The Wonderful Issues of Climate Change - Post #5 - Modelling the Future

Original Date: January 17, 2024; Revised February 25, 2024

Author: Tim Platnich

The data supports the proposition that the average temperature of the globe has increased by about 1C since 1850. Will this trend continue into the future? Will the rate of warming increase, decrease or stay the same? How can we predict?

This is where modelling comes in. Everyone is familiar with weather forecasting. Weather forecasting is done through modelling. Climate change predictions are also a question of modelling.

Weather forecasting is difficult beyond a few days out. This is because weather is a complex, multivariate, non-linear and chaotic system. Such systems involve multivariate equations (more than 2 variables) to make predictions. These multivariate equations are largely unsolvable making the systems they attempt to describe indeterminate and unpredictable when looking into the future.

Chaotic systems are very sensitive to initial conditions. A slight variation in initial conditions can lead the same equations to arrive at very different results. Furthermore, even if initial conditions are identical, over time, the same multivariate equations will diverge. In other words, even if we had exactly correct initial conditions (data) plugged into the correct equations, weather would not be predictable beyond a week or two.

The global climate is also a complex, multivariate, non-linear and chaotic system.

One variable of climate is temperature. If we assume that global warming is the major driver of climate change, we must first focus on the modelling of this warming. Warming itself is the product of many influences (variables). The first and foremost warming influence is the sun. The earth’s climate system is powered by solar radiation [IPCC AR5]. All of the earth’s heat energy comes from the sun through solar radiation. Solar radiation consists of light across the whole spectrum of wavelengths.

Virtually all climate models assume that incoming sunlight is a constant. This constant is contested by many scientists. According to them, incoming sunlight is subject to variation. Changes in the sun’s intensity can change the amount of sunlight reaching the earth [“Unsettled: What Climate Science Tells Us, What It Doesn’t, and Why It Matters” by Steven Koonin].

The second most important warming influence is the earth’s albedo. The earth on average absorbs about 70% of the sunlight that reaches it. The other 30% is reflected directly back into space. Of this 30%, 23% is reflected by clouds and 7% is reflected by the earth’s surface. [earthobservatory.nasa.gov]

Albedo has many elements (or variables). Cloud cover is one very important variable. Other variables include: snow and ice cover on the ground and sea; and aerosols in the air. An increase in the earth’s albedo from 30% to 31% would completely compensate for the warming influence of doubling the CO2 in the atmosphere ["Unsettled"].

The third most important warming influence is the greenhouse gases. When the surface of the earth warms due to solar radiation, mostly ultra-violet radiation, it subsequently emits that energy back into the atmosphere in the form of infra-red long wave radiation. While ultra-violet light reflecting from the earth's surface mostly passes through the atmosphere without being absorbed, infra-red radiation is absorbed by the green house gases, and then re-emitted. Some of the re-emitted infra-red radiation returns to the surface of the earth causing further warming.

But for greenhouse gasses in the atmosphere, the earth’s equilibrium surface temperature would average -18 degrees C ["Unsettled"]. With the insulating effect of greenhouse gases, the earth’s actual global average temperature is about 15 degrees C ["Unsettled"]. It is evident that a certain amount of greenhouse gases are necessary in order to make the earth habitable.

The major greenhouse gases are: water vapour; carbon dioxide and methane. The various gases absorb infra-red radiation across a spectra of wavelengths. Some wavelengths overlap; some do not. Water vapour accounts for more than 90% of the atmosphere’s ability to intercept heat ["Unsettled"]. Water vapour intercepts infrared light at various wavelengths. Many of these wavelengths are shared with other greenhouse gases.

Assessing water vapour as a variable in global warming is complex for a number of reasons not the least of which is that water vapour can form into clouds. Clouds are a complex variable as they both contribute to albedo and to infra-red light absorption. It is a matter of controversy whether clouds have a net warming or a net cooling effect on the globe. It is also a matter of controversy whether increasing temperatures are a factor affecting the average amount of cloud cover.

The next most significant greenhouse gas after water vapour is carbon dioxide. Carbon dioxide currently accounts for about 7% of the atmosphere’s ability to intercept heat ["Unsettled"]. The presence of carbon dioxide in the atmosphere is part of the carbon cycle. All carbon originates with the earth’s crust before being distributed to the atmosphere, the hydrosphere and the biosphere. “Human-emitted CO2 is a relatively small add-on to a vast natural cycle of carbon moving among the earth’s crust, oceans, plants and atmosphere.”["Unsettled"]. Apart from human contributions, carbon dioxide finds its way into the atmosphere through various powerful natural processes including fires, volcanic eruptions and vegetation decay. 

CO2 intercepts some wavelengths of infra-red radiation that water vapour (and other greenhouse gases) do not ["Unsettled"]. Without CO2, the earth would radiate much more heat back into space.

Some scientists now concede that increased CO2 by itself is not a major direct contributor to global warming. However, it is argued that knock-on effects (positive feedbacks) from increased CO2 are a major contributor to global warming. [“Primacy of Doubt”, Tim Palmer, Chapter 6]

Methane is the next most important green house gas after CO2. Although methane is about 1/200 of the concentration of CO2 in the atmosphere, it is potentially 30 times more potent in intercepting infra-red radiation. Most methane emissions are from the digestive system of cattle (mostly front end). Other sources of emission include rice cultivation, sewage and the decay of material in landfills ["Unsettled"].

Although a potentially strong absorber of infra-red radiation, the combined absorptive effect of methane and nitrous oxide is less than 3%. This is due to the overlap of the absorption bands of other green house gases. [David Cole, Walter Fabinski, Gerhard Wiegleb. "The Impact of CO2, H2O and Other “Greenhouse Gases” on Equilibrium Earth Temperatures"]. It would take a huge increase in atmospheric concentration of methane and nitrous oxide to have significant impact on total atmospheric infra-red absorption. [Cole, supra].

Above, we have referenced several variables involved in global warming. These variables (and others) inter-react. There are feedbacks, both negative and positive. A negative feedback is one that counteracts potential warming. A positive feedback is one that amplifies warming. Let's look at some examples. If warming causes less snow and ice surface coverage, albedo is decreased. Thus, declining snow and ice coverage is a positive feedback. Another example is melting permafrost. If warming is melting permafrost and melting permafrost releases methane, this is a positive feedback. If warming increases evaporation and increased evaporation increases cloud cover, this may be either a negative or positive feedback. There is much debate concerning feedbacks, especially concerning clouds.

The question of whether clouds act as a positive or negative feedback on climate change cannot at present be unambiguously answered.[Primacy of Doubt, supra, Chapter 6]“…. climate-change science is unquestionably complicated and clouds are the most complicated and uncertain part of it all”. [ibid]

Now we can get back to the question of climate modelling. Climate modelling must take all of these variables, and many others, into account in predicting the future.

Climate models are not just ‘physics’["Unsettled"] The best evidence of this is the fact that various climate models conducted by different groups do not agree [ibid]. In fact, they vary substantially.

Many assumptions are built into different models. These assumptions can and do lead to differing results. These assumptions are made necessary by the limitations of the modelling methodology and by limitations in the data available. One example where assumptions are required relates to cloud cover.

The first test for any climate model is how well it can reproduce the past. If a model cannot reproduce the past, it is a big red flag.["Unsettled", pp. 89-95]. Without “tuning”, model simulation results generally don’t match the observed climate system past or present [ibid, p.84] Modellers must adjust parameters to get a better match with features of the real climate system [ibid]. Tuning is not a minor detail. It is the process of “adjusting the model to deal with troublesome inconsistencies or [to] paper over irksome uncertainties.”[ibid] Some modellers adjust parameters to produce the desired result [ibid].

The idea is that if a model accurately ‘predicts’ past climate and climate change, it will accurately predict future climate and climate change. Without “tuning”, model simulation results generally don’t match the observed climate system past or present ["Unsettled", pp.68, 84]

Calibration involves the tuning of parameters. Parameters are tuned to produce agreement between the model and existing observational data. Calibration is intended to reduce prediction error. However, ‘agreement between model and observational data does not imply that the model gets the correct answer for the right reasons’.[Curry, "Climate Uncertainty and Risk", p. 68]. Different models will tune the same, or different parameters, differently. Which tuning is the correct one? Further, what observational data should be used? Selecting the time period 1975-2000 ‘tunes the model to a warming phase of natural internal variability, resulting in oversensitivity of the models to C02 [warming]. [Curry, p. 69]

This is not to say that climate models have no use. Modelling has several purposes. One general purpose is to understand how multiple variables work within a system. Models can add or delete variables to gain an understanding of how these variables may affect the system. This is a classic use of models in various scientific fields including climate science.

In fact, Global Climate Models (GCMs) were originally designed as a tool to help understand how the climate system works. [Curry, p.65]

The use of GCMs to predict the future raises an issue of 'fitness for purpose' [Curry, p. 74]. There are dozens of GCMs. In constructing a GCM, there are thousands of different choices made regarding different variables. Different sets of choices produce different models. Different models have different results

As a result of all the different models and mode results, 'ensembles' are used. Various forms of single and multi-model ensembles (SMILESs, MMEs, PPMs) are used to provide ranges of results. [Curry, p. 69] The same model may use several different sets of initial conditions to arrive at a range of outcomes. Or, different models may use the same initial conditions to arrive at a range of outcomes. The same model may use different parameter settings to obtain the same or different outcomes. The range of results may establish outer boundaries for predictions and deal with uncertainty quantification.

In “Primacy of Doubt”, author Tim Palmer argues that model divergence within an ensemble is a positive rather than a negative attribute. Models using different initial conditions and different parameterizations, taken together, can show areas of relative certainty. For example, regarding the issue of climate sensitivity, all of the models in the ensemble show that, with a doubling of CO2, temperature will increase by at least 2 degrees C. This proves, according to Palmer, that a doubling of CO2 will lead to a temperature increase of at least 2 degrees C. Similarly, the models within the ensemble set an upper limit to what temperature increase may be expected. Which exact model within the ensemble is correct, becomes irrelevant. This reasoning assumes that all possible outcomes are captured by the models within the ensemble.

So, what to make of all this? Predictions of future warming are based on models. Most models focus on CO2 as the control knob for future warming. Most models attempt to predict what will happen with a doubling of CO2. It is assumed that CO2 will double at some point, sooner or later, depending on which 'scenario' is adopted. The various models vary wildly in their predictions with a range of warming from 2C to 8.5C. Which model is to be believed? Palmer says this doesn't matter. What matters is that we know that a doubling of CO2 is very likely to cause warming of at least 2C and that is enough to cause concern.