Chapter 4
Modelling future climate
The whole of human society operates on knowing the future weather. For example, farmers in India know when the monsoon rains will come next year and so they know when to plant the crops. Farmers in Indonesia know there are two monsoon rains each year, so next year they can have two harvests. This is based on their knowledge of the past, as the monsoons have always come at about the same time each year in living memory. But the need to predict goes deeper than this; it influences every part of our lives. Our houses, roads, railways, airports, offices, cars, trains, and so on are all designed for the local climate. For example, in England all the houses have central heating, as the outside temperature is usually below 20°C, but no air-conditioning, as temperatures rarely exceed 26°C, while in Australia the opposite is true: most houses have air-conditioning but rarely central heating. Predicting future climate is now essential, as we can no longer rely on records of past weather to tell us what the future will hold. We also need to understand the consequences of our actions. For example, if we continue to emit GHGs at the same rate as today, how much climate change will occur? So we have to develop new ways of understanding potential futures. We model the future (Figure 16).
16. Generic structure of a global climate model.
Models
There is a whole hierarchy of climate models, from relatively simple box models to extremely complex three-dimensional GCMs. Each has a role in examining and furthering our understanding of the global climate system. It is the complex three-dimensional GCMs that are used to predict future global climate. These comprehensive climate models are based on physical laws represented by mathematical equations, which are solved using a three-dimensional grid over the globe. To obtain the most realistic simulations, all the major parts of the climate system must be represented in sub-models, including atmosphere, ocean, land surface (topography), cryosphere, and biosphere, as well as the processes that go on within them and between them.
Over the past 40 years there has been a huge improvement in climate models. This has been due to our increased knowledge of the climate system but also because of the nearly exponential growth in computer power. There has been a massive improvement in spatial resolution of the models from the very first IPCC report in 1990 to the latest in 2021. The current generation of GCMs have multiple layers in the atmosphere, land, and ocean and can have a spatial resolution greater than one point every 30 km by 30 km. Equations are typically solved for every simulated ‘half-hour’ of a model run. Many physical processes, such as atmospheric chemistry, the formation of clouds, production and movement of aerosols (particles suspended in the air), and ocean convection, take place on a much smaller scale than the main model can resolve. The effects of small-scale processes have to be lumped together, which is referred to as ‘parameterization’. All of these parameterizations are checked with separate ‘small-scale-process models’ to validate the scaling up of these smaller influences.
The biggest unknown in the models is not the physics or the chemistry or the biology: it is the estimation of future global GHG emissions over the next 80 years. This includes many variables, from the global economy to personal lifestyles. Individual models are therefore run many times with different emission scenarios to provide a range of changes that could occur in the future. In fact, the latest (sixth) IPCC Assessment Report (AR6) has compiled the results of multiple runs from over a hundred distinct climate models being produced across forty-nine different international modelling groups, which are all part of the latest (sixth) Coupled Model Intercomparison Projects (CMIP6). Of course, as computer processing power continues to increase, both the representation of coupled climate systems and the spatial scale will continue to improve.
Carbon cycle
At the heart of the climate models is the carbon cycle, central to estimating what happens to anthropogenic CO2 and CH4 emissions. The Earth’s carbon cycle is complicated, with both large sources and sinks of CO2. Currently half of all our carbon emissions are absorbed by the natural carbon cycle and do not end up in the atmosphere but rather in the oceans and the terrestrial biosphere. Figure 17 shows the global reservoirs in gigatonnes of carbon (GtC; or 1,000 million tonnes) and fluxes (the ins and outs of carbon in GtC per year). These indicative figures show the changes since the Industrial Revolution. Evidence is accumulating that many of the fluxes can vary significantly from year to year.
17. The carbon cycle, in gigatonnes of carbon.
This is because in contrast to the static view conveyed in figures like this one, the carbon system is dynamic, and coupled to the climate system on seasonal, interannual, and decadal timescales. What has become clear is that the ocean surface and the land biosphere each take up about 25% of our carbon emissions every year. As the oceans continue to warm they can hold less dissolved CO2, which means their uptake will reduce. As we continue to deforest and substantially alter land use, the ability of the land biosphere to absorb carbon diminishes.
Warming and cooling effects
As well as the warming effects of the GHGs, the Earth’s climate system is complicated in that there are also cooling effects (see Figure 18). These include the amount of aerosols in the air (many of which come from human pollution, such as sulfur emissions from power stations), which have a direct effect on the proportion of solar radiation that hits the Earth’s surface. Aerosols have a significant local or regional impact on temperature. Computer simulations of climate change demonstrate that industrial areas of the planet have not warmed as much as would be predicted just from rising GHGs. This so called ‘global dimming’, or more precisely ‘regional dimming’, has been confirmed with real temperature and aerosol measurements. Water vapour is a GHG, but, at the same time, the upper white surface of clouds reflects solar radiation back into space. The level of reflectivity of a surface is called ‘albedo’. Clouds and ice have a high albedo, which means that they reflect large quantities of solar radiation away from surfaces on Earth. Increasing aerosols in the atmosphere increases the amount of cloud cover, as they provide points on which the water vapour can nucleate. Predicting what will happen to the amount and types of clouds, and their warming or cooling potential, has been one of the key challenges for climate scientists.
18. Radiative forcings between 1750 and 2018.
Emission models of the future
As noted earlier, a critical problem with trying to predict future climate is predicting the amount of CO2 emissions that will be produced in the future. This will be influenced by population growth, economic growth, development, fossil-fuel usage, the rate at which we switch to alternative energy, the rate of deforestation, and the effectiveness of international agreements to cut emissions. Out of all the systems that we are trying to model into the future, humanity is by far the most complicated and unpredictable. If you want to understand the problem of predicting what will happen in the next 80 years, imagine yourself in 1920 and what you would have predicted the world to be like in the 21st century. At the beginning of the 20th century, the British Empire was the dominant world power due to the Industrial Revolution and the use of coal. Would you have predicted the switch to a global economy based on oil after the Second World War? Or the explosion of car use? Or the general availability of air travel? Even 30 years ago, it would have been difficult to predict that there would be budget airlines, allowing for cheap flights throughout Europe, the USA, and Asia.
The first IPCC reports used simplistic assumptions of GHG emissions over the next 100 years. From 2000 onwards, the climate models used more detailed scenarios set down in an IPCC special report (Special Report on Emission Scenarios by the IPCC, or SRES, 2000). The 2013 IPCC AR5 used more sophisticated representative concentration pathways (RCPs), which considered a much wider variable input to the socioeconomic models, including population, land use, energy intensity, energy use, and regional differentiated development. The RCPs were defined by the final radiative forcing achieved by the year 2100, and they range from 2.6 to 8.5 watts per square metre (W/m2). Radiative forcing is defined as the difference between sunlight (radiant energy) received by the Earth and the energy radiated back to space and is measured in units of W/m2 of the Earth’s surface. For the 2021 IPCC AR6, the RCP1.9 was added, to represent the Paris 2015 agreement ambition to keep global temperature rise to only 1.5°C above pre-industrial levels.
The IPCC AR6 also uses shared socioeconomic pathways (SSPs), which were developed to cover the full range of possible futures. The SSPs are a set of five narratives and driving forces that may shape the global economy and global emissions in the future (Figure 19). The SSPs were defined in 2017 by Keywan Riahi and colleagues in a paper in the journal Global Environmental Change as described below.
19. Future carbon-emission scenarios.
Low challenges to mitigation and adaptation: In this scenario the world shifts gradually but continually towards a more sustainable path, with inclusive and environmentally aware economic development. Management of the global commons slowly improves, educational and health investments accelerate the demographic transition towards reducing population, and the emphasis on economic growth shifts towards a broader emphasis on human wellbeing. Driven by an increasing commitment to achieving development goals, inequality is reduced both across and within countries. Consumption is oriented towards low material growth and lower resource and energy intensity.
Medium challenges to mitigation and adaptation: In this scenario the world follows a path in which social, economic, and technological trends do not shift markedly from historical patterns. Development and income growth proceeds unevenly, with some countries making relatively good progress while others fall short of expectations. Global and national institutions work towards but make slow progress in achieving sustainable development goals. Environmental systems experience degradation, although there are some improvements and overall the intensity of resource and energy use declines. Global population growth is moderate and levels off in the second half of the century. Income inequality persists or improves only slowly, and challenges to reducing vulnerability to societal and environmental changes remain.
High challenges to mitigation and adaptation: In this scenario there is a resurgence of nationalism, concerns about competitiveness and security, and regional conflicts that push countries to increasingly focus on domestic or, at best, regional issues. Policies shift over time to become increasingly oriented towards national and regional security issues. Countries focus on achieving energy and food security goals within their own regions at the expense of broader based development. The scenario also assumes that investments in education and technological development decline. Economic development is slow, consumption is material-intensive, and inequalities persist or worsen over time. Population growth is low in industrialized and high in developing countries. A low international priority for addressing environmental concerns leads to strong environmental degradation in some regions.
Low challenges to mitigation, but high challenges to adaptation: In this scenario there are highly unequal investments in human capital, combined with increasing disparities in economic opportunity and political power, lead to increasing inequalities and stratification both across and within countries. Over time, a gap widens between an internationally connected society that contributes to knowledge- and capital-intensive sectors of the global economy, and a fragmented collection of lower income, poorly educated societies that work in a labour-intensive, low-tech economy. Social cohesion degrades, and conflict and unrest become increasingly common. Development is substantial in the high-tech economy. The globally connected energy sector diversifies, with investments in both carbon-intensive fuels like coal and unconventional oil, but also low-carbon energy sources. Environmental policies focus on local issues around middle- and high-income areas.
High challenges to mitigation, but low challenges to adaptation: In this scenario the world places increasing faith in competitive markets, innovation, and participatory societies to produce rapid technological progress and development of human capital as the path to sustainable development. Global markets are increasingly integrated. There are also strong investments in health, education, and institutions to enhance human and social capital. At the same time, the push for economic and social development is coupled with the exploitation of abundant fossil-fuel resources and the adoption of resource- and energy-intensive lifestyles around the world. All these factors lead to rapid growth of the global economy, while global population peaks and declines in the 21st century. Local environmental problems like air pollution are successfully managed. There is faith in the ability to effectively manage social and ecological systems, including by geoengineering if necessary.
These are narratives that describe different pathways our future society could take. SSP1 and SSP5 are optimistic about human development, with both allowing for ‘substantial investments in education and health, rapid economic growth, and well-functioning institutions’. The difference is that SSP5 is fossil-fuel energy intensive, while SSP1 assumes a shift to renewable energy. SSP3 and SSP4 are pessimistic about the future and SSP2, as it says, is a middle-of-the-road scenario. The scenarios used in the AR6 are a combination of the SSPs and RCPs, providing a clear narrative and an outcome. This is because the SSPs do not include any mitigation measures, so a high-emission global economy could have a lower concentration pathway by employing huge amounts of mitigation. If all the SSP and RCP combinations are examined, then some are extremely unlikely and others are almost impossible, for example SSP5 and RCP1.9. The AR6 focuses on five main scenarios SSP1–1.9, SSP1–2.6, SSP2–4.5, SSP3–7.0, and SSP5–8.5 (Table 2).
Modelling uncertainty
In the most recent IPCC AR6, the past- and future-emission scenarios were used in about 100 distinct, independent GCMs. Each of these models has its own independent design and parameterizations of key processes. The independence of each model is important, as confidence may be derived from multiple runs on different models providing similar future climate predictions. In addition, the differences between the models can help us to learn about their individual limitations and advantages. Within the IPCC, due to political expediency, each model and its output is assumed to be equally valid. This is despite the fact that some are known to perform better than others when tested against the reality provided by the historical and palaeoclimate records. Moreover, though we understand uncertainty within a single model, the notion of quantifying uncertainty from many models currently lacks any real theoretical background or basis. The IPCC combines all the models used for each run and then presents the mean and the uncertainty between the models. This way it is clear that there are differences in the models’ output but that in general they agree and show very different futures based on which scenario we take. The uncertainties in the IPCC 2021 report are slightly higher than those in previous reports. This is because of our greater understanding of the processes and our ability to quantify the uncertainty of our knowledge. So although our confidence in the climate models has increased, so has the range of possible answers for any specific GHG forcing. One way to test the models and their uncertainty is to compare their predictions with the real world outcome. The CMIP3 took place just before the IPCC AR3 in 2001, and we can use those model predictions to compare with the following 20 years of real data. As can be seen from Figure 20 the predicted warming of the world by the earlier climate models was very good, and we have now had over 20 years to improve the models and expand the number we use in our predictions.
20. Climate model predictions compared with climate data (2000–20).
Future global temperatures, precipitation, sea ice, and sea level
Between ten and thirty-six climate models have been run for each of the five SSP-RCPs for the IPCC 2021 report to produce scenarios of global temperature, precipitation, sea-ice and sea-level changes that may occur by 2100. These climate models suggest that depending on our GHG emissions the global surface temperature could, by 2081–2100, rise between 1.3°C and 5.5°C compared with the pre-industrial period (1850‒1900): see Table 3. In all the scenarios except SSP1–1.9, a global temperature rise of over 1.5°C is reached between 2021 and 2041 with the best estimate being 2030. The models also show that the temperature rise will be unevenly distributed, with the largest rises in temperature observed on land.
Future sea-level rise is dependent on which SSP we follow and could be between 0.32 m and 0.82 m in the last two decades of the century (Table 3 and Figure 21). With the 20 cm rise which has already occurred, this would represent a total rise of 0.52 m and 1.02 m. If we look at the final projected sea level at 2100, the models show an increase in global mean sea level of between 27 cm and 98 cm. This is similar but more extreme than the projection made by the IPCC 2007 report, which suggests a sea-level rise of between 28 cm and 79 cm by 2100.
21. Global temperatures, Arctic sea ice, and sea level in the 21st century.
Both average land and ocean precipitation are very likely to increase under all five of the SSPs (see Table 3). The annual global average land precipitation by 2081–2100 relative to 1995–2014 will increase by 2.7% (with a range of 0.6–4.8%) in the low-emission scenario SSP1–1.9 and 8.2% (with a range of 2.5–13.8%) in the high-emission scenario SSP5–8.5. Based on all the scenarios the average land precipitation will increase approximately 1–3% per 1°C of global warming.
In the three worst-case SSP scenarios (SSP2–4.5, SSP3–7.0, and SSP5–8.5), the Arctic Ocean will become effectively ice-free (coverage below 1 million km2) in September (the minimum ice month) by 2081–2100.
What do the climate change deniers say?
One of the best ways to summarize the perceived problems of modelling climate change is to review what the climate change deniers say.
Different models give different results, so how can we trust any of them? This is a frequent response from many people not familiar with modelling, as there is a feeling that somehow science must be able to predict an exact future. However, in no other walk of life do we expect this precision. For example, you would never expect to get a perfect prediction on which horse will win a race or which football team in a match will emerge triumphant. The truth is that none of the climate models is right, because they provide a range of potential futures. Our view of the future is strengthened by the use of more than one model, because each model has been developed by different groups of scientists around the world, using different assumptions, different computers, and different programming languages; thus they provide their own independent future predictions. What causes scientists to have confidence in the model results is that they all predict the same trend in global temperature and sea level for the next 80 years. One of the great strengths of the 2021 IPCC reports is that they used over a hundred distinct models from forty-nine different modelling groups around the world compared to forty models in 2013, twenty-three models in 2007, and seven in 2001.
Climate models are too sensitive to CO2. To dismiss the importance of climate change, many deniers argue that the models are oversensitive to changes in GHGs. This is a classic ‘it is not as bad as you think’ argument. The strength of having so many climate models is that scientists can also give an estimation of how confident they are in the model results and check how sensitive their models are compared with each other and real world data. One key test of climate models is the equilibrium climate sensitivity (ECS), whereby the model predicts what the global temperature change would be if pre-industrial CO2 levels were doubled. These results have been very consistent over the past 50 years (see Figure 22), and the 2021 IPCC report suggests the most up to date modelled range is between 2.50°C and 5.43°C (average 3.74°C), which is consistent with other measures.
22. Equilibrium climate sensitivity.
Climate models fail to reconstruct natural variability. Many climate deniers argue that the current warming trend is due to natural variations. But scientists include all the natural variables, including ones that cool the climate, in the climate models. Combining all our scientific knowledge of natural (solar, volcanic, aerosol, and ozone) and human-made (GHGs and land-use change) factors warming and cooling the climate shows that 100% of the warming observed over the past 150 years is due to humans.
Clouds have negative feedbacks on global climate that will reduce the effects of climate change. As has been the case since the very first IPCC report in 1990, one of the uncertainties in the models is the role of the clouds and their interaction with radiation. Clouds can both absorb and reflect radiation, thereby cooling the surface, and absorb and emit long-wave radiation, warming the surface. The competition between these effects depends on a number of factors: the height, thickness, and radiative properties of clouds. The radiative properties and formation and development of clouds depend on the distribution of atmospheric water vapour, water drops, ice particles, atmospheric aerosols, and cloud thickness. The physical basis of how clouds are represented or parametrized in the climate models has greatly improved through the inclusion of representations of cloud microphysical properties in the cloud water budget equations. Figure 18 shows that even if the most extreme cooling value is applied for clouds, the warming factors due to GHGs are still three times larger.
Climate change must be caused by galactic cosmic rays (GCRs). GCRs are high-energy radiation that originates outside our solar system and may even be from distant galaxies. It has been suggested that they may help to seed or ‘make’ clouds. So reduced GCRs hitting the Earth would mean fewer clouds, which would reflect less sunlight back into space and would cause the Earth to warm up.
But there are two problems with this idea. First, the scientific evidence shows that GCRs are not very effective at seeding clouds. Second, over the past 50 years, the flux of GCRs has actually increased, hitting record levels in recent years. If this idea were correct, GCRs should be cooling the Earth, which they aren’t.
Modelling future climate change is about understanding the fundamental physical processes of the climate system. Five new emission scenarios were produced for the 2021 IPCC science report, using a much wider set of inputs to the socioeconomic models, including population, land use, energy intensity, energy use, and regional differentiated development. One of these emissions pathways (SSP1-1.9) was developed to indicate to policy makers how they could achieve the aspired target of just 1.5°C warming set at the Paris 2015 climate change conference. Over a hundred climate models were used in developing the IPCC scenarios, providing a huge ‘weight of evidence’. Using the three main realistic carbon-emission pathways over the next 80 years, the climate models suggest the global mean surface temperature could rise by between 2.1°C and 5.5°C by 2100. However, it must be remembered that global temperatures will not stop changing once we get to the year 2100. Figure 23 shows how temperatures could continue to rise way beyond the levels of this century, depending on the chosen emission pathway. Using the three main realistic carbon-emission pathways, the models also predict an increase in global mean sea level of between 0.50 m and 1.3 m by 2100 compared with pre-industrial times.
23. Global surface temperatures (1950–2300).