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People matter in climate change models

Date:
December 5, 2011
Source:
Inderscience Publishers
Summary:
Climate change does not discriminate among regions or their inhabitants, but the continued growth of the human population will most likely contribute to the ill-effects of climate change. US researchers suggest an interdisciplinary approach, recruiting expertise from the social sciences, is best for conducting the needed research and model development to move forward in the study of climate change.

Climate change does not discriminate among regions or their inhabitants, but the continued growth of the human population will most likely contribute to the ill-effects of climate change. US researchers writing in the International Journal of System of Systems Engineering suggest an interdisciplinary approach, recruiting expertise from the social sciences, is best for conducting the needed research and model development to move forward in the study of climate change.

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Catherine M. Banks and John A. Sokolowski of the Virginia Modeling, Analysis, and Simulation Center (VMASC) at Old Dominion University in Suffolk, Virginia, re-emphasize that the consensus among scientists is that there has been and will continue to be an overall rise in global average temperatures. The Intergovernmental Panel Climate Change (IPCC) supports the scientific predictions that climate change will cause extreme weather conditions including more droughts, heavy precipitation, heat-waves, and hurricanes globally as well as rising sea levels as glaciers and ice shelves melt. The VMASC team points out that there is a significant gap in research regarding the nature of the human-environment interaction with water resources management on policy areas such as health, food security, and nature conservation.

There are many calls for urgent solutions to the problem of climate change itself including geo-engineering and carbon capture technologies which attempt to reduce concentrations of the greenhouse gas carbon dioxide in the atmosphere. The biggest obstacle in the path of such programs is that individual nations are unlikely to find a way to coordinate the significant effort and resources needed. Banks and Sokolowski concede that such suggestions obviously recognize the urgency of the situation, however, there could be underlying problems with these programs. For instance, localized geo-engineering projects might ameliorate a problem in one region but cause additional problems elsewhere. Thus, it is important to represent, or model, a community before implementing a program like geo-engineering.

The team has now suggested that the modeling of any aspect of climate change and the detailed modeling of the potential human effect on climate change must take into account the human-environment relationship as a complex system; i.e., models must fully characterize and represent the human behavior component on the environment. "Direct experiments on the real world system are not feasible," the team says, "[our] modeling methodology offers an alternative investigation as to how the complex systems found within the human-environment relationship might react to proposed efforts to reverse the effects of climate change."

Journal reference: "Assessing the human-environment relationship: a complex-systems methodology to modeling climate change" in Int. J. System of Systems Engineering, 2011, 2, 329-346


Story Source:

The above story is based on materials provided by Inderscience Publishers. Note: Materials may be edited for content and length.


Cite This Page:

Inderscience Publishers. "People matter in climate change models." ScienceDaily. ScienceDaily, 5 December 2011. <www.sciencedaily.com/releases/2011/12/111205102629.htm>.
Inderscience Publishers. (2011, December 5). People matter in climate change models. ScienceDaily. Retrieved November 27, 2014 from www.sciencedaily.com/releases/2011/12/111205102629.htm
Inderscience Publishers. "People matter in climate change models." ScienceDaily. www.sciencedaily.com/releases/2011/12/111205102629.htm (accessed November 27, 2014).

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