Optimizing Seasonal‐To‐Decadal Analog Forecasts With a Learned Spatially‐Weighted Mask

Abstract

Seasonal‐to‐decadal climate prediction is crucial for decision‐making in a number of industries, but forecasts on these timescales have limited skill. Here, we develop a data‐driven method for selecting optimal analogs for seasonal‐to‐decadal analog forecasting. Using an interpretable neural network, we learn a spatially‐weighted mask that quantifies how important each grid point is for determining whether two climate states will evolve similarly. We show that analogs selected using this weighted mask provide more skillful forecasts than analogs that are selected using traditional spatially‐uniform methods. This method is tested on two prediction problems using the Max Planck Institute for Meteorology Grand Ensemble: multi‐year prediction of North Atlantic sea surface temperatures, and seasonal prediction of El Niño Southern Oscillation. This work demonstrates a methodical approach to selecting analogs that may be useful for improving seasonal‐to‐decadal forecasts and understanding their sources of skill.

Improving the forecast quality of near-term climate projections by constraining internal variability based on decadal predictions and observations

“…based on temporal or spatial analogues of ocean temperature variability (Befort et al 2020, Mahmood et al 2021, 2022. Other methods have been developed to derive seasonal to decadal climate predictions by constraining large climate model ensembles based on model analogues that represent the state of climate variability (Ding et al 2018, Rader and Barnes 2023. All these methods make use of existing climate simulations to derive the climate predictions, and therefore do not involve substantial additional computational cost for providing initialised climate simulations beyond the decadal prediction horizon.…”