Many things affect global temperature, and the three best-known other than greenhouse-gases those are the el Niño southern oscillation (ENSO), atmospheric aerosols from volcanic eruptions, and variations in the sun. We can use historical data to estimate how strongly those factors affect global temperature. I’ve done so in the past, and by requests here’s an updated version which includes recent data.
ENSO, when in its warm el Niño phase, warms up the atmosphere (and hence our weather), but in its cool la Niña phase cools us down. Large volcanic eruptions put sulfates in the atmosphere, cooling us off for a few years. When the sun gets hotter or cooler, Earth tends to as well. Here are the estimated impacts of these factors on global temperature (using temperature data from NASA) since 1950 (where “MEI” stands for the Multivariate El niño Index):
The effect of all three factors is statistically significant, although the impact of solar variation tends to be much smaller than that of either volcanoes or ENSO. When we add all three factors together we get an estimate of their total impact together:
The strongest peak warming due to these factors was during the super-strong el Niño event leading up to 1998, while the peak cooling was in 1976, when all three combinated to cool us off: volcanic eruptions, the cool (la Niña) phase of ENSO, and the cool part of the solar cycle.
We can subtract the estimate of how these factors have affected temperature, to get an estimate of how temperature has changed apart from these known factors. I’ll show both the data from NASA as is, and the NASA data with the impact of known factors removed, and I think it’s clearer when showing yearly averages rather than monthly data (note: 2018 isn’t complete yet), so here goes (open blue dots for data as is, filled red for adjusted data with known factors removed):
The difference seems rather plain, that after removing the impact of these known factors, we end up with a temperature series with less fluctuation but about the same trend. One way to estimate the present trend rate is by fitting a “piecewise linear” model, which is just two straight lines which meet at their endpoints:
Before adjustment the estimated trend rate (now) is 1.83 +/- 0.21 °C/century, after adjustment it’s 1.75 +/- 0.13 °C/century; the difference is not statistically significant. The standard deviation (a measure of the amount of fluctuation) of yearly averages before adjustment is 0.094 °C, after adjustment it’s 0.057 °C.
I’ve analyzed the adjusted data, just as I have the “as is” data, to look for any significant sign of deviation from the piecewise-linear trend (in particular, anything else we can say with confidence since about 1975). It’s not there. Just for a clear view, here’s the adjusted data with its estimated trend:
That doesn’t mean it has followed two straight lines since 1950, but it does mean we don’t have enough statistical evidence to claim that it hasn’t.
I’m also interested in how this looks using data over land areas only. I’ll do two different data sets, in part to show how well they agree with each other, which is partly a response to the recent “study” of Hadley Centre/Climate Research Unit data (the CRUTEM4 data for land areas only) referred to by a reader recently. The “study” calls itself an audit but in my opinion is nothing more than a hatchet job. I’ll also use the land-only estimate from the Berkeley Earth Surface Temperature project.
Here they both are (annual averages, note that 2018 isn’t complete yet and I’ve set them to the same baseline) “as is”:
Here they both are after estimating and removing the impact of ENSO, volcanoes, and solar variations:
Again it’s abundantly clear that the two data sets are in excellent agreement. The Berkeley data show greater fluctuation, so let’s take a look at just that. Here’s the Berkeley data for land areas only, together with a piecewise-linear trend approximation in blue and a smooth-fit trend approximation in red:
Of particular interest is the rate of warming, which we can approximate using either the smooth-fit or piecewise-linear fit. Here are the results:
An important thing to note is that the rates based on the piecewise-linear fit are really the average rates over each piece, which is why is has smaller probable errors than the rates based on the smooth fit.
Just for those who are interested in numbers, here is the data (both “as is” and adjusted for ENSO, volcanic, solar) for the NASA data covering the entire globe:
Important note: wordpress won’t allow me to upload a “csv” file, so I changed the suffix to “xls” to get it there. Change the name suffix from “xls” to “csv” and treat it as a csv.
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