There are two major challenges to improving interannual to decadal forecasts: (a) consistently initializing the coupled system so that variability is not dominated by initial imbalances, and (b) having a large sample of different initial conditions on which to test forecast skill. The second challenge requires consideration of time periods not only outside the recent period of intensive ocean observation, but also before the instrumental era, which increases the importance of the first challenge. Forecasts prior to the 1850s isolate internally generated sources of variability by removing the majority of anthropogenic forcing, and the sparse observational record during this time period motivates the use of paleoclimate proxy data. We address these issues by using a linear inverse model (LIM) approach and a recent proxy‐based reconstruction over the last millennium at annual resolution. The reconstruction is used to train, initialize, and validate LIM forecasts. The LIM trained on paleo‐data assimilated using a LIM trained on global climate model (GCM) simulation data outperforms a LIM trained on raw GCM data at forecast leads longer than 2 years for in‐sample experiments, and beyond 4‐year leads in most out‐of‐sample experiments validated on instrumental data. The most skillful normal mode of the paleo‐data LIM for the instrumental experiment represents a persistent pattern with a longer decay time than for the GCM‐LIM's modes, which accounts for the outperformance at longer leads. The paleo‐data LIM is consequently more sensitive to ocean initialization, which is reflected in forecasts during the instrumental era where ocean reanalyses exhibit large uncertainty.
Skillfully predicting the North Atlantic Oscillation (NAO), and the closely related northern annular mode (NAM), on ‘subseasonal’ (weeks to less than a season) timescales is a high priority for operational forecasting centers, because of the NAO’s association with high-impact weather events, particularly during winter. Unfortunately, the relatively fast, weather-related processes dominating total NAO variability are unpredictable beyond about two weeks. On longer timescales, the tropical troposphere and the stratosphere provide some predictability, but they contribute relatively little to total NAO variance. Moreover, subseasonal forecasts are only sporadically skillful, suggesting the practical need to identify the fewer potentially predictable events at the time of forecast. Here we construct an observationally based linear inverse model (LIM) that predicts when, and diagnoses why, subseasonal NAO forecasts will be most skillful. We use the LIM to identify those dynamical modes that, despite capturing only a fraction of overall NAO variability, are largely responsible for extended-range NAO skill. Predictable NAO events stem from the linear superposition of these modes, which represent joint tropical sea-surface temperature-lower stratosphere variability plus a single mode capturing downward propagation from the upper stratosphere. Our method has broad applicability because both the LIM and the state-of-the-art European Centre for Medium-Range Weather Forecasts Integrated Forecast System (IFS) have higher (and comparable) skill for the same set of predicted high skill forecast events, suggesting that the low-dimensional predictable subspace identified by the LIM is relevant to real-world subseasonal NAO predictions.
more » « less- Award ID(s):
- 1756958
- NSF-PAR ID:
- 10361720
- Publisher / Repository:
- IOP Publishing
- Date Published:
- Journal Name:
- Environmental Research Letters
- Volume:
- 16
- Issue:
- 4
- ISSN:
- 1748-9326
- Page Range / eLocation ID:
- Article No. 044024
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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