We have all become conscious of just how much our lives depend on changing weather patterns, and how much global climate models are essential predicting and assessing the impacts of climate change across large areas. In fact, global models can simulate the earth’s climate hundreds of years into the future, and have been used to evaluate climate impacts on water, air temperature, human health, extreme precipitation, wildfire, agriculture, snowfall, and other applications.
However, both global climate models and models that have been downscaled to increase the data’s spatial resolution, analogous to increasing the number of pixels used in a digital image have proven to be inaccurate at local and regional levels. As a result, researchers note that makes them insufficient for modeling of potential climate impacts on small watersheds, such as those in the mountainous northeastern United States, which are a critical socioeconomic resource for Vermont, New York, New Hampshire, Maine and southern Quebec.
To remedy this problem, a team from Dartmouth, the University of Vermont and Columbia University has developed a new method to project future climate scenarios at the local level by generating high-resolution climate datasets for assessing local climate change impacts on the Lake Champlain basin in Vermont, including changes in water quantity and quality flowing into the Lake, itself, as part of a National Science Foundation-funded project to help create policies on land use and management to reduce toxic algal blooms caused by nutrient pollution there.
According to their report in the Journal of Hydrometeorology they accomplished this by finding the relationships between temperature and elevation and between precipitation and elevation, and then using those relationships to create a high-resolution temperature and precipitation dataset from a relatively coarse-resolution dataset and high-resolution elevation data. The method is particularly suited to the northeast although it can be any mountainous or hilly area with a reasonable number of weather stations measuring temperature and precipitation.
“Compared to weather station observations, our high-resolution dataset better captures both temperature and precipitation, especially in cases where there is a large error in the coarse-resolution dataset and the elevation adjustment is large,” stated lead author Jonathan Winter, an assistant professor of geography whose research explores climate prediction and the impacts of climate variability and change on water resources and agriculture. “Improved climate datasets at higher resolutions make assessments of climate variability and climate change impacts both more accurate and more location specific.”