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Abstract Accurate and interpretable marine heatwave (MHW) forecasts allow decision makers and industries to plan for and respond to extreme ocean temperature events. Recent work demonstrates skillful pointwise prediction of MHWs. Here, we evaluate a method of detecting and predicting spatially connected MHW objects. We apply object‐based forecast verification to the Community Earth Systems Model Seasonal‐to‐Multiyear Large Ensemble (SMYLE) experiment, a set of initialized hindcasts with 20‐member ensembles of 24‐month simulations initialized quarterly from 1970 to 2019. We demonstrate that SMYLE predicts MHWs that occur near observed MHWs with high skill at long lead times, but with errors in location, area, and intensity that grow with lead time. SMYLE exhibits improved skill in predicting the intensity of MHWs in December and January, and worse skill from August to October. This work illustrates the capacity to forecast connected MHW objects and to quantify the uncertainty in those forecasts with potential applications for future community use.
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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.
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- PAR ID:
- 10477117
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Geophysical Research Letters
- Volume:
- 50
- Issue:
- 23
- ISSN:
- 0094-8276
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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