In this post I set both UAH and GISS global temperature anomaly series to a common baseline period (1981-2010) and compare them. Even though the UAH series is satellite based and GISS series is station based, the series exhibit striking similarities.
In this previous post, I showed how to convert temperature anomaly time series from one baseline period to another period. I use this technique in this post to directly compare UAH (baseline 1981-2010) and GISS (baseline 1951-1980) series.
The offsets are as follows:
- UAH: -0.000978
- GISS: 0.34958
Since the UAH TLT 5.4 series is based on a 1981-2010 baseline, the offset is nearly zero (-0.00098 versus 0.0).
Users can reproduce my analysis on their own by downloading my CTS.csv file and applying the offsets to the UAH and GISS series.
Comparison of 1981-2010 Baseline Series
Here is a plot of UAH and GISS 12 month moving averages for 1979 to current: Click to Enlarge Continue reading
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.
Click Image to Enlarge
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.
In this post I compare GISS LOTAs during 5 El Nino – La Nina cycles (2010, 1998, 19883, 1973 and 1970).
El Nino – La Nina Cycles
In a previous post I showed the Nino 34 SSTA cycles for 2010, 1998, 1983, 1973 and 1970 here. In this post, I want to see how GISS Land Ocean Temperature Anomalies (LOTA) vary over El Nino – La Nina cycles. Here is my RClimate chart showing GISS anomalies for 6 months prior to cycle year,the cycle year and the 12 months after cycle year (30 month period).
Click chart to enlarge
While the 2010 cycle is only partially complete, there are a number of interesting aspects in this chart. The average temperatures during the cycles have clearly risen with the latest cycle showing the highest maximum anomaly. All 5 cycles all have similar patterns, with a buildup in 6 months prior to cycle year. The maximum – minimum range for the 5 cycles are comparable, ranging from 0.45 (2010) to 0.60 (1998).
Here is a data summary of the 5 cycles.
In this post, I use a quadratic regression model to forecast the September, 2011 Arctic Sea Ice Extent. The model was developed with 1980 – 2010 data. Links to the R script, source data and how-to article on polynomial regression are provided.
Arctic Sea Ice Extent Forecast for September, 2011
First, here is my forecast: (Click image to enlarge)
Based on the 1980 – 2010 downward Arctic Sea Ice trend, my forecast is that September, 2011 SIE will decline 0.36 below 2010 levels, to 4.54 million km^2, with a confidence band of +- 0.59.
How Did I Develop My Forecast?
I have written a number of posts on Arctic Sea Ice Extent (here, here, here). In this post, I used the NSDIC‘s monthly data file (link) to construct a quadratic regression model of September sea ice extent for the 1980 – 2010 period. I then used this model to predict the September, 2011 Arctic Sea Ice Extent.
I have 2 main learning curve sources for this model:
- Tamino‘s post on Arctic Sea Ice decline provided the basic idea of using a quadratic model to fit Arctic SIE decline.
- John Quick’s tutorial on polynomial regression provided the how-to instructions I needed to implement Tamino’s approach in R.
RClimate Script and Links
Here is the link to my RClimate script.
I am pleased to announce my CTS.csv file which includes 18 climate monthly time series in one easy to access csv file. This is part of my goal of having a user friendly way for do-it-yourself citizen climate scientists to get up-to-date agency climate time series in a painless way.
Update 1: Reader Scott asked if I could provide meta data for the columns in my CTS.csv. This page lists the source agency and data links for the climate data series.
Here’s a snap shot of the first 6 rows of my CTS.csv file. The data extends from 1880 until the most recent month. Click image to enlarge
My hope is to make the CTS.csv the go-to file for citizen climate scientists who may want to:
- Check temperature anomalies trends by series (GISS, HAD, NOAA, RSS, UAH)
- Assess climate oscillations(AMO, AO, MEI, Nino34, PDO) trends
- Evaluate CO2 versus temperature anomaly relationships
- Evaluate relationship between Sunspot numbers and anomaly temperature anomaly trends
- Compare atmospheric transmission, SATO index and volcanic activity
- Assess impact of volcanoes on temperature anomaly trends
- Compare MEI versus Nino ENSO 34 indicators
- Assess lower stratospheric trends using RSS’s TLS series
By having these climate time series in a single csv file, R and Excel users can work with up to date data in a convenient form. The file will be automatically updated monthly as the climate agencies release their latest data.
How can CTS.csv Help Do-It-Yourself Citizen Climate Scientists?
Interested climate observers who want to compare global SSTA versus Nino34 trends, for example, have to follow a multiphase process:
- Find data file – even with Google this can take time
- Download files
- Merge 2 or more files to get data into a usable format – source files all have different formats
- Perform analysis
Steps 1-3 can be very time consuming, so many users don’t bother checking out their ideas. Rather, they may rely on climate blog comments. With CTS.csv and some R or Excel analysis, they can find the facts themselves rather than just having opinions. They can submit their analysis and charts to blog posts, hopefully increasing the rigor of blog discussions.
Climate bloggers can request that their readers submit charts to back up their climate trend claims.
Data & RClimate Scripts Are All Open Book
All of the RClimate script that I use to produce the CTS.csv is available on-line at this link. Source data links are included in the function for each series.
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)