NASA’s Goddard Institute for Space Studies (GISS)  has released their December, 2014 anomaly data, showing that 2014 was the warmest year in the warmest decade in the 1880 – 2014 instrumental temperature record period.

NASA’s results are consistent with the Japanese Meteorological Agency report (here)

Here is my R script for those who would like to reproduce my chart.

############## RClimate Script: GISS Annual Temperature Anomaly ###########################
##                   http:chartsgraphs.wordpress.com    1/16/15                            ##
############################################################################################
  library(plyr); library(reshape)
 ## File Download and File
  url <- c("http://data.giss.nasa.gov/gistemp/tabledata/GLB.Ts+dSST.txt")
  file <- c("GLB.Ts+dSST.txt")
  download.file(url, file)

## 1st 8 rows and the last 12 rows contain instructions
## Find out the number of rows in the file, and exclude the last 12
    rows <- length(readLines(file)) - 12
## Read file as  char vector, one line per row, Exclude first 8 rows
    lines <- readLines(file, n=rows)[8:rows]
## Data Manipulation, R vector
## Use regexp to replace all the occurences of **** with NA
    lines2 <- gsub("\*{3,5}", " NA", lines, perl=TRUE)
## Convert the character vector to a dataframe
    df <- read.table(
      textConnection(lines2), header=TRUE, colClasses = "character")
    closeAllConnections()
## Select monthly data in first 13 columns
    df <- df[,c(1,14)]
## Convert all variables (columns) to numeric format
    df <- colwise(as.numeric) (df)
    df[,2] <- df[,2]/100
    names(df) <- c("Year", "anom")
## Remove rows where Year=NA from the dataframe
    df <- df [!is.na(df$Year),]
## Find last report month and last value
    GISS_last <- nrow(df)
    GISS_last_yr <- df$Year[GISS_last]
    GISS_last_temp <- df$anom[GISS_last]
## Calc decade averages
  dec_mean<- as.numeric(14)
  dec_st <- as.numeric(14)
  dec_end <- as.numeric(14)
 # yr_n <- as.integer(df$Year)
  base_yr <- 1870
  df$dec_n <-  (as.numeric((df$Year - base_yr) %/% 10) * 10) + base_yr
 # df <- data.frame(df, dec_n)
  for (i in 1:13) {dec_st[i] = base_yr+ i*10
     dec_sub <- subset(df, dec_n == dec_st[i], na.rm=T)
     dec_mean[i] <- mean(dec_sub$anom)
     }
 dec_st[14] <- 2020              # Need to have for last step line across decade
 dec_mean[14] <- dec_mean[13]
 dec<- data.frame(dec_st, dec_mean)
#### Plot function
  plot_func <- function() {
  par(las=1); par(ps=12)
  par(oma=c(2.5,1,1,1)); par(mar=c(2.5,4,2,1))
# specify plot yr min & max
  p_xmin <- 1880;   p_xmax <- GISS_last_yr+10
  title <- paste("GISS Land and Sea Temperature Annual Anomaly Trend n", p_xmin, " to ",
   GISS_last_yr, sep="")
  plot(df[,1], df[,2], type = "l", col = "grey",
     xlim = c(p_xmin, p_xmax), ylab = "Temperature Anomaly - C (1951-1980 Baseline)",
     xlab="", main = title,cex.main = 1,cex.lab=0.8,cex.axis=0.85)
  points(GISS_last_yr, GISS_last_temp, col = "red", pch=19)
  last_pt <- paste( GISS_last_yr, ", ", GISS_last_yr, " @ ", GISS_last_temp, "C",sep="")
  points(dec$dec_st, dec$dec_mean, type="s", col="blue")
## add legend
  legend(1880,0.6, c("Decade Mean Anomaly", "Annual Anomaly" ,GISS_last_temp), col = c("blue", "grey", "red"),
       text.col = "black", lty = c(1,1,0),pch=c(0,0,16),pt.cex=c(0,0,1),
       merge = T, bg = "white", bty="o", cex = .75, box.col="white")