Category Archives: R Climate Data Analysis Tool

Arctic Sea Ice Extent Trends: 1979-2010; Update 1

Now that the 2010 Arctic sea ice melt season is over, we can see how 2010 fits into the long-term trends Arctic  Sea Ice Extent. This post shows an R Climate chart that I have made to look at the annual  NSIDC Arctic Sea Ice Extent maximum, minimum and seasonal melt trends for the 32 year period, 1979 to 2010. Data and RClimate scripts are provided.
Update 1 (10/6/10) Added trend lines to plots based on suggestion from reader.

Here’s my RClimate script trend chart of 1979-2010  NSIDC Arctic Sea Ice Extent data.  I have plotted NSIDC’s maximum and minimum sea ice extent for each year and my calculated value for seasonal melt (maximum – minimum). (Click image to enlarge)

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RClimate Tools for Do It Yourself Climate Trend Analysis

In this post I introduce my RClimate functions which allow R users to easily download and plot monthly temperature anomaly data for the 5 major global temperature anomaly data series: GISS, HAD, NOAA, RSS, UAH.

Consolidated LOTA Data File

In this previous post I introduced my global  Land Ocean Temperature Anomaly (LOTA) monthly csv file that Excel and R users can download to conduct climate trend analysis.

In this post, I introduce my RClimate.txt R scripts that users can source() to simplify access to the LOTA data.  Please note that I have used the “.txt” descriptor  for my file type to avoid download problems encountered when I use the standard R file descriptor.

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Time Series Regression of Temperature Anomaly Data: 1 – Don’t Use OLS

Phil Jones Statement (February , 2010)

Phil  Jones of the  Climate Research Unit (CRU) responded to a series of questions from the BBC in early February, 2010 (link). Question B dealt with global warming trends in the 1995 – 2009 period. Here’s the BBC question and Phil Jones answer:

BBC Question B: B – “Do you agree that from 1995 to the present there has been no statistically-significant global warming?”
Phil Jones Answer: “Yes, but only just. I also calculated the trend for the period 1995 to 2009. This trend (0.12C per decade) is positive, but not significant at the 95% significance level. The positive trend is quite close to the significance level. Achieving statistical significance in scientific terms is much more likely for longer periods, and much less likely for shorter periods.”

Phil Jones’ statement provided a time series regression learning moment for  many of us citizen climate observers who quickly checked his statement with our Excel, R or other handy regression analysis tools.  I sure did. Two readers, J and S, contacted me with questions – comments:

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Tracking Long Term and Recent UAH Channel 5 Anomally Trends

In this post I present  combined long term and recent trend charts of UAH Channel 5 temperature anomalies using R’s figure inside figure capability.  This approach provides a better picture of global temperature trends than UAH’s day-of-year plots.


The University of Alabama Huntsville (UAH) provides a daily update to their Channel 5 global average temperature at 14,000 feet. I have previously posted about this data set here. The source data file link is here.

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UAH Channel 5 Anomaly Trends

Lucia at The Blackboard has on on-going bet where readers submit predictions of the monthly UAH anomaly, the April bet post is here.

Lucia does some very interesting climate analysis, some of her charts, however,  can give me a headache if I stare at them to long. Let’s look at her April UAH bet chart(Warning, may give you a headache if you look for too long)

What can we say about this chart? Well, um, ah, – - -  let’s be positive:

  • It was done in Excel. The ugly grey shading gives Excel away every time
  • It’s colorful, yes, very colorful
  • It has lots of data, yes, lots of data
  • It is clearly labeled

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Climate Oscillations and GISS Temperature Anomaly Trends

In this post, I examine the combined impacts of Pacific Decadal Oscillation (PDO), Atlantic Multidecadal Oscillation (AMO) and El Nino – Southern Oscillation (ENSO)   on the long-term GISS Land and Ocean Temperature Anomaly (LOTA) trend.


Professor Don Easterbrook of Western Washington University  has stated …

“The PDO cool mode has replaced the warm mode in the Pacific Ocean, virtually assuring us of about 30 years of global cooling, perhaps much deeper than the global cooling from about 1945 to 1977.”  source

Easterbrook’s PDO theory is repeated  here and here. Clearly he believes that the shift in PDO phase from warm to cool will have a significant impact on global temperatures for the next 30 years.

In this post I take a closer look at PDO, AMO and ENSO indexes to see how they are related to the GISS anomaly trends.

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RClimate Script: Pacific Decadal Oscillation Trend

This RClimate Script lets users retrieve and plot the monthly and moving average  Pacific Decadal Oscillation (PDO) data  from the University of Washington’s JISAO website. The script retrieves the PDO data from January, 1900 until latest month available at time script is run.  The trend chart shows the JISAO PDO trend and user selected moving average period.

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Tracking Climate Trends with RClimate Scripts and Links

Some of my visitors may have noticed that I have added a new Climate Images page and have been adding climate data images to my right side panel. So far, I have 6 trend charts, 4 map images, 1 photo image and 1  data value  showing the CO2 concentrations, recent total solar irradiance (TSI) , temperature anomalies and  Arctic sea ice extent trends and map images of global and SST anomalies and  Arctic sea ice extent.

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RClimate Script: Polar Amplification – 2000 to 2009

This RClimate Script lets users retrieve and plot the NASA GISS temperature anomaly data for 2000 to 2009 by latitude zone during the past decade. A link is provided to the NASA GISS data generation query page as well as links to my saved file of the GISS data and my RClimate script that users can run with a simple R source() statement.


I’ve discussed polar amplification in this previous post.

NASA has reported that 2009 was the 2nd warmest year on record and that 2000 to 2009 was the warmest decade on record, based on global mean anomaly values. The increased anomalies over the decade were not uniform, as shown in this NASA image of 2000 – 2009 anomalies  compared to the 1951-1980 baseline period. (Click image to enlarge it)

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RClimate Script Introduction

Would you like to be able to generate a GISS temperature anomaly trend chart on your PC with just 1 line of R script? What about downloading the R script to your PC so that you can edit the script to fit your needs?

In this post I present my first RClimate Script so that users with just a little R experience who have R and 2 R libraries up and running on their PC  can  retrieve the latest NASA file on monthly GISS temperature anomalies, generate a trend chart and calculate the anomaly trend rate in 1-2 minutes.

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