Nicolas Gruber, Pierre Friedlingstein, Christopher B. Field, Riccardo Valentini, Martin Heimann, Jeffrey E. Richey, Patricia Romero Lankao, E.-Detlef Schulze, and Chen-Tung Arthur Chen
In most scenario calculations to date, emissions from fossil-fuel burning are prescribed, and a carbon cycle model computes the time evolution of atmospheric CO2 as the residual between emissions and uptake by land and ocean, typically without considering feedbacks of climate on the carbon cycle (see, e.g., Schimel et al. 1996). The global carbon cycle is, however, intimately embedded in the physical climate system and tightly interconnected with human activities. As a consequence, climate, the carbon cycle, and humans are linked in a network of feedbacks, of which only those between the physical climate system and the carbon cycle have been explored so far (Friedlingstein, Chapter 10, this volume). One example of a carbon-climate feedback begins with the modification of climate through increasing atmospheric CO2 concentration. This modification affects ocean circulation and consequently ocean CO2 uptake (e.g., Sarmiento et al. 1998; Joos et al. 1999; Matear and Hirst 1999). Similar feedbacks occur on land. For example, rising temperatures lead to higher soil respiration rates, which lead to greater releases of carbon to the atmosphere (e.g., Cox et al. 2000; Friedlingstein et al. 2003). Human actions can also lead to feedbacks on climate. If climate change intensifies pressure to convert forests into pastures and cropland, then the climate change may be amplified by the human response (Raupach et al., Chapter 6, this volume). These positive feedbacks increase the fraction of the emitted CO2 that stays in the atmosphere, increasing the growth rate of atmospheric CO2 and accelerating climate change. Negative feedbacks are also possible. For example, a northward extension of forest or increased rates of plant growth in a warmer climate could increase rates of carbon storage, constraining further climate change.
A detailed quantitative assessment of a broad range of positive and negative feedbacks will require the use of fully coupled carbon-climate-human models. Such models do not exist yet. For some kinds of interactions (e.g., changes in institutional incentives and constraints on land use), predictive frameworks may be far in the future. Nevertheless, some experience has been gained already using coupled climate-carbon cycle models.
Two such coupled models that simulate the anthropogenic perturbation during the 21st century based on emission scenarios of the Intergovernmental Panel on Climate Change (IPCC) (Cox et al. 2000; Dufresne et al. 2002) show dramatically different magnitudes of climate-carbon cycle interactions (Figure 3.1). Both models simulate an accelerated increase of atmospheric CO2 as a result of impacts of climate change on the carbon cycle. The magnitude of this feedback varies, however, by a factor of 4 between the two simulations. Without the carbon-climate interaction, both models reach an atmospheric concentration of700 parts per million (ppm) by 2100. When the carbon-climate feedback is operating, the Hadley Centre model (Cox et al. 2000) reaches 980 ppm, leading to an average near-surface warming of +5K, while the IPSL model (Dufresne et al. 2002) attains only 780 ppm and a warming of +3K. This different behavior can be traced to the higher sensitivity of the land carbon cycle to warming in the Hadley Centre model, and to the larger ocean uptake in the IPSL model (Friedlingstein et al. 2003).
Although these pioneering model simulations represent a large step forward in scientists' ability to elucidate the interactions of the physical climate system with the global carbon cycle, they are also subject to important limitations. In both models, key processes are highly parameterized and poorly constrained. For example, emissions from land use changes are prescribed as an external input, and the associated changes in land cover are not explicitly modeled. In addition, a substantial number of processes and carbon pools are not included in such models. Several of these pools and processes could be vulnerable—that is, they are at risk of losing large amounts of carbon to the atmosphere as a result of a changing climate and/or human drivers (e.g., markets, policies, and demographic dynamics). Incorporating of all these pools and processes into coupled models is a difficult task, particularly because some processes operate through low-probability, high-consequence stochastic events. As a result, coupled climate-carbon models currently have limited capability for assessing the full magnitude and all risks associated with carbon-climate-human feedbacks, and their capability will continue to be limited for the foreseeable future.
An alternative and in many respects complementary approach is that of risk assessment, a method often used to identify and assess the risks associated with the operation of complex systems, such as nuclear power plants or chemical factories (Kammen and Hassenzahl 2001). In a risk analysis, the magnitude and likelihood of certain processes that may lead to catastrophic failures are determined and assessed in order to arrive at
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