Skillful high-resolution seasonal climate forecasts using model-analogs

Seasonal climate forecasts provide important decision support tools that help stakeholders manage a variety of socioeconomically-relevant resources, including food, water, energy, and infrastructure. As a result, there has been a concerted effort in recent years to develop, evaluate, and maintain seasonal forecasting systems. Despite these advances, however, high-resolution climate modeling in support of operational forecasting efforts remains a computational challenge. Here, we use the model-analog technique to overcome computational bottlenecks associated with model resolution and data availability, generating a suite of high-resolution ocean (0.1˚) and atmosphere (0.25˚) reforecasts at 1-12 month leads from an existing high-resolution global climate simulation—CESM-HR. In our model-analog framework, we compare past observed climate states to the CESM-HR data library, with the best matches retained as “analogs”. The subsequent model evolution of each analog is then treated as a forecast. Without the computational constraints of traditional initialized forecasting, we are able to produce large forecast ensembles (>20 members) and evaluate the skill of important climate variables that are not usually prioritized in operational forecasting (such as subsurface ocean temperature). We show that high-resolution model-analog ocean forecasts of surface and subsurface variables in the California Current System (CCS) match, and in some cases even exceed, the skill of similar high-resolution initialized forecasts derived from regional models, but at a fraction of the computational cost. High-resolution model-analog forecasts of precipitation over the Continental United States (CONUS) are similarly impressive. Our research highlights the model-analog technique as an extremely cost-effective method for generating skillful, high-resolution seasonal climate forecasts in support of operational management strategies.