03 February 2020
James Hansen
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Scientific references are included in the PDF version of this paper on our website.
Climate models are most useful when
used so as to help us understand climate mechanisms in the real world,
and thus improve our ability to understand ongoing and future climate
change.
Comparison of climate model predictions against real world outcome
provides one way to gain improved understanding. There is recent
discussion in the media of early predictions of human-caused global
warming, including simulations made with early climate models at the
Goddard Institute for Space Studies (GISS), specifically (1) 1981 paper
in Science that used a simple one-dimensional (1-D) column climate
model, and (2) 1988 paper in JGR that used our first coarse-resolution
3-D climate model. The media discussions miss the most important
lessons.
The 1981 model did a pretty good job, slightly underpredicting global
warming. The main reason for this was that the 1-D model has ocean heat
capacity at its foot. Although we reduced the ocean’s heat capacity by
the factor 0.7 to account for the fact that ocean covers only 70
percent of Earth’s surface, a 1-D model using that procedure yields a
result closer to that for the ocean (see figure above) than it should.
This is discussed in Chapter 20 of Sophie’s Planet.
A 1-D model can be doctored to do a better job of accounting for land
and ocean fractions, but, because of the dynamical exchange of marine
and continental air, it is still better to use a 3-D model that allows
realistic mixing of marine and continental air.
Our group had the good fortune to interact with Jule Charney
when we started to build a 3-D climate model in the late 1970s. We
were at least a decade behind Suki Manabe, and our computer, more than
10 years old, was much slower than those at GFDL and NCAR. Yet Charney
treated us with the respect accorded more established researchers,
despite the coarse resolution and unpublished status of our climate
model (Chapter 17 of Sophie’s Planet).
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Fig. 2. Climate model simulations in our 1988 paper and subsequent real-world data.
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Jule called me four or five times while
writing his famous 1979 report on climate sensitivity, as he was trying
to understand the physical mechanisms that caused our model to have a
sensitivity of almost 4°C for doubled CO2, while Manabe’s current model had 2°C sensitivity. Cloud feedbacks were to be the biggest factor causing this difference.
We had to keep our 3-D model fixed once the climate runs were started,
because it required a few years to complete them on our computer! So
all runs were done with the model having sensitivity near 4°C, even
though we had reasons to believe that real-world climate sensitivity was
closer to 3°C for doubled CO2. The 1-D model used in our 1981 paper specified climate sensitivity as 2.8°C, which was probably a good choice.
Real world climate forcing turned out to be close to that in our
Scenario B, which is the scenario that we expected to be most
realistic. So what are the main reasons for the moderate overshoot of
Model B (see figure) compared to the real world?
In the spirit of Jule Charney, we should look not only under the street
light (factors that we can quantify), but also at other factors that we
know about and believe to be important. The list I come up with in Sophie’s Planet is, in order of estimated importance:
- Aerosol (direct and indirect) forcing: no aerosol forcing is included in our 1988 model
- Model sensitivity (equilibrium and response time, i.e. modeling of ocean inertia effect)
- Energy imbalance at model start (warming in pipeline): zero imbalance in 1958
- Ice sheet/ice shelf freshening effect: Southern Ocean/North Atlantic cooling
- Greenhouse gas (GHG) forcing errors
1. Global aerosols presumably increased during 1958-present, reducing
the global warming caused by increasing GHGs, but we have no good
observations. This is a sad state of affairs that I dwell on in Sophie’s Planet, so much so that I am removing the aerosol chapters and some of the planetary chapters to another book (Battleship Galactica,
if I can get away with that name). Ignorance allows speculation.
Could aerosols from the enormous upshoot in global coal burning between
2000 and 2015 (Fig. 3) contribute to the minimal warming during that
period (Fig.2)?
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Fig. 3. Global energy consumption and fossil fuel CO2 emissions.
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2. The equilibrium sensitivity of our GCM in 1988 (4°C for doubled CO2) is higher than our best estimate for the real world (3°C for doubled CO2). This difference by itself might approximately account for the difference between the model B simulation and observations.
3. By starting the model in energy balance in 1958 we do not account for
any ‘unrealized warming’ that is ‘in the pipeline.’ This reduces the
warming, perhaps as much as 0.1-0.2°C, thus partially compensating for
the above two overestimates.
4. We do not include the effect of increasing meltwater on the North
Atlantic and Southern Oceans (our ocean ‘model’ consisted of specified,
unchanging dynamical transport of heat), which has been shown to already
be occurring. Observations confirm that minimal warming, or even
slight cooling, is occurring southeast of Greenland and in the Southern
Ocean.
5. Real-world GHG forcing turned out to be almost exactly Scenario B,
when we examine effective forcings. There are several definitions for
radiative forcings (instantaneous forcing, adjusted forcing, effective
forcing, etc.). The most relevant forcing should be the effective
forcing, which accounts for the ‘efficacy’ of each forcing.
Furthermore, it makes sense to include within the effective forcings
those indirect GHG forcings that are reasonably well understood. For
example, an increase of methane leads to an increase of stratospheric
water vapor via simple chemistry. A methane increase also causes an
increase of tropospheric ozone, because methane and ozone are competing
for the hydroxyl radical (OH), which is the cleansing agent in the
troposphere.
The bottom line is that #1 and #2 almost surely caused our model to
yield too much warming, but this was partly compensated by #3. #4
reduces the real-world warming, contributing to the gap between
predicted and observed global temperatures. #4 will become more
important in the future, if the rate of mass loss from the ice sheets
and ice shelves increases.
The sad part of this story is that the biggest uncertainty, #1, is not
being measured to a useful accuracy. It is hard to measure, because it
includes the effect of aerosols on clouds, but there is no good excuse
for why we are not monitoring the aerosol direct and indirect climate
forcings. That is the main subject of Battleship Galactica.
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