Probabilistic Estimation And Validation Of Regional Climate Change Using Statistical Downscaling Methods
Open Access
- Author:
- Ning, Liang
- Graduate Program:
- Meteorology
- Degree:
- Doctor of Philosophy
- Document Type:
- Dissertation
- Date of Defense:
- March 12, 2012
- Committee Members:
- Michael Mann, Dissertation Advisor/Co-Advisor
Robert George Crane, Committee Chair/Co-Chair
Raymond Gabriel Najjar Jr., Committee Member
Thorsten Wagener, Committee Member - Keywords:
- regional climate change
statistical downscaling
probabilistic esitmation - Abstract:
- ABSTRACT Two applications of regional climate changes are presented of a statistical downscaling method based on self-organizing maps (SOMs). The first application produces high-resolution, downscaled precipitation estimates over the state of Pennsylvania in the Mid-Atlantic region of the U.S. using synoptic circulation data from the National Center for Environmental Prediction (NCEP) and nine General Circulation Models (GCMs). The downscaling approach provides a faithful reproduction of the observed probability distributions and temporal characteristics of precipitation on both daily and monthly time scales. The downscaled precipitation field shows significant improvement over the raw GCM precipitation fields with regard to observed average monthly precipitation amounts, average monthly numbers of rainy days, and standard deviations of monthly precipitation amounts. When applied to the future period 2046-2065, downscaling predicts an increase in annual and winter precipitation and a decrease in summer precipitation when ensemble averaged across the nine GCMs. In order to examine the sensitivity of precipitation change to the water vapor increase brought by global warming, two downscaling approaches are used: one includes the specific humidity in the downscaling algorithm and the other does not. The downscaled precipitation increases employing specific humidity are larger than those without it, and both of them are smaller than those increases from raw GCM simulations. And application of downscaling reduces the inter-GCM variation, suggesting that some of spread among models in the raw projected precipitation may result from differences in precipitation parameterization schemes rather than fundamentally different climate responses. Projected changes in the North Atlantic Oscillation (NAO) are found to be significantly related to changes in winter precipitation in the downscaled results but not for the raw GCM results, suggesting that the downscaling more effectively captures the influence of climate dynamics on projected changes in winter precipitation. The second application of the downscaling method is the probabilistic estimation of temperature change over Central Africa as input for a malaria transmission model. The downscaled temperature data are then evaluated using the malaria transmission model requirements. The downscaled annual cycles and PDFs of maximum temperature, minimum temperature, average temperature, and diurnal temperature range (DTR), closely match observed annual cycles and probability distributions. And the downscaled time series of the monthly number of days with daily maximum temperature and minimum temperature within malaria development thresholds closely reproduce the observed variability on seasonal to interannual scales. From period 1961-2000 to period 2046-2065, the downscaled ensemble average maximum temperature, minimum temperature, and average temperature increase by 1.5 ºC and the DTR decreases. The annual and boreal winter average temperature increases over West Africa are larger than those over East Africa and West Coastal Africa. The annual and boreal summer DTR decreases over West Africa are larger than those over the other two regions, and the results for boreal winter months are opposite. The validations and probabilistic projections on the downscaling method show that this statistical downscaling method is capable of bridging the gap between coarse-resolution GCM output and the requirements of high-resolution input from the end users, on the inter-disciplinary studies about future climate impacts to hydrology, ecology, and human health.