Several warm winter events have occurred across the Southeast in the past decade, including 2 major events in 2017 and 2018 in Georgia and South Carolina. Plants will begin their spring growth sooner than climatology would suggest and then be damaged by early spring frosts in what is commonly known as a "false spring" event. Some species of plants, like peaches and blueberries, which produce buds early in the season, are just an example of some of the agricultural products more at risk than others. As an important measure of dormancy time in plants, chill hours present a measurement capable of tracking phenological shifts in plants. While a lack of required chill hours can delay spring emergence, intense warm periods can override the chilling hour requirement and induce spring emergence. This project involves training self-organizing maps (SOMs) to identify periods of anomalous winter warming based on a reduced number of chill hours within a 5-day temporal period compared to the period's climatological average. A second SOM is nested in the node that produced the most anomalous events to identify the range of warming that occurs in the most anomalous events, the synoptic setups of these events, and when these occurred. Hourly 2-meter temperature from ERA5 is used to conduct this analysis over a domain centered primarily over South Carolina and Georgia with a temporal period of 1980-2020. Climatological examination of chill hour accumulations in the past 4 decades show an overall decrease in chill hour accumulation across the past decade (2011-2020) Results indicated that periods of higher-than-average temperatures are increasing with time while periods of average or lower than average temperatures are decreasing with time. Both results were statistically significant by Mann-Kendall test. The results of the nested SOMs suggest that an increase in patterns of southerly flow (a common pattern for warmer temperatures) is occurring through time. A third SOM investigating early spring hard freezes was inconclusive but illustrated that some years had more early spring frosts than others independent of how many warmer than average periods occurred in the main winter. The use of SOMs for investigating climatological and synoptic changes in winter and early spring proved successful and effective. Future modifications to these SOMs could be used to identify more trends that exist within these seasons. / Master of Science / Several warm winter events have occurred across the Southeast in the past decade, including 2 major events in 2017 and 2018 in Georgia and South Carolina. Plants will begin their spring growth sooner than climatology would suggest and then be damaged by early spring frosts in what is commonly known as a "false spring" event. Some species of plants, like peaches and blueberries, which produce buds early in the season, are just an example of some of the agricultural products more at risk than others. As an important measure of dormancy time in plants, chill hours present a measurement capable of tracking shifts from normal winter to spring transition in plants. While a lack of required chill hours can delay leaf emergence and spring blooms, intense warm periods can override the chilling hour requirement and induce this spring emergence. This project involves training self-organizing maps (SOMs), a machine learning model, to identify periods of anomalous winter warming based on a reduced number of chill hours within a 5-day temporal period compared to the period's climatological average. A second SOM is nested in the node that produced the most anomalously warm events to identify the range of warming that occurs in the most anomalous events, the large-scale meteorological setups of these events, and when these occurred. Hourly 2-meter temperature from ERA5, a climatological dataset, is used to conduct this analysis over a domain centered primarily over South Carolina and Georgia with a temporal period of 1980-2020. Climatological examination of chill hour accumulations in the past 4 decades show an overall decrease in chill hour accumulation across the past decade (2011-2020) Results indicated that periods of higher-than-average temperatures are increasing with time while periods of average or lower than average temperatures are decreasing with time. Both of these trend findings were statistically significant by Mann-Kendall test. The results of the nested SOMs suggest that an increase in patterns of southerly flow (a common pattern for warmer temperatures) is occurring through time. A third SOM investigating early spring hard freezes (temperatures low enough to cause damage to plant cellular structures) was inconclusive but illustrated that some years had more early spring frosts than others independent of how many warmer than average periods occurred in the main winter. The use of SOMs for investigating climatological and synoptic changes in winter and early spring proved successful and effective. Future modifications to these SOMs could be used to identify more trends that exist within these seasons.

Since 2011, California has been under drought conditions. These conditions have not only affected water availability for farmers, but also production. California’s second most valuable crop, almonds, has been affected by drought conditions. This study used three models (Model 1-3) to describe almond yield variability from year to year and almond yield variability within a year in Kern County, CA. The study evaluated 185 almond farms that were classified in three locations (east side, west side and north west side). The years of the study were 2011 (wet year) and 2013-2015 (drought condition years). Model 1 determined a functional regression between almond yield and annual evapotranspiration during the 4 years of the study. The R2was 7.9%, meaning low association between both variables and high unexplained variability (92.1%). Model 2 evaluated year to year variation. A regression function between almond yield and annual evapotranspiration after adjusting for location, precipitation, chilling hours and year was made. The R2of this model 62.6%, and all the variables used had a p2was higher than Model 1; however, there was high unexplained variability (47.4%). Model 3 evaluated within-year variation. A regression function between almond yield and annual evapotranspiration after adjusting for tree age and location (east, west and northwest side) was made for each year (2011 and 2013 -2015). Coefficient of variation of evapotranspiration and soil available water storage were analyzed as additional variables in Model 3; however, they were not introduced in Model 3 due to the low increase in R2 in each year (2 of Model 3 for each year were, 60.4%, 49.7%, 53.8% and 53.2% for the years 2011, 2013-2015, respectively. Model 3 also had high unexplained almond yield variability in each year (39.6%-50.3%). This high unexplained variability leads to introduce additional variables to the functional regression model for further studies. Identifying these additional variables and having a functional regression model with high R2 would lead to understand howlow evapotranspiration could potentially lead to a positive response on yield in drought conditions; thus, making farmers improve water use efficiency and hence, lowering production cost. However, the high unexplained variability clearly indicates that evapotranspiration is only one of many factors that influence yield. If improved yield is an important outcome, future studies must examine large- scale almond-producing farms with multiple agricultural system variables.