sylhet Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec DJF MAM JJAS ON Annual

10 626 MD. NAZRUL ISLAM ET AL. Figure 12. Rainfall amount projected by PRECIS for 2071 (left panel) and rainfall anomaly between 2071 and an average for the period (right panel). Table IV. C T for ten locations using the point-to-point analysis method throughout Bangladesh. C T for Temperature (blsula) in C Barisal Bhola Chittagong Coxsbazar Dhaka Jessore Rangpur Satkhira Srimongal Sylhet Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec DJF MAM JJAS ON Annual whereas that for temperature will vary from 2.1 to 3.4 C. Finally, PRECIS calculates about 92% of surface rainfall and underestimates temperature by about 0.61 C. Using the look-up table proposed in this analysis for different months, seasons, and years at different locations, it is possible to generate climate change scenarios for the two parameters. Such scenarios will provide a clear understanding of both temperature and rainfall well before climate changes appreciably, which may be utilized by stakeholders, policy and decision makers for multipurpose uses including agricultural planning. However, before any application of projected scenarios, checking of meta-data and analyses of increasing number of projected years may help in proper understanding. Acknowledgements The authors would like to express their thanks to the collaborative efforts led by the Climate Change Cell of Bangladesh, in cooperation with the Department for

11 CALIBRATION OF PRECIS IN EMPLOYING FUTURE SCENARIOS IN BANGLADESH 627 Figure 13. PRECIS projected temperature for 2071 (left panel) and anomaly of temperature for 2071 and an average for the period (right panel). International Development (DFID), UK; United Nations Development Programme (UNDP); and Department of Environment (DoE), Ministry of Environment and Forest, Government of the People s Republic of Bangladesh. The authors are grateful to the Hadley Centre of the United Kingdom for providing the PC-based PRECIS model with LBC data. The BMD is acknowledged for providing observational data. References Ahmed AU Adaptability of Bangladesh s crop agriculture to climate change: possibilities and limitations. Asia Pacific Journal on Environment and Development 7(1): Ahmed AU, Alam M, Rahman AA Adaptation to climate change in Bangladesh: future outlook. In Vulnerability and Adaptation to Climate Change for Bangladesh, Huq S, Karim Z, Asaduzzaman M, Mahtab F (eds). Kluwer Academic Publishers: Dordrecht; Ali A Climate change impacts and adaptation assessment in Bangladesh. Climate Research 12: Beraki AF Climate change scenario simulations over Eritrea by using a fine resolution limited area climate model: temperature and moisture sensitivity, Masters Thesis etd , University of Pretoria, Department of Geography, Geoinformatics and Meteorology, Eritrea, Niger. Bhaskaran B, Murphy JM, Jones RG Intraseasonal oscillation in the Indian summer monsoon simulated by global and nested regional climate models. Monthly Weather Review 126: Bhaskaran B, Jones RG, Murphy JM, Noguer M Simulations of the Indian summer monsoon using a nested regional climate model: domain size experiments. Climate Dynamics 12: Chase TN, Kanaff JA, Pielke RA Sr, Kalnay E Changes in global monsoon circulations since Natural Hazards 29: Dickinson R, Errico R, Giorgi F, bates G A regional climate model for the Western United States. Climatic Change 15(3): Georgi F, Hewitson B Regional climate IUnformation evaluation and projections. In Climate Change 2001: The Scientific Basis, Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change, Houghton JT, Ding Y, Griggs DJ, Noguer M, van der Linden PJ, Xioaosu D (eds). Cambridge University Press: Cambridge. Giorgi F, Mearns L Approaches to regional climate change simulation: a review. Review of Geophysics and Space Physics 29(2): Giorgi F, Shields BC, Bates GT Regional climate change scenarios over the United States produced with a nested regional climate model. Journal of Climate 7: Grell G, Dudhia J, Stauffer D A description of the Fifth- Generation Penn State/NCAR Mesoscale Model (MM5). Technical Note TN-398+IA, technical report. National Center for Atmospheric Research (NCAR): Colorado. Hack J, Boville B, Briegleb B, Kiehl J, Tasch P, Williamson D Description of the NCAR community climate model (CCM2). Technical Note, NCAR/TN-382 STR. National Center for Atmospheric Research (NCAR): Colorado. Houghton JT, Meira Filho LG, Callander BA, Harris N, Kattenberg A, Maskell K (eds) Climate Change 1995, Intergovernmental Panel on Climate Change (IPCC). Cambridge University Press: Cambridge. Houghton JT, Ding Y, Griggs DJ, Noguer M, van der Linden PJ, Xioaosu D (eds) Climate Change 2001: The Scientific Basis, Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press: Cambridge. Huq S, Karim Z, Asaduzzaman M, Mahtab F (eds) Vulnerability and Adaptation to Climate Change for Bangladesh. Kluwer Academic Publishers: Dordrecht; 135. Islam MN, Uyeda H Use of TRMM in determining the climatic characteristics of rainfall over Bangladesh. Remote Sensing of Environment, Elsevier Science 108(3): DOI: /j.rse Islam MN, Uyeda H Comparison of TRMM 3B42 products with surface Rainfall over Bangladesh. In Preprints in the Proceedings of International. Geoscience and Remote Sensing Symposium (IGARSS) 2005, Seoul, South Korea, July 25 29, 4, 4TH127A 05. Islam MN, Uyeda H TRMM observed vertical structure and diurnal variation of precipitation in south Asia. In Proceedings in the IEEE International Geoscience and Remote Sensing Symposium (IGARSS06), August, Denver, USA, 31 July 4, 4. Islam MN, Hayashi T, Uyeda H, Terao T, Kikuchi K Diurnal variations of cloud activity in Bangladesh and north of the Bay of Bengal in Remote Sensing of Environment, Elsevier Science 93(3): Islam MN, Terao T, Uyeda H, Hayashi T, Kikuchi K Spatial and temporal variations of precipitation in and around Bangladesh. Journal of the Meteorological Society of Japan 83(1): Jones RG, Murphy JM, Noguer M Simulation of climate change over Europe using a nested regional climate model. Part I: assessment of control climate, including sensitivity to location of lateral

12 628 MD. NAZRUL ISLAM ET AL. boundaries. Quarterly Journal of the Royal Meteorological Society 121: Jones RG, Noguer M, Hassell DC, Hudson D, Wilson SS, Jenkins GJ, Mitchell JFB Generating High Resolution Climate Change Scenarios Using PRECIS. Met Office Hadley Centre: Exeter. Karim Z, Hussain SkG, Ahmed AU Climate change vulnerability of crop agriculture. In Vulnerability and Adaptation to Climate Change for Bangladesh, Huq S, Karim Z, Asaduzzaman M, Mahtab F (eds). Kluwer Academic Publishers: Dordrecht; Kolli RK, Sahai AK, Kumar KK, Patwardhan SK, Mishra PK, Revadekar JV, Kamala K, Pant GB High-resolution climate change scenarios for India for the 21st century. Current Science 90(3): Krishnamurti TN, Subrahmanyam D The day mode at 850 mb during MONEX. Journal of Atmospheric Sciences 39: McCarthy JJ, Canziani OF, Leary NA, Dokken DJ, White KS (eds) Climate Change 2001: Impacts, Adaptation. and Vulnerability, Inter-Governmental Panel on Climate Change (IPCC), Work Group II Input to the Third Assessment Report. Cambridge University Press: Cambridge. McGuffie K, Henderson-Sellers A A Climate Modelling Primer. John Wiley and Sons: New York; 253. Murphy JM, Mitchell JFB Transient response of the Hadley Centre coupled ocean-atmosphere model to increasing carbon dioxide. Part II. Spatial and temporal structure of response. Journal of Climate 8: Pal J, Small E, Eltahir E Simulation of regional-scale water and energy budgets: representation of subgrid cloud and precipitation processes within RegCM. Journal of Geophysical Research- Atmospheres 105(D24): Robinson PJ, Finkelstein PL The development of impact oriented climate scenarios. Bulletin of the American Meteorological Society 72: Simmons AJ, Burridge DM An energy and angular-momentum conserving finite-difference scheme and hybrid coordinates. Monthly Weather Review 109: Simon W, Hassell D, Hein D, Jones R, Taylor R Installing and Using the Hadley Centre Regional Climate Modelling System, PRECIS, Version 1.1. Met Office Hadley Centre: Exeter. Uchiyama T, Mizuta R, Kamiguchi K, Kitoh A, Noda A Changes in Temperature-based Extremes Indices Due to Global Warming Projected by a Global 20-km-mesh Atmospheric Model, SOLA, 2, 68 71, DOI: /sola Vernekar AD Modelling interannual variations of the Indian summer monsoon. Proceedings Symposium in Honor of Professor Aksel Wiin Nielsen. Royal Academy of Sciences: Copenhagen; Yasunari T Quasi-stationary appearance of day period in the cloudiness fluctuations during summer monsoon over India. Journal of the Meteorological Society of Japan 58: Yasunari T Structure of an Indian summer monsoon system with around 40-day period. Journal of the Meteorological Society of Japan 59: Yinlong X, Xiaoying H, Yong Z, Wantao L, Erda L Statistical analyses of climate change scenarios over China in the 21st Century. Advances in Climate Change Research 2(Suppl. 1):