Successful adaptation reduces vulnerability to an extent that depends greatly on adaptive capacitythe ability of an affected system, region, or community to cope with the impacts and risks of climate change (see Chapter 18). Enhancement of adaptive capacity can reduce vulnerability and promote sustainable development across many dimensions.
Adaptive capacity in human systems varies considerably among regions, countries, and socioeconomic groups. The ability to adapt to and cope with climate change impacts is a function of wealth, technology, information, skills, infrastructure, institutions, equity, empowerment, and ability to spread risk. Groups and regions with adaptive capacity that is limited along any of these dimensions are more vulnerable to climate change damages, just as they are more vulnerable to other stresses. Enhancement of adaptive capacity is a necessary condition for reducing vulnerability, particularly for the most vulnerable regions, nations, and socioeconomic groups. To be sure, some development paths can increase some types of vulnerabilities, whereas others can reduce those vulnerabilities.
Adaptive capacity in natural systems tends to be more limited than adaptive capacity in human systems. Many species have limited ability to migrate or change behavior in response to climate change. What may be of greater concern is the harm that already has been done to natural systems by societal development. Habitat fragmentation and destruction, as well as creation of barriers to migration, will make it much more difficult for species to cope with climate change than if natural systems were undisturbed.
We do not address adaptation explicitly in this chapter, except to the extent that the literature cited here considers adaptation. Adaptation may have the potential to reduce vulnerability and, in many cases, shift the threshold for negative impacts to higher magnitudes of climate change. The degree to which adaptation can do so is not addressed here; it should be the subject of future research.
Box 19-1. Uncertainties in Future Warming
Does a given atmospheric concentration of GHGs cause a specific change in global mean temperature (or other climate variables, for that matter)? To answer this question, we quantify uncertainties in the change in global mean temperature for a given CO2 concentration level. This is accomplished by using the same simple models that are used in the TAR Working Group I report (TAR WGI Chapter 9). These models are updated versions of models used previously by the IPCC in the Second Assessment Report (SAR) (Kattenberg et al., 1996; see also Raper et al., 1996). We consider the effects of uncertainties in future emissions of all radiatively important gases (particularly the relative importance of CO2 to other forcing factors) and climate sensitivity, but not uncertainties in translating emissions to concentrations.
These uncertainty issues are addressed by comparing CO2 concentrations (not other GHGs) and the corresponding temperature projections for 5-year time steps from 1990 to 2100 (i.e., using results for 1995, 2000, 2005, etc.) for the six illustrative emissions scenarios from the IPCC Special Report on Emissions Scenarios (SRES) (Nakicenovic et al., 2000) under a range of climate sensitivity assumptions. The six emissions scenarios provide a sampling of the space of the relative effects of CO2 compared with other GHGs and sulfur dioxide (SO2)-derived sulfate aerosols. Climate sensitivity (3T2x) values of 1.5, 2.5, and 4.5°C are used.
The results are plotted as a simple scatter diagram of temperature change against CO2 concentration (see Figure 19-1). The scatter plot has 22 5-year values (1990 values are zero in each case) by six scenarios by three sensitivities (396 points). The diagram is meant only to illustrate a range of possibilities. One cannot associate any specific confidence intervals with the ranges shown; however, simultaneous use of realistic values in several input parameters with the judgment that the climate sensitivity range of 1.5-4.5°C represents approximately the 90% confidence interval (see, e.g., Morgan and Keith, 1995) suggests that the probability of a result outside the ranges shown, during the interval 1990-2100, is less than 10%.
The results are shown in Figure 19-1. For example, for a future CO2 level of 550 ppmv, the global mean warming range is 1-3°C relative to 1990. Thus, a specific CO2 concentration could lead to a range of increases in global mean temperature. Note that this is a transient result; in other words, if CO2 concentrations were stabilized at 550 ppmv, substantial additional warming would occur beyond this range as the climate system slowly relaxed toward a new equilibrium state. The levels of increase in global mean temperature displayed in the diagram are less than what would eventually happen if CO2 concentrations were stabilized at a particular level. Note also that there is no time (or date) associated with any particular concentration level. For, example, in the SRES scenarios, 550 ppmv is reached at a range of dates from about 2050 onward.
Other reports in this collection