scholarly journals The northern hemisphere circumglobal teleconnection in a seasonal forecast model and its relationship to European summer forecast skill

2018 ◽  
Vol 52 (5-6) ◽  
pp. 3759-3771 ◽  
Author(s):  
Jonathan D. Beverley ◽  
Steven J. Woolnough ◽  
Laura H. Baker ◽  
Stephanie J. Johnson ◽  
Antje Weisheimer
2021 ◽  
Author(s):  
Jonathan D. Beverley ◽  
Steven J. Woolnough ◽  
Laura H. Baker ◽  
Stephanie J. Johnson ◽  
Antje Weisheimer ◽  
...  

AbstractThe circumglobal teleconnection (CGT) is an important mode of circulation variability, with an influence across many parts of the northern hemisphere. Here, we examine the excitation mechanisms of the CGT in the ECMWF seasonal forecast model, and the relationship between the Indian summer monsoon (ISM), the CGT and the extratropical northern hemisphere circulation. Results from relaxation experiments, in which the model is corrected to reanalysis in specific regions, suggest that errors over northwest Europe are more important in inhibiting the model skill at representing the CGT, in addition to northern hemisphere skill more widely, than west-central Asia and the ISM region, although the link between ISM precipitation and the extratropical circulation is weak in all experiments. Thermal forcing experiments in the ECMWF model, in which a heating is applied over India, suggest that the ISM does force an extratropical Rossby wave train, with upper tropospheric anticyclonic anomalies over east Asia, the North Pacific and North America associated with increased ISM heating. However, this eastward-propagating branch of the wave train does not project into Europe, and the response there occurs largely through westward-propagating Rossby waves. Results from barotropic model experiments show a response that is highly consistent with the seasonal forecast model, with similar eastward- and westward-propagating Rossby waves. This westward-propagating response is shown to be important in the downstream reinforcement of the wave train between Asia and North America.


2020 ◽  
Author(s):  
Lisa Degenhardt ◽  
Gregor Leckebusch ◽  
Adam Scaife

<p>Severe Atlantic winter storms are affecting densely populated regions of Europe (e.g. UK, France, Germany, etc.). Consequently, different parts of the society, financial industry (e.g., insurance) and last but not least the general public are interested in skilful forecasts for the upcoming storm season (usually December to March). To allow for a best possible use of steadily improved seasonal forecasts, the understanding which factors contribute to realise forecast skill is essential and will allow for an assessment whether to expect a forecast to be skilful or not.</p><p>This study analyses the predictability of the seasonal forecast model of the UK MetOffice, the GloSea5. Windstorm events are identified and tracked following Leckebusch et al. (2008) via the exceedance of the 98<sup>th</sup> percentile of the near surface wind speed.</p><p>Seasonal predictability of windstorm frequency in comparison to observations (based e.g., on ERA5 reanalysis) are calculated and different statistical methods (skill scores) are compared.</p><p>Large scale patterns (e.g., NAO, AO, EAWR, etc.) and dynamical factors (e.g., Eady Growth Rate) are analysed and their predictability is assessed in comparison to storm frequency forecast skill. This will lead to an idea how the forecast skill of windstorms is depending on the forecast skill of forcing factors conditional to the phase of large-scale variability modes. Thus, we deduce information, which factors are most important to generate seasonal forecast skill for severe extra-tropical windstorms.</p><p>The results can be used to get a better understanding of the resulting skill for the upcoming windstorm season.</p>


2012 ◽  
Vol 25 (9) ◽  
pp. 3155-3172 ◽  
Author(s):  
T. Jung ◽  
M. J. Miller ◽  
T. N. Palmer ◽  
P. Towers ◽  
N. Wedi ◽  
...  

The sensitivity to the horizontal resolution of the climate, anthropogenic climate change, and seasonal predictive skill of the ECMWF model has been studied as part of Project Athena—an international collaboration formed to test the hypothesis that substantial progress in simulating and predicting climate can be achieved if mesoscale and subsynoptic atmospheric phenomena are more realistically represented in climate models. In this study the experiments carried out with the ECMWF model (atmosphere only) are described in detail. Here, the focus is on the tropics and the Northern Hemisphere extratropics during boreal winter. The resolutions considered in Project Athena for the ECMWF model are T159 (126 km), T511 (39 km), T1279 (16 km), and T2047 (10 km). It was found that increasing horizontal resolution improves the tropical precipitation, the tropical atmospheric circulation, the frequency of occurrence of Euro-Atlantic blocking, and the representation of extratropical cyclones in large parts of the Northern Hemisphere extratropics. All of these improvements come from the increase in resolution from T159 to T511 with relatively small changes for further resolution increases to T1279 and T2047, although it should be noted that results from this very highest resolution are from a previously untested model version. Problems in simulating the Madden–Julian oscillation remain unchanged for all resolutions tested. There is some evidence that increasing horizontal resolution to T1279 leads to moderate increases in seasonal forecast skill during boreal winter in the tropics and Northern Hemisphere extratropics. Sensitivity experiments are discussed, which helps to foster a better understanding of some of the resolution dependence found for the ECMWF model in Project Athena.


2021 ◽  
Author(s):  
Lisa Degenhardt ◽  
Gregor C. Leckebusch ◽  
Adam A. Scaife

<p>The seasonal forecast of extreme events is gaining more and more interest in science, for stakeholders and the general public. The most important extreme events with a seasonal variance for Europe, including the British Isles, are winter windstorms.</p><p>This study is investigating the prediction of seasonal accumulated storm frequency and intensity based on one state-of-the-art seasonal forecast model, the UK Met Office (GloSea5 GC2) and analyses the dynamical and physical reasons for skill.</p><p>Winter (DJF) windstorm events are individually identified and tracked using 10m wind speed once exceeding the local 98<sup>th</sup> percentile. The intensity of the season is calculated via an integrated measure based on the Storm Severity Index (Leckebusch et al., 2008).  Thus, the total seasonal intensity is investigated as grid cell accumulated index over all storm events and as storm count normalised sum. The forecast skill is assessed via different skill measures (e.g. Kendall-Correlation or RPSS) and validated in a hindcast approach with ERA5 for 23 seasons (1993-2015).</p><p>This presentation will give an overview about three main topic areas: the prediction skill for storm frequency and intensity; a multi-linear regression analysis to identify dominant large-scale modes, and finally, an outlook on first results on chosen dynamical parameters influencing the skill.</p><p>This investigation shows significant positive correlations over the British Isles for all three different storm parameters (frequency and both intensity measures). The positive skill pattern of the storm intensity is shifted north-west-wards compared to the positive skill in the storm frequency results. The accumulated intensity shows slightly higher correlations as the storm frequency. The normalised intensity reveals the lowest skills but still significant values downstream of the British Isles. Hence, three different storm parameters show positive prediction over UK; pure frequency, pure intensity and a combined measure of intensity and frequency.</p><p>Additionally to the model skill investigation, a regression analysis based on the three dominant teleconnection patterns over Europe (NAO, SCA and EA) was performed in order to gain better understanding in the connection of storms and these modes. This regression predicts the three storm parameters out of the given indices and explains up to 40-50% of variance. A statistical-model based approach of the storm parameters using three large-scale modes is showing improvements in skill compared to previous studies with NAO as only predictor. But the forecast model output shows still the best storm predictions.</p><p>Further studies will investigate the dynamical and physical reasons of the skill and their connections between the windstorm parameters, the dominant large-scale modes, and other atmospheric parameters.</p>


2020 ◽  
Vol 146 (733) ◽  
pp. 4055-4066 ◽  
Author(s):  
Christopher H. O'Reilly ◽  
Antje Weisheimer ◽  
David MacLeod ◽  
Daniel J. Befort ◽  
Tim Palmer

2017 ◽  
Vol 44 (11) ◽  
pp. 5729-5738 ◽  
Author(s):  
Christopher H. O'Reilly ◽  
James Heatley ◽  
Dave MacLeod ◽  
Antje Weisheimer ◽  
Tim N. Palmer ◽  
...  

2021 ◽  
Author(s):  
Nicola Cortesi ◽  
Verónica Torralba ◽  
Llorenó Lledó ◽  
Andrea Manrique-Suñén ◽  
Nube Gonzalez-Reviriego ◽  
...  

AbstractIt is often assumed that weather regimes adequately characterize atmospheric circulation variability. However, regime classifications spanning many months and with a low number of regimes may not satisfy this assumption. The first aim of this study is to test such hypothesis for the Euro-Atlantic region. The second one is to extend the assessment of sub-seasonal forecast skill in predicting the frequencies of occurrence of the regimes beyond the winter season. Two regime classifications of four regimes each were obtained from sea level pressure anomalies clustered from October to March and from April to September respectively. Their spatial patterns were compared with those representing the annual cycle. Results highlight that the two regime classifications are able to reproduce most part of the patterns of the annual cycle, except during the transition weeks between the two periods, when patterns of the annual cycle resembling Atlantic Low regime are not also observed in any of the two classifications. Forecast skill of Atlantic Low was found to be similar to that of NAO+, the regime replacing Atlantic Low in the two classifications. Thus, although clustering yearly circulation data in two periods of 6 months each introduces a few deviations from the annual cycle of the regime patterns, it does not negatively affect sub-seasonal forecast skill. Beyond the winter season and the first ten forecast days, sub-seasonal forecasts of ECMWF are still able to achieve weekly frequency correlations of r = 0.5 for some regimes and start dates, including summer ones. ECMWF forecasts beat climatological forecasts in case of long-lasting regime events, and when measured by the fair continuous ranked probability skill score, but not when measured by the Brier skill score. Thus, more efforts have to be done yet in order to achieve minimum skill necessary to develop forecast products based on weather regimes outside winter season.


2018 ◽  
Vol 31 (2) ◽  
pp. 655-670 ◽  
Author(s):  
YuJia You ◽  
Xiaojing Jia

The interannual variations and the prediction of the leading two empirical orthogonal function (EOF) modes of spring (April–May) precipitation over China for the period from 1951 to 2014 are investigated using both observational data and the seasonal forecast made by six coupled climate models. The leading EOF mode of spring precipitation over China (EOF1-prec) features a monosign pattern, with the maximum loading located over southern China. The ENSO-related tropical Pacific SST anomalies in the previous winter can serve as a precursor for EOF1-prec. The second EOF mode of spring precipitation (EOF2-prec) over China is characterized by a dipole structure, with one pole near the Yangtze River and the other one with opposite sign over the Pearl River delta. A North Atlantic sea surface temperature (SST) anomaly dipole in the preceding March is found contribute to the prec-EOF2 and can serve as its predictor. A physics-based empirical (P-E) model is then formulated using the two precursors revealed by the observational analysis to forecast the variations of EOF1-prec and EOF2-prec. Compared to coupled climate models, which have little skill in forecasting the time variations of the two EOF modes, this P-E model can significantly improve the forecast skill of their time variations. A linear regression model is further established using the time series forecast by the P-E model to forecast the spring precipitation over China. Results suggest that the seasonal forecast skill of the spring precipitation over southeastern China, especially over the Yangtze River area, can be significantly improved by the regression model.


2013 ◽  
Vol 6 (2) ◽  
pp. 98-102 ◽  
Author(s):  
M. Sigmond ◽  
J. F. Scinocca ◽  
V. V. Kharin ◽  
T. G. Shepherd

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