The GISS Model E is the workhorse of NASA’s climate models. I got interested in the
GISSE hindcasts of the 20th century due to an interesting posting by Lucia over at
the Blackboard. She built a simple model (which she calls “Lumpy”) which does a pretty
good job of emulating the GISS model results, using only a model including forcings
and a time lag. Stephen Mosher points out how to access the NASA data here (with
a good discussion), so I went to the NASA site he indicated and got the GISSE results
he points to. I plotted them against the GISS version of the global surface air temperature
record in Figure 1.
1. To a very close approximation (R^2 = 0.91, average error less than a tenth of
a degree C) the GISS model output can be replicated by a simple linear transformation
of the total forcing and the elapsed time. Since the climate is known to be a non-linear,
chaotic system, this does not bode well for the use of GISSE or other similar models.
2. The GISSE model illustrates that when hindcasting the 20th century, the modelers
were free to design their own forcings. This explains why, despite having climate
sensitivities ranging from 1.8 to 4.2, the various climate models all provide hindcasts
which are very close to the historical records. The models are tuned, and the forcings
are chosen, to do just that.
3. The GISSE model results show a climate sensitivity of half a degree per doubling
of CO2, far below the IPCC value.
4. Most of the assumed GISS forcings vary little from a straight line (except for
some of them going flat in 1990).
5. The modelers truly must believe that the future evolution of the climate can be
calculated using a simple linear function of the forcings. Me, I misdoubts that …
In closing, let me try to anticipate some objections that people will likely have
to this analysis.
1. But that’s not what the GISSE computer is actually doing! It’s doing a whole bunch
of really really complicated mathematical stuff that represents the real climate
and requires 160 teraflops to calculate, not some simple equation. This is true.
However, since their model results can be replicated so exactly by this simple linear
model, we can say that considered as black boxes the two models are certainly equivalent,
and explore the implications of that equivalence.
2. That’s not a new finding, everyone already knew the models were linear. I also
thought the models were linear, but I have never been able to establish this mathematically.
I also did not realize how rigid the linearity was.
3. Is there really an inherent linear warming trend built into the model? I don’t
know … but there is something in the model that acts just like a built-in inherent
linear warming. So in practice, whether the linear warming trend is built-in, or
the model just acts as though it is built-in, the outcome is the same. (As a side
note, although the high R^2 of 0.91 argues against the possibility of things improving
a whole lot by including a simple lagging term, Lucia’s model is worth exploring
4. Is this all a result of bad faith or intentional deception on the part of the
modelers? I doubt it very much. I suspect that the choice of forcings and the other
parts of the model “jes’ growed”, as Topsy said. My best guess is that this is the
result of hundreds of small, incremental decisions and changes made over decades
in the forcings, the model code, and the parameters.