The Use of a CMIP5 Climate Model to Assess Regional Temperature and Precipitation Variation due to Climate Change: A Case Study of Dhaka Megacity, Bangladesh

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https://doi.org/10.1007/s41748-019-00117-w ORIGINAL ARTICLE

The Use of a CMIP5 Climate Model to Assess Regional Temperature and Precipitation Variation due to Climate Change: A Case Study of Dhaka Megacity, Bangladesh

Md. Masudur Rahman¹ · Md. Abdur Rob¹

Received: 19 October 2018 / Accepted: 26 August 2019 / Published online: 7 September 2019

Abstract

The Dhaka megacity is highly vulnerable to anthropogenic climate change. In addition to the risks associated with high population density and unplanned infrastructures, temperature and precipitation changes are two environmental factors which have the greatest potential to negatively impact the residential population, both now and into the future. This study uses historical climate data recorded in the Dhaka area for the 1995–2014 period, as well as a multi-model dataset, to understand existing climate variability and possible future climate change scenarios. Future climate scenarios and predictions for this area have been carried out with CMIP5 40 GCMs using the three new representative concentration pathways (RCP 4.5, RCP 6.0 and RCP 8.5) adopted by the IPCC. Climate model projections suggest that the average temperature would increase approximately 2.56 °C by the end of the twenty-first century and future monsoonal rainfall events would also substantially increase in frequency, particularly in the month of July. The results indicate that the long, hot and humid (pre-monsoon) and humid and wet (monsoon) season will persist over Dhaka for an increased length of time. A multi-model ensemble projection clearly showed that the risks associated with the modeled climate change parameters could increase Dhaka’s vulnerability to climate change by the end of the twenty-first century. It also indicated that issues associated with waterlogging, public health, transport system, and water supply would impact many areas within the Dhaka megacity. This study provides information, which can be used to assist in the development of measures to support the sustainable growth of Dhaka.

Keywords Climate change · Climate variability · Projections · Future climate · RCPs

1 Introduction

Bangladesh is one of the most populous countries in the world. It currently has a population density of approximately 1115 people per square kilometer; therefore, ranking it 10th in the world (World Population Review 2019). The capital, Dhaka, is already facing problems due to its increasing population (Dewan and Yamaguchi 2009; Dewan et al.

2012; Dewan 2013). According to the Bangladesh Bureau of Statistics (BBS 2015), the projected population in Dhaka will be 13.798 million (m) in 2021, 14.777 m in 2031, 15.291 m in 2041 and 15.323 m in 2051. It is expected that the increasing population will be heavily impacted by the

effects of ongoing climate change. A study has found that Dhaka already ranks in the top in terms of climate change risk in Asian countries (David 2009) and anthropogenic influences have already substantially increased the regional climate change risk (Solomon et al. 2007). According to the Intergovernmental Panel on Climate Change (IPCC), future climate scenarios are already being impacted by warming from past anthropogenic emissions as well as by changes in the predicted future anthropogenic emissions and the natural variability of the climate (IPCC 2014). As a result, many developing and developed countries are at risk of being significantly impacted by climate change (Kundzewicz et al.

2007; Hayat et al. 2019).

The Coupled Model Intercomparison Project Phase 5 (CMIP5) was designed to advance existing knowledge in regard to climate variability and future climate change (Tay- lor et al. 2012) and to help in the interpretation of inter- model differences in climate change projections (Meehl and Bony 2011). It is the successor to the successful CMIP3

* Md. Masudur Rahman [email protected]

1 Department of Geography and Environment, University of Dhaka, Dhaka 1000, Bangladesh

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(Siew et al. 2014), providing not only emission-driven Earth System Model (ESM) experiments (Meehl and Bony 2011), but also long-term, concentration-driven Atmosphere–Ocean General Circulation Model (AOGCM) experiments and include four new representative concentration pathway (RCP) mitigation scenarios (Moss et al. 2010). The newer models and finer spatial resolution added to the CMIP5 (AR5) dataset are proving useful in generating spatiotem- poral and site-specific scenarios, therefore, enhancing the credibility of future climate projections generated by the modeling. The multi-model ensemble produces better results (Krishnamurti et al. 2000; Gleckler et al. 2008; Mishra et al.

2018) and reduces variations in the individual model outputs (Harrison et al. 1995) as well as the modeled climate variability (Pierce et al. 2009). Sabeerali et al. (2013) found that the ensemble of CMIP5 models perform quite well in simulation mode and is widely used for climate change projections (Mishra et al. 2018). Multi-model studies may vary over both space and time. In studies in India, it was found that temperature variations were higher over the northern and northeastern areas (Preethi et al. 2010; DelSole and Shukla 2012; Mishra et al. 2018). This was also noted by Basha et al. (2017), who observed similarities with the monsoon period of the northwestern part of the country. Using the CMIP5 models, the variations were relatively low over southern and southeastern parts of India. Using CanESM2, CSIRO, IPSL-MR, and IPSL-LR, a consistent increase in temperature over India was noted by Maity et al. (2016).

Numerous CMIP3 Atmosphere–Ocean General Circulation Model (AOGCMs) outputs have been evaluated in different regions (Lal and Harasawa 2000; Cook and Vizy 2006;

Annamalai et al. 2007; Kripalani et al. 2007; Bombardi and Carvalho 2009). Monsoon precipitation has been simu- lated moderately well in CCCMA, GISS-EH, and INMCM modeling over North Africa (Cook and Vizy 2006), and MIROC3.2-hires, MIROC3.2-medres, and MRI-CGCM3.2.3 modeling over South America (Bombardi and Carvalho 2009). Only GFDL-CM2.0, GFDL-CM2.1, ECHAM/MPI- OM, MRI-CGCM2.3.2, PCM, and HadCM3 performed well for summer precipitation over India (Annamalai et al. 2007).

In addition, HadCM2, ECHAM4, CSIRO, and CCSR/NIES AOGCMs models are able to simulate the broad features of climate over Asia (Lal and Harasawa 2000). Earlier, Bao (2012) noted that when using CMIP5 models under the RCP 4.5 and RCP 8.5 scenarios, summer monsoons were projected to increase over the South Asian area. Regional climate model simulations, including those focused on precipitation (Lucas-Picher et al. 2012; Luca et al. 2012; Zou and Zhou 2013) have added new knowledge to this area of study (Castro et al. 2005; Feser 2006; Prömmel et al. 2010;

Diaconescu and Laprise 2013). The climate predictions developed using the various integrated tools and models are deemed essential when developing any mitigation measures

for decreasing identified risks of a changing climate on the general population (Warrick 2009). To date, there has been little use of CMIP5 modeling over the Dhaka area and the current work aims to contribute to the existing body of knowledge.

On a regional scale, further research is needed to determine whether the multi-models are capable of simulating the dynamics of regional climate (Solomon et al. 2007). An assessment of these multi-models is vital in ensuring the pre- dictive accuracy of the outputs, and the correct interpretation of future climate change possibilities (Kundzewicz and Som- lyódy 1997). For the future projections, the CMIP5 Fifth Assessment Report (AR5) dataset supports four key representative concentration pathways (RCPs) scenarios—RCP 2.6, RCP 4.5, RCP 6.0 and RCP 8.5. The Bangladesh AR5 dataset estimates the return periods of extreme events, esti- mations which are useful when assessing possible changes in climatic parameters such as mean, maximum, minimum temperature and precipitation.

Porter et al. (2014) indicated that extreme events could elevate both temperature and precipitation in many parts of the world. Bangladesh is regarded as one country extremely vulnerable to these extreme climate change events. Due to this vulnerability, the assessment of climate change at the regional level is critical in determining the likelihood and consequences of change in these areas, and any possible mitigation options available. Christensen et al. (2007) indicated that regional climate change has been a strong driver for the use of regional climate models. Climate modeling at a local level (larger scale) can potentially be used to develop suitable mitigation and adaptation strategies.

In this current work, site-specific scenarios for extreme climate events were evaluated, at different temporal scales, using a multi-model ensemble. The aim was to evaluate past climate and compute the projected change of the two major climate variables (temperature and precipitation) for Dhaka, under the Köppen–Geiger climate classification (Peel et al.

2007). Using the regional climate model (RCM), predictions were made for these two parameters for the years 2025, 2050, 2075 and 2100. Data from the years 1995 to 2014 were used to provide a baseline.

2 Data and Methodology

2.1 Data

Bangladesh is located between 20°34′ and 26°38′N latitudes, and 88°01′ and 92°41′E longitudes (Banglapedia contributors 2018). Dhaka is the capital and the region has four dis- tinct seasons, namely pre-monsoon (March–May), monsoon (June–September), post-monsoon (October–November) and winter (December–February) (Khatun et al. 2016). The

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Fig. 1 Dhaka study area [Source: elevation data are from Shuttle Radar Topographic Mission (SRTM)]

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annual average temperature of Dhaka is 25.47 °C (77.85 °F) and monthly mean varies from 18.30 °C (64.94 °F) in Janu- ary to 28.76 °C (83.77 °F) in May. Approximately 71.91%

of the average rainfall of 1719 mm occurs during monsoon season. The region is mostly dominated by the tropical monsoon (humid and rainy) climate (Rabbani et al. 2011; Kha- tun et al. 2016). The study is focused on Dhaka, located at

23.78^o latitude and 90.38^o longitude. The average elevation of the area is 8.45 meters (m) above sea level as shown in Fig. 1. Bangladesh Meteorological Department (BMD) datasets were obtained and CMIP5 multi-models AR5 dataset were developed for the last 20 years (1995–2014) to evaluate timescales and determine mean, maximum, minimum temperature and precipitation values. Climate models were

Table 1 Climate models used including their CMIP5 identification, model resolution, institution and country of origin

S.L CMIP5 model Institution and country of origin Atmospheric horizontal resolution (lat × lon)

1 ACCESS1-0 CSIRO-BOM, Australia 1.9 × 1.2

2 ACCESS1-3 CSIRO-BOM, Australia 1.9 × 1.2

3 BCC-CSM1-1 BCC, CMA, China 2.8 × 2.8

4 BCC-CSM1-1-M BCC, CMA, China 1.0 × 1.0

5 BNU-ESM BNU, China 2.8 × 2.8

6 CanESM2 CCCMA, Canada 2.8 × 2.8

7 CCSM4 NCAR, USA 1.2 × 0.9

8 CESM1-BGC NSF-DOE-NCAR, USA 1.2 × 0.9

9 CESM1-CAM5 NSF-DOE-NCAR, USA 1.2 × 0.9

10 CMCC-CM CMCC, Italy 0.7 × 0.7

11 CMCC-CMS CMCC, Italy 1.9 × 1.9

12 CNRM-CM5 CNRM-CERFACS, France 1.4 × 1.4

13 CSIRO-MK3-6-0 CSIRO-QCCCE, Australia 1.9 × 1.9

14 EC-EARTH EC-EARTH, Europe 1.1 × 1.1

15 FGOALS-G2 LASG-CESS, China 2.8 × 2.8

16 FGOALS-S2 LASG, China 1.7 × 2.8

17 GFDL-CM3 NOAA, GFDL, USA 2.5 × 2.0

18 GFDL-ESM2G NOAA, GFDL, USA 2.5 × 2.0

19 GFDL-ESM2 M NOAA, GFDL, USA 2.5 × 2.0

20 GISS-E2-H NASA/GISS, NY, USA 2.5 × 2.0

21 GISS-E2-H-CC NASA/GISS, NY, USA 1.0 × 1.0

22 GISS-E2-R NASA/GISS, NY, USA 2.5 × 2.0

23 GISS-E2-R-CC NASA/GISS, NY, USA 1.0 × 1.0

24 HadCM3 MOHC, UK 3.7 × 2.5

25 HadGEM2-AO NIMR-KMA, Korea 1.9 × 1.2

26 HadGEM2-CC MOHC, UK 1.9 × 1.2

27 HadGEM2-ES MOHC, UK 1.9 × 1.2

28 INMCM4 INM, Russia 2.0 × 1.5

29 IPSL-CM5A-LR IPSL, France 3.7 × 1.9

30 IPSL-CM5A-MR IPSL, France 2.5 × 1.3

31 IPSL-CM5B-LR IPSL, France 3.7 × 1.9

32 MIROC-ESM JAMSTEC, Japan 2.8 × 2.8

33 MIROC-ESM-CHEM JAMSTEC, Japan 2.8 × 2.8

34 MIROC4H JAMSTEC, Japan 0.56 × 0.56

35 MIROC5 JAMSTEC, Japan 1.4 × 1.4

36 MPI-ESM-LR MPI-N, Germany 1.9 × 1.9

37 MPI-ESM-MR MPI-N, Germany 1.9 × 1.9

38 MRI-CGCM3 MRI, Japan 1.1 × 1.1

39 NorESM1-M NCC, Norway 2.5 × 1.9

40 NorESM1-ME NCC, Norway 2.5 × 1.9

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Fig. 2 Spatial patterns and site-specific time-series projections of mean, maximum, minimum temperature and precipitation for the baseline period (1995–2014) for Bangladesh

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developed using CMIP5 and information on these is sum- marized in Table 1.

Regional climate models are normally non-hydrostatic and of a high spatial resolution. Non-hydrostatic model are regarded as generally more robust than the hydrostatic model and produce smoother solutions (Janjic et al. 2001).

Both models have been widely used in both the research and operational communities during the last two decades (Jang and Hong 2016; Qi et al. 2018) and many researchers have used the non-hydrostatic models. These include the Weather Research and Forecasting (WRF) model (Qi et al. 2018),

Spectral Model (SM) (Jang and Hong 2016), Simulating WAves till SHore (SWASH) model (Zijlema et al. 2011), Regional Oceanic Modeling System (ROMS) (Kanarska et al. 2007), Mesobeta-Scale Model (MSM) (Stoelinga and Warner 1999). In this study, non-hydrostatic modeling work has used the SimCLIM 2013 simulation package with a downscaled horizontal spatial resolution of 0.00833° in both latitude and longitude under three different RCPs (RCP 4.5, RCP 6.0 and RCP 8.5). A feature of the SimCLIM simulation is that an integrated assessment tool is embedded within the model, enabling an assessment of the effects of climate

Fig. 3 Projected spatial pattern and time-slice analysis for mean temperature for the 40 GCMs (ensemble) with RCP 4.5 sensitivity for Bangla- desh

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change (Kenny et al. 1995), as well as an evaluation of the uncertainties caused by future greenhouse gas (GHG) emissions (Warrick 2009; Yin et al. 2013). This tool can be used to assess the risks and impacts to long-term sustainability when using the GHG concentration pathway generator and extreme event analyzer (Amin et al. 2016). The resulting simulation results tend to be more reliable as a result of these capabilities.

Fig. 4 Timescale evaluation for historical climatic data of 1995–2014 between SimCLIM simulation RCP 4.5 40 GCMs (ensemble) and the observed data

18 20 22 24 26 28 30

mean temperature (oc)

Historical Climate Data (1995–2014) SimCLIM projected Station data

24 26 28 30 32 34

maximum temperature (0c)

12 14 16 18 20 22 24 26

minimum temperature (0c)

0 50 100 150 200 250 300 350 400

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

precipitation (mm)

2.2 Analytical Techniques

Temperature and precipitation were used as inputs to the CMIP5 40 (GCMs) ensemble model. Bilinear interpola- tion was applied to the AR5 dataset to provide 40 (GCMs) ensembles at various national, regional, local, and site- specific scales for the many countries involved (Bao et al.

2015). Three representative concentration pathways (RCPs)

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were used—RCP 4.5 with low climate sensitivity, RCP 6.0 with mid-climate sensitivity and RCP 8.5 with high climate sensitivity. The first two represent the mid-range of global climate change scenarios with a median projection, while the third represents high climate sensitivity, with low and high bounds indicating the range of uncertainties. Fifty

percentiles of the GCMs value were applied in this work to obtain better results than any individual GCM (Reichler and Kim 2008; Amin et al. 2016). A multi-model pattern scaling method was also applied in producing the future climate change scenarios.

0 50 100 150 200 250 300 350 400 450 500 550

1 2 3 4 5 6 7 8 9 10 11 12

normal rain (mm/month)

months

(a) ^1957-1986

1967-1996 1977-2006 1987-2016

400 405 410 415 420 425 430 435 440

1957-1986 1967-1996 1977-2006 1987-2016

monsoon rain (mm)

Time period

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Fig. 5 Normal rainfall variability (a) and monsoon rainfall (b) in Dhaka

Fig. 6 MME mean temperature and precipitation for three (RCPs) RCP 4.5 (blue), RCP 6.0 (green) and RCP 8.5 (red) in the period (2011–2040), (2041–2070) and (2071–2100), respectively

19.5 20.5 21.5 22.5 23.5 24.5 25.5 26.5 27.5 28.5 29.5 30.5 31.5 32.5

J F M A M J J A S O N D temperature (oc/month)

months 2041-2070

19.5 20.5 21.5 22.5 23.5 24.5 25.5 26.5 27.5 28.5 29.5 30.5 31.5 32.5

months 2071-2100

19.5 20.5 21.5 22.5 23.5 24.5 25.5 26.5 27.5 28.5 29.5 30.5 31.5 32.5

months 2011-2040

RCP 4.5 RCP 6.0 RCP 8.5

0 50 100 150 200 250 300 350 400 450 500

J F M A M J J A S O N D

precipitation (mm/month)

months 2011-2040

RCP 4.5 RCP 6.0 RCP 8.5

0 50 100 150 200 250 300 350 400 450 500

months 2041-2070

0 50 100 150 200 250 300 350 400 450 500

months 2071-2100

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The modeled impacts of climate change vary according to the individual climate model dynamics, grid sizes, and parameterization processes. Use of a single model will not provide an optimum outcome in regard to risk, so it is important to consider all uncertainties using the GCM outputs. The use of multi-model ensembles has been expanding;

however, they are currently limited in the ability to capture the full range of climatic uncertainties (Tebaldi and Knutti 2007). For a specific site, the use of a multi-model ensemble is recommended. 20 years of data was used for the current

work (spanning the period 1995–2014) and the baselines in both observed (BMD) and modeled data for the historical period were compared. Future climate change scenarios for the early twenty-first (2011–2040), middle twenty-first (2041–2070) and late twenty-first (2071–2100) century periods, under three different RCPs, were generated.

2.3 Justification for Multi‑model SimCLIM Simulation Use

There are currently many methods used to produce regional climate change patterns, including statistical or dynamic downscaling. SimCLIM uses a pattern-scaling method which has particular advantages when compared with

0 5 10 15 20 25 30

mean temperature (0c)

RCP 4.5 RCP 6.0 RCP 8.5 2025

0 5 10 15 20 25 30

mean temperature (0c)

2050

0 5 10 15 20 25 30

mean temperature (0c) 2075

0 5 10 15 20 25 30

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

mean temperature (0c) 2100

Fig. 7 Projected mean temperature by 40 GCMs (ensemble) with different RCPs

Table 2 MME monthly mean temperature for the baseline years (1995–2014) and predictions for 2025, 2050, 2075 and 2100 using the median scenario for Dhaka

Month Mean temperature (°C)

Baseline 2025 2050 2075 2100

January 19.00 19.80 20.53 21.29 22.03

February 21.60 22.40 23.13 23.89 24.63

March 25.70 26.50 27.23 27.99 28.73

April 28.80 29.48 30.10 30.74 31.37

May 29.10 29.74 30.32 30.92 31.51

June 28.90 29.46 29.96 30.48 30.99

July 28.60 29.13 29.62 30.11 30.61

August 28.90 29.44 29.92 30.09 30.92

September 28.80 29.39 29.93 30.49 31.03

October 27.50 28.16 28.75 29.37 29.98

November 23.80 24.53 25.20 25.88 26.56

December 20.10 20.91 21.65 22.42 23.17

0 4 8 12 16 20 24 28 32

Pre-Monsoon Monsoon Post-Monsoon Winter mean temperature (oc)

20252050 20752100

Fig. 8 MME seasonal mean temperature using the median scenario for the study area

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other methods. To provide a valid climate assessment, data sources should be combined from multiple GCM/RCM, and various methods should be tested (IPCC 2014). Pattern-scaling methods focus principally on ‘climate change signals’

which are represented by GCM data differences between the baseline and the future periods (SimCLIM 2017). Individual model responses may vary (Bao et al. 2015), and variations in geographical size and multi-model climate sensitivity can reduce uncertainties corresponding to the 90% confidence

interval as used in the IPCC Fourth Assessment Report (AR4). Resource constraints may also affect the number of GCMs used for a particular work (Hulme et al. 2000).

The data representation and visualization methodologies used currently do have some shortcomings and, to a certain extent, pattern scaling can provide a viable solution to some of the issues encountered. In contrast, downscaling strategies are not as good in providing solutions (SimCLIM 2017).

0.0 0.2 0.4 0.6 0.8 1.0

change in mean temp (%) 2025RCP 4.5 RCP 6.0 RCP 8.5

0.0 0.3 0.6 0.9 1.2 1.5 1.8 2.1

change in mean temp (%) 2050

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5

change in mean temp (%)

2075

0.0 0.7 1.4 2.1 2.8 3.5 4.2 4.9

change in mean temp (%) 2100

Fig. 9 Changes in mean temperature from baseline year by 40 GCMs (ensemble) using various RCPs

0 6 12 18 24 30 36

maximum temperature (0c) 2025RCP 4.5 RCP 6.0 RCP 8.5

0 6 12 18 24 30 36

maximum temperature (0c) 2050

0 6 12 18 24 30 36

Fig. 10 Projected maximum temperature by 40 GCMs (ensemble) with different RCPs

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3 Results

3.1 Baseline Parameter

The Bangladesh data show a large variation in the precipitation recorded over the year. As a consequence, future climatic projections of temperature and precipitation may vary significantly in the different regions of the country. A notable increase in climate-related extreme events such as heavy rainfall events (> 20 mm), hot days (> 32 °C) and hot nights (> 25 °C) have already been observed (Shahid et al. 2016).

These events require the development of specific climate adaptation and mitigation strategies. These strategies can be formulated using future climate predictions developed through the use of simulation models such as SimCLIM (Shirazi et al. 2006; Abbas et al. 2014). Figure 2 shows the current spatial pattern and site-specific scenarios. The results are similar to those shown in other studies (Sohail and Burke 2013). To estimate change in climatic parameters, the Sim- CLIM simulation 40 GCMs (ensemble) median scenario is used to predict baseline information for 2025, 2050, 2075 and 2100 (Fig. 3). Timescale evaluation and identification of the regional variation of climatic variables, the observed data, BMD dataset, and 40 GCMs ensemble climate model dataset were used for the period of 20 years (1995–2014) at the 95% confidence level (CI), and the results are shown in Fig. 4.

The monthly mean, maximum, minimum temperature and precipitation had similar readings among the different grids, i.e., little variation. The analysis between the SimCLIM derived and observed climate data revealed a subtle variation in the annual mean, maximum, minimum temperatures, and precipitation, recording 26.14–26.43 °C, 30.86–30.88 °C, 21.42–21.98 °C, and 170.59–164.03 mm, respectively. In regard to monthly values, the analysis show that the variation of the baseline parameter for mean temperature in January had a positive variation of 0.37 °C, while other months had a negative variation of ≤ 0.85 °C.

The maximum temperature was positive in the month of January, February, April, and December, whereas the other months indicated negative values ≤ 0.70 °C. The monthly minimum temperature was positive in the months of August and September, while the other months had a negative variation of ≤ 1.66 °C. The precipitation results from both products indicated that January to July were positive with ≤ 43.50 mm while the period August to October and December recorded a negative variation. The highest recorded precipitation was in July (386.2–384.6 mm) and the lowest was in January (6.86–6.45 mm). The temperature and precipitation datasets provide the basis for the calculation of extreme event periods and an assessment of

the modeling data is a first step in understanding variations between baseline and regional climatic patterns.

3.2 Climate Variability

IPCC recommends that, where possible, the most recent 30-year climate ‘normal’ period should be adopted as the climatological baseline period when conducting impact and adaptation assessments. Two datasets of the recorded precipitation, in different regions within Bangladesh, have been developed by the Bangladesh Meteorological Department (BMD). This is a 10-year interval compiled from 30 years of data (1971–2000 and 1981–2010). A similar study (Fig. 5) shows that current annual monsoonal rainfall in Dhaka is increasing and that normal rainfall total has increased by approximately 7.5% period. This is particularly noticeable in July. Conversely, normal rainfall appears to be relatively

Table 3 MME monthly maximum temperature of Dhaka for baseline year (1995–2014) and predictions for 2025, 2050, 2075 and 2100 using the median scenario

Month Maximum temperature (°C)

Baseline 2025 2050 2075 2100

January 25.80 26.64 27.40 28.18 28.95

February 28.50 29.21 29.86 30.53 31.19

March 32.40 33.09 33.80 34.54 35.26

April 34.40 35.09 35.72 36.38 37.01

May 33.40 34.01 34.56 35.13 35.69

June 31.90 32.49 33.02 33.57 34.11

July 31.20 31.72 32.20 32.69 33.17

August 31.60 32.08 32.52 32.97 33.42

September 31.80 32.47 32.95 33.51 34.06

October 31.30 31.96 32.55 33.16 33.76

November 29.20 29.94 30.60 31.29 31.97

December 26.50 27.24 27.92 28.62 29.31

0 5 10 15 20 25 30 35 40

Pre-Monsoon Monsoon Post-Monsoon Winter maximum temperature (oc)

20252050 20752100

Fig. 11 MME seasonal maximum temperature using the median scenario

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unchanged during the winter months, while January rainfall has decreased by 0.49%. The analysis also shows that there have been some ‘extremely heavy rain days’ over Dhaka during the observed period. This includes 333 mm (28-07- 2009) and 341 mm (14-09-2004) as reported by Khatun et al.

(2016). The correlation among periods demonstrates that the amounts of monsoon rainfall over Dhaka have increased during the 1987–2016 period.

In Fig. 6, the upper graphs of mean temperature and the lower graphs of precipitation show the multi-model ensemble (MME) for three different RCPs during 2011–2040, 2041–2070 and 2071–2100. In general, the pre-monsoon and monsoon temperatures are projected to gradually increase during the twenty-first century with the possibility of extended hot and humid pre-monsoon and humid and wet monsoon seasons. A previous study had noted that higher nighttime temperatures and a relatively lower mean diurnal temperature range in the two largest urban areas of Bangla- desh (e.g., Dhaka and Chittagong) (Shahid et al. 2016). Due to the urban heat island (UHI) effect, urbanized areas often show a narrower diurnal temperature range than rural areas (Easterling et al. 1997). Further analysis has indicated an increasing trend of temperature under the RCP 8.5 scenarios at the end of the twenty-first century (2071–2100).

The southwest monsoon is a significant feature control- ling the climate of Bangladesh. Tropical depressions (also known as monsoon depressions) carry moisture-laden air from the Bay of Bengal into the inland areas and are the main source for the rainfall in the region (Ahmed and Kim 2003; Shahid and Khairulmaini 2009; Shahid 2010b; Shahid et al. 2012). It can be seen from the observed data that about 57.68% (1995–2014) of the annual average rainfall occurs during monsoon in Dhaka. Under the RCP 8.5 scenarios, the model results show rainfall would increase during the periods 2011–2040, 2041–2070 and 2071–2100 by 66.32%, 66.77%, and 67.25%, respectively. Conversely, the results

0.0 0.2 0.4 0.6 0.8 1.0

change in max temp (%) 2025RCP 4.5 RCP 6.0 RCP 8.5

0.0 0.4 0.8 1.2 1.6 2.0

change in max temp (%) 2050

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5

0.0 0.8 1.6 2.4 3.2 4.0 4.8

Fig. 12 Changes in maximum temperature from baseline year by 40 GCMs (ensemble) using various RCPs

Table 4 MME monthly minimum temperature for the baseline years (1995–2014), and predictions for 2025, 2050, 2075 and 2100 using the median scenario

Month Minimum temperature (°C)

Baseline 2025 2050 2075 2100

January 12.20 13.03 13.79 14.57 15.34

February 14.70 15.59 16.41 17.24 18.07

March 19.20 20.03 20.78 21.56 22.32

April 23.30 24.02 24.68 25.35 26.02

May 24.80 25.46 26.06 26.68 27.29

June 25.90 26.52 27.08 27.66 28.23

July 26.10 26.67 27.18 27.72 28.24

August 26.20 26.76 27.27 27.46 28.32

September 25.90 26.54 27.11 27.71 28.30

October 23.80 24.54 25.20 25.89 26.57

November 18.40 19.18 19.89 20.63 21.35

December 13.70 14.58 15.38 16.21 17.03

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also appear to show a slight decrease in winter rainfall under the RCP’s.

3.3 Future Climate Projection 3.3.1 Mean Temperature

The projections for mean temperature over Dhaka for the years 2025, 2050, 2075 and 2100 are shown in Fig. 7. The results reveal that annual mean temperature would increase for the years 2025, 2050, 2075 and 2100 by 26.58, 27.20, 27.81 and 28.46 °C, respectively. The projected increase in temperature for Dhaka of 0.64 °C per 25 years is higher than the current maximum temperature of 0.62 °C and lower than the current minimum temperature of 0.68 °C (Table 2;

Fig. 8). The results also indicate that the mean temperature increases significantly in the warmer seasons (pre-monsoon and monsoon). Using the BMD data from 1948 to 2016, it was noted that there appears to be an upward trend (per century) of 1.72 °C for Dhaka and 0.82 °C for Bangladesh.

Shahid (2010a) has analyzed the historical trends in temperature for Bangladesh. Results indicate an increase of 0.097 °C per decade during the period 1961–2008, thus giv- ing a theoretical increase of 0.97 °C per century. A notable feature of these results is that if the mean temperature con- tinues to increase at this rate, it will reach 2.56 °C by 2100.

This value is higher than the tipping point of 2 °C limit flagged by the IPCC report (IPCC 2014). Inclusion of climate uncertainty (5th–95th percentile range) in the temperature model showed that the differences for 2025, 2050, 2075 and 2100 are 0.34–1.00 °C, 0.61–1.76 °C, 0.77–2.24 °C and 0.85–2.46 °C, respectively. However, the mean temperature changes were found to be (1) RCP 4.5 (0.67, 1.18, 1.51 and 1.66%), (2) RCP 6.0 (0.61, 1.09, 1.61 and 2.14%), and (3) RCP 8.5 (0.76, 1.62, 2.68 and 3.89%) (Fig. 9). The highest positive change is recorded in December and the lowest positive change in July.

3.3.2 Maximum Temperature

Temperature pattern changes may also lead to spatial changes in cloud cover in Bangladesh. Rai et al. (2012) stated that an increase in cloud cover tended to decrease maximum temperature. In this study, the model-projected data showed that the maximum temperature would increase by 0.62 °C every 25 years, a lower rate than the minimum

0 5 10 15 20 25 30

minimum temperature (oc) 2025RCP 4.5 RCP 6.0 RCP 8.5

0 5 10 15 20 25 30

minimum temperature (o 2050

0 5 10 15 20 25 30

minimum temperature (oc) 2075

0 5 10 15 20 25 30

minimum temperature (oc) 2100

c)

Fig. 13 Projected minimum temperature by 40 GCMs (ensemble) with different RCPs

0 5 10 15 20 25 30

Pre-Monsoon Monsoon Post-Monsoon Winter minimum temperature (oc)

20252050 20752100

Fig. 14 MME seasonal minimum temperature using the median scenario

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temperature. A significant increase in annual maximum temperatures (in the order of 1.02–1.10 °C per century) has been noted in previous works (Hasan and Rahman 2013;

Shahid et al. 2016) and this study also indicated that the monsoon and post-monsoon seasons would be extended.

The current work indicates that the annual maximum temperature would increase to 31.33 °C in 2025, 31.92 °C in 2050, 32.55 °C in 2075 and 33.16 °C in 2100 (Fig. 10). The length of the pre-monsoon and monsoon season could also

increase significantly (Table 3; Fig. 11). An analysis of the BMD data indicated that the highest temperature in Dhaka (42.3 °C) was recorded on April 30, 1960. Dhaka citizens experienced the highest temperature recorded in 54 years (40.2 °C) on April 24, 2014. The modeled trend of 0.64 °C indicated that the pre-monsoon period, specifically April, would be the warmest season by the end of the twenty-first century. The maximum temperature changes for the RCPs

0.0 0.2 0.4 0.6 0.8 1.0

change in min temp (%) 2025RCP 4.5 RCP 6.0 RCP 8.5

0.0 0.4 0.8 1.2 1.6 2.0 2.4

change in min temp (%) 2050

0.0 0.6 1.2 1.8 2.4 3.0 3.6

0.0 1.0 2.0 3.0 4.0 5.0

Fig. 15 Changes in minimum temperature from baseline year by 40 GCMs (ensemble) using various RCPs

0 100 200 300 400

precipitation (mm)

RCP 4.5 RCP 6.0 RCP 8.5 2025

0 100 200 300 400

precipitation (mm)

2050

0 100 200 300 400 500

precipitation (mm)

2075

0 100 200 300 400 500

precipitation (mm)

2100

Fig. 16 Projected precipitation by 40 GCMs (ensemble) with different RCPs

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were found to be (1) RCP 4.5 (0.66, 1.15, 1.50 and 1.59%), (2) RCP 6.0 (0.60, 1.07, 1.58 and 2.09%), and (3) RCP 8.5 (0.74, 1.58, 2.62 and 3.80%) (Fig. 12). At the same time, the highest positive change was found to occur in January and the lowest positive change was noted in August.

3.3.3 Minimum Temperature

An analysis of monthly minimum temperature trends shows a significant increase in Dhaka’s temperature in all months (Table 4; Fig. 13). It should be noted that minimum temperatures in winter would have the greatest rate of increase (0.81 °C per 25 years) (Fig. 14). The minimum temperature increases at a rate higher than maximum temperature. A recent study by Shahid et al. (2016) reported that a seasonal analysis of the minimum temperature trend showed a significant increase in all seasons. This study also observed that the seasonal analysis of monsoon showed the lowest rate of increase (0.54 °C per 25 years). The analysis also show an annual minimum temperature increase of 21.91 °C by 2025, 22.57 °C by 2050, 23.22 °C by 2075, and 23.92 °C by 2100.

However, the minimum temperature change has been esti- mated to be (1) RCP 4.5 (0.72, 1.26, 1.61 and 1.80%), (2) RCP 6.0 (0.65, 1.17, 1.73 and 2.29%), and (3) RCP 8.5 (0.81, 1.73, 2.87 and 4.16%) (Fig. 15). February has the highest positive change and August has the lowest positive change.

3.3.4 Precipitation

The observed annual average rainfall increased at a rate of 6.48 mm/year in Dhaka during the 1953–2016 period. A recent study noted that the annual average rainfall increased at a rate of 5.53–5.68 mm/year over Bangladesh as a whole (Shahid 2010b; Mullick et al. 2019). An analysis of the

current model precipitation projections indicate that annual precipitation would significantly increase during 2025, 2050, 2075 and 2100 at a rate of 5.93, 6.10, 6.35 and 6.55 mm, respectively (Fig. 16). Monsoon precipitation might increase significantly for the three RCPs by the end of the twenty- first century. In contrast, winter season precipitation appears to decrease slightly. Mullick et al. (2019) also observed a decrease in total rainfall during winter period. A seasonal analysis of model-projected data for 1995–2014 shows that precipitation during the monsoon season would increase at a rate of 14.95 mm every 25 years. For the three RCPs, precipitation is negative in January, April, November, and December, while positive in the rest of the months. The seasonal analysis also shows that the monsoon season is the dominant season in regard to total precipitation.

4 Discussion

Bangladesh has a distinctive monsoonal climate due to its location and size. The particular characteristics of the climatic patterns evident during the year delineate the seasons—hot and humid (pre-monsoon), humid and wet (monsoon), hot and dry (post-monsoon) and cold and dry (winter). The southwest monsoon controls the general climate of the region and changes in rainfall patterns have a large impact on all regions of Bangladesh. This study is focused on the densely populated megacity of Dhaka.

The city already experiences high temperatures in the pre- monsoon period and variable amounts of rainfall during the monsoon. The current study has focused on revealing climatic spatial patterns, possible long-term climatological variability, and climate projections, to understand possible future climate change impacts.

Forty GCM (ensemble) higher spatial resolutions and non-hydrostatic climate models have been used to produce smoother solutions (Janjic et al. 2001). The research looked at modeling climate variability every 30 years and developed

Table 5 MME monthly precipitation for the baseline years (1995–

2014), and predictions for 2025, 2050, 2075 and 2100 using the median scenario

Month Precipitation (mm)

Baseline 2025 2050 2075 2100

January 7.00 6.54 6.13 5.70 5.28

February 21.00 22.57 23.36 24.52 25.66

March 56.00 58.98 61.67 64.46 67.21

April 135.00 133.43 132.00 130.52 129.07

May 272.00 287.29 301.15 315.47 329.58

June 369.00 392.92 414.61 437.01 459.09

July 381.00 397.27 412.01 427.25 442.26

August 315.00 327.22 338.30 349.75 361.02 September 270.00 282.35 293.54 305.11 316.50 October 159.00 165.90 172.15 178.61 184.97

November 32.00 31.03 30.15 29.23 28.34

December 7.00 6.81 5.64 6.46 6.28

0 50 100 150 200 250 300 350 400

Pre-Monsoon Monsoon Post-Monsoon Winter

precipitation (mm)

20252050 20752100

Fig. 17 MME seasonal precipitation using median scenario

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a projection for every 25-year interval. The results showed that Dhaka’s overall rainfall would increase substantially in the monsoon season, while rainfall totals remain essen- tially unchanged in the winter months. Rainfall in January decreased by 0.49%. There is a general trend of increasing temperature in the warmer months, indicating that a longer, hot and humid pre-monsoon than is currently experienced will develop by the end of the twenty-first century.

Modeling overall temperature trends indicate that mean temperature will increase by 2.56 °C in Dhaka by the end of the twenty-first century, a figure which is higher than the tipping point of 2 °C noted by the 2014 IPCC report as being the threshold above which climate change risks and associated impacts substantially increase (IPCC 2014). The results also indicate that annual mean temperature would increase to 26.58 °C by 2025, 27.20 °C by 2050, 27.20 °C by 2075 and 28.46 °C by 2100 in the different RCPs. The minimum temperature increase is higher than the maximum temperature, which supports the results of Shahid (2010a) and Shahid et al. (2016). A decrease in the diurnal temperature range is due to the increase in minimum temperature (Shahid et al.

2012). Maximum temperatures increase significantly in the pre-monsoon and monsoon seasons (at a rate of 0.64 °C).

The pre-monsoon period and specifically the month of April would become the hottest season. The high positive changing pattern found in January indicated that the length of the winter period might be shorter in future. Conversely, the minimum temperature in winter shows a large rate of increase of approximately 0.81 °C. Research by Hasan and Rahman (2013) indicated that the minimum temperature had increased significantly throughout the winter season.

Previous work indicated the potential for a significant increase in annual precipitation during the periods 2025 (5.9 mm), 2050 (6.1 mm), 2075 (6.35 mm) and 2100 (6.55 mm) (Shahid 2011). Other studies have shown annual average rainfall increases of 5.53–5.68 mm/year over the whole of Bangladesh (Shahid 2010b; Mullick et al. 2019).

In the current investigation, a decreasing trend of rainfall is noted from November to January and April, and an increasing trend is observed from February to March, and May to October (Table 5; Fig. 17). These projections show that the monsoon period is generally going to be wetter, although some of the winter, pre-monsoon and the post-monsoon season is going to be drier. Changes in the pattern of precipitation show the greatest positive change in February, March, May, and June, while the greatest negative was observed in January. In regard to the precipitation parameter, high precipitation change was also observed during the periods 2025, 2050, 2075 and 2100 by 2.24, 4.27, 6.38 and 8.45%, respectively (Fig. 18).

Most climate modeling studies which have been undertaken in the South Asian region show overall increases in temperature and precipitation during the twenty-first century (IPCC 2013). Islam et al. (2008) developed future climate scenarios for Bangladesh using the PRECIS simulation software and found both temperature and rainfall increased through to 2071. Using an ensemble climate model for future precipitation projections in Bangladesh, Rajib et al. (2011) reported that an increase could be expected in all months under the SRES A1B scenario. Using a regional climate model to project the temperature and rainfall of Bangla- desh into the middle of the twenty-first century, Rahman

-8 -6 -4 -2 0 2 4 6 8

change in precipitation (%) 2025

RCP 4.5 RCP 6.0 RCP 8.5

-16 -12 -8 -4 0 4 8 12 16

-28 -21 -14 -7 0 7 14 21 28

-40 -32 -24 -16-80 8 16 24 32 40

Fig. 18 Changes in precipitation from baseline year by 40 GCMs (ensemble) using various RCPs

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et al. (2012a) suggested that monthly temperatures will increase by 0.5–2.1 °C. Rahman et al. (2012b) indicated that an increase in temperature variability as a result of climate change would have a major impact on Bangladesh. In summary, the various climate model simulations undertaken have produced differing predictions in regard to temporal trends in temperature and precipitation.

5 Conclusion

Historical records were obtained and used to provide data for the development of future regional climate scenarios. CMIP5 40 GCMs (ensemble) climate models were used to simulate temperature and precipitation over the Dhaka megacity in Bangladesh. The spatial patterns of long-term climate variation in temperature and precipitation were studied and trends reported. The modeling indicates that monsoon rainfall in Dhaka may increase substantially and would be most noticeable in the month of July. By the end of the twenty-first century, monsoon rainfall would increase by approximately 7.5% and the projected mean temperature by approximately 2.56 °C. Dhaka is one of the largest megacities in the world and is already facing climate-related adverse events. The modeling confirms that the urban population of Bangladesh will be subjected to further extreme climate change impacts.

Information derived from the current study is expected to add to the understanding of these changes, both currently and into the future. This will assist in the development of targeted climate change mitigation measures for the Dhaka area, measures which will also have applicability to other areas of Bangladesh.

Acknowledgements The authors wish to thank the ICT Division, Min- istry of Posts, Telecommunications and Information Technology and the Government of the People’s Republic of Bangladesh, for providing a Master of Philosophy (M. Phil.) Research Fellowship for this work.

The authors are also grateful to the International Global Change Insti- tute (IGCI) and the University of Waikato, Hamilton, New Zealand, for software sponsorship (SimCLIM 2013), the provision of licensed software and the CMIP5 AR5 Global and Bangladesh spatial dataset used for future climate change projections.

Compliance with Ethical Standards

Conflict of interest The authors declare that they have no conflicts of interest.

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