Visualizing Arctic Sea Ice Extent Trends

Reader GH sent me an e-mail asking about a previous Arctic sea ice extent trend post (click). GH asked ….

“Why is there such a difference between this type of representation and the chart at link ? What you’ve written above seems to imply that the definitions of extent are the same.   Just looking at 2002 – present,  I’m not clear why the JAXA chart doesn’t appear to demonstrate the same clear trend. ..”

Tracking Nino34 Trends with ImageJ

Here’s a NOAA/NESDIS image of sea surface temperature anomalies (SSTA).

This image shows the land areas in black and has color codes for SSTAs, ranging from -5 to + 5 C. The yellow – orange color range shows positive anomalies while the blue – purple range show negative anomalies.

Many climate data sites show these NOAA images. Lucia at Blackboard, for example,  compared Oct, Nov and Dec 2007 and 2008 by displaying an image montage.

Lucia  said “..

“I have to admit I always have trouble integrating color images to estimate whether the net effect is a positive or negative anomaly. But, it is fun to look at the images…”

I have the same problem. While the images are great for giving the reader a sense for the spatial distribution of SSTAs,  it sure would be nice to be able to evaluate the anomalies in a defined areas like NINO34 or even better to specify an area and see the trend over  time!

There’s a free tool that I think is great for that!

RCimate Script: RSS Global Temperature Anomaly Trends

This RClimate Script lets users retrieve and plot the latest data file on monthly global RSS temperature anomaly data based for the lower troposphere.   The trend chart shows monthly and annual anomalies since 1979.

RSS Temperature Anomalies – Lower troposphere

I’ve discussed RSS temperature anomalies in this earlier post.

Here’s Remote Sensing Systems (RSS)  global land and ocean temperature  anomaly data series trend since 1979.

This chart shows the monthly and annual mean satellite based global temperature anomalies s and highlights the latest monthly anomaly.

Here are the data and RClimate Script links:

• Data Link
• RClimate Script link

RClimate Script: Sea Surface Temperature (SST) Anomaly Trends

This RClimate Script lets users retrieve and plot the National Climatic Data Center’s monthly Sea Surface Temperature (SST) Anomaly dat series .  Links to the NCDC data file and my RClimate script are included. Users can run my script with a simple R source() statement.

NCDC  SST Anomaly

I’ve discussed the Hadley SST anomaly trends in this earlier post.

Here’s NCDC’s global SST anomaly data series trend since 1880.

This chart shows the decadal means and highlights the latest monthly SST anomaly.

Here are the data and RClimate Script links:

• Data Link
• RClimate Script link

RClimate Script: NINO 3.4 SST Anomaly Trends

This RClimate Script lets users retrieve and plot the weekly NOAA NINO 3.4 SST  anomaly data for 1990 to the most recent value.  Links to the NOAA data file and my RClimate script are included. Users can run my script with a simple R source() statement.

NINO 3.4 SST Anomaly

I’ve discussed ENSO and NINO 3.4 in this earlier post.

SST’s in 4 equatorial Pacific zones are closely monitored to assess the status of El Nino – Southern Oscillation (ENSO).

NINO 3.4 SST anomaly provides a quick and effective indication of ENSO conditions. NOAA updates the NINO 1,2,3,3.4 and 4 SST and SSTA series weekly.

Here’s the weekly NINO 3.4 trend from 1990 to the most recent weekly reading. Click image to enlarge.

Here are the data and RClimate Script links:

• Data Link
• RClimate Script link

RCimate Script: Recent Total Solar Irradiance (TSI) Trends

This RClimate Script lets users retrieve and plot the latest data file on daily total solar irradiance (TSI) data from NASA’s  Solar Radiation and Climate Experiment (SORCE) Mission. The trend trend chart shows daily values from 2/25/03 to about one week before when the R script is run.

Recent TSI Trends

Here’s the 1/21/10 plot of NASA’s SORCE TSI data.

Here are the data and RClimate Script links:

• Data Link
• RClimate Script link

RClimate Script: CO2 Trends

This RClimate Script lets users retrieve the latest data file on monthly Mauna Loa  CO2 levels and generate a trend chart with the latest reading highlighted.

CO2 Trends – Keeling Curve

Here’s the Mauna Loa Observatory CO2 trend from 1958 to Dec., 2009.

Here are the data and RClimate Script links:

• Data Link
• RClimate Script link

Understanding the Science of CO2’s Role in Climate Change: 3 – How Green House Gases Trap Heat

This post, the 3rd (1^st 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.

Understanding the Science of CO2’s Role in Climate Change: 2 – Electromagnetic Radiation and Earth’s Climate

This post, the 2nd (1^st 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.

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.