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ABSTRACT Snow cover amount and duration have a large role in both the natural and anthropogenic systems of the Northeast United States. Any changes in the climatology of the amount, duration, and timing of the snowpack may have long-lasting environmental and economic consequences. At present, the principal tools used to examine future climatic changes are general circulation models; however, they do not provide information at the scales required for investigating many of the impacts in which snow plays a major role. In order to address this gap, a statistical downscaling methodology for simulating climatological snow cover parameters was developed. The methodology utilizes the Statistical DownScaling Model to construct climate change scenarios at daily time scales that are subsequently evaluated based on their ability to reproduce seasonal snow cover statistics. Two different observational datasets (station and gridded) were used as predictands and empirical relationships were created between the predictands and regional scale predictors. The methodology was tested at 20 locations across the Northeast and was then applied to output from HadCM3 under the A2 and B2 emissions scenarios. The methodology performed well at capturing key properties of station-based snow cover over a range of climates and was found to perform better than the technique used in previous work in the Northeast. By the end of the century, the projections revealed characteristics that are consistent with declining snow cover, yet there are likely to be regional variations in the next several decades, especially when elevation is considered. [ABSTRACT FROM AUTHOR]

Accurate estimates of local changes in extreme precipitation are valuable for informing local policy decisions and estimating potential impacts on areas such as health, infrastructure, ecosystems and agriculture. To bridge the gap between the coarse spatial resolution of climate model output and the need for weather and climate information at a higher resolution, downscaling methods have been developed. This research compares several different downscaling methods in their ability to simulate rainfall extremes over the Northeastern United States. The methods compared are a bias-correction and spatial disaggregation technique, the statistical downscaling model (SDSM) and a regional climate model, HadRM3. Validation was done using generalized extreme value (GEV) distributions of annual maxima and return-period estimates. All the different techniques simulated reasonable estimates of extreme rainfall across the northeast. However, the agreement of simulations with observations depended upon the downscaling technique used and the location. The SDSM simulations matched observed extreme climatology the best. HadRM3 tended to overestimate mean precipitation and gave a GEV distribution that did not replicate the observed distribution well and, thus, overestimated the extremes. We then developed high-resolution projections of future changes to rainfall across the Northeast United States, using IPCC SRES emission scenario A2 combined with these downscaling methods. Future projections based on SDSM indicate increases in the magnitude of extreme rainfall events by ∼7% through the 2041-2060 period with considerable regional variation particularly at the higher 50- and 100-year return periods. However, these projected increases rarely fall outside the 95% confidence intervals based on the observation period from 1961 to 1980. Copyright © 2010 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

No Abstract available. [ABSTRACT FROM AUTHOR]

To assess the influence of global climate change at the regional scale, we examine past and future changes in key climate, hydrological, and biophysical indicators across the US Northeast (NE). We first consider the extent to which simulations of twentieth century climate from nine atmosphere-ocean general circulation models (AOGCMs) are able to reproduce observed changes in these indicators. We then evaluate projected future trends in primary climate characteristics and indicators of change, including seasonal temperatures, rainfall and drought, snow cover, soil moisture, streamflow, and changes in biometeorological indicators that depend on threshold or accumulated temperatures such as growing season, frost days, and Spring Indices (SI). Changes in indicators for which temperature-related signals have already been observed (seasonal warming patterns, advances in high-spring streamflow, decreases in snow depth, extended growing seasons, earlier bloom dates) are generally reproduced by past model simulations and are projected to continue in the future. Other indicators for which trends have not yet been observed also show projected future changes consistent with a warmer climate (shrinking snow cover, more frequent droughts, and extended low-flow periods in summer). The magnitude of temperature-driven trends in the future are generally projected to be higher under the Special Report on Emission Scenarios (SRES) mid-high (A2) and higher (A1FI) emissions scenarios than under the lower (B1) scenario. These results provide confidence regarding the direction of many regional climate trends, and highlight the fundamental role of future emissions in determining the potential magnitude of changes we can expect over the coming century. [ABSTRACT FROM AUTHOR]