1. Montserrat Fuentes Statistics Department NCSU Research directions in climate change SAMSI workshop, September 14, 2009.

2. Observed data and a climate simulation Graph provided by D. Nychka

3. Calibrating general circulation climate models. Calibration of climate models is generally limited to matching the mean and variance of models and data. Need to consider different quantiles of models and data to compare frequency of extreme events Spatial quantile regression might provide a more comprehensive evaluation. Need to consider different quantiles of models and data to compare frequency of extreme events Spatial quantile regression might provide a more comprehensive evaluation.

4. Calibration plots for temperature and wind speed in the South-East. Using spatial quantile regression (Reich, Fuentes, Dunson, 2009).

5. Graph provided by Nychka

6. Estimating future climate: Ensemble climate prediction. Multi model ensembles help with statistical assessment of model performance. But, how do we weight different circulation models? Bayesian model averaging does not have physical justification, can they provide helpful information about model performace?

7. Graph provided by Nychka

8. Impact of climate change on weather extremes.  Most of the emphasis has been on global mean temperatures. Not much is known about the impact of climate change on regional weather extremes.  Modeling of extreme events across space is challenging Need of new methods for spatial modeling of extreme events.

9. IPCC (2001) IPCC (2001) The IPCC (2001) has estimated that the global average temperature will rise by several degrees centigrade during this century. There is unavoidable uncertainty in this estimate. There is a lot of emphasis on global averages. What about regional extreme temperatures? There is a lot of emphasis on global averages. What about regional extreme temperatures?

10. Fuentes, Henry and Reich (2009). Spatial modeling of maximum temperatures Going beyond Gaussian models. Spatial modeling of extremes: important and active area of research.

11. Impact of climate change on air pollution regulation. Due to the strong dependence on weather conditions. Ozone levels may be sensitive to climate change. Using future numerical climate models, we could forecast potential future increases or decreases in ozone levels. Statistical models for forecasting air pollution.

12. Reich, Fuentes and Dunson (2009)

13. Probabilities that the 3 year (2003-2005 left graph; and 2041- 2043 right) average of the fourth highest daily max. 8-hour average ozone exceeds 75 ppb. Reich, Fuentes and Dunson (2009). Bayesian spatial quantile regression.

14. Impact of climate change on human health. There is great interest in studying the potential effect of climate change on ozone levels, and how this change may affect public health. There is also public health consequences of rising sea-levels. Climate change could also alter the geographic range (latitude and altitude) and seasonality of certain infectious diseases. Climate change could also alter the geographic range (latitude and altitude) and seasonality of certain infectious diseases. Spatial environmental health epidemiological modeling. Space-time dynamic risk parameters.

15. Topics  Calibrating general circulation climate models. Spatial quantile regression.  Estimating future climate: Ensemble climate prediction. How to weight different circulation models?  Impact of climate change on weather extremes. Spatial modelling of extreme events.  Impact of climate change on air pollution regulation. Statistical models for forecasting pollution.  Impact of climate change on human health. Spatial environmental health modeling.