Seasonal prediction of the boreal winter stratosphere

Portal, Alice; Ruggieri, Paolo; Palmeiro, Froila M.; Garcı́a-Serrano, Javier; Domeisen, Daniela I.V.; Gualdi, Silvio

doi:10.1007/s00382-021-05787-9

Cited by 20 publications

(41 citation statements)

References 113 publications

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“…A potential reason for the lack of a predictable signal in NAO− could lie in the role of stratospheric sudden warmings (SSWs), which are known to induce negative NAO states (Baldwin and Dunkerton, 2001;Hitchcock and Simpson, 2014;Domeisen et al, 2020). Portal et al (2021) have shown that seasonal forecast models, including the SEAS5 model studied here, tend to overpredict the SSW response to ENSO. Consistent with that, there is a strong NAO− response to ENSO (Figure 8), which is not seen in observations.…”

Section: Regimementioning

confidence: 88%

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

Falkena

Wiljes

Weisheimer

et al. 2021

Quart J Royal Meteoro Soc

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To study the forced variability of atmospheric circulation regimes, the use of model ensembles is often necessary for identifying statistically significant signals as the observed data constitute a small sample and are thus strongly affected by the noise associated with sampling uncertainty. However, the regime representation is itself affected by noise within the atmosphere, which can make it difficult to detect robust signals. To this end we employ a regularised k-means clustering algorithm to better identify the signal in a model ensemble. The approach allows for the identification of six regimes for the wintertime Euro-Atlantic sector and leads to more pronounced regime dynamics, compared to results without regularisation, both overall and on sub-seasonal and interannual time-scales. We find that sub-seasonal variability in the regime occurrence rates is mainly explained by changes in the seasonal cycle of the mean climatology. On interannual time-scales relations between the occurrence rates of the regimes and the El Niño Southern Oscillation (ENSO) are identified. The use of six regimes captures a more detailed response of the circulation to ENSO compared to the common use of four regimes. Predictable signals in occurrence rate on interannual time-scales are found for the two zonal flow regimes, namely a regime consisting of a negative geopotential height anomaly over the Norwegian Sea and Scandinavia, and the positive phase of the NAO. The signal strength for these regimes is comparable between observations and model, in contrast to that of the NAO-index where the signal strength in the observations is underestimated by a factor of 2 in the model. Our regime analysis suggests that this signal-to-noise problem for the NAO-index is primarily related to those atmospheric flow patterns associated with the negative NAO-index as we find poor predictability for the corresponding NAO− regime.

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Section: Regimementioning

confidence: 88%

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

Falkena

Wiljes

Weisheimer

et al. 2021

Quart J Royal Meteoro Soc

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“…Note that since the figure is composited based on initializations, the probabilities for March include verification times in April, and so the corresponding probabilities for easterly winds are likely influenced by final warmings. Regardless, this seasonal cycle is only partially reproduced in the subseasonal models, which particularly underestimate the probability of events for December and January initializations at weeks 3 and 4, and of in January, and seasonal prediction models, which also fail to reproduce the SSW peak in January (Portal et al, 2021) despite the seasonal average of SSWs often being well reproduced (Domeisen et al, 2015). Interestingly, the NCEP system consistently predicts a higher occurrence of easterly winds than other systems.…”

Section: Northern Hemisphere Polar Vortex Variabilitymentioning

confidence: 94%

“…Indeed, tropospheric biases identified independently of the stratosphere can sometimes be traced back to stratospheric anomalies in subseasonal forecasts; in the ECMWF extended-range prediction system, the persistence of the NAO was found to be constrained too strongly by the state of the polar vortex at initialization (Kolstad et al, 2020). Since stratospheric variability and extreme events are primarily governed by wave-mean flow interactions, biases in wave driving due to either resolved or parameterized processes can lead to biases in the mean state of the stratosphere, which can further alter its response to subsequent forcing (McLandress et al, 2012;Richter et al, 2014) and limit stratospheric predictability (Portal et al, 2021). More fundamentally, a model's vertical resolution in the stratosphere can influence its ability to realistically represent the stratosphere.…”

Section: Introductionmentioning

confidence: 99%

“…Subseasonal forecast systems that poorly represent tropospheric stationary waves also tend to have low vertical resolution in the stratosphere, connected to larger stratospheric biases (Schwartz et al, 2021). On seasonal timescales, surface prediction skill can also in part be traced back to model properties such as vertical resolution in the stratosphere (Butler et al, 2016;Portal et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

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Quantifying stratospheric biases and identifying their potential sources in subseasonal forecast systems

Lawrence

Ábalos

Ayarzagüena

et al. 2022

Preprint

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Abstract. The stratosphere can be a source of predictability for surface weather on timescales of several weeks to months. However, the potential predictive skill gained from stratospheric variability can be limited by biases in the representation of stratospheric processes and the coupling of the stratosphere with surface climate in forecast systems. This study provides a first systematic identification of model biases in the stratosphere across a wide range of subseasonal forecast systems. It is found that many of the forecast systems considered exhibit warm global mean temperature biases from the lower to middle stratosphere, too strong/cold wintertime polar vortices, and too cold extratropical upper troposphere/lower stratosphere regions. Furthermore, tropical stratospheric anomalies associated with the Quasi-Biennial Oscillation tend to decay toward each system's climatology with lead time. In the Northern Hemisphere (NH), most systems do not capture the seasonal cycle of extreme vortex event probabilities, with an underestimation of sudden stratospheric warming events and an overestimation of strong vortex events in January. In the Southern Hemisphere (SH), springtime interannual variability of the polar vortex is generally underestimated, but the timing of the final breakdown of the polar vortex often happens too early in many of the prediction systems. These stratospheric biases tend to be considerably worse in systems with lower model lid heights. In both hemispheres, most systems with low-top atmospheric models also consistently underestimate the upward wave driving that affects the strength of the stratospheric polar vortex. We expect that the biases identified here will help guide model development for sub-seasonal to seasonal forecast systems, and further our understanding of the role of the stratosphere for predictive skill in the troposphere.

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“…9c), our analysis reveals that the snow cover anomaly reinforces the Ural ridge, allowing for increased wave flux into the stratosphere. This location of a tropospheric ridge interferes constructively with climatological stationary wave-1 and wave-2 patterns (Garfinkel et al, 2010) and seems to be key for a skilled forecast of the polar winter stratosphere (Portal et al, 2021).…”

Section: Discussionmentioning

confidence: 94%

Impact of Eurasian autumn snow on the winter North Atlantic Oscillation in seasonal forecasts of the 20th century

Wegmann

Orsolini

Weisheimer

et al. 2021

Weather Clim. Dynam.

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Abstract. As the leading climate mode of wintertime climate variability over Europe, the North Atlantic Oscillation (NAO) has been extensively studied over the last decades. Recently, studies highlighted the state of the Eurasian cryosphere as a possible predictor for the wintertime NAO. However, missing correlation between snow cover and wintertime NAO in climate model experiments and strong non-stationarity of this link in reanalysis data are questioning the causality of this relationship. Here we use the large ensemble of Atmospheric Seasonal Forecasts of the 20th Century (ASF-20C) with the European Centre for Medium-Range Weather Forecasts model, focusing on the winter season. Besides the main 110-year ensemble of 51 members, we investigate a second, perturbed ensemble of 21 members where initial (November) land conditions over the Northern Hemisphere are swapped from neighboring years. The Eurasian snow–NAO linkage is examined in terms of a longitudinal snow depth dipole across Eurasia. Subsampling the perturbed forecast ensemble and contrasting members with high and low initial snow dipole conditions, we found that their composite difference indicates more negative NAO states in the following winter (DJF) after positive west-to-east snow depth gradients at the beginning of November. Surface and atmospheric forecast anomalies through the troposphere and stratosphere associated with the anomalous positive snow dipole consist of colder early winter surface temperatures over eastern Eurasia, an enhanced Ural ridge and increased vertical energy fluxes into the stratosphere, with a subsequent negative NAO-like signature in the troposphere. We thus confirm the existence of a causal connection between autumn snow patterns and subsequent winter circulation in the ASF-20C forecasting system.

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Seasonal prediction of the boreal winter stratosphere

Cited by 20 publications

References 113 publications

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

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

Quantifying stratospheric biases and identifying their potential sources in subseasonal forecast systems

Impact of Eurasian autumn snow on the winter North Atlantic Oscillation in seasonal forecasts of the 20th century

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