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Degree-days are a temperature index used for understanding the impact of climate change. Different methods to deal with climate model biases, termed bias correction or more generally calibration, yield different projections of such indices, something not widely understood for temperature indices in many impact sectors. An analytical expression is derived for the expected value of degree-days given parameters of the underlying statistical distribution (assumed to be Gaussian). It is demonstrated that the uncertainty introduced by calibration methodology is driven by the magnitude of the nonlinearity in this expression. In a climate where mean temperature is, and remains, far from (approximately three standard deviations) the threshold used in defining the index, the equation is approximately linear, and methodological choice makes little difference relative to the absolute number of degree-days. However, case studies for U.K. cities London and Glasgow for heating and cooling degree-days (HDD and CD...

What is the Uncertainty in Degree-Day Projections due to Different Calibration Methodologies?