Developments in synoptic climatology in the 1990s included advances in traditional synoptic climatology, empirical downscaling, and dynamical downscaling (i.e. regional climate modelling). The research emphasis in traditional, empirical-statistical approaches to synoptic climatology shifted from methodological development to applications of widely accepted classification techniques, including manual, correlation-based, eigenvector-based, compositing and indexing schemes. In contrast, most efforts in empirical downscaling, which became a well-established field of synoptic climatology during the 1990s, were directed to model development; applications were of secondary concern. Similarly, regional climate models (RCMs) burst onto the scene during the decade and focused on model development, although important progress was made in linking or coupling RCMs to regional or local surface climate systems. This paper discusses prospects for the future of traditional synoptic climatology, empirical downscaling and regional climate modelling. It concludes by looking at the present role of geographic information system (GIS) concepts in synoptic climatology and the potential future role of GIS to the field.

The variability of winter precipitation across the western United States has important implications for a wide range of physical and socioeconomic systems. While El Niño‐Southern Oscillation (ENSO) teleconnections explain a high degree of interannual variance in western U.S. winter precipitation, their influence on decadal time scales is less well understood. In this study, we examine the relationship between ENSO conditions and winter precipitation in the western U.S. within the context of decadal‐scale variability, as represented by phasing of the Pacific Decadal Oscillation (PDO). We identify spatial inconsistencies in the ENSO‐precipitation relationship, commensurate with PDO phase shifts, which take the form of a ‘dipole’ signature across the western U.S. This finding has implications for the knowledge of uncertainty of ENSO teleconnections, and may prove meaningful for users of climate information throughout the region.

Humans experience climate variability and climate change primarily through changes in weather at local and regional scales. One of the most effective means to track these changes is through detailed analysis of meteorological data. In this work, monthly and seasonal trends in recent winter climate of the northeastern United States (NE‐US) are documented. Snow cover and snowfall are important components of the region's hydrological systems, ecosystems, infrastructure, travel safety, and winter tourism and recreation. Temperature, snowfall, and snow depth data were collected from the merged United States Historical Climate Network (USHCN) and National Climatic Data Center Cooperative Network (COOP) data set for the months of December through March, 1965–2005. Monthly and seasonal time series of snow‐covered days (snow depth >2.54 cm) are constructed from daily snow depth data. Spatial coherence analysis is used to address data quality issues with daily snowfall and snow depth data, and to remove stations with nonclimatic influences from the regional analysis. Monthly and seasonal trends in mean, minimum, and maximum temperature, total snowfall, and snow‐covered days are evaluated over the period 1965–2005, a period during which global temperature records and regional indicators exhibit a shift to warmer climate conditions. NE‐US regional winter mean, minimum, and maximum temperatures are all increasing at a rate ranging from 0.42° to 0.46°C/decade with the greatest warming in all three variables occurring in the coldest months of winter (January and February). The regional average reduction in number of snow‐covered days in winter (−8.9 d/decade) is also greatest during the months of January and February. Further analysis with additional regional climate modeling is required to better investigate the causal link between the increases in temperature and reduction in snow cover during the coldest winter months of January and February. In addition, regionally averaged winter snowfall has decreased by about 4.6 cm/decade, with the greatest decreases in snowfall occurring in December and February. These results have important implications for the impacts of regional climate change on the northeastern United States hydrology, natural ecosystems, and economy.

The development of a statistical modeling technique suitable for producing mean and interannual gridded climate datasets for a topographically varying domain is undertaken. Stepwise regression models at 1 × 1 km resolution are generated to estimate mean winter temperature and precipitation for the Southwest United States for the years . Topographic predictor variables are used to explain spatial variance in the datasets. Kriging and inverse distance weighting interpolation algorithms are utilized to account for model residuals. The final regression models show a high degree of explained variance for temperature (R 2 = 0.98, mean bias error [MBE] = -0.15°C, rootmean-squared error [RMSE] = 0.74°C) and a moderate degree of explained variance for precipitation (R 2 = 0.63, MBE = -1.4 mm, RMSE = 27.0 mm). Several smaller-scale precipitation regression models are developed for comparison to the domain-wide model, but do not show marked accuracy improvements. Observed values of winter temperature and precipitation from the years 1961-1999 are compared to the 30 yr modeled means, and the differences are interpolated using kriging (temperature) and inverse distance weighting (precipitation). The result is a 39 yr time series of maps and datasets of winter temperature and precipitation at 1 × 1 km resolution for the Southwest United States.

During 2012, flash drought developed and subsequently expanded across large areas of the Central United States (US) with severe impacts to overall water resources and warm-season agricultural production. Recent efforts have yielded a methodology to detect and quantify flash drought occurrence and rate of intensification from climatological datasets via the standardized evaporative stress ratio (SESR). This study utilizes the North American Regional Reanalysis and applied the SESR methodology to quantify the spatial and temporal development and expansion of flash drought conditions during 2012. Critical results include the identification of the flash drought epicenter and subsequent spread of flash drought conditions radially outward with varying rates of intensification. Further, a comparison of the SESR analyses with surface-atmosphere coupling metrics demonstrated that a hostile environment developed across the region, which limited the formation of deep atmospheric convection, exacerbated evaporative stress, and perpetuated flash drought development and enhanced its radial spread across the Central US.