The logic behind the approach starts with an inventory of what each information source can and cannot deliver. Non-initialized historical simulations, such as the 163 CMIP6 transient runs the team used, capture the forced response to greenhouse gases and aerosols but scatter widely around it because each member carries its own random realization of internal variability. Initialized decadal predictions, of which the study used 92 hindcasts from the CMIP6 Decadal Climate Prediction Project, are nudged toward the observed state of the ocean and atmosphere at their start date, which should in principle phase-lock the simulated modes of variability with the real ones. In practice, however, their skill often fades after a few years, and they suffer from initialization shock — a drift caused by the mismatch between biased models and the observed states they are fed. Observations alone can also constrain projections, as earlier work by Bonnet and others showed when selecting ensemble members using observed proxies of the Atlantic meridional overturning circulation. What had not been tried until now was blending all three sources in a single, computationally cheap framework.

The method, which the authors call BLEND, works as a two-stage member selection from the pool of historical simulations. In the first stage, each of the 163 historical runs is scored against an observational metric over a calibration window — typically twenty years — preceding the forecast start date. The similarity is quantified by combining normalized root-mean-square error and correlation, and the fifty best-matching simulations are retained. In the second stage, those fifty candidates are further filtered down to thirty by checking which ones come closest to the ensemble-mean temperature that the initialized decadal hindcasts actually predict for the forecast window over the region of interest. The result is a constrained subset of projections that agrees both with what the real climate has recently done and with what the prediction systems say it is about to do. Because the selection operates on non-initialized simulations, the method sidesteps the drift problem that plagues decadal forecasts by construction, while still harvesting their information.

A crucial design question is which observational metric to use as the first-stage constraint. The team tested four candidate indices, each chosen for its documented influence on European climate at annual to multidecadal timescales. The Atlantic Multidecadal Variability index captures basin-wide North Atlantic sea surface temperature fluctuations after removing the externally forced signal, using a ten-year low-pass Lanczos filter. The subpolar gyre index tracks SSTs over the 50–60° N, 15–40° W region, a hotspot where decadal predictability is known to be high. A global index measures nine-year pattern correlations between simulated and observed sea surface temperature fields worldwide, following earlier constraining studies. Finally, the winter North Atlantic Oscillation index, computed as the pressure difference between broad southern and northern boxes over the Atlantic, probes the dominant mode of atmospheric circulation variability. Observational references came from the NOAA ERSSTv5 dataset for the ocean indices and the ERA5 reanalysis for the NAO.

To test whether the blending actually helps, the researchers ran a retrospective evaluation every year from 1966 to 2000, producing five-, ten-, and fifteen-year forecasts of summer and winter surface temperature averaged over three IPCC reference regions: Northern Europe, West Central Europe, and the Mediterranean. They compared spread, defined as the range between the minimum and maximum ensemble members, and absolute error of the ensemble mean against the ERA5 observations. Three skill scores anchored the assessment: the mean squared skill score for deterministic performance, and the ranked probability skill score and continuous ranked probability skill score for probabilistic skill, all benchmarked against the full unconstrained historical ensemble. Critically, the team used a resampling test — drawing one thousand random thirty-member subsets from the historical pool — to verify that any improvement was not just an artifact of working with a smaller ensemble. A result counted as significant only when it beat the 95th percentile of those random draws.

The headline finding is that the constrained forecasts significantly narrow the uncertainty range. Nearly all observation-constrained subsets showed a pronounced spread reduction relative to both the full historical ensemble and the decadal hindcasts, across regions, seasons, and lead times, and the double-constrained BLEND subsets reduced spread even further, particularly at the five-year horizon. In an illustrative case, a five-year summer temperature forecast for West Central Europe starting in 1983 saw the historical ensemble predict −0.09 ± 0.81 °C while the blended forecast landed at −0.23 °C with a tighter spread and an ensemble mean closer to the observed value. The decadal hindcasts, by contrast, showed spread and error statistics broadly similar to the unconstrained projections, a disappointing result the authors attribute to possible initialization shocks, structural model biases such as a southward-shifted NAO pattern, and the fact that European decadal temperature variability is intrinsically weak compared to chaotic atmospheric noise.

Regional and seasonal texture matters enormously in the results. Over West Central Europe in summer, both the observation-only and blended forecasts using regional surface temperature as the predictor delivered large, statistically significant improvements at all three forecast horizons, and the blended versions generally outscored their observation-only counterparts — direct evidence that adding decadal prediction information pays off there. A spatial case study showed the advantage extending into Germany and Poland for ten-year summer forecasts. Over Northern Europe, significant gains appeared for five- and ten-year summer forecasts, and blended forecasts using the global SST pattern index improved fifteen-year predictions. In the Mediterranean in winter, the Atlantic Multidecadal Variability-based blends captured part of the low-frequency variability, consistent with the known role of the AMV in modulating the region’s decadal climate, though the regional gains were more modest and concentrated over parts of Spain and the Maghreb. Notably, the second, region-specific selection step could improve skill over the targeted region while degrading it elsewhere, a trade-off the authors flag for anyone applying the method.

The study is equally candid about failures and caveats. The NAO-based constraint performed poorly over Northern Europe despite the well-established physical teleconnection, possibly because CMIP-class models misplace the NAO’s centers of action or underestimate its decadal variability, or because a twenty-year calibration window is too long for an index with short decorrelation times. Over the Mediterranean in summer, where anthropogenic forcing dominates, none of the blended subsets beat random ensemble subsets in absolute error. The authors also stress that conclusions can flip depending on which skill score is used: improvements in the ensemble mean sometimes vanished once the full probability distribution was penalized for overconfidence, which is why they recommend deploying multiple complementary metrics aligned with the end user’s decision context. The optimal predictor, they conclude, varies by region and must be evaluated case by case.

The broader significance lies in what the framework makes possible. It runs at very low computational cost, requires no new simulations, and produces seamless climate information that stays consistent from the historical record through the coming decades — exactly the property adaptation planners need when they cannot afford a discontinuity between what has been observed and what is forecast. The authors suggest natural extensions to climate extremes such as heatwaves and droughts, and to other regions where a variable exhibits genuine multi-annual to decadal internal variability with identifiable large-scale drivers. If those extensions succeed, the humble act of cherry-picking the right simulations — guided jointly by what the real world has done and what initialized models expect — could become a standard tool for turning the noisiest part of the climate forecast into actionable, regionally tailored information.

Subject of Research: Reducing near-term regional climate forecast uncertainty by constraining climate model ensembles with combined observational and decadal prediction information

Article Title: Reduction of uncertainty in near-term climate forecast by combining observations and decadal predictions

Article References: Bonnet, R., Boé, J., Sanchez-Gomez, E., & Cassou, C. (2026). Reduction of uncertainty in near-term climate forecast by combining observations and decadal predictions. Earth System Dynamics, 17(5), 1299-1314. https://doi.org/10.5194/esd-17-1299-2026

Image Credits: AI Generated

DOI: 10.5194/esd-17-1299-2026

Keywords: decadal climate prediction, internal climate variability, CMIP6, climate projections, member selection, Europe, Atlantic Multidecadal Variability, North Atlantic Oscillation, forecast uncertainty, adaptation planning, Earth System Dynamics, ensemble forecasting