LONG-RANGE TEMPERATURE FORECASTING

In an approach, a computer receives an observation dataset that identifies one or more ground truth values of an environmental variable at one or more times and a reforecast dataset that identifies one or more predicted values of the environmental variable produced by a forecast model that correspond to the one or more times. The computer then trains a climatology on the observation dataset to generate an observed climatology and trains the climatology on the reforecast dataset to generate a forecast climatology. The computer identifies observed anomalies by subtracting the observed climatology from the observation dataset and forecast anomalies by subtracting the forecast climatology from the reforecast dataset. The computer then models the observed anomalies as a function of the forecast anomalies, resulting in a calibration function, which the computer can then use to calibrate new forecasts received from the forecast model.