In this post I show one simple and 2 multiple regression models to assess the role of time, El Nino – La Nina SSTA and volcanic activity (SATO) on UAH global temperature anomaly trends. The 3rd model provides a reasonable approximation of the actual UAH oscillations over the 1979 – Feb, 2011 period.
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This analysis is similar to previous temperature anomaly regressions (here, here, here) that I have done.
The simple trend line regression shows the overall trend is upward, however, there are several oscillations that the linear trend misses. The yr_frac and Nino34 regression improves on the linear model, however, it undershoots in the early 1980s, overshoots in the 1992-1994 period, periods following significant volcanic activity.
The yr_frac, Nino34 and SATO model improves the fit in the early 1980s and 1992-1994 period and is slightly better in the 1998 and 2010 El Nino periods.
The 3rd model matches the observed 2010 El Nino – La Nina oscillation pretty well so far, indicating that the 2010 – 2011 UAH anomalies are following a predictable pattern.
This post shows how to use RClimate.txt to retrieve a climate time series and write a csv file in 5 lines of R script.
One of my readers, Robert, wants to be able to download climate time series data and write it to a csv file. The R script below shows how to download the MEI data series and write a csv file. For this example I will use the RClimate function (func_MEI) to retrieve the data. I then simply specify the path and file name link for the output file (note quotes around the output file name and then write a csv file.
m <- func_MEI()
output_link <- "C://R_Home/mei.csv"
write.csv(m, output_link, quote=FALSE, row.names = F)
In previous posts I have shown plots of global temperature anomaly, volcano and Nino34 trends (here , here). In this post , I want to further explore the role of volcanic eruptions and Nino34 phases (El Nino, La Nina) on temperature anomalies.
This post shows a 5-panel chart of monthly climate trend data: 1) time line of major volcanoes and Volcanic Explosivity Index (VEI), 2) Mauna Loa Observatory (MLO) Atmospheric Transmission (AT) measurements, 3) Stratospheric Aerosol Optical Thickness (SATO) Index, 4) , Nino 34 as an indicator of ENSO and 5) GISS land-ocean temperature anomaly.
The RClimate script and Climate Time Series data file (CTS.csv) links are provided.
First, here is the 5-panel chart that I have made showing the monthly volcano time line with Volcano Explosivity Index (VEI) , Atmospheric Transmission at Mauna Loa Observatory, SATO Index as well as the Nino34 SSTA and GISS LOTA. (Click Image to Enlarge)
While there are many online climate data resources, the source data files are in numerous data formats, presenting a challenge to climate citizen scientists who want to retrieve and analyze several climate indicators at the same time.
I have been working to develop a consolidated open access data file and RClimate scripts that users can use to retrieve climate data, conduct their own analysis and generate their own climate charts. My goal is to make it easier for climate citizen scientists to get their hands on the data in a simple, usable format (CSV). This post updates the status of my RClimate efforts.
The 5 global land-ocean temperature anomaly (LOTA) series use different baseline periods, making direct comparisons between the series more difficult than it would be if each series had the same baseline period.
This post shows how to convert the 5 major LOTA series to a common baseline. Links to on-line source data file and RClimate script are provided. Here is long term LOTA trends using a 133 month moving average and 1979-2008 baseline.
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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.
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.
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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.
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