Conlon, K. C., Kintziger, K. W., Jagger, M., Stefanova, L., Uejio, C. K., & Konrad, C. (2016). Working with Climate Projections to Estimate Disease Burden: Perspectives from Public Health.
Int J Environ Res Public Health, 13(8).
Abstract: There is interest among agencies and public health practitioners in the United States (USA) to estimate the future burden of climate-related health outcomes. Calculating disease burden projections can be especially daunting, given the complexities of climate modeling and the multiple pathways by which climate influences public health. Interdisciplinary coordination between public health practitioners and climate scientists is necessary for scientifically derived estimates. We describe a unique partnership of state and regional climate scientists and public health practitioners assembled by the Florida Building Resilience Against Climate Effects (BRACE) program. We provide a background on climate modeling and projections that has been developed specifically for public health practitioners, describe methodologies for combining climate and health data to project disease burden, and demonstrate three examples of this process used in Florida.
Kirtman, B. P., Misra, V., Anandhi, A., Palko, D., & Infanti, J. (2017). Future Climate Change Scenarios for Florida. In E. P. Chassignet, J. W. Jones, V. Misra, & J. Obeysekera (Eds.),
Florida's climate: Changes, variations, & impacts (pp. 533–555). Gainesville, FL: Florida Climate Institute.
Li, H., Kanamitsu, M., & Hong, S. - Y. (2012). California reanalysis downscaling at 10 km using an ocean-atmosphere coupled regional model system.
J. Geophys. Res., 117(D12).
Lu, J., Hu, A., & Zeng, Z. (2014). On the possible interaction between internal climate variability and forced climate change.
Geophys. Res. Lett., 41(8), 2962–2970.
Mirhosseini, G., Srivastava, P., & Stefanova, L. (2013). The impact of climate change on rainfall Intensity-Duration-Frequency (IDF) curves in Alabama.
Reg Environ Change, 13(S1), 25–33.
Misra, V., & DiNapoli, S. M. (2013). Understanding the wet season variations over Florida.
Clim Dyn, 40(5-6), 1361–1372.
Seitz, C. (2014).
Estimating the Effects of Climate Change on Tropical Cyclone Activity. Master's thesis, Florida State University, Tallahassee, FL.
Selman, C., & Misra, V. (2017). The impact of an extreme case of irrigation on the southeastern United States climate.
Clim Dyn, 48(3-4), 1309–1327.
Selman, C., Misra, V., Stefanova, L., Dinapoli, S., & Smith III, T. J. (2013). On the twenty-first-century wet season projections over the Southeastern United States.
Reg Environ Change, 13(S1), 153–164.
Smith, R. A. (2007).
Trends in Maximum and Minimum Temperature Deciles in Select Regions of the United States. Master's thesis, Florida State University, Tallahassee, FL.
Abstract: Daily maximum and minimum temperature data from 758 COOP stations in nineteen states are used to create temperature decile maps. All stations used contain records from 1948 through 2004 and could not be missing more than 5 consecutive years of data. Missing data are replaced using a multiple linear regression technique from surrounding stations. For each station, the maximum and minimum temperatures are first sorted in ascending order for every two years (to reduce annual variability) and divided into ten equal parts (or deciles). The first decile represents the coldest temperatures, and the last decile contains the warmest temperatures. Patterns and trends in these deciles can be examined for the 57-year period. A linear least-squares regression method is used to calculate best-fit lines for each decile to determine the long-term trends at each station. Significant warming or cooling is determined using the Student's t-test, and bootstrapping the decile data will further examine the validity of significance. Two stations are closely examined. Apalachicola, Florida shows significant warming in its maximum deciles and significant cooling in its minimum deciles. The maximum deciles seem to be affected by some localized change. The minimum deciles are discontinuous, and the trends are a result of a minor station move. Columbus, Georgia has experienced significant warming in its minimum deciles, and this appears to be the result of an urban heat-island effect. The discontinuities seen in the Apalachicola case study illustrate the need for a quality control method. This method will eliminate stations from the regional analysis that experience large changes in the ten-year standard deviations within their time series. The regional analysis shows that most of the region is dominated by significant cooling in the maximum deciles and significant warming in the minimum deciles, with more variability in the lower deciles. Field significance testing is performed on subregions (based on USGS 2000 land cover data) and supports the findings from the regional analysis; it also isolates regions, such as the Florida peninsula and the Maryland/Delaware region, that appear to be affected by more local forcings.