Forget global climate models, 'local climate models' to predict change are the next big thing

From the “climate is just global weather on a local scale” department:

Dartmouth-Led Team Develops Method to Predict Local Climate Change

HANOVER, N.H. – Feb. 18, 2016 – Global climate models are essential for climate prediction and assessing the impacts of climate change across large areas, but a Dartmouth College-led team has developed a new method to project future climate scenarios at the local level.

The method can be used in any mountainous or hilly area with a reasonable number of weather stations measuring temperature and precipitation.

The findings appear in the Journal of Hydrometeorology. The team includes researchers from Dartmouth, the University of Vermont and Columbia University.

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. But both global climate models — and global models that have been downscaled to increase the data’s spatial resolution, analogous to increasing the number of pixels used in a digital image — aren’t accurate at local and regional levels. 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 address this limitation, the researchers developed a method to generate 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 Lake Champlain.  They did 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.

“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,” says 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.”

This work is 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 in Lake Champlain.

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Development and Evaluation of High-Resolution Climate Simulations over the Mountainous Northeastern United States

Abstract

The mountain regions of the Northeastern US are a critical socioeconomic resource for Vermont, New York State, New Hampshire, Maine, and Southern Quebec. While global climate models (GCMs) are important tools for climate change risk assessment at regional scales, even the increased spatial resolution of statistically downscaled GCMs (commonly ~1/8°) is not sufficient for hydrologic, ecologic, and land-use modeling of small watersheds within the mountainous Northeast. To address this limitation, we develop an ensemble of topographically downscaled, high-resolution (30”), daily 2-m maximum air temperature, 2-m minimum air temperature, and precipitation simulations for the mountainous Northeast by applying an additional level of downscaling to intermediately downscaled (1/8°) data using high-resolution topography and station observations. We first derive observed relationships between 2-m air temperature and elevation, and precipitation and elevation. Then, these relationships are combined with spatial interpolation to enhance the resolution of intermediately downscaled GCM simulations. The resulting topographically downscaled dataset is analyzed for its ability to reproduce station observations. We find that topographic downscaling adds value to intermediately downscaled maximum and minimum 2-m air temperature at high elevation stations, as well as moderately improves domain-averaged maximum and minimum 2-m air temperature. Topographic downscaling improves mean precipitation but not daily probability distributions of precipitation. Overall, we show that the utility of topographic downscaling is dependent on the initial bias of the intermediately downscaled product and the magnitude of the elevation adjustment. As the initial bias or elevation adjustment increase, more value is added to the topographically downscaled product.