Exogenous Redux

A reader recently requested that I revisit the issue of adjusting temperature data to allow for known factors, the ones that don’t affect the trend but do cause fluctuations, in order better to isolate the trend changes, which are mainly due to greenhouse gases. We can call those known influences exogenous factors. I’ve dealt with this many times, and published research about it, so I’m not going to say very much about the methodology.

The factors which will be estimated are the el Niño southern oscillation (estimated by MEI, the multivariate el Niño index), solar variation, and reflective aerosols (one of the consequences of volcanic eruptions). Then their estimated influence will be removed to show what global temperature might have looked like without these fluctuation influences.

We’ll start with data from NASA’s Goddard Institute for Space Studies. Here are annual averages (2015 is included although it’s not yet complete) of the basic data, without adjustment:

To estimate the influence of exogenous factors we create a mathematical model using monthly rather than annual averages. If we compare the model (shown as a red line) to the raw data (black line), it looks like this:

It’s actually impressive how well the model fits; clearly some of the big ups and downs in temperature fluctuations turn out to be due to these exogenous factors. For example, the exceptional heat in 1998, well above the overall trend, is because of the very strong el Niño of that year, while the temperature dip in the early-to-mid 1990s is due to reflective aerosols from the explosion of the Mt. Pinatubo volcano.

When we remove the estimated influence of the exogenous factors, then once again compute annual averages, we end up with this:

If you compare this with the first graph, you’ll see that the temperature trend is pretty much the same: steady increase from 1970 up to the present. However, the year-to-year fluctuations are reduced, which makes the steady trend even clearer than it was before. It also makes any talk of a so-called “pause” in global warming, look ridiculous. But then, such talk is ridiculous.

There are, of course, still plenty of fluctuations. One reason is that not all fluctuations are caused by el Niño, solar variation, or reflective aerosols. Another is that our model is a pretty good, but not perfect, estimate of their influence. Fluctuation still remains in our “adjusted” data — but it has been notably reduced.

NASA’s data are an estimate of surface temperature. Let’s also take a look at temperature in the atmosphere, specifically in the troposphere (the lower layer of earth’s atmosphere). I’ll do so with two data sets. One is based on measurements from thermometers carried aloft by balloons, which has been used to compute a global average called RATPAC (Radiosonde Atmospheric Temperature Products for Accessing Climate). We’ll use the RATPAC data for the lower troposphere (from atmospheric pressure 850mb to 300mb), so it leaves out the stratosphere.

The data are seasonal averages, with the seasons being DJF for winter, MAM spring, JJA summer, SON fall. I used the seasonal value for each month of the season in order to create a pseudo-monthly data set. And here’s the data averaged over each calendar year:

Here’s how the model based on exogenous factors (red line) compares to the data:

Once again the model is pretty good, although not perfect. Once again we can remove the estimated influence of exogenous factors, then re-compute annual averages:

Again, the steady rise is evident.

Balloon-borne thermometers aren’t the only data source for tropospheric temperature; there are also estimates from satellites. But they don’t actually measure temperature, they measure microwave brightness, from which we try to infer temperature at different levels within the atmosphere. Different channels (different microwave frequencies) respond with different strength to different levels of the atmosphere. But, none of them responds to a thin layer at a definitive altitude, they all respond broadly to pretty much the entire atmosphere, although they’re more sensitive to certain levels. This makes it a tricky problem to disentangle the information from different channels in order to estimate temperature in a well-defined atmospheric layer. It’s especially problematic because all channels respond, at least in part, to the stratosphere, which is known to be cooling because of increased CO₂.

There is also a serious difficulty splicing together data from all the different satellites that have been used (more than a dozen). And, there’s the issue of different response depending on the angle at which the satellite is looking at the atmosphere, and some dispute about how the satellites’ orbits have altered over the years. All in all, it makes piecing together an accurate satellite temperature estimate quite difficult — which is one of the reasons satellite data have been revised and updated so often. The upshot is, that all those claims deniers constantly make about how the satellite data are somehow “better” than surface temperature data or balloon-borne thermometer data, are wrong. There’s very good reason to doubt.

Here’s the data from RSS, the satellite data that deniers seem to like best because it shows the least warming:

Here’s the model (in red) based on el Niño, solar variation, and cooling aerosols:

The model is definitely showing the real impact of these exogenous factors, but not quite as well as it does for surface temperature from thermometers. It is especially noteworthy that it doesn’t capture how extreme was the warming from the 1998 el Niño. Yet the exogenous-compensated data still shows consistent warming:

The warming, however, gives a visual impression of not being as steady as it was in thermometer data, although the idea of a so-called “pause” is still just as ridiculous.

We can see how different the satellite data is from the thermometer data for the troposphere, by plotting the difference between the two (annual averages):

This suggests that something happened around 2000 to cause these data sets to diverge. Thermometers didn’t change how they measure temperature, nor balloons how they rise through the atmosphere. But satellite instruments have gone through many changes, satellite orbits have altered, and the satellites themselves change over time. I strongly suspect that there’s a serious problem with the satellite data after about the year 2000, as indicated by their divergence from thermometer data.

We can also see the divergence in the data compensated for el Niño, solar, and aerosols, for which I’ll compare the monthly data:

Another sign is how the overall warming rates compare between sources. For surface temperature data from NASA, the warming rate is about 0.0166 +/- 0.0028 deg.C/yr. It’s slightly higher (but not significantly so) for RATPAC data, at 0.0174 +/- 0.0036 deg.C/yr. But the odd man out is the data from RSS, warming at only 0.0127 +/- 0.0048 deg.C/yr.

In my opinion, it’s high time to take a much closer look at the satellite data, how it’s processed, and how it compares to other sources.