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Martha D. Shulski and Mark W. Seeley

Abstract

Models were utilized to determine the snow accumulation season (SAS) and to quantify windblown snow for the purpose of snowdrift control for locations in Minnesota. The models require mean monthly temperature, snowfall, density of snow, and wind frequency distribution statistics. Temperature and precipitation data were obtained from local cooperative observing sites, and wind data came from Automated Surface Observing System (ASOS)/Automated Weather Observing System (AWOS) sites in the region. The temperature-based algorithm used to define the SAS reveals a geographic variability in the starting and ending dates of the season, which is determined by latitude and elevation. Mean seasonal snowfall shows a geographic distribution that is affected by topography and proximity to Lake Superior. Mean snowfall density also exhibits variability, with lower-density snow events displaced to higher-latitude positions. Seasonal wind frequencies show a strong bimodal distribution with peaks from the northwest and southeast vector direction, with an exception for locations in close proximity to the Lake Superior shoreline. In addition, for western and south-central Minnesota there is a considerably higher frequency of wind speeds above the mean snow transport threshold of 7 m s−1. As such, this area is more conducive to higher potential snow transport totals. Snow relocation coefficients in this area are in the range of 0.4–0.9, and, according to the empirical models used in this analysis, this range implies that actual snow transport is 40%–90% of the total potential in south-central and western areas of the state.

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Barbara Mayes Boustead, Martha D. Shulski, and Steven D. Hilberg

Abstract

The story of the winter of 1880/81 in the central United States has been retold in historical fiction, including Laura Ingalls Wilder’s The Long Winter, as well as in local histories and folklore. What story does the meteorological data tell, and how does it measure up when compared to the fiction and folklore? What were the contributing factors to the severity of the Long Winter, and has it been or could it be repeated? Examining historical and meteorological data, reconstructions, and reanalysis, including the Accumulated Winter Season Severity Index, the Long Winter emerges as one of the most severe since European-descended settlers arrived to the central United States and began documenting weather. Contributing factors to its severity include an extremely negative North Atlantic Oscillation pattern, a mild to moderate El Niño, and a background climate state that was much colder than the twentieth-century average. The winter began early and was particularly cold and snowy throughout its duration, with a sudden spring melt that caused subsequent record-setting flooding. Historical accounts of the winter, including The Long Winter, prove to be largely accurate in describing its severity, as well as its impacts on transportation, fuel availability, food supplies, and human and livestock health. Being just one of the most severe winters on record, there are others in the modern historical record that do compare in severity, providing opportunity for comparing and contrasting the impacts of similarly severe winters.

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Barbara E. Mayes Boustead, Steven D. Hilberg, Martha D. Shulski, and Kenneth G. Hubbard

Abstract

The character of a winter can be defined by many of its features, including temperature averages and extremes, snowfall totals, snow depth, and the duration between onset and cessation of winter-weather conditions. The accumulated winter season severity index incorporates these elements into one site-specific value that defines the severity of a particular winter, especially when examined in the context of climatological values for that site. Thresholds of temperature, snowfall, and snow depth are assigned points that accumulate through the defined winter season; a parallel index uses temperature and precipitation to provide a snow proxy where snow data are unavailable or unreliable. The results can be analyzed like any other meteorological parameter to examine relationships to teleconnection patterns, determine trends, and create sector-specific applications, as well as to analyze an ongoing winter or any individual winter season to place its severity in context.

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Andrea J. Coop, Kenneth G. Hubbard, Martha D. Shulski, Jinsheng You, and David B. Marx

Abstract

Climate data are increasingly scrutinized for accuracy because of the need for reliable input for climate-related decision making and assessments of climate change. Over the last 30 years, vast improvements to U.S. instrumentation, data collection, and station siting have created more accurate data. This study explores the spatial accuracy of daily maximum and minimum air temperature data in Nebraska networks, including the U.S. Historical Climatology Network (HCN), the Automated Weather Data Network (AWDN), and the more recent U.S. Climate Reference Network (CRN). The spatial structure of temperature variations at the earth’s surface is compared for timeframes 2005–09 for CRN and AWDN and 1985–2005 for AWDN and HCN. Individual root-mean-square errors between candidate station and surrounding stations were calculated and used to determine the spatial accuracy of the networks. This study demonstrated that in the 5-yr analysis CRN and AWDN were of high spatial accuracy. For the 21-yr analysis the AWDN proved to have higher spatial accuracy (smaller errors) than the HCN for both maximum and minimum air temperature and for all months. In addition, accuracy was generally higher in summer months and the subhumid area had higher accuracy than did the semiarid area. The findings of this study can be used for Nebraska as an estimate of the uncertainty associated with using a weather station’s data at a decision point some distance from the station.

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Toward Regional Climate Services

The Role of NOAA's Regional Climate Centers

Arthur T. DeGaetano, Timothy J. Brown, Steven D. Hilberg, Kelly Redmond, Kevin Robbins, Peter Robinson, Martha Shulski, and Marjorie McGuirk

For 25 yr, the Regional Climate Center (RCC) program has provided climate services to six regions encompassing the United States. The service provided by the RCCs has evolved through this time to become an efficient, user-driven program that exemplifies many of the components that have been cited for effective national climate services. To illustrate the RCCs' role as operational climate service providers, a brief history of the program is presented with recent examples of RCC innovations in the provision and creation of data products and decision tools, computer infrastructure, and the integration of climate data across networks. These strengths complement the missions of other federal climate service providers and regional and state-based programs, such as the Regional Integrated Sciences and Assessments, state climatologist programs, and National Weather Service climate services program managers and local focal points with which the RCCs actively partner.

Building on this expertise, a vision for the RCC role in climate services during the next quarter century is presented. This strategy includes five main components encompassing 1) operational linkage of an array of climate data sources with climate products, tools, and monitoring systems; 2) engagement of new and existing climate service partners to reduce the risk associated with climate impacts; 3) implementation of innovative user-driven approaches to regional and local climate services; 4) climate data stewardship; and 5) scientifically sound assessments and solutions to climate-related problems through active stakeholder collaboration and engagement. These elements will be equally applicable and important to decisions related to the historical climate record, real-time interannual climate variations, or future climate change assessment and adaptation activities.

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Eugene S. Takle, Christopher J. Anderson, Jeffrey Andresen, James Angel, Roger W. Elmore, Benjamin M. Gramig, Patrick Guinan, Steven Hilberg, Doug Kluck, Raymond Massey, Dev Niyogi, Jeanne M. Schneider, Martha D. Shulski, Dennis Todey, and Melissa Widhalm

Abstract

Corn is the most widely grown crop in the Americas, with annual production in the United States of approximately 332 million metric tons. Improved climate forecasts, together with climate-related decision tools for corn producers based on these improved forecasts, could substantially reduce uncertainty and increase profitability for corn producers. The purpose of this paper is to acquaint climate information developers, climate information users, and climate researchers with an overview of weather conditions throughout the year that affect corn production as well as forecast content and timing needed by producers. The authors provide a graphic depicting the climate-informed decision cycle, which they call the climate forecast–decision cycle calendar for corn.

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