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This page started out as an attempt to see how easy it would be to replicate the global temperature data of the last several decades with freely available data.  Numerous individuals have described an apparent "global warming slowdown" after the 1998 El Nino event and some scientists have even tried to explain this with various analyses of climate data.  I searched for climate forcing data available on the web and information indicating the contribution of these forcings to global average temperature.  I was surprised by the relative ease by which I was apparently able to re-create the global temperature record.  I converted my original spreadsheet to a google document so that it could be edited by others and update the graphs dynamically.  This tool is meant for general information as something fun and somewhat informative, but is by no means meant to replace results from more sophisticated models or the results of studies from climate scientists.

Data Sources

This tool is quite simple in that it attempts to "model" global average temperature with the following natural forcings: solar activity and volcanic activity.  Although not considered a true "forcing" of climate, data for the El Nino Southern Oscillation (ENSO) was also used because of the apparent warming/cooling effect seen over short-term periods, which is basically noise on top of the signal.  Cooling/warming from ENSO is not typically considered actual cooling or warming of the climate system, but is related to pacific sea surface temperatures and how heat energy in the climate system is transferred between surface and deep ocean waters.  Another climate factor was also considered, which can be referred to as the "temperature influence not explained by known natural forcings."

Solar Activity

The best representation of solar activity is total solar irradiance, which has been measured from satellites for several decades and estimated through proxies (such as sunspot numbers) for several hundred years prior.  Data for this tool came from the daily Total Solar Irradiance (TSI) Composite data retrieved from PMOD-WRC.  Data was averaged to a monthly timestep to match other data.

Volcanic Activity

A good estimate of sunlight blocking particulate matter in the stratosphere from volcanic eruptions comes from the Aerosol Optical Depth (AOD).  NASA uses the AOD data in their GISS climate model.  Information on converting AOD values to W/m2 forcing can be found with the data.


El Nino/La Nina are represented by several different indeces (MEI, ONI, etc) which may cover different regions of the equatorial Pacific Ocean or be measures of pressure differences instead of sea surface temperature.  I found a few papers that used the Southern Oscillation Index (SOI) for their representation of ENSO, and this is what I chose to use.  It should be noted that in different data sets for SOI found online, the values seem to be slightly different, although the overall oscillation appears similar.

Anthropogenic ("temperature influence not explained by natural forcings")

For now, I have simply estimated an anthropogenic component with a linear trend and a starting value.  At some point in the future I might use the greenhouse gas index or some other estimate of CO2 concentration.  There are several papers which have estimated the human influence on global temperature, either through modeling or by removing natural factors from temperature data.

Temperature Observations

Temperature observations come from the NASA GISTemp dataset.


Each of these indeces was analyzed on a monthly timestep and converted to an estimated temperature influence.  Data was available for indeces for at least the 1979-2012 period and I started with this range for now.

Solar activity generally follows a ~11yr cycle from peak to peak which has an estimated 0.1-0.2C impact on global temperature.  I calculated a mean value for TSI over the period and converted monthly averages to anomalies.  TSI anomalies had an amplitude of almost 2 W/m2, so I used a short term temperature sensitivity of 0.1 C/[W/m2] for solar activity to match 0.2C impact.  Foster and Rahmstorf (2011) estimated about 1 month for the temperature lag to solar activity, which I assumed for default.

The NASA page for volcanic aerosol data indicated a value of -23 for effective efficacy of AOD.  This is the W/m2 change in forcing for each unit of AOD.  I assumed the same short term temperature sensitivity for volcanic activity as used for solar activity.  Foster and Rahmstorf (2011) estimated about 6 month lag for volcanic activity's influence on temperature.

Because ENSO is not really a climate forcing, it's effects on global temperature are more of an apparent effect.  I used the same short term temperature sensitivity as with the other indeces but had to estimate the efficacy-equivalent based upon the observed temperature data used (ENSO affects each temperature dataset differently).  A default value of 4 months was used for the lag based upon numerous sources, including Foster and Rahmstorf (2011).

I estimated the warming influence from human greenhouse gas emissions to be about 0.18C/decade based upon some recent papers I read and also the estimate from Foster and Rahmstorf (2011).  A starting anomaly of +0.12C was used to better match the observed data.

Changing the Parameters

This simple tool provides a way to see the relative contribution of several factors to global temperature.  If you do not believe that gradual warming from human activities is part of the equation, you can set the anthropogenic warming rate to 0C/decade and play with the temperature sensitivities of other natural factors to see if any combinations could replicate the observations.  The relative temperature sensitivities and lags can be changed to see how that will affect the noise and trend.  When you change a parameter, you are changing an actual google documents spreadsheet, and you are changing the parameters for everyone.  The graphs are updated based upon the data in the document, and may not be the default values I selected.  Find the default values again by viewing the "Parameters_Default" tab.

Please note: because this is not a sophisticated climate model and slow response warming from the oceans is not simulated, the temperature sensitivities used are likely not the same as temperature sensitives estimated by other sources.  The sensitivity used for this tool is for the short-term impact only, and I have seen the true sensitivity estimated as 7x the change that I use by default (roughly 0.7 C/[W/m2] instead of 0.1).

Click here to change the model parameters.

Look at the resulting graphs here.


This concept isnt new.  In fact, I got the idea from another person on the internet trying to do the same thing, who also ended up with similar results.  See: https://sites.google.com/site/refsdefred/warming-actors

Foster and Rahmstorf (2011) also put much more effort into finding the true global warming signal in the observed temperature record.  See: http://skepticalscience.com/foster-and-rahmstorf-measure-global-warming-signal.html

Thompson (2008) also attempted to remove the climate variability from ENSO.  See: http://www.skepticalscience.com/el-nino-southern-oscillation.htm