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: 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.

CO2 and Temperature Trends

In a previous post I discussed problems with an Excel based CO2 and temperature trend chart that used 2 Y axes. Double axis charts can be misleading because they may distort the Y axis for one of the series.

In this post I show another example of am ineffective double Y axis CO2 and temperature trend chart and present 2 alternative ways to show the same data more effectively. Links to my  R scripts and Google spreadsheet based data file are included.

CO2 Emission Trends

“Since 1751 approximately 329 billion [metric] tons of carbon have been released to the atmosphere from the consumption of fossil fuels and cement production. Half of these emissions have occurred since the mid 1970s. The 2006 global fossil-fuel carbon emission estimate, 8230 million metric tons of carbon, represents an all-time high and a 3.2% increase from 2005.” CDIAC

This is the 1st in a series of posts on CO2 emissions, CO2’s fate in the atmosphere and the long term climate and ocean impacts of these emissions.

In this 1st post, I use  CDIAC  CO2 emission data to prepare a trend chart and CSV file showing annual global CO2 emissions for  the period 1751-2006. The data and R script are available on-line as a Google spreadsheet and Google document  so that interested readers can easily download CSV files of the data and generate analysis and charts on their own in Excel, R or any software that will accept a CSV file.

Segmented Regression of GISS Temperature Trends

In this post I show how to use the R strucchange package to prepare a segmented regression of the annual GISS temperature anomaly data series.