Abstract

Interannual variations in Beaufort Sea summer ice conditions influence a wide range of socioeconomic activities, including merchant shipping in the Beaufort Sea and subsistence lifestyles on the Alaskan North Slope. Each year, the National Ice Center quantifies Beaufort Sea summer ice conditions based on the Barnett severity index (BSI), which is based on distances from Point Barrow, Alaska, to the sea ice edge, as well as characteristics of the shipping season from Prudhoe Bay to the Bering Sea. Long-range forecasts (monthly to seasonal) of the BSI would be valuable for the above-mentioned users, provided that the forecasts are communicated effectively and used properly. Utilizing mean monthly sea ice and atmospheric data from 1979 through 2000, multiple linear regression models are developed here to forecast the BSI at monthly intervals from October of the previous year through to July of the prediction year. The final models retain between three and five variables, with decreased multiyear sea ice (MYI) and total sea ice (CT) concentration in the Beaufort Sea, and increased MYI in the transpolar drift stream leading to less severe summer ice conditions. Variations in antecedent autumn and spring wind patterns associated with the October east Atlantic index and the March North Atlantic Oscillation index also play a key role in defining the ensuing summer's ice severity, with fluctuations in July heating degree days being somewhat valuable. Monte Carlo simulations suggest that the final models are not adversely influenced by artificial skill, while Durbin–Watson and variance inflation factor (VIF) statistics indicate the final models are statistically valid. Model accuracy, as defined by the coefficient of determination, ranges from 0.74 with October data to 0.92 with July data. Categorical forecasts (e.g., forecasted ice conditions are ranked from heaviest to lightest) are provided as an example of effectively communicating the model output for all users, while a probability of exceedance curve is shown as an example of communicating uncertainty information to more advanced users. It is important to note that this method does not show good skill on historical (1953–78) data, likely due to a regime shift in the mid-1970s, and that if the Arctic climate changes, the methods described here will need to be altered.

Abstract

Prediction of Great Lakes ice cover is important for winter operations and planning activities. Current 30-day forecasts use accumulated freezing degree-days (AFDDs) to identify similar historical events and associated ice cover. The authors describe statistical models that relate future ice cover to current ice cover, AFDDs, and teleconnection indices, available on the day the forecast is made. These models are evaluated through Monte Carlo simulation and assess the potential of a perfect AFDD forecast in a regression between ice cover and AFDDs between the forecast date (first day of month) and the date for which the forecast is made (first day of next month).

In 2008, the American Meteorological Society (AMS) Board on Enterprise Planning (BEP) established the Committee on Mobile Observations to discuss the application and utilization of mobile weather and road condition data in the context of supporting the weather and transportation communities and how these data could be used to improve safety and mobility across the nation's surface transportation system. The goal of the committee is to articulate a clear vision for mobile data that captures the immense opportunities for these data to improve road weather services and transportation safety and mobility. The Committee on Mobile Observations is engaged in numerous activities to accomplish its goal, which includes a nationwide survey of the traveling public to obtain better information on their preferences for and interests in obtaining weather and road condition information, their willingness to share vehicle data, and their willingness to pay for enhanced services. This paper outlines the results of the survey. Working through Survey Sampling International, the survey obtained 1627 responses. Results show that people are strongly interested in obtaining road weather information, though they remain wary of sharing data, and they are disinclined to pay for the data. Stratifications note some regional differences in the level of interest in data, as well as dependencies between the amount of information desired, and the willingness to pay for it and to share vehicle information.

Abstract

A better understanding of the interannual variability in temperature and precipitation datasets used as forcing fields for hydrologic models will lead to a more complete description of hydrologic model uncertainty, in turn helping scientists study the larger goal of how the Arctic terrestrial system is responding to global change. Accordingly, this paper investigates temporal and spatial variability in monthly mean (1992–2000) temperature and precipitation datasets over the Western Arctic Linkage Experiment (WALE) study region. The six temperature datasets include 1) the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5); 2) the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40); 3) the Advanced Polar Pathfinder all-sky temperatures (APP); 4) National Centers for Environmental Prediction– National Center for Atmospheric Research (NCEP–NCAR) reanalyses (NCEP1); 5) the Climatic Research Unit/University of East Anglia CRUTEM2v (CRU); and 6) the Matsuura and Wilmott 0.5° × 0.5° Global Surface Air Temperature and Precipitation (MW). Comparisons of monthly precipitation are examined for MM5, ERA-40, NCEP1, CRU, and MW. Results of the temporal analyses indicate significant differences between at least two datasets (for either temperature or precipitation) in almost every month. The largest number of significant differences for temperature occurs in October, when there are five separate groupings; for precipitation, there are four significantly different groupings from March through June, and again in December. Spatial analyses of June temperatures indicate that the greatest dissimilarity is concentrated in the central portion of the study region, with the NCEP1 and APP datasets showing the greatest differences. In comparison, the spatial analysis of June precipitation datasets suggests that the largest dissimilarity is concentrated in the eastern portion of the study region. These results indicate that the choice of forcing datasets likely will have a significant effect on the output from hydrologic models, and several different datasets should be used for a robust hydrologic assessment.

Abstract

The Western Arctic Linkage Experiment (WALE) is aimed at understanding the role of high-latitude terrestrial ecosystems in the response of the Arctic system to global change through collection and comparison of climate datasets and model results. In this paper, a spatiotemporal approach is taken to compare and validate model results from the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) with commonly used analysis and reanalysis datasets for monthly averages of temperature and precipitation in 1992–2000 and for a study area at 55°–65°N, 160°–110°W in northwestern Canada and Alaska.

Objectives include a quantitative assessment of similarity between datasets and climate model fields, and identification of geographic areas and seasons that are problematic in modeling, with potential causes that may aid in model improvement. These are achieved by application of algebraic similarity mapping, a simple yet effective method for synoptic analysis of many (here, 45) different spatial datasets, maps, and models. Results indicate a dependence of model–data similarity on seasonality, on climate variable, and on geographic location. In summary, 1) similarity of data and models is better for temperature than for precipitation; and 2) modeling of summer precipitation fields, and to a lesser extent, temperature fields, appears more problematic than that of winter fields. The geographic distribution of areas with best and worst agreement shifts throughout the year, with generally better agreement between maps and models in the northeastern and northern inland areas than in topographically complex and near-coastal areas. The study contributes to an understanding of the geographic complexity of the Arctic system and modeling its diverse climate.

Weather and Society*Integrated Studies (WAS*IS) is a grassroots movement to change the weather enterprise by comprehensively and sustainably integrating social science into meteorological research and practice. WAS*IS is accomplishing this by establishing a framework for a) building an interdisciplinary community of practitioners, researchers, and stakeholders who are dedicated to the integration of meteorology and social science, and b) providing this community with a means to learn and further examine ideas, methods, and examples related to integrated weather-society work.

In its first year, WAS*IS focused on achieving its mission primarily through several workshops. Between July 2005 and August2006, there were three WAS*IS workshops with a total of 86 selected participants. The workshops focused on the following: laying the groundwork for conducting interdisciplinary work, teaching basic tools and concepts relevant to integrated weather-society efforts, using real-world examples to learn about effective integrated work, and developing opportunities and relationships for doing WAS*IS-type work. By emphasizing the importance of developing a lifelong cohort, as well as helping participants learn and apply social science tools and concepts, WAS*IS can address societal impacts of weather in powerful and sustained ways.

This article discusses the need and motivation for creating WAS*IS; the development, scope, and implementation of WAS*IS through summer of 2006; and WAS*IS-related outcomes thus far, as well as future prospects of the WAS*IS movement.

Abstract

Accurate estimates of the spatial and temporal variation in terrestrial water and energy fluxes and mean states are important for simulating regional hydrology and biogeochemistry in high-latitude regions. Furthermore, it is necessary to develop high-resolution hydroclimatological datasets at finer spatial resolutions than are currently available from global analyses. This study uses a regional climate model (RCM) to develop a hydroclimatological dataset for hydrologic and ecological application in the Western Arctic. The fifth-generation Penn State–NCAR Mesoscale Model (MM5) forced by global reanalysis products at the boundaries is used to perform 12 yr of simulation (1990 through 2001) over the Western Arctic. An analysis that compares the RCM simulations with independent observationally derived data sources is conducted to evaluate the temporal and spatial distribution of the mean states, variability, and trends during the period of simulation. The RCM simulation of sea level pressure agrees well with the reanalysis in terms of mean states, seasonality, and interannual variability. The RCM also simulates major spatial patterns of the observed climatology of surface air temperature (SAT), but RCM SAT is generally colder in the summertime and warmer in the wintertime in comparison with other datasets. Although there are biases in the mean state of SAT, the RCM simulations of the seasonal and interannual variability of SAT are similar to variability in observationally derived datasets. The RCM also simulates general spatial patterns of observed rainfall, but the modeled mean state of precipitation is characterized by large biases relative to observationally derived datasets. In particular, the RCM tends to overestimate coastal region precipitation but underestimates precipitation in the interior of the Western Arctic. The Arctic terrestrial surface climate trends for the period of 1992 to 2001 of the RCM are similar to those derived from observations, with sea level pressure decreasing 0.15 hPa decade⁻¹, SAT increasing 0.10°C decade⁻¹, and precipitation decreasing slightly in the RCM simulations. In summary, the RCM dataset produced in this study represents an improvement over data currently available from large-scale global reanalysis and provides a consistent meteorological forcing dataset for hydrologic and ecological applications.

Abstract

Applied meteorology is an important and rapidly growing field. This chapter concludes the three-chapter series of this monograph describing how meteorological information can be used to serve society’s needs while at the same time advancing our understanding of the basics of the science. This chapter continues along the lines of Part II of this series by discussing ways that meteorological and climate information can help to improve the output of the agriculture and food-security sector. It also discusses how agriculture alters climate and its long-term implications. It finally pulls together several of the applications discussed by treating the food–energy–water nexus. The remaining topics of this chapter are those that are advancing rapidly with more opportunities for observation and needs for prediction. The study of space weather is advancing our understanding of how the barrage of particles from other planetary bodies in the solar system impacts Earth’s atmosphere. Our ability to predict wildland fires by coupling atmospheric and fire-behavior models is beginning to impact decision-support systems for firefighters. Last, we examine how artificial intelligence is changing the way we predict, emulate, and optimize our meteorological variables and its potential to amplify our capabilities. Many of these advances are directly due to the rapid increase in observational data and computer power. The applications reviewed in this series of chapters are not comprehensive, but they will whet the reader’s appetite for learning more about how meteorology can make a concrete impact on the world’s population by enhancing access to resources, preserving the environment, and feeding back into a better understanding how the pieces of the environmental system interact.

Abstract

The 2010 Development Test Environment Experiment (DTE10) took place from 28 January to 29 March 2010 in the Detroit, Michigan, metropolitan area for the purposes of collecting and evaluating mobile data from vehicles. To examine the quality of these data, over 239 000 air temperature and atmospheric pressure observations were obtained from nine vehicles and were compared with a weather station set up at the testing site. The observations from the vehicles were first run through the NCAR Vehicle Data Translator (VDT). As part of the VDT, quality-checking (QCh) tests were applied; pass rates from these tests were examined and were stratified by meteorological and nonmeteorological factors. Statistics were then calculated for air temperature and atmospheric pressure in comparison with the weather station, and the effects of different meteorological and nonmeteorological factors on the statistics were examined. Overall, temperature measurements showed consistent agreement with the weather station, and there was little impact from the QCh process or stratifications—a result that demonstrated the feasibility of collecting mobile temperature observations from vehicles. Atmospheric pressure observations were less well matched with surface validation, the degree of which varied with the make and model of vehicle. Therefore, more work must be done to improve the quality of these observations if atmospheric pressure from vehicles is to be useful.