I have made several updates to RClimate tools for do-it-yourself climate scientists. The downloadable monthly climate trends file (link to csv file) now includes the 5 major global land-ocean temperature anomaly time series (GISS, HAD, NOAA, RSS, UAH) as well as PDO, AMO and NINI34 indexes. Stay tuned, I plan to add several more series in the next few weeks. Do you have any suggestions?
I have also added several functions to my on-line RClimate.txt file to help DIY citizen climate scientists to quickly and easily retrieve up to date climate trend data so that they can spend their time analyzing the temperature anomaly and climate oscillation trends rather than slugging through data downloads and reformatting.
In this post I present a 5 panel trend charts which show the year-to-date anomaly trends for the 5 major global temperature anomaly series and a table that shows how 2010 YTD ranks over the entire record for each series. The source data and RClimate script file links are provided.
Click to Enlarge
Now that the
2010 2013 Arctic sea ice melt season is over, we can see how 2010 2013 fits into the long-term trends Arctic Sea Ice Extent. This post shows an R Climate chart that I have made to look at the annual NSIDC Arctic Sea Ice Extent maximum, minimum and seasonal melt trends for the 35 year period, 1979 to 2013. Data and RClimate scripts are provided.
Update 1 (10/6/10) Added trend lines to plots based on suggestion from reader. Update 2: Extended to 2014, included R script.
Here’s my RClimate script trend chart of 1979-2010 NSIDC Arctic Sea Ice Extent data. I have plotted NSIDC’s maximum and minimum sea ice extent for each year and my calculated value for seasonal melt (maximum – minimum). (Click image to enlarge)
arctic oscillation (AO): 1 – trends Since 1950
In this post, I begin a series on the Arctic Oscillation (AO) . This post presents a chart of monthly AO Index from 1950 to the present and introductory information on AO . I will be updating this chart each month as NOAA updates the data series. A link to the RClimate script that downloads the source data from NOAA is provided.
Update 1: Reader skrafner noticed that my plot legend indicated a 60-day moving average while the script actually calculated a 60-month moving avg. I’ve updated the script and plot.
In this post I introduce my RClimate functions which allow R users to easily download and plot monthly temperature anomaly data for the 5 major global temperature anomaly data series: GISS, HAD, NOAA, RSS, UAH.
Consolidated LOTA Data File
In this previous post I introduced my global Land Ocean Temperature Anomaly (LOTA) monthly csv file that Excel and R users can download to conduct climate trend analysis.
In this post, I introduce my RClimate.txt R scripts that users can source() to simplify access to the LOTA data. Please note that I have used the “.txt” descriptor for my file type to avoid download problems encountered when I use the standard R file descriptor.
This post discusses my updated and enhanced UAH Channel 5 daily trend chart. Updated 3/29/11
Update 1: 3/29/11
Since I have received a number of comments and questions about this post, I am updating it to address these comments and improve the chart.
I plot the Channel 5 data because it is available in rear real time so that readers can get a sense for how the monthly global temperature anomaly is shaping up. However, the comments tell me that there is some confusion about Channel 5 and how it compares to the UAH TLT data.
Lucia at The Blackboard has a detailed discussion of UAH TLT and Channel 5 here. Bob Illis has an interesting chart that shows the differences between UAH TLT and Channel 5 here.
Dr. Roy Spencer discussed tracking daily global temperature anomalies here.
I have revised my chart to show both the UAH TLT 5.4 and Channel 5 monthly trends as well as the daily Channel 5 data for the current month.
I’ve added this UAH Channel 5 trend chart to my sidebar: (Click to enlarge)
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.
Phil Jones Statement (February , 2010)
Phil Jones of the Climate Research Unit (CRU) responded to a series of questions from the BBC in early February, 2010 (link). Question B dealt with global warming trends in the 1995 – 2009 period. Here’s the BBC question and Phil Jones answer:
BBC Question B: B – “Do you agree that from 1995 to the present there has been no statistically-significant global warming?”
Phil Jones Answer: “Yes, but only just. I also calculated the trend for the period 1995 to 2009. This trend (0.12C per decade) is positive, but not significant at the 95% significance level. The positive trend is quite close to the significance level. Achieving statistical significance in scientific terms is much more likely for longer periods, and much less likely for shorter periods.”
Phil Jones’ statement provided a time series regression learning moment for many of us citizen climate observers who quickly checked his statement with our Excel, R or other handy regression analysis tools. I sure did. Two readers, J and S, contacted me with questions – comments:
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.