Researcher: Vladimir Kattsov
Funding Source: NSF*
Collaborators: IARC modeling group; Prof. J. Overland and Dr. M.Wang (NOAA)
This project will be a comprehensive evaluation of state-of-the-art global climate models aimed at reducing uncertainties in arctic climate change projection.
Varying sets of strengths and weaknesses that models display lead to the conclusion that no single model can be considered the "best," and it is important to utilize results from a range of models, i.e. multi-model ensembles. There is a possibility that metrics based on observations might be used to constrain model projections of climate change, through the analysis of ensembles of model simulations. Nevertheless, a proven set of model metrics that might be used to narrow the range of plausible climate projections has yet to be developed.
Anticipated results are as follows; 1) quantification of model biases and errors based on the model inter-comparison and comparison against observations of different types of global and arctic climate simulations by about 20 state-of-the-art global models; 2) a comprehensive set of diagnostic tests and metrics based on observations to constrain model projections of climate change in the Arctic; 3) conclusion on possibilities of objective discriminating (weighting) models used in ensembles to narrow the range of plausible arctic climate projections.
The results of the project are expected to contribute to reducing (currently huge) uncertainties in arctic climate change projections, which in turn should result in reducing uncertainties in arctic climate impacts and vulnerabilities. Reducing of the above uncertainties is one of major goals of Arctic Sciences.
*An additional funding source is the Russian Foundation for Basic Research (RFBR). The RFBR project "Objective discrimination of climate models to be used in ensemble projections of regional climate change" is not specifically focused on the Arctic, but also considers a number of other regions of the Earth.