Calibration of PRECIS in employing future scenarios in Bangladesh

INTERNATIONAL JOURNAL OF CLIMATOLOGYInt. J. Climatol. 28: 617–628 (2008)Published online 1 June 2007 in Wiley InterScience(www.interscience.wiley.com) DOI: 10.1002/joc.1559

Calibration of PRECIS in employing future scenarios inBangladesh

Md. Nazrul Islam,a* M. Rafiuddin,a Ahsan Uddin Ahmedb and Rupa Kumar Kollica Department of Physics, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh

b Centre for Global Change, 12-Ka/A/1 Shaymoli, Dhaka-1207, Bangladeshc Institute of Tropical Meteorology, Pune, India

ABSTRACT: Providing Regional Climates for Impacts Studies (PRECIS) is a regional climate model, which is used forthe simulation of regional-scale climatology at high resolution (i.e. 50-km horizontal resolution). The calibration of rainfalland temperature simulated by PRECIS is performed in Bangladesh with the surface observational data from the BangladeshMeteorological Department (BMD) for the period 1961–1990. The Climate Research Unit (CRU) data is also used forunderstanding the performance of the model. The results for the period 1961–1990 are used as a reference to find thevariation of PRECIS-projected rainfall and temperature in 2071, in and around Bangladesh, as an example. Analyses areperformed using the following two methods: (1) grid-to-grid and (2) point-to-point analyses. It is found that grid-to-gridanalysis provides overestimation of PRECIS in Bangladesh because of downscaling of observed data when gridded fromasymmetric low-density data network of BMD. On the other hand, model data extracted at observational sites provide betterperformance of PRECIS. The model overestimates rainfall in dry and pre-monsoon periods, whereas it underestimates it inthe monsoon period. Overall, PRECIS is found to be able to estimate about 92% of surface rainfall. Model performance inestimating rainfall increases substantially with the increase in the length of time series of datasets. Systematic cold bias isfound in simulating the annual scale of the surface temperature. In the annual scale, the model underestimates temperatureof about 0.61 °C that varies within a range of +1.45 °C to −3.89 °C in different months. This analysis reveals that rainfalland temperature will be increased in Bangladesh in 2071. On the basis of the analyses, look-up tables for rainfall andtemperature were prepared in a bid to calibrate PRECIS simulation results for Bangladesh. The look-up tables proposedin this analysis can be employed in the application of the projected rainfall and temperature in different sectors of thecountry. These look-up tables are useful only for the calibration of PRECIS simulation results for future climate projectionfor 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 2007

1. Introduction

In a country like Bangladesh (88.05–92.74 °E, 20.67–26.63 °N), where about 60% of the population findsemployment from agriculture, the importance of pre-dicted rainfall and temperature towards planning forthe sector and ensuring food security of 140 millionpeople is paramount. Bangladesh is amongst the mostdensely populated areas of the world where proper plan-ning and management of water resources are essential.Model-simulated climate scenarios can play an importantrole in developing such types of plans. Bangladesh isregarded as one of the most vulnerable countries underclimate change. A climate change is likely to exacer-bate frequently occurring climatic hazards such as floods,cyclones, storm surges, droughts, and heavy rain (Huqet 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. E-mail: [email protected]

country is primarily agrarian, the projection of rain-fall and temperature, and their effects on water-relatedhazards and subsequent implications for peoples’ livesand livelihoods are very important (Ahmed et al., 1998;Ahmed, 2000).

Climate models are the main tools available fordeveloping projections of climate change in the future(Houghton et al., 1995, 2001). In recent years, atmo-sphere–ocean general circulation models (AOGCMs)have been used to predict the climatic consequencesof increasing atmospheric concentrations of greenhousegases (McGuffie and Henderson-Sellers, 1997; McCarthyet al., 2001). These predictions may be adequate forareas where the terrain is reasonably flat, uniform, andaway from coasts. However, in areas where coasts andmountains have significant effects on weather, scenariosbased on global models are unable to capture the local-level details needed for assessing impacts at national andregional scales. Also, at such coarse resolutions, extremeevents such as cyclones or heavy rainfall episodes areeither not captured or their intensities are unrealistically

Copyright 2007 Royal Meteorological Society

618 MD. NAZRUL ISLAM ET AL.

low. The highest horizontal resolution of any AOGCMpublished is around 300 km (Murphy and Mitchell,1995). Yet, in order to assess potential impacts of cli-mate change, regional information at a scale of 100 kmor finer is needed (Robinson and Finkelstein, 1991). Aregional climate model (RCM), therefore, is the besttool for dynamic downscaling of climate features in caseof obtaining detailed information for a particular region(Georgi and Hewitson, 2001; Jones et al., 2004).

A regional model generally covers a limited area ofthe globe at a higher resolution (typically around 50 km)for which conditions at its boundary are specified froman AOGCM (Dickinson et al., 1989; Hack et al., 1993;Grell et al., 1994). The RCM is better able to resolvemesoscale forcings associated with coastlines, mountains,lakes, and vegetation characteristics that exert a stronginfluence on the local climate (Giorgi and Mearns, 1991;Vernekar, 1995; Pal et al., 2000). In particular, previ-ous investigations (Giorgi et al., 1994; Jones et al., 1995)have shown that the precipitation distributions simulatedby RCMs contain a strong orographically related compo-nent on scales not resolved by the AOGCM. The RCMsimulates a strong precipitation signal, which appears torepresent an orographic component of the response tocirculation anomalies associated with the intra-seasonaloscillation (ISO), whereas this precipitation signal isabsent in the AOGCM (Bhaskaran et al., 1998). Severalobservational studies have been carried out to understandthe spatial structure and phase propagation of the 30- to50-day mode (e.g. Yasunari, 1980, 1981; Krishnamurtiand Subrahmanyam, 1982) of the ISO. Bhaskaran et al.(1996) demonstrated the superior ability of an RCM tocapture fine scale details of the observed rainfall dis-tribution. The spatial patterns of precipitation and tem-perature over Europe are well simulated by RCM andare validated against the observed climatology for GreatBritain (Jones et al., 1995). However, there is very littleresearch work carried out so far using the climate modelin Bangladesh. Since climate scenarios will determine theresponse options for agricultural and water managementfor Bangladesh in future decades, it is expected that theavailable RCMs will be employed towards the develop-ment of such scenarios. Therefore, calibration of an RCMsuch as Providing Regional Climates for Impacts Studies(PRECIS) is essential in order to develop future climatescenarios for the country.

As the Earth’s surface, on average, gets warm becauseof the increase in the concentrations of greenhouse gases,it will not become warm uniformly. The pattern of cli-mate response in any given area due to the increasedradiative forcing depends substantially on how the mainatmospheric circulation patterns as a whole respond to theforcing. The distribution of atmospheric temperature andprecipitation depends substantially on this local climate.RCMs are appropriate tools for elaborating this local cli-mate. The United Kingdom Hadley Centre has developeda regional climate model named PRECIS that can be runon a PC and applied easily to any area of the globe togenerate detailed climate change projections. One may

find examples of PRECIS applications in China (Yin-long et al., 2006), in Niger (Beraki, 2005), and in India(Kolli et al., 2006). Projection on future climate usingPRECIS leads to substantially improved assessments ofa country’s vulnerability to climate change, which in turnallows policy makers to decide on adaptation options.

Since the adverse implication of climate change will beof paramount importance to water- and agriculture-sectorplanning, it is equally important to develop PRECIS-generated climate scenarios for Bangladesh. In this pur-suit, the model outputs need to be calibrated with theobservational data. Once the calibration is completed andthe performance is reasonable, model projected scenarioscan be generated and utilized for application purposes.This article explains where the calibration of PRECISoutputs will be required and how they may be usefultowards the development of future climate scenarios forBangladesh.

2. Model description and methodology

2.1. Model description

The PRECIS is a hydrostatic, primitive equation grid-point model containing 19 levels described by a hybridvertical coordinate (Simmons and Burridge, 1981; Simonet al., 2004). The present version of PRECIS has a hori-zontal resolution of 50 km with the option of downscal-ing to 25 km horizontal resolution. It has the provisionto include the sulphur cycle and it can generate out-puts for more than 150 parameters. PRECIS is madefreely available for use by scientists of developing coun-tries involved in vulnerability and adaptation studies.PRECIS runs with 50-km horizontal resolution for thepresent climate (1961–1990) using different base-line lat-eral boundary conditions (LBCs) and for future scenarios(2070–2100) using the special report on emissions sce-narios (SRES) of the Intergovernmental Panel on ClimateChange (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 andmesoscale circulations generated within the RCM are notundesirably damped and, simultaneously, (2) the chosendomain is sufficiently small so that the deviation of thelarge-scale seasonally averaged RCM circulation from thedriving AOGCM is not overwhelmingly large to implya significant perturbation to the planetary-scale diver-gent circulation. These conditions are necessary to ensurephysical consistency between the RCM solution and thepre-determined AOGCM solution external to the RCMdomain (Jones et al., 1995).

2.2. Methodology

Observational data of BMD throughout Bangladesh(Figure 1) have been used for the purpose of calibra-tion. The BMD observation network density is low andthe distribution is poor; in some cases, observation sitesare located about 25 km apart, whereas these are about145 km apart in some other areas. When the coverage of

Copyright 2007 Royal Meteorological Society Int. J. Climatol. 28: 617–628 (2008)DOI: 10.1002/joc

CALIBRATION OF PRECIS IN EMPLOYING FUTURE SCENARIOS IN BANGLADESH 619

Figure 1. Location of the BMD observation sites throughoutBangladesh. 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 gridsare found that do not contain any observation site. For theapplication of PRECIS for climate change impact studiesin Bangladesh, it is important to find out the appropriatecalibration method. Having this in mind, analyses havebeen performed on both grid-to-grid and point-to-pointbasis.

1. Grid-to-grid basis: In this method, observationaldata collected at 26 locations are gridded using theKriging average technique. The regional value isobtained for both observation and model data atfour regions, namely north–west (NW: 88.8–90.4 °E;23.5–25.2 °N), north-east (NE: 90.4–92.0 °E; 23.5–25.2 °N), south-east (SE: 90.4–92.0 °E; 21.8–23.5 °N),and south-west (SW: 88.8–90.4 °E; 21.8–23.5 °N), asshown by dashed lines in Figure 1. Averages obtainedfrom the four regional values are considered as equiva-lent to the coverage of Bangladesh (BD: 88.8–92.0 °E;21.8–25.2 °N). Monthly, seasonal, annual, decadal,and long-term analyses are performed using rainfalland temperature data.

2. Point-to-point basis: In this procedure, observed dataat a particular site are considered as being the rep-resentative of that location (Islam and Uyeda, 2007).Grid value of the model data is compared with theobserved data representing that grid. If more than oneobservation site exists within a grid, the average valueof all the observational sites is considered as being therepresentative value for that grid. Rain-gauge rainfalland temperature data collected daily by BMD are pro-cessed to obtain monthly, seasonal, annual, decadal,and long-term values. The model data of rainfall andtemperature are extracted at 26 sites of BMD and are

then converted to monthly, seasonal, annual, decadal,and long-term values.

Rainfall is simulated by PRECIS for different ensem-bles (a, b and c) of LBCs, which are (1) blsula (base-line with the sulphur cycle and ensemble category (a),(2) blnosula (baseline without the sulphur cycle andensemble category (a), (3) blsulb (baseline with the sul-phur cycle and ensemble category (b), (4) blnosulb (base-line without the sulphur cycle and ensemble category (b),(5) blsulc (baseline with the sulpher cycle and ensem-ble category (c), (6) blnosulc (baseline without the sul-phur cycle and ensembles category (c), and (7) ERA15[ECMWF (European Centre for Medium-Range WeatherForecasting) re-analyses]. On the other hand, temperaturedata are simulated for blsula and blnosula. Future scenar-ios are generated for a2sul, a2nosul, b2sul, and b2nosulfor 2070–2100.

Look-up tables are prepared for the two analysedparameters at different sites to ascertain the amountthat needs to be added or subtracted from the modelresolved values for future scenarios in order to obtain theprojected value. Correction expressions were developedto obtain the projected rainfall and temperature frommodel scenarios with the help of look-up tables. Theperformance of a model option described above is definedby the difference between the model and observed valuesdivided by the observed value, which is expressed inpercentage.

3. Results

3.1. Calibration of rainfall

The model outputs are available from 1961 to 1990,whereas the ERA15 option has provided values for1980–1993. ERA15 and blsula output data are analysedin detail.

3.1.1. Monthly rainfall

Rainfall obtained from observation (BMD) and simu-lation (blsula) using the point-to-point analysis methodis shown in Figure 2. The model overestimates rainfallfrom the dry month of December to the monsoon monthof June. During July–September, it underestimates rain-fall. In the post-monsoon months October and November,the model estimates are almost closer to the observedrainfall. These results are consistent with the TropicalRainfall Measuring Mission (TRMM) reported by Islamand Uyeda (2005, 2006). The fact is that the characteris-tics of precipitation systems, especially the vertical heightand precipitation strength, in this region are different indifferent rainy periods, whereas the use of the same cloudparameterization cannot represent variable atmosphericconditions in different periods.

3.1.2. Seasonal rainfall

Seasonal rainfall averaged for NW, NE, SE, and SWregions and for the whole of Bangladesh (BD) using the



Figure 2. Comparison of monthly average rainfall (mm/day) obtainedfrom observation (RNG) and simulation (blsula), considering val-ues from 26 BMD observation sites and averaged for the period

1961–1990.

Table I. Seasonal rainfall estimated by model and rain-gaugesusing the grid-to-grid analysis method in Bangladesh.

Rainfall (mm/day) averages from 1961 to1990

NW NE SE SW BD BDBias

DJF RNG 0.31 0.37 0.26 0.32 0.32 –Model 0.71 0.76 0.60 0.55 0.65 0.33

MAM RNG 3.51 5.86 3.63 2.48 3.87 –Model 6.43 9.10 6.32 4.88 6.68 2.81

JJAS RNG 9.40 11.97 12.09 7.97 10.36 –Model 7.90 8.72 11.34 10.83 9.70 −0.65

ON RNG 3.51 5.86 3.63 2.48 3.87 –Model 3.32 3.19 3.72 3.52 3.44 −0.43

grid-to-grid analysis method are obtained as tabulated inTable I.

It is seen that the model has overestimated rainfall inwinter (DJF) and pre-monsoon (MAM) periods, whereasit has underestimated in monsoon (JJAS) and post-monsoon (ON) periods for BD. Large spatial differencesare also found. For example, during the monsoon period,

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 inBangladesh is region dependent (Islam et al., 2004).

Figure 3 shows the comparison of seasonal rainfallobtained from observation (RNG) and model simulationusing the point-to-point analysis method for 1961–1990.The results are similar to that for the monthly valuesas explained in Figure 2 and Table I: during winter andpre-monsoon periods, the model overestimates, whereasduring monsoon it underestimates the observed rainfall.During the post-monsoon period, model simulated valuesare almost the same as the observed values.

3.1.3. Annual rainfall

The time sequence of the annual rainfall calculatedby the BMD rain-gauge (RNG) and PRECIS (blsulaand ERA40) using the point-to-point analysis method isshown in Figure 4. Out of 30 years, blsula overestimatesfor 10 years (1966, 1969, 1971, 1976, 1977, 1980–83,and 1987). In case of ERA15, the model overestimatesfor 2 years (1980 and 1982) out of 10 years. Here, it isclear that the annual rainfall varies from year to year andthe simulation does not always provide the same trend i.e.

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 averagedfrom all observational sites throughout Bangladesh.



the bias (= model − observation) is not systematic. So,an average of long-term data may provide a reasonablecalibration.

3.1.4. Decadal rainfall

The decadal average of the extracted rainfall at the BMDobservation sites are compared with observed decadalvalues (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 1961–1970,1971–1980, and 1981–1990 respectively. From obser-vations, rainfall has decreased from 1971 to 1980 buthas increased again from 1981 to 1990. These trendsare detected well by the model (values in parenthesis),whereas the extent of changes are not equal for all thethree analysed decades.

3.1.5. Long-term rainfall

The simulated rainfall data for the entire analysisdomain are compared with the CRU data (Figure 5) for1981–1990. Model and CRU rainfall patterns are foundto be almost similar. The model detects heavy rain in theNE of Bangladesh (Shilong hill of India), NE of the Bayof Bengal, and Western Ghat of India, which are veri-fied by the CRU data. Even the lack of rainfall along thewestern parts of India and southern parts of Pakistan arewell captured.

Figure 6 shows the long-term average (1961–1990)rainfall estimated by PRECIS (blsula) and observation(RNG) over Bangladesh. For RNG, rainfall values fromall observation sites are gridded and displayed in thesame procedure as applied for the model data. Applyingthe grid-to-grid analysis method, the obtained patternsare similar with a few exceptions. This may be partlyattributed to the lack of observational data sites through-out the country. This may also have resulted from theinherent uncertainties of the model. Significantly, rainfallin the north-eastern parts of the country is well simulatedby the model, which are the heaviest rainfall areas inand around Bangladesh (Islam et al., 2005). Considering

rainfall averages for all analysed stations using the point-to-point analysis method and for 30 years (1961–1990),RNG and blsula estimate 6.71 and 6.22 mm/day respec-tively. Overall, the model slightly underestimates long-term rainfall over Bangladesh.

3.2. Calibration of temperature

In this section, model output temperature for blsula iscompared with the observational data.

3.2.1. Monthly and seasonal temperature

Figure 7 shows the monthly averaged temperature (°C)obtained from PRECIS (sula) and BMD (Obs) usingthe point-to-point analysis method. It is found that thereexists a hot bias in favour of the model for 5 months(March, April, May, June, and July), while for therest of the year there exists a cold bias. One can findcomparable seasonal temperatures for both observationaland 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, themodel 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 variationin temperature (i.e. cold bias in the dry season and hotbias in the rainy season) may be due to the decreaseand increase of latent heat flux for the two seasonsrespectively, (Uchiyama et al., 2006) which may not bewell distinguished by the model.

The temperature in different seasons and at differentregions obtained using the grid-to-grid analysis methodis tabulated in Table II. It is seen that, in DJF and ON,the temperature is underestimated by PRECIS for thenorthern parts (NW and NE), whereas it is overesti-mated for the southern parts (SE and SW) of the country.In MAM and JJAS, the model overestimates through-out all regions. Overall, for BD, it is overestimated.The magnitude of overestimation is higher during pre-monsoon (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 1981–1990.



Figure 6. Distribution of average rainfall (mm/day) obtained from the model (left panel) and observation (right panel) for the period 1961–1990.

Table II. Comparison of model and observed temperature obtained by the grid-to-grid analysis method at different regions.

Average Temp 1961–1990 in °C

NW NE SE SW BD BD Bias

DJF Obs 17.92 17.445 15.955 15.84 16.785 –Model 15.88 16.005 18.825 17.63 17.09 0.305

MAM Obs 25.48 24.07 21.33 22.2 23.26 –Model 29.575 27.53 28.145 29.76 28.755 5.49

JJAS Obs 27.155 25.99 22.05 22.155 24.325 –Model 28.87 28.33 28.255 28.3 28.435 4.115

ON Obs 24.77 23.66 20.745 20.435 22.39 –Model 22.37 22.65 24.38 23.33 23.19 0.795

Figure 7. Comparison of PRECIS-simulated monthly average temper-ature with observational data for the period 1961–1990. This figure is

available in colour online at www.interscience.wiley.com/ijoc

to post-monsoon and dry periods. Here, one observes thedifference between the results of point-to-point and grid-to-grid analyses. Regional analysis using the grid-to-gridanalysis method shows overestimation of temperature in

all regions for BD, whereas point-to-point analysis showsunderestimation of temperature during DJF and ON. Thefact is that when observational data is gridded, the actualvalue is reduced.

3.2.2. Annual and decadal temperature

Annual temperatures provided by the model are com-pared with that obtained from observations, as shown inFigure 8. The time sequence of both datasets obtainedusing the point-to-point analysis method is similar intrend with a few exceptions. The model underestimatestemperature in all the years between 1961 and 1990. Onan average, the model simulated temperature is 24.8 °C,whereas the observed value appears to be 25.51 °C. Asimilar cold bias is found for decadal temperatures whenvalues from observations are compared with model values(in parenthesis) as 25.33 (24.74), 25.56 (24.77), and 25.65(24.89)°C for 1960–1970, 1971–1980, and 1981–1990respectively. A gradual increase in temperature with timeis also evident. This result is consistent with IPCC find-ings (McCarthy et al., 2001) as well as the recent results



Figure 8. Comparison of average annual temperatures and temperaturetrends obtained from the model and observation.

of Uchiyama et al. (2006), who found that mean tempera-ture has increased worldwide. Hence, PRECIS can detectthe rise in temperature in Bangladesh well, which maybe considered as a signature of global warming havingsignificant implications for monsoon-influenced regions(Chase et al., 2003).

Figure 9 shows the spatial distribution of temperaturesimulated by PRECIS (blsula) and CRU for the period1981–1990 and processed by using the grid-to-gridmethod. It is seen that the patterns are almost similar:low temperature regions are in the east and north ofBangladesh, whereas high temperature regions are in thesouth-eastern parts. Overall, a cold bias persists in modelsimulation.

3.2.3. Long-term temperature

Figure 10 shows the spatial distribution of long-termaverage temperatures obtained from the model (blsula,left panel) and from observation (BMD, right panel) forthe period 1961–1990. Simulation shows low tempera-tures in and around the Shillong hill in India, north andeast to Bangladesh. A high-temperature zone is observed

in the western parts of the country. The distribution oflong-term observed temperature is obtained from the grid-to-grid method, which shows somewhat similarities to thesimulated distribution. For decadal analysis, as explainedin Figure 9, distribution patterns of simulated temperatureare also very similar to CRU data. As shown in Figure 8,point-to-point analysis also shows that the temperaturedistribution patterns for the model and observed valuesare similar. The model outputs based on the point-to-point method are better correlated to the observed valuesin comparison with values obtained from the grid-to-gridmethod. Therefore, it may be inferred that, point-to-pointanalysis can provide a better understanding of model-simulated temperature data at any location, which can beutilized with confidence in many applications, especiallyin 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 anal-ysis where a bias may be defined as the differencebetween the model and observed value (bias = model −observed). Model outputs for rainfall are also foundto vary with model options as well as timeframe ofanalysis. Figure 11(a) shows seasonal and annual biasesfor ERA15 and blsula options, while the latter hasbeen resolved for two distinctly different time frames(blsula for 1981–1990 and blsula for 1961–1990).The simulation overestimates during DJF and MAM,and underestimates during JJAS. The model overes-timates during ON except for blsula 1981–1990. Onan annual scale, dry biases amount to −0.36, −0.69,and −0.10 mm/day for ERA15, blsula 1981–1990 andblsula 1961–1990 options respectively. From the aboveanalysis, 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 period1981–1990.



Figure 10. Spatial distribution of temperature obtained from the model and BMD observation averages for the period 1961–1990.

Figure 11. (a) Rainfall biases and (b) performance of PRECIS.

using the point-to-point method can provide a consistentcalibration factor for a region.

Figure 11(b) shows the PRECIS performance in esti-mating rainfall in Bangladesh using the point-to-pointmethod. It shows that PRECIS underperforms by 9.91,10.86, and 7.32% for ERA15, blsula 1981–90, andblsula 1961–1990 options respectively. This representsthat PRECIS can calculate about 90.09, 89.14, and92.68% of the observed rainfall for ERA15, blsula 1981–1990, and blsula 1961–1990 options respectively. Theperformance of PRECIS increases (underestimationdiminishes) when averages from a longer simulationperiod (i.e. 1961–1990) are considered. Using the grid-to-grid method, it is found that for blsula 1961–1990option the estimated rainfall is 111, 97, 111, and 149%with respect to observed values for NW, NE, SE, andSW regions respectively. For the whole of Bangladesh,the estimated rainfall amount is 114%. Hence, the estima-tion becomes overrated for grid-to-grid analysis. In SW,

PRECIS overrates by about 49% and for BD it overratesonly by about 14%. Therefore, point-to-point analysis andaverages for all observation sites throughout the countrygive better performance of PRECIS in estimating rainfallin Bangladesh.

PRECIS can be used to generate future climate scenar-ios and those scenarios can be used in rainfall forecastingwith some tolerance of biases at different locations ofBangladesh. To assess the future scenario, one can adjustthe biases by adding or subtracting, as needed, monthly,seasonal, and annual average rainfall from 1961 to 1990at any particular location. The proposed correction equa-tion for rainfall in Bangladesh is given as follows:

ObsRF = ModelRF − CRF (1)

where CRF is the constant amount of rainfall at a certainlocation. As an example, CRF values for ten stationsobtained by point-to-point method are shown in Table III.



Such type of look-up table (Table III) may be helpful forfuture planning.

4.2. PRECIS projected rainfall

PRECIS projected rainfall for the year 2071 is shownin Figure 12 (left panel). The right panel of Figure 12shows the anomaly of average rainfall of 2071 withrespect to the average rainfall of the period 1961–1990.The rainfall is expected to increase almost throughoutBangladesh in 2071. The rate of increase will be about1–2.5 mm/day. The rainfall seems to decrease in theSW and NW tips of the country. The rate of decreasein rainfall will be 0.5–1 mm/day. These amounts areobtained without model calibration. One can estimatethe projected amount of rainfall at a particular locationfor any specific timeframe in future using Equation 1 forplanning purposes.

4.3. Temperature bias and future scenario

As explained earlier, there is a cold bias as shown bythe simulation results. Similar to the corrective treatmentfor rainfall, a correction equation for temperature isdeveloped, which is proposed as follows:

ObsT = ModelT − CT (2)

where CT is a constant value that is obtained by the point-to-point method and tabulated for different locations andmonths (in Table IV).

One can prepare a look-up table for temperature biasesat monthly, seasonal, and annual scales for different sitesof the country as shown in Table IV.

The temperature projection in 2071 and the differ-ence of average temperatures of 2071 and 1961–1990are shown in Figure 13. It is seen that the average tem-perature in and around Bangladesh will be increased by

2.1–3.4 °C in 2071 with respect to the 1961–1990 period.The rate of increase in temperature in the northern sidesis higher than that in the southern sides. Using Equa-tion (2) and Table IV, one can calculate the projectedtemperature from PRECIS simulation at different loca-tions of the country and these values can be used in manyapplications, especially for agricultural planning in future.

5. Conclusions

Results from simulations using various options of aregional climate model, PRECIS, with horizontal resolu-tion of 50 km, the following conclusions can be drawn:

1. The blsula option of PRECIS is able to estimate about92.68% of surface rainfall in Bangladesh.

2. Data extracted and averaged from all observationalsites and analysed using the point-to-point methodprovides reasonable calibration of PRECIS in Bang-ladesh.

3. The regional analysis with gridded observational dataand analysed in the grid-to-grid method providesan overestimation of PRECIS resolved data towardscalculating rainfall at different regions of the country.

4. The blsula option of PRECIS shows a cold bias fortemperature. On an average, PRECIS underestimatestemperature by about 0.61 °C. However, there is amonth-wise variability in the model resolved tempera-ture, which varies within a range of +1.45 to −3.89 °Cwith respect to observed monthly average tempera-tures.

5. PRECIS projected rainfall and temperature indicatesthat in Bangladesh rainfall and temperature will beincreased throughout the country in 2071. The rate ofincrease in rainfall will vary from 1 to 2.5 mm/day,

Table III. CRF for ten locations using the point-to-point analysis method throughout Bangladesh.

CRF for Rainfall 1961–1990 (blsula) in mm/day

Barisal Bhola Chittagong Coxsbazar Dhaka Jessore Rangpur Satkhira Srimongal Sylhet

Jan 0.42 0.46 0.39 0.38 0.54 0.42 0.11 0.44 0.61 0.31Feb −0.21 0.50 0.49 0.30 0.17 −0.15 0.11 −0.34 1.29 −0.01Mar −0.51 1.46 0.40 0.49 −0.43 −0.29 1.12 −0.11 6.65 −0.26Apr −1.51 4.41 −1.16 −1.16 −1.84 −0.37 4.39 −0.66 12.12 −2.91May 5.03 6.95 7.12 9.08 1.74 5.88 4.84 6.13 13.63 12.05Jun 2.79 0.91 0.66 −0.57 0.13 3.72 −1.20 4.87 −4.49 11.87Jul −4.23 −7.63 −18.70 −18.96 −6.03 −2.74 −8.08 −3.10 −10.84 −5.29Aug −4.62 −2.96 −11.16 −13.68 −4.48 −3.33 −3.70 −2.65 −6.45 −3.78Sep −2.81 −1.82 −4.52 −4.20 −3.94 −0.75 −3.24 −2.35 −5.49 −2.34Oct −1.13 1.89 −1.89 −1.39 −1.04 1.38 2.19 0.51 2.13 −0.31Nov −0.64 0.65 −0.14 −0.15 −0.25 0.11 0.41 0.18 1.04 0.01Dec −0.25 0.19 0.20 0.29 0.04 0.08 0.11 −0.04 0.30 0.16DJF −0.01 0.38 0.36 0.32 0.25 0.12 0.11 0.02 0.74 0.15MAM 1.00 4.27 2.12 2.80 −0.18 1.74 3.45 1.79 10.80 2.96JJAS −2.22 −2.87 −8.43 −9.35 −3.58 −0.77 −4.05 −0.81 −6.82 0.12ON −0.89 1.27 −1.02 −0.77 −0.65 0.74 1.30 0.35 1.59 −0.15Annual −0.64 0.42 −2.36 −2.46 −1.28 0.33 −0.24 0.24 0.87 0.79



Figure 12. Rainfall amount projected by PRECIS for 2071 (left panel) and rainfall anomaly between 2071 and an average for the period1961–1990 (right panel).

Table IV. CT for ten locations using the point-to-point analysis method throughout Bangladesh.

CT for Temperature 1961–1990 (blsula) in °C

Barisal Bhola Chittagong Coxsbazar Dhaka Jessore Rangpur Satkhira Srimongal Sylhet

Jan 3.11 2.49 2.32 3.00 3.73 3.99 3.84 3.82 3.12 5.31Feb 1.23 1.32 1.71 3.17 1.60 1.51 0.80 1.34 1.52 4.78Mar −0.76 1.45 1.58 3.67 −1.11 −2.18 −2.12 −1.85 −0.98 4.51Apr −1.65 2.13 1.00 3.27 −2.59 −3.33 −0.86 −2.61 −2.53 4.56May −0.60 0.96 1.14 2.54 −1.52 −1.31 0.61 −0.80 −2.00 3.18Jun 1.03 2.06 0.97 1.03 0.34 1.59 0.08 2.22 −1.87 2.13Jul −0.34 0.80 0.11 0.75 −1.24 −0.08 −1.29 0.44 −2.57 1.41Aug −0.42 1.01 0.35 0.97 −1.22 −0.35 −0.73 0.35 −1.81 1.90Sep 0.55 1.21 0.79 1.51 −0.37 0.84 −0.32 −0.06 −1.56 2.09Oct 1.55 1.68 1.38 2.22 1.05 1.93 2.23 2.29 0.59 3.89Nov 3.67 3.44 2.63 3.10 4.06 5.17 5.00 5.19 2.92 4.57Dec 4.36 3.87 2.99 7.69 5.01 5.52 5.76 5.60 3.93 5.23DJF 2.90 2.56 2.34 4.62 3.45 3.68 3.46 3.59 2.86 5.11MAM −1.00 1.51 1.24 3.16 −1.74 −2.27 −0.79 −1.75 −1.84 4.09JJAS 0.21 1.27 0.55 1.07 −0.62 0.50 −0.57 0.74 −1.95 1.88ON 2.61 2.56 2.00 2.66 2.55 3.55 3.62 3.74 1.76 4.23Annual −2.29 −4.99 0.69 1.91 −0.76 −0.53 −5.51 −1.37 −1.85 −0.29

whereas that for temperature will vary from 2.1 to3.4 °C.

Finally, PRECIS calculates about 92% of surface rain-fall and underestimates temperature by about 0.61 °C.Using the look-up table proposed in this analysis fordifferent months, seasons, and years at different locations,it is possible to generate climate change scenariosfor the two parameters. Such scenarios will providea clear understanding of both temperature and rainfallwell before climate changes appreciably, which may

be utilized by stakeholders, policy and decision mak-ers for multipurpose uses including agricultural planning.However, before any application of projected scenarios,checking of meta-data and analyses of increasing numberof projected years may help in proper understanding.

Acknowledgements

The authors would like to express their thanks to thecollaborative efforts led by the Climate Change Cellof Bangladesh, in cooperation with the Department for



Figure 13. PRECIS projected temperature for 2071 (left panel) and anomaly of temperature for 2071 and an average for the period 1961–1990(right panel).

International Development (DFID), UK; United NationsDevelopment Programme (UNDP); and Department ofEnvironment (DoE), Ministry of Environment and Forest,Government of the People’s Republic of Bangladesh. Theauthors are grateful to the Hadley Centre of the UnitedKingdom for providing the PC-based PRECIS modelwith LBC data. The BMD is acknowledged for providingobservational data.

References

Ahmed AU. 2000. Adaptability of Bangladesh’s crop agriculture toclimate change: possibilities and limitations. Asia Pacific Journal onEnvironment and Development 7(1): 71–93.

Ahmed AU, Alam M, Rahman AA. 1998. Adaptation to climatechange in Bangladesh: future outlook. In Vulnerability andAdaptation to Climate Change for Bangladesh, Huq S, Karim Z,Asaduzzaman M, Mahtab F (eds). Kluwer Academic Publishers:Dordrecht; 125–143.

Ali A. 1999. Climate change impacts and adaptation assessment inBangladesh. Climate Research 12: 109–116.

Beraki AF. 2005. Climate change scenario simulations over Eritreaby using a fine resolution limited area climate model: temperatureand moisture sensitivity, Masters Thesis etd-02102006-152327,University of Pretoria, Department of Geography, Geoinformaticsand Meteorology, Eritrea, Niger.

Bhaskaran B, Murphy JM, Jones RG. 1998. Intraseasonal oscillation inthe Indian summer monsoon simulated by global and nested regionalclimate models. Monthly Weather Review 126: 3124–3134.

Bhaskaran B, Jones RG, Murphy JM, Noguer M. 1996. Simulations ofthe Indian summer monsoon using a nested regional climate model:domain size experiments. Climate Dynamics 12: 573–587.

Chase TN, Kanaff JA, Pielke RA Sr, Kalnay E. 2003. Changes inglobal monsoon circulations since 1950. Natural Hazards 29:229–254.

Dickinson R, Errico R, Giorgi F, bates G. 1989. A regional climatemodel for the Western United States. Climatic Change 15(3):383–422.

Georgi F, Hewitson B. 2001. Regional climate IUnformation –evaluation and projections. In Climate Change 2001: The ScientificBasis, Contribution of Working Group I to the Third AssessmentReport 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. 1991. Approaches to regional climate changesimulation: a review. Review of Geophysics and Space Physics 29(2):129–216.

Giorgi F, Shields BC, Bates GT. 1994. Regional climate changescenarios over the United States produced with a nested regionalclimate model. Journal of Climate 7: 375–399.

Grell G, Dudhia J, Stauffer D. 1994. A description of the Fifth-Generation Penn State/NCAR Mesoscale Model (MM5). TechnicalNote TN-398+IA, technical report. National Center for AtmosphericResearch (NCAR): Colorado.

Hack J, Boville B, Briegleb B, Kiehl J, Tasch P, Williamson D.1993. Description of the NCAR community climate model(CCM2). Technical Note, NCAR/TN-382 STR. National Center forAtmospheric Research (NCAR): Colorado.

Houghton JT, Meira Filho LG, Callander BA, Harris N, Kattenberg A,Maskell K (eds). 1995. Climate Change 1995, IntergovernmentalPanel on Climate Change (IPCC). Cambridge University Press:Cambridge.

Houghton JT, Ding Y, Griggs DJ, Noguer M, van der Linden PJ,Xioaosu D (eds). 2001. Climate Change 2001: The Scientific Basis,Contribution of Working Group I to the Third Assessment Reportof the Intergovernmental Panel on Climate Change. CambridgeUniversity Press: Cambridge.

Huq S, Karim Z, Asaduzzaman M, Mahtab F (eds). 1998. Vulnerabilityand Adaptation to Climate Change for Bangladesh. KluwerAcademic Publishers: Dordrecht; 135.

Islam MN, Uyeda H. 2007. Use of TRMM in determining theclimatic characteristics of rainfall over Bangladesh. RemoteSensing of Environment, Elsevier Science 108(3): 264–276 DOI:10.1016/j.rse.2006.11.011.

Islam MN, Uyeda H. 2005. Comparison of TRMM 3B42 products withsurface Rainfall over Bangladesh. In Preprints in the Proceedings ofInternational. Geoscience and Remote Sensing Symposium (IGARSS)2005 , Seoul, South Korea, July 25–29, 4, 4TH127A 05.

Islam MN, Uyeda H. 2006. TRMM observed vertical structure anddiurnal variation of precipitation in south Asia. In Proceedings inthe 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. 2004. Diurnalvariations of cloud activity in Bangladesh and north of the Bay ofBengal in 2000. Remote Sensing of Environment, Elsevier Science93(3): 378–388.

Islam MN, Terao T, Uyeda H, Hayashi T, Kikuchi K. 2005. Spatialand temporal variations of precipitation in and around Bangladesh.Journal of the Meteorological Society of Japan 83(1): 23–41.

Jones RG, Murphy JM, Noguer M. 1995. Simulation of climate changeover Europe using a nested regional climate model. Part I: assessmentof control climate, including sensitivity to location of lateral



boundaries. Quarterly Journal of the Royal Meteorological Society121: 1413–1449.

Jones RG, Noguer M, Hassell DC, Hudson D, Wilson SS, Jenkins GJ,Mitchell JFB. 2004. Generating High Resolution Climate ChangeScenarios Using PRECIS. Met Office Hadley Centre: Exeter.

Karim Z, Hussain SkG, Ahmed AU. 1998. Climate change vul-nerability of crop agriculture. In Vulnerability and Adaptationto Climate Change for Bangladesh, Huq S, Karim Z, Asaduzza-man M, Mahtab F (eds). Kluwer Academic Publishers: Dordrecht;39–54.

Kolli RK, Sahai AK, Kumar KK, Patwardhan SK, Mishra PK,Revadekar JV, Kamala K, Pant GB. 2006. High-resolution climatechange scenarios for India for the 21st century. Current Science90(3): 334–345.

Krishnamurti TN, Subrahmanyam D. 1982. The 30–50 day mode at850 mb during MONEX. Journal of Atmospheric Sciences 39:2088–2095.

McCarthy JJ, Canziani OF, Leary NA, Dokken DJ, White KS (eds).2001. Climate Change 2001: Impacts, Adaptation. and Vulnerability,Inter-Governmental Panel on Climate Change (IPCC), Work GroupII Input to the Third Assessment Report. Cambridge University Press:Cambridge.

McGuffie K, Henderson-Sellers A. 1997. A Climate Modelling Primer.John Wiley and Sons: New York; 253.

Murphy JM, Mitchell JFB. 1995. Transient response of the HadleyCentre coupled ocean-atmosphere model to increasing carbondioxide. Part II. Spatial and temporal structure of response. Journalof Climate 8: 57–80.

Pal J, Small E, Eltahir E. 2000. Simulation of regional-scale water andenergy budgets: representation of subgrid cloud and precipitation

processes within RegCM. Journal of Geophysical Research-Atmospheres 105(D24): 29579–29594.

Robinson PJ, Finkelstein PL. 1991. The development of impactoriented climate scenarios. Bulletin of the American MeteorologicalSociety 72: 481–490.

Simmons AJ, Burridge DM. 1981. An energy and angular-momentumconserving finite-difference scheme and hybrid coordinates. MonthlyWeather Review 109: 758–766.

Simon W, Hassell D, Hein D, Jones R, Taylor R. 2004. Installingand 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. 2006.Changes in Temperature-based Extremes Indices Due to GlobalWarming Projected by a Global 20-km-mesh Atmospheric Model ,SOLA, 2, 68–71, DOI: 10.2151/sola.2006-018.

Vernekar AD. 1995. Modelling interannual variations of the Indiansummer monsoon. Proceedings Symposium in Honor of ProfessorAksel Wiin Nielsen. Royal Academy of Sciences: Copenhagen;183–187.

Yasunari T. 1980. Quasi-stationary appearance of 30–40 day periodin the cloudiness fluctuations during summer monsoon over India.Journal of the Meteorological Society of Japan 58: 225–229.

Yasunari T. 1981. Structure of an Indian summer monsoon system witharound 40-day period. Journal of the Meteorological Society of Japan59: 336–354.

Yinlong X, Xiaoying H, Yong Z, Wantao L, Erda L. 2006. Statisticalanalyses of climate change scenarios over China in the 21st Century.Advances in Climate Change Research 2(Suppl. 1): 50–53.


Calibration of PRECIS in employing future scenarios in Bangladesh

Documents

Transcript of Calibration of PRECIS in employing future scenarios in Bangladesh