A Synoptic Climatological Analysis of the Atmospheric Drivers of Water Clarity Variability in the Great Lakes

Details

• Journal Title:
  Journal of Applied Meteorology and Climatology
• Personal Author:
  Smith, Erik T. ; Lee, Cameron C. ; Barnes, Brian B. ; Adams, Ryan E. ; Pirhalla, Douglas E. ; Ransibrahmanakul, Varis ; Hu, Chuanmin ; Sheridan, Scott C.
• NOAA Program & Office:
  NOS (National Ocean Service) ; NCCOS (National Centers for Coastal Ocean Science)
• Description:
  A historical water clarity index (K<jats:sub>d</jats:sub> index or KDI) was developed through the use of satellite-derived and validated diffuse light attenuation (K<jats:sub>d</jats:sub>; m<jats:sup>−1</jats:sup>) for each of the Great Lakes (and subbasins) on a daily level from 1998 to 2015. A statistical regionalization was performed with monthly level KDI using k-means clustering to subdivide the Great Lakes into regions with similar temporal variability in water clarity. The KDI was then used to assess the relationship between water clarity and atmospheric circulation patterns and stream discharge. An artificial neural-network-based self-organized map data reduction technique was used to classify atmospheric patterns using four atmospheric variables: mean sea level pressure, 500-hPa geopotential heights, zonal and meridional components of the wind at 10 m, and 850-hPa temperature. Stream discharge was found to have the strongest relationship with KDI, suggesting that sediments and dissolved matter from land runoffs are the key factors linking the atmosphere to water clarity in the Great Lakes. Although generally lower in magnitude than stream discharge, atmospheric circulation patterns associated with increased precipitation tended to have stronger positive correlations with KDI. With no long-range forecasts of stream discharge, the strong relationship between atmospheric circulation patterns and stream discharge may provide an avenue to more accurately model water clarity on a subseasonal-to-seasonal time scale.
• Keywords:
  Climatic Changes Climatology Machine Learning Climatic Changes Machine Learning
• Source:
  J. Appl. Meteor. Climatol., 59, 915–935
• DOI:
  https://doi.org/10.1175/jamc-d-19-0156.1
• ISSN:
  1558-8424 ; 1558-8432
• Publisher:
  American Meteorological Society
• Document Type:
  Journal Article
• Rights Information:
  Other
• Compliance:
  Library
• Main Document Checksum:
  urn:sha256:7778aea6113914673295027148f889c3ea49585523689241434f24ca8744a83b
• Download URL:
  https://repository.library.noaa.gov/view/noaa/50692/noaa_50692_DS1.pdf
• File Type:
  [PDF - 5.52 MB ]