Key points of post
- Here we analyse air temperature from 3 weather stations along a north-south transect in Bangladesh (Rangpur (1982-2013), Dhaka (1998-2013) and Barisal (1998-2013).
- Air temperatures risen at all 3 stations; Dhaka had the most substantial increases in temperature rising 0.64°C overall between 1998 and 2013.
- Seasonality in temperature varies between years but differences overall are slight and there seems to be no systematic changes in shape.
In this blog post, we ask whether air temperatures are rising in Bangladesh using data available to us from the internet.
Bangladesh is a beautiful, semi-tropical country with India to the north and west and Myanmar to the east. The country is intersected by vast rivers, fed by the mountains of the Himalayas, which meander through the countryside. The normal seasonal cycles of flooding of these rivers regulates food production in many ways, e.g. irrigating rice or triggering fish production. Bangladesh is mostly low-lying and potentially vulnerable to sea-level rise and cyclones. The country is developing rapidly but much of its population remains poor, clinging to precarious livelihoods around rivers, lakes, and the sea.
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| Location of Rangpur, Dhaka and Barisal Weather Stations - Bangladesh |
Bangladesh is a focal
country for our CCAFS program which researches the impact of changing climate and
weather on food production in developing countries; and particularly on the
consequences for the poorest members of society. Temperature is a ‘master’ variable,
ultimately controlling all life in our biosphere including food production,
species distributions and pathogen activity.
Here we examine long-term trends in temperature from the 3 following stations in Bangladesh: Rangpur; Dhaka; and Barisal (see map above). Unfortunately, compared to many other countries, data available for Bangladesh are rather sparse. Even for the capital city of Dhaka, we could only find continuous data from 1998, and there are also substantial gaps. The longest series available to us was from Rangpur which extended 32 years in total from 1982 to 2013. Air temperatures for the 3 stations are plotted below both as simple time-series and 3D, ‘heat-map’ plots, the latter being useful for showing how seasonality changes over longer term time.
The red lines in the 3 time-series plots represent linear models fitted to the data (for all 3 stations) as functions of year. The value of the gradient term (amount of rise/amount of tread) is indicated at the top-left. The gradients were positive in all 3 stations, meaning that temperatures indeed rose but the magnitude of increase is extremely slight compared to other locations we have examined, e.g. in Penang, Malaysia air temperature increases have been comparatively substantial. At Rangpur temperatures rose at 0.0117°C per year over 32 years which means an overall increase of 0.37°C overall (0.0117 x 32). Data were available for shorter periods at Dhaka and Barisal which recorded overall rises of 0.33°C and 0.64°C respectively. In Rangpur, extreme high temperatures (ie. > 35°C) occurred more frequently in the past, between 1982 and 1995, than would be the case for the present (see time-series above). Seasonality indeed varies from year to year (heat-map plots) but there is little evidence of the systematic changes over time that we have seen in other locations.
The shape of the seasonal cycle seemed to vary only slightly between locations (see below). There is a clear Northern Hemisphere winter, summer cycle with lowest temperatures in December and January. In 2013, Dhaka was on average the warmest of all 3 locations, with peak temperatures occurring in June and July. Barisal was slightly warmer than Rangpur in the early part of the year after when Barisal fell relative to Rangpur. Peak temperatures in all 3 locations occurred (on average between June and July.
The results from these
analyses are curious and possibly at odds with the view that the impacts of
climate change on Bangladesh are especially worrying. A rise of 0.64°C between 1998 and 2013 which we see at Dhaka
is substantial, whereas further north at Rangpur we only saw a 0.37°C overall increase between
1982 and 2013. In our view it is
unlikely that these rises are even perceptible to the average human, and are also
unlikely to have had much impact on food production.
Close examination of
actual data can often challenge ‘accepted wisdom’, which is a particular
purpose of these articles. Of course,
temperature is just one variable and Bangladesh’s climate future will also
depend on the interaction between temperature and other variables such as
rainfall and wind speed. Sea-level rise will also be a factor to consider.
These will from the subject of future investigations.
The R code used to produce the plots in this blog post is provided below and the datasets used here are from NOAA National Climatic Data Centre's (NCDC) Integrated Surface Database.
# Load required libraries library(ggplot2) library(scales) library(grid) library(plyr) library(lubridate) library(zoo) # Set working directory setwd("D:/ClimData/BGD") ################################ ## Plot Air Temps for Rangpur ## ################################ dat <- read.table("826316390028dat.txt",header=TRUE,fill=TRUE, na.strings=c("*","**","***","****","*****","******","0.00T*****","0.00T**","0.00T")) colnames(dat)<-tolower(colnames(dat)) Sys.setenv(TZ = "UTC") dat$dates <- as.POSIXct(strptime(dat$yr..modahrmn,format="%Y%m%d%H%M")) + 6 * 60 * 60 dat$tempc <- (dat$temp-32) * (5/9) dat$date <- as.Date(dat$dates,format="%Y%m%d") dat$year <- as.numeric(format(dat$dates,"%Y")) dat$month <- as.numeric(format(dat$dates,"%m")) dat$hour <- as.numeric(format(dat$dates,"%H")) dat$week <- as.numeric(format(dat$dates,"%W")) dat$monthf <- factor(dat$month,levels=as.character(1:12), labels=c("Jan","Feb","Mar","Apr","May","Jun","Jul","Aug","Sep","Oct","Nov","Dec"),ordered=TRUE) # Chuck out data that is < 0 and > 45 dat <- dat[dat$tempc > 0 & dat$tempc < 50, ] datb<-subset(dat, year >= 1982 & year <= 2013) plot.title = 'Daily Air Temperatures in Rangpur (1982-2013)' plot.subtitle = 'Data source: Integrated Surface Database (ISD) - National Climatic Data Centre (NCDC)' b <- ggplot(data=datb, aes(x=dates, y=tempc)) + geom_jitter(colour="grey40",size=1.5,alpha=0.4) + scale_y_continuous(breaks=c(10,15,20,25,30,35,40)) + theme_bw()+ scale_x_datetime(breaks = "5 year", minor_breaks = "5 year", labels=date_format("%Y")) + xlab("") + ylab(as.expression(expression( paste("Temperature (", degree,"C)") ))) + ggtitle(bquote(atop(.(plot.title), atop(italic(.(plot.subtitle)), "")))) + theme(plot.title = element_text(face = "bold",size = 16,colour="black")) + geom_smooth(aes(group = 1), method = "lm",size=0.75,colour="red") b # Get linear equation value and gradient term m <- lm(tempc~year, data=datb ) ms <- summary(m) slope <- coef(m)[2] lg <- list(slope = format(slope, digits=3)) eqg <- substitute(italic(Gradient)==slope,lg) eqgstr <-as.character(as.expression(eqg)) # Add gradient term value to plot b <- b +annotate(geom="text",as.POSIXct(-Inf, origin = '1970-01-01'), y = Inf, hjust = -0.1, vjust = 2, label = eqgstr,parse = TRUE,size=3.5) b col<-c("blue","green","yellow","orange","red") wt <- ggplot(data=datb,aes(x=year,y=week,fill=tempc))+ geom_tile(colour=NA,size=0.65)+ theme_bw()+ scale_fill_gradientn(colours=col,name=as.expression(expression( paste("Temperature (", degree,"C)"))))+ coord_equal(ratio=0.2)+ ylab("WEEK OF YEAR\n")+ xlab("\nYEAR\n\nSource: Integrated Surface Database (ISD) - National Climatic Data Centre (NCDC)")+ scale_x_continuous(expand = c(0,0),breaks = seq(1982, 2015, 2)) + scale_y_discrete(expand = c(0,0),breaks = seq(0,52,4))+ ggtitle("Average weekly air temperatures in Rangpur\n")+ theme(panel.background=element_rect(fill="grey90"), panel.border=element_rect(colour="white"), axis.title.y=element_text(size=10,colour="grey20"), axis.title.x=element_text(size=10,colour="grey20"), axis.text.y=element_text(size=10,colour="grey20",face="bold"), axis.text.x=element_text(size=10,colour="grey20",face="bold"), plot.title = element_text(lineheight=1.2, face="bold",size = 14, colour = "grey20"), panel.grid.major = element_blank(), panel.grid.minor = element_blank(), legend.key.width=unit(c(0.1,0.1),"in")) wt # Exporting file to png ggsave(b, file="Rangpur_AT_Plot.png", width=10, height=6,dpi=400,unit="in",type="cairo") ggsave(wt, file="Rangpur_AT_HeatMap_Plot.png", width=15, height=6,dpi=400,unit="in",type="cairo") ############################## ## Plot Air Temps for Dhaka ## ############################## dat <-read.table("820756390032dat.txt",header=TRUE,fill=TRUE, na.strings=c("*","**","***","****","*****","******","0.00T*****","0.00T**","0.00T")) colnames(dat)<-tolower(colnames(dat)) Sys.setenv(TZ = "UTC") dat$dates <- as.POSIXct(strptime(dat$yr..modahrmn,format="%Y%m%d%H%M")) + 6 * 60 * 60 dat$tempc <- (dat$temp-32) * (5/9) dat$year <- as.numeric(format(dat$dates,"%Y")) dat$month <- as.numeric(format(dat$dates,"%m")) dat$hour <- as.numeric(format(dat$dates,"%H")) dat$week <- as.numeric(format(dat$dates,"%W")) dat$monthf <- factor(dat$month,levels=as.character(1:12), labels=c("Jan","Feb","Mar","Apr","May","Jun","Jul","Aug","Sep","Oct","Nov","Dec"),ordered=TRUE) # Chuck out data that is < 0 and > 45 dat <- dat[dat$tempc > 0 & dat$tempc < 50, ] datb <- subset(dat, year >= 1998 & year <= 2013) plot.title = 'Daily Air Temperatures in Dhaka (1998-2013)' plot.subtitle = 'Data source: Integrated Surface Database (ISD) - National Climatic Data Centre (NCDC)' b <- ggplot(data=datb, aes(x=dates, y=tempc)) + geom_jitter(colour="grey40",size=1.5,alpha=0.4) + scale_y_continuous(breaks=c(10,15,20,25,30,35,40)) + theme_bw()+ scale_x_datetime(breaks = "1 year", minor_breaks = "1 year", labels=date_format("%Y")) + xlab("") + ylab(as.expression(expression( paste("Temperature (", degree,"C)") ))) + ggtitle(bquote(atop(.(plot.title), atop(italic(.(plot.subtitle)), "")))) + theme(plot.title = element_text(face = "bold",size = 16,colour="black")) + geom_smooth(aes(group = 1), method = "lm",size=0.75,colour="red") b # Get linear equation value and gradient term m <- lm(tempc~year, data=datb ) ms <- summary(m) slope <- coef(m)[2] lg <- list(slope = format(slope, digits=3)) eqg <- substitute(italic(Gradient)==slope,lg) eqgstr <-as.character(as.expression(eqg)) # Add gradient term value to plot b <- b +annotate(geom="text",as.POSIXct(-Inf, origin = '1970-01-01'), y = Inf, hjust = -0.1, vjust = 2, label = eqgstr,parse = TRUE,size=3.5) b col<-c("blue","green","yellow","orange","red") wt <- ggplot(data=datb,aes(x=year,y=week,fill=tempc))+ geom_tile(colour=NA,size=0.65)+ theme_bw()+ scale_fill_gradientn(colours=col,name=as.expression(expression( paste("Temperature (", degree,"C)"))))+ coord_equal(ratio=0.15)+ ylab("WEEK OF YEAR\n")+ xlab("\nYEAR\n\nSource: Integrated Surface Database (ISD) - National Climatic Data Centre (NCDC)")+ scale_x_continuous(expand = c(0,0),breaks = seq(1998, 2015,1)) + scale_y_discrete(expand = c(0,0),breaks = seq(0,52,4))+ ggtitle("Average weekly air temperatures in Dhaka\n")+ theme(panel.background=element_rect(fill="grey90"), panel.border=element_rect(colour="white"), axis.title.y=element_text(size=10,colour="grey20"), axis.title.x=element_text(size=10,colour="grey20"), axis.text.y=element_text(size=10,colour="grey20",face="bold"), axis.text.x=element_text(size=10,colour="grey20",face="bold"), plot.title = element_text(lineheight=1.2, face="bold",size = 14, colour = "grey20"), panel.grid.major = element_blank(), panel.grid.minor = element_blank(), legend.key.width=unit(c(0.1,0.1),"in")) wt # Exporting file to png ggsave(b, file="Dhaka_AT_Plot.png", width=10, height=6,dpi=400,unit="in",type="cairo") ggsave(wt, file="Dhaka_AT_HeatMap_Plot.png", width=10, height=5,dpi=400,unit="in",type="cairo") ################################ ## Plot Air Temps for Barisal ## ################################ dat <- read.table("8998396389910dat.txt",header=TRUE,fill=TRUE, na.strings=c("*","**","***","****","*****","******","0.00T*****","0.00T**","0.00T")) colnames(dat)<-tolower(colnames(dat)) Sys.setenv(TZ = "UTC") dat$dates <- as.POSIXct(strptime(dat$yr..modahrmn,format="%Y%m%d%H%M")) + 6 * 60 * 60 dat$tempc <- (dat$temp-32) * (5/9) dat$year <- as.numeric(format(dat$dates,"%Y")) dat$month <- as.numeric(format(dat$dates,"%m")) dat$hour <- as.numeric(format(dat$dates,"%H")) dat$week <- as.numeric(format(dat$dates,"%W")) dat$monthf <- factor(dat$month,levels=as.character(1:12), labels=c("Jan","Feb","Mar","Apr","May","Jun","Jul","Aug","Sep","Oct","Nov","Dec"),ordered=TRUE) # Chuck out data that is < 0 and > 45 dat <- dat[dat$tempc > 0 & dat$tempc < 50, ] datb<-subset(dat, year >= 1998 & year <= 2013) plot.title = 'Daily Air Temperatures in Barisal (1998-2013)' plot.subtitle = 'Data source: Integrated Surface Database (ISD) - National Climatic Data Centre (NCDC)' b <- ggplot(data=datb, aes(x=dates, y=tempc)) + geom_jitter(colour="grey40",size=1.5,alpha=0.4) + scale_y_continuous(breaks=c(10,15,20,25,30,35,40)) + theme_bw()+ scale_x_datetime(breaks = "1 year", minor_breaks = "1 year", labels=date_format("%Y")) + xlab("") + ylab(as.expression(expression( paste("Temperature (", degree,"C)") ))) + ggtitle(bquote(atop(.(plot.title), atop(italic(.(plot.subtitle)), "")))) + theme(plot.title = element_text(face = "bold",size = 16,colour="black"))+ geom_smooth(aes(group = 1), method = "lm",size=0.75,colour="red") b # Get linear equation value and gradient term m <- lm(tempc~year, data=datb ) ms <- summary(m) slope <- coef(m)[2] lg <- list(slope = format(slope, digits=3)) eqg <- substitute(italic(Gradient)==slope,lg) eqgstr <-as.character(paste(as.expression(eqg),as.expression(paste("/year")))) # Add gradient term value to plot b <- b +annotate(geom="text",as.POSIXct(-Inf, origin = '1970-01-01'), y = Inf, hjust = -0.1, vjust = 2, label = eqgstr,parse = TRUE,size=3.5) b col<-c("blue","green","yellow","orange","red") wt <- ggplot(data=datb,aes(x=year,y=week,fill=tempc))+ geom_tile(colour=NA,size=0.65)+ theme_bw()+ scale_fill_gradientn(colours=col,name=as.expression(expression( paste("Temperature (", degree,"C)"))))+ coord_equal(ratio=0.15)+ ylab("WEEK OF YEAR\n")+ xlab("\nYEAR\n\nSource: Integrated Surface Database (ISD) - National Climatic Data Centre (NCDC)")+ scale_x_continuous(expand = c(0,0),breaks = seq(1998, 2015,1)) + scale_y_discrete(expand = c(0,0),breaks = seq(0,52,4))+ ggtitle("Average weekly air temperatures in Barisal\n")+ theme(panel.background=element_rect(fill="grey90"), panel.border=element_rect(colour="white"), axis.title.y=element_text(size=10,colour="grey20"), axis.title.x=element_text(size=10,colour="grey20"), axis.text.y=element_text(size=10,colour="grey20",face="bold"), axis.text.x=element_text(size=10,colour="grey20",face="bold"), plot.title = element_text(lineheight=1.2, face="bold",size = 14, colour = "grey20"), panel.grid.major = element_blank(), panel.grid.minor = element_blank(), legend.key.width=unit(c(0.1,0.1),"in")) wt # Exporting file to png ggsave(b, file="Barisal_AT_Plot.png", width=10, height=6,dpi=400,unit="in",type="cairo") ggsave(wt, file="Barisal_AT_HeatMap_Plot.png", width=10, height=6,dpi=400,unit="in",type="cairo") ############################################ ## Plot Air Temps for All Stations (2013) ## ############################################ # Read raw data from files datr <- read.table("826316390028dat.txt",header=TRUE,fill=TRUE, na.strings=c("*","**","***","****","*****","******","0.00T*****","0.00T**","0.00T")) datd <- read.table("820756390032dat.txt",header=TRUE,fill=TRUE, na.strings=c("*","**","***","****","*****","******","0.00T*****","0.00T**","0.00T")) datb <- read.table("8998396389910dat.txt",header=TRUE,fill=TRUE, na.strings=c("*","**","***","****","*****","******","0.00T*****","0.00T**","0.00T")) # Bind data from 3 stations bgt<-rbind(datr,datb,datd) colnames(bgt)<-tolower(colnames(bgt)) bgt$usaf <- gsub("418590", "Rangpur", bgt$usaf) bgt$usaf <- gsub("419220", "Dhaka", bgt$usaf) bgt$usaf <- gsub("419500", "Barisal", bgt$usaf) Sys.setenv(TZ = "UTC") bgt$dates <- as.POSIXct(strptime(bgt$yr..modahrmn,format="%Y%m%d%H%M")) + 6 * 60 * 60 bgt$tempc <- (bgt$temp-32) * (5/9) bgt$year <- as.numeric(format(bgt$dates,"%Y")) bgt$month <- as.numeric(format(bgt$dates,"%m")) bgt$hour <- as.numeric(format(bgt$dates,"%H")) bgt$week <- as.numeric(format(bgt$dates,"%W")) bgt$monthf <- factor(bgt$month,levels=as.character(1:12), labels=c("Jan","Feb","Mar","Apr","May","Jun","Jul","Aug","Sep","Oct","Nov","Dec"),ordered=TRUE) bgt1 <-subset(bgt,year == 2013 ) plot.title = 'Mean Air Temperatures for 2013 (by Month)' plot.subtitle = 'Source: Integrated Surface Database (ISD) - National Climatic Data Centre (NCDC)' bt <- ggplot(bgt1, aes(x = monthf, y = tempc,colour=factor(usaf),group=usaf))+ geom_smooth(method="loess",se=F,size=1.2,span=1.5)+theme_bw()+ xlab("\nMonth") + ylab(as.expression(expression( paste("Temperature (", degree,"C)"))))+ ggtitle(bquote(atop(.(plot.title), atop(italic(.(plot.subtitle)), "")))) + theme(plot.title = element_text(face = "bold",size = 16,colour="black")) + guides(color = guide_legend(title = "Locality", title.position = "top")) + theme(panel.border = element_rect(colour = "black",fill=F,size=1), panel.grid.major = element_line(colour = "grey",size=0.25,linetype='longdash'), panel.grid.minor = element_blank(), panel.background = element_rect(fill = "ivory",colour = "black"), legend.position="right") bt # Exporting file to png ggsave(bt, file="MonthlyAirTemps_BGD.png", width=10, height=6,dpi=400,unit="in",type="cairo")
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