The Last Millennium Reanalysis (LMR) utilizes an ensemble methodology to assimilate paleoclimate data for the production of annually resolved climate field reconstructions of the Common Era. Two key elements are the focus of this work: the set of assimilated proxy records and the forward models that map climate variables to proxy measurements. Results based on an updated proxy database and seasonal regression-based forward models are compared to the LMR prototype, which was based on a smaller set of proxy records and simpler proxy models formulated as univariate linear regressions against annual temperature. Validation against various instrumental-era gridded analyses shows that the new reconstructions of surface air temperature and 500 hPa geopotential height are significantly improved (from 10 % to more than 100 %), while improvements in reconstruction of the Palmer Drought Severity Index are more modest. Additional experiments designed to isolate the sources of improvement reveal the importance of the updated proxy records, including coral records for improving tropical reconstructions, and tree-ring density records for temperature reconstructions, particularly in high northern latitudes. Proxy forward models that account for seasonal responses, and dependence on both temperature and moisture for tree-ring width, also contribute to improvements in reconstructed thermodynamic and hydroclimate variables in midlatitudes. The variability of temperature at multidecadal to centennial scales is also shown to be sensitive to the set of assimilated proxies, especially to the inclusion of primarily moisture-sensitive tree-ring-width records.

The character of fog in a region centered on New York City, New York, is investigated using 20 yr of historical data. Hourly surface observations are used to identify fog events at 17 locations under the influence of various physiographic features, such as land–water contrasts, land surface character (urban, suburban, and rural), and terrain. Fog events at each location are classified by fog types using an objective algorithm derived after extensive examination of fog formation processes. Events are characterized according to frequency, duration, and intensity. A quantitative assessment of the likelihood with which mechanisms leading to fog formation are occurring in various parts of the region is obtained. The spatial, seasonal, and diurnal variability of fog occurrences are examined and results are related to regional and local influences. The results show that the likelihood of fog occurrence is influenced negatively by the presence of the urban heat island of New York City, whereas it is enhanced at locations under the direct influence of the marine environment. Inland suburban and rural locations also experience a considerable amount of fog. As in other areas throughout the world, the overall fog phenomenon is a superposition of various types. Precipitation fog, which occurs predominantly in winter, is the most common type. Fog resulting from cloud-base lowering also occurs frequently across the region, with an enhanced likelihood in winter and spring. A considerable number of advection fog events occur in coastal areas, mostly during spring, whereas radiation fog occurs predominantly at suburban and rural locations during late summer and early autumn but also occurs during the warm season in the coastal plain of New Jersey as advection–radiation events.

Fog is a weather condition with significant socioeconomic impacts associated with increased hazards and constraints in road, maritime, and air traffic. While current numerical weather prediction models are able to forecast situations that are favorable to fog events, it is very difficult to determine the exact location and time of formation or dissipation. One-dimensional assimilation-forecast models have been implemented at a few airports and provide improved local predictions of fog events, but this approach is limited to specific locations. The occurrence, development, and dissipation of fog result from multiple processes (thermodynamical, radiative, dynamical, microphysical) that occur simultaneously, through a wide range of conditions, and that interact nonlinearly with each other. Hence, to advance our ability to forecast fog processes, we must gain a better understanding of how critical physical processes feed back on each other and improve their parametric representations in models. To provide a d... Fog is a weather condition with significant socioeconomic impacts associated with increased hazards and constraints in road, maritime, and air traffic. While current numerical weather prediction models are able to forecast situations that are favorable to fog events, it is very difficult to determine the exact location and time of formation or dissipation. One-dimensional assimilation-forecast models have been implemented at a few airports and provide improved local predictions of fog events, but this approach is limited to specific locations. The occurrence, development, and dissipation of fog result from multiple processes (thermodynamical, radiative, dynamical, microphysical) that occur simultaneously, through a wide range of conditions, and that interact nonlinearly with each other. Hence, to advance our ability to forecast fog processes, we must gain a better understanding of how critical physical processes feed back on each other and improve their parametric representations in models. To provide a d...