Category Archives: R Climate Data Analysis Tool

Arctic Amplification – November, 2013: Updated 12/20/13

Updated (bold italic) 12/20/13 to reflect comments from David. 

NASA’s GISS temperature anomaly map (link) for November, 2013 is reproduced  below. It uses a 2×2 degree grid cell for the globe. 


The November, 2013 GISS temperature anomaly shows the critical global pattern that is important to recognize because it is fundamental to understanding why global warming is so dangerous.

First, the overall global anomaly for November, 2013 was 0.77 degrees C.  The 2 degree latitude zone mean anomaly varied from a low of 0.068 to a high of 2.33. So the mean global anomaly does not tell the full story, we need to look at the geographical distribution to really understand the global warming pattern.

As we examine the geographical distribution of the November, 2013 anomalies, we see that they tend to increase as we move from the equator toward the poles.  This pattern,  called polar amplification, means that the polar regions, particularly the Arctic region, warms much more rapidly than the overall global mean.

I developed this chart in R to display the mean zonal anomalies by 2 degree latitude zones to help me visualize the November, 2013 anomaly patterns.


Here is the R Script that I used to produce the chart.
#### GISS Temperature Anomaly - Zonal mean by 2 degree latitude
##K O'Day, Dec. 18, 2013
 link <- c("")
 mon <- "November, 2013"
 title <- paste("Mean Temperature Anomaly by 2 Degree Latitude Zones\n", mon, sep="" )
 note_1 <- "GISS Temperature Anomaly\n (1951-1980 base period)"
 df <- read.table(link, skip=4)
 par(las=1, oma=c(3,1,1,1), mar=c(5,5,3,1), ps=11)
 names(df)<- c("Zone", "Anom")
 #png(file="C://R_Home//Charts & Graphs Blog//RClimateTools//a_Revised_Blog//art_amp.png", bg="white")

 plot(df$Anom, df$Zone, xlim=c(0,3), ylim=c(-90,90), type="l", axes=F, xlab="Mean Anomaly for Zone - C",
      ylab = "Latitude",  xaxs="i", yaxs = "i", main=title)
   axis(1, at=NULL)
   axis(2, at=c(-90,-60,-30,0,30,60,90))
   abline(h=40, col="green")
   abline(h=0, col="darkgrey")
   abline(v=0.77,col = "black" )
   abline(h=64, col="blue")
   text(2.5, 43.5, "Philadelphia, Pa.", cex=0.7)
   text(2.7, 67, "Reykjavík, Iceland", cex=0.7)
   text(2.25, -20, note_1, cex=0.75, adj=0)
   rect(0.6,-65,0.85 , -50, col = "white", border = "white")
   text(0.77, -60, "Global Mean @ \n0.77 C", cex=0.7)
 mtext("D Kelly O'Day -", 1,1, adj = 0, cex = 0.8, outer=T)
 mtext(format(Sys.time(), "%m/%d/ %Y"), 1, 1, adj = 1, cex = 0.8, outer=T)

Visualizing the Arctic Sea Ice Extent Decline

Understanding what is happening to Arctic sea ice is critical to recognizing the serious consequences of global warming. So I want to help people visualize the 30+ year trend in Arctic sea ice extent.

The source data file is here:

Comparison of UAH and GISS Time Series with Common Baseline

In this post I set both UAH and GISS global temperature anomaly series to a common baseline period (1981-2010)  and compare them. Even though the UAH series is satellite based and GISS series is station based, the series exhibit striking similarities.

Common Baseline

In this previous post, I showed how to convert temperature anomaly time series from one baseline period to another period.  I use this technique in this post to directly compare UAH (baseline 1981-2010) and GISS (baseline 1951-1980) series.

The offsets are as follows:

  • UAH:  -0.000978
  • GISS: 0.34958

Since the UAH TLT 5.4 series is based on a 1981-2010 baseline, the offset is nearly zero (-0.00098 versus 0.0).

Users can reproduce my analysis on their own by downloading my CTS.csv file and applying the offsets to the UAH and GISS series.

Comparison of 1981-2010 Baseline Series

Here is a plot of UAH and GISS 12 month moving averages for 1979 to current: Click to Enlarge Continue reading

Comparison of GISS LOTAs During 5 El Nino – La Nina Cycles

In this post I compare GISS LOTAs during 5 El Nino – La Nina cycles (2010, 1998, 19883, 1973 and 1970).

El Nino – La Nina Cycles

In a previous post I showed the Nino 34 SSTA cycles for 2010, 1998, 1983, 1973 and 1970 here. In this post, I want to see how GISS Land Ocean Temperature Anomalies (LOTA) vary over El Nino – La Nina cycles.  Here is my RClimate chart showing GISS anomalies for 6 months prior to cycle year,the cycle year and the 12 months after cycle year (30 month period).

Click chart to enlarge


While the 2010 cycle is only partially complete, there are a number of interesting aspects in this chart. The average temperatures during the cycles have clearly risen with the latest cycle showing the highest maximum anomaly. All 5 cycles all have similar patterns, with a buildup in 6 months prior to cycle year. The maximum – minimum range for the 5 cycles are comparable, ranging from 0.45 (2010) to 0.60 (1998).

Here is a data summary of the 5 cycles.


Volcanic Solar Dimming, ENSO and Temperature Anomalies

In previous posts I have shown plots of global temperature anomaly, volcano and Nino34 trends (here , here). In this post , I want to further  explore the role of volcanic eruptions and Nino34 phases (El Nino, La Nina) on temperature anomalies.

This post shows a 5-panel chart of monthly climate trend data: 1) time line of major volcanoes and Volcanic Explosivity Index (VEI),  2) Mauna Loa Observatory (MLO) Atmospheric  Transmission  (AT) measurements,  3) Stratospheric Aerosol Optical Thickness (SATO) Index,   4) , Nino 34 as an indicator of ENSO and 5)  GISS land-ocean temperature anomaly.

The RClimate script and Climate Time Series data file (CTS.csv) links are provided.

First, here is  the 5-panel chart that I have made showing the monthly volcano time line with Volcano Explosivity Index (VEI) , Atmospheric Transmission at Mauna Loa Observatory, SATO Index as well as the Nino34 SSTA and GISS LOTA. (Click Image to Enlarge)


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Climate Charts, Data and RClimate Scripts

While there are many online climate data resources, the source data files are in numerous data formats, presenting a challenge to climate citizen scientists who want to retrieve and analyze several climate indicators at the same time.

I have been working to develop a consolidated open access data file and RClimate scripts that users can use to retrieve climate data, conduct their own analysis  and generate their own climate charts.  My goal is to make it easier for climate citizen scientists to get their hands on the data in a simple,  usable format (CSV). This post updates the status of my RClimate efforts.

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LearnR Toolkit To Help Excel Users Move Up To R (updated 6/14/14)

As a former  Excel chart user, I want to help current Excel users make the transition to more advanced charting R with as little difficulty as possible. This post introduces my LearnR Toolkit to help Excel users move up to R in a systematic, step by step fashion.


As an Excel chart user, I wanted to produce panel charts like this:



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Plotting Atmospheric Temperature Structure and Lapse Rate

This post presents an RClimate script to develop a temperature sounding profile and calculate the ambient lapse rate using University of Wyoming atmospheric sounding data. Understanding atmospheric structure and lapse rate is essential to a full understanding of the role of  greenhouse gases in global warming.

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RClimate Tools for Do It Yourself Climate Trend Analysis – Nov, 2010 Update

I have made several updates to  RClimate tools for do-it-yourself  climate scientists.  The downloadable monthly climate trends file  (link to csv file) now includes the 5 major global land-ocean temperature anomaly time series (GISS, HAD, NOAA, RSS, UAH) as well as  PDO, AMO and NINI34 indexes.  Stay tuned, I plan to add several more series in the next few weeks. Do you have any suggestions?

I have also added several functions to my on-line RClimate.txt file to help DIY  citizen climate scientists to quickly and easily retrieve up to date climate trend data so that they can spend their time analyzing the  temperature anomaly and climate oscillation trends rather than slugging through data downloads and reformatting.

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September Anomaly Trends Show Global Warming Continues: Update 1

In this post I present a trend chart which shows the September anomaly trends for the 5 major global temperature anomaly series and a table that shows how September 2010 ranks over the entire record for each series. The source data and RClimate script file links are provided.

Update 1: In a comment,  ChristianP  suggested the addition of a loess regression fit to the trend line chart. Thanks ChristianP.

Click to Enlarge

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