Seasonal Predictability of the North Atlantic Oscillation and Zonal Mean Fields Associated with Stratospheric Influence in JMA/MRI-CPS2

Saito, Naoaki; Maeda, Shuhei; Nakaegawa, Tosiyuki; Takaya, Yuhei; Imada, Yukiko; Matsukawa, Chihiro

doi:10.2151/sola.2017-038

Cited by 9 publications

(17 citation statements)

References 27 publications

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“…This implies predictability of European and North American winter climate out to a season or even longer ahead. 20,[22][23][24][25][26][27][28][29] All of these studies use ensembles and create an ensemble mean model prediction M to reduce the level of unpredictable noise:…”

Section: Introductionmentioning

confidence: 99%

“…RPC values above 1 were not generally expected, but this second possibility has been considered 32 and examples have now been found in a number of different ensemble seasonal predictions, particularly in winter predictions of the NAO and Arctic Oscillation. 21,23,25,28,29 For example, in the seasonal forecasts of the NAO reported by Scaife et al, 23 the predictable ensemble mean signal was around 2 hPa, the total ensemble variability was around 8 hPa and the correlation was around 0.6 so the RPC = 0.6/(2./8.) > 2.…”

Section: Introductionmentioning

confidence: 99%

“…Note that independent sets of ensemble predictions give a similar result 26 and other climate models show similar effects in their predictions of the NAO and AO. 22,25,28,29 Note also that practical calculations of the RPC are expected to be underestimates of the true value. 20 This is because any practical ensemble is finite in size and so the correlation with observations (r mo ) will likely be lower than that of an infinite ensemble.…”

Section: Introductionmentioning

confidence: 99%

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A signal-to-noise paradox in climate science

2018

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We review the growing evidence for a widespread inconsistency between the low strength of predictable signals in climate models and the relatively high level of agreement they exhibit with observed variability of the atmospheric circulation. This discrepancy is particularly evident in the climate variability of the Atlantic sector, where ensemble predictions using climate models generally show higher correlation with observed variability than with their own simulations, and higher correlations with observations than would be expected from their small signal-to-noise ratios, hence a 'signal-to-noise paradox'. This unusual behaviour has been documented in multiple climate prediction systems and in the response to a number of different sources of climate variability. However, we also note that the total variance in the models is often close in magnitude to the observed variance, and so it is not a simple matter of models containing too much variability. Instead, the proportion of Atlantic climate variance that is predictable in climate models appears to be too weak in amplitude by a factor of two, or perhaps more. In this review, we provide a range of examples from existing studies to build the case for a problem that is common across different climate models, common to several different sources of climate variability and common across a range of timescales. We also discuss the wider implications of this intriguing paradox.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A signal-to-noise paradox in climate science

2018

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“…Recent studies further showed that if a dynamical prediction system is capable of capturing the relation of the winter AO to the sea ice, SST and snow cover, it can predict a portion of the AO's variability (Riddle et al, 2013;Scaife et al, 2014;Sun and Ahn, 2015). Furthermore, some other researches demonstrated that the stratosphere is also one of the sources of the predictability and contributes to the enhancement of the predictive skill of the AO/North Atlantic Oscillation (NAO) in winter (Sigmond et al, 2013;Scaife et al, 2014;Scaife et al, 2016;Saito et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Potential contribution of winter dominant atmospheric mode over the mid‐latitude Eurasia to the prediction of subsequent spring Arctic Oscillation

Han

Sun

2019

Intl Journal of Climatology

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Spring Arctic Oscillation (AO) has been revealed to exert a great impact on the Northern Hemispheric climate; however, its seasonal prediction skill has been limited so far. In this paper, we find that the dominant mode of the winter 500-hPa geopotential height anomalies over the mid-latitude Eurasia has a close connection with the succeeding spring AO. The analysis on the physical processes indicates that the troposphere-stratosphere interaction plays an important role in the connection between the two. This predominant atmospheric mode in the mid-latitude Eurasia during winter can change the vertical wave activity and lead to an anomalous stratospheric polar vortex. Thereafter, the signal of the anomalous stratospheric polar vortex propagates downward to troposphere in the subsequent spring, consequently inducing the anomalous AO pattern in the troposphere. The results in this study imply that this previous winter leading atmospheric mode over the mid-latitude Eurasia may supply a potential source for a spring AO's prediction.

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“…Numerous studies have focused on NAO seasonal prediction [21][22][23][24][25]. Saito et al [26] found that the predictive skill of the NAO increases from late winter to early spring in relation to the high predictability of the zonal mean geopotential height in the stratosphere. The year-to-year increment method has shown advantages in predictions of the winter NAO [21,27].…”

Section: Introductionmentioning

confidence: 99%

Why is the North Atlantic Oscillation More Predictable in December?

Tian

Fan

2019

Atmosphere

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The prediction skill of the Climate Forecast System, version 2 (CFSv2), for the North Atlantic Oscillation (NAO) is evaluated in three winter months (December, January, and February). The results show that the CFSv2 model can skillfully predict the December NAO one month in advance. There are two main contributors to NAO predictability in December. One is the predictability of the relationship between the North Atlantic sea surface temperature anomaly (SSTA) tripole and the NAO and the other is the second empirical orthogonal function (EOF) mode of the geopotential height at 50 hPa (Z50-EOF2). The relationship between the NAO and SSTA tripole index in December is the most significant in the three winter months. The significant monthly differences of surface heat fluxes in December over the whole North Atlantic are favorable for promoting the interaction between the NAO and North Atlantic SSTAs, in addition to improving the predictability of the December NAO. When the NAO is in a positive phase, easterly anomalies are located at the low and high latitudes and westerly anomalies prevail in the mid-latitudes of the troposphere. The correlation between the December Z50-EOF2 and zonal-mean zonal wind anomalies shows a similar spatial structure to that for the NAO. The possible reason why the CFSv2 model can predict the December NAO one month ahead is that it can reasonably reproduce the relationship between the December NAO and both the North Atlantic SST and stratospheric circulation.

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Seasonal Predictability of the North Atlantic Oscillation and Zonal Mean Fields Associated with Stratospheric Influence in JMA/MRI-CPS2

Cited by 9 publications

References 27 publications

A signal-to-noise paradox in climate science

A signal-to-noise paradox in climate science

Potential contribution of winter dominant atmospheric mode over the mid‐latitude Eurasia to the prediction of subsequent spring Arctic Oscillation

Why is the North Atlantic Oscillation More Predictable in December?

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