scholarly journals European Climate Extremes and the North Atlantic Oscillation

2008 ◽  
Vol 21 (1) ◽  
pp. 72-83 ◽  
Author(s):  
Adam A. Scaife ◽  
Chris K. Folland ◽  
Lisa V. Alexander ◽  
Anders Moberg ◽  
Jeff R. Knight
Keyword(s):  
North Atlantic Oscillation ◽  
North Atlantic ◽  
General Circulation ◽  
Climate Model ◽  
Circulation Model ◽  
Extreme Weather Events ◽  
The North ◽  
Weather Events ◽  
The North Atlantic ◽  
The North Atlantic Oscillation

Abstract The authors estimate the change in extreme winter weather events over Europe that is due to a long-term change in the North Atlantic Oscillation (NAO) such as that observed between the 1960s and 1990s. Using ensembles of simulations from a general circulation model, large changes in the frequency of 10th percentile temperature and 90th percentile precipitation events over Europe are found from changes in the NAO. In some cases, these changes are comparable to the expected change in the frequency of events due to anthropogenic forcing over the twenty-first century. Although the results presented here do not affect anthropogenic interpretation of global and annual mean changes in observed extremes, they do show that great care is needed to assess changes due to modes of climate variability when interpreting extreme events on regional and seasonal scales. How changes in natural modes of variability, such as the NAO, could radically alter current climate model predictions of changes in extreme weather events on multidecadal time scales is also discussed.

scholarly journals North Atlantic Oscillation Response to Anomalous Indian Ocean SST in a Coupled GCM

2005 ◽  
Vol 18 (24) ◽  
pp. 5382-5389 ◽  
Author(s):  
Jürgen Bader ◽  
Mojib Latif
Keyword(s):  
Indian Ocean ◽  
North Atlantic Oscillation ◽  
General Circulation Model ◽  
North Atlantic ◽  
General Circulation ◽  
Circulation Model ◽  
Atlantic Sector ◽  
The North ◽  
The Indian Ocean ◽  
The North Atlantic

Abstract The dominant pattern of atmospheric variability in the North Atlantic sector is the North Atlantic Oscillation (NAO). Since the 1970s the NAO has been well characterized by a trend toward its positive phase. Recent atmospheric general circulation model studies have linked this trend to a progressive warming of the Indian Ocean. Unfortunately, a clear mechanism responsible for the change of the NAO could not be given. This study provides further details of the NAO response to Indian Ocean sea surface temperature (SST) anomalies. This is done by conducting experiments with a coupled ocean–atmosphere general circulation model (OAGCM). The authors develop a hypothesis of how the Indian Ocean impacts the NAO.

Evaluation of the North Atlantic Oscillation as simulated by a coupled climate model

1999 ◽  
Vol 15 (9) ◽  
pp. 685-702 ◽  
Author(s):  
T. J. Osborn ◽  
K. R. Briffa ◽  
S. F. B. Tett ◽  
P. D. Jones ◽  
R. M. Trigo
Keyword(s):  
North Atlantic Oscillation ◽  
North Atlantic ◽  
Climate Model ◽  
Coupled Climate Model ◽  
The North ◽  
The North Atlantic ◽  
The North Atlantic Oscillation

Granger Causality of Coupled Climate Processes: Ocean Feedback on the North Atlantic Oscillation

2006 ◽  
Vol 19 (7) ◽  
pp. 1182-1194 ◽  
Author(s):  
Timothy J. Mosedale ◽  
David B. Stephenson ◽  
Matthew Collins ◽  
Terence C. Mills
Keyword(s):  
Time Series ◽  
North Atlantic Oscillation ◽  
Granger Causality ◽  
North Atlantic ◽  
General Circulation ◽  
Careful Examination ◽  
The North ◽  
The North Atlantic ◽  
The North Atlantic Oscillation

Abstract This study uses a Granger causality time series modeling approach to quantitatively diagnose the feedback of daily sea surface temperatures (SSTs) on daily values of the North Atlantic Oscillation (NAO) as simulated by a realistic coupled general circulation model (GCM). Bivariate vector autoregressive time series models are carefully fitted to daily wintertime SST and NAO time series produced by a 50-yr simulation of the Third Hadley Centre Coupled Ocean–Atmosphere GCM (HadCM3). The approach demonstrates that there is a small yet statistically significant feedback of SSTs on the NAO. The SST tripole index is found to provide additional predictive information for the NAO than that available by using only past values of NAO—the SST tripole is Granger causal for the NAO. Careful examination of local SSTs reveals that much of this effect is due to the effect of SSTs in the region of the Gulf Steam, especially south of Cape Hatteras. The effect of SSTs on NAO is responsible for the slower-than-exponential decay in lag-autocorrelations of NAO notable at lags longer than 10 days. The persistence induced in daily NAO by SSTs causes long-term means of NAO to have more variance than expected from averaging NAO noise if there is no feedback of the ocean on the atmosphere. There are greater long-term trends in NAO than can be expected from aggregating just short-term atmospheric noise, and NAO is potentially predictable provided that future SSTs are known. For example, there is about 10%–30% more variance in seasonal wintertime means of NAO and almost 70% more variance in annual means of NAO due to SST effects than one would expect if NAO were a purely atmospheric process.

Applying machine learning for drought prediction using a large ensemble of climate simulations

2021 ◽  
Author(s):  
Elizaveta Felsche ◽  
Ralf Ludwig
Keyword(s):  
Machine Learning ◽  
Regional Climate Model ◽  
North Atlantic Oscillation ◽  
North Atlantic ◽  
North Atlantic Oscillation Index ◽  
Climate Model ◽  
Regional Climate ◽  
The North ◽  
The North Atlantic ◽  
The North Atlantic Oscillation

<p>There is strong scientific and social interest to understand the factors leading to extreme events in order to improve the management of risks associated with hazards like droughts. Recent events like the summer 2018 drought in Germany already had severe und unexpected impacts, e.g. forest fires and crop failures; in order to increase preparedness robust prediction tools are  urgently required. In this study, machine learning methods are applied to predict the occurrence of a drought with lead times of one to three months. The approach takes into account a list of thirty atmospheric and soil variables<strong> </strong>as predictor input parameters from a single regional climate model initial condition large ensemble (CRCM5-LE). The data was produced the context of the ClimEx project by Ouranos with the Canadian Regional Climate Model (CRCM5) driven by 50 members of the Canadian Earth System Model (CanESM2) for the Bavarian and Quebec domains.</p><p>Drought occurrence was defined using the Standardized Precipitation Index. The training and test datasets were chosen from the current climatology (1955-2005) for the Munich and Lisbon subdomain within the CRCM5-LE. The best performing machine learning algorithms managed to obtain a correct classification of drought or no drought for a lead time of one month for around 60 % of the events of each class for the both domains. Explainable AI methods like feature importance and shapley values were applied to gain a better understanding of the trained algorithms. Physical variables like the North Atlantic Oscillation Index and air pressure one month before the event proved to be of high importance for the prediction. The study showed that better accuracies can be obtained for the Lisbon domain, due to the stronger influence of the North Atlantic Oscillation Index on Portugal’s climate.</p>

Subseasonal Predictability of the North Atlantic Oscillation

2021 ◽  
Author(s):  
Matthew Newman ◽  
John Albers
Keyword(s):  
North Atlantic Oscillation ◽  
North Atlantic ◽  
Single Mode ◽  
Linear Superposition ◽  
The North ◽  
Weather Events ◽  
The North Atlantic ◽  
Linear Inverse Model ◽  
Low Dimensional ◽  
The North Atlantic Oscillation

<div> <p>Skillfully predicting the North Atlantic Oscillation (NAO), and the closely related Northern Annular mode (NAM), on “subseasonal” (weeks to a few months) timescales is a high priority for operational forecasting centers, because of the NAO’s association with high-impact weather events. Unfortunately, the relatively fast, weather-related processes dominating overall NAO variability are unpredictable beyond about two weeks. On longer timescales, the tropical troposphere and the stratosphere provide some predictability, but they contribute relatively little to total NAO variance. Moreover, subseasonal forecasts are only sporadically skillful, suggesting the practical need to identify the fewer potentially predictable events at the time of forecast. Here we construct an observationally-based Linear Inverse Model (LIM) that predicts when, and diagnoses why, subseasonal NAO forecasts will be most skillful. We use the LIM to identify those dynamical modes that, despite capturing only a fraction of overall NAO variability, are largely responsible for extended-range NAO skill. Predictable NAO events stem from the linear superposition of these modes, which represent joint tropical sea-surface temperature-lower stratosphere variability plus a single mode capturing downward propagation from the upper stratosphere. Our method has broad applicability because both the LIM (run operationally at NOAA's Climate Prediction Center) and the state-of-the-art European Centre for Medium-Range Weather Forecasts Integrated Forecast System (IFS) have higher (and comparable) skill for the same set of high skill forecast events, suggesting that the low-dimensional predictable subspace identified by the LIM is relevant to real-world subseasonal NAO predictions.</p> </div>

scholarly journals Influence of the North Atlantic oscillation on the atmospheric levels of benzo[a]pyrene over Europe

2021 ◽  
Author(s):  
Pedro Jiménez-Guerrero ◽  
Nuno Ratola
Keyword(s):  
Spatial Pattern ◽  
North Atlantic Oscillation ◽  
North Atlantic ◽  
Transport Model ◽  
Regional Scale ◽  
The South ◽  
Climatic Fluctuations ◽  
The North ◽  
The North Atlantic ◽  
The North Atlantic Oscillation

AbstractThe atmospheric concentration of persistent organic pollutants (and of polycyclic aromatic hydrocarbons, PAHs, in particular) is closely related to climate change and climatic fluctuations, which are likely to influence contaminant’s transport pathways and transfer processes. Predicting how climate variability alters PAHs concentrations in the atmosphere still poses an exceptional challenge. In this sense, the main objective of this contribution is to assess the relationship between the North Atlantic Oscillation (NAO) index and the mean concentration of benzo[a]pyrene (BaP, the most studied PAH congener) in a domain covering Europe, with an emphasis on the effect of regional-scale processes. A numerical simulation for a present climate period of 30 years was performed using a regional chemistry transport model with a 25 km spatial resolution (horizontal), higher than those commonly applied. The results show an important seasonal behaviour, with a remarkable spatial pattern of difference between the north and the south of the domain. In winter, higher BaP ground levels are found during the NAO+ phase for the Mediterranean basin, while the spatial pattern of this feature (higher BaP levels during NAO+ phases) moves northwards in summer. These results show deviations up to and sometimes over 100% in the BaP mean concentrations, but statistically significant signals (p<0.1) of lower changes (20–40% variations in the signal) are found for the north of the domain in winter and for the south in summer.

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