January 17, 2021

Climate Sensitivity

The central question of the whole climate discussion revolves around a single issue: how does the climate, in particular the world average temperature, change if the CO_2 content of the atmosphere doubles. This is called climate sensitivity, the adjusting screw of all climate policy. Based on the different modeling assumptions of the International Panel on Climate Change, the IPCC, we are threatened with an average temperature increase of 2°-5° C by the end of the century. The political “optimal target” of the Paris climate agreement is a limit of 1.5° C.
The problem is that the resulting targets in terms of CO_2 avoidance are based on an assumed climate sensitivity of 2°-3° C with a doubling of CO_2.

Is this correct? Immense costs, the loss of industrial strength and the impoverishment caused by it, not least our freedom depend on the correct answer to this question.

A simple climate model

We want to use the well-established MODTRAN simulation as a one-dimensional mini-climate model to answer the question of climate sensitivity. MODTRAN incorporates a well accepted radiative transfer model. This simplification is legitimate in that the radiative equilibrium can in principle be calculated at any location on Earth, and if a consistent final result emerges under the various conditions, then we consider it reliable. The program is publicly available, so everything is verifiable. At this point we limit ourselves to an example calculation with the standard atmosphere, which is considered to be the optimal global average.

To do this, we set the MODTRAN program as the atmosphere had been in 1850 or so, in particular the CO_2 content was 280 ppm at that time. All other air constituents remain at the preset “standard” average value. The atmosphere model is the so-called US Standard Atmosphere, as used in International Aviation. As cloud model I chose those clouds which are most common, the cumulus clouds between 660m and 2700m altitude. The water vapor content is then adjusted so that the infrared radiation just gets the correct value of about 240 \frac{W}{m^2} (corresponding to the average insolation with an average albedo of 0.3). This is given with an average relative water vapor content of 0.25. The assumed average surface temperature of the standard atmosphere is 15.2° C.

Simulation of the pre-industrial atmosphere

The dark blue spectrum shows the behavior already discussed. The CO_2 hole is very similar to what we already know, the influence of the water vapor at the right and left edges is also clearly visible. As auxiliary lines, the curves are additionally plotted, which mark the ideal radiation behavior without greenhouse gases at the temperatures 220° K to 300° K. The curves are also plotted on the left. This allows one to estimate the radiation temperature and thus the energy at any point in the spectrum.

As our next test, let’s set today’s CO_2 content at 415 ppm. The 1850 curve will remain in the background as the blue reference curve, and the red curve from today will be drawn “above” it.

Simulating today’s atmosphere

What is first noticeable is that the curves are almost identical, and indistinguishable to the naked eye. The red curve almost completely obscures the blue one. Only in the calculated values on the left you can see a slight difference. The about 1 \frac{W}{m^2} lower radiation is compensated by a temperature increase of the earth surface of 0.3°. This average of 0.3° is the effective greenhouse effect from the beginning of industrialization until today, due to the widely accepted MODTRAN model.

Now what if we assume a doubling of CO_2 content from 280 ppm to 560 ppm? Again, the new curve is superimposed on the original blue curve.

Simulating the atmosphere when CO2 levels are doubled

And again, to the naked eye, there is little difference – a few small blue peaks peek out at wavenumber 500, and the “CO_2” well has become a touch wider. To compensate for the reduced infrared radiation due to this minimal greenhouse effect, the ground temperature is increased by a total of 0.5°C. Thus, the climate sensitivity with the MODTRAN simulation made is pretty much half a degree.

Accordingly, there is no reason for any alarmism. This value is far below the lowest assumptions of the Intergovernmental Panel on Climate Change.

Why does the Intergovernmental Panel on Climate Change reach different conclusions?

The natural question after these considerations is why the Intergovernmental Panel on Climate Change, which after all includes the best climate scientists, comes to such much more pessimistic conclusions?
A key problem here is that their climate models are extremely complex and claim to represent the full complexity of climate events. There are good reasons to believe that this is fundamentally impossible under current conditions, for example, because turbulent high-energy phenomena such as ocean currents or tropical storms cannot be adequately represented in these models. Similar models are used for weather forecasting, and these are already known to fail frequently for forecasts that extend beyond a few days.

One important reason to doubt the validity of “global circulation models,” or GCMs, is that they have over-predicted past climate data in past forecasts.
On the left is the average temperature trend (red bar) 1993-2012 – 0.15°/10 years, on the right the same in the period 1998-2012 – 0.03°/10 years, and in addition the results of 110 different climate models. Almost all had estimated much higher temperatures.

Source: http://www.blc.arizona.edu/courses/schaffer/182h/climate/overestimated%20warming.pdf
Simulation of IPCC assumptions

With the MODTRAN simulation program, however, one can reproduce their thinking based on data published by authors close to the IPCC. This is done by first assuming an atmosphere entirely without water vapor and without the other greenhouse gases, and measuring the CO_2 sensitivity there.

With the MODTRAN simulation, this situation is achieved when everything in the standard atmosphere is set to 0 except for the CO_2 content, including no clouds, no water vapor.

This, of course, raises the hypothetical radiation to an unrealistically high value of 347 \frac{W}{m^2}. Clearly, the only deviation from the “ideal curve” is the well-known CO_2-hole.

When the CO_2 content is doubled and the ground temperature remains the same, the radiation now decreases by 3.77 \frac{W}{m^2} due to the greenhouse effect.

This is pretty much the value of CO_2 conditional “radiative forcing” published by the Intergovernmental Panel on Climate Change. The reduced radiative forcing is compensated by temperature increase:

According to this, a temperature increase of 0.75° offsets the doubling of CO_2, which would be the sensitivity according to MODTRAN. However, many scientists arrive at an even higher sensitivity of about 1°.
But this sensitivity is called — in a way rightly — the “pure CO_2 sensitivity” by scientists close to the IPCC, because it does not yet take into account the influence of water vapor. But since water vapor is an even more potent greenhouse gas, and more water vapor is produced by the CO_2-induced temperature increase, in this way of thinking the CO_2 sensitivity is thereby effectively doubled. Thus it is possible to arrive at a sensitivity of 2°, which can then be arbitrarily increased by other catastrophic scenarios such as hypothetical melting of polar ice. They completely disregard cumulus cloud formation, which would also be enhanced by increasing the water vapor content and which would lead to a reduction of the incident energy, i.e. to a strong negative feedback. At best, the cloud issue is used by arguing that the very high cirrus clouds may lead to an enhancement of the greenhouse effect.

In my opinion, however, tearing apart CO_2, clouds, and water vapor when calculating CO_2 sensitivity is unwarranted. If all factors are considered simultaneously, this leads to the above mentioned low sensitivity of 0.5°.

Leave a Reply

Your email address will not be published. Required fields are marked *

This site uses Akismet to reduce spam. Learn how your comment data is processed.