By Marika Lohmus

1. Simulation of Global Warming in the Continental United States Using Agent-Based Modeling By Marika Lohmus Computer Systems Lab 2008-2009 TJSTAR Presentation

2. Purpose • What is the relationship between population and global warming? • Gaia Hypothesis • Does it apply to humans? • Negative Feedback loop • Is there an equilibrium?

3. Background • Sea levels predicted to rise by 18 – 59 cm by 2100 (IPCC, 2007) • Average surface temperature predicted to increase by 1.1 – 6.4°C (EPA, 2007) • From 1750 to 2005, greenhouse gases have increased (IPCC, 2007) • CO2 by 36% • CH4 by 148% • N2O by 18% • U.S. greenhouse gas emissions have increased by 17% from 1990 to 2007 (IPCC, 2009)

4. Background – CO2 CO2 levels will keep on rising (U.S. Climate Change Science Program, 2007) Currently 387 ppmv Estimated increase from 541 to 963 ppmv by 2100 (Prentice, et al., 2001)

5. Methodology • Modeling environment: NetLogo • Simulation of natural and social phenomena • Uri Wilensky 1999 • Written in Java for all major platforms • Components • Interface • Procedures • System Dynamics

6. Methodology Agents and Patches • Agents (People) • Salary • Money • Patches (Land) • Temperature • Elevation • Death Rate • Cities • Average Salary • Poverty Percent • Name • Changeable Variables • Birth Rate • D(Death Rate) • Emissions Per Person • Dependent Variables • Total Emissions • Earth Temperature • System Dynamics • Atmospheric Absorption Coefficient

7. Methodology System Dynamics • Runs parallel with Interface • Atmospheric Absorption Coefficient (AAC) • Greenhouse Gas Effect (Kiehl & Trenberth, 1997) • 60% H2O • 26% CO2 • 8% O3 • 6% CH4 + N2O • AAC of .25628 through trial & error • Mean Earth Temperature: 287.89 K (GISS, 2006) • 60% (.153768) remains constant

8. Methodology Birth Rate: .1418 / 10000 people per year (CIA, 2009) Death Rate: .0827 / 10000 people per year (CIA, 2009) U.S. Greenhouse Gas Emissions in 2007: 7150.1 Tg CO2 Eq. (IPCC, 2009) 17% increase from 1990 Elevation Map from Continental Divide project (Wilensky, 2007) Other Data Used

9. Methodology City Data Percentage under the poverty line $15,800 for a 2.5 person average family (US Census, 2000) 50% below and 50% above average annual salary Move Method Temperature > 25 C Diameter of money/10000 Random spot, if favorable, move Random number generator: move 1 Creation of People and Moving

10. Run With Current Data Birth rate of .1418, d(death rate) of 101, emissions per person of 1

11. Variables Higher temperatures mean higher death rates (IPCC, 2007) What is the connection? High d(death rate) Technological improvements Immunity to diseases Low d(death rate) Disaster hits Chaos due to increasing population New diseases and epidemics D(death rate)

12. D(Death Rate) Data Clear increase Dampened Oscillation Better technology = more people Chaos due to population increase? (IPCC, 2007)

13. Birth Rate Data Clear increase in both temperature and number of turtles NOTE: D(Death Rate) = 101 meaning that the population size increases due to a slow increase in the death rate

14. Emissions Data The 40% of AAC is not entirely contributed to by humans Cold weather – more deaths 30% of current emissions optimal

15. Emissions Cap on temperature? People die quickly at high emissions Inverse relationship with turtles

16. Conclusion • Sudden drop in temperature and population at first, followed by a sharp increase • Shock value • Adaptation • Model does not show a system as change out of equilibrium (Janssen, 1998) • Humans have learned to adapt to changes • Oscillations seem to level off • Emissions kept at current level: Scenarios not too bleak • From 1990, 1.3% annual increase in emissions (EPA, 2009)

17. Batten, D. (2007). Are some human ecosystems self-defeating?. Environmental Modelling & Software, 22, 649-655. EPA (2009). Inventory of U.S. Greenhouse Gas Emissions and Sinks . Washington, DC: U.S. Environmental Protection Agency. Damaceanu, R. (2008). An agent-based computational study of wealth distribution in function of resource growth interval using NetLogo. Applied Mathematics and Computation, 201, 371-377. Grimm, V., Revilla, E., Berger, U., Jeltsch, F., Mooij, W., Railsback, S., Thulke, H., Weiner, J., Wiegand, T., & Deangelis, D. (2005). Pattern-Oriented Modeling of Agent-Based Complex Systems: Lessons from Ecology. Science, 310(5750), 987-991. IPCC (2001). Climate Change 2001: The Scientific Basis: Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change. New York: Cambridge University Press. IPCC (2007). Climate Change 2007 - The Physical Science Basis: Working Group I Contribution to the Fourth Assessment Report of the IPCC (Climate Change 2007). New York: Cambridge University Press. IPCC (2007). Climate Change 2007: Impacts, Adaptation and Vulnerability: Summary for Policymakers, a Report of Working Group II of the Intergovernmental. New York: Cambridge University Press. Janssen, M. (1998). Use of complex adaptive systems for modeling global change. Ecosystems, 1, 457-463. Keenlyside, N., Latif, M., Jungclaus, J., Kornblueh, L., & Roeckner, E. (2008). Advancing decadal-scale climate prediction in the North Atlantic sector. Nature, 453(7191), 84-88. NASA GISS Surface Temperature Analysis. (n.d.). Retrieved May 27, 2009, from http://data.giss.nasa.gov/gistempSemtner, A. (2000). Ocean and Climate Modeling. Communications of the ACM, 43(4), 81-89.Trenberth, K., Fasullo, J., & Kiehl, J. (2009). Earth's Global Energy Budget. Bulletin of the American Meteorological Society, 90(3), 311-323. Weather Records and Averages. (n.d.). Retrieved September 27, 2008, from http://www.weatherbase.com Bibliography