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Synoptic ClimatologySynoptic climatology, as defined by Brent Yarnal in his 1993 book, 'integrates the simultaneous atmospheric dynamics and coupled response of the surface environment'. Synoptic climatological methods typically aim at identifying the holistic nature of a component or region of the atmosphere. This can mean a collective assessment of the spatial pattern of a single atmospheric variable, or of multiple variables at a given location, although there are many permutations.
Interpreting Yarnal’s quote further, synoptic methods can be used to assess climate variability in and of itself, as well as to connect climate variability and trends with a surface environmental ‘response’, and this response can be insect populations, human health, snow cover, or agriculture. Indeed it is the application of our holistic understanding of the atmosphere to a better understanding of the surface environment that many synoptic climatologists assert is at the heart of our discipline.
One commonality of many synoptic climatological studies is that they involve the classification or grouping of different types of atmospheric circulation. There are a number of ways in which this is done, using many statistical methodologies, levels of the atmosphere, spatial and temporal domains, and variables analyzed. Most of my synoptic work is based upon work I did initially for my dissertation, in which I redeveloped the Spatial Synoptic Classification (SSC) system. The SSC takes in surface weather observations for a station and classifies them into one of seven weather types. Day-by-day "calendars" of weather types are available for over 800 stations, for periods of up to 80 years. Over 31,000,000 days have been classified. If you're interested in knowing more about the SSC, check out the Spatial Synoptic Classification information page.
Starting in 2019, I worked on redeveloping the SSC into a new automated system that reduces human inputs, and is thus more readily usable for larger data sets, whether gridded reanalysis products or GCM output. This new version can be found at the Version 3.0 website. Globally, nearly 2000 stations are available, with over 45,000,000 days categorized into weather types.
The SSC has already been utilized as a tool in much research; a bibliography can be found at the link above. Personally, I have worked on weather-type variability across different teleconnections (such as El Nino), the change in weather-type frequencies over time, how the urban heat island varies according to weather type, as well as variability in atmospheric aerosol concentrations across North America. I have also used statistical methods to use GCM output to project future SSC types for the state of California, and am currently working on historical reconstructions as well. Other researchers have incorporated the SSC into assessments of variability in snow cover, snow water-equivalent, transportation, physical activity, forest regeneration, and atmospheric pollution transport, among others.
Beyond the weather typing systems, such as the SSC, there are a number of other methods for atmospheric classification of patterns. One technique which we have used frequently in the last several years is self-Organizing maps (SOMs), in which atmospheric patterns are classified in a continuum of patterns along a multi-dimensional grid. My colleague Cameron Lee and I wrote review paper on the method and have been using it in a number of research projects; this has become the standard method we use when categorizing atmospheric circulation patterns in our research.
Students of mine have also utilized the synoptic climatological methods in their work. Jason Senkbeil utilized the SSC in his dissertation as part of his assessment of irrigation's influences on precipitation in the Great Plains. He also utilized the SSC in evaluating tree-ring growth in Alabama as a follow up to his master's thesis work. Tom Ballinger has used the SSC as well as other circulation pattern catalogues to assess Arctic sea ice variability and its relationship with North American climate. Cameron Lee used future climate model data in his thesis to predict circulation patterns that are associated with tornadic outbreaks, and has also developed a gridded weather-type classification system similar in philosophy to the SSC. Three current advisees of mine are using self-organizing maps in their research. Rafiq Islam is using SOMs to analyze the variability in the South Asian Monsoon. Tyler Smith and Ryan Adams are using SOMs of atmospheric patterns across eastern North America to analyze cold air outbreaks, and bomb cyclones, respectively.
My main applied climatological interest is in bioclimatology, specifically the impacts of climate upon human health. My main contribution in this regard has been in working on the development and implementation of heat watch-warning systems for more than 50 cities worldwide, including Rome, Toronto, Phoenix, Dayton, Philadelphia, New Orleans, Seoul, and Chicago. These systems are all based on analyzing patterns of how human health in each locale in the past varied by weather conditions. The SSC has been used here, and large differences among the weather types have appeared. From city to city, the oppressive weather type varies. These systems, once developed, can forecast if weather conditions over the next two or three days may fall into one of these "offensive" categories. The forecast output goes to a webpage, which is then read by weather forecasters as well as civic and health agencies.
After working on heat warning systems for a number of years, I wondered if people actually listened. To answer this question, thanks to an EPA grant, I was able to interview 900 people over age 65 in Toronto, Philadelphia, Dayton, and Phoenix, to gauge their perception of heat vulnerability. The results showed that while the majority of people knew that there was a heat warning in place, only around half changed their behavior. Moreover, even though people tended to recall more specific advice, few people did anything other than "stay inside." I hope to further explore this issue of vulnerability perception, especially a comparison between urban and rural residents, or among agricultural workers.
Over the last several years, I’ve shifted away from work directly in heat warning systems, and more on broad analysis of the weather-health relationship. I've worked with researchers in California and Sweden on different means of assessing future heat-related health problems, with the output from projections of future weather conditions as well as demographic changes. I have also worked with researchers at the New York State Department of Health to evaluate weather-related variability in morbidity outcomes across the state, connecting the SSC and extreme events with variability in hospital admissions due to heat, cold, asthma, and other respiratory ailments. Newer projects with New York State have allowed me to extend this research to look at the concomitant impact of other disruptive events, such as power outages.
I’m also particularly interested in exploring how weather-health relationships change over the seasons, as well as from year-to-year. This interest has led to collaborative research on temporal trends in heat- and cold-related mortality. I am currently a co-investigator with Dr. Cameron Lee on a NOAA project exploring temporal trends in extreme biometeorological index events.
With the availability of sensors that allow much more personalized data collection than has ever been available before, I am starting to pursue research on personally-experienced environments. The first of these projects involves working with an undergraduate student, combining our bicycle commutes with pollution monitors to explore how exposure varies based on route.
A number of my students have looked at climate and health issues for their graduate work. Tim Dolney completed his master's thesis analyzing ambulance call patterns in Toronto on hot days. Paul Butke evaluated the spatial variability of crime across Cleveland in relation to weather conditions. Candace Olszak followed the Kent State football team, evaluating their perceptions of weather and its effects on their game. Michael Allen examined weather-related mortality patterns during the cold season, along with the seasonality of mortality patterns and how they relate to when a season 'begins'. Jeremy Spencer has looked at thresholds that are associated with increases in hypothermia deaths. Brad Austin has looked at how weather conditions are associated with the overall sentiment of tweets.
Coastal Water Quality
Over the past decade, an emerging research focus of mine has been to examine atmosphere – ocean interactions, in particular with regard to coastal water quality, sea-levels, and nuisance flooding. Working with scientists from NOAA, wehave collaborated on projects that examine atmospheric circulation patterns in the Gulf of Mexico, and their impact on water clarity, chlorophyll levels, and extreme sea-surface temperature events across the region. This has led to two NASA-funded projects that have examining water clarity in both the Gulf of Mexico and the Great Lakes, and assessing how extreme events have affected water clarity, as well as whether trends in water clarity can be related to climate change.
I’ve also collaborated on several projects to examine coastal sea levels and their connection with anomalous atmospheric circulations. With sea-level rise, coastal flooding is becoming more frequent in many places, and understanding the atmospheric drivers of these floods is critical. A current NOAA-funded project I’m leading is examining these connections for a number of sites along the US coastline, with the ultimate goal of producing seasonal forecasts.