Improved Teleconnection‐Based Dynamical Seasonal Predictions of Boreal Winter

Dobrynin, Mikhail; Domeisen, Daniela I. V.; Müller, Wolfgang A.; Bell, Louisa; Brune, Sebastian; Bunzel, Felix; Düsterhus, André; Fröhlich, Kristina; Pohlmann, Holger; Baehr, Johanna

doi:10.1002/2018gl077209

Cited by 66 publications

(126 citation statements)

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“…Seasonal (DJF) means of sea-level pressure (SLP) were used from the region limited to 20 • N and 90 • N latitudes and 90 • W and 60 • E longitudes. In this study, we use the ERA-Interim NAO as a reference, and the ensemble-based seasonal prediction of NAO from Dobrynin et al (2018), normalized by their respective standard deviations. In this study, we use the ERA-Interim NAO as a reference, and the ensemble-based seasonal prediction of NAO from Dobrynin et al (2018), normalized by their respective standard deviations.…”

Section: Nao Index and Subsamplingmentioning

confidence: 99%

“…The EOF-based NAO index is represented by the principal component of the leading EOF of SLP (Kutzbach, 1970). Dobrynin et al (2018) reported a subsampling method, which considers well-established NAO teleconnection such as links between the October sea surface temperature in the North Atlantic, snow depth in Eurasia, the stratospheric air temperature in the Northern Hemisphere, September sea ice volume in the Arctic, and the subsequent winter NAO. We define years of strongly positive NAO + and negative NAO − phases as years with anomalies of the NAO that exceed the mean NAO value by ±1 standard deviation.…”

Section: Nao Index and Subsamplingmentioning

confidence: 99%

“…In this study, we use the ERA-Interim NAO as a reference, and the ensemble-based seasonal prediction of NAO from Dobrynin et al (2018), normalized by their respective standard deviations. Therefore, following Dobrynin et al (2018), we apply subsampling to the dynamical ensemble of ocean waves WAM-30. A mean NAO value over the entire period of the simulation is used as a reference for anomalies calculation.…”

Section: Nao Index and Subsamplingmentioning

confidence: 99%

“…For example, the 10-member seasonal prediction system based on the mixed resolution Coupled Model Intercomparison Project Phase 5 version of the Max Planck Institute for Meteorology Earth system model (MPI-ESM-MR; Giorgetta et al, 2013) shows a correlation (prediction skill) between observed and forecasted NAO index of 0.43. In the recent study by Dobrynin et al (2018) this MPI-ESM-MR ensemble was extended to 30 members, where the prediction skill is improved to 0.52. Moreover, Dobrynin et al (2018) suggested a teleconnection-based subsampling approach that increased the winter NAO prediction skill to up to 0.86.…”

Section: Introductionmentioning

confidence: 99%

“…Gray squares represent each individual ensemble member (gray line represents ensemble mean), red-filled squares indicate selected members with the accurate representation of the winter NAO according toDobrynin et al (2018; red line indicates the subsampled ensemble mean), black line represents assimilation simulation, and blue line represents ERA-Interim. December, January, and February (DJF) mean (left column) and DJF 99th percentile (right column) of total (a, b), sea (c, d), and swell (e, f) significant wave heights.…”

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confidence: 99%

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Skilful Seasonal Prediction of Ocean Surface Waves in the Atlantic Ocean

Dobrynin

Kleine

Düsterhus

et al. 2019

Geophysical Research Letters

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Ocean surface wave height in the Atlantic Ocean is strongly influenced by the North Atlantic Oscillation (NAO). Here we demonstrate for the first time a skilful seasonal forecast for wave height in the Atlantic Ocean, produced by a seasonal prediction system with an enhanced prediction skill of winter NAO. The improved seasonal prediction skill of the wave height reaches 0.8 in major parts of the North Atlantic. Prediction skill in the Central and South Atlantic is significantly improved due to swell propagation from better represented active wave generation regions in the North Atlantic. By subsampling, the modeling of climatological anomalies of seasonal wave height for strongly positive and negative NAO phases is considerably improved. We demonstrate the potential of an improved, subsampling‐based approach for the dynamical seasonal prediction of waves, specifically for extreme seasons during strong NAO phases, which can be implemented for operational purposes.

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Section: Nao Index and Subsamplingmentioning

confidence: 99%

Section: Nao Index and Subsamplingmentioning

confidence: 99%

Section: Nao Index and Subsamplingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Skilful Seasonal Prediction of Ocean Surface Waves in the Atlantic Ocean

Dobrynin

Kleine

Düsterhus

et al. 2019

Geophysical Research Letters

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What potential for improving sub‐seasonal predictions of the winter NAO?

Kent

Scaife

Dunstone

2022

Atmospheric Science Letters

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The North Atlantic Oscillation (NAO) is the leading mode of variability across the Atlantic sector and is a key metric of extratropical forecast performance.Skilful predictions of the NAO are possible at medium-range (1-2 weeks) and seasonal time scales. However, in a leading dynamical prediction system, we find that sub-seasonal predictions (1 month NAO with a lead time of 20-30 days) are not statistically significant and represent a gap in forecast skill.In this study, we have investigated the potential for improving predictions using a large ensemble of dynamical hindcasts. First, we find that monthly predictions of the NAO are only weakly related to forecast errors at the mediumrange. This implies that improving medium-range forecast performance is unlikely to drive significant improvements at longer lead times. Second, the Madden-Julian Oscillation (MJO) is the leading mode of sub-seasonal variabil-

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Seasonal Forecasts of Winter Temperature Improved by Higher-Order Modes of Mean Sea Level Pressure Variability in the North Atlantic Sector

Dalelane

Dobrynin

Fröhlich

2020

Preprint

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The variability of the sea level pressure in the North Atlantic sector is the most important driver of weather and climate in Europe. The main mode of this variability, the North Atlantic Oscillation (NAO), explains up to 50% of the total variance. Other modes, known as the Scandinavian index, East Atlantic, and East Atlantic/West Russian pattern, complement the variability of the sea level pressure, thereby influencing the European climate. It has been shown previously that a seasonal prediction system with enhanced winter NAO skill due to ensemble subsampling entails an improved prediction of the surface climate variables as well. Here, we show that a refined subselection procedure that accounts both for the NAO index and for the three additional modes of sea level pressure variability is able to further increase the prediction skill of wintertime mean sea level pressure, near-surface temperature, and precipitation across Europe.Plain Language Summary Atmospheric winter conditions in Europe are primarily controlled by the varying pressure field over the North Atlantic, influencing temperature and precipitation in Europe. Current seasonal forecasts of European winter climate, though highly desirable for society and economy, are as yet not fully reliable. There exist a number of autumn predictors, such as sea surface and stratospheric temperature, Eurasian snow depth, and Arctic sea ice, that impact on the upcoming pressure regimes in a predictable way. The present dynamical seasonal forecast systems respond still too weakly to these known seasonal predictors. But the relationship is reproduced quite well by means of statistics. In combination, statistical and dynamical forecasts have the potential to improve forecasts of the North Atlantic pressure conditions and thereby affected variables like temperature and precipitation in Europe considerably. We extend an existing hybrid seasonal forecast procedure by considering more modes of variability of the Atlantic pressure regimes than just the North Atlantic Oscillation. In this way, we are able to improve the forecasts for temperature and precipitation over wider regions in Europe. Cohen et al. (2019) argue that new statistical techniques can increase the accuracy of seasonal forecasts and advocate the development of hybrid dynamical-statistical forecasts to produce more robust seasonal predictions. Hybrid forecasts based on circulation specification were presented, for example, by Baker, Shaffrey, and Scaife (2018) and Dobrynin et al. (2018).In boreal winter, European weather and climate is dominated by the zonal propagation of planetary and synoptic-scale waves. This large-scale circulation is an extremely high-dimensional phenomenon in real RESEARCH LETTER

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Improved Teleconnection‐Based Dynamical Seasonal Predictions of Boreal Winter

Cited by 66 publications

References 47 publications

Skilful Seasonal Prediction of Ocean Surface Waves in the Atlantic Ocean

Skilful Seasonal Prediction of Ocean Surface Waves in the Atlantic Ocean

What potential for improving sub‐seasonal predictions of the winter NAO?

Seasonal Forecasts of Winter Temperature Improved by Higher-Order Modes of Mean Sea Level Pressure Variability in the North Atlantic Sector

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