Demystifying Climate Models

What is climate? What is a model? How do we describe the uncertainty in a model? This chapter introduces key terms and concepts. We start with basic definitions of climate and weather. Then, we discuss models. Even if we do not realize it, we use models all the time. So we describe a few different conceptual types of models and put climate models in context. Finally we introduce the concept of uncertainty. As we discuss later in the book, models may have errors and still be useful, but this requires understanding the errors (the uncertainties) and understanding where they come from. These concepts are common to many types of modeling.

The surface of the earth is the intersection of distinct parts of the climate system. Understanding the different parts or components of the climate system is critical for modeling (or simulating) the system. This chapter describes the basic parts of the Earth that comprise the climate system, and the key scientific principles and critical processes necessary to model each of these components. These components include the Atmosphere, Ocean, Ice and Land. The climate system is typically represented as a set of building blocks, with individual processes collected into a model of one component of the system. The components are coupled to other components to represent the entire climate system. Understanding and then representing the interactions between processes and between components is critical for being able to build a representation of the system: a climate model.

Why does climate change? How does climate change?  This chapter describes flows of energy in the climate system. Climate can change when there are changes to energy flows between the different components of the climate system—internal changes.  Climate can also change as a response to a change in total input or output energy—external changes. The energy budget of the planet is critical for understanding how the climate system may change over time, because on long timescales, climate is governed largely by the total amount of energy in the system and how that energy is stored and moves. Examples of interactions and internal feedbacks from past changes to the climate are described, and where the energy may go in the future is discussed.

Climate models are constructed from mathematical equations that describe the behavior of its components: Atmosphere, Ocean, Ice and Land. This chapter describes the structure of a coupled climate model, including common features and concepts used across different components. Coupling is the term used to define the modeling of the interactions of the components. The equations of the climate model are programmed into a computer, much like budget-management equations are coded in a spreadsheet.  The equations represent of the physical, chemical and biological laws that quantify the climate. There can be simple or comprehensive sets of equations, ranging from ‘simple’ models of the energy balance to complex models of the full climate system. The complexity in some ways mirrors the history of climate models. The construction and form of these different models are described, and the challenges of using climate models on large computers are also discussed.

How is the atmosphere modeled? What are the energy flows, the circulation, and the behavior of water as it freezes and evaporates? Different types of atmosphere models are described. The atmosphere is important for connecting the different parts or components of the earth system. Two of these connections are related to greenhouse gases: the water (or hydrologic) cycle, and the carbon cycle. The similarities and differences between models used to simulate climate and those used to simulate weather are discussed. Details about the challenges involved in simulating the future are presented. As an example, atmospheric models are applied to studying tropical cyclones (hurricanes).

This chapter discusses ocean and sea-ice models. The ocean is a much larger reservoir of heat than the atmosphere. Therefore the heat content of the ocean is a critical part of the climate system. The ocean circulation is influenced by density, ocean boundaries (topography), the rotation of the earth and surface winds. The exchange of heat and water at the surface between the ocean and atmosphere is important for understanding the variability of the climate on time lengths of years to decades. Density plays an important role in the ocean: heavy water sinks; light water rises. The general nature of the ocean circulation cannot be understood without it. The density is related to temperature and salt, and the exchanges of heat and water with the atmosphere influence density. The cryosphere (“ice” sphere) contains land ice (ice sheets and glaciers), seasonal snow on land, and sea ice. Freezing and melting represent flows of energy between ice and atmosphere, land, and oceans. Models of sea ice are tightly coupled to the ocean. Sea ice is a critical part of the climate system because it strongly affects the reflection and absorption of solar energy (albedo). Sea ice also affects the surface energy coupling between the atmosphere and ocean. Thus, even though the cryosphere is a small area of the planet, it is an important part of the climate system, and it is critical at high latitudes. The role of the ocean and ice in sea-level rise projections is analyzed.

How are the pieces of the terrestrial system (land surface, glaciers, and ice sheets) modeled? Although terrestrial systems are often thought of as just modeling the land and its biology surface, the system also includes two other important components: the cryosphere (ice and snow) that sits on land and the anthroposphere (the role of humans). Plants are critical for modeling the land surface because they help govern the exchange of heat, water and carbon between the soil and the atmosphere. The coupling between plants, soil and atmosphere is discussed, along with the role of glaciers and ice sheets. Some of the major challenges in terrestrial models are discussed. The interaction of human systems and the climate system is also discussed as a framework to think about climate change. A national park in North America is used as an example of modeling effects of climate change on land ecosystems.

How do we bring all of the components together? To describe the Earth’s climate all the interactions of all of the individual components have to be accounted for. The accounting is done by coupling, which defines a coupled climate (or earth system) model. Different types of coupled models can be constructed, representing regions, the whole planet, or even focusing on human systems. Some important aspects of the coupled system are obvious (e.g., precipitation falling from atmosphere onto land). Other aspects of coupling are more complex, especially interactions that result in strong feedbacks to the climate system. That is, when there is a change in one component of the climate system, how do the other components respond? A critical coupled aspect of the Earth’s climate with certain effects on humans is sea- level rise. Sea-level rise is related to interactions of the ocean, ice, land and atmosphere. The integrated assessment of water resources in California is used as an example of how the water cycle in the climate system is coupled to different components and to the human system.

Why should we trust climate models? Trust in a model comes through testing, evaluation and validation of the model. Evaluation requires comparisons against observations. Both the observations and the model may be uncertain, and this uncertainty must be addressed. Observational and model uncertainties are described in detail. An essential part of climate science uses models to make projections. Projections of historical time periods allow evaluation of models against the observational record. Success in representing the past increases our confidence in future predictions. Weather forecasting and seasonal climate prediction improve our ability to validate climate models. The different way models are run is described in detail. When we run models for the future, we make either projections or forecasts, and these are different and need to be understood. A projection is usually dependent on an assumed scenario for model inputs, like future emissions of greenhouse gases. As an example of model evaluation, we discuss the evaluation of models in the assessment and projection of ozone depletion.

Once there is some trust in a model, how well can the climate be predicted?  How are models used to generate predictions and projections? The description and quantification of uncertainty is an element of scientific research. To be useful, predictions and projections require a description and estimate of uncertainty.  Key uncertainties in model predictions and projections are discussed. Then methods of computational experimentation to understand uncertainty are described. Attention is given to ensembles of multiple simulations and multiple models. As an example and application of these ideas, the development of scenarios for future greenhouse gas emissions is examined.

What are the results provided by state-of-the-art climate models? This chapter provides some perspective on current results and modeling efforts, taking into account the description of the climate system, climate models, and uncertainty. Selected results of recent climate model simulations are used to characterize and frame model uncertainties. The goal is to understand the uncertainty in climate model predictions of the future. A prediction without uncertainty, or with the wrong uncertainty, may be worse than no prediction at all. First, we briefly review some of the history and organization of modeling efforts. Second, we discuss what we want to know (predict) and how to use uncertainty. Third, we review the confidence in current predictions. Some climate model predictions have high confidence, for example, global average temperature. Other predictions are less certain, such as regional precipitation, sea ice and the carbon cycle. Highly uncertain predictions are most likely to be ‘wrong’ in that the actual result is out of the range of uncertainty. Sea level rise predictions dependent on ice sheet melt are an example of this. Predicting changes in extreme events such as tropical cyclones or floods presents unique issues. An example of prediction of regional climate and extremes in Colorado is used as an example.

How does a non-expert actually use climate models? Climate models aim to provide useful projections of future climate for practitioners who need to make policy, planning and management decisions. The challenges of communication and use of model projections in planning and management is not trivial. This chapter explores the use of model information, both conceptually and with case studies. The goal is to examine the processes involved in the use of model information to help the reader overcome barriers to use of climate model output. Conceptually models are useful because they compare well to observations (credibility), are produced by a known or reputable process (legitimacy) and produce relevant outputs for a particular problem (salience). Dealing with uncertainty in ways that policy makers can understand is critical. A key component of communication is a need for interpreters who can evaluate model output for particular disciplines and frame uncertainty.

This chapter sets out to synthesize the key points from the preceding chapters. The synthesis includes a summary of what is understood about predicting climate and what is uncertain. We summarize the basic principles behind climate models. We describe in a qualitative fashion the mechanics of how the different components of a climate model are constructed. In the process, we focus on critical aspects of the climate system that make the different pieces complex, uncertain, and interesting. For most parts of the earth system, important mechanisms for how climate works are not necessarily intuitive. Finally, we lay out some of the methods for evaluating models, and examined what climate models are good for, and what they are not good for. This includes a detailed look at uncertainty, and a look at the applications of models for decision making. To this we also add a summary of the future directions and challenges for climate modeling.