Animated Images of Arctic Sea Ice Extent Decline

This post shows how to download and animate a series of Arctic Sea Ice Extent images using R and the animation package.

In my previous post, I showed how to download the daily arctic sea ice extent  data and generate an animation of the time series plots. In this post I show how to animate the monthly images provided by NOAA’s Nation Snow and Ice Data Center.

###### RClimate Script: NSIDC_Monthly_Sea_Ice_Extent_Images.R
## Download and process 1981 to 2012 Monthly Arctic SIE Images
## Sept. 5, 2012: http://chartsgraphs.wordpress.com; DK O'Day
##############################################################
  library(animation)
## Establish work dir to place downloaded images and gif file
  setwd(getwd())
  where <- getwd()

## Monthly NSIDC Extent Immage Link
# ftp://sidads.colorado.edu/DATASETS/NOAA/G02135/Aug/N_198108_extn.png
  part_1 <- "ftp://sidads.colorado.edu/DATASETS/NOAA/G02135/Aug/N_"
  part_3 <- "08_extn.png"

# Specify month number for animation
  m <- "08"

# Loop through years to create link and download images
  for (y in 1981:2012) {
     file_name <- paste(part_1, y, part_3, sep="")
     copy_name <- paste(where, "//asie",y,m,".png",sep="")
     download.file(file_name, copy_name, mode="wb")
     }

## copy last file c times to extend gif animation
    for (c in 1:2)
    {
     file_name <- paste("asie2012",c, ".png",sep="")
     file.copy(from= copy_name, to = file_name, overwrite=T)
    }

## Use animation package to generate gif file
  ani.options(convert=shQuote('C:\\Program Files (x86)\\ImageMagick-6.7.9-Q16\\convert.exe'))
  ani.options(outdir = getwd()) # direct gif output file to working dir
  ani.options(interval= 0.70)
  im.convert("asie*.png", "asie_image_animation.gif")

Here is a link to the R script file.

R Script to Build Animation of Arctic Sea Ice Extent – Update 9/3/12

In my previous post I showed an animation of Arctic Sea Ice Extent from the 1980′s through August, 2012 (link).  In this post, I show how to build this Arctic Sea ice Extent  animated chart.

Source Data

The Arctic Ice Sea Monitor (link)   updates their daily csv file with the latest satellite based arctic sea ice measurements.  Here is the daily csv file link.

R script

To develop my animation of the daily Arctic Sea Ice extent, I decided to produce a plot for each year that showed the current year in red and the previous years in grey.  I go this idea from Tamino at Open Mind.

Here is my R script: link.

Once I have the individual png files for each year, I use Photoshop Elements to generate my animation.

After getting Pierre’s comment about using the animation package, I gave it another try. The 2nd time,  I got it to work with ease.

I can now update the daily ASIE plot automatically without any Photoshop Elements involvement.

Thanks Pierre.

Global Sea Level Rise and El Nino – La Nina

Here is an informative interview with NASA’s Josh Willis about global sea level rise and El Nino – La Nina.  I first saw the video on Zeke Hausfather’s  YALE Forum on CLIMATE CHANGE & THE MEDIA

Comparison of UAH and RSS Time Series with Common Baseline

In this post I set both UAH 5.4 and RSS 3.3 global temperature anomaly series to a common baseline period (1981-2010)  to compare them. Since both the UAH 5.4 and RSS 3.3 series are satellite based , they 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 then used this technique in this post to directly compare UAH 5.4 (baseline 1981-2010) and GISS.

In this post, I compare the satellite based UAH 5.4 (baseline 1981-2010) and RSS 3.3 (baseline 1979-1998) series.

The offsets are as follows:

• UAH:  -0.000978
• RSS:      0.098772

Since the UAH TLT 5.4 series is based on a 1981-2010 baseline, the offset is nearly zero (-0.00098 versus 0.0). The RSS offset changes the baseline from 1979-1998 to 1981-2010.

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

Comparison of 1981-2010 Baseline Series

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

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

UAH Temperature Anomalies Following Predictable Pattern

In this post I show one simple  and 2 multiple regression models to assess the role of time, El Nino – La Nina SSTA and volcanic activity (SATO) on UAH global temperature anomaly trends. The 3rd model provides a reasonable  approximation of the actual UAH oscillations over the 1979 – Feb, 2011 period.

Click Image to Enlarge

This analysis is similar to previous temperature anomaly regressions (here, here, here) that I have done.

The simple trend line regression shows the overall trend is upward, however, there are several oscillations that the linear trend misses.  The yr_frac and Nino34 regression improves on the linear model, however, it undershoots in the early 1980s,  overshoots in the 1992-1994 period, periods following significant volcanic activity.

The yr_frac, Nino34 and SATO model improves the fit in the early 1980s and 1992-1994 period and is slightly better in the 1998 and 2010 El Nino periods.

The 3rd model matches the observed 2010 El Nino – La Nina oscillation pretty well so far, indicating that the 2010 – 2011 UAH anomalies are following a predictable pattern.

Assessing Climate Trends: Eyeball versus Regression

In a previous post, I showed the Lower Stratospheric Temperature Anomaly (TLS) Trends (link).  A reader submitted the following comment:

“The lower stratosphere temperature profile is essentially flat from ca. 1995 to the present. This approximately mirrors the temperature trend for the surface temperature. From 1980 to about 1995, the surface temperature increased while the lower stratospheric temperature decreased. After that both went flat.” tony

In the words of Edwards Deming:

In God we Trust, All Others Must  Bring Data”

Since tony didn’t bring any data to back up his  claims, I’ll do the analysis for him.

Atmospheric Temperature Structure : 2 – Stratospheric Cooling

In this  post I review the temperature structure of the atmosphere and lower stratosphere temperature (TLS) anomaly trends.

Temperature Structure in the Atmosphere

In post 1 of this series, I developed this RClimate chart of temperature soundings which I update daily: (Click to enlarge)

Using RClimate To Retrieve Climate Series Data

This post shows how to use RClimate.txt to retrieve a climate time series and write a csv file in 5 lines of R script.

One of my readers, Robert, wants to be able to download climate time series data and write it to a csv file.  The R script below shows how to  download the MEI data series and write a csv file.  For this example I will use the RClimate function (func_MEI) to retrieve the data. I then simply specify the path and file name link for the output file (note quotes around the output file name and then write  a csv file.

source("http://processtrends.com/files/RClimate.txt"
m <- func_MEI()
head(m)
output_link <- "C://R_Home/mei.csv"
write.csv(m, output_link, quote=FALSE, row.names = F)

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)