ABSTRACT
Attribution of climate change to causal factors has been based predominantly on simulations using physical climate models, which have inherent limitations in describing such a complex and chaotic system. We propose an alternative, data centric, approach that relies on actual measurements of climate observations and human and natural forcing factors. Specifically, we develop a novel method to infer causality from spatial-temporal data, as well as a procedure to incorporate extreme value modeling into our method in order to address the attribution of extreme climate events, such as heatwaves. Our experimental results on a real world dataset indicate that changes in temperature are not solely accounted for by solar radiance, but attributed more significantly to CO2 and other greenhouse gases. Combined with extreme value modeling, we also show that there has been a significant increase in the intensity of extreme temperatures, and that such changes in extreme temperature are also attributable to greenhouse gases. These preliminary results suggest that our approach can offer a useful alternative to the simulation-based approach to climate modeling and attribution, and provide valuable insights from a fresh perspective.
Supplemental Material
- Climate change 2007 - the physical science basis IPCC Fourth Assessment Report on scientific aspects of climate change for researchers, students, and policymakers.Google Scholar
- Barnett, T.P., Pierce,D.W. and Schnur, R. (2001). Detection of anthropogenic climate change in the world's oceans. Science, 292.Google Scholar
- Beirlant, J., Goegebeur, Y., Segers, J., and Teugels, J. (2004). Statistics of Extremes: Theory and Applications. New York: Wiley.Google Scholar
- Banerjee, S., Carlin, B., and Gelfand, A. (2004). Hierarchical Modeling and Analysis for Spatial Data. Boca Ration, Florida: Chapman&Hall.Google Scholar
- Christidis, N., Peter, S.A., Brown, S., Office, M. and Hegerl, J-C.G.C. (2005). Detection of changes in temperature extremes during the second half of the 20th century. Geophys. Res. Lett., 32(L20716), 2005.Google Scholar
- Carter, C. K. and Kohn, R. (2001). On Gibbs sampling for state space models. Biometrica, 81, 541--553.Google ScholarCross Ref
- Coles, S. (2001). An Introduction to Statistical Modeling of Extreme Values. Berlin: Springer.Google Scholar
- Gillett, N.P., Zwiers,F.W., Weaver,A.J. and Stott, P.A. (2003). Detection of human influence on sea level pressure. Nature, 422(b).Google Scholar
- Granger, C. (1980). Testing for causlity: A personal viewpoint. Journal of Economic Dynamics and Control 2, 329--352.Google ScholarCross Ref
- Karoly, D. J., Braganza, K., Stott, P. A., Arblaster, J.M. Meehl, Anthony, G.A., Broccoli, J. and Dixon, K.W. (2003) Detection of a human influence on north american climate. Science, 302.Google Scholar
- Luo, L. Wahba, G. and Johnson, D.R. (1998) Spatial-temporal analysis of temperature using smoothing spline anova. J. Climate, 11.Google Scholar
- Matern, B. (1960). Spatial Variation. New York: Springer.Google Scholar
- New, M., Hulme, M. and Jones, P.D. (1999) Representing twentieth century space-time climate variability. Part 1: development of a 1961-90 mean monthly terrestrial climatology. Journal of Climate 12, 829--856.Google ScholarCross Ref
- Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. J. Royal. Statist. Soc B., Vol. 58 (1), 267--288.Google ScholarCross Ref
- P.A. Stott, D.A. Stone, and M.R. Allen. (2004) Human contribution to the european heatwave of 2003. Nature, 432.Google Scholar
- Yuan, M. and Lin, Y. (2006) Model selection and estimation in regression with grouped variables. J. R. Stat. B 68, 49--67.Google ScholarCross Ref
- Zou, H., Hastie T. (2005) Regularization and variable selection via the Elastic Net. J. R. Statist. Soc. B 67(2) 301--320.Google ScholarCross Ref
Index Terms
- Spatial-temporal causal modeling for climate change attribution
Recommendations
Effects of climate change on primary production in the Inner Mongolia Plateau, China
Climate change has significant effects on primary productivity and ecosystem function. To assess these effects in the Inner Mongolia Plateau of China, we analysed the 8 km normalized difference vegetation index NDVI time-series from the Global Inventory ...
Spatial-Temporal Changes of Vegetation Coverage and Its Responses to Global Climate Changes in the Tibetan Plateau
ESIAT '09: Proceedings of the 2009 International Conference on Environmental Science and Information Application Technology - Volume 02Recent global climate changes have affected vegetation coverage in the Tibetan Plateau. Based on the remote sensing images and the meteorological data from 1986 to 2000, using the model of extracting vegetation coverage, the spatiotemporal changes of ...
Climate Change Modeling: Computational Opportunities and Challenges
High-fidelity climate models are the workhorses of modern climate change sciences. In this article, the authors focus on several computational issues associated with climate change modeling, covering simulation methodologies, temporal and spatial ...
Comments