A Spatial Markov Model for Climate Extremes

References

• Banerjee, S., Carlin, B. P., and Gelfand, A. E. (2014), Hierarchical Modeling and Analysis for Spatial Data (2nd ed.), Boca Raton, FL: CRC Press.
   Google Scholar
• Carroll, A. L., Taylor, S. W., Régnière, J., and Safranyik, L. (2003), “Effect of Climate Change on Range Expansion by the Mountain Pine Beetle in British Columbia,” in Mountain Pine Beetle Symposium: Challenges and Solutions (Vol. BC-X-399), Natural Resources Canada, pp. 223–232.
   Google Scholar
• Davison, A. C., Huser, R., and Thibaud, E. (2013), “Geostatistics of Dependent and Asymptotically Independent Extremes,” Mathematical Geosciences, 45, 511–529.
   Web of Science ®Google Scholar
• Director, H., and Bornn, L. (2015), “Connecting Point-Level and Gridded Moments in the Analysis of Climate Data,” Journal of Climate, 28, 3496–3510.
   Web of Science ®Google Scholar
• Donat, M. G., Alexander, L. V., Yang, H., Durre, I., Vose, R., and Caesar, J. (2013), “Global Land-Based Datasets for Monitoring Climatic Extremes,” Bulletin of the American Meteorological Society, 94, 997–1006.
   Web of Science ®Google Scholar
• Fiedler, J., Mays, F., and Siry, J. (2001), “Feeling the Heat in Florida: Global Warming on the Local Level,” Report, Natural Resources Defense Council and Florida Climate Alliance, available at https://assets.nrdc.org/sites/default/files/florida.pdf?_ga=2.239024000.1432284223.1530583826-1373023488.1530583826
   Google Scholar
• Gilleland, E., and Katz, R. W. (2011), “New Software to Analyze How Extremes Change Over Time,” Eos, 92, 13–14.
   Google Scholar
• Goldsmith, J., Huang, L., and Crainiceanu, C. M. (2014), “Smooth Scalar-on-Image Regression via Spatial Bayesian Variable Selection,” Journal of Computational and Graphical Statistics, 23, 46–64.
   PubMed Web of Science ®Google Scholar
• Green, P. J. (1995), “Reversible Jump Markov Chain Monte Carlo Computation and Bayesian Model Determination,” Biometrika, 82, 711–732.
   Web of Science ®Google Scholar
• Green, P. J., and Richardson, S. (2002), “Hidden Markov Models and Disease Mapping,” Journal of the American Statistical Association, 97, 1055–1070.
   Web of Science ®Google Scholar
• Hansen, J., Ruedy, R., Sato, M., and Lo, K. (2010), “Global Surface Temperature Change,” Reviews of Geophysics, 48, RG4004.
   Web of Science ®Google Scholar
• Huser, R., and Davison, A. (2014), “Space–Time Modelling of Extreme Events,” Journal of the Royal Statistical Society, Series B, 76, 439–461.
   Google Scholar
• Huser, R., Opitz, T., and Thibaud, E. (2017), “Bridging Asymptotic Independence and Dependence in Spatial Extremes Using Gaussian Scale Mixtures,” Spatial Statistics, 21, 166–186.
   Web of Science ®Google Scholar
• Huser, R. G., and Wadsworth, J. L. (2018), “Modeling Spatial Processes With Unknown Extremal Dependence Class,” Journal of the American Statistical Association, Forthcoming.
   Google Scholar
• IPCC (2007), IPCC Fourth Assessment Report: Working Group II Report “Impacts, Adaptation and Vulnerability,” Cambridge: Cambridge University Press. Available at: http://www.ipcc.ch/ipccreports/ar4-wg2.htm
   Google Scholar
• ——— (2013), Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge, United Kingdom and New York, NY, USA: Cambridge University Press. Available at: www.climatechange2013.org
   Google Scholar
• Jalbert, J., Favre, A. C., Bélisle, C., and Angers, J. F. (2017), “A Spatio-Temporal Model for Extreme Precipitation Simulated by a Climate Model, With an Application for Assessing Changes in Return Levels and IDF Curves Over North America,” Journal of the Royal Statistical Society, Series C, 66, 941–962.
   Google Scholar
• Jones, P. D., Lister, D. H., Osborn, T. J., Harpham, C., Salmon, M., and Morice, C. P. (2012), “Hemispheric and Large-Scale Land-Surface Air Temperature Variations: An Extensive Revision and an Update to 2010,” Journal of Geophysical Research: Atmospheres, 117, D05127.
   Web of Science ®Google Scholar
• Kabluchko, Z., Schlather, M., and de Haan, L. (2009), “Stationary Max-Stable Fields Associated to Negative Definite Functions,” Annals of Probability, 37, 2042–2065.
   Web of Science ®Google Scholar
• Karl, T. R., Nicholls, N., and Ghazi, A. (1999), “CLIVAR/GCOS/WMO Workshop on Indices and Indicators for Climate Extremes: Workshop Summary,” Climatic Change, 42, 3–7.
   Web of Science ®Google Scholar
• Lawrimore, J. H., Menne, M. J., Gleason, B. E., Williams, C. N., Wuertz, D. B., Vose, R. S., and Rennie, J. (2011), “An Overview of the Global Historical Climatology Network Monthly Mean Temperature Data Set, Version 3,” Journal of Geophysical Research: Atmospheres, 116, D19121.
   Web of Science ®Google Scholar
• Liang, F. (2010), “A Double Metropolis-Hastings Sampler for Spatial Models With Intractable Normalizing Constants,” Journal of Statistical Computation and Simulation, 80, 1007–1022.
   Web of Science ®Google Scholar
• Liu, W., Awate, S, P., Anderson, J. S., and Fletcher, P. T. (2014), “A Functional Networks Estimation Method of Resting-State fMRI Using a Hierarchical Markov Random Field,” Neuroimage, 100, 520–534.
   PubMed Web of Science ®Google Scholar
• Mead, M. E. (2015), “Generalized Inverse Gamma Distribution and its Application in Reliability,” Communications in Statistics - Theory and Methods, 44, 1426–1435.
   Web of Science ®Google Scholar
• Møller, J., Pettitt, A. N., Reeves, R., and Berthelsen, K. K. (2006), “An Efficient Markov Chain Monte Carlo Method for Distributions With Intractable Normalizing Constants,” Biometrika, 93, 451–458.
   Web of Science ®Google Scholar
• Morris, S. A., Reich, B. J., Thibaud, E., and Cooley, D. (2017), “A Space-Time Skew-t Model for Threshold Exceedances,” Biometrics, 73, 749–778.
   PubMed Web of Science ®Google Scholar
• Murray, I., Ghahramani, Z., and MacKay, D. J. C. (2006), “MCMC for Doubly-Intractable Distributions,” in Proceedings of the 22nd Annual Conference on Uncertainty in Artificial Intelligence, available at https://dl.acm.org/citation.cfm?id=3020463
   Google Scholar
• Nolan, J. P. (2015), Stable Distributions: Models for Heavy Tailed Data, Washington, DC: American University.
   Google Scholar
• Papastathopoulos, I., and Strokorb, K. (2016), “Conditional Independence Among Max-Stable Laws,” Statistics and Probability Letters, 108, 9–15.
   Web of Science ®Google Scholar
• Peterson, T. C., and Vose, R. A. (1997), “An Overview of the Global Historical Climatology Network Temperature Database,” Bulletin of the American Meteorological Society, 78, 2837–2849.
   Web of Science ®Google Scholar
• Reich, B. J., and Shaby, B. A. (2012), “A Hierarchical Max-Stable Spatial Model for Extreme Precipitation,” Annals of Applied Statistics, 6, 1430–1451.
   PubMed Web of Science ®Google Scholar
• Reich, B. J., Shaby, B. A., and Cooley, D. (2014), “A Hierarchical Model for Serially-Dependent Extremes: A Study of Heat Waves in the Western US,” Journal of Agricultural, Biological, and Environmental Statistics, 19, 119–135.
   Web of Science ®Google Scholar
• Ross, K. (2013), “Agricultural Statistics Review,” Report, California Department of Food and Agriculture, available at https://www.cdfa.ca.gov/statistics/PDFs/2016Report.pdf
   Google Scholar
• Rue, H., and Held, L. (2005), “Gaussian Markov Random Fields: Theory and Applications (Monographs on Statistics and Applied Probability, Vol. 104), Boca Raton, FL: Chapman & Hall/CRC..
   Google Scholar
• Schlather, M. (2002), “Models for Stationary Max-Stable Random Fields,” Extremes, 5, 33–44.
   Google Scholar
• Seneviratne, S. I., Nicholls, N., Easterling, D., Goodess, C. M., Kanae, S., Kossin, J., Luo, Y., Marengo, J., McInnes, K., Rahimi, M., and Reichstein, M. (2012), “Changes in Climate Extremes and Their Impacts on the Natural Physical Environment,” in Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation, pp. 109–230.
   Google Scholar
• Sillmann, J., Donat, M. G., Fyfe, J. C., and Zwiers, F. W. (2014), “Observed and Simulated Temperature Extremes During the Recent Warming Hiatus,” Environmental Research Letters, 9, 064023.
   Web of Science ®Google Scholar
• Stacy, E. (1962), “A Generalization of the Gamma Distribution,” Annals of Mathematical Statistics, 335, 1187–1192.
   Google Scholar
• Thibaud, E., and Opitz, T. (2015), “Efficient Inference and Simulation for Elliptical Pareto Processes,” Biometrika, 102, 855–870.
   Web of Science ®Google Scholar
• Traboulsi, M. R. (2013), “Effect of Climate Change on Supply Response of Florida Citrus Crops 1980–2010,” in 2013 Annual Meeting, February 2-5, 2013, Orlando, Florida, Southern Agricultural Economics Association.
   Google Scholar
• Zhang, Z., and Smith, R. L. (2010), “On the Estimation and Application of Max-Stable Processes,” Journal of Statistical Planning and Inference, 140, 1135–1153.
   Web of Science ®Google Scholar