On this Picostat.com statistics page, you will find information about the bomregions2012 data set which pertains to Australian and Related Historical Annual Climate Data, by region. The bomregions2012 data set is found in the DAAG R package. You can load the bomregions2012 data set in R by issuing the following command at the console data("bomregions2012"). This will load the data into a variable called bomregions2012. If R says the bomregions2012 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. 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 bomregions2012 R data set. The size of this file is about 15,950 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))

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