The Earth’s radiation balances are being altered by a number of changes in the composition of the atmosphere, and as a consequence the climate system is being ‘forced’, almost certainly in the direction of higher temperatures. In earlier chapters we examined the physics of the processes involved. What are likely to be the effects of such changes? The only way to answer questions such as this is to model the Climate System in sufficient detail. Firstly, we need to model the atmosphere, something we have been doing for half a century to forecast the weather. Because of the significant exchanges of both heat and water between the ocean and the atmosphere, it is clearly necessary to couple an ocean model. Ice sheets are likely to be affected by warming, as they are one of the key feedback processes just mentioned. The land surface also has significant interactions on various timescales.

Research on the interaction between climate science and policy has pointed to the production of so-called ‘boundary objects’ as one way in which the IPCC has influenced climate policymaking and broader climate discourses. By providing a common framework that enables interaction across social worlds, while still allowing more localised use by different groups of actors, such objects have been key in bringing together climate science and policy, in turn shaping the trajectory of both. This chapter reviews several concepts that have been analysed as boundary objects — such as the concept of climate sensitivity and the 2 °C and 1.5 °C targets — and explains how they have been productive of new science/policy relations. It also points to new challenges for the IPCC as climate policy development moves towards implementation and increases demand for more ‘solution-oriented’ knowledge.

This is our climate change status check, and the first three things to understand are that climate change is real, anthropogenic (i.e., caused by humans), and dangerous. To drive home those points, this chapter relies heavily on the science of the landmark IPCC climate assessments and particularly the most recent AR5 report. We drill down on a key figure from the AR5 to clarify the observed data since 1850 in respect of global average surface temperatures, sea level rise, atmospheric concentrations of several greenhouse gases, and annual CO2 emissions. With a second figure we examine the sources of climate forcing since 1950 and clarify the degree to which they are caused by humanity or by nature. We then divert to key facets that we don’t yet fully understand about climate change, including tipping points, “climate sensitivity” and the likely emissions pathway that humanity will choose over the remainder of the century. We conclude with the observation that irrespective of how these mysteries play out, substantial climate change is in our future. It’s coming.

Old climate models are often evaluated on whether they made correct predictions of global warming. But, if the old models were missing processes that we know now to be important, any correctness of their predictions would have to be attributed to a fortuitous compensation of errors, creating a paradoxical situation. Climate models are also tested for falsifiability by using them to predict the impact of short-term events like volcanic eruptions. But climate models do not exhibit the numeric convergence to a unique solution characteristic of small-scale computational fluid dynamics (CFD) models, like the ones that simulate flow over a wing. Compensating errors may obscure the convergence of individual components of a climate model. Lack of convergence suggests that climate modeling is facing a reducibility barrier, or perhaps even a reducibility limit.

The media often lends more credence to dramatic predictions from individual climate models than does the climate science community as a whole. Models are metaphors of reality; they should be taken seriously, not literally. Predictions made by simple climate models need to be confirmed using more comprehensive climate models. Deep uncertainty surrounds estimates of climate metrics like climate sensitivity, meaning that the error bars presented with these metrics may have their own unquantifiable error bars. It is important to make the distinction between precision and accuracy when evaluating uncertainty estimates of climate parameters. Overconfidence in numerical predictions of extreme climate scenarios may lead to a “doomist” belief that a climate catastrophe is inevitable and that there is nothing we can do to prevent it.

The greenhouse effect is a fundamental property of the Earth’s climate that is responsible for keeping the planet warm and habitable for life. The concept of the Goldilocks zone describing the habitability of planets is briefly introduced. Basic concepts in atmospheric physics, such as solar and infrared radiation, are introduced using concepts from everyday life, including an analogy with blankets to explain the greenhouse effect. The two main gases responsible for the greenhouse effect – water vapor and carbon dioxide – are described. The historical background of the discovery of the greenhouse effect, including the very first climate model that predicted global warming (formulated by Svante Arrhenius in the 1890s), as well as the recently uncovered role of Eunice Foote, is presented. Climate feedbacks, which can amplify the global warming, are explained.

The Rumsfeld knowledge matrix – which spans the knowledge categories “known knowns,” “known unknowns,” and “unknown unknowns” – is used to illustrate the process of model improvement. Two new knowledge subcategories – “poorly known unknowns” and “well-known unknowns” – are introduced to distinguish between accuracy of parameterizations. A distinction is made between “downstream benefits” of parameterizations, which improve prediction skill, and “upstream benefits,” which improve understanding of the phenomenon being parameterized but not necessarily the prediction skill. Since new or improved parameterizations add to the complexity of models, it may be important to distinguish between essential and nonessential complexity. The fourth knowledge category in the Rumsfeld matrix is “unknown knowns” or willful ignorance, which can be used to describe contrarian views on climate change. Contrarians dismiss climate models for their limitations, but typically only offer alternatives born of unconstrained ideation.

The fundamental difference between weather prediction and climate prediction is explained, using a “nature versus nurture” analogy. To predict weather, we start from initial conditions of the atmosphere and run the weather forecast model. To predict climate, the initial conditions matter less, but we need boundary conditions, such as the angle of the sun or the concentration of carbon dioxide in the atmosphere, which control the greenhouse effect. Charles David Keeling began measuring carbon dioxide in the late 1950s, and found that its concentration was steadily increasing. Carbon dioxide concentrations for the past 800,000 years can also be measured using ice cores that contain trapped air. These ice core data show that the rise in carbon dioxide concentrations measured by Keeling was unprecedented. Manabe, and another scientist, Jim Hansen, used climate models to predict that increasing carbon dioxide could cause global warming.

The Geophysical Fluid Dynamics Laboratory (GFDL) is a pioneering institution in the field of climate modeling. Its founding director, Joseph Smagorinsky, was a member of the Princeton Meteorology Group. He hired a Japanese scientist, Syukuro Manabe, who formulated a one-dimensional model of climate, known as the radiative–convective model, that was able to calculate the amplifying climate feedback due to water vapor. This model provided one of the first reliable estimates of global warming. Manabe worked with other scientists to build three-dimensional climate models, including the first model that coupled an atmospheric model to an ocean model. The concepts of reductionism and emergentism, which provide the philosophical context for these scientific developments, are introduced.

Future projections are uncertain, for multiple reasons. Limits to scientific understanding of natural variability, structural and parametric uncertainty in scientific modeling, climate sensitivity, bias correction and downscaling all play a role. The uncertainty due to human choices that will determine emissions of heat-trapping gases becomes increasingly important over time, to the point where it dominates the uncertainty in many aspects of global and regional change by the end of century. Quantifying how a given system will respond to a changing climate adds yet another layer of uncertainty that can be prohibitively large in systems that are complex and/or not well understood. Understanding the source of these uncertainties and how they can be addressed when applying downscaled climate projections to assess future impacts is essential to quantifying the range of future change and resulting impacts on human and natural systems