An official website of the United States government
The Laurentian Great Lakes have substantial influences on regional climatology, particularly with impactful lake-effect snow events. This study examines the snowfall, cloud-inferred snow band morphology, and environment of lake-effect snow days along the southern shore of Lake Michigan for the 1997–2017 period. Suitable days for study were identified based on the presence of lake-effect clouds assessed in a previous study and extended through 2017, combined with an independent classification of likely lake-effect snow days based on independent snowfall data and weather map assessments. The primary goals are to identify lake-effect snow days and evaluate the snowfall distribution and modes of variability, the sensitivity to thermodynamic and flow characteristics within the upstream sounding at Green Bay, WI, and the influences of snowband morphology. Over 300 lake-effect days are identified during the study period, with peak mean snowfall within the lake belt extending from southwest Michigan to northern Indiana. Although multiple lake-effect morphological types are often observed on the same day, the most common snow band morphology is wind parallel bands. Relative to days with wind parallel bands, the shoreline band morphology is more common with a reduced lower-tropospheric zonal wind component within the upstream sounding at Green Bay, WI, as well as higher sea-level pressure and 500-hPa geopotential height anomalies to the north of the Great Lakes. Snowfall is sensitive to band morphology, with higher snowfall for shoreline band structures than for wind parallel bands, especially due south of Lake Michigan. Snowfall is also sensitive to thermodynamic and flow properties, with a greater sensitivity to temperature in southwest Michigan and to flow properties in northwest Indiana.
more »
« less
The Contribution of Lake-Effect Snow to Annual Snowfall Totals in the Vicinity of Lakes Erie, Michigan, and Ontario
In the Great Lakes region, total cold-season snowfall consists of contributions from both lake-effect systems (LES) and non-LES snow events. To enhance understanding of the regional hydroclimatology, this research examined these separate contributions with a focus on the cold seasons (October–March) of 2009/2010, a time period with the number of LES days substantially less than the mean, and 2012/2013, a time period with the number of LES days notably greater than the mean, for the regions surrounding Lakes Erie, Michigan, and Ontario. In general, LES snowfall exhibited a maximum contribution in near-shoreline areas surrounding each lake while non-LES snowfall tended to provide a more widespread distribution throughout the entire study regions with maxima often located in regions of elevated terrain. The percent contribution for LES snowfall to the seasonal snowfall varied spatially near each lake with localized maxima and ranged in magnitudes from 10% to over 70%. Although total LES snowfall amounts tended to be greater during the cold season with the larger number of LES days, the percent of LES snowfall contributing to the total cold-season snowfall was not directly dependent on the number of LES days. The LES snowfall contributions to seasonal totals were found to be generally larger for Lakes Erie and Ontario during the cold season with a greater number of LES days; however, LES contributions were similar or smaller for areas in the vicinity of Lake Michigan during the cold season with a smaller number of LES days.
more »
« less
- Award ID(s):
- 2040594
- PAR ID:
- 10319206
- Date Published:
- Journal Name:
- Frontiers in Water
- Volume:
- 4
- ISSN:
- 2624-9375
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Synoptic Conditions and Lake-to-Lake Connections for Days with Lake Effect on All of the Great LakesAbstract An investigation of lake effect (LE) and the associated synoptic environment is presented for days when all five lakes in the Great Lakes (GL) region had LE bands [five-lake days (5LDs)]. The study utilized an expanded database of observed LE clouds over the GL during 25 cold seasons (October–March) from 1997/98 to 2021/22. LE bands occurred on 2870 days (64% of all cold-season days). Nearly a third of all LE bands occurred during 5LDs, although 5LDs consisted of just 17.1% of LE days. A majority of 5LDs (56.5%) had lake-to-lake (L2L) bands, and these days comprised 43.5% of all L2L occurrences. 5LDs occurred with a mean of 26.1 (SD = 6.2) days per cold season until 2008/09 and then decreased to a mean of 13.8 (SD = 5.5) days during subsequent cold seasons. January and February had the largest number of consecutive LE days in the GL with a mean of 5.7 and 5.4 days, respectively. As the number of consecutive LE days increases, both the number of 5LDs and the occurrence of consecutive 5LD increase. This translates to an increased potential of heavy snowfall impacts in multiple, localized areas of the GL for extended time periods. The mean composite synoptic pattern of 5LDs exhibited characteristics consistent with lake-aggregate disturbances and showed similarity to synoptic patterns favorable for LE over one or two of the GL found by previous studies. The results demonstrate that several additional areas of the GL are often experiencing LE bands when a localized area has active LE bands occurring.more » « less
-
This dataset contains a record of daily mean air temperature for each of the U.S. Great Lakes from January 1, 1897 to October 22, 2023. These temperatures were derived using the following method. Daily maximum and minimum air temperature data were obtained from the Global Historical Climatology Network-Daily (GHCNd, Menne, et al. 2012) and the Great Lakes Air Temperature/Degree Day Climatology, 1897-1983 (Assel et al. 1995). Daily air temperature was calculated by taking a simple average of daily maximum and minimum air temperature. Following Cohn et al. (2021), a total of 24 coastal locations along the Great Lakes were selected. These 24 locations had relatively consistent station data records since the 1890s. Each of the selected locations had multiple weather stations in their proximity covering the historical period from 1890s to 2023, representing the weather conditions around the location. For most of the locations, datasets from multiple stations in the proximity of each location were combined to create a continuous data record from the 1890s to 2023. When doing so, data consistency was verified by comparing the data during the period when station datasets overlap. This procedure resulted in almost continuous timeseries, except for a few locations that still had temporal gaps of one to several days. Any temporal data gap less than 10 days in the combined timeseries were filled based on the linear interpolation. This resulted in completely continuous timeseries for all the locations. Average daily air temperature was calculated from by simply making an average of timeseries data from corresponding locations around each lake. This resulted in daily air temperature records for all five Great Lakes (Lake Superior, Lake Huron, Lake Michigan, Lake Erie, and Lake Ontario).more » « less
-
Abstract Extreme water temperatures impact the ecological and economic value of freshwater systems. They disrupt fisheries habitat, trigger harmful algal blooms, and stress coastal infrastructure. This study examines the spatiotemporal patterns of heatwaves and cold‐spells in the Great Lakes using 82 years of simulated surface temperature data. Significant increasing trends in heatwave duration were observed in Lake Superior and Lake Michigan‐Huron, while cold‐spell duration increased on all lakes except Ontario. Temperature anomalies during these events varied from the climatological mean by as much as ±10C, but did not change significantly over time. Analysis revealed substantial spatial variability in heatwaves and cold‐spells, both within and across lakes, with differences driven by air temperature and ice cover anomalies as well as associated climate teleconnections (i.e., the East Pacific/North Pacific and Atlantic Multidecadal Oscillation). These findings highlight the importance of both climatic and lake processes in shaping extreme temperature events.more » « less
-
The Laurentian Great Lakes (hereafter the Great Lakes) comprise the world’s largest surface freshwater system. Over the past two decades, water levels in the Great Lakes have fluctuated drastically, reaching both record highs and lows. Accurate water level forecasting is critical due to the extensive ecosystem and millions of US and Canadian citizens that rely on this valuable resource. One of the most dominant variables for water supply in any freshwater system is surface runoff, which is directly impacted by precipitation amount, type, magnitude, and timing across the system’s land surfaces. Lake Superior, the most upstream of the Great Lakes, receives the greatest amount of seasonal snowfall annually out of all the great Lakes. This snowfall affects both the timing and quantity of runoff into the Great Lakes system and impacts the water supply of the Great Lakes. In this study, I analyzed the patterns of snow water equivalent and its effect on surface runoff in the Lake Superior basin. My results indicate important changes in snow water equivalent and runoff patterns over time. Specifically, I found that, as of 1971, maximum seasonal snow water equivalent is occurring on average 12 days earlier in the spring season. I also found that maximum seasonal runoff is occurring earlier; however, the change in the timing of peak runoff occurred in 1983 and is found to now be on average 11 days earlier than it was before 1983. By advancing an understanding of these relationships and ensuring they are reflected in state-of-the-art modeling systems, I provided critical information for improving the skill of water level forecasts and preparing water managers and communities for future hydrologic changes, including those associated with climate change.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3389/frwa.2022.782910
