Regional climate trends in south-west England and the North Atlantic Oscillation

Weather ◽  
2004 ◽  
Vol 59 (2) ◽  
pp. 39-41 ◽  
Author(s):  
N. L. H. Wood
Keyword(s):  
North Atlantic Oscillation ◽  
North Atlantic ◽  
Regional Climate ◽  
Climate Trends ◽  
The North ◽  
South West ◽  
The North Atlantic ◽  
The North Atlantic Oscillation ◽  
South West England

scholarly journals Quantifying the rarity of extreme multi-decadal trends: how unusual was the late twentieth century trend in the North Atlantic Oscillation?

2021 ◽  
Author(s):  
R. Eade ◽  
D. B. Stephenson ◽  
A. A. Scaife ◽  
D. M. Smith
Keyword(s):  
North Atlantic Oscillation ◽  
North Atlantic ◽  
Serial Correlation ◽  
Climate Models ◽  
Climate Indices ◽  
Positive Trend ◽  
Climate Trends ◽  
The North ◽  
The North Atlantic ◽  
The North Atlantic Oscillation

AbstractClimate trends over multiple decades are important drivers of regional climate change that need to be considered for climate resilience. Of particular importance are extreme trends that society may not be expecting and is not well adapted to. This study investigates approaches to assess the likelihood of maximum moving window trends in historical records of climate indices by making use of simulations from climate models and stochastic time series models with short- and long-range dependence. These approaches are applied to assess the unusualness of the large positive trend that occurred in the North Atlantic Oscillation (NAO) index between the 1960s to 1990s. By considering stochastic models, we show that the chance of extreme trends is determined by the variance of the trend process, which generally increases when there is more serial correlation in the index series. We find that the Coupled Model Intercomparison Project (CMIP5 + 6) historical simulations have very rarely (around 1 in 200 chance) simulated maximum trends greater than the observed maximum. Consistent with this, the NAO indices simulated by CMIP models were found to resemble white noise, with almost no serial correlation, in contrast to the observed NAO which exhibits year-to-year correlation. Stochastic model best fits to the observed NAO suggest an unlikely chance (around 1 in 20) for there to be maximum 31-year NAO trends as large as the maximum observed since 1860. This suggests that current climate models do not fully represent important aspects of the mechanism for low frequency variability of the NAO.

scholarly journals Historic record of pasture soil water and the influence of the North Atlantic Oscillation in south-west England

2016 ◽  
Vol 48 (1) ◽  
pp. 277-294
Author(s):  
Anita Shepherd ◽  
Wellen Atuhaire ◽  
Lianhai Wu ◽  
David Hogan ◽  
Robert Dunn ◽  
...  
Keyword(s):  
Soil Moisture ◽  
North Atlantic ◽  
Moisture Sensor ◽  
Soil Moisture Sensor ◽  
The North ◽  
South West ◽  
The North Atlantic ◽  
The North Atlantic Oscillation ◽  
South West England

The North Wyke Farm Platform for sustainable grassland research in south-west England contains infrastructure measuring soil moisture and field runoff. Its time series of sensor data is used to validate the parsimonious SH2O-NW model for soil water at field-scale. Thirty-four years of daily soil moisture and runoff is simulated, and used to detect long-term trends and produce a risk analysis. The model accounts for wetter periods of soil moisture and the main summer soil deficit and autumn re-wetting; limitations involve short-term, rapid changes in drying and re-wetting. The soil moisture sensor observations however do not reflect field variability. Analysis of more than one field allows an assessment of unexpected sensor anomalies. The paper recommends that soil moisture sensor confidence levels be provided, for comparison against modelled data. The simulations show a historic reduction in the occurrence of summer soil moisture deficits above a third of water capacity, while the winter precipitation and runoff simulation shows a stable long-term trend, matching the direction and magnitude of the North Atlantic Oscillation Index. A large runoff of 400 m3/day from a 1.75 ha pasture has a 0.07% probability, having a return period of once in 4 years during the 34-year period.

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>

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.

Sign in / Sign up

Export Citation Format

Share Document