Ensemble Forecasts: Probabilistic Seasonal Forecasts Based on a Model Ensemble

• Published in: Climate
• Main Authors: Hannah Aizenman, Michael Grossberg, Nir Krakauer, Irina Gladkova
• Format: Text
• Language: English
• Published: Multidisciplinary Digital Publishing Institute 2016
• Subjects: seasonal forecasting; hindcast; surface temperature; information gain; bias correction; climate change; Arctic; Climate change
• Online Access: https://doi.org/10.3390/cli4020019

Ensembles of general circulation model (GCM) integrations yield predictions for meteorological conditions in future months. Such predictions have implicit uncertainty resulting from model structure, parameter uncertainty, and fundamental randomness in the physical system. In this work, we build probabilistic models for long-term forecasts that include the GCM ensemble values as inputs but incorporate statistical correction of GCM biases and different treatments of uncertainty. Specifically, we present, and evaluate against observations, several versions of a probabilistic forecast for gridded air temperature 1 month ahead based on ensemble members of the National Centers for Environmental Prediction (NCEP) Climate Forecast System Version 2 (CFSv2). We compare the forecast performance against a baseline climatology based probabilistic forecast, using average information gain as a skill metric. We find that the error in the CFSv2 output is better represented by the climatological variance than by the distribution of ensemble members because the GCM ensemble sometimes suffers from unrealistically little dispersion. Lack of ensemble spread leads a probabilistic forecast whose variance is based on the ensemble dispersion alone to underperform relative to a baseline probabilistic forecast based only on climatology, even when the ensemble mean is corrected for bias. We also show that a combined regression based model that includes climatology, temperature from recent months, trend, and the GCM ensemble mean yields a probabilistic forecast that outperforms approaches using only past observations or GCM outputs. Improvements in predictive skill from the combined probabilistic forecast vary spatially, with larger gains seen in traditionally hard to predict regions such as the Arctic.