R Dataset / Package DAAG / bomregions

On this R-data statistics page, you will find information about the bomregions data set which pertains to Australian and Related Historical Annual Climate Data, by region. The bomregions data set is found in the DAAG R package. You can load the bomregions data set in R by issuing the following command at the console data("bomregions"). This will load the data into a variable called bomregions. If R says the bomregions data set is not found, you can try installing the package by issuing this command install.packages("DAAG") and then attempt to reload the data with the library() command. If you need to download R, you can go to the R project website. You can download a CSV (comma separated values) version of the bomregions R data set. The size of this file is about 15,817 bytes.

Australian and Related Historical Annual Climate Data, by region

Description

Australian regional temperature data, Australian regional rainfall data, and Annual SOI, are given for the years 1900-2008 or 1900-2011 or 1900-2012. The regional rainfall and temperature data are area-weighted averages for the respective regions. The Southern Oscillation Index (SOI) is the difference in barometric pressure at sea level between Tahiti and Darwin.

Usage

bomregions

Format

This data frame contains the following columns:

Year: Year
eastAVt: Eastern temperature
seAVt: Southeastern region average temperature (degrees C)
southAVt: Southern temperature
swAVt: Southwestern temperature
westAVt: Western temperature
northAVt: Northern temperature
mdbAVt: Murray-Darling basin temperature
auAVt: Australian average temperature, area-weighted mean
eastRain: Eastern rainfall
seRain: Southeast Australian annual rainfall (mm)
southRain: Southern rainfall
swRain: Southwest rainfall
westRain: Western rainfall
northRain: Northern rainfall
mdbRain: Murray-Darling basin rainfall
auRain: Australian average rainfall, area weighted
SOI: Annual average Southern Oscillation Index
co2mlo: Moana Loa CO2 concentrations, from 1959
co2law: Moana Loa CO2 concentrations, 1900 to 1978
CO2: CO2 concentrations, composite series
sunspot: Annual average sunspot counts

Source

Australian Bureau of Meteorology web pages:

http://www.bom.gov.au/climate/change/index.shtml

The CO2 series co2law, for Law Dome ice core data. is from http://cdiac.ornl.gov/trends/co2/lawdome.html.

The CO2 series co2mlo is from Dr. Pieter Tans, NOAA/ESRL (www.esrl.noaa.gov/gmd/ccgg/trends/)

The series CO2 is a composite series, obtained by adding 0.46 to he Law data for 1900 to 1958, then following this with the Moana Loa data that is avaiable from 1959. The addition of 0.46 is designed so that the averages from the two series agree for the period 1959 to 1968

Sunspot data is from http://sidc.oma.be/sunspot-data/

References

D.M. Etheridge, L.P. Steele, R.L. Langenfelds, R.J. Francey, J.-M. Barnola and V.I. Morgan, 1998, Historical CO2 records from the Law Dome DE08, DE08-2, and DSS ice cores, in Trends: A Compendium of Data on Global Change, on line at Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, U.S. Department of Energy, Oak Ridge, Tenn., U.S.A. http://cdiac.ornl.gov/trends/co2/lawdome.html

Lavery, B., Joung, G. and Nicholls, N. 1997. An extended high-quality historical rainfall dataset for Australia. Australian Meteorological Magazine, 46, 27-38.

Nicholls, N., Lavery, B., Frederiksen, C.\ and Drosdowsky, W. 1996. Recent apparent changes in relationships between the El Nino – southern oscillation and Australian rainfall and temperature. Geophysical Research Letters 23: 3357-3360.

SIDC-team, World Data Center for the Sunspot Index, Royal Observatory of Belgium, Monthly Report on the International Sunspot Number, online catalogue of the sunspot index: http://www.sidc.be/sunspot-data/, 1900-2011

Examples

plot(ts(bomregions[, c("mdbRain","SOI")], start=1900),
 panel=function(y,...)panel.smooth(bomregions$Year, y,...))
avrain <- bomregions[,"mdbRain"]
xbomsoi <- with(bomregions, data.frame(Year=Year, SOI=SOI,
cuberootRain=avrain^0.33))
xbomsoi$trendSOI <- lowess(xbomsoi$SOI, f=0.1)$y
xbomsoi$trendRain <- lowess(xbomsoi$cuberootRain, f=0.1)$y
xbomsoi$detrendRain <-
with(xbomsoi, cuberootRain - trendRain + mean(trendRain))
xbomsoi$detrendSOI <-
with(xbomsoi, SOI - trendSOI + mean(trendSOI))
## Plot time series avrain and SOI: ts object xbomsoi
plot(ts(xbomsoi[, c("cuberootRain","SOI")], start=1900),
 panel=function(y,...)panel.smooth(xbomsoi$Year, y,...),
 xlab = "Year", main="", ylim=list(c(250, 800),c(-20,25)))
par(mfrow=c(1,2))
rainpos <- pretty(xbomsoi$cuberootRain^3, 6)
plot(cuberootRain ~ SOI, data = xbomsoi,
 ylab = "Rainfall (cube root scale)", yaxt="n")
axis(2, at = rainpos^0.33, labels=paste(rainpos))
mtext(side = 3, line = 0.8, "A", adj = -0.025)
with(xbomsoi, lines(lowess(cuberootRain ~ SOI, f=0.75)))
plot(detrendRain ~ detrendSOI, data = xbomsoi,
 xlab="Detrended SOI", ylab = "Detrended rainfall", yaxt="n")
axis(2, at = rainpos^0.33, labels=paste(rainpos))
with(xbomsoi, lines(lowess(detrendRain ~ detrendSOI, f=0.75)))
mtext(side = 3, line = 0.8, "B", adj = -0.025)
par(mfrow=c(1,1))

Dataset imported from https://www.r-project.org.

Attachments: csv, json

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