|Home||<< 1 2 3 4 5 6 7 8 9 10 >> [11–11]|
Briggs, K. (2006). ENSO Event Reproduction: A Comparison of an EOF vs. A Cyclostationary (CSEOF) Approach. Master's thesis, Florida State University, Tallahassee, FL.
Abstract: In past studies, El Niño-Southern Oscillation (ENSO) events have been linked to devastating weather extremes. Climate modeling of ENSO is often dependent on limited records of the pertinent physical variables, thus longer records of these variables is desirable. Noisy signals, such as monthly sea surface temperature, are good candidates for reproduction by several existing auto-regression techniques. Through auto-regression, influential principal component modes are broken down into a series of time points that are each dependent upon an optimal weighting of the surrounding points. Using these unique numerical relationships, a noisy signal can be reproduced by thus processing the leading modes and adding an artificial record of properly distributed noise. Statistical measures of important ENSO regions suggest that the nature of oceanic and atmospheric anomalous events is cyclic with respect to certain timescales; for example, the monthly timescale. To detect ENSO signals in the presence of a varying background noise field, the detection method should take into account the signal's strong phase-locking with this nested variation. Cyclostationary Emperical Orthogonal Functions (CSEOFs) are built upon the idea of nested cycles, unlike traditional EOFs, which incorporate a design that is better detailed for stationary processes. In this study, both EOF and CSEOF modes of a 50-year Pacific SST record are processed using an auto-regression technique, and several sets of artificial SST records are constructed. Appropriate statistical indices are applied to these artificial time series to ensure an acceptable consistency with the real record, and then artificial data is produced using the artificial time series. In all cases, the cyclostationary approach produces more realistic warm ENSO events with respect to timing, strength, and other traits than does the stationary approach. However, both methods produce only a fair representation of cold events, suggesting that further study is necessary for improvement of La Niña modeling. Shorter records of variables such as sea level height and Pacific wind stress anomalies can hinder the usefulness of auto-regression, owing to time point dependence on surrounding points. Using a regression technique to find an evolutionary consistency (i.e. physically consistent patterns) between one of these variables and a variable with a longer record (such as SST) can eliminate this problem. Once a regression relationship is found between two variables, the variable with the shorter record can be re-written to match the time evolution of the variable with the longer record. Here regression, both EOF and CSEOF, is performed on both sea surface temperature and sea level height (a 20-year record), and sea surface temperature and wind stress (a 39-year record). Once the regression relationships are found, artificial SST time series are incorporated in place of the original time series to produce several artificial 50-year SLH and wind stress data sets. 5 Pacific regions are chosen, and statistics and behavior of the artificial sets within these regions are compared to those of the original data. Once again the cyclostationary approach fares better than the stationary. In particular the EOF assumption of cross correlational symmetry fails to capture the direction-dependence of ENSO evolution, causing inconsistent ENSO behavior. This renders an EOF method insufficient for climate modeling and prediction, and implies that a better aim is to incorporate physical cyclic features via a cyclostationary method.
Keywords: EOF, Autoregression, Wind Stress, Sea Level Height, SST, ENSO, Regression, CSEOF, Cyclostationary
Brolley, J. M. (2004). Experimental Forest Fire Threat Forecast. Master's thesis, Florida State University, Tallahassee, FL.
Abstract: Climate shifts due to El Niño (warmer than normal ocean temperatures in the tropical Pacific Ocean) and La Niña (cooler than normal) are well known and used to predict seasonal temperature and precipitation trends up to a year in advance. These climate shifts are particularly strong in the Southeastern United States. During the winter and spring months, El Niño brings plentiful rainfall and cooler temperatures to Florida. Recent los Niños occurred in 1997-1998, one of the strongest on record, with another mild El Niño in 2002-2003. Conversely, La Niña is associated with warm and dry winter and spring seasons in Florida. Temperature and precipitation affect wildfire activity; interannual drivers of climate, like ENSO, have an influence on wildfire activity. Studies have shown a strong connection between wildfires in Florida and La Niña, with the more than double the average number of acres burned (O'Brien et al 2002; Jones et al. 1999). While this relationship is important and lends a degree of predictability to the relative activity of future wildfire seasons, human activities such as effective suppression, prescribed burns, and ignition can play an equally important role in wildfire risks. This study forecasts wildfire potential rather than actual burn statistics to avoid complications due to human interactions. This wildfire threat potential is based upon the Keetch-Byram Drought Index (KBDI). The KBDI is well suited as a seasonal forecast medium. It is based on daily temperature and rainfall measurements and responds to changing climate and weather conditions on time scales of days to months, and this index is high during dry warm weather patterns and low during wet cool patterns. The KBDI has been widely used in forestry in the Southeastern United States since its development in the 1970's, with foresters and firefighters have a good level of familiarity with the index and its applications. The KBDI is calculated daily and used as an index by wildfire managers. This study calculates wildfire potential using a statistical method known as bootstrapping. Many datasets contain approximately a half-century of data, and the limited dataset will introduce biases. Bootstrapping can remedy bias by simulating thousands of years of data, which retain the climatology for the past half-century. Bootstrapping preserves the mean but not the variance. By incorporating this method, this study will improve long-term forest fire risks that will become useful for those living or working near forests and assist in managing forests and wildfires. The Southeast Climate Consortium will also be issuing wildfire risk forecast for Florida and parts of Alabama and Georgia based on ENSO phase and the KBDI. Climate information and ENSO predictions are better served by incorporating them with known climate indices that are routinely used in the forestry sector. Wildfire managers and foresters operationally use the KBDI to monitor and predict wildfire activity (O'Brien et al. 2002). Meteorologists at the Florida Division of Forestry have demonstrated the validity of the KBDI as an indicator of potential wildfire activity in Florida (Long 2004). They showed that the value of the KBDI is not as important as the deviation from the monthly average. The wildfire risk forecast is based on the probabilities of KBDI anomalies and will present the probabilities associated with large deviations from the seasonal normal.
Keywords: Forest Fire, El Nino, ENSO, Seasonal Forecast, KBDI, Keetch-Byram Drought Index, Bootstrapping
Coles, V. J., Stukel, M. R., Brooks, M. T., Burd, A., Crump, B. C., Moran, M. A., et al. (2017). Ocean biogeochemistry modeled with emergent trait-based genomics. Science, 358(6367), 1149–1154.
Abstract: Marine ecosystem models have advanced to incorporate metabolic pathways discovered with genomic sequencing, but direct comparisons between models and “omics” data are lacking. We developed a model that directly simulates metagenomes and metatranscriptomes for comparison with observations. Model microbes were randomly assigned genes for specialized functions, and communities of 68 species were simulated in the Atlantic Ocean. Unfit organisms were replaced, and the model self-organized to develop community genomes and transcriptomes. Emergent communities from simulations that were initialized with different cohorts of randomly generated microbes all produced realistic vertical and horizontal ocean nutrient, genome, and transcriptome gradients. Thus, the library of gene functions available to the community, rather than the distribution of functions among specific organisms, drove community assembly and biogeochemical gradients in the model ocean.
Keywords: Atlantic Ocean; Biochemical Phenomena/genetics; Metabolic Networks and Pathways/*genetics; Metagenome; *Metagenomics; Microbial Consortia/*genetics; Models, Biological; Seawater/*microbiology; Transcriptome
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.
Keywords: *Climate Change/statistics & numerical data; Florida; Forecasting; Humans; Models, Theoretical; Public Health/*trends; United States; adaptation; attributable fraction; climate modeling; project disease burden; public health
|Danabasoglu, G., Yeager, S. G., Bailey, D., Behrens, E., Bentsen, M., Bi, D., et al. (2014). North Atlantic simulations in Coordinated Ocean-ice Reference Experiments phase II (CORE-II). Part I: Mean states. Ocean Modelling, 73, 76–107.|
|Danabasoglu, G., Yeager, S. G., Kim, W. M., Behrens, E., Bentsen, M., Bi, D., et al. (2016). North Atlantic simulations in Coordinated Ocean-ice Reference Experiments phase II (CORE-II). Part II: Inter-annual to decadal variability. Ocean Modelling, 97, 65–90.|
|Downes, S. M., Farneti, R., Uotila, P., Griffies, S. M., Marsland, S. J., Bailey, D., et al. (2015). An assessment of Southern Ocean water masses and sea ice during 1988-2007 in a suite of interannual CORE-II simulations. Ocean Modelling, 94, 67–94.|
|Dukhovskoy, D. S., Morey, S. L., & O'Brien, J. J. (2009). Generation of baroclinic topographic waves by a tropical cyclone impacting a low-latitude continental shelf. Continental Shelf Research, 29(1), 333–351.|
|Dukhovskoy, D. S., Myers, P. G., Platov, G., Timmermans, M. - L., Curry, B., Proshutinsky, A., et al. (2016). Greenland freshwater pathways in the sub-Arctic Seas from model experiments with passive tracers. J. Geophys. Res. Oceans, 121(1), 877–907.|
|Dukhovskoy, D. S., Ubnoske, J., Blanchard-Wrigglesworth, E., Hiester, H. R., & Proshutinsky, A. (2015). Skill metrics for evaluation and comparison of sea ice models. J. Geophys. Res. Oceans, 120(9), 5910–5931.|