The Long Winter of 1880-1881

The story of the winter of 1880-1881 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

The Accumulated Winter Season Severity Index (AWSSI)

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

The Accumulated Winter Season Severity Index (AWSSI)

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

Climate Change: What Does It Mean for Nebraska?

Because Nebraska’s location on the North American continent is far removed from large bodies of water, Nebraskans experience a strong continental type climate. As such, residents do not benefit from the moderating influence of the ocean, and temperatures can have wide swings from day to day and season to season. Typical characteristics for a continental climate at this latitude are large temperature variability with warm summers dominated by convective thunderstorms, and cold winters influenced by snow and wind from mid-latitude cyclones

Development of a Long-Term (1884-2006) Serially Complete Dataset of U.S. Temperatures and Precipitation for Climate Services

Serially complete climate datasets with no missing data are necessary for a diverse group of users working in many economic sectors. In this article we describe the procedures used to create a Serially Complete Data set (SCD) for the U.S. We include the selection criterion applied to potential SCD stations, the various procedural steps and the details applied to each step. A few observations that were not previously digitized were obtained from observers official paper reports. The methods used to estimate missing data are the Spatial Regression Test and the Inverse Distance Weighting technique. Using the criterion for selecting stations we were able to include 2144 stations for the SCD that had at least 1 element (maximum/minimum temperature and/or precipitation) for a continuous period of at least 40 years. In addition, the quality control procedure assigned confidence intervals to all observations and many of the estimates. We continue to explore the options for estimating any missing data that remain after our 3 step approach and we look forward to changing the base data set form TD 3200 to GHCN

Quality Assessment of Meteorological Data for the Beaufort and Chukchi Sea Coastal Region using Automated Routines

Meteorological observations from more than 250 stations in the Beaufort and Chukchi Sea coastal, interior, and offshore regions were gathered and quality-controlled for the period 1979 through 2009. These stations represent many different observing networks that operate in the region for the purposes of aviation, fire weather, coastal weather, climate, surface radiation, and hydrology and report data hourly or sub-hourly. A unified data quality control (QC) has been applied to these multi-resource data, incorporating three main QC procedures: the threshold test (identifying instances of an observation falling outside of a normal range); the step change test (identifying consecutive values that are excessively different); and the persistence test (flagging instances of excessively high or low variability in the observations). Methods previously developed for daily data QC do not work well for hourly data because they flag too many data entries. Improvements were developed to obtain the proper limits for hourly data QC. These QC procedures are able to identify the suspect data while producing far fewer Type I errors (the erroneous flagging of valid data). The fraction of flagged data for the entire database illustrates that the persistence test was failed the most often (1.34%), followed by the threshold (0.99%) and step change tests (0.02%). Comparisons based on neighboring stations were not performed for the database; however, correlations between nearby stations show promise, indicating that this type of check may be a viable option in such cases. This integrated high temporal resolution dataset will be invaluable for weather and climate analysis, as well as regional modeling applications, in an area that is undergoing significant climatic change.Des observations météorologiques provenant de plus de 250 stations des régions côtières, intérieures et extracôtières de la mer de Beaufort et de la mer des Tchouktches ont été recueillies pendant la période allant de 1979 à 2009, puis elles ont fait l’objet d’un contrôle de la qualité. Ces stations relèvent de plusieurs réseaux d›observation différents qui existent dans la région à des fins d›aviation, de météorologie forestière, de météorologie côtière, de climat, de rayonnement de surface et d’hydrologie, et elles fournissent des données horaires ou subhoraires. Un contrôle de la qualité (CQ) unifié des données a été appliqué à ces données provenant de sources multiples en faisant appel à trois méthodes principales de CQ, soit le test d’acceptabilité (qui a permis de déterminer dans quels cas une observation ne faisait pas partie de la gamme normale); le test de la variation discrète (qui a permis de détecter les valeurs consécutives qui sont excessivement différentes); et le test de la persistance (qui a permis de repérer les cas de variabilité excessivement élevée ou basse). Les anciennes méthodes de CQ des données quotidiennes ne donnent pas de bons résultats dans le cas des données horaires parce qu’elles se trouvent à signaler un trop grand nombre d’entrées de données. Des améliorations ont été apportées afin d’obtenir les bonnes limites en vue du CQ des données horaires. Ces méthodes de CQ permettent de repérer les données douteuses et produisent beaucoup moins d’erreurs de type I (le signalement erroné de données valables). La fraction de données signalées pour l’ensemble de la base de données illustre que le test de persistance a échoué le plus souvent (1,34 %), suivi du test d’acceptabilité (0,99 %) et des tests de la variation discrète (0,02 %). Des comparaisons effectuées avec les données de stations avoisinantes n’ont pas été effectuées pour la base de données. Cependant, des corrélations entre les stations annexes s’avéraient prometteuses, ce qui a laissé entendre que ce type de vérification pourrait présenter une option viable dans de tels cas. Cet ensemble de données intégrées à haute résolution temporelle aura une très grande valeur pour l’analyse météorologique et climatique ainsi que pour les applications de modélisation régionale dans une région où le changement climatique est important

Mesoscale Modeling of the Meteorological Impacts of Irrigation during the 2012 Central Plains Drought

In the summer of 2012, the central plains of the United States experienced one of its most severe droughts on record. This study examines the meteorological impacts of irrigation during this drought through observations and model simulations using the Community Land Model coupled to the Weather Research and Forecasting (WRF) Model. A simple parameterization of irrigation processes is added into the WRF Model. In addition to keeping soil moisture in irrigated areas at a minimum of 50% of soil moisture hold capacity, this irrigation scheme has the following new features: 1) accurate representation of the spatial distribution of irrigation area in the study domain by using a MODIS-based land surface classification with 250-m pixel size and 2) improved representation of the time series of leaf area index (LAI) values derived from crop modeling and satellite observations in both irrigated and nonirrigated areas. Several numerical sensitivity experiments are conducted. The WRF-simulated temperature field when including soil moisture and LAI modification within the model is shown to be most consistent with ground and satellite observations, all indicating a temperature decrease of 2–3K in irrigated areas relative to the control run. Modification of LAI in irrigated and dryland areas led to smaller changes, with a 0.2-K temperature decrease in irrigated areas and up to a 0.5-K temperature increase in dryland areas. Furthermore, the increased soil moisture and modified LAI are shown to lead to statistically significant increases in surface divergence and surface pressure and to decreases in planetary boundary layer height over irrigated areas

Mesoscale Modeling of the Meteorological Impacts of Irrigation during the 2012 Central Plains Drought

In the summer of 2012, the central plains of the United States experienced one of its most severe droughts on record. This study examines the meteorological impacts of irrigation during this drought through observations and model simulations using the Community Land Model coupled to the Weather Research and Forecasting (WRF) Model. A simple parameterization of irrigation processes is added into the WRF Model. In addition to keeping soil moisture in irrigated areas at a minimum of 50% of soil moisture hold capacity, this irrigation scheme has the following new features: 1) accurate representation of the spatial distribution of irrigation area in the study domain by using a MODIS-based land surface classification with 250-m pixel size and 2) improved representation of the time series of leaf area index (LAI) values derived from crop modeling and satellite observations in both irrigated and nonirrigated areas. Several numerical sensitivity experiments are conducted. The WRF-simulated temperature field when including soil moisture and LAI modification within the model is shown to be most consistent with ground and satellite observations, all indicating a temperature decrease of 2–3K in irrigated areas relative to the control run. Modification of LAI in irrigated and dryland areas led to smaller changes, with a 0.2-K temperature decrease in irrigated areas and up to a 0.5-K temperature increase in dryland areas. Furthermore, the increased soil moisture and modified LAI are shown to lead to statistically significant increases in surface divergence and surface pressure and to decreases in planetary boundary layer height over irrigated areas

A Multi-sensor View of the 2012 Central Plains Drought from Space

In summer of 2012, the Central Plains of the United States experienced its most severe drought since the ground-based data record began in the late 1900s. By using comprehensive satellite data from MODIS (Moderate Resolution Imaging Spectroradiometer) and TRMM (Tropical Rainfall Measuring Mission), along with in-situ observations, this study documents the geophysical parameters associated with this drought, and thereby providing, for the first time, a large-scale observation-based view of the extent to which the land surface temperature and vegetation can likely be affected by both the severe drought and the agricultural response (irrigation) to the drought. Over non-irrigated area, 2012 summer daytime land surface temperature (LSl) , and Normalized Difference Vegetation Index (NDVI) monthly anomalies (with respect to climate in 2002-2011) are often respectively greater than 5 K and negative, with some extreme values of 10K and -0.2 (Le., no green vegetation). In contrast, much smaller anomalies \u3c 2 K) of LST and nearly the same NDVI are found over irrigated areas. Precipitation received was an average of 5.2 cm less, while both fire counts and fire radiative power were doubled, thus contributing in part to a nearly 100% increase of aerosol optical depth in many forested areas (close to intermountain west). Water vapor amount, while decreased over the southern part, indeed slightly increased in the northern part of Central Plains. As expected, cloud fraction anomaly is negative in the entire Central Plains; however, the greatest reduction of cloud fraction is found over the irrigated areas, which is in contrast to past modeling studies showing that more irrigation, because of its impact on LST, may lead to increase of cloud fraction