Lake-effect Snowstorms Research Articles

Vertical Profile Climatology of Polarimetric Radar Variables and Retrieved Microphysical Parameters in Synoptic and Lake Effect Snowstorms

AbstractThis study derives polarimetric radar vertical profiles and microphysical retrievals for 25 Synoptic Snow (SS) and 23 Lake Effect Snow (LES) cases using the Range‐Defined Quasi‐Vertical Profiles (RD‐QVP), Columnar Vertical Profiles (CVP), and Process‐oriented Vertical Profiles (POVP) methods. For all vertical profile techniques, SS cases exhibit a near‐linear increase in reflectivity from −30 to 0°C whereas ZDR and Kdp locally peak in the dendritic growth layer. LES cases universally exhibit negative ZDR, rather high Z, negligible Kdp, and near‐unity ρhv. Ground measurements from the past OWLeS campaign provide direct evidence that conical graupel may strongly affect these polarimetric measurements in LES bands. Aggregation efficiencies for SS cases are estimated by optimizing the theoretical number concentration (Nt) and mean volume diameter (Dm) steady‐state vertical profiles against radar‐retrieved profiles derived from 20 of the 25 synoptic storm RD‐QVPs. The median estimated aggregation efficiency is approximately 0.15 with a relatively narrow interquartile range that spans from 0.1 to just over 0.2. Values of optimized aggregation efficiencies are nearly independent of the assumed gamma distribution shape parameter. These results are used to derive temperature‐dependent, climatological steady‐state relations for vertical profiles of Nt, Dm, and liquid‐equivalent snowfall rates. These results can be used in numerical weather prediction model aggregation parameterizations and can also provide climatologically representative vertical profiles of radar and microphysical quantities.

Identification of Convective Boundary Layer Depth over the Great Lakes Region Using Aircraft Observations: A Comparison of Various Methods

Abstract This study evaluates the methods of identifying the height zi of the top of the convective boundary layer (CBL) during winter (December and January) over the Great Lakes and nearby land areas using observations taken by the University of Wyoming King Air research aircraft during the Lake-Induced Convection Experiment (1997/98) and Ontario Winter Lake-effect Systems (2013/14) field campaigns. Since CBLs facilitate vertical mixing near the surface, the most direct measurement of zi is that above which the vertical velocity turbulent fluctuations are weak or absent. Thus, we use zi from the turbulence method as the “reference value” to which zi from other methods, based on bulk Richardson number (Rib), liquid water content, and vertical gradients of potential temperature, relative humidity, and water vapor mixing ratio, are compared. The potential temperature gradient method using a threshold value of 0.015 K m−1 for soundings over land and 0.011 K m−1 for soundings over lake provided the estimates of zi that are most consistent with the turbulence method. The Rib threshold-based method, commonly used in numerical simulation studies, underestimated zi. Analyzing the methods’ performance on the averaging window zavg we recommend using zavg = 20 or 50 m for zi estimations for lake-effect boundary layers. The present dataset consists of both cloudy and cloud-free boundary layers, some having decoupled boundary layers above the inversion top. Because cases of decoupled boundary layers appear to be formed by nearby synoptic storms, we recommend use of the more general term, elevated mixed layers. Significance Statement The depth zi of the convective atmospheric boundary layer (CBL) strongly influences precipitation rates during lake-effect snowstorms (LES). However, various zi approximation methods produce significantly different results. This study utilizes extensive concurrently collected observations by project aircraft during two LES field studies [Lake-Induced Convection Experiment (Lake-ICE) and OWLeS] to assess how zi from common estimation methods compare with “reference” zi derived from turbulent fluctuations, a direct measure of CBL mixing. For soundings taken both over land and lake; with cloudy or cloud-free conditions, potential temperature gradient (PTG) methods provided the best agreement with the reference zi. A method commonly employed in numerical simulations performed relatively poorly. Interestingly, the PTG method worked equally well for “coupled” and elevated decoupled CBLs, commonly associated with nearby cyclones.

The Seasonal Snowfall Contributions of Different Snowstorm Types in Central New York State

Located at the eastern extent of the Great Lakes snowbelt, Central New York averages some of the highest annual snowfall totals east of the Rocky Mountains. This is in large part due to the variety of snowstorms that affect the region including lake-effect storms, coastal storms, and overrunning storms. Previous estimates suggest that lake-effect snowstorms account for approximately half of the seasonal snow in the Great Lakes basin, but ignore the spatial variability that exists within the region. Therefore, this study examines the seasonal snowfall contributions of the different snowstorm types to affect Central New York. Results suggest that although lake-effect snowstorms are the dominant snowstorm type in the region, their seasonal snowfall contributions vary between 13 and 48%. Although lake-effect snowstorms produce more snow during the peak and mid-seasons, their relative contribution is greatest during the early and mid-winter seasons. Generally, higher contributions occur near the Tug Hill Plateau, with lower contributions in southern Central New York. Instead, snowfall in southern Central New York is mostly dominated by Nor'easters (16–35%), with lesser contributions from Rocky lows (14–29%). Overrunning storms that originate in Canada (e.g., Alberta clippers) and non-cyclonic storms contribute the least to seasonal snowfall totals across Central New York; however, they are often the catalyst for lake-effect snowstorms in the region, as they advect continental polar air masses that destabilize across the lake. Understanding the actual snowfall contribution from different snowstorm types is needed for future climate predictions. Since the potential trajectory of future snowfall varies according to the type of storm, climate models must accurately predict the type of storm that is producing the snow.

The Role of a Tropopause Polar Vortex in the Generation of the January 2019 Extreme Arctic Outbreak

AbstractAn extreme Arctic cold air outbreak took place across the Midwest, Great Lakes, and Northeast during 29 January to 1 February 2019. The event broke numerous long-standing records with wide-reaching and detrimental societal impacts. This study found that this rare and dangerous cold air outbreak (CAO) was a direct consequence of a tropopause polar vortex (TPV) originating at high latitudes and subsequently tracking southward into the United States. The tropopause depression at the center of this TPV extended nearly to the surface. Simulations using the atmospheric component of the Model for Prediction Across Scales (MPAS) were conducted, revealing excellent predictability at 6–7-day lead times with the strength, timing, and location of the CAO linked to the earlier characteristics of the TPV over the Arctic. Within the middle latitudes, the TPV subsequently developed a tilt with height. Warming and the destruction of potential vorticity also took place as the TPV passed over the Great Lakes initiating a lake-effect snow storm. The climatological investigation of CAOs suggests that TPVs frequently play a role in CAOs over North America with a TPV located within 1000 km of a CAO 85% of the time. These TPVs tended to originate in the northern Canadian Arctic and are ejected equatorward into the Great Lakes/Upper Midwest and then to the Northeast over Labrador. This study also provides insight into how the impact of Arctic circulations on middle latitudes may vary within the framework of a rapidly changing Arctic.

The Flexible Array of Radars and Mesonets (FARM)

AbstractThe Flexible Array of Radars and Mesonets (FARM) Facility is an extensive mobile/quickly deployable (MQD) multiple-Doppler radar and in situ instrumentation network. The FARM includes four radars: two 3-cm dual polarization, dual frequency (DPDF), Doppler on Wheels (DOW6/DOW7), the Rapid-Scan DOW (RSDOW), and a quickly deployable (QD) DPDF 5-cm C band on Wheels (COW). The FARM includes three mobile mesonet (MM) vehicles with 3.5-m masts, an array of rugged QD weather stations (PODNET), QD weather stations deployed on infrastructure such as light/power poles (POLENET), four disdrometers, six MQD upper-air sounding systems and a Mobile Operations and Repair Center (MORC). The FARM serves a wide variety of research/educational uses. Components have deployed to >30 projects during 1995–2020 in the United States, Europe, and South America, obtaining pioneering observations of a myriad of small spatial- and temporal-scale phenomena including tornadoes, hurricanes, lake-effect snow storms, aircraft-affecting turbulence, convection initiation, microbursts, intense precipitation, boundary layer structures and evolution, airborne hazardous substances, coastal storms, wildfires and wildfire suppression efforts, weather modification effects, and mountain/alpine winds and precipitation. The radars and other FARM systems support innovative educational efforts, deploying >40 times to universities/colleges, providing hands-on access to cutting-edge instrumentation for their students. The FARM provides integrated multiple radar, mesonet, sounding, and related capabilities enabling diverse and robust coordinated sampling of three-dimensional vector winds, precipitation, and thermodynamics increasingly central to a wide range of mesoscale research. Planned innovations include S-band on Wheels Network (SOWNET) and Bistatic Adaptable Radar Network (BARN), offering more qualitative improvements to the field project observational paradigm, providing broad, flexible, and inexpensive 10-cm radar coverage and vector wind field measurements.

The Effects of Lake Representation on the Regional Hydroclimate in the ECMWF Reanalyses

AbstractLakes are an integral part of the geosphere, but they are challenging to represent in Earth system models which either exclude lakes or prescribe properties without simulating lake dynamics. In ECMWF Interim reanalysis (ERA-Interim), lakes are represented by prescribing lake surface water temperatures (LSWT) from external data sources, while the newer generation ERA5 introduces the FLake parameterization scheme to the modelling system with different LSWT assimilation data sources. This study assesses the performance of these two reanalyses over three regions with the largest lakes in the world (Laurentian Great Lakes, African Great Lakes, and Lake Baikal) to understand the effects of their simulation differences on hydrometeorological variables. We find that differences in lake representation alter the associated hydrological and atmospheric processes and can affect regional hydroclimatic assessments. There are prominent differences in LSWT between the two datasets which influence the simulation of lake-effect snowstorms in the Laurentian winters and lake-land breeze circulation patterns in the African region. Generally, ERA5 has warmer LSWT in all three regions for most months (by 2-12 K) and its evaporation rates are up to twice the magnitudes in ERA-Interim. In the Laurentian lakes, ERA5 has strong biases in LSWT and evaporation magnitudes. Over Lake Baikal and the African Great Lakes, ERA5 LSWT magnitudes are closer to satellite-based datasets, albeit with warm bias (1-4 K), while ERA-Interim underestimates the magnitudes. ERA5 also simulates intense precipitation hotspots in lake proximity that are not visible in ERA-Interim and other observation-based datasets. Despite these limitations, ERA5 improves the simulation of lake-land circulation patterns across the African Great Lakes.

An Updated Synoptic Climatology of Lake Erie and Lake Ontario Heavy Lake-Effect Snow Events

Lake-effect snow (LES) storms pose numerous hazards, including extreme snowfall and blizzard conditions, and insight into the large-scale precursor conditions associated with LES can aid local forecasters and potentially allow risks to be mitigated. In this study, a synoptic climatology of severe LES events over Lakes Erie and Ontario was created using an updated methodology based on previous studies with similar research objectives. Principal component analysis (PCA) coupled with cluster analysis (CA) was performed on a case set of LES events from a study domain encompassing both lakes, grouping LES events with similar spatial characteristics into the primary composite structures for LES. Synoptic scale composites were constructed for each cluster using the North American Regional Reanalysis (NARR). Additionally, one case from each cluster was simulated using the Weather Research and Forecast (WRF) model to analyze mesoscale conditions associated with each of the clusters. Three synoptic setups were identified that consisted of discrepancies, mostly in the surface fields, from a common pattern previously identified as being conducive to LES, which features a dipole and upper-level low pressure anomaly located near the Hudson Bay. Mesoscale conditions associated with each composite support differing LES impacts constrained to individual lakes or a combination of both.

Event‐Based Precipitation Isotopes in the Laurentian Great Lakes Region Reveal Spatiotemporal Patterns in Moisture Recycling

AbstractLake effect snowstorms influence climate, ecology, and agriculture in the Laurentian Great Lakes region and can be costly to surrounding communities in the United States and Canada. Stable isotopes of lake effect precipitation events throughout the year display a distinct signature that can be used to better understand how these storms may respond to a changing climate. Here we present event‐based δ18O and δ2H of precipitation from a site in Skaneateles, NY, downwind of Lakes Ontario and Erie, between April 2015 and February 2018. We find a seasonal isotopic cycle with a well‐defined signature of high deuterium excess (d‐excess) during National Weather Service‐defined lake effect snowstorms. Application of a previously developed moisture recycling model to this data set shows that up to 25% more moisture recycling takes place when the lake water and air temperature difference is large, air temperature is below freezing, and wind direction permits storms to move over Lake Ontario or Lake Erie. Moisture recycling occurs less frequently during spring, summer, and early fall due to meteorological and lake parameters that are less conducive to moisture recycling. Comparison of annual mean precipitation d‐excess at sites both upwind and downwind of the Laurentian Great Lakes provides evidence that this high d‐excess signature is characteristic of mean annual precipitation isotopic composition at downwind sites and therefore may be used to quantify changes in moisture recycling that occur on event to annual time scales in response to past and future climate changes.

Influence of Lake Erie on a Lake Ontario Lake-Effect Snowstorm

AbstractAnnual lake-effect snowstorms, which develop through surface buoyant instability and upward moisture transport from the Laurentian Great Lakes, lead to important local increases in snowfall to the south and east. Surface wind patterns during cold-air outbreaks often result in areas where the air is modified by more than one Great Lake. While it is known that boundary layer air that has crossed multiple lakes can produce particularly intense snow, few observations are available on the process by which this occurs. This study examines unique observations taken during the Ontario Winter Lake-effect Systems (OWLeS) field project to document the process by which Lake Erie influenced snowfall that was produced over Lake Ontario on 28 January 2014. During the event, lake-effect clouds and snow that developed over Lake Erie extended northeastward toward Lake Ontario. OWLeS and operational observations showed that the clouds from Lake Erie disappeared (and snow greatly decreased) as they approached the Lake Ontario shoreline. This clear-air zone was due to mesoscale subsidence, apparently due to the divergence of winds moving from land to the smoother lake surface. However, the influence of Lake Erie in producing a deeper lake-effect boundary layer, thicker clouds, increased turbulence magnitudes, and heavier snow was identified farther downwind over Lake Ontario. It is hypothesized that the combination of a low-stability, high-moisture boundary layer as well as convective eddies and limited snow particles crossing the mesoscale subsidence region locally enhanced the lake-effect system over Lake Ontario within the plume of air originating over Lake Erie.

A new moisture tagging capability in the Weather Research and Forecasting model: formulation, validation and application to the 2014 Great Lake-effect snowstorm

Abstract. A new moisture tagging tool, usually known as water vapor tracer (WVT) method or online Eulerian method, has been implemented into the Weather Research and Forecasting (WRF) regional meteorological model, enabling it for precise studies on atmospheric moisture sources and pathways. We present here the method and its formulation, along with details of the implementation into WRF. We perform an in-depth validation with a 1-month long simulation over North America at 20 km resolution, tagging all possible moisture sources: lateral boundaries, continental, maritime or lake surfaces and initial atmospheric conditions. We estimate errors as the moisture or precipitation amounts that cannot be traced back to any source. Validation results indicate that the method exhibits high precision, with errors considerably lower than 1 % during the entire simulation period, for both precipitation and total precipitable water. We apply the method to the Great Lake-effect snowstorm of November 2014, aiming at quantifying the contribution of lake evaporation to the large snow accumulations observed in the event. We perform simulations in a nested domain at 5 km resolution with the tagging technique, demonstrating that about 30–50 % of precipitation in the regions immediately downwind, originated from evaporated moisture in the Great Lakes. This contribution increases to between 50 and 60 % of the snow water equivalent in the most severely affected areas, which suggests that evaporative fluxes from the lakes have a fundamental role in producing the most extreme accumulations in these episodes, resulting in the highest socioeconomic impacts.

The Ontario Winter Lake-Effect Systems Field Campaign: Scientific and Educational Adventures to Further Our Knowledge and Prediction of Lake-Effect Storms

Abstract Intense lake-effect snowstorms regularly develop over the eastern Great Lakes, resulting in extreme winter weather conditions with snowfalls sometimes exceeding 1 m. The Ontario Winter Lake-effect Systems (OWLeS) field campaign sought to obtain unprecedented observations of these highly complex winter storms. OWLeS employed an extensive and diverse array of instrumentation, including the University of Wyoming King Air research aircraft, five university-owned upper-air sounding systems, three Center for Severe Weather Research Doppler on Wheels radars, a wind profiler, profiling cloud and precipitation radars, an airborne lidar, mobile mesonets, deployable weather Pods, and snowfall and particle measuring systems. Close collaborations with National Weather Service Forecast Offices during and following OWLeS have provided a direct pathway for results of observational and numerical modeling analyses to improve the prediction of severe lake-effect snowstorm evolution. The roles of atmospheric boundary layer processes over heterogeneous surfaces (water, ice, and land), mixed-phase microphysics within shallow convection, topography, and mesoscale convective structures are being explored. More than 75 students representing nine institutions participated in a wide variety of data collection efforts, including the operation of radars, radiosonde systems, mobile mesonets, and snow observation equipment in challenging and severe winter weather environments.

Dynamically Downscaled Projections of Lake-Effect Snow in the Great Lakes Basin*,+

Abstract Projected changes in lake-effect snowfall by the mid- and late twenty-first century are explored for the Laurentian Great Lakes basin. Simulations from two state-of-the-art global climate models within phase 5 of the Coupled Model Intercomparison Project (CMIP5) are dynamically downscaled according to the representative concentration pathway 8.5 (RCP8.5). The downscaling is performed using the Abdus Salam International Centre for Theoretical Physics (ICTP) Regional Climate Model version 4 (RegCM4) with 25-km grid spacing, interactively coupled to a one-dimensional lake model. Both downscaled models produce atmospheric warming and increased cold-season precipitation. The Great Lakes’ ice cover is projected to dramatically decline and, by the end of the century, become confined to the northern shallow lakeshores during mid-to-late winter. Projected reductions in ice cover and greater dynamically induced wind fetch lead to enhanced lake evaporation and resulting total lake-effect precipitation, although with increased rainfall at the expense of snowfall. A general reduction in the frequency of heavy lake-effect snowstorms is simulated during the twenty-first century, except with increases around Lake Superior by the midcentury when local air temperatures still remain low enough for wintertime precipitation to largely fall in the form of snow. Despite the significant progress made here in elucidating the potential future changes in lake-effect snowstorms across the Great Lakes basin, further research is still needed to downscale a larger ensemble of CMIP5 model simulations, ideally using a higher-resolution, nonhydrostatic regional climate model coupled to a three-dimensional lake model.

Impact of Microphysics Parameterizations on Simulations of the 27 October 2010 Great Salt Lake–Effect Snowstorm

Abstract Simulations of moist convection at cloud-permitting grid spacings are sensitive to the parameterization of microphysical processes, posing a challenge for operational weather prediction. Here, the Weather Research and Forecasting (WRF) Model is used to examine the sensitivity of simulations of the Great Salt Lake–effect snowstorm of 27 October 2010 to the choice of microphysics parameterization (MP). It is found that the simulated precipitation from four MP schemes varies in areal coverage, amount, and position. The Thompson scheme (THOM) verifies best against radar-derived precipitation estimates and gauge observations. The Goddard, Morrison, and WRF double-moment 6-class microphysics schemes (WDM6) produce more precipitation than THOM, with WDM6 producing the largest overprediction relative to radar-derived precipitation estimates and gauge observations. Analyses of hydrometeor mass tendencies show that WDM6 creates more graupel, less snow, and more total precipitation than the other schemes. These results indicate that the rate of graupel and snow production can strongly influence the precipitation efficiency in simulations of lake-effect storms, but further work is needed to evaluate MP-scheme accuracy across a wider range of events, including the use of aircraft- and ground-based hydrometeor sampling to validate MP hydrometeor categorization.

Spatiotemporal Snowfall Trends in Central New York

AbstractCentral New York State, located at the intersection of the northeastern United States and the Great Lakes basin, is impacted by snowfall produced by lake-effect and non-lake-effect snowstorms. The purpose of this study is to determine the spatiotemporal patterns of snowfall in central New York and their possible underlying causes. Ninety-three Cooperative Observer Program stations are used in this study. Spatiotemporal patterns are analyzed using simple linear regressions, Pearson correlations, principal component analysis to identify regional clustering, and spatial snowfall distribution maps in the ArcGIS software. There are three key findings. First, when the long-term snowfall trend (1931/32–2011/12) is divided into two halves, a strong increase is present during the first half (1931/32–1971/72), followed by a lesser decrease in the second half (1971/72–2011/12). This result suggests that snowfall trends behave nonlinearly over the period of record. Second, central New York spatial snowfall patterns are similar to those for the whole Great Lakes basin. For example, for five distinct regions identified within central New York, regions closer to and leeward of Lake Ontario experience higher snowfall trends than regions farther away and not leeward of the lake. Third, as compared with precipitation totals (0.02), average air temperatures had the largest significant (ρ < 0.05) correlation (−0.56) with seasonal snowfall totals in central New York. Findings from this study are valuable because they provide a basis for understanding snowfall patterns in a region that is affected by both non-lake-effect and lake-effect snowstorms.

Characteristics of easterly-induced snowfall in Yeongdong and its relationship to air-sea temperature difference

Characteristics of snowfall episodes have been investigated for the past ten years in order to study its association with lowlevel stability and air-sea temperature difference over the East Sea. In general, the selected snowfall episodes have similar synoptic setting such as the Siberian High extended to northern Japan along with the Low passing by the southern Korean Peninsula, eventually resulting in the easterly flow in the Yeongdong region. Especially in the heavy snowfall episodes, convective unstable layers have been identified over the East sea due to relatively warm sea surface temperature (SST) about 8∼10°C and specifically cold pool around 1∼2 km above the surface level (ASL), which can be derived from Regional Data Assimilation and Prediction System (RDAPS), but that have not been clearly exhibited in the weak snowfall episodes. The basic mechanism to initiate snowfall around Yeongdong seems to be similar to that of lake-effect snowstorms around Great Lakes in the United States (Kristovich et al., 2003). Difference of equivalent potential temperature (θ e ) between 850 hPa and surface as well as difference between air and sea temperatures altogether gradually began to increase in the pre-snowfall period and reached their maximum values in the course of the period, whose air (850 hPa) — sea temperature difference and snowfall intensity in case of the heavy snowfall episodes are almost larger than 20°C and 6 tims greater than the weak snowfall episodes, respectively. Interestingly, snowfall appeared to begin in case of an air-sea temperature difference exceeding over 15°C. The current analysis is overall consistent with the previous finding (Lee et al., 2012) that an instabilityinduced moisture supply to the lower atmosphere from the East sea, being cooled and saturated in the lower layer, so to speak, East Sea-Effect Snowfall (SES), would make a low-level ice cloud which eventually moves inland by the easterly flow. In addition, a longlasting synoptic characteristics and convergence-induced invigoration also appear to play the important roles in the severe snowstorms. Improvements in our understanding of mesoscale sea-effect snowstorms require detailed in-situ and remote sensing observations over and around East Sea since observations of the concurrent thermodynamic and microphysical characteristics have not been available there and this study emphasizes the importance of low level stability as quantitative estimation of moist static energy generation over the East Sea.

Multivariate analysis of lake-effect snowstorms in western New York

The Great Lakes region of the United States is subjected to a wintertime convective phenomenon known as lake-effect snow (LES). These events are capable of producing significant quantities of snow over localized areas by developing elongated bands that tap into heat and moisture exchanges between the warm lake surface and the overlying continental polar air. Several factors are believed to affect the snowfall intensity associated with LES events, including the ice coverage over the lake where the snowbands originate. Improvements in the quality of snowfall forecasts associated with these LES events require an approach that uses a larger number of events than used in previously investigated case studies. The intensity of 91 LES events was assessed using the total volume of snow (snow depth multiplied by snow coverage area) per day in western New York as reported by the National Weather Service in Buffalo, New York, from 1998–2011. These events were cross-referenced against the fetch, capping inversion height, thermodynamic instability, wind speed, and ice coverage over Lake Erie during each event. A multivariate regression revealed that only fetch, inversion height, and ice coverage were statistically significant in determining the intensity of the LES event, with ice coverage having the greatest impact. These parameters accounted for approximately 30% of the overall snowfall volume variability in our LES events. An examination of two events demonstrated that other environmental controls—such as orography and low-level moisture—may have affected the quality of the snowfall predicted by our regression.

Simulation of Heavy Lake-Effect Snowstorms across the Great Lakes Basin by RegCM4: Synoptic Climatology and Variability

AbstractA historical simulation (1976–2002) of the Abdus Salam International Centre for Theoretical Physics Regional Climate Model, version 4 (ICTP RegCM4), coupled to a one-dimensional lake model, is validated against observed lake ice cover and snowfall across the Great Lakes Basin. The model reproduces the broad temporal and spatial features of both variables in terms of spatial distribution, seasonal cycle, and interannual variability, including climatological characteristics of lake-effect snowfall, although the simulated ice cover is overly extensive largely due to the absence of lake circulations. A definition is introduced for identifying heavy lake-effect snowstorms in regional climate model output for all grid cells in the Great Lakes Basin, using criteria based on location, wind direction, lake ice cover, and snowfall. Simulated heavy lake-effect snowstorms occur most frequently downwind of the Great Lakes, particularly to the east of Lake Ontario and to the east and south of Lake Superior, and are most frequent in December–January. The mechanism for these events is attributed to an anticyclone over the central United States and related cold-air outbreak for areas downwind of Lakes Ontario and Erie, in contrast to a nearby cyclone over the Great Lakes Basin and associated cold front for areas downwind of Lakes Superior, Huron, and Michigan.

The Role of Ice Cover in Heavy Lake-Effect Snowstorms over the Great Lakes Basin as Simulated by RegCM4

Abstract A 20-km regional climate model, the Abdus Salam International Centre for Theoretical Physics Regional Climate Model version 4 (ICTP RegCM4), is employed to investigate heavy lake-effect snowfall (HLES) over the Great Lakes Basin and the role of ice cover in regulating these events. When coupled to a lake model and driven with atmospheric reanalysis data between 1976 and 2002, RegCM4 reproduces the major characteristics of HLES. The influence of lake ice cover on HLES is investigated through 10 case studies (2 per Great Lake), in which a simulated heavy lake-effect event is compared with a companion simulation having 100% ice cover imposed on one or all of the Great Lakes. These experiments quantify the impact of ice cover on downstream snowfall and demonstrate that Lake Superior has the strongest, most widespread influence on heavy snowfall and Lake Ontario the least. Ice cover strongly affects a wide range of atmospheric variables above and downstream of lakes during HLES, including snowfall, surface energy fluxes, wind speed, temperature, moisture, clouds, and air pressure. Averaged among the 10 events, complete ice coverage causes major reductions in lake-effect snowfall (>80%) and turbulent heat fluxes over the lakes (>90%), less low cloudiness, lower temperatures, and higher air pressure. Another important consequence is a consistent weakening (30%–40%) of lower-tropospheric winds over the lakes when completely frozen. This momentum reduction further decreases over-lake evaporation and weakens downstream wind convergence, thus mitigating lake-effect snowfall. This finding suggests a secondary, dynamical mechanism by which ice cover affects downstream snowfall during HLES events, in addition to the more widely recognized thermodynamic influence.

Lake-Effect Thunderstorms in the Lower Great Lakes

Abstract Cloud-to-ground (CG) lightning, radar, and radiosonde data were examined to determine how frequently lake-effect storms (rain/snow) with lightning occurred over and near the lower Great Lakes region (Lakes Erie and Ontario) from September 1995 through March 2007. On average, lake-effect lightning occurred on 7.9 days and with 5.8 storm events during a particular cool season (September–March). The CG lightning with these storms had little inland extent and was usually limited to a few flashes per storm. Some storms had considerably more, with the most intense storm (based on National Lightning Detection Network observations) producing 1551 CG flashes over a 4-day period. Thundersnow events were examined in more detail because of the rarity of this phenomenon across the United States. Most lake-effect thundersnow events (75%) occurred in November and December. An analysis of model sounding data using the Buffalo Toolkit for Lake Effect Snow (BUFKIT) software package in which lower boundary conditions can be modified by lake surfaces showed that thundersnow events had an 82% increase in the mean height of the −10°C level when compared with nonelectrified lake-effect snowstorms (1.2 vs 0.7 km AGL), had higher lake-induced equilibrium levels (EL; above 3.6 km AGL) and convective available potential energy (CAPE; >500 J kg−1), had low wind shear environments, and were intense, single-band storms. A nomogram of the altitude of the −10°C isotherm and EL proved to be useful in predicting lake-effect thundersnowstorms.

MODIS-derived surface temperature of the Great Salt Lake

The surface temperature of Utah's hypersaline Great Salt Lake is examined between 2000 and 2007 using 3345 images from the Moderate Resolution Imaging Spectroradiometer (MODIS) on board the NASA Earth Observing System Terra and Aqua platforms. This study shows the utility of using a multi-year record of the easily accessible and fully processed MODIS thermal imagery to monitor spatial, diurnal, seasonal, and annual variations in the surface water temperature (SWT) of lakes where long-term in situ measurements are rarely available. A cloud-free Terra image is available on average every day during the summer and early fall, every other day during spring and late fall, and every third day during the winter. MODIS-derived lake SWT exhibits a cool bias (~ − 1.5 °C) relative to in situ temperature observations gathered from three buoys and a slowly-moving watercraft. The dominant SWT signal is the annual cycle (with a range of 26 °C and peak temperature in mid-July) while the diurnal range is as large as 4 °C during the spring season. Year-to-year variations in SWT are largest during the fall with over 1 °C anomalously warm (cold) departures from the 8-year monthly medians observed during fall 2001 (2006). The MODIS imagery provides an updated SWT climatology for operational weather forecasting applications (e.g., lake-effect snow storm prediction) as well as for input into operational and research numerical weather prediction models.