Improving reporting of uncertainties in sea level rise assessments

Sea level rise (SLR) assessments are commonly used to identify the extent that coastal populations are at risk to flooding. However, the data and assumptions used to develop these assessments contain numerous sources and types of uncertainty, which limit confidence in the accuracy of modeled results. This study illustrates how the intersection of uncertainty in digital elevation models (DEMs) and SLR lead to a wide range of modeled outcomes. SLR assessments are then reviewed to identify the extent that uncertainty is documented in peer-reviewed articles. The paper concludes by discussing priorities needed to further understand SLR impacts. (PDF contains 4 pages)

Otto Kelland’s Model Dories and the Digital Archives Initiative

Otto Kelland was a truly unique individual in Newfoundland. During his long life he had several careers from being a prison superintendant to being an instructor at Marine Institute. During his life Kelland made hundreds of wooden boat models. They are beautifuly hand-crafted and represented the type of watercraft used by fishermen in Newfoundland. The collection of boat models made by Otto Kelland and owned by Marine Institute made an ideal object to be digitalized. In particular the collection of dories was an ideal group to be digitized. They were housed in one cabinet and accompanied by hand-written documents describing each model. The Digital Archives Initiative (DAI) is a “gateway to the learning and research-based cultural resources held by Memorial University of Newfoundland and partnering organizations.” The DAI hosts a variety of collections which together reinforce the importance, past and present, of Newfoundland and Labrador's history and culture. I will give an oral presentation of the project followed by a demonstration of the Otto Kelland Dories exhibit on the Digital Archives Initiative (DAI) at Memorial University of Newfoundland. I will be happy to answer questions following my presentation.

A multi-basin seasonal streamflow model for the Sierra Nevada

Linear regression models are constructed to predict seasonal runoff by fitting streamflow to temperature, precipitation, and snow water content across a range of elevations. The models are quite successful in capturing the differences in discharge between different elevation watersheds and their interannual variations. This exercise thus provides insight into seasonal changes in streamflow at different elevation watersheds that might occur under a changed climate.

A stochastic model of temporal variations in monthly temperature, precipitation, snowfall, and resulting snowpack

We report a Monte Carlo representation of the long-term inter-annual variability of monthly snowfall on a detailed (1 km) grid of points throughout the southwest. An extension of the local climate model of the southwestern United States (Stamm and Craig 1992) provides spatially based estimates of mean and variance of monthly temperature and precipitation. The mean is the expected value from a canonical regression using independent variables that represent controls on climate in this area, including orography. Variance is computed as the standard error of the prediction and provides site-specific measures of (1) natural sources of variation and (2) errors due to limitations of the data and poor distribution of climate stations. Simulation of monthly temperature and precipitation over a sequence of years is achieved by drawing from a bivariate normal distribution. The conditional expectation of precipitation. given temperature in each month, is the basis of a numerical integration of the normal probability distribution of log precipitation below a threshold temperature (3°C) to determine snowfall as a percent of total precipitation. Snowfall predictions are tested at stations for which long-term records are available. At Donner Memorial State Park (elevation 1811 meters) a 34-year simulation - matching the length of instrumental record - is within 15 percent of observed for mean annual snowfall. We also compute resulting snowpack using a variation of the model of Martinec et al. (1983). This allows additional tests by examining spatial patterns of predicted snowfall and snowpack and their hydrologic implications.

Analysis of four decades of high elevation climate data

Four decades of instrumented climate records at D1 on Niwot Ridge suggest that high elevation data are an important - and even unique - part of the full climate picture. High elevation data provide information on changing lapse rates as well as model verification for global warming, which is predicted to occur earliest in high latitudes and at high elevations. The D1 records show climatic trends that arguably support global warming, assuming that greater planetary wave amplitude is verification of warming. Lapse rates reflect conditions of air mass stability, atmospheric moisture, and could [sic] cover, which contribute to feedback processes involving temperature, precipitation, and snowpack. The D1 record show a period, 1981-1985, when the lapse rate increased significantly, and this change was not detected by other data.