Detection of interannual ensemble forecast signals over the North Atlantic and Europe using atmospheric circulation regimes

Falkena, Swinda; Wiljes, Jana de; Weisheimer, Antje; Shepherd, Theodore G.

doi:10.1002/qj.4213

Cited by 10 publications

(16 citation statements)

References 53 publications

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“…From k-means clustering an estimate of the regime dynamics is known, which is characterised by the climatological regime frequencies P c and transition probabilities T c i j between the regimes. For SEAS5 these are given by (Falkena et al, 2022) Starting from the regime probabilities at time t − 1, a best estimate of the prior probabilities for the next time step is…”

Section: Methodsmentioning

confidence: 99%

“…The regimes for the SEAS5 hindcast ensemble are shown in Figure 2 and are the two phases of the North Atlantic Oscillation (NAO), the Atlantic Ridge (AR), Scandinavian Blocking (SB) and both their counterparts. Note that these regimes are slightly different in their patterns from those of ERA-Interim (see Falkena et al (2022) for details on this), thereby providing an inherent bias correction between the model and reanalysis. These hard, categorical, regime assignments are used to compute the likelihood functions that are used in the Bayesian approach, for which a detailed discussion is given in Section 3.1.…”

Section: Data and Clusteringmentioning

confidence: 99%

“…To see whether the found response to ENSO for some regimes also reflects a predictable signal in the observations we regress the ERA-Interim interannual variability onto the SEAS5 one, as in Falkena et al (2022). The results for this, looking at the sequential and ensemble Bayesian approach, are shown in Table 1.…”

Section: Interannual Variabilitymentioning

confidence: 99%

“…Hannachi and O'Neill, 2001;Smyth et al, 1999), but are not widely used. In studies that look into forecasting of regimes on sub-seasonal timescales, the probability of being in a regime is often considered by looking at the empirical distribution of the (hard) regime assignment across an ensemble (Vigaud et al, 2018;Cortesi et al, 2021;Büeler et al, 2021;Falkena et al, 2022). A limitation of this method is that it requires availability of ensemble data, where typically the ensemble size is small, and verification is done against a hard regime assignment from reanalysis.…”

Section: Introductionmentioning

confidence: 99%

“…an ENSO index or by making use of the availability of ensemble data. In a previous study a regularised clustering method helped to identify a more pronounced interannual regime signal by making use of the information available in an ensemble (Falkena et al, 2022). Similarly, having a more informative prior for Bayes Theorem (1), incorporating information from external processes, can help in identifying a stronger non-stationary regime signal.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

A Bayesian Approach to Atmospheric Circulation Regime Assignment

Falkena¹,

Wiljes²,

Weisheimer³

et al. 2022

Preprint

Self Cite

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The standard approach when studying atmospheric circulation regimes and their dynamics is to use a hard, categorical, regime assignment. That is, each atmospheric state is assigned to the regime it is closest to in distance. However, this may not always be the most appropriate approach as the regime assignment may be affected by small deviations in the distance to the regimes due to noise. To mitigate this we develop a probabilistic regime assignment using Bayes theorem. Bayes theorem tells us that the probability of a regime given the data can be determined by combining climatological likelihood with prior information. The regime probabilities at time t can be used to inform the prior probabilities at time t + 1, which then is used to sequentially update the regime probabilities. We apply this approach to both reanalysis data and a seasonal hindcast ensemble incorporating knowledge of the transition probabilities between regimes. Furthermore, making use of the signal present within the ensemble to better inform the prior probabilities allows for identifying more pronounced interannual variability. The signal within the interannual variability of wintertime North Atlantic circulation regimes is assessed using both a categorical and regression approach, with the strongest signals found during very strong El Niño years.

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Section: Methodsmentioning

confidence: 99%

Section: Data and Clusteringmentioning

confidence: 99%

Section: Interannual Variabilitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Bayesian Approach to Atmospheric Circulation Regime Assignment

Falkena¹,

Wiljes²,

Weisheimer³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

A hypothesis on ergodicity and the signal‐to‐noise paradox

Brener

2024

Atmospheric Science Letters

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This letter raises the possibility that ergodicity concerns might have some bearing on the signal‐to‐noise paradox. This is explored by applying the ergodic theorem to the theory behind ensemble weather forecasting and the ensemble mean. Using the ensemble mean as our best forecast of observations amounts to interpreting it as the most likely phase‐space trajectory, which relies on the ergodic theorem. This can fail for ensemble forecasting systems if members are not perfectly exchangeable with each other, the averaging window is too short and/or there are too few members. We argue these failures can occur in cases such as the winter North Atlantic Oscillation (NAO) forecasts due to intransitivity or regime behaviour for regions such as the North Atlantic and Arctic. This behaviour, where different ensemble members may become stuck in different relatively persistent flow states (intransitivity) or multi‐modality (regime behaviour), can in certain situations break the ergodic theorem. The problem of non‐ergodic systems and models in the case of weather forecasting is discussed, as are potential mitigation methods and metrics for ergodicity in ensemble systems.

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Robustness of hydrometeorological extremes in surrogated seasonal forecasts

Klehmet,

Berg,

Bozhinova

et al. 2024

Intl Journal of Climatology

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Water and disaster risk management require accurate information about hydrometeorological extremes. However, estimation of rare events using extreme value analysis is hampered by short observational records, with large resulting uncertainties. Here, we present a surrogate world setup that makes use of data samples from meteorological and hydrological seasonal re‐forecasts to explore extremes for long return periods. The surrogate timeseries allow us to pool the re‐forecasts into 1000‐year‐long timeseries. We can then calculate return values of extremes and explore how they are affected by the size of sub‐samples as method for estimating the uncertainty. The approach relies on the fact that probabilistic seasonal re‐forecasts, initialized with perturbed initial conditions, have limited predictive skill with increasing lead time. At long lead times re‐forecasts will diverge into independent samples. The meteorological seasonal re‐forecasts are taken from the SEAS5 system, and hydrological re‐forecasts are generated with the E‐HYPE process‐based model for the pan‐European domain. Extreme value analysis is applied to annual maxima of precipitation and streamflow for return periods of 100 years. The analysis clearly demonstrates the large uncertainty in long return period estimates with typical available samples of only few decades. The uncertainty is somewhat reduced for 100‐year samples, but several 100 years seem to be necessary to have robust estimates. The bootstrap with replacement approach is applied to shorter timeseries, and is shown to well reproduce the uncertainty range of the longer samples. However, the main estimate of the return value can be significantly offset. Although the method is model based, with the associated uncertainties and bias compared to the real world, the surrogate approach is likely useful to explore rare and compounding extremes.

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Detection of interannual ensemble forecast signals over the North Atlantic and Europe using atmospheric circulation regimes

Cited by 10 publications

References 53 publications

A Bayesian Approach to Atmospheric Circulation Regime Assignment

A Bayesian Approach to Atmospheric Circulation Regime Assignment

A hypothesis on ergodicity and the signal‐to‐noise paradox

Robustness of hydrometeorological extremes in surrogated seasonal forecasts

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