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.
This post shows how to retrieve on-line Arctic Sea Ice Extent data from the National Snow and Ice Data Center (NSIDC), consolidate the data files, generate a csv file, summarize and plot the data and post it as a Google Docs so that interested readers can download and analyze this data series themselves.
Links to the NSIDC source data files, R script and my Google Docs csv file are provided.
Sea Ice Extent Data and Definition
The Japan Aerospace Exploration Agency (JAXA) provides daily updates to the Arctic Sea Ice Extent and maintains a downloadable csv file of daily values from June, 2002 at this link.
JAXA and other climate science data sites define sea ice extent as the area of the ocean where sea-ice concentration is 15% of more.
NSIDC maintains a series of 12 ASCII files (one for each month) that include the monthly average sea ice extent values for the period 1979 to present. I am using the NSIDC data set because it covers a longer period (31 years) than the JAXA data series.
Here’s the 1979 – 2009 trend chart of monthly Arctic sea ice extent measurement data based on NSIDC files. (Click image to enlarge)
This post, the 3rd (1st here, 2nd here) in the series on Understanding the Science of CO2’s Role in Climate Change, discusses how water vapor, CO2, CH4, O3 and N2O absorb and emit the Earth’s longwave radiation, changing the Earth’s energy balance.
I’ve made a 5 panel chart that shows spectra data for 5 greenhouse gases (GHG). Molecules of these gases in the atmosphere absorb and emit the Earth’s infrared radiation at specific frequencies, trapping some of the Earth’s radiation, warming the planet.
I’ve included a link to my R script so that readers can access the online spectra data and generate your own GHG spectra.
This post, the 2nd (1st post here) in the series on Understanding the Science of CO2‘s Role in Climate Change, discusses several electromagnetic radiation topics: 1) electromagnetic spectrum basics, 2) essential climate related electromagnetic radiation physics, and 3) the Sun and Earth’s electromagnetic radiation spectra.
I present the basic formulas and 3 R based charts that I have developed to help readers get a sense for the underlying physics and to provide a basic foundation for understanding the climate related properties of greenhouse gases and the energy balance models presented in upcoming posts.I have also included an Excel workbook with these basics formulas.
Moving Beyond Trend Charts
I have made many trend charts and regressions over the past 4 years as I try to learn as much as I can about climate change and CO2’s role in global warming. Here are links to some of my previous Climate Charts & Graphs work:
While these trend charts and regressions help to show the relationships between climate factors, I keep asking myself the fundamental question “how does atmospheric CO2 actually affect the earth’s temperature”. To answer this, I need to delve more deeply into the physics of CO2 in the atmosphere to really understand the CO2 – climate change relationship. As I read climate science papers and visit climate change websites, I get a sense for the physics of CO2 and climate change, but not a real solid understanding.
David Archer’s “Global Warming, Understanding the Forecast” book and accompanying videos of his Chicago University lectures and Dennis Hartmann’s “Global Physical Climatology” have helped my understand the role that solar radiation, electromagnetic spectrum, blackbody radiation, Stefan-Boltzmann Equation, Planck’s Law, atmospheric absorption bands and greenhouse gases play in the global energy balance and climate change.
To really understand CO2’s role, I need to work with the numbers and formulas myself so that I can see the cause and effect relationships. I have developed a series of Excel tools to help me understand the physics behind global warming and to be able to reproduce several of the critical charts necessary to understanding climate change.
This series of posts will outline and graphically display the key topics and present the Excel tools that I found helpful to me to understand basic climate change science. Where possible, I present downloadable Excel workbooks to let readers work with the fundamental equations and check out the basic mathematical relationships for themselves.
This post is part of Blog Action Day. Since I write about climate charts and graphs regularly, I struggled to find a topic that would fit Blog Action Day beyond what I usually write.
I am currently reading David Archer’s Global Warming – Understanding the Forecast and realized that telling my readers about this excellent book, accompanying lecture videos and on-line models would make a great post.
This book is a must read for anyone who wants to understand the science behind global warming, how sunlight warms the earth, how the earth emits infrared radiation (earth light), how greenhouse gases affect the earth’s climate, and how climate models work. David Archer, a professor in the Geophysical Sciences Department at the University of Chicago, wrote this book for his class on global warming for non-science majors.
The book is accompanied with video lectures from his Fall, 2009 class and access to 8 on-line state of the art interactive models used by climate scientists.
David Archer’s book is the best climate science book that I have found.
This post continues my analysis of solar trends, this time comparing reconstructed total solar irradiance (TSI) and temperature anomaly (GISS) trends for the 1880 – 2009 period. I use 11 year moving averages to smooth both series so that we can see the longer term trends. Links to a download-able copy of my R script is provided for those readers who wish to prepare their own charts.
Here’s a don’t miss image of the sun’s 9/25/09 sunspots from the spaceweather.com website archive.
Here’s the link to spaceweather.com’s current activity page.
This is the 2nd in a series of posts I will be doing on solar trends. In this post, I show how to retrieve online daily satellite and reconstructed TSI data, plot the daily data as well as annual and 11 year moving average smooths for the data series. Links to the original data sources and my R script Google document are provided. Updated 10/1/09.
In previous posts, I have shown the 1750-2008 global CO2 emission trends and the atmospheric CO2 concentrations at Mauna Loa, Hawaii. In this post, I compare annual CO2 emissions with annual changes in atmospheric CO2. The resulting chart shows the portion of CO2 emissions that remains in the atmosphere and the portion that is soaked up by the land & ocean. Links to the R script and source data files are provided.