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.
Frumkin, A. (2011).
Predictability of Dry Season Reforecasts over the Tropical South American Region. Master's thesis, Florida State University, Tallahassee, FL.
Goto, Y. (2008).
Improved Vegetation Characterization and Freeze Statistics in a Regional Spectral Model for the Florida Citrus Farming Region. Ph.D. thesis, Florida State University, Tallahassee, FL.
Abstract: This study focused on the effective use of a numerical climate model for agriculture in Florida, especially in the citrus farming region of the Florida peninsula, because of the impact of agriculture to Florida's economy. For the analyses of the ensemble, the climate models used in this study were the FSU/COAPS Global Spectral Model and FSU/COAPS Regional Spectral Model (FSU/COAPS RSM) coupled with a land-surface model. The multi-convective scheme method and variable initial conditions were used for the ensembles. Severe freezes impacting agriculture in Florida were associated with some major climate patterns, such as El Niño and Southern Oscillation (ENSO) and North Atlantic Oscillation (NAO). In the first part of this study, seasonal ensemble integrations of the regional model were examined for the tendencies of freezes in the Florida peninsula during each ENSO or NAO phase is examined. Mean excess values of minimum temperatures from thresholds on the basis of the Generalized Pareto Distribution (GPD), which represents the extreme data in a dataset, were used to analyze the freezes in the regional model. According to some previous studies, El Niño winters obtain fewer freezes than the other ENSO phases. Although the ensemble comprised only 19 winters, the ensemble found variability patterns in minimum temperatures in each climate phase similar to the findings in the previous studies which were based on the observed data. The FSU/COAPS RSM was coupled with Community Land Model 2.0 (CLM2), to represent the land-surface conditions. Although the coupling improved the temperature forecast of the RSM, it still has a cold bias and simulates smaller diurnal temperature changes than actually occur in southern Florida. Among the prescribed surface data, Leaf Area Index (LAI) for southern Florida in the CLM2 is lower than those observed by MODIS (Moderate Resolution Imaging Spectroradiometer). In the first experiment of this part, the sensitivity of the temperature forecast to the LAI in the climate models was investigated, by modifying the LAI data in the CLM2 based on the monthly MODIS observations. In the second experiment, newly created prescribed datasets of LAI and plant functional types for the CLM2 based on the MODIS observations were applied to the RSM. The substitution increased the diurnal temperature change in southern Florida slightly but almost consistently.
Hong, S. - Y., Park, H., Cheong, H. - B., Kim, J. - E. E., Koo, M. - S., Jang, J., et al. (2013). The Global/Regional Integrated Model system (GRIMs).
Asia-Pacific J Atmos Sci, 49(2), 219–243.
Michael, J. - P. (2010).
ENSO Fidelity in Two Coupled Models. Master's thesis, Florida State University, Tallahassee, FL.
Abstract: This study examines the fidelity of the ENSO simulation in two coupled model integrations and compares this with available global ocean data assimilation. The two models are CAM-HYCOM coupled model developed by the HYCOM Consortium and CCSM3.0. The difference between the two climate models is in the use of different ocean general circulation model (OGCM). The hybrid isopycnal-sigma-pressure coordinate ocean model Hybrid Coordinate Ocean Model (HYCOM) replaces the ocean model Parallel Ocean Program (POP) of the CCSM3.0. In both, the atmospheric general circulation model (AGCM) Community Atmosphere Model (CAM) is used. In this way the coupled systems are compared in a controlled setting so that the effects of the OGCM may be obtained. Henceforth the two models will be referred to as CAM-HYCOM and CAM-POP respectively. Comparison of 200 years of model output is used discarding the first 100 years to account for spin-up issues. Both models (CAM-HYCOM and CAM-POP) are compared to observational data for duration, intensity, and global impacts of ENSO. Based on the analysis of equatorial SST, thermocline depth, wind stress and precipitation, ENSO in the CAM-HYCOM model is weaker and farther east than observations while CAM-POP is zonal and extends west of the international dateline. CAM-POP also has an erroneous biennial cycle of the equatorial pacific SSTs. The analysis of the subsurface ocean advective terms highlights the problems of the model simulations.
Misra, V., & Bhardwaj, A. (2019). Defining the Northeast Monsoon of India.
Mon. Wea. Rev., 147(3), 791–807.
Abstract: This study introduces an objective definition for onset and demise of the Northeast Indian Monsoon (NEM). The definition is based on the land surface temperature analysis over the Indian subcontinent. It is diagnosed from the inflection points in the daily anomaly cumulative curve of the area-averaged surface temperature over the provinces of Andhra Pradesh, Rayalseema, and Tamil Nadu located in the southeastern part of India. Per this definition, the climatological onset and demise dates of the NEM season are 6 November and 13 March, respectively. The composite evolution of the seasonal cycle of 850hPa winds, surface wind stress, surface ocean currents, and upper ocean heat content suggest a seasonal shift around the time of the diagnosed onset and demise dates of the NEM season. The interannual variations indicate onset date variations have a larger impact than demise date variations on the seasonal length, seasonal anomalies of rainfall, and surface temperature of the NEM. Furthermore, it is shown that warm El Niño�Southern Oscillation (ENSO) episodes are associated with excess seasonal rainfall, warm seasonal land surface temperature anomalies, and reduced lengths of the NEM season. Likewise, cold ENSO episodes are likely to be related to seasonal deficit rainfall anomalies, cold land surface temperature anomalies, and increased lengths of the NEM season.
Roads, J. (2003). International Research Institute/Applied Research Centers (IRI/ARCs) regional model intercomparison over South America.
J. Geophys. Res., 108(D14).
Schoof, J. T., Shin, D. W., Cocke, S., LaRow, T. E., Lim, Y. - K., & O'Brien, J. J. (2009). Dynamically and statistically downscaled seasonal temperature and precipitation hindcast ensembles for the southeastern USA.
Int. J. Climatol., 29(2), 243–257.
Selman, C., & Misra, V. (2015). Simulating diurnal variations over the southeastern United States.
J. Geophys. Res. Atmos., 120(1), 180–198.
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.