Spectral (interior) nudging is a way of constraining a model to be more consistent with observed behavior. However, such control over model behavior raises concerns over how much nudging may affect unforced variability and extremes. Strong nudging may reduce or filter out extreme events since nudging pushes the model toward a relatively smooth, large-scale state. The question then becomes: what is the minimum spectral nudging needed to correct biases while not limiting the simulation of extreme events? To determine this, case studies were performed using a six-member ensemble of the Pan-Arctic Weather Research and Forecasting model (WRF) with varying spectral nudging strength, using WRF’s standard nudging as a reference point. Two periods were simulated, one in a cold season (January 2007) and one in a warm season (July 2007).

Precipitation and 2-m temperature were analyzed to determine how changing spectral nudging strength impacts temperature and precipitation extremes and selected percentiles. Results suggest that there is a marked lack of sensitivity to varying degrees of nudging. Moreover, given that nudging is an artificial forcing applied in the model, an outcome of this work is that nudging strength can be considerably smaller than the WRF standard strength and still produce climate simulations that are much better than using no nudging.