Understanding the Science of CO2’s Role in Climate Change: 1 – Introduction

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:

• Early Excel trend charts (link)
• R trend charts (link, link, link, link)
• R regressions (link, link, link)

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.

Learn R Toolkit Update

My Learn R Toolkit went on-line in April, 2009. I’m happy to announce that the 10,000th  file download benchmark was reached on October 30.

Here are a few comments that Learn R Toolkit users have submitted:

• “… I think your instructional material is simply excellent. Your explanations are uncomplicated, unhurried and clear.”

• “I’ve enjoyed your R tutorial series. Thank you. It was worth the $19.”

• “You’ve given me some fishing poles to catch what I want to catch.”

• “Your tutorials are a good starting point for learning about R. I’m interested in manipulating data … and find myself referencing your tutorials on different issues.”
  

The first 3 modules are free, so be sure  to check out my Learn R Toolkit for yourself.

Worth Reading: Global Warming – Understanding the Forecast

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.

Solar Trends: Comparison of TSI and GISS Temperature Anomaly Trends

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.

Solar Trends: Solar Cycle 24 Sunspots

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.

Solar Trends: Total Solar Irradiance Since 1611 – Update

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.

Solar Trends: Sunspot Numbers Since 1749

This is the 1st in a series of  posts I will be doing on solar trends. In this post, I show how to retrieve online monthly sunspot data back to 1749, calculate average annual sunspot numbers (SSN),  plot the monthly and annual average SSN as well as a lowess smooth, add the Solar Cycle number to the plot and generate  a csv file that will be used in future posts. Links to the original data source, my annual SSN and cycle date Google spreadsheet files, and my R script Google document file are provided.

CO2: Emissions & Changes in Atmospheric Levels

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.

Global Mean Sea Level Trends

In this post, I show an R script that downloads the University  of Colorado, Boulder’s 1993-2009 global mean sea level (msl) change (link) data, converts the ASCII file into a usable R data frame, calculates moving average and msl change  trend rate and develops a trend  chart that shows msl change  and trend rates and writes a csv file that I upload to Google Docs.

Global Sea Surface Temperature Trends (1850-2009)

In this post, I show an R script that downloads the Hadley Centre’s 1850-2009 monthly sea surface temperature (HadSST2) anomaly data, converts the ASCII file into 2 usable R data frames, calculates overall and post 1980 SST anomaly trend rates and develops a 2 panel chart that shows SST anomalies and trend rates and the % global coverage for the SST series.