In a previous post, I showed the Lower Stratospheric Temperature Anomaly (TLS) Trends (link). A reader submitted the following comment:
“The lower stratosphere temperature profile is essentially flat from ca. 1995 to the present. This approximately mirrors the temperature trend for the surface temperature. From 1980 to about 1995, the surface temperature increased while the lower stratospheric temperature decreased. After that both went flat.” tony
In the words of Edwards Deming:
In God we Trust, All Others Must Bring Data”
Since tony didn’t bring any data to back up his claims, I’ll do the analysis for him.
In this post I review the temperature structure of the atmosphere and lower stratosphere temperature (TLS) anomaly trends.
Temperature Structure in the Atmosphere
In post 1 of this series, I developed this RClimate chart of temperature soundings which I update daily: (Click to enlarge)
This post shows the YTD global land – ocean temperature anomaly (LOTA) trends for the 5 major series through November, 2010 and how 2010 YTD ranks over the entire record for each series. The source data file link is provided.
This post shows the YTD global land – ocean temperature anomaly (LOTA) trends for the 5 major series through October, 2010 and how 2010 YTD ranks over the entire record for each series. The source data file link is provided.
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
In this post I present a trend chart which shows the September anomaly trends for the 5 major global temperature anomaly series and a table that shows how September 2010 ranks over the entire record for each series. The source data and RClimate script file links are provided.
Update 1: In a comment, ChristianP suggested the addition of a loess regression fit to the trend line chart. Thanks ChristianP.
Click to Enlarge
Do-it-yourself citizen scientists need to conduct proper data analysis to reach valid conclusions.
In this post I show how the blogger Inconvenient Skeptic misleads himself on the role of the Atlantic Multi-decadal Oscillation in global warming because he misinterprets his own trend charts and implies causation from correlation.
Now that the 2010 Arctic sea ice melt season is over, we can see how 2010 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 32 year period, 1979 to 2010. Data and RClimate scripts are provided.
Update 1 (10/6/10) Added trend lines to plots based on suggestion from reader.
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.