This post describes my consolidated global temperature anomaly CSV file that users can easily download to Excel or R to do their own trend analysis.
Do It Yourself Global Temperature Anomaly Trend Analysis
As I wrote in my July 10, 2009 post,
“There are many blogs and web sites (small sample: 1, 2, 3, 4, 5, 6) with multiple opinions on global climate trends. Some sites are data oriented and others are opinion oriented. What is a [data analyst] charter to think?
My advice, take a look at the data for yourself. As an Excel or R charter, why not analyze it yourself to get a better appreciation for what is going on.
To help you get started, I’ve developed a consolidated monthly CSV file that presents the 5 major global land and ocean temperature anomaly data series: GISS, NOAA, HADCrut3, RSS and UAH.
Here’s the link to my consolidated temperature anomaly CSV file.
I update the consolidated file regularly by downloading the latest agency source files so that the consolidated file includes all source agency data revisions. This way you can get the most up-to-date temperature anomaly data without having to reformat/ manipulate the 5 individual files.
In this post I present a chart that tracks the daily Arctic Sea ice Extent (SIE) for 2007 and 2010. I chose 2007 as the comparison year because it had the record minimum and I wanted to be able to directly compare 2010 with the record minimum year to get a quick comparison of 2010 with the minimum year.
I will update this chart regularly on my Arctic Update page to help Arctic Sea Ice observers get a quick sense of the 2010 – 2007 comparison.
2010 – 2007 Comparison Chart
Here’s my R based 2010 – 2007 Arctic SIE extent chart. (Click image to enlarge)
I’ve added a number of features to this chart to help me quickly asses the situation:
We are at the half way point in June, 2010 and the Arctic Sea Ice Extent is melting at a record-breaking pace. Please note I have adjusted my post based on JAXA’s 6/15/10 data update.
I have added a new page – Arctic Update where I will regularly have the latest JAXA day of year chart, my month to date data table and my month to date Sea Ice Extent chart. 6/17/10
Here’s the JAXA day-of-year chart which clearly shows how ASIE has dropped dramatically in May and so far in June. The mid June levels are the lowest in JAXA’s 2003-2010 period.
Here’s my JAXA data trend chart which shows the daily June values for each year. The blue dots reflect the daily values and the red dots reflect the ASIE values for the latest data date for each year. The green line is intended to assist reader in comparing the same values for the same data in each year.
Clearly the June 2010 ASIE is dropping rapidly, with the 6/15/2010 value significantly less than the values on this data in previous years.
This table provides additional information on the magnitude of the June ASIE decrease.
June, 2010 started at the lowest point in the 2003-2010 period, 32,000 km^2 less than the previous June 1 low in 2003. So far in the first half of June, 2010, ASIE has decreased 0.898 million km^2, beating the previous full June record of 0.797 million km^2. The mid June 2010 rate of decrease has been 60,000 million km^2 per day, considerably greater than the previous maximum mid June rate of 53,000 in 2008. (Corrected 7/7/10 based on Derek McCreadie comment)
Details on my RClimate script are available in this previous post.
Arctic Sea Ice Extent (SIE) follows an annual cycle, with maximum levels usually in March and minimum values in September. Many analysts use day-of-year charts to compare the SIE cycle by years so that they can assess the current years trend with previous years. In this post, I present an alternative to the day-of-year chart which shows the daily values a calendar month previous years and the current year.
Arctic Sea Ice Extent (SIE)
I have discussed Arctic Sea Ice Extent (SIE) here and here. Both of those posts used the NSDIC monthly Arctic SIE data. In this post, I use the Japan Aerospace Exploration Agency (JAXA) daily data series, available at this link.
Here’s the JAXA day-of-year chart . Click image to enlarge.
In this post, I examine the combined impacts of Pacific Decadal Oscillation (PDO), Atlantic Multidecadal Oscillation (AMO) and El Nino – Southern Oscillation (ENSO) on the long-term GISS Land and Ocean Temperature Anomaly (LOTA) trend.
Professor Don Easterbrook of Western Washington University has stated …
“The PDO cool mode has replaced the warm mode in the Pacific Ocean, virtually assuring us of about 30 years of global cooling, perhaps much deeper than the global cooling from about 1945 to 1977.” source
Easterbrook’s PDO theory is repeated here and here. Clearly he believes that the shift in PDO phase from warm to cool will have a significant impact on global temperatures for the next 30 years.
In this post I take a closer look at PDO, AMO and ENSO indexes to see how they are related to the GISS anomaly 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. ..”
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!