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.
As a former Excel chart user, I want to help current Excel users make the transition to more advanced charting R with as little difficulty as possible. This post introduces my LearnR Toolkit to help Excel users move up to R in a systematic, step by step fashion.
As an Excel chart user, I wanted to produce panel charts like this:
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.
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 presents an RClimate script to develop a temperature sounding profile and calculate the ambient lapse rate using University of Wyoming atmospheric sounding data. Understanding atmospheric structure and lapse rate is essential to a full understanding of the role of greenhouse gases in global warming.