Climate Forcing Scenarios

In order to project future changes in the climate system, scientists must first estimate how GHG emissions and other climate forcings will evolve over time. Since the future cannot be known with certainty, a large number of scenarios of future emissions are developed using different assumptions about future economic, social, technological, and environmental conditions. Emissions scenarios are not forecasts and do not attempt to predict "short-term" fluctuations such as business cycles or oil market price spikes. Instead, they focus on long-term (e.g., decades to centuries) trends in energy and land use that ultimately affect the radiation balance of the Earth.

For the past decade, the most widely used scenarios of 21st-century GHG emissions have been those produced for the IPCC's Special Report on Emissions Scenarios (SRES) (Nakicenovic, 2000). The SRES scenarios are quantitative realizations of qualitative storylines that sketched a range of alternative assumptions regarding 21st-century population growth and economic and technological development. The SRES scenarios were all intended to represent alternative baseline (or "business as usual") GHG emissions trajectories, with no explicit policy interventions to limit emissions. In addition, probability distributions were not estimated for either the range or individual SRES scenarios, and so there was no explicit characterization of the likelihood that actual emissions might fall outside the range of the included scenarios.

Since 2000, major scenario exercises have put less emphasis on alternative no-policy baselines and instead concentrated primarily on elaborating the socioeconomic, technological, and policy aspects of alternative GHG trajectories over the next century, with an emphasis on changes over the next few decades; on improving the realism and comprehensiveness of both individual scenarios and the suite of scenarios, for example by adding or improving representations of all important forcing agents and developing scenarios with widely spaced total radiative forcing estimates; and on developing these trajectories in a more integrated and iterative manner with climate model projections and assessments of current and future climate impacts. Recent scenario development exercises stressing these characteristics have been undertaken by the CCSP (2007c), the Energy Modeling Forum (Clarke et al., 2009), and other groups (Moss et al., 2010). These exercises have yielded a number of important insights, such as the challenges associated with reaching certain GHG emissions or temperature goals.

The aim of developing more useful climate forcing scenarios is subject to several pressures that are in tension with each other, such as providing more sophisticated and increasingly detailed representations of socioeconomic, environmental, and policy factors, while at the same time keeping the origin of the assumptions used transparent, plausible, and understandable. Additional challenges to scenario development include balancing and integrating the qualitative and quantitative elements of scenarios; developing scenarios that provide socioeconomic and environmental information (which is useful, for example, for adaptation planning) that is consistent with the corresponding emissions trajectories; and making more explicit, transparent, and defensible judgments of probabilities associated with scenario-based ranges of key variables (CCSP, 2007b; Parson, 2008).

In response to these issues, climate modelers, integrated assessment modelers, and researchers focusing on impacts, adaptation, and vulnerability collaborated to develop a new process for preparing and applying scenarios in climate research (Moss et al., 2010). In contrast to the traditional approach in which scenarios are developed and applied in a linear causal chain from socioeconomic "drivers" of emissions, to atmospheric and climate processes, to impacts, the new process starts with four scenarios of future radiative forcing called "Representative Concentration Pathways." These pathways are defined by their radiative forcing in 2100 and include (1) a high scenario of 8.5 W/m2, and still rising; (2) an "overshoot scenario" in which radiative forcing peaks midcentury and then declines to a level of 2.6 W/m2 (which is lower than any of the SRES scenarios) in 2100, and (3) two intermediate scenarios that stabilize in 2100 at 6 and 4.5 W/m2. These representative concentration pathways will be used to conduct new climate model experiments and produce new climate change scenarios. In parallel, new socioeconomic and emissions scenarios will be developed to explore detailed scenarios of socioeconomic drivers, adaptation, mitigation, and other issues such as feedbacks. The process rests on the simple observation that any particular radiative forcing trajectory can be realized by many different socioeconomic, technology, and policy futures. The new process facilitates research into a number of key issues including feedbacks, the ease or difficulty of achieving overshoot scenarios (and the climate and ecosystem consequences of these trajectories, which are highly uncertain), as well as process issues discussed in the previous paragraph.

Climate Models

Climate models encapsulate scientists' best understanding of climate and related Earth system processes and are important tools for understanding past, present, and future climate change. While there are many different kinds of climate models, all are based fundamentally on the laws of physics that govern atmospheric and oceanic motions, including the conservation of mass, energy, and angular momentum and laws that govern the propagation of radiation through the atmosphere. Most modern climate models also include representations of the oceans, cryosphere, and land surface, as well as the exchanges of energy, moisture, and materials among these components. Earth system models additionally simulate a wide range of biophysical processes including atmospheric chemistry and the biogeochemistry of ecosystems on land and in the oceans (Figure 6.17).

Climate and Earth system models (for simplicity, referred to hereafter as climate models) use computer-based numerical techniques to solve a system of mathematical equations that embody these laws, systems, and processes, yielding a predicted evolution of the climate system over time (see, e.g., DOE, 2008b, 2009b; Donner and

FIGURE 6.17 Schematic illustration of the components of climate and Earth system models. The components of climate models are in gray and the additional components in Earth system models are in green. The connecting arrows indicate exchanges that couple the model components. SOURCE: Donner and Large (2008).

FIGURE 6.17 Schematic illustration of the components of climate and Earth system models. The components of climate models are in gray and the additional components in Earth system models are in green. The connecting arrows indicate exchanges that couple the model components. SOURCE: Donner and Large (2008).

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FIGURE 6.18 Schematic overview of the translation from a specified trajectory of emissions of GHGs and other climate forcing agents to trajectory to climate response. Simulated climate changes will include both the forced response and internal (natural) variability. The specific model results in the bottom row are for illustrative purposes only. SOURCE: Meehl et al. (2007a).

Large, 2008). Climate models are based on the same basic equations that are used to predict short-term weather variations. However, rather than trying to predict the exact future evolution of the atmosphere (i.e., the weather), climate models instead focus on accurately simulating the processes that govern interannual and longer-term climate trends (see Box 6.1).

Climate models are used to simulate both natural climate variability and the evolution of the climate system under specified climate forcing, including both historical data and scenarios of future forcing changes (Figure 6.18). Our confidence in the ability of climate models to reliably project certain aspects of future climate stems from the extensive development and testing processes used to design models and evaluate their performance—including simulations of 20th-century climate when the climate forcing and response are both reasonably well known (up to the limits of observations and recordkeeping) and simulations of the response to volcanic eruptions (e.g., Randall et al., 2007). Moreover, by assessing many different models, each with different emphases, strengths, and weaknesses, or many different runs of the same model (which provides an indication of natural variability), the most robust features of future projections emerge. These results are presented in the next section of the chapter.

Advances in climate modeling over the past 50 years have been driven by two main factors: (1) increases in computer power, which have allowed improved spatial resolution, the inclusion of additional Earth system components, more explicit representa tions of processes, and multiple model experiments to explore different assumptions and model specifications; and (2) improvements in theoretical and mechanistic understanding of the climate system and the processes being modeled, which in turn are tied to basic research and improvements in observational capabilities. Today, continued improvements in computational power, scientific understanding, and supporting observations are still the primary factors driving improvements in climate models—or stated conversely, even if the evolution of future climate forcing were known exactly, limits in computer power, observational data, and scientific understanding of the climate system would still constrain the ability of models to produce perfect predictions of future climate (Shapiro et al., in press).

For example, the typical horizontal grid spacing of a state-of-the-art global climate model is on the order of 60 miles (100 km), but climatically relevant features such as clouds, topography, and land cover often vary at a scales of a half-mile or less. These subgridscale features and processes must be parameterized—approximated using numerical techniques that specify the large-scale influence of small-scale processes—or upscaled through statistical or "nested model" approaches that extend representative small-scale simulations to larger spatial scales. As a result of these approximations and other factors (described below), global climate models generally only provide consistent and reliable simulations of temperature, precipitation, and other relevant climate variables at continental to global scales.

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